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Advancement in technology (ICONAT), International Conference for

作者： K. Kalaiselvi r. Sankar S Supriya G Kaushik Hemanth Swamy M devika Department of Data Analytics Saveetha College of Liberal Arts and Sciences SIMATS Chennai Tamilnadu India Department of Electrical and Electronics Engineering Chennai Institute of Technology Chennai Tamil Nadu 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 Electronics and Communication Engineering MLR Institute of Technology Hyderabad India Senior Software Engineer Motorola Solutions Department of AI& DS Dhanalakshmi College of Engineering Chennai Tamil Nadu India

ISBN: (数字)9798350354171

ISBN: (纸本)9798350354188

Advanced wireless networks are essential given the increase in demand for smart services enabled by the Internet of Everything (IoE). despite the fact that 5G networks could enable Internet of Everything (IoE)-based services, they can’t meet all of the requirements needed to create intelligent applications. In order to overcome the shortcomings of the current infrastructure, there is an increasing demand to investigate the potential of 6G wireless communication technologies. Complex network optimization problems can be resolved by integrating artificial intelligence into sixth generation networks. An increasing user base and support for large-scale data-driven applications are essential given the fast evolving 6G landscape. In addition to highlighting network needs, critical supporting technologies, and a comparison of 5G and 6G networks, this study looks at recent advancements and trends in 6G technology. The report also explores new approaches to 6G connectivity, such as edge computing, backscatter communications, AI-driven IoT networks, and holographic beamforming, all of which are meant to support intelligent communities. In order to achieve the goal of 6Gbased IoT networks, it concludes by outlining potential research avenues.

关键词： 6G mobile communication 5G mobile communication Wireless networks Complex networks Market research Communications technology Internet of Things Optimization Next generation networking Edge computing

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design and Comparison of Collaborative Filtering technology for Product Suggestions in E-Commerce 1

Design and Comparison of Collaborative Filtering Technology ...

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1st IEEE International Conference on Automation, Computing and renewable Systems, ICACrS 2022

作者： rajasekar, P. Mohanraj, B. Padhi, S.N. Sivakumar, N. Lavanya, J. Charles Prabu, V. Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Tamil Nadu Chennai603203 India Department of Information Technology Karpagam College of Engineering Tamil Nadu Coimbatore641032 India Department of Mechanical Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522302 India Department of Computer Science and Engineering Panimalar Engineering College Chennai City Campus Tamil Nadu Chennai600029 India Department of Fashion Designing SRM Institute of Science and Technology CSH Kattankulathur campus Tamil Nadu Chennai603203 India Department of Computer Science Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai600 062 India

ISBN: (纸本)9781665460842

E-commerce websites and internet purchasing have never been more necessary. Product recommendations are a key tactic employed by online buying platforms. This study compares two different filtering algorithms that are used in product recommendationson websites like Amazon and Flipkart. reviews of electrical devices, user data, and timestamps were all gathered from Kaggle and put into a dataset. These specifics then undergo several preprocessing steps. The steps are data visualization, duplicate value analysis, missing value analysis, and datatype analysis. The exhibited data is then used to train and test the machine learning models. For this purpose, two machine learning models are developed using two different algorithms. The algorithms K-Nearest Neighbor(KNN) and Support Vector decomposition (SVd) are employed. The models are then trained to acquire used in the prediction procedure after that. The best models for the prediction process are then determined by scrutinizing the results of both models. The root Mean Square Error (rMSE), Mean Square Error (MSE), and Mean Absolute Error (MAE) values are located and compared because the findings of the analysis weren't *** erroneous data are plotted into a graph and evaluated to further demonstrate the point. As the SVd method is discovered to be the best of the group, the algorithm's greatest and worst predictions are discovered and examined. Even the poorest model constructed for the study's predictions turned out to be superior to several other prediction algorithms used for product suggestions. This algorithm can be deployed into the backend of any e-commerce website making it more effective. © 2022 IEEE

关键词： Value engineering

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Enhanced Condition Monitoring of Low Voltage Motors in Harsh Environments with Ladybug Beetle Optimization and Context-Aware Graph Convolutional Networks

Enhanced Condition Monitoring of Low Voltage Motors in Harsh...

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Sustainable Expert Systems (ICSES), International Conference on

作者： P. rajasekar Praveen Prakash Tiwari L. Sharmila Akriti Garg rayudu Srinivas V V Satyanarayana Tallapragada Department of Data Science and Business System SRM Institute of Science and Technology Kattankulathur Tamil Nadu 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 Electrical Engineering Mahatma Jyotiba Phule Rohilkhand University Bareilly Uttar Pradesh India Department of Electrical and Electronics Engineering Aditya College of Engineering & Technology Surampalem Andhra Pradesh India Department of Electronics and Communication Engineering School of Engineering Mohan Babu University Tirupati Andhra Pradesh India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

Low voltage motors are essential in many applications in contemporary industrial settings, as they power machines and guarantee efficient operations. However, these motors often operate in harsh environments where they are exposed to factors such as extreme temperatures, moisture, dust, and vibrations, which can lead to premature wear and failure. This paper proposes a hybrid method for low voltage motor condition monitoring in challenging industrial environments. The proposed method is the combined execution of Ladybug Beetle Optimization algorithm (LBOA) and Heterogeneous Context-Aware Graph Convolutional Network (HCAGCN). Hence it is named as LBOA-HCAGCN technique. Initially, the input data is collected from Triboelectric vibration sensors. Then, the collected data is fed into preprocessing utilizing Generalized Multi-kernel Maximum Correntropy Kalman Filter (GMKMCKF). This filter normalizes and cleans the data by effectively removing noise. recursive Hilbert Transform (rHT) is used to extract time domain features such as mean, root Mean Square (rMS), Peak-to-Peak Value, Skewness, and Kurtosis. Then the extracted features are given to HCAGCN for classify the motor health conditions such as normal, broken-bar, bowed-bar, bowed-rotor, faulty bearing, and voltage imbalance. In general, HCAGCN does not express adapting optimization strategies to determine optimal parameters to classify motor conditions. Hence, the LBOA is used to optimize the weight parameter of the HCAGCN which accurately classify motor conditions. The proposed LBOA-HCAGCN is implemented in MATLAB. Performance indicators such as Mean Absolute Error (MAE), Precision, and Accuracy were used to analyze the effectiveness of the proposed approach. Comparing the proposed LBOA-HCAGCN methodology to other existing techniques like deep residual Neural Network (drNN), Convolutional Neural Network (CNN), and recursive Neural Network (rNN), it achieves 25.8%, 26.4%, and 24.7% greater accuracy, 26.7%, 29.4%, and

关键词： Vibrations Condition monitoring Low voltage Accuracy Graph convolutional networks Transforms Motors Feature extraction Kalman filters Optimization

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AI Tools for Enhanced English Learning: Leveraging Machine Learning for Improved Outcomes

AI Tools for Enhanced English Learning: Leveraging Machine L...

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Green Industrial Electronics and Sustainable Technologies (GIEST), IEEE International Conference on

作者： Priya Sharma divya Nimma Sweetline S K S Punithaasree Sreela B M. K. Mohamed Faizal UITTR Department Chandigarh University Mohali India Computational Science University of Southern Mississippi Data Analyst in UMMC USA Department of English Vel Tech Rangarajan Dr.Sagunthala R&D Institue of Science and Technology Chennai India Department of English Nandha Engineering College (Autonomous) Erode India Department of English Prathyusha Engineering College India Department of AI & DS K.Ramakrishnan College of Engineering Samayapuram Tamil Nadu India

ISBN: (数字)9798331540142

ISBN: (纸本)9798331540159

First, AI has implemented English language learning by providing a tailor-made approach to education. Yet, the current methods based on AI have certain drawbacks, including exclusive reliance on such static characteristics of a learner as study routine, rigid flexibility, and poor interpretation of the context in which the speech is delivered’, which may lead to speech suppression; unaddressed individual learning requirements – low learning attainment and engagement; This paper outlines a new approach to English as a foreign language based on the application of AI and advanced ML methods such as NLP and adaptive The proposed system can also support flexible learning and delivery mode that engages learners with their own skills, writing and speaking feedback, and participation with on gamified learning experience. Further, the rationale for the middle section focuses on the utilization of real-time speech recognition to enhance word pronunciation of students besides employing text-recognition technologies enhanced by artificial intelligence to augment learning activities. The general approach of the overall methodology comprises of NLP based context comprehension, along with the process of gamification, and the coming together of flexibility of curriculum and simulation of actual practice. Algorithms collected data to employ it for the customization of the learning interactions and improving the structure of the platform throughout learners’ engagement. In addition to overcoming the existing shortcomings, the present approach suggests elements that can promote the most suitable and effective method of teaching English to diverse groups of learners.

关键词： Industrial electronics Adaptive learning Adaptive systems Machine learning Speech recognition Learning (artificial intelligence) Writing Speech Natural language processing real-time systems

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Transforming Skin disease diagnosis: Harnessing Compound-Scaled AI Network for Enhanced Image Analysis and Precision Medicine

Transforming Skin Disease Diagnosis: Harnessing Compound-Sca...

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disruptive Technologies (ICdT), International Conference on

作者： S. P. Santhoshkumar Torthi ravichandra Nandipati Mounika r ranjani S Gajalakshmi Jim Mathew Philip Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Electronics and Communication Engineering Ellenki College of Engineering and Technology Sangareddy India Department of Computer Science and Engineering Ellenki College of Engineering and Technology Sangareddy India Department of Artificial intelligence and data science Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai India Department of Computer Science and Engineering Rathinam Technical Campus Coimbatore Tamil Nadu India Department of Computer Science and Engineering Sri Ramakrishna Institute of Technology Coimbatore Tamil Nadu India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

Millions worldwide suffer from skin conditions, making prompt and precise diagnosis essential to successful treatment. However, conventional diagnostic techniques are time-consuming, dependent on physician knowledge, and frequently unavailable in low-resource locations. The compound-scaled AI network, an advanced deep-learning model, addresses these challenges by providing precise and scalable image analysis. Utilizing high-resolution imaging and compound scaling, it can accurately detect, classify, and segment various skin disorders, including benign and malignant lesions. While questions remain about whether AI matches human specialists in diagnosing all pigmented skin lesions, studies have compared AI performance against experts in identifying clinically significant cases. This research highlights the compound-scaled AI network's potential in dermatology, particularly for telemedicine, early cancer detection, and clinical workflow integration. By enhancing diagnostic accessibility and accuracy, this AI model could transform dermatology, enabling more personalized and equitable treatment worldwide. Impressively, it achieved 99.6% training accuracy and 98.4% testing accuracy, demonstrating deep learning's effectiveness in automating skin disorder detection and classification.

关键词： Training Accuracy Image analysis Precision medicine Computational modeling dermatology Transforms Skin Lesions Artificial intelligence

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IoT-Genomics Analysis for Personal Health with Cybergenomics and Cloud technology

IoT-Genomics Analysis for Personal Health with Cybergenomics...

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International Conference on Communications and Signal Processing

作者： T. Prabakaran A. ramesh Babu S. Hemalatha Jayabharathi ramasamy rajesh Kambattan Kovarasan B Elango Department of Computer Science and Engineering Joginpally B.R. Engineering College Hyderabad Telangana India School of Computer Applications Vidya Vikas Institute of Engineering And Technology Mysore Karnataka India Department of Artificial Intelligence and Data Science Tagore Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering RMK College of Engineering and Technology Chennai Tamil Nadu 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 Electronics and Communication Engineering Adhiparasakthi Engineering College Melmaruvathur Tamil Nadu India

ISBN: (数字)9798350353068

ISBN: (纸本)9798350353075

recent innovations at the convergence of genomics and the Internet of Things (IoT) have opened the way for an innovative strategy for administering individualized health care. This research study combines the potential of wearable health monitoring technologies with genetic insights to analyze the personal healthcare. A comprehensive knowledge of health dynamics may be achieved by seamlessly integrating real-time health data collected from IoT-enabled wearables with individual genetic profiles. Using algorithms for machine learning, connections between genetic differences and instantaneous health responses may be uncovered, paving the way for the development of individualized therapies and lifestyle recommendations. The suggested framework allows people to improve their health and gives medical professionals access to data-driven insights that can be used for the early diagnosis of diseases and the development of individualized treatment plans. The work that has been done sheds light on the revolutionary potential of merging genetics with the Internet of Things to establish a new paradigm of preventative and individualized medical treatment.

关键词： Technological innovation Merging Medical treatment Genomics real-time systems Internet of Things Bioinformatics

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Agent-Based Uncertainty Awareness Improves Automated radiology report Labeling with an Open-Source Large Language Model

arXiv

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arXiv 2025年

作者： Ben-Atya, Hadas Gavrielov, Naama Badash, Zvi Focht, Gili Cytter-Kuint, ruth Hagopian, Talar Turner, dan Freiman, Moti Faculty of Biomedical Engineering Technion - Israel Institute of Technology Haifa Israel Faculty of Data and Decision Sciences Technion - Israel Institute of Technology Haifa Israel Juliet Keidan Institute of Pediatric Gastroenterology Hepatology and Nutrition Shaare Zedek Medical Center The Hebrew University School of Medicine Jerusalem Israel Pediatric Radiology Unit Radiology Department The Eisenberg R&D Authority Shaare Zedek Medical Center The Hebrew University of Jerusalem Jerusalem Israel

Purpose: To improve the reliability and performance of Large Language Models (LLMs) in extracting structured data from radiology reports, particularly in domains with complex and non-English texts (e.g., Hebrew), by incorporating agent-based uncertainty-awareness method to achieve trustworthy predictions in medical applications. Materials and Methods: This retrospective study analyzed 9,683 Hebrew radiology reports from Crohn’s disease patients (2010–2023) across three medical centers. A subset of 512 reports was manually annotated for six gastrointestinal organs and 15 pathological findings, while the remaining data was automatically annotated using the HSMP-BErT model. Structured data extraction used Llama 3.1 (Llama 3-8b-instruct), an open-source LLM, with Bayesian Prompt Ensembles (BayesPE) leveraging six semantically equivalent prompts for uncertainty estimation. An Agent-Based decision Model synthesized multiple prompt outputs into five confidence levels for calibrated uncertainty, compared against three entropy-based models. Performance was assessed via accuracy, F1 score, precision, recall, and Cohen’s Kappa, both before and after filtering high-uncertainty cases. results: The agent-based model outperformed the baseline across all metrics, achieving an F1 score of 0.3967, recall of 0.6437, and Cohen’s Kappa of 0.3006. After filtering high uncertainty cases (≥ 0.5), the F1 score increased to 0.4787 and Kappa to 0.4258. Uncertainty histograms showed clear separation between correct and incorrect predictions, with the agent-based model exhibiting the best-calibrated uncertainty predictions. Conclusion: Incorporating uncertainty-aware prompt ensembles and an agent-based decision model significantly enhances the performance and reliability of LLMs in structured data extraction from radiology reports, offering a calibrated and interpretable approach for high-stakes medical applications. © 2025, CC BY-NC-Nd.

关键词： radiology

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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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Enhancing Alzheimer's disease diagnosis Using Multi-relation Graph Convolutional Networks and Structural MrI data

Enhancing Alzheimer's Disease Diagnosis Using Multi-Relation...

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IoT Based Control Networks and Intelligent Systems (ICICNIS), International Conference on

作者： P. rajesh Kanna C. Gunasundari M. Senthamarai P. Pandiaraja P. Nithin K. Chitra Department of Computer Science and Engineering Bannari Amman Institute of Technology Erode Tamil Nadu India Department of Computer Science and Engineering SRM Institute of Science and Technology Trichy Tamil Nadu India Department of Artificial Intelligence and Data Science Nandha Engineering College Erode Tamil Nadu 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 Artificial Intelligence and Machine Learning Bannari Amman Institute of Technology Erode Tamil Nadu India Department of Computer Applications Kongu Engineering College Erode Tamil Nadu India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518103

Alzheimer's disease (Ad) has substantial obstacles to early detection, which frequently leads to therapy delays. In this article a unique method that uses structural MrI data and Multi-relation Graph Convolutional Networks (Mr-GCN) to improve Ad diagnostic accuracy. Which can capture the nuanced interactions that characterize neurodegenerative disorders by representing the intricate relationships between various brain regions as a multi-relational graph. This strategy provides a comprehensive picture of brain function and pathology through the combination of relational data and structural MrI features. Using a large dataset, the resusts are evaluated and contrasted in Mr-GCN method with other machine-learning techniques and single-relation graph models. The findings demonstrate that this method outperforms current models in terms of classification accuracy and resilience, achieving higher sensitivity and specificity in distinguishing between individuals in good health and various stages of Ad. Then for further exploration the interpretability of the model is identified in critical brain regions that support the diagnosing process. This work provides a promising framework for the integration of sophisticated graph-based medical imaging tools, which could lead to more sophisticated clinical applications in the diagnosis and monitoring of neurodegenerative diseases in addition to improving our understanding of how Ad progresses.

关键词： Neuroimaging Pathology Accuracy Graph convolutional networks Magnetic resonance imaging Machine learning Sensitivity and specificity Brain modeling Alzheimer's disease resilience

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Challenges and Best Practices in Corporate AI Governance: Lessons from the Biopharmaceutical Industry

SSRN

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SSrN 2022年

作者： Mökander, Jakob Sheth, Margi Gersbro-Sundler, Mimmi Blomgren, Peder Floridi, Luciano Oxford Internet Institute University of Oxford 1 St Giles’ OxfordOX1 3JS United Kingdom Center for Information Technology Policy Princeton University PrincetonNJ08544 United States R&D Data Office Data Sciences and AI BioPharmaceuticals R&D AstraZeneca Cambridge United Kingdom Department of Legal Studies University of Bologna Via Zamboni 33 Bologna40126 Italy

While the use of artificial intelligence (AI) systems promises to bring significant economic and social benefits, it is also coupled with ethical, legal, and technical challenges. Business leaders thus face the question of how to best reap the benefits of automation whilst managing the associated risks. As a first step, many companies have committed themselves to various sets of ethics principles aimed at guiding the design and use of AI systems. So far so good. But how can well-intentioned ethical principles be translated into effective practice? And what challenges await companies that attempt to operationalize AI governance? In this article, we address these questions by drawing on our first-hand experience of shaping and driving the roll-out of AI governance within AstraZeneca, a biopharmaceutical company. The examples we discuss highlight challenges that any organization attempting to operationalize AI governance will have to face. These include questions concerning how to define the material scope of AI governance, how to harmonize standards across decentralized organizations, and how to measure the impact of specific AI governance initiatives. By showcasing how AstraZeneca managed these operational questions, we hope to provide project managers, CIOs, AI practitioners, and data privacy officers responsible for designing and implementing AI governance frameworks within other organizations with generalizable best practices. In essence, companies seeking to operationalize AI governance are encouraged to build on existing policies and governance structures, use pragmatic and action-oriented terminology, focus on risk management in development and procurement, and empower employees through continuous education and change management. © 2022, The Authors. All rights reserved.

关键词： Artificial intelligence

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