1.
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
2.
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.
3.
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.
4.
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.
5.
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.
6.
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.
7.
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.
8.
Sapna, G. S.; Revanna, S. D.
An Efficient Internet of Things Interoperability Model Using Secure Access Control Mechanism Journal Article
In: International Journal of Intelligent Engineering and Systems, vol. 16, pp. 41-56,, 2023, ISBN: 2185310X (ISSN), (0).
@article{47,
title = {An Efficient Internet of Things Interoperability Model Using Secure Access Control Mechanism},
author = {G. S. Sapna and S. D. Revanna},
doi = {10.22266/ijies2023.1031.05},
isbn = {2185310X (ISSN)},
year = {2023},
date = {2023-01-01},
journal = {International Journal of Intelligent Engineering and Systems},
volume = {16},
pages = {41-56,},
publisher = {Intelligent Network and Systems Society},
abstract = {Internet of Things (IoT) is a revolutionary innovation in many aspects of our society like financial activities, communication activities, and global security such as the military and battlefields’ internet. Security and energy play a crucial role in data transmission across IoT and edge networks. In this research, a trust mechanism based on privacy access control is proposed for IoT devices’ interoperability. Most of the existing researches on achieving interoperability for IoT devices has drawbacks such as overlapping of systems, uneven distribution of data, lack of data security, high power consumption, and low optimization of resources. The main objective of this research is to focus and overcome these challenges by introducing a privacy access control mechanism that includes trust parameters of IoT device interoperability. A routing protocol for low-power and lossy networks (RPL) mode of operation is set in the direction of multipoint-to-point traffic flow, except in the downward flow direction. Sensor nodes send data packets to the sink node, which transmits the information to the server to determine the trust values in this mode. To validate the performance, a widely used lightweight low-power wireless simulator Contiki/cooja simulator is implemented. The simulation results of the proposed model have shown a transmission ratio of 100%, a receiver ratio of 30 to 100%, and the detection of malicious nodes in a simulation time of 60 minutes. With the use of the proposed trust mechanism based on privacy access control, a less packet loss ratio of 0.43% is achieved along with less power consumption of 0.4%, and the highest average residual energy of 0.87mJoules at node 30. © (2023), (Intelligent Network and Systems Society). All Rights Reserved.},
note = {0},
keywords = {ISE},
pubstate = {published},
tppubtype = {article}
}
Internet of Things (IoT) is a revolutionary innovation in many aspects of our society like financial activities, communication activities, and global security such as the military and battlefields’ internet. Security and energy play a crucial role in data transmission across IoT and edge networks. In this research, a trust mechanism based on privacy access control is proposed for IoT devices’ interoperability. Most of the existing researches on achieving interoperability for IoT devices has drawbacks such as overlapping of systems, uneven distribution of data, lack of data security, high power consumption, and low optimization of resources. The main objective of this research is to focus and overcome these challenges by introducing a privacy access control mechanism that includes trust parameters of IoT device interoperability. A routing protocol for low-power and lossy networks (RPL) mode of operation is set in the direction of multipoint-to-point traffic flow, except in the downward flow direction. Sensor nodes send data packets to the sink node, which transmits the information to the server to determine the trust values in this mode. To validate the performance, a widely used lightweight low-power wireless simulator Contiki/cooja simulator is implemented. The simulation results of the proposed model have shown a transmission ratio of 100%, a receiver ratio of 30 to 100%, and the detection of malicious nodes in a simulation time of 60 minutes. With the use of the proposed trust mechanism based on privacy access control, a less packet loss ratio of 0.43% is achieved along with less power consumption of 0.4%, and the highest average residual energy of 0.87mJoules at node 30. © (2023), (Intelligent Network and Systems Society). All Rights Reserved.