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Sustainable Expert Systems (ICSES), International Conference on

作者： V. Sumathi ramesh S Chethan Chandra S Basavaraddi Visumathi J Ishwarya M.V S. Srinivasan Department of Mathematics Sri Sairam Engineering College Chennai Tamil Nadu India Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai Tamil Nadu India Department of Artificial Intelligence and Machine Learning R&D Don Bosco Institute of Technology VTU Belagavi Karnataka India Department of Information Technology Veltech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Artificial intelligence and Data science Department Agni College of Technology Chennai Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

Optimization of industrial activities is significantly helped by Advanced Process Control (APC), which increases efficiency, decreases costs, and improves product quality. Artificial Neural Networks (ANNs) and the Internet of Things (IoT) have greatly improved APC in manufacturing. It examines how APC systems may include ANNs and IoT devices in industrial processes. By connecting to a wide range of sensors placed strategically throughout a production facility, IoT devices may gather data in real time, allowing for managing and monitoring crucial parameters. To APC systems, ANNs are invaluable for predictive modeling and control due to their adaptability to changing process conditions and capacity to represent complicated nonlinear interactions. Manufacturers may gain better control over their operations, improving productivity and product quality, by using data from the IoT to train and update ANN models. recent advances in APC using IoT devices and ANNs are reviewed in this study. Their applications in various industrial industries are discussed, and case examples are shown to demonstrate their usefulness. Further improvements in APC for manufacturing using IoT devices and ANNs may be guided by highlighting the field's problems and future research goals. Challenges with data security, integration challenges, and reliability impact the accuracy of predictive modeling, scalability, and performance of IoT systems.

关键词： Performance evaluation Adaptation models Process control Artificial neural networks Predictive models real-time systems Manufacturing Sensors Internet of Things Monitoring

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FdM: NutriGuide - A Cross-Platform Flutter Application for Managing and Educating on Nutritional deficiencies

FDM: NutriGuide - A Cross-Platform Flutter Application for M...

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

作者： Vidhya K T.M Thiyagu Antony Taurshia B. Nagarajan Jenefa A Joshua Flower Jebas I School. of CST Karunya Institute of Tech. and Sci. Coimbatore India Dept. of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Sci. and Tech. Dept. of Artificial Intelligence and Data Science KPR Institute of Engineering and Technology Coimbatore India

ISBN: (数字)9798331540661

ISBN: (纸本)9798331540678

In today's fast-paced world, many individuals strug-gle to maintain a balanced diet, leading to various nutritional de-ficiencies. Effective management and education on these deficien-cies are crucial for improving public health. Traditional methods of addressing nutritional deficiencies often lack personalization and user engagement. The NutriGuide application, developed using the Flutter framework, aims to revolutionize nutritional management by providing a user-centric, cross-platform solution. Utilizing a comprehensive database of nutrients and symptoms, the application offers personalized dietary recommendations based on user inputs. The dataset includes detailed information on common deficiencies, symptoms, and nutrient-rich foods. Experimental results indicate high user satisfaction, with an average satisfaction score of 4.5 out of 5, and significant im-provements in nutritional intake, such as a 28.6 % increase in Vitamin A intake and a 50% increase in Vitamin d intake. In conclusion, NutriGuide presents an innovative approach to nutritional health, combining modern technology with evidence-based recommendations to empower users in managing their dietary needs.

关键词： databases Education Nutrients Electronic healthcare Internet of Things Public healthcare

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Optimizing Vehicle-to-Vehicle (V2V) Communication Efficiency with KNN-based dynamic Time slot Allocation

Optimizing Vehicle-to-Vehicle (V2V) Communication Efficiency...

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Social and Sustainable Innovations in technology and Engineering (SASI-ITE), International Conference on

作者： N devakirubai Anitha Velu d Sumathi A Prasanth Department of Artificial Intelligence and Data Science R P Sarathy Institute of Technology Salem Tamilnadu India Department of Electronics and Communication Engineering Sri Sairam College of Engineering Bengaluru India Department of Computer Science and Engineering Alliance College of Engineering and Design Alliance University Bengaluru India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (数字)9798350360790

ISBN: (纸本)9798350360806

In the continually evolving field of vehicular communications, the efficient allocation of time slots for Vehicle-to-Vehicle (V2V) communication is of utmost importance. This work introduces a novel approach that employs a K-Nearest Neighbors (KNN) algorithm to allocate time slots in Nr V2X sidelink communications. The allocation is specifically tailored to dynamically adjust to the current density of traffic in real-time. The efficacy of the KNN-based time slot allocation system is assessed through simulating the V2X environment, encompassing a fleet of vehicles. The efficacy of the suggested methodology is assessed in relation to a random allocation technique and it is observed that the suggested strategy notably enhances allocation efficiency, slot utilization, contention window, and communication time. The findings illustrate the capacity of a KNN-based dynamic allocation system to adapt to different levels of traffic congestion, leading to the efficient utilization of network resources and the mitigation of communication latency. Furthermore, the examination of the implementation of priority queues in the allocation process is explored, resulting in enhanced efficiency of the system in scenarios involving a substantial amount of traffic.

关键词： Technological innovation Vehicular ad hoc networks Sidelink Nearest neighbor methods dynamic scheduling real-time systems resource management

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detection Of Counterfeit Images Through deep Learning based Yolov3 Model

Detection Of Counterfeit Images Through Deep Learning based ...

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Advancement in Computation & Computer Technologies (InCACCT), International Conference on

作者： Jobez J. C Golden Nancy r Syed rabiya Mohamed Ali Muthukumaran N raghavan Sheeja Jasmine Gnana Malar A Department of AIML Karunya Institute of Technology and Sciences Coimbatore Tamil Nadu India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of ECE Centre for Computational Imaging and Machine Vision Sri Eshwar College of Engineering Coimbatore Tamil Nadu India Department of Artificial Intelligence and Data Science R.M.K. Engineering College Gummidipoondi Tamil Nadu India Department of ECE PSN College of Engineering and Technology Tirunelveli India

ISBN: (数字)9798331543891

ISBN: (纸本)9798331543907

Image forgery is the procedure of analysing and detecting any fraudulent or unauthorised alterations made to digital images. The detection in images is difficult for achieving a high accuracy rate and the process is time-consuming. researchers and forensic experts are developing tools for detecting fake images. The identification of computer-generated picture forgeries is a notable area of study, aiming to provide a comprehensive review of detection tools and techniques. In this work, a novel deep-learning model is proposed for detecting forgery in images. The images are pre-processed using an adaptive Gaussian bilateral (AGB) filter to enhance the quality of the images. The enhanced photos are fed into the Mobile Net for extracting the relevant features in the pictures. The modified Yolov3 is utilized for object detection in images and detecting the real and forgery images. According to the result, the proposed model attains 99.55% for forgery detection in images. The modified Yolov3 yields 99.55% accuracy as opposed to Mask rCNN, rCNN, and SSd's 0.68%, 0.65%, and 1.16% accuracy. CNN, resNetV2 and dCNN are all outperformed the proposed model in terms of overall accuracy by 0.11%, 1.10%, and 5.55%, respectively.

关键词： YOLO deep learning Adaptation models Accuracy reviews Computational modeling Forensics digital images Feature extraction Forgery

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Advancements in Artificial Intelligence for Better Image Analysis, recognition, and Interpretation in the Field of Image Processing

Advancements in Artificial Intelligence for Better Image Ana...

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Communications and Information Technologies (GCCIT), Global Conference on

作者： V. Saravanan A. Adaikkammai G. Sasikala Iyswariya A A. Babisha M. Latha 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 Computer Science and Business Systems R.M.D. Engineering College Department of ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Department of ECE R.M.K. Engineering College Chennai Department of Artificial Intelligence and Data Science Panimalar Engineering College Department of IT SRM Institute of Science and Technology

ISBN: (数字)9798350388916

ISBN: (纸本)9798350388923

The rapid advancement of artificial intelligence (AI) has brought about a significant revolution in image processing. This revolutionary technology has enabled the analysis, recognition, and interpretation of images in ways that were previously inconceivable. An investigation into the most recent developments in artificial intelligence techniques that have improved the efficiency and precision of image processing tasks is presented in this study. We survey the emergence of transformer architectures and the integration of deep learning models, namely Convolutional Neural Networks (CNNs), to explore how these technologies have transformed the understanding and application of images in several industries. An overview of existing image processing methods and the limits of those approaches is presented at the beginning of our research. This highlights the necessity of more advanced AI-driven solutions. After that, we examine the advancements that have been made in artificial intelligence, such as the incorporation of attention processes, the development of more effective CNN architectures, and the utilisation of generative adversarial networks (GANs) for the purpose of picture synthesis and augmentation. The purpose of this research is to investigate the impact that artificial intelligence has on particular applications of image processing, such as autonomous car navigation systems, facial recognition, medical imaging, and satellite images analysis. Specifically, we focus on the enhancements in diagnostic accuracy, environmental monitoring, security, and safety that arose as a result of the adoption of artificial intelligence in these sectors through the use of case studies. Furthermore, we discuss the issues that are associated with artificial intelligence in image processing. These challenges include the necessity for explainable AI models, the requirement for computational resources, and the protection of data privacy. A number of potential solutions to these problems

关键词： Industries Image recognition Image analysis Transformers Satellite navigation systems Generative adversarial networks Satellite images Convolutional neural networks Security Artificial intelligence

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AI Governance in the Context of the EU AI Act: A Bibliometric and Literature review Approach

arXiv

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arXiv 2025年

作者： Kim, Byeong-Je Jeong, Seunghoo Cho, Bong-Kyung Chung, Ji-Bum The Institute of Social Data Science Pohang University of Science and Technology Pohang Korea Republic of Division of Advanced Nuclear Engineering Pohang University of Science and Technology Pohang Korea Republic of R&D Planning Strategy Division Korea Railroad Research Institute Uiwang Korea Republic of Strategy Group INTERX Seoul Korea Republic of Department of Civil Urban Earth and Environmental Engineering Ulsan National Institute of Science and Technology Ulsan Korea Republic of

The rapid advancement of artificial intelligence (AI) has brought about significant societal changes, necessitating robust AI governance frameworks. This study analyzed the research trends in AI governance within the framework of the EU AI Act. This study conducted a bibliometric analysis to examine the publications indexed in the Web of Science database. Our findings reveal that research on AI governance, particularly concerning AI systems regulated by the EU AI Act, remains relatively limited compared to the broader AI research landscape. Nonetheless, a growing interdisciplinary interest in AI governance is evident, with notable contributions from multi-disciplinary journals and open-access publications. dominant research themes include ethical considerations, privacy concerns, and the growing impact of generative AI, such as ChatGPT. Notably, education, healthcare, and worker management are prominent application domains. Keyword network analysis highlights education, ethics, and ChatGPT as central keywords, underscoring the importance of these areas in current AI governance research. Subsequently, a comprehensive literature review was undertaken based on the bibliometric analysis findings to identify research trends, challenges, and insights within the categories of the EU AI Act. The findings provide valuable insights for researchers and policymakers, informing future research directions and contributing to developing comprehensive AI governance frameworks beyond the EU AI Act. © 2025, CC BY.

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A Novel Approach to Identify dynamic deficiency in Cell using Gaussian NB Classifier 7

A Novel Approach to Identify Dynamic Deficiency in Cell usin...

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7th International Conference on Communication and Electronics Systems, ICCES 2022

作者： Pushpakumar, r. Prabu, r. Priscilla, M. renisha, P.S. Prabu, r. Thandaiah Muthuraman, U. Department of IT Veltech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Chennai India Vel Tech Multi Tech Dr Rangarajan Dr Sakunthala Engineering Department of It Chennai India S.A. Engineering College Department of Ece Chennai India Srinivas University College of Engineering and Technology Department of Cloud Technology and Data Science Karnataka Mukka Mangalore India Simats Saveetha School of Engineering Department of Ece Chennai India Francis Xavier Engineering College Dept of Eee Tirunelveli India

ISBN: (数字)9781665496346

ISBN: (纸本)9781665496346

For the identification of intra-cell faults that may arise in returned products, a novel learning-guided technique has been described in this study. Assumed static flaws described by stuck-at errors are used in the first half of the study. Many supervised learning methods are investigated, with varied degrees of efficiency. The second section of the study is built on the earlier research by addressing more complex dynamic flaws. As part of the diagnostic procedure, a Naive Bayesian classification algorithm is applied to identify and categorise the type of each new data fault. The suggested approach's reliability and clarity have been shown via testing on standard circuits and compared with a conventional cell-aware diagnostic tool. © 2022 IEEE.

关键词： Machine learning

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Increasing Octupole Collectivity across the Z=64 Isotopic Chain: B(E3) Values in Gd150

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Physical review Letters 2025年第9期134卷 092501-092501页

作者： S. Pascu E. Yüksel Abhishek P. Stevenson G. H. Bhat r. N. Mao K. Nomura C. Costache Z. P. Li School of Mathematics and Physics University of Surrey Guildford GU2 7XH United Kingdom “Horia Hulubei” National Institute for R&D in Physics and Nuclear Engineering R-77125 Bucharest-Magurele Romania Department of Higher Education (GDC Shopian) Jammu and Kashmir 192 303 India School of Physical Science and Technology Southwest University Chongqing 400715 China Department of Physics Hokkaido University Sapporo 060-0810 Japan Nuclear Reaction Data Center Hokkaido University Sapporo 060-0810 Japan

An enhanced electric octupole (E3) strength of 45(5) W.u. has been measured for the 3−→0+ transition in Gd150, providing direct evidence for large octupole collectivity in this nucleus. The value was determined following two sensitive experiments, to measure the mean lifetime of the Jπ=3− level and the weak E3 decay branch direct to the ground state. The systematics of the E3 strength across the Gd isotopic chain reveals that enhanced octupole collectivity is obtained for Gd150. The increasing low-lying octupole strength in the Gd isotopes is analyzed with five state-of-the-art theoretical models. It is found to be best reproduced by quasiparticle random phase approximation calculations, providing a good understanding of octupole collectivity in the rare-earth region.

关键词： Gadolinium alloys

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Exploring the Efficacy of U-Net-CNN Architecture for Automated detection of Psoriatic Arthritis from Medical Imaging data

Exploring the Efficacy of U-Net-CNN Architecture for Automat...

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Self Sustainable Artificial Intelligence Systems (ICSSAS), International Conference on

作者： divya Muralitharan S Tamilselvi J daphney Joann Sajeev ram Arumugam Sankar Ganesh Karuppasamy C S Shylaja Department of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of AI&DS Panimalar Engineering college Chennai India Department of CSE(Data Science) Sri Venkateswara College Of Engineering & Technology(Autonomous) Chittoor India Department of AI&DS Sri Krishna College of Engineering and Technology Coimbatore India Independent Researcher Coimbatore India

ISBN: (数字)9798350368413

ISBN: (纸本)9798350368420

Psoriatic arthritis (PsA) is a chronic inflammatory joint disease that often exists with psoriasis, causing substantial morbidity and disability if left untreated. Timely intervention and better patient outcomes depend on the early and precise identification of PsA. This work proposes an innovative method for PsA detection from medical imaging data that uses the U-Net-CNN architecture. Precise localization and characterization of arthritis-associated features are made possible by the U-Net-CNN model, which combines the U-Net architecture for precise segmentation with convolutional neural networks (CNNs) for feature extraction and classification. Using a dataset of medical images from radiopaedia, the effectiveness of the proposed method is evaluated. It shows that, compared with traditional architectures, it achieves a high accuracy of $\mathbf{0. 9 7 4 6}$, precision of 0.9335, recall of 0.9035, dice coefficient of $\mathbf{0. 8 9 0 5}$, and Jaccard Index of 0.9092.

关键词： Image segmentation Accuracy Sensitivity Psoriasis Feature extraction Arthritis Vectors Planning Indexes Convolutional neural networks

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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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