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A Novel Machine Learning technique for Diabetic Prediction in IoT-based Healthcare Monitoring System

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

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Sustainable Computing and Smart Systems (ICSCSS), International Conference on

作者： S Aravinth Vijay Jesuraj R. Ganesh Vinda Manjramkar M. Sridharan Vivek Ravishankar Dubey 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 diabetes in the Internet of Things (IoT) e-healthcare scenario has gained considerable attention to implement accurate diabetes diagnosis. IoT is playing an increasingly important role in healthcare environment by 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-based 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 diabetic disease, and relevant medical data is produced to precisely predict those who would be seriously impacted by the condition. For predicting the illness and its severity, a brand-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. besides, the efficiency is tested and confirmed by comparing over SOTA methods.

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Introduction and Development of Cyber Physical Systems in Smart Grid with blockchain technology to Enhance Sustainability

Introduction and Development of Cyber Physical Systems in Sm...

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Augmented Intelligence and Sustainable Systems (ICAISS), International Conference on

作者： P. Valarmathi G. Manoj Sandip Kumar Das Sujo Oommen Mohit Tiwari Department of Computer Science & Engineering B. S Abdur Rahman Crescent Institute Tamil Nadu India Department of Management Studies Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu India School of Electrical Engineering Kalinga Institute of Industrial Technology (KiiT) Bhubaneswar Odisha India School of Electrical and Electronics Engineering REVA University Bangalore Karnataka India Department of Computer Science and Engineering Bharati Vidyapeeth’s College of Engineering Delhi India

The need for increased sustainability and efficiency in the energy industry has led to a major increase in interest in the development of cyber-physical systems (CPS) in the smart grid domain in recent years. In the context of the smart grid, the term "CPS" refers to a network of physical objects, sensors, and communication systems that are interconnected and allow for the smooth exchange of data and control signals to optimize the production, distribution, and use of electricity. Demand response, energy management, and grid optimization are just a few of the smart grid applications that can be successfully implemented by utilizing CPS's capabilities. On the other side, blockchain technology provides a decentralized and secure framework for logging and validating transactions among numerous organizations. Since it is distributed, there is no need for middlemen, transparency is improved, and data integrity and immutability are all guaranteed. These qualities make blockchain an excellent choice for tackling the issues that the smart grid is facing, such as data privacy, security, and stakeholder confidence. There are many advantages to integrating blockchain and CPS in the smart grid. First and foremost, a decentralized and democratized energy market is made possible by blockchain, which enables safe and effective peer-to-peer energy trade among prosumers. This increases sustainability by encouraging the use of renewable energy sources and reducing dependency on centralized power generation. In conclusion, there is enormous potential for improving sustainability through the smart grid's integration of blockchain technology with cyber-physical systems.

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Comparative Analysis of ResNet, EfficientNet, and MobileNetV2 for Detecting Skin Conditions in Deep Learning Models

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

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Sustainable 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 MobileNetV2 deep learning models. The 3 skin conditions that have been considered are Acrochordon, Lichenoid Keratosis and Vascular Lesions. The ResNet Model, EfficientNet Model and MobileNetV2 Model have been assessed against one another. ResNet and EfficientNet both predicted the skin condition with an accuracy of 92.1875% which is greater than MobileNetV2, which predicted the skin condition with 90.625% accuracy. Through this research, it has been concluded that ResNet and EfficientNet are significantly better than MobileNetV2 in terms of training models to accurately detect skin conditions.

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

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brain 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： Ambaji. S. Jadhav Rashmi Pawar Sunilkumar. M. Hattaraki Shwetha bagali Rajeshwari S. Patil bhagyashree 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 body. brain tumors, which disrupt normal brain 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 brain 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 both local and global features for better image recognition. Our proposed model outperforms over existing methods in terms of accuracy, robustness, and efficiency in brain tumor detection.

关键词： Image segmentation technological innovation Accuracy Thresholding (Imaging) Magnetic resonance imaging Noise brain tumors Training data X-rays Robustness

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Cloud Computing-Enhanced and Raspberry 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 capabilities of Raspberry Pi devices, powerful artificial intelligence, and cloud computing, the system allows otoscopic tests to be performed without interruption while securely transmitting data to the cloud. On a web-based platform, interactive patient-professional consultations are made possible via real-time communication, which is made possible by WebRTC. Cloud services are very important because of their ability to provide scalable 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 beginning to finish. This integrated approach promises scalability, reliability, and advanced diagnostic capabilities, and it reshapes the landscape of remote ENT care with accessible 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 Scalability Nose Medical services Ear

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Development of a Current-Position Control Strategy for Motion Systems Utilizing Nonlinear Reluctance Actuators

Development of a Current-Position Control Strategy for Motio...

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IEEE Conference on Decision and Control

作者： Mohammad Al Saaideh Yazan M. Al-Rawashdeh Natheer Alatawneh Khaled Aljanaideh Almuatazbellah boker Mohammad Al Janaideh Department of Electrical and Computer Engineering Memorial University St. John’s NL Canada Electrical Engineering Department at Al-Zaytoonah University of Jordan 130 Amman Jordan Department of Mechanical and Mechatronics Engineering Memorial University St. John’s NL A1B 3X5 Canada Department of Aeronautical Engineering Faculty of Engineering Jordan University of Science and Technology Irbid Jordan Department of Electrical and Computer Engineering Virginia Tech Blacksburg VA USA School of Engineering University of Guelph Guelph Canada

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

Reluctance actuators (RA) can replace the current Lorentz actuators in the next generation of positioning and scanning motion systems, such as the wafer stage in lithography machines. However, the nonlinear output force characteristic and the gap dependency of the RA are the main challenges in using the RA to drive motion systems. In this paper, we design a two-loop control approach for a reluctance actuator motion system (RAMS) to achieve tracking performance for a desired motion profile. First, the current control loop linearizes the RA under different conditions. Next, the position control loop is designed using a PID control based on an extended-high gain observer to achieve a desired motion profile considering unknown dynamics in the system. The simulation results show the efficiency of the proposed current control in linearizing the dynamic behavior under different desired forces and nominal air gaps and achieving a frequency response similar to the spring-mass-damper system. Moreover, the position control can achieve different long-stroke and short-stroke motion profiles with different amplitudes and ranges of motion.

关键词： Current control Actuators Tracking loops Tracking Dynamics Air gaps Force Position control Random access memory Frequency response

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Innovative techniques in the Construction of Very Large Scale Integration Circuits for Low-Power and High-Performance Circuits

Innovative Techniques in the Construction of Very Large Scal...

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Communications and Information technologies (GCCIT), Global Conference on

作者： V. Saravanan G. Sasikala P. Gomathi D. Kirubakaran A. Rajalingam Jubert R. Pagunuran Department of Nano Electronics Materials and Sensors Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamilnadu India Department of ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Department of ECE V.S.B. Engineering College Department of Computer Science and Engineering R.M.K Engineering College Engineering Department College of Engineering and Technology University of Technology and Applied Sciences Shinas Sultanate of Oman

ISBN: (数字)9798350388916

ISBN: (纸本)9798350388923

Portable electronic gadgets and high-performance computing systems have increased demand for low-power, high-performance integrated circuits (ICs). VLSI technology, which creates integrated circuits with dozens to millions of transistors on a chip, is leading these efforts. This research investigates low-power, high-performance VLSI circuit construction methods. We start with VLSI design fundamentals and power management and performance optimization concerns. The constraints of traditional VLSI design methods in satisfying current electronics' strict requirements are examined. VLSI design methodology development and evaluation are the focus of this research. Advanced power optimization methods include MTCMOS, adaptive body biasing, and power gating. We also study clock tree improvement, signal integrity enhancement, and new materials and device topologies for high performance. In addition, we study how machine learning techniques automate and optimize circuit placement, routing, and timing in VLSI design. AI handles the complexity of current VLSI designs and finds optimal design parameters for minimal power and high performance. Scaling VLSI circuits to newer technology nodes raises process variance, leakage current, and thermal management difficulties, which the research tackles. These issues can be overcome with new ways to keep VLSI circuits scaling while preserving performance and power efficiency. Finally, case studies and experimental findings show that the offered methods work in real-world applications. These findings demonstrate significant power usage and performance benefits over typical VLSI design. In conclusion, our research advances VLSI technology by developing low-power, high-performance integrated circuits. The proposed VLSI design methods and solutions could lead to more efficient and powerful electronic systems.

关键词： Performance evaluation Design methodology Machine learning Very large scale integration Thermal management Routing Topology Timing Leakage currents Signal integrity

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Enhanced Agricultural Methods and Sustainable Farming Through IoT and AI technology

Enhanced Agricultural Methods and Sustainable Farming Throug...

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Intelligent Cyber Physical Systems and Internet of Things (ICoICI), International Conference on

作者： Manivannan K. V Dankan Gowda bH. V.V.S.R.K.K. Pavan S. Aravindh C. Nithisha Rama chaithanya Tanguturi Department of Information Technology V.S.B. Engineering College Karur Tamilnadu India Department of Electronics and Communication Engineering BMS Institute of Technology and Management Bangalore Karnataka Department of ECE Bonam Venkata Chalamayya Institute of Technology and Science Autonomous Amalapuram Andhrapradesh India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Data Science School of Computing Mohan Babu University Sree Sainath Nagar Tirupati Andhra Pradesh India Department of Computer Science and Engineering PACE Institute of Technology and Sciences Ongole Andhra Pradesh India

ISBN: (数字)9798331540661

ISBN: (纸本)9798331540678

The combination of IoT and AI technologies in the future promises to disrupt agriculture positively while trying to solve problems affecting the sector through embracing technological advances. The following paper focuses on the development of an IoT and AI framework for enhancing practices in agriculture through real-time control and smart control. Wireless IoT sensors are placed in the farms to monitor important parameters of farms like moisture content, temperature, humidity etc and health of crops. Consequently, data is harvested to assist with accurate irrigation, climatic regulation, and early diagnosis of disease. From the experimental results observed it became clear that resource utilization was boosted by 25% of lesser water usage while crop yields were boosted by as much as 30%. It also early identifies crop diseases, hence cutting crop losses by 15%. Such is crucial to stress that the results obtained reveal the ability of the system to improve the sustainability and productivity of agriculture. Future work will comprise the fine-tuning of the AI model, enhancement of sensors, and their incorporation with other smart technologies besides adapting this strategy for various agricultural conditions, hence the importance of smart solutions in the promotion of eco-friendly farming.

关键词： Temperature sensors Productivity Irrigation Crops Internet of Things Artificial intelligence Sustainable development Water resources Monitoring Farming

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Hybrid Competitive Swarm Optimization Algorithm based Scheduling in the Cloud Computing Environment

Hybrid Competitive Swarm Optimization Algorithm Based Schedu...

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International Conference on Inventive Research in Computing Applications (ICIRCA)

作者： GV. Shrichandran V S Narayana Tinnaluri J. Senthil Murugan T. Meeradevi Vijay Kumar Dwivedi S. Suma Christal Mary Department of Computer Science & Applications B.Sc -CS Programme SRM Institute of Science and Technology Ramapuram Chennai -89 Department of Computer Science Koneru lakshmaiah education foundation Vaddeswaram Vel Tech High Tech Dr. Rangarajan Dr.Sakunthala Engineering College Avadi Chennai Department of ECE Kongu Engineering College Perundurai Erode V.E.C. Ambikapur Dist Surguja (C.G.) Department of Information Technology Panimalar Engineering College Poonamalle Chennai Tamil Nadu

Cloud computing (CC) platform not only offers resource sharing for users and also provide many on-demand services. business procedures can be managed to utilize the workflow technology on the cloud which signifies most of the problems in employing the resources in an effectual approach because of the dependencies between the tasks. Task scheduling (TS) in a CC platform applies to the procedure of effectively allocating resources computing to tasks or jobs submitted to the CC environment. In a cloud platform, tasks are separated as smaller subtasks that are applied in parallel on distinct machines. There are many features to consider if the TS is in CC platform, comprising the kind of tasks like resources cost, resources required, and resources availability. Therefore, this study presents a Hybrid Competitive Swarm Optimization Algorithm based Task Scheduling (HCSOA-TS) technique in the CC platform. The presented HCSOA-TS technique schedules the tasks proficiently in such a way that maximum resource utilization and performance gets accomplished. In the design of HCSOA, Cauchy mutation operator is included in the CSO algorithm and thereby improves the overall performance. In addition, the derivation of the fitness function by the HCSOA-TS technique undergoes optimal scheduling process and decreases energy usage. The experimental result analysis of the HCSOA-TS technique is tested using a series of measures. The comprehensive comparison study highlighted the improvements of the HCSOA-TS technique over recent approaches.

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Advancing HRM with AI Powered Recruitment Performance Management and Employee Engagement Using One Class SVMs

Advancing HRM with AI Powered Recruitment Performance Manage...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Rafiya banu Ruchi Tripathi Saumya Sharma Loganathan Chenniappan K. Chandrasekaran Umanesan R Department of Commerce B. S. Abdur Rahman Crescent Institute of Science and Technology Chennai India Faculty of Management Studies SRM-IST Ghaziabad India Department of Management & Commerce The Northcap University Gurugram India Business Administration IBRI Sultanate of Oman Department of Mechanical Engineering K.S.R College of Engineering Tiruchengode India Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

AI is making labour-intensive HRM operations like hiring, performance review, and employee engagement more efficient and effective. The incorporation of AI technology in HRM could change several sectors. Through efficient applicant sourcing, screening, and selection, AI-driven solutions simplify hiring processes and ensure fair and accurate picks. Performance management using AI-powered solutions provides data-driven feedback and customized development plans to align individual progress with organizational goals. Predictive analytics improve employee engagement by personalizing experiences and meeting needs. The method focuses on preprocessing and model training. Data are cleansed, ordinal and categorical variables encoded, scaling characteristics and engineering features applied, and dimensional homogeneity assured before processing. For model training, the simple and robust one-class SVM technique is used. One-class SVM model outperformed others with 95.28%. This method worked best compared to GAN and GRU models. AI could improve HRM by improving decision-making and offering data-driven solutions. The findings show that the proposed model outperforms current options, providing the framework for HRM AI applications.

关键词： Support vector machines Training Productivity Accuracy Uncertainty Reviews Artificial intelligence Integrated circuit modeling Predictive analytics Recruitment

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