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3rd International Conference on electronics and Renewable Systems, ICEARS 2025

作者： Sivamurugan, J. Naveen, G. Rajalakshmi, D. Reddy, P. Venkata Subba Taher, Shaik Deepak, Sannapu Madha Institute of Engineering and Technology Department of Computer Science and Engineering Chennai India Madha Institute of Engineering and Technology Department of Electronics and Communication Engineering Chennai India R.M.D. Engineering College Department of Computer Science and Engineering Thiruvallur India

ISBN: (纸本)9798331509675

The economic growth of a nation entirely depends upon the agriculture and agricultural products. In developing countries like India, agriculture is the primary source of income and its contributing 17% to the total GDP. There are plenty of factors lead to plant disease which impacts the quality and yield of plants. Though manual method of detection is time consuming and it may have chance for errors. This method is not enough to identify and limit the spread of plant disease. To establish an automated plant disease detection in farms will reduce the risk of plant disease and promotes real-time monitoring of crops in a daily basis. Artificial Intelligence (AI) took part in supporting farmers to get instant solution in selection of fertilizers, classifying the quality of agricultural products, weather prediction and soil nutrient level detection. In this study, we proposes a novel segmentation approach namely Hybrid U-Net with active contours to segment the disease affected portion on leaf which support farmers to identify disease at early stage. This study provides a comprehensive analysis of plant disease segmentation by proposed method with conventional approaches. The publically available dataset is chosen for this analysis and performance of conventional studies was compared. This study presented current trends of plant disease segmentation and several other image classification techniques. Experimental results evaluating that the proposed study improved segmentation better than conventional methods. © 2025 IEEE.

关键词： Wiener filtering

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Improved Soil Quality Prediction Model Using Deep Learning for Smart Agriculture Systems

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computer Systems Science & engineering 2023年第5期45卷 1545-1559页

作者： P.Sumathi V.V.Karthikeyan M.S.Kavitha S.Karthik Department of Information Technology SNS College of EngineeringCoimbatoreTamilnaduIndia Department of Electrical and Electronics Engineering SNS College of TechnologyCoimbatoreTamilnaduIndia Department of Computer Science&Engineering SNS College of TechnologyCoimbatoreTamilnaduIndia

Soil is the major source of infinite lives on Earth and the quality of soil plays significant role on Agriculture practices all ***,the evaluation of soil quality is very important for determining the amount of nutrients that the soil require for proper *** present decade,the application of deep learning models in many fields of research has created greater *** increasing soil data availability of soil data there is a greater demand for the remotely avail open source model,leads to the incorporation of deep learning method to predict the soil *** that concern,this paper proposes a novel model called Improved Soil Quality Prediction Model using Deep Learning(ISQP-DL).The work considers the chemical,physical and biological factors of soil in particular area to estimate the soil ***,pH rating of soil samples has been collected from the soil testing laboratory from which the acidic range has been categorized through soil test and the same data has been taken as input to the Deep Neural Network Regression(DNNR)***,soil nutrient data has been given as second input to the DNNR *** utilizing this data set,the DNNR method is used to evaluate the fertility rate by which the soil quality has been *** training and testing,the model uses Deep Neural Network Regression(DNNR),by utilizing the *** results show that the proposed model is effective for SQP(Soil Quality Prediction Model)with efficient good fitting and generality is enhanced with input features with higher rate of classification *** results show that the proposed model achieves 96.7%of accuracy rate compared with existing models.

关键词： Soil quality classification accuracy deep learning neural network soil features training and testing

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Circuit Design using Quantum Dot technology 1

Circuit Design using Quantum Dot Technology

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Manjunath, T.C. Kuravatti, Prakash Rajarajeswari College of Engineering Department of Computer Science & Engineering Bangalore India Rajeev Institute of Technology Department of Electronics and Communication Engineering Hassan India

ISBN: (纸本)9798331529246

In this paper, the harnessing QCA for High-Performance, Low-Power Arithmetic Circuits w.r.t. the focusing on Multipliers and Square Circuits is presented. The background in relation to this work is presented next. Quantum-dot Cellular Automata (QCA) is an emerging technology in nano-electronics that offers promising advantages over traditional CMOS (Complementary Metal-Oxide-Semiconductor) technology, including low power consumption, high operational speeds, and exceptional integration capabilities. These features position QCA as a potential successor to CMOS in various applications. Multipliers, critical components in digital signal processing, and square circuits, essential for mathematical computations, significantly impact system throughput and power efficiency. This research focuses on designing QCA-based multiplier and square circuits to leverage these advantages. Methods, Results, and Concluding Remarks w.r.t. the work is given here. The QCA-based multiplier was constructed using binary adders and AND gates, while a comparative analysis was conducted between QCA and CMOS technologies to evaluate performance. The evaluation considered parameters such as cell count, area, and energy dissipation, utilizing the QCA Designer tool for validation. Results demonstrate that QCA designs offer significant improvements in terms of reduced power dissipation and area requirements compared to CMOS. These findings highlight the potential of QCA technology to enhance the efficiency and integration of critical components in digital systems, marking it as a transformative approach in nano-electronics. © 2025 IEEE.

关键词： CMOS integrated circuits

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Lung Cancer Classification Using Deep Learning: A Comprehensive Approach with Modified Convolutional Neural Networks 2

Lung Cancer Classification Using Deep Learning: A Comprehens...

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2nd International Conference on Computational Intelligence and Network Systems, CINS 2024

作者： Lad, Saket Chafekar, Bhavisha Bide, Pramod Department of Electronics and Telecommunication Engineering Mumbai India Department of Computer Engineering Sardar Patel Institute of Technology Mumbai India

ISBN: (纸本)9798331504106

This paper presents a strong deep learning technique to identify lung cancer through computed tomography (CT) imagery, using the IQ-OTH/NCCD lung cancer set of data. We have come up with a convolutional neural network (CNN) that is to classify CT scans in normal, benign and malignant *** CNN architecture consists of multiple convolutional and pooling layers, dropout mechanisms as a regularizer and fully connected layers for feature extraction. We perform comprehensive data pre-processing which involves image resizing in order to improve model performance. The proposed model having achieved excellent accuracy during testing *** results reveal how deep learning could revolutionize the way we diagnose and classify lung cancer accurately with CT scans.T By arguing for the application of deep learning in clinical work to increase accuracy of diagnosis, the research shows just how significant use of artificial intelligence has become in the analysis of images taken during medical procedures. This proposed CNN based approach achieved a test accuracy of 99.55%, precision of 100%, recall of 99% and a f1-score of 98%. This approach might help radiologists make diagnoses sooner and plan treatments better, so patients do well because doctors can treat them quickly and accurately. ©2024 IEEE.

关键词： Lung cancer

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Upshot of unified power flow controller on the minimization of the severity of overloading on electric power grid

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Journal of King Saud University - engineering Sciences 2024年第6期36卷 400-408页

作者： Adetona, S.O. Olaboyin, E.B. Salawu, R.I. Okafor, F.N. Department of Electrical & Electronics Engineering University of Lagos Lagos Nigeria Department of Electronic & Computer Engineering Bells University of Technology Ogun State Ota Nigeria Nigerian Electricity Regulatory Commission Abuja Nigeria

With the objective of discovering the vital line in an electrical power grid (EPG) where the unified power flow controller (UPFC) could be inserted to minimize the severity of overloading (SOL) during contingencies, an investigation into the stiffness of single line contingencies (SLC) on the EPG is presented in this study. An overloading index (OVI) is developed and used in this contribution to arrange the transmission branches of the EPG based on their cruciality and criticality throughout SLC. A harmony search algorithm (HSA), an optimization method, has been adopted to optimally set the parameters of the UPFC to achieve the best minimum SOL of the system at the optimal location. The IEEE 30-bus network was used as the test bed. Results based on the test bed show that placing a UPFC based on the ranking of a well-known contingency severity index (CSI) minimized the SOL to 2.40×1010 as against the 8.05×105 obtained when the placement is done on the ranking based on the proposed OVI. The minimization of SOL is achieved with a reactance and reactive power of 0.036pu and 0.096pu, respectively, of the UPFC. The result also reveals that the proposed OVI identifies with a higher level of precision the vital line in the test bed for placing the UPFC for the purpose of minimizing the SOL during contingencies. © 2021 The Authors

关键词： Flow control

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Exploratory Data Analytics of Ota Transmission Sub-Station Electricity Load Patterns: Insights for Forecasting and Policy Direction

Exploratory Data Analytics of Ota Transmission Sub-Station E...

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2024 International Conference on Science, engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Oyebisi, Rafiu O. Olusanya, Olamide O. Bells University of Technology Department of Electrical/Electronics and Computer Engineering Ogun State Ota Nigeria Bells University of Technology Department of Computer Engineering Ogun State Ota Nigeria

ISBN: (纸本)9798350358155

Electricity supply and planning play a vital role in supporting economic development and improving the quality of life for communities. Effective decision-making in this domain requires a thorough understanding of electricity load patterns and consumption trends. Exploratory Data Analysis offers valuable opportunities to gain insights from electricity load data, as a use case of data science. In this study, 33KV feeders' peak load electricity measurements are acquired from Ota transmission stations, spanning four years (2019 to 2022). This data provides a rich source of information on seasonal variations in electricity consumption toward understanding demand patterns for optimizing resource allocation and infrastructure planning. Data analytics was therefore implemented on the historical peak load measurements to gather actionable insights for industry practitioners. Experimental results returns mean load value as a range from 4.24 to 20.16, indicating variations in electricity demand across the areas. The median values, ranging from 4.00 to 18.00, shows load measurements tend to center around a relatively stable level. Standard deviations value, between 1.27 and 2.61, illustrate the degree of variability in load measurements. The maximum load value discovered as 23.5 signifies the highest demand observed during the four-year study period, while the minimum load value of 1.0 signifies periods of low electricity consumption. The results also highlight variations in total load, emphasizing the importance of location-specific policies and infrastructure planning. For instance, AMJE exhibits the highest total load of 967.5, followed by Sango with 850.5 and IDI-IROKO with 689.5. Positive correlations are observed between SANGO and SUMO, indicating a tendency for these locations to experience similar load patterns. Whereas, negative correlations observed between IDI-IROKO and SUMO indicate contrasting load patterns between these locations. These correlations provide valua

关键词： Decision making

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Implementation of Secure and Verifiable Access Control Procedures using the NTRU Cryptosystem to Store Big Data in the Cloud Environment

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Recent Patents on engineering 2025年第2期19卷 57-70页

作者： Babu B, Ravindra Saxena, Swati Widjaja, Gunawan Vinodha D, Vedha Omarov, Batyrkhan Ramaiah, Gurumurthy B. Vats, Prashant Adama Ethiopia Department of Computer Science KCG College of Technology Chennai India Universitas Krisnadwipayana Jawa Barat 13077 Indonesia Department of Electronics and Communication Engineering JCT College Of Engineering and Technology Pichanur Coimbatore India International University of Tourism and Hospitality Turkistan Kazakhstan Technical and Vocational Training Institute Addis Ababa Ethiopia Department of Computer Science and Engineering Faculty of Engineering and Technology SGT University Haryana Gurugram India

Background: Due to their complexity and size, deploying ciphertexts for clouds is considered the most useful approach to accessing large data stores. Methods: However, access to a user's access legitimacy and improving a decrypted text on the cloud depending on an improved access policy (AP) specified by the data owner are the key challenges for making large data storage realistic and effective in clouds. The traditional ways either totally remove the problem of AP development or offer renewal to arbiter power, but in real-time, enhancing the AP is essential to maximising security and handling agility. Results: In this patent paper, a safe and verifiable access control program characterised by the NTRU cryptographic system for large storage of data in the clouds is proposed. Primarily, an improved NTRU decryption protocol to deal with the decryption failures of the prime NTRU is established, and in addition, the program is analysed for its security strength and computational performance. When a new AP is specified by the data owner, the cloud server allows the program to improve ciphertext and allows the owner to verify the upgrade to oppose the cloud's fraudulent behaviour. Conclusion: It enables (i) checking the user's legitimacy to access the data owner and qualified users, and (ii) allowing the user to verify the data provided by other users for the recovery of the right user. Strong analysis can prevent and block delinquency from various attacks, namely the collusion attack that could potentially target fraud users. © 2025 Bentham Science Publishers.

关键词： Cloud security

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Enhancing face recognition performance: a comprehensive evaluation of deep learning models and a novel ensemble approach with hyperparameter tuning

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Soft Computing 2024年第20期28卷 12399-12424页

作者： Selvaganesan, Jana Sudharani, B. Shekhar, S. N. Chandra Vaishnavi, K. Priyadarsini, K. Raju, K. Srujan Rao, T. Srinivasa Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R & ampD Institute of Science and Technology Avadi Tamil Nadu Chennai600062 India Guntur Dist. Andhra Pradesh Vadlamudi India Department of Electronics and Communication Engineering Sreenidhi Institute of Science and Technology Telangana Hyderabad501301 India Department of Computer Science and Engineering Sona College of Technology Tamil Nadu Salem636005 India Department of Data Science and Business Systems School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India Department of Computer Science and Engineering CMR Technical Campus Medchal Telangana Hyderabad501401 India Department of Electronics and Communication Engineering Aditya Engineering College Surampalem533437 India

In response to growing security concerns and the increasing demand for face recognition (FR) technology in various sectors, this research explores the application of deep learning techniques, specifically pre-trained Convolutional Neural Network (CNN) models, in the field of FR. The study harnesses the power of five pre-trained CNN models—DenseNet201, ResNet152V2, MobileNetV2, SeResNeXt, and Xception—for robust feature extraction, followed by SoftMax classification. A novel weighted average ensemble model, meticulously optimized through a grid search technique, is introduced to augment feature extraction and classification efficacy. Emphasizing the significance of robust data pre-processing, encompassing resizing, data augmentation, splitting, and normalization, the research endeavors to fortify the reliability of FR systems. Methodologically, the study systematically investigates hyperparameters across deep learning models, fine-tuning network depth, learning rate, activation functions, and optimization methods. Comprehensive evaluations unfold across diverse datasets to discern the effectiveness of the proposed models. Key contributions of this work encompass the utilization of pre-trained CNN models for feature extraction, extensive evaluation across multiple datasets, the introduction of a weighted average ensemble model, emphasis on robust data pre-processing, systematic hyperparameter tuning, and the utilization of comprehensive evaluation metrics. The results, meticulously analyzed, unveil the superior performance of the proposed method, consistently outshining alternative models across pivotal metrics, including Recall, Precision, F1 Score, Matthews Correlation Coefficient (MCC), and Accuracy. Notably, the proposed method attains an exceptional accuracy of 99.48% on the labeled faces in the wild (LFW) dataset, surpassing erstwhile state-of-the-art benchmarks. This research represents a significant stride in FR technology, furnishing a dependable and accurate

关键词： Convolutional neural networks

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Joint trust-based detection and signature-based authentication technique for secure localization in underwater wireless sensor network

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Multimedia Tools and Applications 2025年 1-20页

作者： Kumar, Manni Goyal, Nitin Qaisi, Ramy Mohammed Aiesh Najim, Mohd Gupta, Sachin Kumar Department of Computer Science & Engineering Chandigarh University Gharuan Punjab India Department of Computer Science Engineering School of Engineering and Technology Central University of Haryana Haryana Mahendergarh123031 India Department of Electrical and Electronic Engineering College of Engineering University of Jeddah Jeddah21589 Saudi Arabia Department of Electronics and Communication Engineering Central University of Jammu UT of J&K Jammu181143 India

Ensuring secure and accurate node localization in Underwater Wireless Sensor Networks (UWSN) is a significant challenge, as conventional methods tend to neglect the security risks associated with malicious node interference. This research addresses this issue by proposing a Joint Trust-based Detection and Signature-based Authentication (JTDSA) technique, aimed at improving both the security and accuracy of node localization in UWSNs. The technique utilizes Intrusion Detection System (IDS) methods to calculate the trust values of secondary-anchor nodes (SNs), ensuring only the most trusted SNs are selected. Furthermore, a digital signature algorithm secures the Received Signal Strength (RSS) data from tampering. The method incorporates a hybrid approach combining Angle of Arrival (AoA) and RSS ranging techniques to achieve robust localization. Simulation results demonstrate that JTDSA improves localization accuracy by 4% compared to existing methods, achieving an overall accuracy of 99.2%. The technique also reduces packet drop by 35% and enhances attack detection accuracy by 3%. These results indicate that the proposed JTDSA technique offers an effective solution for secure and reliable localization in UWSNs. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Sensor nodes

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Fuzzy clustering for electric field characterization and its application to thunderstorm interpretability

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Digital Communications and Networks 2025年第2期11卷 299-307页

作者： Xu Yang Hongyan Xing Xinyuan Ji Wei Xu Witold Pedrycz School of Electronics and Information Engineering Nanjing University of Information Science and TechnologyNanjing 210044China Department of Electrical and Computer Engineering University of AlbertaEdmonton AB T6G 2R3Canada Systems Research Institute Polish Academy of Sciences00-901 WarsawPoland Department of Computer Engineering Faculty of Engineering and Natural SciencesIstinye University34010 IstanbulTurkey

Changes in the Atmospheric Electric Field Signal(AEFS)are highly correlated with weather changes,especially with thunderstorm ***,little attention has been paid to the ambiguous weather information implicit in AEFS *** this paper,a Fuzzy C-Means(FCM)clustering method is used for the first time to develop an innovative approach to characterize the weather attributes carried by ***,a time series dataset is created in the time domain using AEFS *** AEFS-based weather is evaluated according to the time-series Membership Degree(MD)changes obtained by inputting this dataset into the ***,thunderstorm intensities are reflected by the change in distance from a thunderstorm cloud point charge to an AEF ***,a matching relationship is established between the normalized distance and the thunderstorm dominant MD in the space ***,the rationality and reliability of the proposed method are verified by combining radar charts and expert *** results confirm that this method accurately characterizes the weather attributes and changes in the AEFS,and a negative distance-MD correlation is obtained for the first *** detection of thunderstorm activity by AEF from the perspective of fuzzy set technology provides a meaningful guidance for interpretable thunderstorms.

关键词： Atmospheric electric field(AEF) Thunderstorm Fuzzy C-means(FCM) Attribute

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