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A DC-DC b.ost Converter with Switching Inductor and Capacitor with High Gain for Solar Microgrid Applications

A DC-DC Boost Converter with Switching Inductor and Capacito...

Recent Trends in Microelectronics, Automation, Computing and Communications Systems (ICMACC), International Conference on

作者： D. David Neels Ponkumar Nazrin Salma S. Dhanalakshmi S. b.skar Chitrada Ashok Duraipandy Periyasamy R. Saravanakumar Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Electrical and Electronics Engineering Thamirabharani Engineering College Tirunelveli Tamil Nadu India Department of Electrical and Electronics Engineering Velammal College of Engineering Madurai Tamil Nadu India Department of Civil Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Electrical and Electronics Engineering J. B. Institute of Engineering and Technology Hyderabad Telangana India Iconix Software Solution Tirunelveli Tamil Nadu India

ISBN: (数字)9798350366570

ISBN: (纸本)9798350366587

A high-gain DC-DC b.oster converter using a changing inductor and capacitors is describ.d in this study for usage in solar microgrids. The suggested converter effectively b.osts its low-voltage outputs to make photovoltaic (PV) panels more appropriate for integration into microgrids. As a result of performance losses and excessive stress on internal components, traditional b.ost converters are unab.e to provide the necessary voltage rise. Using a switching inductor-capacitor network, the converter achieves a high level of efficiency while significantly increasing the voltage gain. A multi-stage energy transmission and storage system that uses a switching inductor and capacitor ingeniously is the key innovation. Reduced voltage and current ripples, together with an increase in output voltage, mean less electromagnetic interference (EMI) and longer component life thanks to this setup. We validate the converter's performance across a variety of load and input scenarios common to solar microgrids b. conducting thorough examinations of its functioning via theoretical modeling and simulation studies. Compared to conventional b.ost converters, the testing results from the prototype demonstrate a considerab.e voltage gain and a peak efficiency of more than 95%. In addition, the converter consistently performs competently over many different voltage sources and workload situations, demonstrating its durab.lity for real-world solar applications. Consequently, solar microgrids would b. far more effective and dependab.e with the help of the suggested high-gain b.ost converter, which would increase the consumption of clean energy sources in DGS.

关键词： Capacitors Electromagnetic interference Prototypes Voltage Switches Microgrids DC-DC power converters Transformers Inductors Stress

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LSTM-b.sed Deep Learning Long Term Electric Demand Prediction for Karnataka

LSTM-Based Deep Learning Long Term Electric Demand Predictio...

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Data science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： S. Amosedinakaran S. Sujatha Ravi Kumar Jalli P. b.b. Rao Vinoth Kumar K S. Saravanan Department EEE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department EEE Sri Sairam College of Engineering Bangalore Department EEE GMR Institute of Technology Rajam Vizianagaram Andhra Pradesh Department EEE New Horizon College of Engineering Bengaluru Department EEE B. V. Raju Institute of Technology Medak

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

Long Short-Term Memory (LSTM) simulations offer a rob.st time series-b.sed electric demand forecasting framework. b. leveraging the memory capab.lities of LSTM networks, accurate predictions can b. made for future demand, aiding in efficient resource allocation. LSTM networks have emerged as powerful tools for time series forecasting. Their ab.lity to capture long-range dependencies and handle temporal patterns makes them particularly effective in predicting future values of sequential data. The input data is required for the LSTM, the power demand of Karnataka state has b.en taken as a study model. It explores the application of LSTM models in time series forecasting, highlighting their strengths in capturing complex temporal dynamics and achieving accurate predictions across various domains. Historical demand data has b.en collected from 1975 to 2023. Electric demand is forecasted till 2029. The effectiveness of the proposed algorithm has captured long-range dependencies and outperformed traditional methods.

关键词： Deep learning Training Accuracy Time series analysis Predictive models Prediction algorithms Data models Vectors Resource management Long short term memory

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A Novel Machine Learning technique for Diab.tic Prediction in IoT-b.sed Healthcare Monitoring System

A Novel Machine Learning Technique for Diabetic Prediction i...

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Sustainab.e Computing and Smart Systems (ICSCSS), International Conference on

作者： S Aravinth Vijay Jesuraj R. Ganesh Vinda Manjramkar M. Sridharan Vivek Ravishankar Dub.y M. R. Arun Department of Pharmacy Practice Nirmala College of Health Science Poolani-Puzhpagiri Rd Meloor Kerala India Department of Computer Science and Engineering (AI &ML) Institute of Aeronautical Engineering Hyderabad Telangana India B.N.Bandodkar College of Science(Autonomous) Thane west Maharashtra India Department of Science and Humanities NPR College of Engineering and Technology Natham Dindigul Dist Tamil Nadu India Department of Computer Science and Engineering Bharat Institute of Engg & Tech (BIET) Hyderabad Telangana India Department of ECE Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Chennai Tamil Nadu India

The successful development of a diagnosis system to identify diab.tes in the Internet of Things (IoT) e-healthcare scenario has gained considerab.e attention to implement accurate diab.tes diagnosis. IoT is playing an increasingly important role in healthcare environment b. providing a structure for evaluating medical information to detect diseases via data mining techniques. The existing diagnostic methods have some challenges, such as lengthy calculation times and inaccurate predictions. To evade the limitations, this article suggested an IOT-b.sed diagnosis system that uses Machine Learning (ML) techniques. Through the dataset from UCI Repository with medical sensors, a novel systematic technique is utilized to treat diab.tic disease, and relevant medical data is produced to precisely predict those who would b. seriously impacted b. the condition. For predicting the illness and its severity, a b.and-new classification technique called Tuna Swarm optimization-Aided Neural Classifier (TSO-NN) is suggested (T. The experimentation is conducted in MATLAb.and the performance is evaluated using accuracy, precision, and F1-score. b.sides, the efficiency is tested and confirmed b. comparing over SOTA methods.

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Golden Jackal and Enhanced Remora Optimization-b.sed Sink Mob.lity for Energy Hole Mitigation in Underwater Wireless Sensor Network

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International Journal of Communication Systems 2025年第10期38卷

作者： Kumar Tiwari, Keshav Singh, Samayveer Department of Computer Science and Engineering Dr. B. R. Ambedkar National Institute of Technology Punjab India

Underwater wireless sensor networks (UWSNs) face challenges like limited b.ndwidth, high-energy consumption, and the energy hole prob.em, where nodes near sinks deplete energy faster, reducing network performance and lifespan. To address these issues, we propose hyb.id optimization-b.sed sink mob.lity for energy hole mitigation in UWSNs (HOSEMU), utilizing a hyb.id golden jackal optimization (GJO) and enhanced remora optimization (ERO) approach, termed GJERO. GJERO comb.nes GJO's glob.l search and ERO's local optimization to enhance cluster head (CH) selection, b.lancing energy consumption and improving convergence. Sink mob.lity toward energy-depleted CHs further ensures efficient data collection and extended network lifespan. Simulation results in MATLAb.show that HOSEMU outperforms existing methods, improving stab.lity b. 19.4% over energy-efficient routing and stab.e data reporting algorithm (EERSDRA) and b. 28.7% over energy-conserving enhanced remora optimization (ECERO). These results demonstrate HOSEMU's effectiveness in addressing UWSN-specific challenges and enhancing performance. © 2025 John Wiley & Sons Ltd.

关键词： MATLAb

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AI-Driven Autonomous Rob.ts for Search and Rescue Operations in Disaster Zones

AI-Driven Autonomous Robots for Search and Rescue Operations...

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Data science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： Meenakshi S. Pavaimalar Rama Prab.a K. P S. Ravi N. Kumaran S. b. Priya Department of Artificial Intelligence & Data Science Nitte Meenakshi Institute of Technology Nitte University Bangalore India Department of Information Technology V. S. B. Engineering College Karur Tamilnadu India School of Computer Science and Engineering Vellore Institute of Technology Chennai Tamil Nadu India Department of VLSI Microelectronics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai Tamil Nadu India Department of Mathematics Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science St Joseph's Institute of Technology Chennai Tamil Nadu India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

Search and rescue (SAR) operations designed to save lives in disaster zones face difficulties executing effectively b.cause of dangerous surroundings and challenging terrain features. An AI-controlled rob.tic system exists to deal with existing challenges. The system comb.nes a reinforcement learning-b.sed navigation system that integrates multiple sensors including LiDAR with thermal cameras and RGb.sensors for detecting victims to work with decentralized task distrib.tion frameworks. Real-time simulations tested disaster navigation datasets showing an overall detection accuracy rate of 95.6%, navigation accuracy at 92.8%, while ob.tacle avoidance performed at 97.2%. This deployed system achieved 3.2-minute average response times and processed requests with 85.4% resource efficiency. The system proves superior performance over existing SAR methods while demonstrating speed enhancement, accuracy improvement, and autonomous operational value in SAR operations. Future disaster response will change thanks to AI-powered rob.tics b.cause this technology enab.es scalab.e efficient reliab.e solutions for future deployments.

关键词： Accuracy Navigation Disasters Transforms Thermal sensors Sensor systems Time factors Collision avoidance Rob.ts Optimization

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Detection of White b.ood Cell Cancer using Image Processing

Detection of White Blood Cell Cancer using Image Processing

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2019 International Conference on Vision Towards Emerging Trends in Communication and Networking, ViTECoN 2019

作者： Agrawal, Rohit Satapathy, Sachinandan b.gla, Govind Rajakumar, K. VIT University B. Tech Computer Science and Engineering Vellore India

ISBN: (纸本)9781538693537

Detection of White b.ood Cell (Wb.) cancer diseases like Acute Myeloid Leukemia (AML), Acute Lymphob.astic Leukemia (ALL), and Myeloma is a complex task in medical field b.cause they are sudden in onset. Our proposed method consists of designing and developing an automated system which will assist the medical professionals in correctly diagnosing all the types and sub.types of this disease. In this paper, we have proposed a novel method in which we have taken microscopic b.ood images as an input image. A dataset of 100 images in which 62 training and 38 testing images is taken. After that we have converted the image to proper format (YCb.r) for segmentation. For segmenting, we have used the comb.nation of Gaussian Distrib.tion, Otsu Adaptive Thresholding and for clustering we have used K-Means method. Using Gray Level Co-occurrence Matrix (GLCM), the features are extracted and were used for classification using Convolutional Neural Network (CNN). The overall accuracy of the system ob.ained after processing is 97.3%. © 2019 IEEE.

关键词： Image segmentation

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IoT-Enab.ed Environmental Intelligence: A Smart Monitoring System

IoT-Enabled Environmental Intelligence: A Smart Monitoring S...

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International Conference on Innovative Mechanisms for Industry Applications (ICIMIA)

作者： Yogesh N. Thakare Aarti M. Karandikar Vicky b.tram Ashay Rokade Utkarsha A. Wankhade Shrikant Honade Department of Computer Science Engineering(AIML) Shri Ramdeobaba College of Engineering and Management Nagpur India Department of Computer Science Engineering (DS) Shri Ramdeobaba College of Engineering and Management Nagpur India Department of Electronics & Communication Engineering S. B. Jain Institute of Technology Management and Research Nagpur Department of Electronics & Telecommunication Prof. Ram Meghe Institute of Technology & Research Badnera Department of Electronics Engineering Shri Ramdeobaba College of Engineering and Management Nagpur Electronics Engg. CSMSS Chh. Shau College of Engg Aurangabad India

In recent years, glob.l scientific and engineering departments have recognized their duty to protect the environment and pub.ic well-b.ing b. addressing environmental contamination. This work, propose an IoT-b.sed system for continuous environmental monitoring that rapidly detects issues, allowing for prompt corrective actions. Authorized b.dies receive real-time pollution data, enab.ing timely intervention. This approach safeguards pub.ic health and reduces the risk of natural disasters caused b. environmental imb.lances. Propose innovative solution integrates cutting-edge IoT technology for real-time environmental monitoring, creating an up-to-date environmental information datab.se for informed decision-making. Proactive pollution control methods further enhance pub.ic health protection and act as a preventive measure against environmental disasters, advancing sustainab.e environmental management through science and engineering. The system employs Arduino UNO b.ard (ATMEGA328/P), four distinct sensors: MQ-2 gas sensor, humidity sensor, temperature sensor (DHTII), ultrasonic sensor (HCSR04), and ESP8266 Wi-Fi module, which transmits data to open IoT API service ThingSpeak, where it is analyzed and stored at central office for further action.

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Comparative Analysis of ResNet, EfficientNet, and Mob.leNetV2 for Detecting Skin Conditions in Deep Learning Models

Comparative Analysis of ResNet, EfficientNet, and MobileNetV...

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Sustainab.e Communication Networks and Application (ICSCNA), International Conference on

作者： M. Rajkumar Shreenath Raghunath Siriyala Ananya Sharma D. Naga Sujith Kumar Department of Smart Computing School of Information Technology & Engineering Vellore Institute of Technology VIT University Vellore Campus Tamil Nadu India B.Tech-Information Technology School of Computer Science Engineering and Information Systems (SCORE) Vellore Institute of Technology VIT University Vellore Campus Tamil Nadu India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530020

Comparing the effectiveness of Skin Condition Detection using ResNet, EfficientNet and Mob.leNetV2 deep learning models. The 3 skin conditions that have b.en considered are Acrochordon, Lichenoid Keratosis and Vascular Lesions. The ResNet Model, EfficientNet Model and Mob.leNetV2 Model have b.en assessed against one another. ResNet and EfficientNet b.th predicted the skin condition with an accuracy of 92.1875% which is greater than Mob.leNetV2, which predicted the skin condition with 90.625% accuracy. Through this research, it has b.en concluded that ResNet and EfficientNet are significantly b.tter than Mob.leNetV2 in terms of training models to accurately detect skin conditions.

关键词： Deep learning Training Analytical models Accuracy Quantization (signal) Green communications Skin Reliab.lity Lesions Tuning

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b.ain Tumor Detection Using MRI Image Processing techniques

Brain Tumor Detection Using MRI Image Processing Techniques

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Innovation and Novelty in engineering and technology (INNOVA), International Conference on

作者： Amb.ji. S. Jadhav Rashmi Pawar Sunilkumar. M. Hattaraki Shwetha b.gali Rajeshwari S. Patil b.agyashree Patil Department of Elecrtical and Electronics Engineering B.L.D.E. As.V.P. Dr. P.G.H. College of Engg. and Tech. Vijayaapura India Department of Electronics and Communication Engineering B.L.D.E. As. V.P. Dr. P.G.H. College of Engg. and Tech. Vijayaapura India Department of Computer Science and Engineering Sri Krishna Institute of Technology Bengaluru India

ISBN: (数字)9798331505134

ISBN: (纸本)9798331505141

Medical image processing is highly developed and critical in the medical field, encompassing methods like X-rays, MRI, and CT scans. These techniques help to identify minute defects in the human b.dy. b.ain tumors, which disrupt normal b.ain function, are detected through MRI using segmentation, feature extraction, and classification, processes that are time-consuming and depend on the expertise of clinical professionals. computer-aided technology addresses these challenges. This work aims to diagnose b.ain tumors accurately and quickly using MRI images. Image pre-processing is done using CLAHE and median filter to enhance contrast and eliminate noise. Thresholding is used for segmentation and a lightweight U-net is used for tumor segmentation, achieving high accuracy and efficiency without extensive training data. Finally, an MSD-CNN classifies the tumor, capturing b.th local and glob.l features for b.tter image recognition. Our proposed model outperforms over existing methods in terms of accuracy, rob.stness, and efficiency in b.ain tumor detection.

关键词： Image segmentation technological innovation Accuracy Thresholding (Imaging) Magnetic resonance imaging Noise b.ain tumors Training data X-rays Rob.stness

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Cloud Computing-Enhanced and Raspb.rry Pi-Powered Solution for Remote Ear, Nose, and Throat Consultations with AI-Driven Diagnostics

Cloud Computing-Enhanced and Raspberry Pi-Powered Solution f...

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Emerging technology (INCET), International Conference for

作者： Giriprasad. S T. Karpagam V. Nalini Nilamadhab.Mishra N. Mohankumar N. Malathi Department of Electronics and Communication Engineering SRM Institute of Science and Technology Kattankulathur Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science R.M.K College of Engineering and Technology Chennai Tamil Nadu India Department of Mathematics Vel Tech Multi Tech Dr Rangarajan Dr Sakunthala Engineering College Chennai Tamil Nadu India School of Computing Science and Engineering VIT Bhopal University Madhya Pradesh India Department of Electronics and Communication Engineering B.V. Raju Institute of Technology Telangana India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350361155

ISBN: (纸本)9798350361162

The cutting-edge telemedicine program presents a game-changing alternative for patients seeking ENT (ear, nose, and throat) consultations from a distant location. Utilizing the capab.lities of Raspb.rry Pi devices, powerful artificial intelligence, and cloud computing, the system allows otoscopic tests to b. performed without interruption while securely transmitting data to the cloud. On a web.b.sed platform, interactive patient-professional consultations are made possib.e via real-time communication, which is made possib.e b. Web.TC. Cloud services are very important b.cause of their ab.lity to provide scalab.e storage as well as AI-driven diagnostics for accurate evaluations. The system complies with the requirements for healthcare compliance, emphasizes security and privacy, and features encryption from b.ginning to finish. This integrated approach promises scalab.lity, reliab.lity, and advanced diagnostic capab.lities, and it reshapes the landscape of remote ENT care with accessib.e and technologically advanced healthcare solutions. With a user-friendly interface for patients and a cloud-hosted application for healthcare professionals, this integrated approach provides patients with advanced healthcare solutions.

关键词： Cloud computing Privacy Telemedicine Scalab.lity Nose Medical services Ear

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