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Deep Learning-Based Mask Identification System Using ResNet Transfer Learning Architecture

computer Systems Science & engineering 2024年第2期48卷 341-362页

作者： Arpit Jain Nageswara Rao Moparthi A.Swathi Yogesh Kumar Sharma Nitin Mittal Ahmed Alhussen Zamil S.Alzamil MohdAnul Haq Department of Computer Science and Engineering KoneruLakshmaiah Education FoundationVaddeswaram522302India Department of Computer Science and Engineering Sreyas Institute of Engineering and TechnologyHyderabad500068India University Centre for Research and Development Chandigarh UniversityMohali140413India Department of Computer Engineering College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia

Recently,the coronavirus disease 2019 has shown excellent attention in the global community regarding health and the *** Health Organization(WHO)and many others advised controlling Corona Virus Disease in *** limited treatment resources,medical resources,and unawareness of immunity is an essential horizon to *** all resources,wearing a mask is the primary non-pharmaceutical intervention to stop the spreading of the virus caused by Severe Acute Respiratory Syndrome Coronavirus 2(SARS-CoV-2)*** countries made masks mandatory to prevent *** such enforcement,automatic and effective face detection systems are *** study presents a face mask identification approach for static photos and real-time movies that distinguishes between images with and without *** contribute to society,we worked on mask detection of an individual to adhere to the rule and provide awareness to the public or *** paper aims to get detection accuracy using transfer learning from Residual Neural Network 50(ResNet-50)architecture and works on detection *** experiment is tested with other popular pre-trained models such as Deep Convolutional Neural Networks(AlexNet),Residual Neural Networks(ResNet),and Visual Geometry Group Networks(VGG-Net)advanced *** proposed system generates an accuracy of 98.4%when modeled using Residual Neural Network 50(ResNet-50).Also,the precision and recall values are proved as better when compared to the existing *** outstanding work also can be used in video surveillance applications.

关键词： Transfer learning depth analysis convolutional neural networks(CNN) COVID-19

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Deep Learning-Based Robust Hybrid Approaches for Brain Tumor Classification in Magnetic Resonance Images

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Journal of The Institution of Engineers (India): Series B 2023年 1-15页

作者： Krishna, S. Rama Ravela, Chittibabu Mantena, Srihari Varma Sirajuddin, Mohammad Deol, G. Joel Sunny Hyderabad India Computer Science and Engineering Koneru Lakshmaiah Education Foundation K L University Vaddeswaram India Department of Computer Science and Engineering SRKR Engineering College Bhimavaram India Department of Information Technology Kallam Haranadhareddy Institute of Technology Dasarapalem India Department of Computer Science and Engineering Kallam Haranadhareddy Institute of Technology Andhra Pradesh Dasarapalem India

Classification of brain images is a very challenging problem among the most helpful and commonly employed procedures in the medical system. Deep learning, a subset of artificial intelligence, has pioneered new techniques for automatic medical image investigation. The goal of this study was to create two deep learning-based hybrid models to segment and classify brain MRI data. The first hybrid model is a combination of Fully Convolutional Networks and Residual Networks for segmentation and classification. The second is a hybrid of SegNet for segmentation and MobileNet for classification. The analysis is based on a labelled dataset containing images of glioma, meningioma, pituitary, and no tumor. The hybrid models proposed above are built using Python. The suggested models exhibit a high accuracy of 93.9% and 91.3% for a brain MRI dataset, respectively. Furthermore, the proposed models were compared and evaluated against other existing hybrid models in terms of dice score, Positive predictive value, False predictive value, precision, recall, specificity, and F1 score. As a result, the proposed deep-learning technique can assist doctors as well as radiologists in detecting brain tumors early. © 2023, The Institution of Engineers (India).

关键词： Magnetic resonance imaging

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Achieving Seamless Semantic Interoperability and Enhancing Text Embedding in Healthcare IoT: A Deep Learning Approach with Survey

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SN computer Science 2024年第1期5卷 99页

作者： Purushothaman, Sony Shanmugam, G. Siva Nagarajan, Sureshkumar School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Computer Science Govt. Model Engineering College Kerala Kochi India Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Tamil Nadu Krishnakovil India

Achieving semantic interoperability in healthcare is one of the significant challenges in the rapidly expanding healthcare sector. On the other hand, IoT in healthcare can provide patient-centric treatment mechanisms to help resolve these issues. When several healthcare organizations are generating heterogeneous medical information, semantic interoperability becomes essential to develop better treatment plans and better medical research. In this article, we discuss the applications of healthcare IoT, a comparative study of existing solution paradigms for achieving semantic interoperability and its enabling technologies. The comprehensive analysis of semantic interoperability has versatile dimensions to meet this, and it establishes deep learning-based directions. In continuing the background work, we proposed a new deep learning model called MedRelBERT with six embeddings to remove linguistic ambiguities in medical documents. The survey also examines the bibliographic evaluation of Web of Science articles discussing semantic interoperability solutions. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Deep learning Internet of medical things Privacy Semantic interoperability

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Novel hybrid optimization based adaptive deep convolution neural network approach for human activity recognition system

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Multimedia Tools and Applications 2025年第9期84卷 6519-6543页

作者： Ashwin, M. Jagadeesan, D. Raman Kumar, M. Murugavalli, S. Chaitanya Krishna, A. Ammisetty, Veeraswamy Department of Computer Science and Engineering Presidency University Karnataka Bangalore India Andhra Pradesh Madanapalle517325 India Nalla Malla Reddy Engineering College Telangana Hyderabad500088 India Department of Artificial Intelligence K. Ramakrishnan College of Technology Trichy -2 Kariyamanickam Rd Tamil Nadu Tiruchirappalli621112 India Department of ECE St. Martin’s Engineering College Telangana Secunderabad India Department of Computer Science & Engineering Koneru Lakshmaiah Education Foundation Guntur Andhra Pradesh Vaddeswaram India

Human Activity Recognition (HAR) has emerged as a crucial area of research in computer vision, signal processing, and machine learning, enabling to automatically identify human activities from sensor data. Although various models are developed for automatic HAR, they face challenges like limited accuracy, large training time, etc. To resolve these issues, we developed an innovative hybrid strategy using the combination of hybrid meta-heuristic optimization algorithms and deep learning. The developed work commences with the collection of HAR database and the collected database was pre-processed to enhance image quality. Further, segmentation was performed using optimized Otsu’s methodology to extract the most significant features. Then, an innovative classification model was developed using the combination of Improved Spotted Hyena algorithm with Seagull Optimization Algorithm (ISHO-SOA) and Adaptive Deep Convolutional Neural Networks (ADCNN) for identifying and classifying human activities. The novelty of the algorithm lies in the seamless integration of different meta-heuristic optimization algorithms and deep learning for HAR. In the proposed algorithm, the ADCNN was trained using the database to classify human activities, while the ISHO-SOA for refining the hyperparameters of ADCNN to optimize its training and improve classification performances. The proposed algorithm was modeled in MATLAB software, and validated using HAR dataset from the Kaggle database. The experimental results depict that the developed algorithm achieved higher accuracy of 99.84%, and greater precision of 99.90%. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Classification (of information)

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E-Health Records Stored Over the Cloud with Automated Medication Reminders for Enhanced Patient Care 4

E-Health Records Stored Over the Cloud with Automated Medica...

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4th International Conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Indhuja, E. Angelina, J.Jane Rubel Subhashini, S.J. Ajay Kumar, B. Amulya, L. Gopi, G. Kalasalingam Academy of Research & Education Department of Computer Science and Engineering Krishnankovil India Srm Madurai College for Engineering and Technology Department of Computer Science and Engineering Sivagangai India

ISBN: (纸本)9798350300093

This hospital management system aims to develop a user-friendly and efficient system using PHP as the front-end interface and MySQL as the database. The system enables the management of patient information, doctor information, prescription details, and appointment details. The system provides a centralized platform for the management of these aspects, enabling healthcare providers to access and manage the data in real-time from anywhere. The system allows authorized users to add or remove doctor details, manage patient appointments and claims securely. The system has been designed to ensure the protection of personal data to speed up data processing. The system provides various features such as appointment scheduling, patient record management, doctor record management, prescription management, and billing management. Overall, the hospital management system is a reliable and efficient solution that streamlines the management of healthcare facilities. © 2023 IEEE.

关键词： Information management

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Movie Recommendation System Using Content Based Novel Feature Extraction Information

Movie Recommendation System Using Content Based Novel Featur...

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2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Nagaraj, P. Sumanth Kumar Reddy, P. Venkata Rao, K. Mohan, R. Jaswanth, V. Chethan Kumar, M. Sudar, K. Muthamil Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Virudhunagar Krishnankoil India Mepco Schlenk Engineering College Department of Computer Science and Engineering Virudhnagar India

ISBN: (纸本)9798350348217

Content-Based Filtering and Collaborative Filtering are the two key techniques used by recommender systems to help them provide relevant recommendations. In order to create a report for this paper, we developed a hybrid technique that benefits from both content and collaborative filtering algorithms. The algorithm discussed in this article differs from other work on the topic since it uses a cutting-edge technique to identify content similarities between two items. The study in the paper shows why this new technique is valid and how it can result in useful suggestions. On existing person and object data, the aforementioned strategy outperformed the two most popular approaches, Pure Collaborative Filtering and Singular Value Decomposition, in terms of outcomes. © 2023 IEEE.

关键词： Recommender systems

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The Transformative Impact of AI and Machine Learning on Human Psychology

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Recent Advances in computer Science and Communications 2024年第2期17卷 31-39页

作者： Jyoti, Amrita Yadav, Vikash Pal, Amita Rahul, Mayur Jha, Sonu Kumar ABES Engineering College Uttar Pradesh Ghaziabad India Department of Technical Education Government Polytechnic Bighapur Uttar Pradesh Unnao India Marathwada Mitra Mandal’s Institute of Technology Pune India Department of Computer Applications School of Engineering and Technology CSJM University Kanpur India Krishna Engineering College Uttar Pradesh Ghaziabad India

This journal paper examines the transformative role of Artificial Intelligence (AI) and Machine Learning (ML) in shaping human psychology. It investigates how cognitive processes, emotional states, and social interactions are impacted by AI and ML technology. The use of AI and ML in psychology is covered in this study, covering social behaviour analysis, emotion identification, mental health assessment, and personalised therapies. It also explores the moral issues and prospective effects of AI and ML in comprehending and influencing human psychology. This paper emphasises the enormous influence of AI and ML on the comprehension and research of human psychology through a thorough analysis of pertinent literature and empirical evidence. This paper seeks to offer a thorough explanation of the profound effects that AI and ML have had on psychology. We will offer insight into the possible advantages, difficulties, and ethical issues that occur when integrating AI and ML into the study of human psychology by looking at recent developments and implementations of these technologies in psychological research. We will also look at how other areas of psychology, such as cognitive psychology, clinical psychology, social psychology, and neurology, have been impacted by AI and ML. © 2024 Bentham Science Publishers.

关键词： Machine learning

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Deep Learning Classification Techniques on Detecting Diabetic Retinopathy Dataset 7

Deep Learning Classification Techniques on Detecting Diabeti...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Revathi, B. Usharani, C. Kezial Elizabeth, S.K. Nagaraj, P. Nithya, D. Ramco Institute of Technology Department of Artificial Intelligence And Data Science Virudhunagar India Mangayarkarasi College of Engineering Department of Computer Science And Engineering Madurai India Kalasalingam Academy of Research And Education Department of Computer Science And Engineering Virudhunagar Krishnankoil India Theni Kammavar Sangam College of Technology Department of Computer Science And Engineering Theni India

ISBN: (纸本)9798350359299

Deep learning algorithms can summarize images to understand how to carry out necessary tasks. The purpose of this study is to compare several deep learning methods. Both experience-based and explanation-based learning are possible in deep learning. The most widely utilized algorithms, such as Convolutional Neural Networks (CNN), Multilayer Perceptron (MLP), Generative Adversarial Networks (GAN), Radial Basis Function Networks (RBFN), and Deep Belief Networks (DBN), and the Diabetic Retinopathy dataset is utilized in this study to evaluate the effectiveness of the algorithms. A comparative study of the classifiers reveals that CNN performs more accurately than the other approaches. © 2024 IEEE.

关键词： Classification (of information)

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Economic Design of Generalized Multiple Dependent State Sampling Plan Under Exponentiated Half Logistic Distribution

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American Journal of Mathematical and Management Sciences 2024年第2期43卷 141-162页

作者： Kannan, G. Jeyadurga, P. Balamurali, S. Department of Mathematics Ramco Institute of Technology Rajapalayam India Department of Mathematics Thiagarajar College of Engineering Madurai India Department of Computer Applications Kalasalingam Academy of Research and Education Krishnankoil India

The main objective of this study is to propose an economic design of a generalized multiple dependent state sampling plan based on time truncated life test under exponentiated half logistic distribution for assuring median life. In this study, the optimal plan parameters are determined for various median ratios using the approach of two points on the operating characteristic curve. The performance of the proposed plan is compared with other sampling plans designed under exponentiated half logistic distribution. An investigation on the effect of misspecification of shape parameters on probabilities of lot acceptance is done and the industrial application of the proposed plan is explained with real time data. The results obtained from both conventional as well as economic designing show that the proposed plan outperforms the other existing sampling plans, such as multiple dependent state sampling plan, multiple dependent state repetitive group sampling plan, and single sampling plan. © 2024 Taylor & Francis Group, LLC.

关键词： Economic design exponentiated half logistic distribution generalized multiple dependent state sampling plan median life time truncated life test

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A Three-Tier Architecture Framework with Blockchain Integration to Improve the Security in Healthcare Applications 14

A Three-Tier Architecture Framework with Blockchain Integrat...

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14th International Conference on Cloud Computing, Data Science and engineering, Confluence 2024

作者： Sivashankari, T. Pallikonda Rajasekaran, M. Ramaraj, Kottaimalai Arunprasath, T. Reddy, Muthangi Kantha Thilagaraj, M. Kalasalingam Academy of Research and Education Department of Electronics and Communication Engineering Tamilnadu Krishnankoil India Kalasalingam Academy of Research and Education Department of Biomedical Engineering Tamilnadu Krishnankoil India Sri Vishnu Engineering College Department of Computer Science and Engineering AndhraPradesh Bhimavaram India Mvj College of Engineering Department of Industrial Internet of Things Bengaluru India

ISBN: (纸本)9798350344837

The utilization of online diagnosis as a prevalent data service in contemporary healthcare systems involves the real-time collection of physical data through Internet of Things (IoT) devices, such as body sensors and software. This data informs instantaneous decision-making in the diagnostic process. However, the sensitive nature of the collected user data and diagnosis results poses a significant risk of privacy breaches or hacking incidents, potentially leading to misinformation or even fatal outcomes. The involvement of third-party cloud services exacerbates concerns about data protection. Compounding these issues, resource-constrained IoT devices often lack standard security measures. It is imperative to consider parameters such as accuracy, adaptability, low computation time, and the implementation of appropriate anonymity procedures. Addressing these challenges requires the development of a robust algorithm that prioritizes secure access to health records, with a particular emphasis on blockchain technology. To mitigate these shortcomings and ensure the secure storage of patient health data, this study advocates for the creation of a resilient three-tier architecture leveraging blockchain technology. This approach aims to enhance security measures, maintain data accuracy, and facilitate efficient access to health records while safeguarding patient confidentiality. © 2024 IEEE.

关键词： Internet of things

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