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International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS)

作者： Narendra Kumar S Ravi KM Arahanth M G Satheesh Pragathi Basavaraj Department of Biotechnology R V College of Engineering BE students Department of Computer Science and Engineering R V College of Engineering Scientific officer cum Assistant Professor R V College of Engineering

ISBN: (数字)9798331505462

ISBN: (纸本)9798331505479

Safety and the edibility of products is a must in the food industry. This research paper introduces a machine learning method for predicting whether food products can be eaten or not. A neural network model was created using various characteristics of the dataset including shelf life, manufacture date, optimum storage temperature and product type (solid, liquid or gas) to predict the edibility of a given item. The performance of the model was measured using accuracy, Mean Squared Error (MSE), and R-squared (R2). These findings demonstrate that neural networks are effective in classification tasks as they offer dependable means of forecasting if food is unsafe to eat.[1] Traditional methods have received a significant boost by this approach since it allows faster scalable automated predictions that could be integrated into larger food safety systems

关键词： Temperature measurement Solid modeling Accuracy Temperature Computational modeling Scalability Neural networks Predictive models Solids Food safety

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IoT Enabled Wearable Technology Jacket for Tracking Patient Health and Safety System

IoT Enabled Wearable Technology Jacket for Tracking Patient ...

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International Conference on Smart Technologies For Smart Nation (SmartTechCon)

作者： M. Santhanalakshmi S. Dhanalakshmi M. Radhika G. Kavitha G. Elavel Visuvanathan C. Srinivasan Assistant Professor Department of Computer Science Shrimathi Devkunvar Nanalal Bhatt Vaishnav College for Women Chennai Tamil Nadu India Assistant Professor Department of Electronics and Communication Engineering Sri Muthukumaran Institute of Technology Chennai Tamil Nadu India Assistant Professor Department of Information Technology R.M.D. Engineering College Kavaraipettai Tamil Nadu India Professor Department of Computer Science and Engineering Muthayammal Engineering College (Autonomous) Namakkal Tamil Nadu India Assistant Professor Department of Electronics and Communication Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Adjunct Professor Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

Over the years, there have been ongoing efforts to use clothing as a vehicle for distributing digital capabilities. Information technology (IT) is being incorporated into garments to enable people to work more quickly. Because of their increasing availability, accessibility, and, most significantly, cost, wireless body area networks (WBANs) will undoubtedly be used in healthcare to enhance medical data utilization. Whether it has to do with post-operative recovery or Monitoring, applications for their function in the medical field are already being looked into. Wearable technology allows users to complete activities more efficiently by enhancing one or more of their senses with artificial information. The system proposes a method that displays critical information for doctors on a particular cloud for data analysis. In this concept, sensors are attached to someone's coat to collect real-time data. This data is analyzed, uploaded to the cloud using IoT Medium, and notified in the case of an alarming condition. The data access to real-time data, a website built into this module, and an alert notification system to warn the hospital and the family if the numbers are abnormal. A live stream is recorded using the camera, while the GPS module tracks the location. The doctor can take the proper step based on the patient's health.

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Rice Category Identification through Deep Transfer Learning Features and Machine Learning Classifiers: An Intelligent Approach

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2024年第7期51卷 765-784页

作者： Terlapu, Panduranga Vital Prasan, U.D. Kumar, T. Ravi Bendalam, Vijaya Pappu, Sasibhushana Rao Rao, M. Jayanthi Mohitha, Maddula Ratna Jayaram, D. Dept. of Computer Science and Engineering Aditya Institute of Technology and Management Tekkali Andhra Pradesh Srikakulam India Dept. of CSE Aditya Institute of Technology and Management Tekkali Andhra Pradesh Srikakulam532 201 India Sasibhushana Rao Pappu is an Assistant Professor of Dept. of Department of Computer Science and Engineering GITAM Andhra Pradesh Visakhapatnam India Aditya Institute of Technology and Management Tekkali Andhra Pradesh Srikakulam532 201 India Dept. of CSE Aditya Institute of Technology and Management Tekkali Andhra Pradesh Srikakulam532 201 India Department of Information Technology Chaitanya Bharathi Institute of Technology Gandipet Telangana Hyderabad500075 India

Rice category identification by image analysis is essential to ensure the quality and safety of rice production. In this study, we propose an intellectual approach to improve rice category identification using deep transfer learning features and machine learning classifiers. Specifically, we extracted features from three pre-trained models (Inception V3, VGG-19 and VGG-16) using transfer learning techniques. These were used as inputs to train MultiLayerPerceptron (MLP) and support vector machine (SVM) classifiers. The results of our experiments show that the proposed strategic results achieve high accuracy in identifying rice categories. The SVM (polynomial kernel) achieves the second-highest accuracy among all models and features, with an accuracy of 0.9948 using the VGG-19 and 0.9912 using Inception V3. The MLP classifier with (30 30) hidden layers achieve the first high accuracy, with an accuracy of 0.9972 (99.72%) using VGG-19 features. The results also show that the choice of deep transfer learning model and machine learning (ML) classifier can significantly affect the accuracy of rice category identification. Among the three pretrained models, VGG-19 features consistently perform the best, followed by Inception V3 and VGG-16. The choice of MLP hidden layer size also affects the accuracy, with 30 HL neurons achieving the best performance. Our proposed approach using deep TL features and ML classifiers shows promising results in improving rice category identification. Our study provides valuable insights into optimizing ML models for agricultural image analysis. © (2024) International Association of Engineers. All rights reserved.

关键词： Support vector machines

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Advancing Beyond Contextual Embeddings: Innovations in Word and Document Representations for Natural Language Processing 3rd

Advancing Beyond Contextual Embeddings: Innovations in Word ...

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3rd International Conference on Mechanical and Energy Technologies, ICMET 2023

作者： Nagender, Y. Ravichand, M. Kocherlakota, Nirupama Mary, S. Shyni Carmel Bharathi, S. Kavitha Kalaivaani, P.C.D. School of Computer Science and Artificial Intelligence SR University Telangana Warangal India Vijayawada India Department of Linguistics MIT World Peace University Maharashtra Pune India Assistant Professor in Business Analytics Loyola Institute of Business Administration Loyola College Campus Chennai600 034 India Department of Computer Technology Kongu Engineering College Perundurai India Department of CSE Kongu Engineering College Perundurai638060 India

ISBN: (纸本)9789819727155

Recent years have seen tremendous progress in Natural Language Processing (NLP), particularly in word and document illustration. Conventional methods that relied on contextual embeddings have given way to creative alternatives. In NLP, pre-trained language models have shown remarkable effectiveness, resulting in a fundamental shift away from supervised learning and towards pre-training accompanied by fine-tuning. Enhancing pre-trained models has attracted a lot of research attention in the NLP community. The taxonomy of pre-trained models is introduced, along with a thorough assessment of current advancements and representative work in the field of natural language processing. We initially provide a quick overview of pre-trained models, then go on to distinctive architectures and techniques. Next, we present and examine the implications and difficulties associated with pre-trained models and their subsequent uses. We wrap up quickly and discuss potential future research options in this area. These developments provide more accurate and effective ways to organise and extract data from large textual collections. In conclusion, this study addresses the intrinsic drawbacks of contextual embeddings and emphasises the critical role that word and document illustrations play in advancing NLP. These cutting-edge methods offer a viable avenue to improve natural language comprehension and utilisation as NLP continues to develop, with extensive applications in domains including information retrieval, machine translation, and sentiment analysis. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Embeddings

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Collaborative Defense Strategies: AI, ML, and Cybersecurity: A Comprehensive Study 12th

Collaborative Defense Strategies: AI, ML, and Cybersecurity:...

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12th International Conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2024

作者： Borah, Samarjeet Dhabliya, Dharmesh Dhage, Prashant Shukla, Jyoti Rai, Sumit Kumar Bhattacharya, Saurabh Department of Computer Applications Sikkim Manipal Institute of Technology Sikkim Manipal University Sikkim India Department of Information Technology Vishwakarma Institute of Information Technology Maharashtra Pune India Nagpur Campus Pune India Computer Science and Engineering Arya College of Engineering Jaipur India Department of Mechanical Engineering Dr. D. Y. Patil Institute of Technology Maharashtra Pimpri Pune India Assistant Professor SCSE Galgotias University Uttar Pradesh Greater Noida India

ISBN: (纸本)9789819601462

In the fast-changing field of cybersecurity, the rise of advanced threats requires new and creative defense strategies. This study thoroughly examines collaborative defense strategies, focusing on the utilization of artificial intelligence (AI) and machine learning (ML) techniques. The study aims to evaluate the effectiveness of five prominent machine learning algorithms (random forest, support vector machine (SVM), gradient boosting, isolation forest, and one-class SVM) in the field of cybersecurity. The assessment is carried out utilizing the NSL-KDD dataset, which is a standard in the industry. The study commences by investigating the origins of collaborative defense, analyzing its historical development and fundamental principles. The text explains the importance of AI in collaborative defense, highlighting machine learning algorithms and natural language processing as essential elements. The study explores the uses of artificial intelligence, specifically in identifying, averting, and reacting to threats. A substantial part of the research focuses on evaluating the effectiveness of the five chosen machine learning algorithms. The evaluation includes predictive analysis to forecast cyber threats and proactive defense measures. The study examines how machine learning improves the sharing of threat intelligence by facilitating real-time information exchange between collaborating entities. The challenges and considerations related to incorporating AI and ML into cybersecurity are examined, emphasizing ethical implications, bias, fairness, transparency, and accountability. Case studies and success stories from real-world scenarios showcase examples of successful collaborative defense strategies applied in various industries and sectors. The study evaluates and contrasts the random forest, SVM, gradient boosting, isolation forest, and one-class SVM algorithms based on performance metrics like accuracy, precision, recall, and F1 score, utilizing the NSL-KDD dataset. T

关键词： Adversarial machine learning

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Smart Water Taps Using IoT Sensors and Deep Reinforcement Learning for Personalized Consumption Control

Smart Water Taps Using IoT Sensors and Deep Reinforcement Le...

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International Conference on Energy, Power and Environment: Towards Sustainable Growth (ICEPE)

作者： Mageshkumar Naarayanasamy Varadarajan Viyyapu Lokeshwari Vinya R. Jenin Prabhu G. Swathi N. Mohankumar S. Velmurugan Lead Software Engineer Capital One Chennai Tamil Nadu India Assistant Professor Department of Computer Science and Engineering Vardhaman College of Engineering Ranga Reddy Telangana India Assistant Professor Department of Electrical and Electronics Engineering Vel Tech Multi Tech Dr. Rangarajan Dr. Sakundhala Engineering College Chennai Tamil Nadu India Assistant Professor Department of Computer Science and Engineering CMR Technical Campus Hyderabad Telangana India Professor Symbiosis Institute of Technology (SIT) Symbiosis International (Deemed University) Nagpur Maharashtra India Professor Department of Electronics and Communication Engineering T.J.S. Engineering College Chennai Tamil Nadu India

ISBN: (数字)9798350379297

ISBN: (纸本)9798350379303

This research introduces an innovative approach to water conservation and personalized consumption management by combining Internet of Things (IoT) sensors with Deep Reinforcement Learning (DRL) methods. The suggested approach aims to give people more control over their water use in response to water shortages and the need for sustainable resource management. It installs IoT sensors on taps to track water use in real time. A DRL framework is fed data that these sensors have collected. The DRL algorithm develops individual consumption profiles for each user by factoring in past water use, environmental conditions, and personal tastes. Using this unique profile, water efficiency may be improved instantly. Users may easily enter their water conservation choices, budgets, and the system's user interface. Adjusting the water's flow rate by the user profiles learned by the DRL actively regulates water use while keeping people happy. The results of the experiments reveal that our method effectively reduces both water use and waste. Integrating IoT sensors with DRL methods provides a flexible and scalable answer for smart water management that economically benefits the environment and consumers.

关键词： Water heating Water conservation User interfaces Deep reinforcement learning Real-time systems Sensor systems Internet of Things

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Comprehensive Study on Authentication Methods in Wireless Networks 7th

Comprehensive Study on Authentication Methods in Wireless Ne...

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7th International Conference on Soft Computing and Signal Processing, ICSCSP 2024

作者： Kumar, Gotte Ranjith Babu, K. Suresh Research Scholar Department of Computer Science and Engineering Jawaharlal Nehru Technological University Kukatpally Telangana Hyderabad500085 India Assistant Professor School of CS&AI SR University Telangana Warangal506371 India Professor Department of Computer Science and Engineering Jawaharlal Nehru Technological University Kukatpally Telangana Hyderabad500085 India

ISBN: (纸本)9789819799251

Mobile devices, empowered by advanced wireless networks, have revolutionized information sharing and productivity across industries. However, this ubiquitous connectivity exposes user privacy and security to risks. The core vulnerability lies in the lack of authentication between mobile devices and DHCP (Dynamic Host Configuration Protocol) servers, which assign network configurations. This research explored existing authentication mechanisms in wireless networks to address these DHCP-related security concerns, highlighting the essence of secure data communication in mobile environments. The majority of the authentication mechanisms rely on centralized environments. Some rely on a cryptographic security key, which needs to be exchanged during authentication. Blockchain-based authentication is proposed. This technology is distributed, scalable, and robust. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Mobile security

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Machine Learning Techniques with LowDimensional Feature Extraction for Improving the Generalizability of Cardiac Arrhythmia

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2021年第2期48卷 1-10页

作者： Nurmaini, Siti Tutuko, Bambang Rachmatullah, Muhammad Naufal Darmawahyuni, Annisa Firdaus, Firdaus A Professor of Computer Engineering Intelligent System Research Group Universitas Sriwijaya Palembang30139 Indonesia A lecturer of Computer Engineering Department Faculty of Computer Science Universitas Sriwijaya Palembang30139 Indonesia A research assistant of Intelligent System Research Group Universitas Sriwijaya Palembang30139 Indonesia

Automatic heartbeat classification is an important stage in identifying cardiac arrhythmia. Several machine learning (ML) techniques have been proposed to perform this, but they produce an accuracy result of below 99%. In this study, a deep neural network (DNN) structure is applied to improve ML performance. The feature selection method is based on the combination of discrete wavelet transform (DWT) and principal component analysis (PCA). To avoid computational complexity, the components of PCA are derived by low-dimensional DWT coefficients. The results show that the proposed ML model achieves good performance, producing 99.76% accuracy, 91.80% sensitivity, 99.78% specificity, 93.02% precision, and 92.31% F1-score. To benchmark the proposed model, the support vector machine (SVM) and random forest (RF) techniques are used as the baseline models. The DNNs are 2.3% more sensitive than SVM, while the RF fails to classify the ECG heartbeat. Four datasets are used to analyze the robustness and generalization performance of the proposed model: MIT-BIH, SVDB, MITDB, and IncartDB. All testing results produce satisfying performance. The proposed ML model offers a potential solution to improve the generalizability of a DNN-based model in different cardiac datasets for classifying tasks. © 2021. All Rights Reserved.

关键词： Principal component analysis

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Challenges and Innovations in Multimedia and Real-Time Networking: A Review of Modeling Approaches

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Procedia computer science 2025年 252卷 53-62页

作者： Milind Shah Kajal Parmar Priyanka Padhiyar Naina Parmar Monali Parikh Dhruvansh Patni Assistant Professor Department of Computer Engineering Sardar Vallabhbhai Patel Institute of Technology (S.V.I.T) Vasad Gujarat India Department of Computer Engineering Devang Patel Institute of Advance Technology & Research (DEPSTAR) Faculty of Technology & Engineering (FTE) Charotar University of Science & Technology (CHARUSAT) Changa 388421 India Assistant Professor Department of Information Technology Krishna School of Emerging Technology & Applied Research - Drs. Kiran & Pallavi Patel Global University (KPGU) Vadodara Gujarat India

Over the past decade, the rise of broadband and mobile Internet access has led to the widespread adoption of real-time networking and multimedia applications. These platforms have become essential for connecting individuals and supporting businesses, particularly with the growing trend of remote work. The demand for video streaming services has surged significantly and is expected to continue rising. However, this increased traffic has strained network performance, leading to congestion and diminishing the quality of service, especially during peak evening hours and the COVID-19 pandemic. Besides upgrading network infrastructure, it is crucial to develop intelligent streaming systems that adapt to network conditions and user expectations to enhance customer satisfaction. This review paper explores into the basics and introduction of Multimedia and Real-Time Networking. It also explores the advancements in Adaptive and Intelligent Networking for real-time multimedia communication. The paper formulates research questions based on the discussed topics, such as the distinctions between real-time networking and traditional networking, the ways multimedia applications adjust to fluctuating network conditions, and the challenges and limitations associated with these technologies.

关键词： Real Time Networking Multimedia Challenges Innovations Intelligent Networking

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Relevance of ISO/SAE 21434 in Vehicular Architecture Development

Relevance of ISO/SAE 21434 in Vehicular Architecture Develop...

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IEEE International Conference on Systems, Man and Cybernetics

作者： V Kethareswaran Sanjay Moulik Doctoral Researcher in the Department of Computer Science and Engineering Indian Institute of Information Technology Guwahati Assam India Assistant Professor in the Department of Computer Science and Engineering Indian Institute of Information Technology Guwahati Assam India

Research in terms of security in vehicular architecture has been a focal point of deliberation in the automotive sector. The Automotive industry development has continuously evolved the scenario of adding more integration to networked vehicles or semi-autonomous vehicles. The end product of such manufacturing practices is a vehicle becoming a networked computer on wheels. In spite of providing numerous advantages, these developments in the automotive industry are also bringing more challenges in terms of security. Hence, the industry has been forced to emphasise the principle of Security by Design. Efforts in this direction have resulted in the formulation of the ISO/SAE 21434 standard. In this work, we focus on summarising the ISO/SAE 21434 standard and analysing its relevance for automotive architecture development. Related works in the standard's approach of Security by Design are summarised. Additionally, we propose an application work of the standard in the context of automotive Architecture development as future work.

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