1.
Roja, A.; Srilatha, P.; Khan, U.; Ishak, A.; Verma, A.; Rekha, J. G.; Siddiqui, M. I. H.
Chemically reactive non-Newtonian fluid flow through a vertical microchannel with activation energy impacts: A numerical investigation Journal Article
In: Advances in Mechanical Engineering, vol. 16, 2024, ISBN: 16878132 (ISSN), (0).
@article{3,
title = {Chemically reactive non-Newtonian fluid flow through a vertical microchannel with activation energy impacts: A numerical investigation},
author = {A. Roja and P. Srilatha and U. Khan and A. Ishak and A. Verma and J. G. Rekha and M. I. H. Siddiqui},
doi = {10.1177/16878132241261472},
isbn = {16878132 (ISSN)},
year = {2024},
date = {2024-01-01},
journal = {Advances in Mechanical Engineering},
volume = {16},
publisher = {SAGE Publications Inc.},
abstract = {This work examines the second law analysis of an electrically conducting reactive third-grade fluid flow embedded with the porous medium in a microchannel with the influence of variable thermal conductivity, activation energy, viscous dissipation, joule heating, and radiative heat flux. A suitable non-dimensional variable is included into the governing equations to transform them into an ensemble of equations that are devoid of dimensions. The acquired equations are then tackled using the Runge Kutta Felhberg 4th and 5th order (RKF-45) approach in conjunction with the shooting methodology. Through comparison with the current results, the numerical results are verified, which provides a good agreement. From the present outcomes, it is established that the entropy generation is supreme for the viscous heating constraint, variable thermal conductivity, Frank Kameneski, heat source ratio parameter and third-grade fluid material constraint. The Bejan number boosts up with larger values of activation energy, and Frank Kameneski constraint and the decreasing nature is noticed for increasing third-grade material parameter, viscous heating parameter. With magnetism, the fluid’s velocity slows down because of a resistive force. A similar impact in the channel on velocity is noticed for larger third-grade fluid, activation energy parameter, and Frank-Kameniski parameters and increasing behavior is noticed for variable thermal conductivity, and permeability parameter. Further, it is cleared that the variable thermal conductivity assumption in the channel plate leads to a significant under prediction of the irreversibility rate. © The Author(s) 2024.},
note = {0},
keywords = {MATH},
pubstate = {published},
tppubtype = {article}
}
This work examines the second law analysis of an electrically conducting reactive third-grade fluid flow embedded with the porous medium in a microchannel with the influence of variable thermal conductivity, activation energy, viscous dissipation, joule heating, and radiative heat flux. A suitable non-dimensional variable is included into the governing equations to transform them into an ensemble of equations that are devoid of dimensions. The acquired equations are then tackled using the Runge Kutta Felhberg 4th and 5th order (RKF-45) approach in conjunction with the shooting methodology. Through comparison with the current results, the numerical results are verified, which provides a good agreement. From the present outcomes, it is established that the entropy generation is supreme for the viscous heating constraint, variable thermal conductivity, Frank Kameneski, heat source ratio parameter and third-grade fluid material constraint. The Bejan number boosts up with larger values of activation energy, and Frank Kameneski constraint and the decreasing nature is noticed for increasing third-grade material parameter, viscous heating parameter. With magnetism, the fluid’s velocity slows down because of a resistive force. A similar impact in the channel on velocity is noticed for larger third-grade fluid, activation energy parameter, and Frank-Kameniski parameters and increasing behavior is noticed for variable thermal conductivity, and permeability parameter. Further, it is cleared that the variable thermal conductivity assumption in the channel plate leads to a significant under prediction of the irreversibility rate. © The Author(s) 2024.
2.
Prasanna, B. R.; Nanthini, M.; Sivaranjani, P.; Prabhakaran, K.
Exploring textile industry landscapes Insights from diverse analysis Journal Article
In: Asian Textile Journal, vol. 33, pp. 25-29,, 2024, ISBN: 09713425 (ISSN), (0).
Tags: MBA
@article{4,
title = {Exploring textile industry landscapes Insights from diverse analysis},
author = {B. R. Prasanna and M. Nanthini and P. Sivaranjani and K. Prabhakaran},
isbn = {09713425 (ISSN)},
year = {2024},
date = {2024-01-01},
journal = {Asian Textile Journal},
volume = {33},
pages = {25-29,},
publisher = {G P S Kwatra},
note = {0},
keywords = {MBA},
pubstate = {published},
tppubtype = {article}
}
3.
Bharath, L.; Kumaraswamy, J.; Manjunath, T. V.; Kulkarni, S. K. N.
In: Multiscale and Multidisciplinary Modeling, Experiments and Design, vol. 7, pp. 5387-5399,, 2024, ISBN: 25208179 (ISSN), (5).
@article{6,
title = {Evaluation of microstructure and prediction of hardness of Al–Cu based composites by using artificial neural network and linear regression through machine learning technique},
author = {L. Bharath and J. Kumaraswamy and T. V. Manjunath and S. K. N. Kulkarni},
doi = {10.1007/s41939-024-00525-0},
isbn = {25208179 (ISSN)},
year = {2024},
date = {2024-01-01},
journal = {Multiscale and Multidisciplinary Modeling, Experiments and Design},
volume = {7},
pages = {5387-5399,},
publisher = {Springer Science and Business Media B.V.},
abstract = {Al–Cu alloy with B4C particulates will meet the specific application which includes window panel, seats, aircraft structure and aircraft fittings due to their excellent mechanical properties. In this paper, Al–Cu/B4C composites was fabricated by using three parameters (wt% of B4C, ageing duration and mesh size) with three level each as per the design of experiments. Al–Cu/B4C composites were machined as per IS:1500 standard to evaluate hardness by experimental method. An Artificial Neural Network model was developed by using the Levenberg–Marquardt algorithm to predict the experimental hardness value of formed composites. Linear Regression model is created and evaluated by taking 30% of experimental data set for testing and 70% for training. Polynomial feature is imported with only 2° with their interaction only. It is seen that the established ANN model predicts the closeness with the experimental hardness within ± 10% error. It is seen that 14.58% improvement was been observed after considering polynomial feature for the linear regression model. In addition, microstructure study was discussed for the fabricated composites as per IS:7739 standard and observed that B4C particles were homogeneously dispersed in the Al–Cu based matrix and exhibit good bonding between them. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.},
note = {5},
keywords = {MECH},
pubstate = {published},
tppubtype = {article}
}
Al–Cu alloy with B4C particulates will meet the specific application which includes window panel, seats, aircraft structure and aircraft fittings due to their excellent mechanical properties. In this paper, Al–Cu/B4C composites was fabricated by using three parameters (wt% of B4C, ageing duration and mesh size) with three level each as per the design of experiments. Al–Cu/B4C composites were machined as per IS:1500 standard to evaluate hardness by experimental method. An Artificial Neural Network model was developed by using the Levenberg–Marquardt algorithm to predict the experimental hardness value of formed composites. Linear Regression model is created and evaluated by taking 30% of experimental data set for testing and 70% for training. Polynomial feature is imported with only 2° with their interaction only. It is seen that the established ANN model predicts the closeness with the experimental hardness within ± 10% error. It is seen that 14.58% improvement was been observed after considering polynomial feature for the linear regression model. In addition, microstructure study was discussed for the fabricated composites as per IS:7739 standard and observed that B4C particles were homogeneously dispersed in the Al–Cu based matrix and exhibit good bonding between them. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.
4.
Farooq, U.; Reddy, K. K. S.; Shishira, K. S.; Jayanthi, M. G.; Kannadaguli, P.
Comparing Hindustani Music Raga Prediction Systems using DL and ML Models Proceedings
Institute of Electrical and Electronics Engineers Inc., 2024, ISBN: 979-835037250-2 (ISBN), (1).
@proceedings{9,
title = {Comparing Hindustani Music Raga Prediction Systems using DL and ML Models},
author = {U. Farooq and K. K. S. Reddy and K. S. Shishira and M. G. Jayanthi and P. Kannadaguli},
doi = {10.1109/ICETCS61022.2024.10543647},
isbn = {979-835037250-2 (ISBN)},
year = {2024},
date = {2024-01-01},
journal = {International Conference on Emerging Technologies in Computer Science for Interdisciplinary Applications, ICETCS 2024},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {This project aims to employ DL and ML techniques to predict ragas in Indian classical music accurately. The focus is on developing a system that can process audio recordings and make precise raga predictions. The classification task utilizes CNN and RNN networks, to enhance performance. Extensive evaluation using diverse recordings is conducted, comparing the framework against traditional methods. The outcomes of this project has potential applications for music analysis, archiving, recommendation systems, and education in Indian classical music. The developed raga prediction framework can serve as a valuable tool for automatic raga identification. Additionally, it contributes to the field of music information retrieval by showcasing the capabilities of DL/ML techniques in tackling musical tasks. © 2024 IEEE.},
note = {1},
keywords = {CSE},
pubstate = {published},
tppubtype = {proceedings}
}
This project aims to employ DL and ML techniques to predict ragas in Indian classical music accurately. The focus is on developing a system that can process audio recordings and make precise raga predictions. The classification task utilizes CNN and RNN networks, to enhance performance. Extensive evaluation using diverse recordings is conducted, comparing the framework against traditional methods. The outcomes of this project has potential applications for music analysis, archiving, recommendation systems, and education in Indian classical music. The developed raga prediction framework can serve as a valuable tool for automatic raga identification. Additionally, it contributes to the field of music information retrieval by showcasing the capabilities of DL/ML techniques in tackling musical tasks. © 2024 IEEE.
5.
Naik, J. C.; Pinjare, S. L.; Pooja, P. M.
Design and simulation of underwater acoustic sensor materials Proceedings
American Institute of Physics, vol. 2965, 2024, ISBN: 0094243X (ISSN), (0).
@proceedings{10,
title = {Design and simulation of underwater acoustic sensor materials},
author = {J. C. Naik and S. L. Pinjare and P. M. Pooja},
doi = {10.1063/5.0212194},
isbn = {0094243X (ISSN)},
year = {2024},
date = {2024-01-01},
journal = {AIP Conference Proceedings},
volume = {2965},
pages = {020003+},
publisher = {American Institute of Physics},
abstract = {The Underwater communication is very important field in communication in present time. The sensor and acoustic devices used underwater is mostly corroded by the solvents in the water. The underwater parameters like pressure was applied to the design and the six different piezoresistive materials with four different diaphragm material that have been designed and tabulated with the comparison of different materials. In this paper, pressure is considered to be most important part to track and apply on different material that will give us stress, displacement and change in resistivity. The following Stress, Displacement and Resistivity is applied for the design and the results shows the best material that can be used for the design of any underwater sensors and devices. The maximum resistivity is obtained in PZT, SiC and PDMS with several hundred ohms/m change in resistivity. The maximum resistivity obtained for diaphragm materials are PMMA and Silicon Nitride Si3N4. The optimal change in resistivity and displacement was obtained for PZT as piezoresistive material and PMMA as diaphragm material as 2.976E3 ohm/m and several 17μm to 20μm. The novelty of the paper is that it focuses on detection of the direction of the acoustic signal for the underwater applications. The underwater communication devices can have best material for long life and the financial burden will be reduced for the underwater research. © 2024 Author(s).},
note = {0},
keywords = {ECE},
pubstate = {published},
tppubtype = {proceedings}
}
The Underwater communication is very important field in communication in present time. The sensor and acoustic devices used underwater is mostly corroded by the solvents in the water. The underwater parameters like pressure was applied to the design and the six different piezoresistive materials with four different diaphragm material that have been designed and tabulated with the comparison of different materials. In this paper, pressure is considered to be most important part to track and apply on different material that will give us stress, displacement and change in resistivity. The following Stress, Displacement and Resistivity is applied for the design and the results shows the best material that can be used for the design of any underwater sensors and devices. The maximum resistivity is obtained in PZT, SiC and PDMS with several hundred ohms/m change in resistivity. The maximum resistivity obtained for diaphragm materials are PMMA and Silicon Nitride Si3N4. The optimal change in resistivity and displacement was obtained for PZT as piezoresistive material and PMMA as diaphragm material as 2.976E3 ohm/m and several 17μm to 20μm. The novelty of the paper is that it focuses on detection of the direction of the acoustic signal for the underwater applications. The underwater communication devices can have best material for long life and the financial burden will be reduced for the underwater research. © 2024 Author(s).
6.
Devi, K. K.; Kumar, J. P.
Sustainable Food Development Based on Ensemble Machine Learning Assisted Crop and Fertilizer Recommendation System Journal Article
In: Journal of Machine and Computing, vol. 4, pp. 317-326,, 2024, ISBN: 27891801 (ISSN), (2).
@article{11,
title = {Sustainable Food Development Based on Ensemble Machine Learning Assisted Crop and Fertilizer Recommendation System},
author = {K. K. Devi and J. P. Kumar},
doi = {10.53759/7669/jmc202404030},
isbn = {27891801 (ISSN)},
year = {2024},
date = {2024-01-01},
journal = {Journal of Machine and Computing},
volume = {4},
pages = {317-326,},
publisher = {AnaPub Publications},
abstract = {Agriculture is the most vital sector for the global food supply, and it also provides raw materials for other types of industries. A crop recommendation system is essential for farmers who want to get the most out of their crop-choosing decisions. Over the last several decades, the world's ability to produce food has grown substantially owing to the extensive usage of fertilizers. Therefore, there has to be a more eco-friendly and effective way to utilize fertilizers that include nitrogen (N), phosphorous (P), and potassium (K) to ensure food security. For the reason, this study proposes an ensemble machine learning-assisted crop and fertilizer recommendation system (EML-CFRS) to maximize agricultural output while ensuring the correct use of mineral resources. The research used a dataset obtained from the Kaggle repository like that people can assess several distinct ML algorithms. The databases include data on three climate variables-temperature, rainfall, and humidity-and information on NPK and soil pH. The yields agricultural crops were used to train these models, including Decision Tree, KNN, XGBoost, Support Vector Machine, and Random Forest. Depending on the current weather and soil conditions, the trained model may then recommend the optimal fertiliser for a certain crop. Predicting the ideal kind and quantity of fertilizer for different crops was accomplished with a 96.5% accuracy rate by our suggested strategy. © 2024 The Authors.},
note = {2},
keywords = {CSE},
pubstate = {published},
tppubtype = {article}
}
Agriculture is the most vital sector for the global food supply, and it also provides raw materials for other types of industries. A crop recommendation system is essential for farmers who want to get the most out of their crop-choosing decisions. Over the last several decades, the world's ability to produce food has grown substantially owing to the extensive usage of fertilizers. Therefore, there has to be a more eco-friendly and effective way to utilize fertilizers that include nitrogen (N), phosphorous (P), and potassium (K) to ensure food security. For the reason, this study proposes an ensemble machine learning-assisted crop and fertilizer recommendation system (EML-CFRS) to maximize agricultural output while ensuring the correct use of mineral resources. The research used a dataset obtained from the Kaggle repository like that people can assess several distinct ML algorithms. The databases include data on three climate variables-temperature, rainfall, and humidity-and information on NPK and soil pH. The yields agricultural crops were used to train these models, including Decision Tree, KNN, XGBoost, Support Vector Machine, and Random Forest. Depending on the current weather and soil conditions, the trained model may then recommend the optimal fertiliser for a certain crop. Predicting the ideal kind and quantity of fertilizer for different crops was accomplished with a 96.5% accuracy rate by our suggested strategy. © 2024 The Authors.
7.
Rakesh, S.; Chaithra, S.
Implementation of Local & Remote Controlled Robotic Arm Proceedings
Grenze Scientific Society, vol. 2, 2024, ISBN: 979-833130057-9 (ISBN), (0).
@proceedings{13,
title = {Implementation of Local & Remote Controlled Robotic Arm},
author = {S. Rakesh and S. Chaithra},
isbn = {979-833130057-9 (ISBN)},
year = {2024},
date = {2024-01-01},
journal = {15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024},
volume = {2},
pages = {6119-6126,},
publisher = {Grenze Scientific Society},
abstract = {This paper's primary aim is to design and create a robotic arm system for lifting.The main objective of this project is to design and construct a lifting mechanism for a robotic arm.The advancement of robotic technology has seen significant improvements in the versatility and precision of robotic arms, making them essential tools in various industries such as manufacturing, healthcare, and space exploration.This paper presents the design and implementation of a robotic arm that can be controlled both locally and remotely, offering enhanced flexibility and operational efficiency.The robotic arm is designed to perform a range of tasks including object manipulation, assembly operations, and precise movements required in sensitive environments.The local control mechanism employs direct user interaction through a control panel or joystick, allowing real-time adjustments and immediate response.Remote control capabilities are integrated using Internet of Things (IoT) technologies, enabling operation from distant locations via a web interface or mobile application.This dual-mode control system ensures that the robotic arm can be operated effectively in various scenarios, providing both convenience and reliability.Testing and validation of the robotic arm demonstrate its capability to perform complex tasks with high accuracy and responsiveness under both local and remote control.The system's modular design allows for easy upgrades and customization, making it adaptable to specific user requirements.The Arduino IDE is an open-source computer hardware and software platform that drives servo motors, which in turn control the robotic arm's position.Utilizing wireless command through a Bluetooth module, a smartphone running the Android operating system is also utilized for wireless control.The robotic arm can successfully complete the lifting task, according to the results of its testing and performance validation.In conclusion, the developed robotic arm showcases the potential for integrating local and remote control mechanisms to enhance operational flexibility.This implementation can serve as a prototype for future developments in robotic technology, pushing the boundaries of automation and remote operations. © Grenze Scientific Society, 2024.},
note = {0},
keywords = {ECE},
pubstate = {published},
tppubtype = {proceedings}
}
This paper's primary aim is to design and create a robotic arm system for lifting.The main objective of this project is to design and construct a lifting mechanism for a robotic arm.The advancement of robotic technology has seen significant improvements in the versatility and precision of robotic arms, making them essential tools in various industries such as manufacturing, healthcare, and space exploration.This paper presents the design and implementation of a robotic arm that can be controlled both locally and remotely, offering enhanced flexibility and operational efficiency.The robotic arm is designed to perform a range of tasks including object manipulation, assembly operations, and precise movements required in sensitive environments.The local control mechanism employs direct user interaction through a control panel or joystick, allowing real-time adjustments and immediate response.Remote control capabilities are integrated using Internet of Things (IoT) technologies, enabling operation from distant locations via a web interface or mobile application.This dual-mode control system ensures that the robotic arm can be operated effectively in various scenarios, providing both convenience and reliability.Testing and validation of the robotic arm demonstrate its capability to perform complex tasks with high accuracy and responsiveness under both local and remote control.The system's modular design allows for easy upgrades and customization, making it adaptable to specific user requirements.The Arduino IDE is an open-source computer hardware and software platform that drives servo motors, which in turn control the robotic arm's position.Utilizing wireless command through a Bluetooth module, a smartphone running the Android operating system is also utilized for wireless control.The robotic arm can successfully complete the lifting task, according to the results of its testing and performance validation.In conclusion, the developed robotic arm showcases the potential for integrating local and remote control mechanisms to enhance operational flexibility.This implementation can serve as a prototype for future developments in robotic technology, pushing the boundaries of automation and remote operations. © Grenze Scientific Society, 2024.
8.
Rakesh, V. S.; Vasanthakumar, G. U.
Enhancing Network Security: A Novel Hybrid ML Approach for DDoS Attack Detection in SDN Proceedings
Grenze Scientific Society, vol. 1, 2024, ISBN: 979-833130057-9 (ISBN), (0).
@proceedings{17,
title = {Enhancing Network Security: A Novel Hybrid ML Approach for DDoS Attack Detection in SDN},
author = {V. S. Rakesh and G. U. Vasanthakumar},
isbn = {979-833130057-9 (ISBN)},
year = {2024},
date = {2024-01-01},
journal = {15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024},
volume = {1},
pages = {915-922,},
publisher = {Grenze Scientific Society},
abstract = {Software-defined networking represents a ground breaking advancement in network technology, characterized by its desirable attributes such as enhanced flexibility and manageability. Although ongoing, the issue of DDoS assaults in SDN is characterized by malicious and obtrusive network traffic that overwhelms SDN resources. Despite numerous security methodologies aimed at detecting DDoS attacks, the challenge of effectively addressing this issue continues to persist as an active area of research. The XG-Light Hybrid, a unique hybrid system, has been developed in this work as a solution to this problem. This discovery is significant because it has the potential to dramatically increase the reliability of DDoS attack detection in SDN environments, hence boosting network security and stability. Key findings reveal that the proposed hybrid approach outperforms individual machine learning algorithms with respect to DDoS detection. © Grenze Scientific Society, 2024.},
note = {0},
keywords = {CSE},
pubstate = {published},
tppubtype = {proceedings}
}
Software-defined networking represents a ground breaking advancement in network technology, characterized by its desirable attributes such as enhanced flexibility and manageability. Although ongoing, the issue of DDoS assaults in SDN is characterized by malicious and obtrusive network traffic that overwhelms SDN resources. Despite numerous security methodologies aimed at detecting DDoS attacks, the challenge of effectively addressing this issue continues to persist as an active area of research. The XG-Light Hybrid, a unique hybrid system, has been developed in this work as a solution to this problem. This discovery is significant because it has the potential to dramatically increase the reliability of DDoS attack detection in SDN environments, hence boosting network security and stability. Key findings reveal that the proposed hybrid approach outperforms individual machine learning algorithms with respect to DDoS detection. © Grenze Scientific Society, 2024.
9.
Harish, K. S.; Kotehal, P. U.; Sandesh, M. M.; Reddy, Y. M.; Roopa, K.; Lokesh, G. R.
Artificial Intelligence in Supply Chain Management: Trends and Implications Journal Article
In: Nanotechnology Perceptions, vol. 20, pp. 1113-1120,, 2024, ISBN: 16606795 (ISSN), (0).
@article{18,
title = {Artificial Intelligence in Supply Chain Management: Trends and Implications},
author = {K. S. Harish and P. U. Kotehal and M. M. Sandesh and Y. M. Reddy and K. Roopa and G. R. Lokesh},
doi = {10.62441/nano-ntp.v20iS7.91},
isbn = {16606795 (ISSN)},
year = {2024},
date = {2024-01-01},
journal = {Nanotechnology Perceptions},
volume = {20},
pages = {1113-1120,},
publisher = {Collegium Basilea},
abstract = {Artificial Intelligence (AI) is revolutionizing supply chain management (SCM) by introducing advanced analytics, automation, and data-driven decision-making. This research paper explores the current trends in AI adoption within SCM, including the use of predictive analytics, machine learning, and autonomous systems to optimize logistics, inventory management, and demand forecasting. The study also examines the implications of these technologies on efficiency, cost reduction, and competitive advantage. While AI offers significant benefits, it also presents challenges such as data privacy concerns, the need for specialized skills, and potential disruptions to traditional supply chain roles. This paper aims to provide a comprehensive overview of AI's impact on SCM, offering insights into how businesses can strategically implement AI to enhance their supply chain operations while navigating the associated challenges. © 2024, Collegium Basilea. All rights reserved.},
note = {0},
keywords = {MBA},
pubstate = {published},
tppubtype = {article}
}
Artificial Intelligence (AI) is revolutionizing supply chain management (SCM) by introducing advanced analytics, automation, and data-driven decision-making. This research paper explores the current trends in AI adoption within SCM, including the use of predictive analytics, machine learning, and autonomous systems to optimize logistics, inventory management, and demand forecasting. The study also examines the implications of these technologies on efficiency, cost reduction, and competitive advantage. While AI offers significant benefits, it also presents challenges such as data privacy concerns, the need for specialized skills, and potential disruptions to traditional supply chain roles. This paper aims to provide a comprehensive overview of AI's impact on SCM, offering insights into how businesses can strategically implement AI to enhance their supply chain operations while navigating the associated challenges. © 2024, Collegium Basilea. All rights reserved.
10.
V, S.; Abdullah, A.; Ramadass, P.; Srinivasan, S.; Shivahare, B. D.; Mathivanan, S. K.; P, K.
Context based ranking strategies for renowned instructional methodologies Journal Article
In: Intelligence-Based Medicine, vol. 10, pp. 100186+, 2024, ISBN: 26665212 (ISSN), (0).
@article{20,
title = {Context based ranking strategies for renowned instructional methodologies},
author = {S. V and A. Abdullah and P. Ramadass and S. Srinivasan and B. D. Shivahare and S. K. Mathivanan and K. P},
doi = {10.1016/j.ibmed.2024.100186},
isbn = {26665212 (ISSN)},
year = {2024},
date = {2024-01-01},
journal = {Intelligence-Based Medicine},
volume = {10},
pages = {100186+},
publisher = {Elsevier B.V.},
abstract = {The main objective of this work is to validate the decisions made towards adoption of appropriate instructional methodologies based on the context of a specific region considering the quality of education, the cost of education and the learning outcomes as predominant parameters. The non-deterministic events and uncertain situations that may arise over a long-range period impose a vague and fuzzy environment in the educational system. Investigations have been made to identify suitable educational framework for implementation in the institutions of a specific region in view of these unpredictable events and non-deterministic conditions. Fuzzy decision analysis and rough set theory have been applied to rank the prominent instructional methodologies which are encompassed within each educational framework. Hurwicz Rule is adopted to balance the pessimistic and optimistic opinions about the non-deterministic events while validating the merits of the instructional methodologies. Grey relational analysis is carried out while ranking instructional methodologies in a vague environment. In this work, the instructional methodologies are ranked using fuzzy entropy as well as crisp entropy measures and the outcomes of the fuzzy and rough sets-based decision analysis have been validated. © 2024 The Authors},
note = {0},
keywords = {ISE},
pubstate = {published},
tppubtype = {article}
}
The main objective of this work is to validate the decisions made towards adoption of appropriate instructional methodologies based on the context of a specific region considering the quality of education, the cost of education and the learning outcomes as predominant parameters. The non-deterministic events and uncertain situations that may arise over a long-range period impose a vague and fuzzy environment in the educational system. Investigations have been made to identify suitable educational framework for implementation in the institutions of a specific region in view of these unpredictable events and non-deterministic conditions. Fuzzy decision analysis and rough set theory have been applied to rank the prominent instructional methodologies which are encompassed within each educational framework. Hurwicz Rule is adopted to balance the pessimistic and optimistic opinions about the non-deterministic events while validating the merits of the instructional methodologies. Grey relational analysis is carried out while ranking instructional methodologies in a vague environment. In this work, the instructional methodologies are ranked using fuzzy entropy as well as crisp entropy measures and the outcomes of the fuzzy and rough sets-based decision analysis have been validated. © 2024 The Authors
11.
Gupta, J.; Shaik, H.; Gupta, V. K.; Sattar, S. A.
Perspective of Electrochromic Double Layer Towards Enrichment of Electrochromism: A Review Journal Article
In: Brazilian Journal of Physics, vol. 54, pp. 89+, 2024, ISBN: 01039733 (ISSN), (5).
@article{22,
title = {Perspective of Electrochromic Double Layer Towards Enrichment of Electrochromism: A Review},
author = {J. Gupta and H. Shaik and V. K. Gupta and S. A. Sattar},
doi = {10.1007/s13538-024-01463-5},
isbn = {01039733 (ISSN)},
year = {2024},
date = {2024-01-01},
journal = {Brazilian Journal of Physics},
volume = {54},
pages = {89+},
publisher = {Springer},
abstract = {Electrochromism is the exhibition of reversible optical property changes by certain materials upon administration of voltage across it. Tungsten oxide (WO3) finds a diverse range of applications because of its exceptional electrochromism. Amidst all applications, an electrochromic device (ECD) can be considered the most prominent application due to energy saving perspective. Although, WO3 itself has been noticed as an efficient electrochromic layer for EDCs; however, there exists a lot of space and ideas to enhance the electrochromism and hence the efficiency of an ECD. Recently, scientists are paying close attention to hybrid or composite films such as TiO2/WO3 and TiO2/V2O5. Such hybrid films are known as electrochromic double layer (ECDL). This review article strives to deepen our understanding of ECDL and assess their feasibility in the enrichment of electrochromism in ECDs by replacing a single electrochromic layer with an ECDL toward an energy-saving regime. Graphical Abstract: (Figure presented.). © The Author(s) under exclusive licence to Sociedade Brasileira de Física 2024.},
note = {5},
keywords = {EE},
pubstate = {published},
tppubtype = {article}
}
Electrochromism is the exhibition of reversible optical property changes by certain materials upon administration of voltage across it. Tungsten oxide (WO3) finds a diverse range of applications because of its exceptional electrochromism. Amidst all applications, an electrochromic device (ECD) can be considered the most prominent application due to energy saving perspective. Although, WO3 itself has been noticed as an efficient electrochromic layer for EDCs; however, there exists a lot of space and ideas to enhance the electrochromism and hence the efficiency of an ECD. Recently, scientists are paying close attention to hybrid or composite films such as TiO2/WO3 and TiO2/V2O5. Such hybrid films are known as electrochromic double layer (ECDL). This review article strives to deepen our understanding of ECDL and assess their feasibility in the enrichment of electrochromism in ECDs by replacing a single electrochromic layer with an ECDL toward an energy-saving regime. Graphical Abstract: (Figure presented.). © The Author(s) under exclusive licence to Sociedade Brasileira de Física 2024.
12.
Wollur, C.; Sowmya, H. N.; Reshma, E. K.; Shivananda, P.
Utilization of reservoir sediments for engineering applications Journal Article
In: Proceedings of Institution of Civil Engineers: Waste and Resource Management, 2024, ISBN: 17476526 (ISSN), (0).
@article{23,
title = {Utilization of reservoir sediments for engineering applications},
author = {C. Wollur and H. N. Sowmya and E. K. Reshma and P. Shivananda},
doi = {10.1680/jwarm.23.00013},
isbn = {17476526 (ISSN)},
year = {2024},
date = {2024-01-01},
journal = {Proceedings of Institution of Civil Engineers: Waste and Resource Management},
publisher = {ICE Publishing},
abstract = {In this paper, a detailed characterization of Tungabhadra (TB) Reservoir sediment such as physical, chemical, Geotechnical, morphological and mineralogical attribute is brought off for identifying the suitability of dredged sediment as engineering material. Characteristics of sediment is however compared with Indian Standard codes for specific engineering applications. Through this study it is depicted that, sediment obtained from TB dam reservoirs as such, cannot be used for engineering purpose (such as, as a fill material for highway construction, as sand for concreting and plastering and as foundry sand and other applications) these sediments need prior treatment before using them for above mentioned applications. However, it is also be noted that, to enhance the strength properties of sediment, Air-cooled blast-furnace slag (ABS) is used as a stabilizer. On the other hand, ABS is treated as waste material in steel industry. Thus, it is concluded from this study that, TB dam sediment can be used for many engineering applications provided proper screening and stabilization technic is adopted. Thereby, the cost of dredging is converted into profit by selling the TB dam sediments for engineering applications. © 2024 Emerald Publishing Limited: All rights reserved.},
note = {0},
keywords = {CE},
pubstate = {published},
tppubtype = {article}
}
In this paper, a detailed characterization of Tungabhadra (TB) Reservoir sediment such as physical, chemical, Geotechnical, morphological and mineralogical attribute is brought off for identifying the suitability of dredged sediment as engineering material. Characteristics of sediment is however compared with Indian Standard codes for specific engineering applications. Through this study it is depicted that, sediment obtained from TB dam reservoirs as such, cannot be used for engineering purpose (such as, as a fill material for highway construction, as sand for concreting and plastering and as foundry sand and other applications) these sediments need prior treatment before using them for above mentioned applications. However, it is also be noted that, to enhance the strength properties of sediment, Air-cooled blast-furnace slag (ABS) is used as a stabilizer. On the other hand, ABS is treated as waste material in steel industry. Thus, it is concluded from this study that, TB dam sediment can be used for many engineering applications provided proper screening and stabilization technic is adopted. Thereby, the cost of dredging is converted into profit by selling the TB dam sediments for engineering applications. © 2024 Emerald Publishing Limited: All rights reserved.
13.
Patil, A.; Sanjana, M.; Shilpa, M.; Vaishnavi, R.; Priyadarshini, M.
Brain Tumor Detection and Classification with One-Hot Encoding and EfficientNetB0 using MRI Images Proceedings
Institute of Electrical and Electronics Engineers Inc., 2024, ISBN: 979-835036717-1 (ISBN), (0).
@proceedings{26,
title = {Brain Tumor Detection and Classification with One-Hot Encoding and EfficientNetB0 using MRI Images},
author = {A. Patil and M. Sanjana and M. Shilpa and R. Vaishnavi and M. Priyadarshini},
doi = {10.1109/ICIPCN63822.2024.00023},
isbn = {979-835036717-1 (ISBN)},
year = {2024},
date = {2024-01-01},
journal = {Proceedings - 2024 5th International Conference on Image Processing and Capsule Networks, ICIPCN 2024},
pages = {84-90,},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Convolutional Neural Networks (CNNs) perform well in accurately classifying brain tumors identified in medical scans such as MRI. This study presents a CNN architecture, which contains convolutional layers for extracting features followed by maximum pooling layers for spatial down-sampling, for dimensionality reduction. To mitigate overfitting, dropout layers are employed are strategically integrated, ensuring the generalizability within the model. The task is accomplished, incorporating using fully connected layers with the SoftMax activation function. The Convolutional Neural Network(CNN) proposed architecture demonstrates effectiveness in categorizing brain tumors into three distinct types: meningioma, glioma, and pituitary tumors. Experimental evaluation reveals promising results, with the model achieving an overall classification accuracy of 98%. Specifically, it detects glioma with 96% accuracy, identifies no tumor with 99% accuracy, differentiates meningioma with 97% accuracy, and identifies pituitary tumors with 99% accuracy. The dataset comprises 3264 images, 90% of which are for training and 10% for testing. The approach shows considerable promise to assist clinicians in accurate and timely diagnosis, thereby facilitating tailored treatment planning for patients with brain tumors. Further research can explore improvements to the network architecture and explore its applicability in different medical imaging datasets. © 2024 IEEE.},
note = {0},
keywords = {CSE},
pubstate = {published},
tppubtype = {proceedings}
}
Convolutional Neural Networks (CNNs) perform well in accurately classifying brain tumors identified in medical scans such as MRI. This study presents a CNN architecture, which contains convolutional layers for extracting features followed by maximum pooling layers for spatial down-sampling, for dimensionality reduction. To mitigate overfitting, dropout layers are employed are strategically integrated, ensuring the generalizability within the model. The task is accomplished, incorporating using fully connected layers with the SoftMax activation function. The Convolutional Neural Network(CNN) proposed architecture demonstrates effectiveness in categorizing brain tumors into three distinct types: meningioma, glioma, and pituitary tumors. Experimental evaluation reveals promising results, with the model achieving an overall classification accuracy of 98%. Specifically, it detects glioma with 96% accuracy, identifies no tumor with 99% accuracy, differentiates meningioma with 97% accuracy, and identifies pituitary tumors with 99% accuracy. The dataset comprises 3264 images, 90% of which are for training and 10% for testing. The approach shows considerable promise to assist clinicians in accurate and timely diagnosis, thereby facilitating tailored treatment planning for patients with brain tumors. Further research can explore improvements to the network architecture and explore its applicability in different medical imaging datasets. © 2024 IEEE.
14.
Shankar, M. S.; Adishesha, R.; Kumar, Hemanth; Jayanthi, M. G.; Kannadaguli, P.; Loganathan, D.
Image-Based Plant Disease Classification for the Management of Crop Health Proceedings
Institute of Electrical and Electronics Engineers Inc., 2024, ISBN: 979-835034367-0 (ISBN), (1).
@proceedings{27,
title = {Image-Based Plant Disease Classification for the Management of Crop Health},
author = {M. S. Shankar and R. Adishesha and Hemanth Kumar and M. G. Jayanthi and P. Kannadaguli and D. Loganathan},
doi = {10.1109/ICAECT60202.2024.10469390},
isbn = {979-835034367-0 (ISBN)},
year = {2024},
date = {2024-01-01},
journal = {2024 4th International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Categorizing plant diseases is crucial for ensuring agricultural production and food security. In this research, we investigate two distinct methods for classifying plant diseases: Convolutional neural networks (CNN) for deep learning and logistic regression (LR) along with Random Forest Classifier (RFC) for machine learning. We use a collection of plant pictures representing various diseases to train and evaluate LR and CNN models. The CNN model automatically learns hierarchical representations, while the LR model relies on manually created features extracted from the images. Our analysis reveals that both LR and CNN models achieve high accuracy in classifying plant diseases, with CNN surpassing LR due to its ability to recognize complex image patterns. The CNN model's performance in our experiment outperforms other models in terms of accuracy. The experiment's findings underscore the effectiveness of deep learning and machine learning techniques in classifying plant diseases. © 2024 IEEE.},
note = {1},
keywords = {ISE},
pubstate = {published},
tppubtype = {proceedings}
}
Categorizing plant diseases is crucial for ensuring agricultural production and food security. In this research, we investigate two distinct methods for classifying plant diseases: Convolutional neural networks (CNN) for deep learning and logistic regression (LR) along with Random Forest Classifier (RFC) for machine learning. We use a collection of plant pictures representing various diseases to train and evaluate LR and CNN models. The CNN model automatically learns hierarchical representations, while the LR model relies on manually created features extracted from the images. Our analysis reveals that both LR and CNN models achieve high accuracy in classifying plant diseases, with CNN surpassing LR due to its ability to recognize complex image patterns. The CNN model's performance in our experiment outperforms other models in terms of accuracy. The experiment's findings underscore the effectiveness of deep learning and machine learning techniques in classifying plant diseases. © 2024 IEEE.
15.
C, Kumar; Benazir, A. J.; Ramesh, C. S.
Numerical investigation of heat and mass transfer of SWCNT/MWCNT-water suspension over a porous stretching sheet using Sisko fluid model Journal Article
In: ZAMM Zeitschrift fur Angewandte Mathematik und Mechanik, 2024, ISBN: 00442267 (ISSN), (0).
@article{28,
title = {Numerical investigation of heat and mass transfer of SWCNT/MWCNT-water suspension over a porous stretching sheet using Sisko fluid model},
author = {Kumar C and A. J. Benazir and C. S. Ramesh},
doi = {10.1002/zamm.202300573},
isbn = {00442267 (ISSN)},
year = {2024},
date = {2024-01-01},
journal = {ZAMM Zeitschrift fur Angewandte Mathematik und Mechanik},
publisher = {John Wiley and Sons Inc},
abstract = {This study presents a comprehensive numerical computation of heat-mass transfer characteristics of single-walled carbon nanotube (SWCNT)/multi-walled carbon nanotube (MWCNT)-water suspension flow over a porous stretching sheet with an inclined magnetic field. The governing equations for fluid flow characteristics are formulated using the Sisko fluid model to capture the Newtonian and non-Newtonian behavior of the nanotube-water mixture. The nonlinear coupled partial differential equations are converted into nonlinear dimensionless coupled ordinary differential equations using suitable similarity transformations. These equations are solved using MATLAB by implementing the four-stage Lobatto IIIa formula. The comprehensive set of computations is performed to explore the influence of pertinent parameters, including Sisko fluid parameters, concentration of nanotubes, stretching sheet velocity, and porous medium characteristics on the flow, heat, and mass transfer profiles. From the graphs and statistical analysis, it is clear that the volume fraction of SWCNT and MWCNTs are strongly correlated. The investigation reveals that increasing the inclination angle affects the fluid velocity. The variation in all flow features is negligible for volume fractions of CNTs between 0% and 10% but a significant effect is observed only beyond 10%. Higher volume fractions of CNTs result in enhanced local heat transfer coefficient. This can be attributed due to the outstanding heat transfer capabilities of CNTs owing to their high thermal conductivity. However, Shear thickening fluids exhibit high heat transfer phenomena when compared to shear-thinning and Newtonian fluids. This research provides valuable insights into the optimization of CNT-based nanofluids for efficient heat and mass transfer applications in electronics cooling, heat exchangers, and solar energy systems, offering opportunities to enhance energy efficiency and device performance. © 2024 Wiley-VCH GmbH.},
note = {0},
keywords = {MATH},
pubstate = {published},
tppubtype = {article}
}
This study presents a comprehensive numerical computation of heat-mass transfer characteristics of single-walled carbon nanotube (SWCNT)/multi-walled carbon nanotube (MWCNT)-water suspension flow over a porous stretching sheet with an inclined magnetic field. The governing equations for fluid flow characteristics are formulated using the Sisko fluid model to capture the Newtonian and non-Newtonian behavior of the nanotube-water mixture. The nonlinear coupled partial differential equations are converted into nonlinear dimensionless coupled ordinary differential equations using suitable similarity transformations. These equations are solved using MATLAB by implementing the four-stage Lobatto IIIa formula. The comprehensive set of computations is performed to explore the influence of pertinent parameters, including Sisko fluid parameters, concentration of nanotubes, stretching sheet velocity, and porous medium characteristics on the flow, heat, and mass transfer profiles. From the graphs and statistical analysis, it is clear that the volume fraction of SWCNT and MWCNTs are strongly correlated. The investigation reveals that increasing the inclination angle affects the fluid velocity. The variation in all flow features is negligible for volume fractions of CNTs between 0% and 10% but a significant effect is observed only beyond 10%. Higher volume fractions of CNTs result in enhanced local heat transfer coefficient. This can be attributed due to the outstanding heat transfer capabilities of CNTs owing to their high thermal conductivity. However, Shear thickening fluids exhibit high heat transfer phenomena when compared to shear-thinning and Newtonian fluids. This research provides valuable insights into the optimization of CNT-based nanofluids for efficient heat and mass transfer applications in electronics cooling, heat exchangers, and solar energy systems, offering opportunities to enhance energy efficiency and device performance. © 2024 Wiley-VCH GmbH.
16.
Nagaraja, K. G.; Ramesh, H. R.
In: Electrical Engineering, vol. 106, pp. 5543-5556,, 2024, ISBN: 09487921 (ISSN), (0).
@article{30,
title = {Circulating current mitigation for renewable-based modular seven-level converter using deep learning-optimized fractional-order proportional resonant controller},
author = {K. G. Nagaraja and H. R. Ramesh},
doi = {10.1007/s00202-024-02275-1},
isbn = {09487921 (ISSN)},
year = {2024},
date = {2024-01-01},
journal = {Electrical Engineering},
volume = {106},
pages = {5543-5556,},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {Modular multi-level converters (MMCs) are often used for high and medium voltage applications. However, to reduce losses and costs, many researchers prefer a half-bridge converter. In addition, the half-bridge-based MMC is vulnerable in the event of an error, so the full-bridge MMC is used here to work with faulty network states. The losses and harmonics in the system could be reduced by using an appropriate arm voltage and circulating current control model. In order to operate the MMC in a grid-tied renewable system, both outer and inner loop control were performed. In order to realize outer-loop control, a fractional-order proportional–integral–derivative controller using a deep learning technique is proposed. An active power filter-based fractional-order proportional resonant controller with improved pulse width modulation achieves arm balancing with harmonic mitigated circulating current regulation. The simulation shows that the proposed method reduced the current and voltage harmonics to 71.56% and 10.42% through an improved control strategy based on pulse width modulation. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.},
note = {0},
keywords = {EE},
pubstate = {published},
tppubtype = {article}
}
Modular multi-level converters (MMCs) are often used for high and medium voltage applications. However, to reduce losses and costs, many researchers prefer a half-bridge converter. In addition, the half-bridge-based MMC is vulnerable in the event of an error, so the full-bridge MMC is used here to work with faulty network states. The losses and harmonics in the system could be reduced by using an appropriate arm voltage and circulating current control model. In order to operate the MMC in a grid-tied renewable system, both outer and inner loop control were performed. In order to realize outer-loop control, a fractional-order proportional–integral–derivative controller using a deep learning technique is proposed. An active power filter-based fractional-order proportional resonant controller with improved pulse width modulation achieves arm balancing with harmonic mitigated circulating current regulation. The simulation shows that the proposed method reduced the current and voltage harmonics to 71.56% and 10.42% through an improved control strategy based on pulse width modulation. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.
17.
Chandrasekhar, G. L.; Vijayakumar, Y.; Nagaral, M.; Rajesh, A.; Manjunath, K.; Kaviti, R. V. P.; Auradi, V.
Synthesis and tensile behavior of Al7475-nano B4C particles reinforced composites at elevated temperatures Journal Article
In: Materials Physics and Mechanics, vol. 52, pp. 44-57,, 2024, ISBN: 16052730 (ISSN), (0).
@article{31,
title = {Synthesis and tensile behavior of Al7475-nano B4C particles reinforced composites at elevated temperatures},
author = {G. L. Chandrasekhar and Y. Vijayakumar and M. Nagaral and A. Rajesh and K. Manjunath and R. V. P. Kaviti and V. Auradi},
doi = {10.18149/MPM.5232024_5},
isbn = {16052730 (ISSN)},
year = {2024},
date = {2024-01-01},
journal = {Materials Physics and Mechanics},
volume = {52},
pages = {44-57,},
publisher = {Institute for Problems in Mechanical Engineering, Russian Academy of Sciences},
abstract = {Materials with superior mechanical and wear properties, high strength, high stiffness, and low weight are necessary for modern technology. Mechanical characteristics of metal matrix composites are crucial to their potential use as structural materials. The current research focuses on the preparation of Al7475 alloy with 400 to 500 nm sized B4C a composite using a liquid metallurgy technique. Al7475 alloy was used to make composites with 2, 4, 6, 8 and 10 wt. % of B4C particles. Microstructural analysis was performed on the produced composites using SEM and EDS. Density, hardness, ultimate strength, yield strength, and elongation as a percentage were all measured as per ASTM norms. Further, tensile tests were conducted at room temperature, 50 and 100 °C elevated temperatures. SEM images showed that the boron carbide particles were evenly dispersed throughout the Al7475 alloy. EDS spectrums verified that Al7475 alloy contains boron carbide particles. By incorporating dual particles into the matrix, the density of Al alloy composites was lowered. Al7475 alloy with B4C composites exhibited superior tensile properties at room and elevated temperatures as compared to the base alloy. © G.L. Chandrasekhar, Y. Vijayakumar, M. Nagaral, A. Rajesh, K. Manjunath, R. Vara Prasad Kaviti, Virupaxi Auradi, 2024.},
note = {0},
keywords = {MECH},
pubstate = {published},
tppubtype = {article}
}
Materials with superior mechanical and wear properties, high strength, high stiffness, and low weight are necessary for modern technology. Mechanical characteristics of metal matrix composites are crucial to their potential use as structural materials. The current research focuses on the preparation of Al7475 alloy with 400 to 500 nm sized B4C a composite using a liquid metallurgy technique. Al7475 alloy was used to make composites with 2, 4, 6, 8 and 10 wt. % of B4C particles. Microstructural analysis was performed on the produced composites using SEM and EDS. Density, hardness, ultimate strength, yield strength, and elongation as a percentage were all measured as per ASTM norms. Further, tensile tests were conducted at room temperature, 50 and 100 °C elevated temperatures. SEM images showed that the boron carbide particles were evenly dispersed throughout the Al7475 alloy. EDS spectrums verified that Al7475 alloy contains boron carbide particles. By incorporating dual particles into the matrix, the density of Al alloy composites was lowered. Al7475 alloy with B4C composites exhibited superior tensile properties at room and elevated temperatures as compared to the base alloy. © G.L. Chandrasekhar, Y. Vijayakumar, M. Nagaral, A. Rajesh, K. Manjunath, R. Vara Prasad Kaviti, Virupaxi Auradi, 2024.
18.
Salagare, S.; Sudha, P. N.; Palani, K.
Sustainable energy harvesting system for low-power underwater sensing devices Journal Article
In: Indonesian Journal of Electrical Engineering and Computer Science, vol. 35, pp. 1379-1387,, 2024, ISBN: 25024752 (ISSN), (0).
@article{32,
title = {Sustainable energy harvesting system for low-power underwater sensing devices},
author = {S. Salagare and P. N. Sudha and K. Palani},
doi = {10.11591/ijeecs.v35.i3.pp1379-1387},
isbn = {25024752 (ISSN)},
year = {2024},
date = {2024-01-01},
journal = {Indonesian Journal of Electrical Engineering and Computer Science},
volume = {35},
pages = {1379-1387,},
publisher = {Institute of Advanced Engineering and Science},
abstract = {In marine scientific research, ocean monitoring is crucial where the battery-powered sensor devices are placed under the water to collect different information like temperature, pressure, and turbidity in underwater sensor networks (UWSNs). Thus, keeping these devices active for longer periods is challenging. In the last decades, the piezoelectric transducer (PZT) material has been used widely for constructing more environmentally friendly energy harvesting systems. The PZT harvester offers a promising solution by eliminating the need for batteries for running devices in the future with less maintenance. The PZT harvester allows the system to generate higher voltage to run low-power devices. This paper designed and developed a new renewable energy harvester system using PZT transducers for running different types of underwater sensor devices like temperature, turbidity, and obstacle sensors. The proposed PZT-based energy harvester employs a two-stage amplification model for generating higher voltage and current to run multiple devices. The sensing information collected from these sensors is transmitted to the cloud which is later utilized for analysis and decision-making. Experiment results show the proposed PZT-based energy harvester can generate a voltage of 13 volts (V) and a current of 43.3 milliampere (mA) equivalent to 562 milliwatt (mW) which is very good to run multiple low-power underwater sensor devices. © 2024 Institute of Advanced Engineering and Science. All rights reserved.},
note = {0},
keywords = {ISE},
pubstate = {published},
tppubtype = {article}
}
In marine scientific research, ocean monitoring is crucial where the battery-powered sensor devices are placed under the water to collect different information like temperature, pressure, and turbidity in underwater sensor networks (UWSNs). Thus, keeping these devices active for longer periods is challenging. In the last decades, the piezoelectric transducer (PZT) material has been used widely for constructing more environmentally friendly energy harvesting systems. The PZT harvester offers a promising solution by eliminating the need for batteries for running devices in the future with less maintenance. The PZT harvester allows the system to generate higher voltage to run low-power devices. This paper designed and developed a new renewable energy harvester system using PZT transducers for running different types of underwater sensor devices like temperature, turbidity, and obstacle sensors. The proposed PZT-based energy harvester employs a two-stage amplification model for generating higher voltage and current to run multiple devices. The sensing information collected from these sensors is transmitted to the cloud which is later utilized for analysis and decision-making. Experiment results show the proposed PZT-based energy harvester can generate a voltage of 13 volts (V) and a current of 43.3 milliampere (mA) equivalent to 562 milliwatt (mW) which is very good to run multiple low-power underwater sensor devices. © 2024 Institute of Advanced Engineering and Science. All rights reserved.
19.
Lakshmi, C. R.; Kavitha, D.; Kannadassan, D.; ShivaPanchakshari, T. G.
Slotted Ground Resonator Based Cauer LPF Proceedings
Institute of Electrical and Electronics Engineers Inc., 2024, ISBN: 979-835038328-7 (ISBN), (0).
@proceedings{33,
title = {Slotted Ground Resonator Based Cauer LPF},
author = {C. R. Lakshmi and D. Kavitha and D. Kannadassan and T. G. ShivaPanchakshari},
doi = {10.1109/ICWITE59797.2024.10502769},
isbn = {979-835038328-7 (ISBN)},
year = {2024},
date = {2024-01-01},
journal = {Proceedings of ICWITE 2024: IEEE International Conference for Women in Innovation, Technology and Entrepreneurship},
pages = {65-70,},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {The applicability of Slotted Ground Structure is well documented in various academic literature. One of the design considerations in microwave filter design is the systematic implementation based on empirical modelling. In this study, the implementation of slotted ground structures based on Dumbbell Cauer low-pass filters of different orders is carried out in a methodical manner. The research specification is successfully met by the implementation of empirical modelling techniques for the filter. The design specifications are in alignment with specification of the required filter specifications. © 2024 IEEE.},
note = {0},
keywords = {ECE},
pubstate = {published},
tppubtype = {proceedings}
}
The applicability of Slotted Ground Structure is well documented in various academic literature. One of the design considerations in microwave filter design is the systematic implementation based on empirical modelling. In this study, the implementation of slotted ground structures based on Dumbbell Cauer low-pass filters of different orders is carried out in a methodical manner. The research specification is successfully met by the implementation of empirical modelling techniques for the filter. The design specifications are in alignment with specification of the required filter specifications. © 2024 IEEE.
20.
Priyanka, R.; Teena, K. B.; Rashmi, T. V.; Reshma, J.; Nagaraj, T.; Tejaswini, N.
A Hybrid Cluster Based Intelligent IDS with Deep Belief Network to Improve the Security over Wireless Sensor Network Journal Article
In: International Journal of Intelligent Systems and Applications in Engineering, vol. 12, pp. 225-238,, 2024, ISBN: 21476799 (ISSN), (0).
@article{34,
title = {A Hybrid Cluster Based Intelligent IDS with Deep Belief Network to Improve the Security over Wireless Sensor Network},
author = {R. Priyanka and K. B. Teena and T. V. Rashmi and J. Reshma and T. Nagaraj and N. Tejaswini},
isbn = {21476799 (ISSN)},
year = {2024},
date = {2024-01-01},
journal = {International Journal of Intelligent Systems and Applications in Engineering},
volume = {12},
pages = {225-238,},
publisher = {Ismail Saritas},
abstract = {Numerous inexpensive, compact devices compose a Wireless Sensor Network (WSN). They're usually readily available to some types of attacks due to their location, which is not well protected. A large number of researchers are focusing on WSN security at the moment. This kind of network is characterized by vulnerable characteristics, such as the ability to organize oneself without a stable infrastructure and open-air transmission. To train variables for the probability-based feature vectors, a Deep Neural Network (DNN) framework that is derived from international vehicle network packets shall be applied. The detector is capable of detecting any malicious attack on the vehicle since DNN gives each category a chance to distinguish between attacks and regular packets. Intrusion Detection Systems (IDS), can help to identify and stop security attacks on vehicles. The study proposes a mechanism for enhancing the security of WSNs based on Hybrid Clusters and Intelligent Intrusion Detection Systems with Deep Belief Networks (HCIIDS-DBN). It can provide a protection system for intrusions and an analysis of vehicle attacks in real time. They are designed based on their respective attack probability and ability, to the sensor node, sink, or cluster head. The proposed HCIIDS-DBN is composed of modules designed to detect anomalies and dereliction. The objective is to increase detection rates and decrease false positive incidences by detecting anomalies and abuse. Finally, the detected data are integrated and the various types of vehicle communication attacks are reported using the Decision Support System (DSS). The results of the experiment show that the proposed method may respond to the attack in real-time with a much detection of higher ratio in the Controller Area Network (CAN) bus. © 2024, Ismail Saritas. All rights reserved.},
note = {0},
keywords = {ISE},
pubstate = {published},
tppubtype = {article}
}
Numerous inexpensive, compact devices compose a Wireless Sensor Network (WSN). They're usually readily available to some types of attacks due to their location, which is not well protected. A large number of researchers are focusing on WSN security at the moment. This kind of network is characterized by vulnerable characteristics, such as the ability to organize oneself without a stable infrastructure and open-air transmission. To train variables for the probability-based feature vectors, a Deep Neural Network (DNN) framework that is derived from international vehicle network packets shall be applied. The detector is capable of detecting any malicious attack on the vehicle since DNN gives each category a chance to distinguish between attacks and regular packets. Intrusion Detection Systems (IDS), can help to identify and stop security attacks on vehicles. The study proposes a mechanism for enhancing the security of WSNs based on Hybrid Clusters and Intelligent Intrusion Detection Systems with Deep Belief Networks (HCIIDS-DBN). It can provide a protection system for intrusions and an analysis of vehicle attacks in real time. They are designed based on their respective attack probability and ability, to the sensor node, sink, or cluster head. The proposed HCIIDS-DBN is composed of modules designed to detect anomalies and dereliction. The objective is to increase detection rates and decrease false positive incidences by detecting anomalies and abuse. Finally, the detected data are integrated and the various types of vehicle communication attacks are reported using the Decision Support System (DSS). The results of the experiment show that the proposed method may respond to the attack in real-time with a much detection of higher ratio in the Controller Area Network (CAN) bus. © 2024, Ismail Saritas. All rights reserved.
21.
Kumar, Sandeep; Pai, Vaikunta; Shenoy, Ashwin; Santhosh, S.; Rakesh, V. S.; Prashanth, B. S.
Institute of Electrical and Electronics Engineers Inc., 2024, ISBN: 979-835030641-5 (ISBN), (0).
@proceedings{36,
title = {Optimizing Agricultural Productivity: A Data-Driven Ensemble Model for Crop Recommendation Based on Site-Specific Characteristics and Weather Conditions in India},
author = {Sandeep Kumar and Vaikunta Pai and Ashwin Shenoy and S. Santhosh and V. S. Rakesh and B. S. Prashanth},
doi = {10.1109/IITCEE59897.2024.10467680},
isbn = {979-835030641-5 (ISBN)},
year = {2024},
date = {2024-01-01},
journal = {Proceedings of the 2nd International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2024},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {India's economy and employment are significantly impacted by agriculture. Indian farmers frequently make the mistake of selecting the incorrect crop for the characteristics of their land. The effect is a decrease in productivity. Careful crop selection is necessary for farmers to provide high-quality harvests. We have discovered a solution to the farmers' dilemma. Here, we introduce an ensemble model that uses a majority voting approach recommendation system to provide extremely precise crop recommendations for parameters unique to each site, such as soil nutrients (nitrogen, phosphorus, potassium, and pH level) and local weather conditions (temperature, humidity, and rainfall). The methods we use to do this include Decision Tree, Random Forest, K-Nearest Neighbors, and Naive Bayes. © 2024 IEEE.},
note = {0},
keywords = {CSE},
pubstate = {published},
tppubtype = {proceedings}
}
India's economy and employment are significantly impacted by agriculture. Indian farmers frequently make the mistake of selecting the incorrect crop for the characteristics of their land. The effect is a decrease in productivity. Careful crop selection is necessary for farmers to provide high-quality harvests. We have discovered a solution to the farmers' dilemma. Here, we introduce an ensemble model that uses a majority voting approach recommendation system to provide extremely precise crop recommendations for parameters unique to each site, such as soil nutrients (nitrogen, phosphorus, potassium, and pH level) and local weather conditions (temperature, humidity, and rainfall). The methods we use to do this include Decision Tree, Random Forest, K-Nearest Neighbors, and Naive Bayes. © 2024 IEEE.
22.
Vasantha, M.; Sudarsanan, D.; Santosh, M.; Madhura, G. K.
Developing smart cities by integrating blockchain-based GRNN with CSO-transformed paillier encryption model Book Chapter
In: pp. 94-108,, CRC Press, 2024, ISBN: 978-104011271-7 (ISBN); 978-103260708-5 (ISBN), (0).
@inbook{37,
title = {Developing smart cities by integrating blockchain-based GRNN with CSO-transformed paillier encryption model},
author = {M. Vasantha and D. Sudarsanan and M. Santosh and G. K. Madhura},
doi = {10.1201/9781003460367-7},
isbn = {978-104011271-7 (ISBN); 978-103260708-5 (ISBN)},
year = {2024},
date = {2024-01-01},
journal = {Blockchain for IoT Systems: Concept, Framework and Applications},
pages = {94-108,},
publisher = {CRC Press},
note = {0},
keywords = {CSE},
pubstate = {published},
tppubtype = {inbook}
}
23.
Ganesh, D. R.; William, P.; Biradar, V. S.; Varalatchoumy, M.; Singh, C.; Deepak, A.; Shrivastava, A.
Energy-Efficient Resource Allocation and Relay-Selection for Wireless Sensor Networks Journal Article
In: International Journal of Intelligent Systems and Applications in Engineering, vol. 12, pp. 113-121,, 2024, ISBN: 21476799 (ISSN), (20).
@article{38,
title = {Energy-Efficient Resource Allocation and Relay-Selection for Wireless Sensor Networks},
author = {D. R. Ganesh and P. William and V. S. Biradar and M. Varalatchoumy and C. Singh and A. Deepak and A. Shrivastava},
isbn = {21476799 (ISSN)},
year = {2024},
date = {2024-01-01},
journal = {International Journal of Intelligent Systems and Applications in Engineering},
volume = {12},
pages = {113-121,},
publisher = {Ismail Saritas},
abstract = {We study a cooperative wireless network in the framework of this inquiry. This network is made up of two transceiver nodes that connect with one another through two-way amplify-and-forward (AF) relay nodes that have a limited amount of energy. This network is used to study the problem. The energy that is included inside the signal that has been received is used by the relay nodes in order to magnify the signal before it is retransmitted to the transceiver nodes. As a consequence of this, the transceiver nodes are able to transfer both information and energy at the same time. In order to accomplish simultaneous information extraction and energy harvesting at the relay, we study a time switching-based relaying (TSR) protocol in addition to a power splitting-based relaying (PSR) mechanism. TSR stands for time switching relaying, while PSR stands for power splitting relaying. By using the dual decomposition strategy, we are able to provide a solution to the problem that is close to optimal. The findings of the simulation indicate that the joint resource allocation plan that was recommended fulfils the required requirements for service quality, and that the degree of energy efficiency that may be achieved is greater than that of some projects that are currently being worked on. In addition, the resource allocation approach that has been provided works better in terms of convergence under a variety of topologies. This demonstrates the high scalability of the resource allocation system. The results of an in-depth simulation are presented to illustrate how well our proposed method works in terms of the distribution of transmitting power among the nodes and the overall utility that the network provides. As a consequence of this, there is reason to be positive about the future of practical applications including the joint optimization technique. © 2024, Ismail Saritas. All rights reserved.},
note = {20},
keywords = {CSE},
pubstate = {published},
tppubtype = {article}
}
We study a cooperative wireless network in the framework of this inquiry. This network is made up of two transceiver nodes that connect with one another through two-way amplify-and-forward (AF) relay nodes that have a limited amount of energy. This network is used to study the problem. The energy that is included inside the signal that has been received is used by the relay nodes in order to magnify the signal before it is retransmitted to the transceiver nodes. As a consequence of this, the transceiver nodes are able to transfer both information and energy at the same time. In order to accomplish simultaneous information extraction and energy harvesting at the relay, we study a time switching-based relaying (TSR) protocol in addition to a power splitting-based relaying (PSR) mechanism. TSR stands for time switching relaying, while PSR stands for power splitting relaying. By using the dual decomposition strategy, we are able to provide a solution to the problem that is close to optimal. The findings of the simulation indicate that the joint resource allocation plan that was recommended fulfils the required requirements for service quality, and that the degree of energy efficiency that may be achieved is greater than that of some projects that are currently being worked on. In addition, the resource allocation approach that has been provided works better in terms of convergence under a variety of topologies. This demonstrates the high scalability of the resource allocation system. The results of an in-depth simulation are presented to illustrate how well our proposed method works in terms of the distribution of transmitting power among the nodes and the overall utility that the network provides. As a consequence of this, there is reason to be positive about the future of practical applications including the joint optimization technique. © 2024, Ismail Saritas. All rights reserved.
24.
Joseph, S.; Hegde, A. R.; Gopalakrishnan, V.; Yallappa, S.; Nadzri, N. I. M.; Joseph, K.; Meenakshi, K.
Biodegradable Plastics from Mango Seed Starch for Sustainable Food Packaging-Effect of Citric Acid and Fillers Journal Article
In: ChemistrySelect, vol. 9, pp. e202401312+, 2024, ISBN: 23656549 (ISSN), (1).
Abstract | Links | Tags: CCCIR
@article{40,
title = {Biodegradable Plastics from Mango Seed Starch for Sustainable Food Packaging-Effect of Citric Acid and Fillers},
author = {S. Joseph and A. R. Hegde and V. Gopalakrishnan and S. Yallappa and N. I. M. Nadzri and K. Joseph and K. Meenakshi},
doi = {10.1002/slct.202401312},
isbn = {23656549 (ISSN)},
year = {2024},
date = {2024-01-01},
journal = {ChemistrySelect},
volume = {9},
pages = {e202401312+},
publisher = {John Wiley and Sons Inc},
abstract = {Starch based bioplastics were prepared and characterized with fillers carboxy methyl cellulose (CMC), chitosan (CS) and nanochitosan (NCS); plasticizer sorbitol and cross-linking agent citric acid (CA) at different loading levels. Structural, water resistance, water vapor permeability, solubility, mechanical, antimicrobial and biodegradation properties of the bioplastics were investigated. FTIR confirmed bonding of starch with cross-linking agent, fillers and plasticizer. The water uptake of the CMC bioplastics was decreased up to 70 % with 20 % CA addition. Bioplastics with CS and NCS showed lesser water uptake, only 12.5 % for CS bioplastic. The water vapour permeability (WVP) of all bioplastics was low in the range 2.16×10−7–6.29×10−7 gday−1 m−1 Pa−1. The CA addition increased water resistance and lowered WVP of the bioplastics by restricting the hydrophilic functional groups and forming more tight structures. The fillers CS and NCS additions enhanced the mechanical strength attributing to more hydrogen bonding between NH3+ of chitosan and OH− of starch. All bioplastics demonstrated good antimicrobial activities. These bioplastics offer an attractive alternative to non-biodegradable plastics used in food packaging due to its enhanced water resistance, antibacterial properties and biodegradability. © 2024 Wiley-VCH GmbH.},
note = {1},
keywords = {CCCIR},
pubstate = {published},
tppubtype = {article}
}
Starch based bioplastics were prepared and characterized with fillers carboxy methyl cellulose (CMC), chitosan (CS) and nanochitosan (NCS); plasticizer sorbitol and cross-linking agent citric acid (CA) at different loading levels. Structural, water resistance, water vapor permeability, solubility, mechanical, antimicrobial and biodegradation properties of the bioplastics were investigated. FTIR confirmed bonding of starch with cross-linking agent, fillers and plasticizer. The water uptake of the CMC bioplastics was decreased up to 70 % with 20 % CA addition. Bioplastics with CS and NCS showed lesser water uptake, only 12.5 % for CS bioplastic. The water vapour permeability (WVP) of all bioplastics was low in the range 2.16×10−7–6.29×10−7 gday−1 m−1 Pa−1. The CA addition increased water resistance and lowered WVP of the bioplastics by restricting the hydrophilic functional groups and forming more tight structures. The fillers CS and NCS additions enhanced the mechanical strength attributing to more hydrogen bonding between NH3+ of chitosan and OH− of starch. All bioplastics demonstrated good antimicrobial activities. These bioplastics offer an attractive alternative to non-biodegradable plastics used in food packaging due to its enhanced water resistance, antibacterial properties and biodegradability. © 2024 Wiley-VCH GmbH.
25.
Lakshmipathy, M.; Prasad, M. J. S.; Kodandaramaiah, G. N.
Advanced ambient air quality prediction through weighted feature selection and improved reptile search ensemble learning Journal Article
In: Knowledge and Information Systems, vol. 66, pp. 267-305,, 2024, ISBN: 02191377 (ISSN), (2).
@article{41,
title = {Advanced ambient air quality prediction through weighted feature selection and improved reptile search ensemble learning},
author = {M. Lakshmipathy and M. J. S. Prasad and G. N. Kodandaramaiah},
doi = {10.1007/s10115-023-01947-x},
isbn = {02191377 (ISSN)},
year = {2024},
date = {2024-01-01},
journal = {Knowledge and Information Systems},
volume = {66},
pages = {267-305,},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {Air pollution causes a pivotal impact throughout the world that affects natural resources. It also makes hazardous damage to the environment and defects in human health. The World Health Organization states that the report of air pollution is the major reason of human ailments such as lung cancer, early death, asthma, premature birth, and stroke. Due to the influence of changes in weather and climate caused by air pollution, global warming, acid rain, rainfall declines and depletion of the ozone layer occur. To mitigate these issues, preventive measures for air quality are prerequisites. Therefore, air quality monitoring is considered the main aspect of acquiring decision-making support that yields accurate predictions. In addition, there is a need of evaluating the quality of ambient (outdoor) air depending on the observations of pollutants. To achieve this, an automated air quality prediction model is proposed by using modified probability ratio-based RSA (MPR-RSA) and ensemble-based air quality prediction (EAQP). In the first step, the input data are undergone the preprocessing step. The preprocessing is done through various methods such as data imputation, data cleansing, and data transformation. Then, the preprocessed data are given to extract the significant features. The extracted features are obtained by statistical features, spatial features, and temporal features. To enhance the predictive accuracy, the weighted feature selection is employed, where the weight parameter is optimized by the proposed MPR-RSA algorithm. Then, the classification process is accomplished by EAQP, where the hyper-parameters are optimized by the same MPR-RSA algorithm. Here, the ensemble model is constructed by a single Prediction approach as support vector regression, recurrent neural network, extreme learning, bi-directional long short-term memory, and multi-layer perceptron neural network. Finally, the performance is analyzed with various parameters to prove that the proposed model becomes an advanced air quality prediction. Throughout the analysis, the RMSE of the proposed model achieves 9.96%, which can be a lesser value than the other existing heuristic algorithms. Hence, the proposed prediction model attains the low value of RMSE and MAE, which offers early forecasts of ambient air pollution to evade the damage and impacts to the environment. © 2023, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.},
note = {2},
keywords = {ECE},
pubstate = {published},
tppubtype = {article}
}
Air pollution causes a pivotal impact throughout the world that affects natural resources. It also makes hazardous damage to the environment and defects in human health. The World Health Organization states that the report of air pollution is the major reason of human ailments such as lung cancer, early death, asthma, premature birth, and stroke. Due to the influence of changes in weather and climate caused by air pollution, global warming, acid rain, rainfall declines and depletion of the ozone layer occur. To mitigate these issues, preventive measures for air quality are prerequisites. Therefore, air quality monitoring is considered the main aspect of acquiring decision-making support that yields accurate predictions. In addition, there is a need of evaluating the quality of ambient (outdoor) air depending on the observations of pollutants. To achieve this, an automated air quality prediction model is proposed by using modified probability ratio-based RSA (MPR-RSA) and ensemble-based air quality prediction (EAQP). In the first step, the input data are undergone the preprocessing step. The preprocessing is done through various methods such as data imputation, data cleansing, and data transformation. Then, the preprocessed data are given to extract the significant features. The extracted features are obtained by statistical features, spatial features, and temporal features. To enhance the predictive accuracy, the weighted feature selection is employed, where the weight parameter is optimized by the proposed MPR-RSA algorithm. Then, the classification process is accomplished by EAQP, where the hyper-parameters are optimized by the same MPR-RSA algorithm. Here, the ensemble model is constructed by a single Prediction approach as support vector regression, recurrent neural network, extreme learning, bi-directional long short-term memory, and multi-layer perceptron neural network. Finally, the performance is analyzed with various parameters to prove that the proposed model becomes an advanced air quality prediction. Throughout the analysis, the RMSE of the proposed model achieves 9.96%, which can be a lesser value than the other existing heuristic algorithms. Hence, the proposed prediction model attains the low value of RMSE and MAE, which offers early forecasts of ambient air pollution to evade the damage and impacts to the environment. © 2023, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.
26.
Vimala, P.; Usha, C.
Impact of material in gate engineering of various TFET architectures Book Chapter
In: pp. 333-351,, Springer Nature, 2024, ISBN: 978-981996649-3 (ISBN); 978-981996648-6 (ISBN), (0).
@inbook{44,
title = {Impact of material in gate engineering of various TFET architectures},
author = {P. Vimala and C. Usha},
doi = {10.1007/978-981-99-6649-3_25},
isbn = {978-981996649-3 (ISBN); 978-981996648-6 (ISBN)},
year = {2024},
date = {2024-01-01},
journal = {Handbook of Emerging Materials for Semiconductor Industry},
pages = {333-351,},
publisher = {Springer Nature},
abstract = {Following Moore's law, semiconductor mainstream electronics (processors, memories, etc.) enjoyed a very dynamic evolution over decades. Key to this success was the continuous scaling of the silicon metal-oxide-semiconductor field-effect transistor (Si MOSFET). In 2020, Si MOSFETs with 20-nm gates is in mass production, currently we are in 7-nm gate, and the International Technology Roadmap for Semiconductors (ITRS) predicts 3-nm technology in future. As the MOSFETs are scaled down to nanometer scales, the prime area of concern becomes the drive current deterioration. This is because of the very high vertical and horizontal electrical fields which reduce the carrier mobility and hence the drive current in scaled devices. Field Effect Transistor is the backbone of semiconductor electronics. It represents the basic building block of systems of modern information and communication technology, and progress in the important field critically depends on rapid improvements of FET performance. An efficient option to achieve the goal is the introduction of novel channel materials into FET technology. In the last decade, the Tunnel Field Effect Transistors (TFETs) have received a significant attention in the semiconductor community, as a promising candidate for future low power high-speed applications. The ambipolar behavior and low ON-state current are the major disadvantages of TFETs, and it can be overcome by introducing gate engineering in various TFET models. The book chapter focused on the various devices to improve the drain current and to reduce the ambipolar behavior such as double material double gate (DMDG) TFET, triple material double gate (TMDG) TFET, double material triple gate (DMTG) TFET, triple material triple gate (TMTG) TFET, double material gate-all-around (DMGAA) TFET, and triple material gate-all-around (TMGAA) TFET. © Springer Nature Singapore Pte Ltd. 2024. All rights reserved.},
note = {0},
keywords = {ECE},
pubstate = {published},
tppubtype = {inbook}
}
Following Moore's law, semiconductor mainstream electronics (processors, memories, etc.) enjoyed a very dynamic evolution over decades. Key to this success was the continuous scaling of the silicon metal-oxide-semiconductor field-effect transistor (Si MOSFET). In 2020, Si MOSFETs with 20-nm gates is in mass production, currently we are in 7-nm gate, and the International Technology Roadmap for Semiconductors (ITRS) predicts 3-nm technology in future. As the MOSFETs are scaled down to nanometer scales, the prime area of concern becomes the drive current deterioration. This is because of the very high vertical and horizontal electrical fields which reduce the carrier mobility and hence the drive current in scaled devices. Field Effect Transistor is the backbone of semiconductor electronics. It represents the basic building block of systems of modern information and communication technology, and progress in the important field critically depends on rapid improvements of FET performance. An efficient option to achieve the goal is the introduction of novel channel materials into FET technology. In the last decade, the Tunnel Field Effect Transistors (TFETs) have received a significant attention in the semiconductor community, as a promising candidate for future low power high-speed applications. The ambipolar behavior and low ON-state current are the major disadvantages of TFETs, and it can be overcome by introducing gate engineering in various TFET models. The book chapter focused on the various devices to improve the drain current and to reduce the ambipolar behavior such as double material double gate (DMDG) TFET, triple material double gate (TMDG) TFET, double material triple gate (DMTG) TFET, triple material triple gate (TMTG) TFET, double material gate-all-around (DMGAA) TFET, and triple material gate-all-around (TMGAA) TFET. © Springer Nature Singapore Pte Ltd. 2024. All rights reserved.
27.
Dhanalakshmi, R. V.; Prasanna, B. R.; Tiwari, R.; Pavanathil, R. J.; Kumar, K. S.; Mathiyarasan, M.
In: vol. 536, pp. 421-430,, Springer Science and Business Media Deutschland GmbH, 2024, ISBN: 21984182 (ISSN), (0).
@inbook{50,
title = {Predictive Analytics in Retail: Revealing the Strategic Impact of Advertising Channels on Sales Performance Through Python and Linear Regression Model},
author = {R. V. Dhanalakshmi and B. R. Prasanna and R. Tiwari and R. J. Pavanathil and K. S. Kumar and M. Mathiyarasan},
doi = {10.1007/978-3-031-63402-4_35},
isbn = {21984182 (ISSN)},
year = {2024},
date = {2024-01-01},
journal = {Studies in Systems, Decision and Control},
volume = {536},
pages = {421-430,},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {This research paper presents a comprehensive sales prediction model tailored for the retail industry, specifically focusing on diverse products within this sector. Leveraging advanced machine learning techniques such as Linear Regression and Random Forest Regression, the model assesses the nuanced impact of various advertising channels, with a particular emphasis on television (including a few social media like YouTube advertisements and Mobile Applications like Disney + Hotstar, SunNxt, etc.), radio, and newspaper mediums. Notably, the findings emphasize the pivotal role of TV advertising in driving sales, offering strategic guidance for resource allocation and marketing strategies. This research contributes to the enhancement of decision-making processes within the retail industry, empowering stakeholders to optimize marketing approaches and navigate the dynamic landscape with confidence and precision. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.},
note = {0},
keywords = {MBA},
pubstate = {published},
tppubtype = {inbook}
}
This research paper presents a comprehensive sales prediction model tailored for the retail industry, specifically focusing on diverse products within this sector. Leveraging advanced machine learning techniques such as Linear Regression and Random Forest Regression, the model assesses the nuanced impact of various advertising channels, with a particular emphasis on television (including a few social media like YouTube advertisements and Mobile Applications like Disney + Hotstar, SunNxt, etc.), radio, and newspaper mediums. Notably, the findings emphasize the pivotal role of TV advertising in driving sales, offering strategic guidance for resource allocation and marketing strategies. This research contributes to the enhancement of decision-making processes within the retail industry, empowering stakeholders to optimize marketing approaches and navigate the dynamic landscape with confidence and precision. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
28.
Bharani, B. R.; Murtugudde, G.; Sreenivasa, B. R.; Verma, A.; Al-Yarimi, F. A. M.; Khan, M. I.; Eldin, S. M.
Grey wolf optimization and enhanced stochastic fractal search algorithm for exoplanet detection Journal Article
In: European Physical Journal Plus, vol. 138, pp. 424+, 2023, ISBN: 21905444 (ISSN), (5).
@article{1,
title = {Grey wolf optimization and enhanced stochastic fractal search algorithm for exoplanet detection},
author = {B. R. Bharani and G. Murtugudde and B. R. Sreenivasa and A. Verma and F. A. M. Al-Yarimi and M. I. Khan and S. M. Eldin},
doi = {10.1140/epjp/s13360-023-04024-y},
isbn = {21905444 (ISSN)},
year = {2023},
date = {2023-01-01},
journal = {European Physical Journal Plus},
volume = {138},
pages = {424+},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {Detection of Exoplanet had been an ‘intensely active’ exploration area within Astronomy where several attempts are made. In the proposed research work, exoplanet detection was done using a Kepler Dataset. Data pre-processing was carried out through Mean Imputation which was found to be the most common procedure of replacing missing value. For assessing Imputation Method’s performance, Normalized Root Mean Square Error was calculated. In feature selection method, a novel combination of Grey Wolf Optimizer (GWO) based on Enhanced Stochastic Fractal Search Algorithm (ESFSA) had been utilized, in a more advanced manner, for reducing the number of normalized input values to those which were highly beneficial. Lastly, after finding the best optimum values and delivering them to Random Forest (RF), the exoplanet got classified into 3 categories—False Positive, Not Detected as well as Candidate. The research work also showed the quantitative analysis of proposed GWO-based ESFSA with other feature selection methods and RF classifier with other existing classifiers. Overall comparative analysis of the proposed method with other related works (present in the literature) was also carried out. As observed, GWO-based ESFSA provided outstanding results—99.74% of recall, 99.80% of specificity, 99.81% of accuracy, 99.98% of sensitivity, 98.84% of precision and 97.21% of F1-score, and proved its superiority over existing methods. © 2023, The Author(s), under exclusive licence to Società Italiana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature.},
note = {5},
keywords = {CSE},
pubstate = {published},
tppubtype = {article}
}
Detection of Exoplanet had been an ‘intensely active’ exploration area within Astronomy where several attempts are made. In the proposed research work, exoplanet detection was done using a Kepler Dataset. Data pre-processing was carried out through Mean Imputation which was found to be the most common procedure of replacing missing value. For assessing Imputation Method’s performance, Normalized Root Mean Square Error was calculated. In feature selection method, a novel combination of Grey Wolf Optimizer (GWO) based on Enhanced Stochastic Fractal Search Algorithm (ESFSA) had been utilized, in a more advanced manner, for reducing the number of normalized input values to those which were highly beneficial. Lastly, after finding the best optimum values and delivering them to Random Forest (RF), the exoplanet got classified into 3 categories—False Positive, Not Detected as well as Candidate. The research work also showed the quantitative analysis of proposed GWO-based ESFSA with other feature selection methods and RF classifier with other existing classifiers. Overall comparative analysis of the proposed method with other related works (present in the literature) was also carried out. As observed, GWO-based ESFSA provided outstanding results—99.74% of recall, 99.80% of specificity, 99.81% of accuracy, 99.98% of sensitivity, 98.84% of precision and 97.21% of F1-score, and proved its superiority over existing methods. © 2023, The Author(s), under exclusive licence to Società Italiana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature.
29.
Devi, Vimala; Kavitha, K. S.
Adaptive deep Q learning network with reinforcement learning for crime prediction Journal Article
In: Evolutionary Intelligence, vol. 16, pp. 685-696,, 2023, ISBN: 18645909 (ISSN), (3).
@article{2,
title = {Adaptive deep Q learning network with reinforcement learning for crime prediction},
author = {Vimala Devi and K. S. Kavitha},
doi = {10.1007/s12065-021-00694-8},
isbn = {18645909 (ISSN)},
year = {2023},
date = {2023-01-01},
journal = {Evolutionary Intelligence},
volume = {16},
pages = {685-696,},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {Crime prediction models are very useful for the police force to prevent crimes from happening and to reduce the crime rate of the city. Existing crime prediction models are not efficient in handling the data imbalance and have an overfitting problem. In this research, an adaptive DRQN model is proposed to develop a robust crime prediction model. The proposed adaptive DRQN model includes the application of GRU instead of LSTM unit to store the relevant features for the effective classification of Sacramento city crime data. The storage of relevant features for a long time helps to handle the data imbalance problem and irrelevant features are eliminated to avoid overfitting problems. Adaptive agents based on the MDP are applied to adaptively learn the input data and provide effective predictions. The reinforcement learning method is applied in the proposed adaptive DRQN model to select the optimal state value and to identify the best reward value. The proposed adaptive DRQN model has an MAE of 36.39 which is better than the existing Recurrent Q-Learning model has 38.82 MAE. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.},
note = {3},
keywords = {CSE},
pubstate = {published},
tppubtype = {article}
}
Crime prediction models are very useful for the police force to prevent crimes from happening and to reduce the crime rate of the city. Existing crime prediction models are not efficient in handling the data imbalance and have an overfitting problem. In this research, an adaptive DRQN model is proposed to develop a robust crime prediction model. The proposed adaptive DRQN model includes the application of GRU instead of LSTM unit to store the relevant features for the effective classification of Sacramento city crime data. The storage of relevant features for a long time helps to handle the data imbalance problem and irrelevant features are eliminated to avoid overfitting problems. Adaptive agents based on the MDP are applied to adaptively learn the input data and provide effective predictions. The reinforcement learning method is applied in the proposed adaptive DRQN model to select the optimal state value and to identify the best reward value. The proposed adaptive DRQN model has an MAE of 36.39 which is better than the existing Recurrent Q-Learning model has 38.82 MAE. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
30.
Rekha, J.; Suma, S. P.; Shilpa, B.; Khan, U.; Hussain, S. M.; Zaib, A.; Galal, A. M.
Solute transport exponentially varies with time in an unsaturated zone using finite element and finite difference method Journal Article
In: International Journal of Modern Physics B, vol. 37, pp. 2350089+, 2023, ISBN: 02179792 (ISSN), (4).
@article{5,
title = {Solute transport exponentially varies with time in an unsaturated zone using finite element and finite difference method},
author = {J. Rekha and S. P. Suma and B. Shilpa and U. Khan and S. M. Hussain and A. Zaib and A. M. Galal},
doi = {10.1142/S0217979223500893},
isbn = {02179792 (ISSN)},
year = {2023},
date = {2023-01-01},
journal = {International Journal of Modern Physics B},
volume = {37},
pages = {2350089+},
publisher = {World Scientific},
abstract = {Among several aspects, the one contributing towards the difficulty of groundwater quality evaluation is the large diversity of contamination sources. As contaminants comprising various compounds move from the soil to the water table, they will travel through several hydrologic zones. In constant unidirectional flow fields, a mathematical study of simultaneous adsorption and dispersion of a solute inside homogeneous and isotropic permeable media is described. The solute is adsorbed at a rate proportionate to its concentration in the dispersion systems, which are susceptible to input concentrations that fluctuate exponentially with time. The advection-dispersion equation (ADE) was solved numerically in this work to analyze the pollutants transport bearing in mind the coefficient of distribution and permeability by considering pollutant input concentrations. The solution is derived using the Laplace transform and Duhamel's theorem with moving coordinates. For specified medium and fluid characteristics, mathematical methods are created to forecast the concentration of pollutants in adsorbing porous media. © 2023 World Scientific Publishing Company.},
note = {4},
keywords = {MATH},
pubstate = {published},
tppubtype = {article}
}
Among several aspects, the one contributing towards the difficulty of groundwater quality evaluation is the large diversity of contamination sources. As contaminants comprising various compounds move from the soil to the water table, they will travel through several hydrologic zones. In constant unidirectional flow fields, a mathematical study of simultaneous adsorption and dispersion of a solute inside homogeneous and isotropic permeable media is described. The solute is adsorbed at a rate proportionate to its concentration in the dispersion systems, which are susceptible to input concentrations that fluctuate exponentially with time. The advection-dispersion equation (ADE) was solved numerically in this work to analyze the pollutants transport bearing in mind the coefficient of distribution and permeability by considering pollutant input concentrations. The solution is derived using the Laplace transform and Duhamel's theorem with moving coordinates. For specified medium and fluid characteristics, mathematical methods are created to forecast the concentration of pollutants in adsorbing porous media. © 2023 World Scientific Publishing Company.
31.
Kumar, S.; Setty, S. L. N.
UFS-LSTM: unsupervised feature selection with long short-term memory network for remote sensing scene classification Journal Article
In: Evolutionary Intelligence, vol. 16, pp. 299-315,, 2023, ISBN: 18645909 (ISSN), (1).
@article{7,
title = {UFS-LSTM: unsupervised feature selection with long short-term memory network for remote sensing scene classification},
author = {S. Kumar and S. L. N. Setty},
doi = {10.1007/s12065-021-00660-4},
isbn = {18645909 (ISSN)},
year = {2023},
date = {2023-01-01},
journal = {Evolutionary Intelligence},
volume = {16},
pages = {299-315,},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {The aim of this research is to perform remote sensing scene classification, because it supports numerous strategic research fields like land use and land cover monitoring. However, classifying an enormous amount of remote sensing data is a challenging task in scene classification. In this research work, a new model is introduced to improve the feature extraction ability for better scene classification. A multiscale Retinex technique is employed for color restoration, and contrast enhancement in the aerial images that are collected from UC Merced, aerial image dataset, and RESISC45. Further, the feature extraction is carried out using steerable pyramid transform, gray level co-occurrence matrix features, and local ternary pattern. The feature extraction mechanism reduces overfitting risks, improves training process, and data visualization ability. Generally, the extracted features are high dimension, so an unsupervised feature selection based on multi subspace randomization and collaboration with state transition algorithm is proposed for selecting active features for better multiclass classification. The selected features are fed to long short term memory network for scene type classification. The experimental results showed that the proposed model achieved 99.14 %, 98.09%, and 99.25% of overall classification accuracy on UC Merced, RESISC45 and aerial image dataset. The proposed model showed a minimum of 0.03 % and maximum of 18.6 % improvement in classification accuracy compared to the existing models like self-attention based deep feature fusion, multitask learning system with convolutional neural network, multilayer feature fusion Wasserstein generative adversarial networks, and transfer learning model on UC Merced, RESISC45 and aerial dataset, respectively. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.},
note = {1},
keywords = {CSE},
pubstate = {published},
tppubtype = {article}
}
The aim of this research is to perform remote sensing scene classification, because it supports numerous strategic research fields like land use and land cover monitoring. However, classifying an enormous amount of remote sensing data is a challenging task in scene classification. In this research work, a new model is introduced to improve the feature extraction ability for better scene classification. A multiscale Retinex technique is employed for color restoration, and contrast enhancement in the aerial images that are collected from UC Merced, aerial image dataset, and RESISC45. Further, the feature extraction is carried out using steerable pyramid transform, gray level co-occurrence matrix features, and local ternary pattern. The feature extraction mechanism reduces overfitting risks, improves training process, and data visualization ability. Generally, the extracted features are high dimension, so an unsupervised feature selection based on multi subspace randomization and collaboration with state transition algorithm is proposed for selecting active features for better multiclass classification. The selected features are fed to long short term memory network for scene type classification. The experimental results showed that the proposed model achieved 99.14 %, 98.09%, and 99.25% of overall classification accuracy on UC Merced, RESISC45 and aerial image dataset. The proposed model showed a minimum of 0.03 % and maximum of 18.6 % improvement in classification accuracy compared to the existing models like self-attention based deep feature fusion, multitask learning system with convolutional neural network, multilayer feature fusion Wasserstein generative adversarial networks, and transfer learning model on UC Merced, RESISC45 and aerial dataset, respectively. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
32.
Sharma, M.; Supriya, M.; Kumar, A.; Dhyani, K.; Chaturvedi, P.
Extraction of Water and Riverine Sand using Deep Learning on Multispectral Remote Sensing Images Proceedings
Institute of Electrical and Electronics Engineers Inc., 2023, ISBN: 979-835034060-0 (ISBN), (0).
@proceedings{8,
title = {Extraction of Water and Riverine Sand using Deep Learning on Multispectral Remote Sensing Images},
author = {M. Sharma and M. Supriya and A. Kumar and K. Dhyani and P. Chaturvedi},
doi = {10.1109/ICECA58529.2023.10395559},
isbn = {979-835034060-0 (ISBN)},
year = {2023},
date = {2023-01-01},
journal = {7th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2023 - Proceedings},
pages = {849-854,},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {The study of land-use-land cover (LULC) has become a necessity with the advancement of the urbanization process. Increased erosion of soil, increased silting, and sedimentation of the rivers are key effects that require study and analysis. Deep learning has a significant impact on classification tasks, particularly in the field of remote sensing image analysis. The proposed framework classifies LULC classes by employing the characteristics of deep learning. In this work, we compared the proposed method with the traditional machine learning methods in extracting water and riverine sand from multispectral remote sensing images. Further, we analyse the impact of Stochastic Gradient Descent (SGD) and Adam optimizers. The Adam optimizer implemented in this work gives higher accuracy than other combinations. © 2023 IEEE.},
note = {0},
keywords = {ISE},
pubstate = {published},
tppubtype = {proceedings}
}
The study of land-use-land cover (LULC) has become a necessity with the advancement of the urbanization process. Increased erosion of soil, increased silting, and sedimentation of the rivers are key effects that require study and analysis. Deep learning has a significant impact on classification tasks, particularly in the field of remote sensing image analysis. The proposed framework classifies LULC classes by employing the characteristics of deep learning. In this work, we compared the proposed method with the traditional machine learning methods in extracting water and riverine sand from multispectral remote sensing images. Further, we analyse the impact of Stochastic Gradient Descent (SGD) and Adam optimizers. The Adam optimizer implemented in this work gives higher accuracy than other combinations. © 2023 IEEE.
33.
Usha, C.; Vimala, P.
Evolution of Heterojunction Tunnel Field Effect Transistor and its Advantages Book Chapter
In: pp. 99-123,, CRC Press, 2023, ISBN: 978-100087780-9 (ISBN); 978-103234876-6 (ISBN), (0).
@inbook{12,
title = {Evolution of Heterojunction Tunnel Field Effect Transistor and its Advantages},
author = {C. Usha and P. Vimala},
doi = {10.1201/9781003327035-6},
isbn = {978-100087780-9 (ISBN); 978-103234876-6 (ISBN)},
year = {2023},
date = {2023-01-01},
journal = {Tunneling Field Effect Transistors: Design, Modeling and Applications},
pages = {99-123,},
publisher = {CRC Press},
abstract = {In 1963, Complementary Metal Oxide Semiconductor technology was introduced by Frank Wanlass. CMOS technology is an organization of two types of MOSFET such as P-type and N-type. CMOS technology has dominated the silicon industry due to various advantages. Initially in this chapter, we introduce the limitations of downscaling MOSFET devices which gave rise to short channel effects: Drain Induced Barrier Lowering, threshold roll-off, high leakage current and limits the subthreshold voltage to 60mV/dec. In order to overcome the disadvantages of conventional MOSFET device, a promising device Tunnel Field Effect Transistor was reported. The chapter is continued with the basic structure of the homojunction Double Gate (DG)TFET device. The DGTFET has shown less ON-state performance. Thus, to improve the ON-state performance of the TFET device multigate TFET devices, Gate All Around TFET and gate engineering is reported. Further, we move on to the different materials used for the formation of heterojunction TFET. Heterojunction TFET devices exhibit high forward gain and low reverse gain compared to the homojunction TFETs. And also, band-gap alignment is flexible based on the applications. The combination of silicon with other semiconductors for the formation of heterojunction provides a higher tuning of band-gap than silicon material technology. HeteroJunction Multigate TFET devices such as Double Gate(DG) HJTFET and Gate All Around HJTFET. The operation of the DG HJTFET and the advantages of the device will be discussed. The gate control of the HJTFET device is increased by a cylindrical gate that covers the substrate in all directions. Due to the increase in gate control, the tunneling of electrons increases which further increases the drain current performance. The device structure and operation of the GateAll-Around HJTFET will be discussed in detail. The chapter is further continued with the gate engineering HJTFET devices. Gate engineering is the usage of more than one material across the gate terminal which improves the performance of the device. The gate engineering is used for the Multigate and Gate-All-Around HJTFET devices. The gate engineering HJTFET devices include Double Material Double Gate(DMDG)HJTFET, Triple Material Double Gate (TMDG) HJTFET, Double Material Surrounding Gate(DMSG) HJTFET, and Triple Material Surrounding Gate (TMSG) HJTFET are explained along with device structure and its advantages. Thus, this chapter intends to study the device structure, multigate, gate engineering, electrostatic characteristics, and applications of heterojunction TFET devices. © 2023 selection and editorial matter, T.S. Arun Samuel, Young Suh Song, Shubham Tayal, P. Vimala, and Shiromani Balmukund Rahi; individual chapters, the contributors.},
note = {0},
keywords = {ECE},
pubstate = {published},
tppubtype = {inbook}
}
In 1963, Complementary Metal Oxide Semiconductor technology was introduced by Frank Wanlass. CMOS technology is an organization of two types of MOSFET such as P-type and N-type. CMOS technology has dominated the silicon industry due to various advantages. Initially in this chapter, we introduce the limitations of downscaling MOSFET devices which gave rise to short channel effects: Drain Induced Barrier Lowering, threshold roll-off, high leakage current and limits the subthreshold voltage to 60mV/dec. In order to overcome the disadvantages of conventional MOSFET device, a promising device Tunnel Field Effect Transistor was reported. The chapter is continued with the basic structure of the homojunction Double Gate (DG)TFET device. The DGTFET has shown less ON-state performance. Thus, to improve the ON-state performance of the TFET device multigate TFET devices, Gate All Around TFET and gate engineering is reported. Further, we move on to the different materials used for the formation of heterojunction TFET. Heterojunction TFET devices exhibit high forward gain and low reverse gain compared to the homojunction TFETs. And also, band-gap alignment is flexible based on the applications. The combination of silicon with other semiconductors for the formation of heterojunction provides a higher tuning of band-gap than silicon material technology. HeteroJunction Multigate TFET devices such as Double Gate(DG) HJTFET and Gate All Around HJTFET. The operation of the DG HJTFET and the advantages of the device will be discussed. The gate control of the HJTFET device is increased by a cylindrical gate that covers the substrate in all directions. Due to the increase in gate control, the tunneling of electrons increases which further increases the drain current performance. The device structure and operation of the GateAll-Around HJTFET will be discussed in detail. The chapter is further continued with the gate engineering HJTFET devices. Gate engineering is the usage of more than one material across the gate terminal which improves the performance of the device. The gate engineering is used for the Multigate and Gate-All-Around HJTFET devices. The gate engineering HJTFET devices include Double Material Double Gate(DMDG)HJTFET, Triple Material Double Gate (TMDG) HJTFET, Double Material Surrounding Gate(DMSG) HJTFET, and Triple Material Surrounding Gate (TMSG) HJTFET are explained along with device structure and its advantages. Thus, this chapter intends to study the device structure, multigate, gate engineering, electrostatic characteristics, and applications of heterojunction TFET devices. © 2023 selection and editorial matter, T.S. Arun Samuel, Young Suh Song, Shubham Tayal, P. Vimala, and Shiromani Balmukund Rahi; individual chapters, the contributors.
34.
Gayathri, T.; Mahalakshmi, K.; Shilpa, M.; Jayanthi, M. G.; Kannadaguli, P.
Comparison of Hate Speech Identification in Kannada Language Using ML and DL Models Proceedings
Institute of Electrical and Electronics Engineers Inc., 2023, ISBN: 979-835030816-7 (ISBN), (0).
@proceedings{14,
title = {Comparison of Hate Speech Identification in Kannada Language Using ML and DL Models},
author = {T. Gayathri and K. Mahalakshmi and M. Shilpa and M. G. Jayanthi and P. Kannadaguli},
doi = {10.1109/GCITC60406.2023.10425987},
isbn = {979-835030816-7 (ISBN)},
year = {2023},
date = {2023-01-01},
journal = {2023 Global Conference on Information Technologies and Communications, GCITC 2023},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {The problem at hand is to create a discrimination system specifically for Indian languages, with an emphasis on Automatic Speech Recognition (ASR) implementations. Hate speech poses a serious challenge to online websites and social media, as well as causing harm, such as spreading hate, inciting violence, and promoting inequality. Macro skills are discriminatory, there is an urgent need for a similar system for regional languages as the country has many different languages and unique cultures. Therefore, this paper intends to gauge the overall performance of 4 characteristic engineering strategies and 4 gadget learning algorithms to examine their overall performance on a publicly-to-be-had dataset with two distinct classes. The experimental consequences confirmed that the bigram capabilities when used with the help vector machine set of rules great carried out with 88% accuracy in ML and 91% of accuracy in DL. This observation has practical implications and can be used as a basis for detecting automated hate speech messages. Moreover, the output of different affinity could be utilized as country-of-artwork strategies to compare destiny research for existing computerized text classification techniques. © 2023 IEEE.},
note = {0},
keywords = {ISE},
pubstate = {published},
tppubtype = {proceedings}
}
The problem at hand is to create a discrimination system specifically for Indian languages, with an emphasis on Automatic Speech Recognition (ASR) implementations. Hate speech poses a serious challenge to online websites and social media, as well as causing harm, such as spreading hate, inciting violence, and promoting inequality. Macro skills are discriminatory, there is an urgent need for a similar system for regional languages as the country has many different languages and unique cultures. Therefore, this paper intends to gauge the overall performance of 4 characteristic engineering strategies and 4 gadget learning algorithms to examine their overall performance on a publicly-to-be-had dataset with two distinct classes. The experimental consequences confirmed that the bigram capabilities when used with the help vector machine set of rules great carried out with 88% accuracy in ML and 91% of accuracy in DL. This observation has practical implications and can be used as a basis for detecting automated hate speech messages. Moreover, the output of different affinity could be utilized as country-of-artwork strategies to compare destiny research for existing computerized text classification techniques. © 2023 IEEE.
35.
Sunitha, M.; Gamaoun, F.; Abdulrahman, A.; Malagi, Sanju; Singh, S.; Gowda, Javare; Gowda, R. J.
An efficient analytical approach with novel integral transform to study the two-dimensional solute transport problem Journal Article
In: Ain Shams Engineering Journal, vol. 14, pp. 101878+, 2023, ISBN: 20904479 (ISSN), (11).
@article{15,
title = {An efficient analytical approach with novel integral transform to study the two-dimensional solute transport problem},
author = {M. Sunitha and F. Gamaoun and A. Abdulrahman and Sanju Malagi and S. Singh and Javare Gowda and R. J. Gowda},
doi = {10.1016/j.asej.2022.101878},
isbn = {20904479 (ISSN)},
year = {2023},
date = {2023-01-01},
journal = {Ain Shams Engineering Journal},
volume = {14},
pages = {101878+},
publisher = {Ain Shams University},
abstract = {The q-homotopy analysis method (q-HAM) in combine with the novel integral transform known as Elzaki transform (ET) leads to an efficient analytical technique called, the q-homotopy analysis Elzaki transform method (q-HAETM). In the present study, the two- dimensional advection–dispersion (AD) problem is investigated using an analytical technique q-HAETM. These equations are mainly used to describe the fate of pollutants in aquifers. The analytical solutions to the AD equations are more interesting since they serve as benchmarks against which numerical solutions can be compared. The novelty of the work is to discuss the two-dimensional (2D) solute transport problem in the fractional sense. The reliability and the efficiency of the considered algorithm are demonstrated by employing the 2D fractional solute transport problem. The solute concentration profile is shown in terms of surface plots. The comparison of the exact solution and the approximate solution is done by the 2D plots. The numerical approximate error solutions are presented for different fractional orders. q-HAETM offers us to modulate the range of convergence of the series solution using ℏ, called auxiliary parameter or convergence control parameter. By performing appropriate numerical simulations in comparison with other existing techniques, the effectiveness and reliability of the considered technique are validated. The obtained findings show that the proposed method is very gratifying and examines the complex challenges that arise in science and innovation. © 2022 Faculty of Engineering, Ain Shams University},
note = {11},
keywords = {MATH},
pubstate = {published},
tppubtype = {article}
}
The q-homotopy analysis method (q-HAM) in combine with the novel integral transform known as Elzaki transform (ET) leads to an efficient analytical technique called, the q-homotopy analysis Elzaki transform method (q-HAETM). In the present study, the two- dimensional advection–dispersion (AD) problem is investigated using an analytical technique q-HAETM. These equations are mainly used to describe the fate of pollutants in aquifers. The analytical solutions to the AD equations are more interesting since they serve as benchmarks against which numerical solutions can be compared. The novelty of the work is to discuss the two-dimensional (2D) solute transport problem in the fractional sense. The reliability and the efficiency of the considered algorithm are demonstrated by employing the 2D fractional solute transport problem. The solute concentration profile is shown in terms of surface plots. The comparison of the exact solution and the approximate solution is done by the 2D plots. The numerical approximate error solutions are presented for different fractional orders. q-HAETM offers us to modulate the range of convergence of the series solution using ℏ, called auxiliary parameter or convergence control parameter. By performing appropriate numerical simulations in comparison with other existing techniques, the effectiveness and reliability of the considered technique are validated. The obtained findings show that the proposed method is very gratifying and examines the complex challenges that arise in science and innovation. © 2022 Faculty of Engineering, Ain Shams University
36.
Surendra, D. M.; Chamaraja, N. A.; Yallappa, S.; Bhavya, D. K.; Joseph, S.; Varma, R. S.; Manjanna, J.; Patel, B. M.
Efficacy of phytochemical-functionalized silver nanoparticles to control Flacherie and Sappe silkworm diseases in Bombyx mori L. larvae Journal Article
In: Plant Nano Biology, vol. 5, pp. 100048+, 2023, ISBN: 27731111 (ISSN), (2).
Abstract | Links | Tags: CCCIR
@article{16,
title = {Efficacy of phytochemical-functionalized silver nanoparticles to control Flacherie and Sappe silkworm diseases in Bombyx mori L. larvae},
author = {D. M. Surendra and N. A. Chamaraja and S. Yallappa and D. K. Bhavya and S. Joseph and R. S. Varma and J. Manjanna and B. M. Patel},
doi = {10.1016/j.plana.2023.100048},
isbn = {27731111 (ISSN)},
year = {2023},
date = {2023-01-01},
journal = {Plant Nano Biology},
volume = {5},
pages = {100048+},
publisher = {Elsevier B.V.},
abstract = {Plant extracts comprise a complex mixture of numerous phytochemicals including important alkaloids and polyphenols that can reduce metal ions, and comprise unsaturated compounds such as α-linolenic and carboxylic acid that acts as stabilizing agents in the greener assembly of nanomaterials. The present study demonstrates the role of phytoconstituents from flowers of Tridax trilobata (T. trilobata) in the synthesis of silver nanoparticles (AgNPs) that investigates their effects on the growth and development of the silkworm Bombyx mori L. besides controlling the occurrence of Flacherie and Sappe microbial diseases. FTIR and 13C NMR spectral studies confirmed the in situ role of phytochemicals from the flower extract responsible for the reduction of silver ions to AgNPs with crystalline structure, which is confirmed by XRD analysis. Compared to pure alkaloids and polyphenols, AgNPs synthesized with crude flower extract displayed synergistic antibacterial activity against Flacherie and Sappe microbial strains such as B. subtilis, S. aureus, E. coli, B. cereus, Aerobactercloacae, and S. typhi. Furthermore, AgNPs prevented the growth of biofilms in a concentration-dependent manner and an increase in inhibition is observed with concentration augmentation from 0 to 50 µg/mL. In addition, the biosynthesized AgNPs increased the feeding efficiency and improved the body weight and shell weight of Bombyx mori L. larvae, pupae, and cocoons. Overall, this integrated study found that AgNPs were effective in reducing Flacherie and Sappe disease caused by the consumption of bacterially contaminated mulberry leaves, thus improving the survival rate of Bombyx mori L. and eventually improving the crop yield through insights into the anti-biofilm activity of phytochemical-adorned AgNPs. © 2023 The Authors},
note = {2},
keywords = {CCCIR},
pubstate = {published},
tppubtype = {article}
}
Plant extracts comprise a complex mixture of numerous phytochemicals including important alkaloids and polyphenols that can reduce metal ions, and comprise unsaturated compounds such as α-linolenic and carboxylic acid that acts as stabilizing agents in the greener assembly of nanomaterials. The present study demonstrates the role of phytoconstituents from flowers of Tridax trilobata (T. trilobata) in the synthesis of silver nanoparticles (AgNPs) that investigates their effects on the growth and development of the silkworm Bombyx mori L. besides controlling the occurrence of Flacherie and Sappe microbial diseases. FTIR and 13C NMR spectral studies confirmed the in situ role of phytochemicals from the flower extract responsible for the reduction of silver ions to AgNPs with crystalline structure, which is confirmed by XRD analysis. Compared to pure alkaloids and polyphenols, AgNPs synthesized with crude flower extract displayed synergistic antibacterial activity against Flacherie and Sappe microbial strains such as B. subtilis, S. aureus, E. coli, B. cereus, Aerobactercloacae, and S. typhi. Furthermore, AgNPs prevented the growth of biofilms in a concentration-dependent manner and an increase in inhibition is observed with concentration augmentation from 0 to 50 µg/mL. In addition, the biosynthesized AgNPs increased the feeding efficiency and improved the body weight and shell weight of Bombyx mori L. larvae, pupae, and cocoons. Overall, this integrated study found that AgNPs were effective in reducing Flacherie and Sappe disease caused by the consumption of bacterially contaminated mulberry leaves, thus improving the survival rate of Bombyx mori L. and eventually improving the crop yield through insights into the anti-biofilm activity of phytochemical-adorned AgNPs. © 2023 The Authors
37.
Anuradha, A.; Shilpa, R.; Thirupathi, M.; Padmapriya, S.; Supramaniam, G.; Booshan, B.; Booshan, S.; Pol, N.; Chavadi, C. A.; Thangam, D.
Importance of Sustainable Marketing Initiatives for Supporting the Sustainable Development Goals Book Chapter
In: pp. 149-169,, IGI Global, 2023, ISBN: 978-166848683-2 (ISBN); 978-166848681-8 (ISBN), (2).
@inbook{24,
title = {Importance of Sustainable Marketing Initiatives for Supporting the Sustainable Development Goals},
author = {A. Anuradha and R. Shilpa and M. Thirupathi and S. Padmapriya and G. Supramaniam and B. Booshan and S. Booshan and N. Pol and C. A. Chavadi and D. Thangam},
doi = {10.4018/978-1-6684-8681-8.ch008},
isbn = {978-166848683-2 (ISBN); 978-166848681-8 (ISBN)},
year = {2023},
date = {2023-01-01},
journal = {Handbook of Research on Achieving Sustainable Development Goals With Sustainable Marketing},
pages = {149-169,},
publisher = {IGI Global},
abstract = {Businesses that engage in sustainable marketing can benefit both the world and their bottom line. Earlier, companies could satisfy many customers by simply providing low pricing and high-quality goods. However, people’s concern for the environment and other social concerns have grown, and so has their desire to support groups that share their beliefs. Because they often generate strong market returns and demonstrate durability during economic downturns, many investors want to support businesses that use sustainable business methods. Also, these businesses are more likely to comply with social and environmental laws. Several companies use sustainable marketing to succeed in today’s ethical and ecologically sensitive marketplace. Organizations must finance sustainability programs in order to practice sustainable marketing. But, it can also improve employee engagement, promote regulatory compliance, raise revenues, and build brand loyalty. © 2023 by IGI Global.},
note = {2},
keywords = {MBA},
pubstate = {published},
tppubtype = {inbook}
}
Businesses that engage in sustainable marketing can benefit both the world and their bottom line. Earlier, companies could satisfy many customers by simply providing low pricing and high-quality goods. However, people’s concern for the environment and other social concerns have grown, and so has their desire to support groups that share their beliefs. Because they often generate strong market returns and demonstrate durability during economic downturns, many investors want to support businesses that use sustainable business methods. Also, these businesses are more likely to comply with social and environmental laws. Several companies use sustainable marketing to succeed in today’s ethical and ecologically sensitive marketplace. Organizations must finance sustainability programs in order to practice sustainable marketing. But, it can also improve employee engagement, promote regulatory compliance, raise revenues, and build brand loyalty. © 2023 by IGI Global.
38.
Desai, P.; Preethi, S.; Loganathan, D.; Bharani, B. R.
Institute of Electrical and Electronics Engineers Inc., 2023, ISBN: 979-835034279-6 (ISBN), (0).
@proceedings{25,
title = {Qualitative and Quantitative Data Analysis using Classification, and Ensemble Techniques to Optimize and Predict the Performance of Reviews},
author = {P. Desai and S. Preethi and D. Loganathan and B. R. Bharani},
doi = {10.1109/ICAEECI58247.2023.10370889},
isbn = {979-835034279-6 (ISBN)},
year = {2023},
date = {2023-01-01},
journal = {2023 1st International Conference on Advances in Electrical, Electronics and Computational Intelligence, ICAEECI 2023},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {As an analyst deeply engaged in data analysis, it becomes imperative to discern the inherent nature of the data, classifying it into either qualitative or quantitative forms. Qualitative data necessitates preprocessing to facilitate predictive modeling of its outcomes. In the context of this research, we employ a movie review dataset to predict both negative and positive reviews. The realm of qualitative analysis faces a notable challenge in predictive capabilities, primarily due to the diverse sentiments expressed in various reviews. To address this challenge, we employ a diverse array of classifiers such as bagging, boosting and stacking to evaluate their performance in terms of accuracy, F1 score, and training time by selecting the best performers as an ensemble classifier. Subsequently, we identify the most effective classifier and apply ensemble techniques and stacking methodologies to optimize predictive accuracy. © 2023 IEEE.},
note = {0},
keywords = {ISE},
pubstate = {published},
tppubtype = {proceedings}
}
As an analyst deeply engaged in data analysis, it becomes imperative to discern the inherent nature of the data, classifying it into either qualitative or quantitative forms. Qualitative data necessitates preprocessing to facilitate predictive modeling of its outcomes. In the context of this research, we employ a movie review dataset to predict both negative and positive reviews. The realm of qualitative analysis faces a notable challenge in predictive capabilities, primarily due to the diverse sentiments expressed in various reviews. To address this challenge, we employ a diverse array of classifiers such as bagging, boosting and stacking to evaluate their performance in terms of accuracy, F1 score, and training time by selecting the best performers as an ensemble classifier. Subsequently, we identify the most effective classifier and apply ensemble techniques and stacking methodologies to optimize predictive accuracy. © 2023 IEEE.
39.
Sheela, J.; Meena, S. D.; Rao, T. P. C.; Chetty, B. M.; Gajula, S. K.; Reddy, R. R.; Kumar, M. S.; Nagesh, A. L.
Text clustering using fuzzy rule and lexical term Proceedings
American Institute of Physics Inc., vol. 2869, 2023, ISBN: 0094243X (ISSN); 978-073544684-7 (ISBN), (0).
@proceedings{29,
title = {Text clustering using fuzzy rule and lexical term},
author = {J. Sheela and S. D. Meena and T. P. C. Rao and B. M. Chetty and S. K. Gajula and R. R. Reddy and M. S. Kumar and A. L. Nagesh},
doi = {10.1063/5.0168206},
isbn = {0094243X (ISSN); 978-073544684-7 (ISBN)},
year = {2023},
date = {2023-01-01},
journal = {AIP Conference Proceedings},
volume = {2869},
pages = {050013+},
publisher = {American Institute of Physics Inc.},
abstract = {The fast development of data technology has created a straight system to store and access large quantities of information. The created system has the drawback of extracting potentially valuable knowledge not only in an efficient manner but also in a manner that is easily understood by human beings. One solution to it is in linguistic summarization. This enables the clearing of coherent data summaries that are more consistent with the human cognitive system. The major drawback of the existing applications is in involving high dimensional and distributed info which makes it tough to capture the relevant data. This research focuses on two tasks: one is in selecting the most significant content from source documents and the other is in generating coherent summary by using lexical chaining. In this research paper, an automatic method of text summarization depending on fuzzy sets to extract diversity of structures has been proposed to identify more significant information in the documents. The summary generated by the system is compared to a summary created by domain experts. This method is completely different from other proposed methods described in the literature survey. The text summary is created in the proposed method by testing eight connected features via reduced dimensionality and less fuzzy set rules used for text summarization. In this method, the documents have been summarized by probing connected features and subsequently by different fuzzy sets. The DUC dataset is used during the training and testing phases of the planned summary system. Base Line, weight, accuracy, memory, and F-measure assess the planned system. The outcome of the experiments demonstrations that the proposed technique provides well f-measure than baseline and weight approaches © 2023 Author(s).},
note = {0},
keywords = {CSE},
pubstate = {published},
tppubtype = {proceedings}
}
The fast development of data technology has created a straight system to store and access large quantities of information. The created system has the drawback of extracting potentially valuable knowledge not only in an efficient manner but also in a manner that is easily understood by human beings. One solution to it is in linguistic summarization. This enables the clearing of coherent data summaries that are more consistent with the human cognitive system. The major drawback of the existing applications is in involving high dimensional and distributed info which makes it tough to capture the relevant data. This research focuses on two tasks: one is in selecting the most significant content from source documents and the other is in generating coherent summary by using lexical chaining. In this research paper, an automatic method of text summarization depending on fuzzy sets to extract diversity of structures has been proposed to identify more significant information in the documents. The summary generated by the system is compared to a summary created by domain experts. This method is completely different from other proposed methods described in the literature survey. The text summary is created in the proposed method by testing eight connected features via reduced dimensionality and less fuzzy set rules used for text summarization. In this method, the documents have been summarized by probing connected features and subsequently by different fuzzy sets. The DUC dataset is used during the training and testing phases of the planned summary system. Base Line, weight, accuracy, memory, and F-measure assess the planned system. The outcome of the experiments demonstrations that the proposed technique provides well f-measure than baseline and weight approaches © 2023 Author(s).
40.
Patel, S.; Dinesh, P. A.; Suma, S. P.; Sushma, T. C.; Gayathri, M. S.
Characteristic Analysis of Soret and Corolis Forces on a Natural Convection in a Finite Cavity with Isotropic and Anisotropic Permeable Media Journal Article
In: Journal of Mines, Metals and Fuels, vol. 71, pp. 2708-2717,, 2023, ISBN: 00222755 (ISSN), (0).
@article{35,
title = {Characteristic Analysis of Soret and Corolis Forces on a Natural Convection in a Finite Cavity with Isotropic and Anisotropic Permeable Media},
author = {S. Patel and P. A. Dinesh and S. P. Suma and T. C. Sushma and M. S. Gayathri},
doi = {10.18311/jmmf/2023/41762},
isbn = {00222755 (ISSN)},
year = {2023},
date = {2023-01-01},
journal = {Journal of Mines, Metals and Fuels},
volume = {71},
pages = {2708-2717,},
publisher = {Informatics Publishing Limited and Books and Journals Private Ltd},
abstract = {Using 3D transmission in a definite cavity with anisotropic and isotropic permeable media rotating at a fixed rotational velocity, the Rayleigh-Benard issue for a viscous, unstable, laminar, incompressible fluid heated from below a horizontal layer is extended in this paper's research. Seven controlling PDEs from the given physical configuration are similarly transformed to produce a system of non-dimensional ODEs. The Rayleigh, Taylor, and Prandtl numbers are examined for their impacts on temperature gradient and velocity in both isotropic and anisotropic conditions using the Fourier series approach. It has been discussed and determined that the results of the stream function and isotherms on a variety of factors are good. © 2023, Informatics Publishing Limited and Books and Journals Private Ltd. All rights reserved.},
note = {0},
keywords = {MATH},
pubstate = {published},
tppubtype = {article}
}
Using 3D transmission in a definite cavity with anisotropic and isotropic permeable media rotating at a fixed rotational velocity, the Rayleigh-Benard issue for a viscous, unstable, laminar, incompressible fluid heated from below a horizontal layer is extended in this paper's research. Seven controlling PDEs from the given physical configuration are similarly transformed to produce a system of non-dimensional ODEs. The Rayleigh, Taylor, and Prandtl numbers are examined for their impacts on temperature gradient and velocity in both isotropic and anisotropic conditions using the Fourier series approach. It has been discussed and determined that the results of the stream function and isotherms on a variety of factors are good. © 2023, Informatics Publishing Limited and Books and Journals Private Ltd. All rights reserved.