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An Intelligent Agent Based Optimal Scheduling & Congestion Free Routing Scheme to Enhance QoS of NFV Enabled Heterogeneous Wireless Sensor Network Framework 15

An Intelligent Agent Based Optimal Scheduling & Congestion F...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Prathap, C. Sivaranjani, S. Sathya, M. Prabhakaran Rajalakshmi, J. Kavin Kumar, K. Gokulchandrasekaran NadarSaraswathi College of Engineering and Technology Department of Computer Science and Engineering Theni India SethuInstituteofTechnology Department of Computer Science and Engineering Virudhnagar India Nadar Saraswathi College of Engineering and Technology Department of Information Technology Theni India Nandha Engineering College Department of Electronics and Communication Engineering Erode India Sethu Institute of Technology Department of Electronics and Communication Engineering Virudhunagar India Kongu Engineering College Perundurai Department of Electronics and Communication Engineering Erode638060 India Karpagam Institute of Technology Department of Electronics and Communication Engineering Coimbatore India

ISBN: (纸本)9798350370249

In wireless sensor network (WSN), congestion is regarded as an unavoidable issue as of today's scenario at which the data traffic is increased to their aggregated channel capacity. This results in the consequence of buffer overflowing which leads to packets drop thereby reducing packet delivery ratio and degrades network throughput. Also, routing is the most favored approaches for the minimization of nodes energy consumption and the enhancement of Heterogeneous WSNs throughput. Since, the problem of routing has been proved to be an NP-hard, it has been realized that optimization-based technique offers better performance. For solving these issues, this paper proposes a congestion free optimal routing scheme based on Genetic algorithm-based distance aware-Low Energy Adaptive Clustering Hierarchy (GADA-LEACH) protocol to predict intelligent agent path. The major intention of this work is to enhance the efficiency of energy and QoS (throughput and delay) in edge-cloud based IoT environment of data transmission. Initially, the system model is initialized and the graph construction is made for the process of clustering using Spectral graph theory (SGT) at which the cluster head (CH) selection is made. Then, the scheduling process is carried by means of Packet weighted based ensemble optimization scheduling (PW-EOS) scheme. The NFV enabled optimal edge node is selected using Reconfigured Cuckoo Search optimizer (RCSO) after which the intelligent agent moving path in the Heterogeneous Network (HetNet) are predicted by GADA-LEACH technique. From this, an effective routing path is determined among CH and an optimized Edge node. Eventually, the performance assessment is carried in terms of throughput, energy consumption, survival rate, PDR, End-to-End delay and the outcomes conquered are compared with existing methods to attest the proficiency of proposed scheme. © 2024 IEEE.

关键词： Sensor nodes

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Investigation on compressed air energy storage system for efficient utilisation of renewable power sources

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International Journal of Power and Energy Conversion 2024年第3期15卷 281-293页

作者： Karthika, J. Annamalai, T. Kumar, P. Veera Sanjeeva Olana, Firew Dereje Rasu, N. Govindha Department of Electrical and Electronics Engineering Sri Krishna College of Engineering and Technology Kuniamuthur Coimbatore641008 India Department of Electrical and Electronics Engineering Sri Venkateswara College of Engineering Tamil Nadu Sriperumbudur602117 India Department of Mechanical Engineering Annamacharya Institute of Technology and Sciences Andhra Pradesh Rajampet516126 India Department of Electrical and Computer Engineering Mattu University Post-318 Mattu Ethiopia School of Mechanical Engineering Vellore Institute of Technology Vellore India

In recent years, emerging nations have shifted their focus to renewable energy sources for electricity production. This shift has happened in both industrialised and underdeveloped countries. The percentage of electricity produced overall in most developed countries that originates from renewable sources of energy is rising significantly. Currently, the grid is going through a number of modifications to improve its intelligence and stability. These adjustments are becoming more pronounced. Although there are many various types of storage systems available, the CAES system is the most appropriate for large-capacity storage that is ideal for grid integration. However, the overall roundtrip efficiency is less than that of a pumped hydro storage system when it comes to large-scale renewable energy storage. © 2024 Inderscience Enterprises Ltd.

关键词： Compressors

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Ensembled Machine Learning Models for Antenna Optimization in Wireless Communication/ Biomedical Applications 3

Ensembled Machine Learning Models for Antenna Optimization i...

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3rd International Conference on Communication, Control, and Intelligent Systems, CCIS 2024

作者： Upadhyay, Nidhi Kaur, Amanpreet Singh, Ashima Pattanayak, Arnab Thapar Institute of Engineering and Technology Department of Electronics and Communication Engineering Punjab India Thapar Institute of Engineering and Technology Department of Computer Science Engineering Punjab India

ISBN: (纸本)9798331528201

In this work, the antenna optimisation process is done by using ML algorithms such as KNN, random forest, Gradient Boost, XG Boost and MLP. Then the predicted results are ensembled using Voting Regressor. A Cylindrical Dielectric Resonator Antenna (CDRA) was designed and simulated in CST MWS'23 for this purpose. Dataset is created by varying the radius of the DR using parametric sweep function of the EM simulator. A close prediction between actual values and the predicted ones is obtained using these ML models. It is observed that by ensembling these varied approaches, a model that can handle different types of relationships in the data, can be created. This algorithm can be further utilized for optimisation of other antenna parameters and thus helping in reducing time in optimising complex antenna designs. The accuracy achieved by the ensemble model is 98.6% while maintaining a small MSE of 0.001. The optimized design shows stable characteristics within the ISM band and hence can be used for biomedical as well as wireless communication applications. © 2024 IEEE.

关键词： Random forests

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Salp Swarm Incorporated Adaptive Dwarf Mongoose Optimizer with Lévy Flight and Gbest-Guided Strategy

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Journal of Bionic engineering 2024年第4期21卷 2110-2144页

作者： Gang Hu Yuxuan Guo Guanglei Sheng Department of Applied Mathematics Xi’an University of TechnologyXi’an710054People’s Republic of China School of Computer Science and Engineering Xi’an University of TechnologyXi’an710048People’s Republic of China Department of Electronics and Information Engineering Bozhou UniversityBozhou236800People’s Republic of China

In response to the shortcomings of Dwarf Mongoose Optimization(DMO)algorithm,such as insufficient exploitation capability and slow convergence speed,this paper proposes a multi-strategy enhanced DMO,referred to as ***,we propose an improved solution search equation that utilizes the Gbest-guided strategy with different parameters to achieve a trade-off between exploration and exploitation(EE).Secondly,the Lévy flight is introduced to increase the diversity of population distribution and avoid the algorithm getting stuck in a local *** addition,in order to address the problem of low convergence efficiency of DMO,this study uses the strong nonlinear convergence factor Sigmaid function as the moving step size parameter of the mongoose during collective activities,and combines the strategy of the salp swarm leader with the mongoose for cooperative optimization,which enhances the search efficiency of agents and accelerating the convergence of the algorithm to the global optimal solution(Gbest).Subsequently,the superiority of GLSDMO is verified on CEC2017 and CEC2019,and the optimization effect of GLSDMO is analyzed in *** results show that GLSDMO is significantly superior to the compared algorithms in solution quality,robustness and global convergence rate on most test ***,the optimization performance of GLSDMO is verified on three classic engineering examples and one truss topology optimization *** simulation results show that GLSDMO achieves optimal costs on these real-world engineering problems.

关键词： Dwarf mongoose optimization algorithm Gbest-guided Lévy flight Adaptive parameter Salp swarm algorithm engineering optimization Truss topological optimization

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Suppression of over voltage in SiC-based inverter fed induction motor

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Australian Journal of Electrical and electronics engineering 2024年第1期21卷 11-28页

作者： Salau, Ayodeji Olalekan Kanchana, Kadirvel Anoop, Kuttichakku Johny Markus, Elisha Didam Braide, Sepiribo Lucky Department of Electrical/Electronics and Computer Engineering Afe Babalola University Ado-Ekiti Nigeria Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India Department of Electrical Electronics Engineering Saveetha Engineering College Chennai India Department of Electrical and Electronics Engineering VISAT Engineering College Kerala Ernakulam India Department of Electrical/Electronic and Computer Engineering Central University of Technology Free State Bloemfontein South Africa Department of Electrical/Electronic Engineering University of Port Harcourt Rivers State Choba Nigeria

Silicon carbide (SiC) based inverters are widely used due to their fast switching and ability to withstand high temperatures and Voltage. This helps to increase efficiency and reduces switching losses compared to the silicon-based inverter. However, this fast-switching device causes an increase in the Voltage at the junction of the cable and motor due to the reflected wave. The main causes of overvoltage are cable/motor impedance mismatch, fast-switching inverter devices (IGBT), and inverter-motor separation. To extend the induction motor lifespan, overvoltage reduction is necessary. Passive and active filters solve the problem, but passive filters are bulky and have high power loss. This paper presents a shunt active filter approach for an inverter-fed induction motor to reduce overvoltage. To show the effectiveness of the proposed approach, the findings were compared to the topology of the clamp-type active filter already in use. The shunt active filter used Proportional Integral (PI) and fuzzy controllers to generate reference current according to the Synchronous Reference Frame (SRF) theory. Harmonics in the power system were removed using the SRF theory. The unique aspect of this work is how the reference current is produced in the shunt active filter using the SRF theory. The proposed shunt active filter with a fuzzy controller was added to the hardware implementation to achieve better results than previous active filters. ©, Engineers Australia.

关键词： Silicon carbide

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Synergistic applications of nanostructured boron nitride in photocatalysis and targeted drug delivery

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Nanotechnology for Environmental engineering 2024年第2期9卷 179-188页

作者： Mali, Priyadarshani Shivakumar Jagtap, Bhagyashree Krishnarao Gowda V, Dankan Ashreetha, B. Vasanthakumar, G.U. kaur, Mandeep Hariram, Venkatesan Department of Electronics and Telecommunications Engineering Bharati Vidyapeeth’s college of Engineering Kolhapur Maharashtra416013 India Department of Electronics and Computer Engineering Sharad Institute of Technology college of Engineering Yadrav Maharashtra Ichalkaranji Kolhapur416115 India Department of Electronics and Communication Engineering BMS Institute of Technology and Management Bangalore Karnataka560064 India Department of Electronics and Communication Engineering School of Engineering Mohan Babu University Andhra Pradesh Tirupati India Department of Computer Science and Engineering Nitte Meenakshi Institute of Technology Yelahanka Bengaluru560064 India Department of Electronics and Communication Engineering Punjabi University Punjab Patiala147002 India Department of Practice of Medicine Vinayaka Mission’s Homoeopathic Medical College and Hospital A Constituent College of Vinayaka Mission’s Research Foundation Deemed to be University Tamilnadu Salem636 308 India

This study explores the applications of nanostructured boron nitride (BN) in photocatalysis and targeted drug delivery, highlighting its notable thermal conductivity, chemical stability, and wide bandgap. Through the synthesis and detailed characterization of BN nanoparticles, employing Scanning Electron Microscopy (SEM) and Transmission Electron Microscopy (TEM), the material’s efficacy in light-induced chemical reactions and potential as a nanocarrier for drug delivery systems are investigated. Photocatalytic experiments reveal an enhanced ability of BN to absorb light and facilitate charge separation, suggesting a promising approach for environmental detoxification and sustainable energy generation. In parallel, investigations into BN’s drug loading capacity, release kinetics, and targeting efficiency, particularly in the context of cancer therapy, are conducted. Findings indicate that BN nanoparticles significantly enhance photocatalytic performance and offer a safe, effective platform for controlled drug release. This research underscores the dual utility of BN in addressing critical challenges in environmental engineering and medical treatment, pointing towards novel interdisciplinary applications of this versatile nanomaterial. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Photocatalysis

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Single-Frequency Self-Alignment RF Resonant Beam for Information and Power Transfer

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IEEE Internet of Things Journal 2025年第11期12卷 16622-16636页

作者： Jiang, Qingwei Liu, Mingqing Xu, Mengyuan Fang, Wen Xiong, Mingliang Liu, Qingwen Zhou, Shengli Tongji University College of Computer Science and Technology Shanghai201804 China Tongji University College of Electronics and Information Engineering Shanghai201804 China University of Connecticut Department of Electrical and Computer Engineering StorrsCT06250 United States

Due to power attenuation, improving transmission efficiency in the radio-frequency (RF) band remains a significant challenge, which hinders advancements in various fields of the Internet of Things (IoT), such as wireless power transfer (WPT) and wireless communication. Array design and retro-directive beamforming (RD-BF) techniques offer simple and effective ways to enhance transmission efficiency. However, when the target is an array or in the near field, the RD-BF system (RD-BFS) cannot radiate more energy to the target due to phase irregularities in the target region, resulting in challenges in achieving higher efficiency. To address this issue, we propose the RF-based resonant beam system (RF-RBS), which adaptively optimizes phase and power distribution between transmitting and receiving arrays by leveraging the resonance mechanism to achieve higher transmission efficiency. We analyze the system structure and develop an analytical model to evaluate power flow and resonance establishment. Numerical analysis demonstrates that the proposed RF-RBS achieves self-alignment without beam control and provides higher transmission efficiency compared to RD-BFS, with improvements of up to 16%. This self-alignment capability allows the system to effectively transfer power and information across varying distances and offsets. The numerical results indicate the capability to transmit watt-level power and achieve 21 bps/Hz of downlink spectral efficiency in indoor settings, highlighting the advantages of RF-RBS in information and power transfer for mobile applications. © 2014 IEEE.

关键词： Inductive power transmission

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Enhancing Human Action Recognition with Adaptive Hybrid Deep Attentive Networks and Archerfish Optimization

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computers, Materials & Continua 2024年第9期80卷 4791-4812页

作者： Ahmad Yahiya Ahmad Bani Ahmad Jafar Alzubi Sophers James Vincent Omollo Nyangaresi Chanthirasekaran Kutralakani Anguraju Krishnan Department of Accounting and Finance Faculty of BusinessMiddle East UniversityAmman11831Jordan Faculty of Engineering Al-Balqa Applied UniversitySalt19117Jordan Department of Mathematics Kongunadu College of Engineering and Technology(Autonomous)TholurpattiTrichy621215India Department of Computer Science and Software Engineering Jaramogi Oginga Odinga University of Science and TechnologyBondo210-40601Kenya Department of Electronics and Communication Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennai602105India Department of Electronics and Communication Engineering Saveetha Engineering College(Autonomous)Chennai602105India Department of Computer Science and Engineering Kongunadu College of Engineering and Technology(Autonomous)TholurpattiTrichy621215India

In recent years,wearable devices-based Human Activity Recognition(HAR)models have received significant *** developed HAR models use hand-crafted features to recognize human activities,leading to the extraction of basic *** images captured by wearable sensors contain advanced features,allowing them to be analyzed by deep learning algorithms to enhance the detection and recognition of human *** lighting and limited sensor capabilities can impact data quality,making the recognition of human actions a challenging *** unimodal-based HAR approaches are not suitable in a real-time ***,an updated HAR model is developed using multiple types of data and an advanced deep-learning ***,the required signals and sensor data are accumulated from the standard *** these signals,the wave features are *** the extracted wave features and sensor data are given as the input to recognize the human *** Adaptive Hybrid Deep Attentive Network(AHDAN)is developed by incorporating a“1D Convolutional Neural Network(1DCNN)”with a“Gated Recurrent Unit(GRU)”for the human activity recognition ***,the Enhanced Archerfish Hunting Optimizer(EAHO)is suggested to fine-tune the network parameters for enhancing the recognition *** experimental evaluation is performed on various deep learning networks and heuristic algorithms to confirm the effectiveness of the proposed HAR *** EAHO-based HAR model outperforms traditional deep learning networks with an accuracy of 95.36,95.25 for recall,95.48 for specificity,and 95.47 for precision,*** result proved that the developed model is effective in recognizing human action by taking less ***,it reduces the computation complexity and overfitting issue through using an optimization approach.

关键词： Human action recognition multi-modal sensor data and signals adaptive hybrid deep attentive network enhanced archerfish hunting optimizer 1D convolutional neural network gated recurrent units

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DeepFakeGuard: Safeguarding Digital Platforms Against Fake Profiles Using AI 4

DeepFakeGuard: Safeguarding Digital Platforms Against Fake P...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Swarna Sudha, M. Manjula, S. Bharathi, Indira Krishnasamy, Valarmathi Vijayalakshmi, K. Ramco Institute of Technology Department of Computer Science and Engineering Rajapalayam India School of Computer Science and Engineering Vellore Institute of Technology Chennai India PSR Engineering College Department of Electronics and Communication Engineering Sivakasi India

ISBN: (纸本)9798331523923

This research introduces DeepFakeGuard, a hybrid deep learning framework designed to detect fake profiles on social media platforms, addressing the growing threat of fraudulent accounts online. DeepFakeGuard integrates multiple data modalities, including textual, visual, behavioral, and network features, to accurately classify profiles as real or fake. Leveraging advanced deep learning models such as BERT for textual analysis, ResNet for image processing, and LSTM for modeling behavioral patterns, DeepFakeGuard provides a comprehensive and robust solution for identifying fake profiles across diverse platforms. The framework processes a rich set of profile characteristics, including account information, posting behavior, post sentiment, and network relationships, ensuring a multi-dimensional analysis of social media accounts. The hybrid architecture of DeepFakeGuard merges outputs from individual sub-models, which are then processed through dense layers for final classification using a sigmoid activation function. This multi-faceted approach significantly enhances detection accuracy, precision, and recall compared to traditional methods. The framework is highly scalable and adaptable to various social media platforms, offering a versatile tool for strengthening online security. With strong performance metrics, including high classification accuracy, DeepFakeGuard demonstrates its potential for real-world applications in mitigating the risks associated with fake profiles. By fostering secure and trustworthy social media environments, this research contributes to the ongoing fight against fraudulent accounts, paving the way for safer online interactions. © 2025 IEEE.

关键词： Contrastive Learning

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A novel approach to detect, analyze and block adversarial web pages

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International Journal of Information technology (Singapore) 2024年第6期16卷 3617-3622页

作者： Kumar, M. Ranjith Naik, Darshana A. Kapila, Neha Mohan, Chinnem Rama Prasad, Ch Raghava Shelke, Chetan Rao, C. V. Guru School of Computer Science and Engineering Vellore Institute of Technology Department of Computer Science and Engineering M S Ramaiah Institute of Technology Department of Electronics and Electrical Engineering Lovely Professional University Department of Computer Science and Engineering Narayana Engineering College Electronics and Communication Engineering Koneru Lakshmaiah Education Foundation Alliance College of Engineering and Design Alliance University School of CS & amp AI SR Universtity

The phenomenon of distraction is very common, and its adverse effects are seen among people. The major cause underlying this issue is the ease with which adversarial web sites and web pages can be accessed. It is of utmost importance to locate, evaluate, and actively block such web pages in an effort to comprehensively and globally solve this societal issue. Thus, the given paper proposes an extension or plug-in to detect, analyze, and block websites smartly. The proposed approach takes keywords entered by the user as input into consideration, which eventually leads to the generation of web pages and web links. The filtering of web links is done by the proposed extension, followed by the extraction of features, the utilization of support vector machines (SVM) for binary classification, and the summarization of textual data using natural language processing (NLP). Lastly, the precise results corresponding to relevant web links are presented to the users, which will increase their productivity, thereby reducing their distraction levels under all working circumstances. The performance of the proposed approach is validated against existing recent studies based on evaluation metrics such as adversarial website detection time (ms) and accuracy (%). © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Adversarial web pages Distraction Natural language processing (NLP) Support vector machine (SVM) Text summarization

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