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DIFFERENTIAL EVOLUTION ALGORITHM SUPPORTED RANDOM FOREST CLASSIFIER FOR EFFECTIVE FEATURE SELECTION AND CLASSIFICATION OF POWER QUALITY DISTURBANCES

UPB Scientific Bulletin, Series C: Electrical Engineering an...

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UPB Scientific Bulletin, Series C: Electrical engineering and Computer science 2023年第1期85卷 131-142页

作者： Vidhya, S. Balaji, M. Fantin Irudaya Raj, E. Kamaraj, V. Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India Department of Electrical and Electronics Engineering Sri Sivasubramaniya Nadar College of Engineering Chennai India Department of Electrical and Electronics Engineering Dr. Sivanthi Aditanar College of Engineering Chennai India

The rise in the usage of power electronic equipment, which induces a deviation in the voltage and current waveforms, has raised the relevance of power quality (PQ) issues. It is important to classify them accurately in order to address the PQ problems. One of the most effective methods to classify PQ problems is to combine Machine Learning (ML) algorithms with efficient feature extraction and feature selection techniques. In the present work, the S-transform-based feature extraction technique is adopted and extracts around 20 features from the PQ disturbance signal. And then, Differential Evolution (DE) based feature selection technique is utilized for feature selection and five optimized features are selected and given as input to the ML classifiers. It is a method that is essential in data mining in order to reduce dimensionality and enhance the performance of ML classifiers. Eight different ML classifiers namely the K-Means, Adaptive Boosting (AdaBoost), K-nearest neighbor (KNN), Naive Bayes, Recursive Support Vector Machine (R-SVM), Linear SVM, Decision Tree, and Random Forest (RF) algorithms have been employed in this work. The effectiveness of the ML Classifiers is measured in terms of various performance evaluation measures namely precision, accuracy, F1-score, recall, and specificity. The results revealed that the DE-supported RF classifier is more efficient when compared with other classifiers in classifying the PQ disturbances even in environments with noise inference. © 2023, Politechnica University of Bucharest. All rights reserved.

关键词： Support vector machines

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Dynamic Data Placement Strategy with Network Security Issues in Distributed Cloud Environment for Medical Issues: An Overview

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Recent Advances in Computer science and Communications 2024年第6期17卷 25-38页

作者： Pradhan, Devasis Behera, Majusha Gheisari, Mehdi Department of Electronics and Communication Engineering Acharya Institute of Technology Karnataka Bangalore560107 India School of Electronics Engineering Campus 12 KIIT deemed to be University Odisha Bhubaneswar India Department of Cognitive Computing Institute of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

The rapid integration of distributed cloud systems in the healthcare industry has profoundly impacted the management of valuable medical data. While this advancement has significantly improved data handling, protecting sensitive healthcare information in such a complex environment remains daunting. This comprehensive study explores the crucial intersection between dynamic data placement strategies and network security concerns in distributed cloud environments, particularly healthcare. After establishing the significance and context of this research, the survey delves into the growing need to safeguard medical data within the everevolving landscape of cloud-based healthcare systems. It lays out fundamental concepts, such as dynamic data placement and network security, highlighting their unique implications in the medical domain. Ultimately, this survey sheds light on the most effective approaches for balancing dynamic data placement and network security in the healthcare sector. This research delves into examining many tactics, evaluating their effectiveness in handling delicate medical information, and presenting tangible use cases. A key focus of this investigation is the fusion of data organization and network safety within the healthcare industry. It investigates the adaptability of dynamic data positioning techniques in fortifying network security and safeguarding against potential threats unique to the healthcare sector. Case studies of the successful implementation of these strategies in healthcare establishments are also included. © 2024, Bentham science Publishers. All rights reserved.

关键词： Electronic health record

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Collaborative learning-based inter-dependent task dispatching and co-location in an integrated edge computing system

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Digital Communications and Networks 2024年第6期10卷 1837-1850页

作者： Uchechukwu Awada Jiankang Zhang Sheng Chen Shuangzhi Li Shouyi Yang School of Software Henan Institute of Science and TechnologyXinxiang 453003China School of Electrical and Information Engineering Zhengzhou UniversityZhengzhou 450001China Department of Computing and Informatics Bournemouth UniversityPoole BH125BBUK School of Electronics and Computer Science University of SouthamptonSouthampton SO171BJUK Faculty of Information Science and Engineering Ocean University of ChinaQingdao 266100China

Recently,several edge deployment types,such as on-premise edge clusters,Unmanned Aerial Vehicles(UAV)-attached edge devices,telecommunication base stations installed with edge clusters,etc.,are being deployed to enable faster response time for latency-sensitive *** fundamental problem is where and how to offload and schedule multi-dependent tasks so as to minimize their collective execution time and to achieve high resource *** approaches randomly dispatch tasks naively to available edge nodes without considering the resource demands of tasks,inter-dependencies of tasks and edge resource *** approaches can result in the longer waiting time for tasks due to insufficient resource availability or dependency support,as well as provider ***,we present Edge Colla,which is based on the integration of edge resources running across multi-edge *** Colla leverages learning techniques to intelligently dispatch multidependent tasks,and a variant bin-packing optimization method to co-locate these tasks firmly on available nodes to optimally utilize *** experiments on real-world datasets from Alibaba on task dependencies show that our approach can achieve optimal performance than the baseline schemes.

关键词： Edge computing Collaborative learning Resource utilization Execution time Edge federation Gang scheduling

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SMART DRIVING LICENSING SYSTEM AND AUTHORIZATION OF AUTONOMOUS VEHICLE INTEGRATING WIRELESS SENSOR NETWORKS AND IoT

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Telecommunications and Radio engineering (English translation of Elektrosvyaz and Radiotekhnika) 2024年第9期83卷 69-85页

作者： Santhakumar, D. Sudhan, M.B. Prasanna, R. Manikandan, S.P. Prathaban, Banu Priya Kanna, Rajesh R. Saravanan, G. Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Computer Science and Engineering SRM Institute of Science and Technology Chennai Ramapuram India Department of Electronics and Communication Engineering SRM Institute of Science and Technology Kattankulathur Chennai India Department of Electronics and Communication Engineering Sree Sastha Institute of Engineering and Technology Chennai India Department of Networking and Communications School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India Department of Electronics and Communication Engineering Sri Sairam Institute of Technology Tamil Nadu Chennai India

The advent of autonomous vehicles has revolutionized the automotive industry, offering promising advancements in safety, efficiency, and mobility. To integrate these autonomous vehicles into our society seamlessly, it is imperative to establish a robust licensing system that ensures the safe operation of automated vehicles on public roads. This article presents an effective smart Driving Licensing System (SDLS), a digital framework designed to regulate the existing licensing system and authorize licensing for autonomous vehicles. Through a comprehensive assessment process, the system assesses the drivers and the autonomous vehicle’s ability to navigate various road conditions, respond to dynamic environments, and prioritize safety for passengers and other road users. SDLS integrates high-level security through facial and fingerprint verification for identity verification to preserve licensing data. Hacking and unauthorized access to autonomous vehicles is prevented by robust cybersecurity protocols. The key benefits of SDLS include automated data collection, integration of wireless sensor networks (WSN) and Internet of Things (IoT), smart application processing, real-time license checks, cybersecurity measures, automated testing, and digital certification. The proposed system offers a comprehensive approach to safely integrating autonomous vehicles into our transportation ecosystem. The autonomous vehicles are integrated with antennas at rooftops or into bumpers, depending upon the vehicle design which can be connected with the SDLS for signal communication. Furthermore, the SDLS establishes a framework for continuous monitoring and updates, enabling ongoing assessments to keep pace with evolving technology and safety standards. © 2024 by Begell House, Inc.

关键词： Autonomous vehicles

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Chaotic Map-Based Authentication and Key Agreement Protocol with Low-Latency for Metasystem

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Computers, Materials & Continua 2024年第3期78卷 4471-4488页

作者： Guojun Wang Qi Liu School of Electronics&Information Engineering Nanjing University of Information Science&TechnologyNanjing210044China Yancheng Polytechnic College Yancheng224000China Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services School of SoftwareNanjing University of Information Science and TechnologyNanjingChina

With the rapid advancement in exploring perceptual interactions and digital twins,metaverse technology has emerged to transcend the constraints of space-time and reality,facilitating remote AI-based *** this dynamic metasystem environment,frequent information exchanges necessitate robust security measures,with Authentication and Key Agreement(AKA)serving as the primary line of defense to ensure communication ***,traditional AKA protocols fall short in meeting the low-latency requirements essential for synchronous interactions within the *** address this challenge and enable nearly latency-free interactions,a novel low-latency AKA protocol based on chaotic maps is *** protocol not only ensures mutual authentication of entities within the metasystem but also generates secure session *** security of these session keys is rigorously validated through formal proofs,formal verification,and informal *** confronted with the Dolev-Yao(DY)threat model,the session keys are formally demonstrated to be secure under the Real-or-Random(ROR)*** proposed protocol is further validated through simulations conducted using VMware workstation compiled in HLPSL language and C *** simulation results affirm the protocol’s effectiveness in resisting well-known attacks while achieving the desired low latency for optimal metaverse interactions.

关键词： Metasystem authentication and key agreement chaotic map secure communication

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Deep Learning for Enhanced Detection and Characterization of Pulmonary Nodules 4

Deep Learning for Enhanced Detection and Characterization of...

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4th International Conference on Intelligent Technologies, CONIT 2024

作者： Lavanya, G. Muthulakshmi, M. Latha, Manohar Keerthinathan, A. Vishal Krishh, P. Sulthana, Sheba Meghana, Meka Kavya Uma Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Chennai India Amrita School of Computing Department of Electronics and Communication Engineering Amrita Vishwa Vidyapeetham Chennai India Rajalakshmi Engineering College Department of Electronics and Communication Engineering India

ISBN: (纸本)9798350349900

This study aims to develop, an effective customized Convolutional Neural Network model that can accurately identify areas on CT scans that have lung nodules and those that do not. The study utilizes two datasets, the LUNA-16 dataset for training and the IQ-OTH/NCCD dataset for testing. Additionally, we employ a U-Net model for precise lung nodule segmentation. This segmentation approach aimed to achieve a comprehensive characterization of nodules based on their spatial characteristics. The testing phase involves evaluating the performance of our trained models on the IQ-OTH/NCCD dataset, particularly focusing on localizing nodules in malignant cases. Overall, the research highlights the effectiveness of deep learning in early detection and diagnosis of lung cancer. This work contributes to improving the accuracy and reliability of automated pulmonary nodule detection systems, with a focus on identifying nodules in patients with suspected malignancies. © 2024 IEEE.

关键词： Lung cancer

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Intelligent Energy Management Strategies for Renewable Energy Sources 1

Intelligent Energy Management Strategies for Renewable Energ...

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1st International Conference on AIML-Applications for engineering and Technology, ICAET 2025

作者： Patil, Sangita Deotale, Disha Mali, Ramesh MIT School of Computing MIT ADT University Department of Electrical & Electronics Engineering Pune412201 India MIT School of Computing MIT ADT University Department of Computer Science & Engineering Pune412201 India

ISBN: (纸本)9798350355611

There have been several developments in renewable resources, standby sources of energy, and storage technologies. Because renewable sources are inconsistent, the best method to ensure supply continuity is to combine them with traditional energy to serve mini-grids or the isolated loads that are not linked to the main grid. In hybrid power system, many different energy sources are blended. Every time, effective and efficient Energy Management Strategies (EMS) are necessary when more than a source of energy is employed to supply a given load. This strategy directs the energy flow via the supply chain. This is necessary not just for standalone hybrid systems, but for hybrid power system that are linked to the main network. The intelligent control of hybrid MG has been the subject of a large number of serious research projects. A comprehensive assessment of the approaches used by a number of writers in their research work on applying energy management approaches is conducted. Standalone hybrid energy systems and the hybrid systems those are linked to the electrical grids are examples of these solutions. The aim of this paper is to give summary of various energy management schemes utilized in sustainable hybrid systems. Brief study is conducted for a number of freestanding and grid-linked hybrid system designs in order to make sure and find that which energy management technique that works well and will be applied for one renewable source is not always the best for another. © 2025 IEEE.

关键词： Hybrid power

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Efficient protection of golden nutri cereal implementing enhanced disease identification injecting hybrid split-attention mechanism through Novel SANDNES Mechanism

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Multimedia Tools and Applications 2024年第38期83卷 85499-85521页

作者： Divya, A. Sungeetha, D. Ramesh, Sundar Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai602105 India Department of Networking and Communications School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Tamilnadu Kattankulathur Chennai India

Millets, often referred to as "Nutri Cereals," hold significant potential for enhancing future food availability. Over the past 50 years, advancements such as the use of fertilizers, insecticides, and controlled irrigation have markedly increased the productivity of pearl millet crops worldwide. According to the 2020 review report by the Directorate of Millets Development, 693 million hectares of agricultural land are dedicated to pearl millet cultivation. However, managing crop diseases remains a substantial challenge for farmers. Effective crop disease management is crucial for sustainable agriculture, yet traditional algorithms often struggle with accurate detection and computational inefficiency especially in the case of golden cereal termed as "Pearl Millet". Our study introduces a novel approach for disease detection in pearl millet crops using aerial imagery, named Split-Attention Networks with Disentangled Nonlocal and Edge Supervision (SANDNES). This method leverages advanced neural network architectures to enhance disease detection accuracy and efficiency. Our comprehensive numerical evaluations demonstrate that SANDNES outperforms traditional U-Net and U-Net + Nonlocal baseline models Comprehensive evaluations demonstrate SANDNES' exceptional performance, achieving an impressive average F1 score of 89.04%, significantly surpassing other methods in accuracy and efficiency. Specifically, SANDNES achieved notable performance improvements, with F1 scores of 94.88% for healthy crops, 85.96% for honeydew, 82.97% for smut, and 92.69% for ergot. This method leverages split-attention mechanisms for precise feature localization, with Disentangled Nonlocal and Edge Supervision enhancing contextual reasoning and boundary delineation. SANDNES superior computational efficiency and high accuracy make it suitable for real-time precision agriculture applications. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024

关键词： Crops

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Ruppert's Delaunay Triangulation Refinement Scheme for Optimal RSUs Placement in Vehicle to Infrastructure Communication Network

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China Communications 2023年第7期20卷 303-320页

作者： Selvakumari P Chinnasamy A Sree Rathna Lakshmi NVS Sheela D Department of Computer Science and Engineering Tagore Engineering CollegeChennai 600048India Department of Data Science and Business Systems School of ComputingSRMIST kattankulathur CampusChennai 603203India Department of Electronics and Communication Engineering Department of ECE at Agni College of TechnologyChennai 603103India Department of Electronics and Communication Engineering Saveetha School of Engineering SIMATSChennai 600124India

Road Side Units(RSUs)are the essential component of vehicular communication for the objective of improving safety and mobility in the road *** are generally deployed at the roadside and more specifically at the intersections in order to collect traffic information from the vehicles and disseminate alarms and messages in emergency situations to the neighborhood vehicles cooperating with the ***,the development of a predominant RSUs placement algorithm for ensuring competent communication in VANETs is a challenging issue due to the hindrance of obstacles like water bodies,trees and *** this paper,Ruppert’s Delaunay Triangulation Refinement Scheme(RDTRS)for optimal RSUs placement is proposed for accurately estimating the optimal number of RSUs that has the possibility of enhancing the area of coverage during data *** RDTRS is proposed by considering the maximum number of factors such as global coverage,intersection popularity,vehicle density and obstacles present in the map for optimal RSUs placement,which is considered as the core improvement over the existing RSUs optimal placement *** is contributed for deploying requisite RSUs with essential transmission range for maximal coverage in the convex map such that each position of the map could be effectively covered by at least one RSU in the presence of *** simulation experiments of the proposed RDTRS are conducted with complex road traffic *** results of this proposed RDTRS confirmed its predominance in reducing the end-to-end delay by 21.32%,packet loss by 9.38%with improved packet delivery rate of 10.68%,compared to the benchmarked schemes.

关键词： road side units(RSUs) ruppert’s delau-nay triangulation refinement area of global coverage optimal RSUs placement

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Playing Maze using Voice Commands 14

Playing Maze using Voice Commands

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14th International Conference on computing Communication and Networking Technologies, ICCCNT 2023

作者： Sainadh, K. Vishnu Satwik, K. Ashrith, V. Vekkot, Susmitha Amrita School of Computing Department of Computer Science and Engineering Bengaluru India Department of Electronics & Communication Amrita School of Computing Bengaluru India

ISBN: (纸本)9798350335095

In recent years, speech recognition has become increasingly well-known as a result of the numerous applications it has in virtually every industry. These applications include wake-word, sentiment, command, and interactive game play. Interest in voice in games has increased recently. Voice-controlled interaction expanded video game accessibility. However, in order to prevent an unwelcome delay, using one's voice as a controller for a game calls for real-time processing. This research paper puts forward the idea of controlling the well-known maze game with speech commands recognized by a trained convolutional neural network (CNN). A dataset of the speech commands "Left", "Right", "Up", "Down", "Go", and "Stop"was prepared for training, validation, and testing. In order to recognize all six speech commands, an optimal bark spectrogram and the CNN-based spoken command recognition model is used. The results from this work indicated that, the proposed algorithm was able to recognize all six commands with a high recognition accuracy of 94.57% and recall, precision, F1-Score of 94.8%, 94.43%, 94.53% respectively. The proposed algorithm is incorporated into a maze game in the final step. © 2023 IEEE.

关键词： Statistical tests

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