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Soft Computing for Security Applications (ICSCSA), International Conference on

作者： M. Ilampari M. Amina Begum N Mahesh Babu S. Gopi M. Mythreyee Shanmugavel deivasigamani Saveetha School of Law SIMATS Department of ECE Oxford College of Engineering Chennai India Department of Information Technology Anurag Engineering College Suryapet Kodad Telangana India Department of CSE Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Department of EEE Dr. MGR Educational and Research Institute Chennai Department of Artificial Intelligence and Data Science Jeppiaar Institute of Technology Chennai India

ISBN: (数字)9798331515720

ISBN: (纸本)9798331515737

Wireless Sensor Networks (WSNs) are a very important technology widely used in diverse application domains of environmental monitoring, disaster management, health care, industrial automation etc. These are networks in which spatially distributed sensor nodes monitor physical or environmental conditions and communications data are processed through a base station. Here, we introduced a novel method for enhancing the efficiency and lifetime of WSNs by using the WSN to process images data and genetic programming (GA) to find the best route. A system model was developed to balance a network consisting of homogenous sensor nodes placed randomly in a particular geographical area, representing realistic scenarios. Each sensor node has limited energy resources, hence the operation of the network was partitioned into clustering phase and routing phase to optimal utilization of energy. To improve both the accuracy and efficiency of clustering, the genetic algorithm then identified the optimal path through these clusters using the best combination of pivot nodes for transmission. The proposed method applies image processing techniques to process sensor data, preprocesses sensor data, and then improves the classification process, clustering results, and the genetic algorithm obtains the more energy-efficient paths. Our extensive evaluation results prove substantial optimizations in network performance, energy efficiency and lifetime over known protocols. This method is expected to be effective in such applications as environmental monitoring, disaster management, and other applications that will utilize reliable and sustainable WSNs.

关键词： Wireless sensor networks Protocols Image processing disaster management routing Energy efficiency Environmental monitoring Security reliability Genetic algorithms

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Machine Learning for Network Traffic in Home Automation IoT Systems

Machine Learning for Network Traffic in Home Automation IoT ...

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Communication, Computing and Energy Efficient Technologies (I3CEET), International Conference on

作者： S. P. Shingare d.Suresh Chetan K. Verma S. Lakshminarasimhan raghunandan K r r. Navaneetha Krishnan Department of Chemical Engineering Bharati Vidyapeeth College of Engineering Navi Mumbai Department of ECE GMR Institute of Technology Andhra Pradesh India MBIT CVM University Anand Gujrat India Department Artificial Intelligence and Data Science Department of Computer Science and Engineering NMAM Institute of Technology Nitte (Deeemed to be a University) Department of Mathematics Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

IOT device security, which should protect the astute homeowner and family, has left hundreds of thousands, if not millions, of devices vulnerable to hacking. It is not practical to handle various sorts of gadgets under the same system monitoring approach since each of these devices has distinct requirements for Quality of Service (QoS). IoT Governance and Security: In combination with device Categorization, IoT security and management may monitor device activity and automate network activities based on type or function to address concerns with rogue or susceptible devices. One of the most amazing applications of machine learning-based traffic analysis is this one, which can automatically de-anonymize devices and, as a result, reveal a variety of IoT data-hiding behaviors (as shown by an expanding body of work on the subject). This paper offers a detailed investigation into each approach to evaluate its strengths and limits. Initially, we provide a high-level procedure for classifying IoT devices and explain how various attack/fix combinations may be applied at each step of the workflow. Slides displaying specifics of the main components, like: Categorization of pubdata examination of IoT traffic data gathering situations and techniques as well. The literature on the extraction of features from IoT traffic is reviewed in Chapter 5 and includes information on related classes, comparisons to common feature types, and a brief description of a free tool that can be used to extract different types of OT or IT layered data using machine learning techniques for automatic device classification. These result in the taxonomies we have created, which enumerate patterns found in various literary corpora. Additionally, the study looks at and suggests several less-traveled paths that still need more research in this emerging sector.

关键词： Machine learning Telecommunication traffic Smart homes Quality of service Feature extraction Telecommunication computing Internet of Things Security Monitoring Unsupervised learning

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Photovoltaic Powered Electric Vehicle Using direct Torque Control and IoT Monitoring

Photovoltaic Powered Electric Vehicle Using Direct Torque Co...

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Sathyanathan, P. Manikandan, S. Chandrakala, d. raian, V.r. Ishwarya, M.V. Sujatha, S. VelTech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electrical and Electronics Engineering Tamil Nadu Chennai India Meenakshi Sundararajan Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Chennai India Easwari Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Chennai India Mvj College of Engineering Department of Electrical and Electronics Engineering Karnataka Bangalore India Agni College of Technology Artificial Intelligence and Data Science Department Tamil Nadu Thazhambur India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350357769

The concept of speed control of a Permanent Magnet Synchronous Motor (PMSM) using direct Torque Control (dTC) is proposed for use in electric vehicles (EVs). A solar Photo Voltaic (PV) system powers the PMSM, and they are monitored using Internet of Things (IoT). In this configuration, a PMSM serves as the EV's main propulsion engine, with speed control achieved by dTC which provides fast and accurate torque and flux control. By using dTC to accurately control the PMSM speed, EV operation may be more reliable and efficient. This integration lessens the dependence on conventional energy sources and enables the functioning of the EV in a sustainable and ecologically responsible manner. The PV system offers a sufficient and clean source of energy, enhancing the EV's overall performance and range. IoT technology is used to keep monitor the motor speed and data from the PV system. The IoT allows instantaneous information exchange and connection across systems and objects. © 2023 IEEE.

关键词： direct torque control Electric vehicle Internet of things and Permanent magnet synchronous motor Photovoltaic

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A Hybrid Blockchain Framework for Secure, Scalable, and Energy-Efficient digital Identity Management with Token-based Selective disclosure

A Hybrid Blockchain Framework for Secure, Scalable, and Ener...

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Electronics and renewable Systems (ICEArS), International Conference on

作者： Velmurugan M Shinzeer C K G. Maya r. ramya r Kanimozhi Syed Ismail Abdul Lathif Department Of Computer Science & Engineering Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science And Technology Chennai Department of School of Computer Applications Lovely Professional University Phagwara Punjab Faculty of Management SRM Institute of Science and Technology Kattankulathur Department of Electronics and Communication Engineering K.Ramakrishnan College of Engineering Samayapuram Computer Science and Engineering Muthayammal Engineering College Rasipuram Department of Data Science and Business Systems SRM Institute of Science and Technology Chennai

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

data breaches, identity theft, and the lack of user control within traditional digital identity management systems, it begs a more secure and decentralized alternative. In this study we propose a blockchain based digital identity management framework that implements privacy, security and transparency by employing distributed ledger technology. To provide tamper proof identity storage and automated verification, the proposed model uses cryptographic hashing and smart contracts. The use of a novel token-based access mechanism enables users to safely share their identity data with third parties, while keeping sensitive information out of sight. Security metrics are enhanced by 30% for data integrity with 45% reduction in unauthorized access attempts all through simulations. The framework also achieves improved efficiency reducing identity verification time by 40 percent compared to traditional systems. It offers a promising approach to address important vulnerabilities in centralized identity management in line with the development of marketable and user-friendly digital identity solutions.

关键词： resistance Privacy Fault tolerance Smart contracts Fault tolerant systems Throughput Energy efficiency Blockchains Identity management systems Interoperability

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A New dual-Input Single-Output Step-up dC-dC Converter for Grid-Connected Photovoltaic Applications

A New Dual-Input Single-Output Step-up DC-DC Converter for G...

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Smart Power & Internet Energy Systems (SPIES), International Conference on

作者： Priyabrata Shaw Muhammad Mubashir Alam Saad Ul Hasan Yam P. Siwakoti dylan dah-Chuan Lu Department of Electronics & Control Research and Development (R&D) Shakti Pumps (India) Ltd. India School of Electrical & Data Engineering University of Technology Sydney Australia

This paper proposes a dual-input single-output (dISO) step-up dc-dc converter exhibiting high voltage gain for grid-connected applications. The main advantage of the proposed concept is the common ground between input and output ports with a continuous input current profile at both input ports. The operating principle and steady-state analysis are presented in detail. The voltage gain expressions for both inputs are obtained using equivalent circuit analysis. The voltage and current stresses of all components and the design equations for L-C elements are derived. Preliminary simulation results are presented using the LTspice simulator to demonstrate the key operational characteristics of the proposed dISO step-up converter. Finally, the converter operation and analytical studies are verified through sample experimental results captured using a prototype converter.

关键词： Photovoltaic systems Simulation Mathematical analysis Voltage dC-dC power converters High-voltage techniques Topology

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IoT-Enabled Asthma Trackers for Children with KNN for Improved Breathing Control 5

IoT-Enabled Asthma Trackers for Children with KNN for Improv...

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5th IEEE International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2025

作者： Sathyanathan, P. Balachander, K. Pandey, Pramod Srinivasan, V. Prasanna Ganeshbabu, T.r. Muthulekshmi, M. Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Electrical and Electronics Engineering Tamil Nadu Chennai India R.M.K. College of Engineering and Technology Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Nagpur Campus Maharashtra Pune India R.M.D. Engineering College Department of Information Technology Tamil Nadu Chennai India Department of Electronics and Communication Tamil Nadu Namakkal India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350356236

Managing asthma in children is a complex task that needs constant vigilance and personalized treatments to get the best potential control. For better respiratory management, an Internet of Things (IoT)-enabled pediatric asthma tracking system that uses machine learning (ML) methods, particularly for K-nearest neighbors (KNN), is presented. The system's integration of wearable sensors, which continuously monitor vital signs and environmental conditions, provides a comprehensive view of the child's respiratory health. Asthma exacerbation causes, and trends may be discovered by analyzing the obtained data using KNN algorithms. Analysis allows for the generation of real-time warnings and feedback, allowing caregivers to take proactive steps in reducing risks and optimizing treatment procedures. Our IoT-enabled method for improving respiratory control and decreasing asthma-related consequences in children is feasible and effective in pilot research, including children with asthma. Our research highlights the promise of IoT devices and ML algorithms to transform how pediatric asthma is treated by providing tailored, evidence-based treatments that lead to better health outcomes. © 2025 IEEE.

关键词： Pediatrics

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Endoscopic Image Processing: Evaluating the robustness of deep Neural Networks

Endoscopic Image Processing: Evaluating the Robustness of De...

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Smart Electronics and Communication (ICOSEC), International Conference on

作者： C N Gireesh Babu Kaparapu Satish Babu Sajja Suneel K.Srinivasa rao divya Muralitharan r Aarthi Dept.of CSE/ISE BMS Institute of Technology and Management Bengaluru Karnataka Department of Electronics and Communication Engineering Geetanjali College of Engineering and Technology Hyderabad Telangana India Department of CSE(Data Science) Institute of Aeronautical Engineering Dundigal Hyderabad Telangana India Department of AI&ML K.S.R.M. College of Engineering Kadapa Andhra Pradesh Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of ECE R.M.D Engineering College R. S. M Nagar Gummidipoondi

ISBN: (数字)9798331504403

ISBN: (纸本)9798331504410

The Hybrid CNN-Transformer Model (HCNNT) is a novel model which exploits Convolutional Neural Networks (CNN) for spatial feature extraction and Transformers for capturing contextual and long range dependencies in order to carry complex image classification. In order to evaluate the algorithm proposed, a new model has been introduced and trained on a data set of gastrointestinal (GI) image classification, which is derived from a balanced data set with 226 images available on Kaggle. Although the cascaded CNN part is to learn elaborate visual characteristics, the improved understanding of complex patterns and relationships is achieved by the Transformer component to process these features. When both are integrated, we gain a powerful system that has high accuracy in distinguishing GI bleeding condition from normal condition. Extensive training and optimization of the hybrid model using binary cross-entropy loss and Adam optimizer has achieved state-of-the-art performance. Through the experiments evaluated on the HCNNT model, it is evident that an accuracy of 97.34% proves to be significantly beneficial, its practicality in terms of reliability for medical image classification that essentially improves the accuracy of diagnosis in gastrointestinal healthcare.

关键词： Training Visualization Accuracy Transformers Feature extraction Gastrointestinal tract robustness Convolutional neural networks Image classification Context modeling

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residual VGGNet for Unveiling Optimal Process Parameters of Oil rig Welding Through Mathematical Modeling and Optimization

Residual VGGNet for Unveiling Optimal Process Parameters of ...

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International Conference on Computing and Networking technology (ICCNT)

作者： John T Mesia dhas Utpal Saikia K K Arun C.Vimala Josphine M.Theodore Kingslin N Padmavathi Department of Computer Science and Engineering School of Computing Veltech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology (deemed to be university) Chennai Tamilnadu India Department of Mathematics Silapathar College Silapathar Dhemaji Assam India Department of Mechanical Engineering Kumaraguru College of Technology Coimbatore Tamilnadu India Artificial Intelligence and Data Science R.M.K Engineering College Kavaraipettai Department of Electronics & Communication Engineering R.M.K College of Engineering & Technology Puduvoyal Tamilnadu India Department of Electronics and Instrumentation Engineering R.M.D. Engineering College Tamilnadu India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

The optimization of welding techniques for oil rig components is crucial to upholding the structural integrity and safety of offshore drilling activities. In this study, we utilize a residual VGGNet architecture, which is a modified version of the well-known VGGNet, for the purpose of extracting crucial information from images of welding processes. The methodology employed in this study involves the collection of a complete dataset comprising welding process images obtained from diverse oil rig welding activities. The provided images are accompanied by annotations that provide the associated welding parameters, including current, voltage, and welding speed. The residual VGGNet is subsequently trained to acquire knowledge about the complex associations between picture features and welding parameters. In order to develop a reliable mathematical model, regression techniques are utilized to precisely establish the relationship between the extracted features and welding parameters. The performance of the model is evaluated using cross-validation and compared to traditional regression models to emphasize its greater ability to capture intricate patterns within the data.

关键词： Accuracy Welding Oils Voltage Feature extraction Mathematical models data models Safety reliability Optimization

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Actively Targeting redox-responsive Multifunctional Micelles for Synergistic Chemotherapy of Cancer

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ACS Omega 2024年第32期9卷 34268-34280页

作者： darge, Haile Fentahun Addisu, Kefyalew dagnew Tsai, Hsieh-Chih Birhan, Yihenew Simegniew Hanurry, Endris Yibru Mekonnen, Tefera Worku Gebrie, Hailemichael Tegenu Arunagiri, Vinothini Thankachan, darieo Wu, Tsung-Yun Lai, Juin-Yih Chang, Hao-Ming Huang, Chun-Chiang Wu, Szu-Yuan Graduate Institute of Applied Science and Technology National Taiwan University of Science and Technology College of Medicine and Health Science Bahir Dar University Centre for Ocular Research & Education (CORE) School of Optometry and Vision Science University of Waterloo Institute of Technology Bahir Dar University Advanced Membrane Materials Center National Taiwan University of Science and Technology R&D Center for Membrane Technology Chung Yuan University Department of Chemistry College of Natural and Computational Sciences Debre Markos University School of Medicine Health Science College Addis Ababa University Division of General Surgery Tri-Service General Hospital National Defense Medical Center Taiwan Instrument Research Institute National Applied Research Laboratories Department of Food Nutrition and Health Biotechnology College of Medical and Health Science Asia University Big Data Center Lo-Hsu Medical Foundation Lotung Poh-Ai Hospital Division of Radiation Oncology Department of Medicine Lo-Hsu Medical Foundation Lotung Poh-Ai Hospital Department of Healthcare Administration College of Medical and Health Science Asia University Cancer Center Lo-Hsu Medical Foundation Lotung Poh-Ai Hospital Graduate Institute of Business Administration Fu Jen Catholic University Centers for Regional Anesthesia and Pain Medicine Taipei Municipal Wan Fang Hospital Taipei Medical University

Stimuli-responsive polymeric micelles decorated with cancer biomarkers represent an optimal choice for drug delivery applications due to their ability to enhance therapeutic efficacy while mitigating adverse side effects. Accordingly, we synthesized a digoxin-modified novel multifunctional redox-responsive disulfide-linked poly(ethylene glycol-b-poly(lactic-co-glycolic acid) copolymer (Bi(dig-PEG-PLGA)-S₂) for the targeted and controlled release of doxorubicin (dOX) in cancer cells. Within the micellar aggregate, the disulfide bond confers redox responsiveness, while the presence of the digoxin moiety acts as a targeting agent and chemosensitizer for dOX. Upon self-assembly in aqueous solution, Bi(dig-PEG-PLGA)-S₂ formed uniformly distributed spherical micelles with a hydrodynamic diameter (d_h) of 58.36 ± 0.78 nm and a zeta potential of −24.71 ± 1.01 mV. The micelles exhibited desirable serum and colloidal stability with a substantial drug loading capacity (dLC) of 6.26% and an encapsulation efficiency (EE) of 83.23%. In addition, the release of dOX demonstrated the redox-responsive behavior of the micelles, with approximately 89.41 ± 6.09 and 79.64 ± 6.68% of dOX diffusing from dOX@Bi(dig-PEG-PLGA)-S₂ in the presence of 10 mM GSH and 0.1 mM H₂O₂, respectively, over 96 h. Therefore, in HeLa cell lines, dOX@Bi(dig-PEG-PLGA)-S₂ showed enhanced intracellular accumulation and subsequent apoptotic effects, attributed to the targeting ability and chemosensitization potential of digoxin. Hence, these findings underscore the promising characteristics of Bi(dig-PEG-PLGA)-S₂ as a multifunctional drug delivery vehicle for cancer treatment. © 2024 The Authors. Published by American Chemical Society

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diabetic retinopathy detection Using Capsule Network Modelled deep Learning

Diabetic Retinopathy Detection Using Capsule Network Modelle...

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Emerging Technologies and Innovation for Sustainability (EmergIN), International Conference on

作者： Govindharaj I Vijayarajeswari r Anbuthiruvarangan K Hannah rose Esther T Anandh r dinesh Kumar K Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Information Technology Sona College of Technology Salem Tamil Nadu India Department of Artificial Intelligence and Data Science Velammal Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering Sri Manakula Vinayagar Engineering College Puducherry Tamil Nadu India

ISBN: (数字)9798350391268

ISBN: (纸本)9798350391275

Presently, glaucoma, an acute eye disorder, if untreated, leads to permanent blindness is a major challenging in healthcare system for the complexities of diagnosis. Historical diagnosis models greatly rely on practical assessments of retinal images in addition to, rim width, optic cup and optic disc size, and any abnormalities within the eyes. Advancements of the last few years in AI have brought recent opportunities in diagnosis of glaucoma disorder. The work focuses on a composite learning model that combines UN et++ and CapsN et design for accurate detection and diagnosis of glaucoma. Using UNet++, semantic segmentation is applied together with Od and OC segmented region being critical to the identification of illness. The hybrid model leverage of CapsNet ability to detect hierarchical relationship Successful in better detection of glaucomatous change in comparison to standard Convolutional Neural Networks. The method uses state-of-the-art approaches like CLAHE method to preprocess retinal images, as a result, to improve the image quality. When examined with algorithm models, this research survey has been trained and tested on benchmark datasets for optic curves, optic segmentation and glaucoma detection wherein it has outperformed all of them. This automated solution for glaucoma detection offers promising potential in the fight against blindness, as evidenced by the methods robust trained performance and diagnostic accuracy. The integration in UNet++ and CapsNet Offers a unique and effective solution for prior and accuracy in detection of glaucoma, potentially revolutionizing the field of ophthalmic diagnostics.

关键词： Glaucoma deep learning diabetic retinopathy Technological innovation Accuracy Biomedical optical imaging Optical computing Blindness retina Optical imaging

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