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1st International Conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Bhardwaj, Diwakar Bordoloi, Dibyhash Deepak, A. Srivastava, Arun Pratap Mayuri, K. Khan, Akhilesh Kumar Institute of Engineering and Technology Gla University Department of Computer Engineering and Applications Mathura India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamilnadu Chennai India Lloyd Institute of Engineering and Technology Greater Noida India Institute of Aeronautical Engineering Department of Computer Science and Engineering Telangana Hyderabad India Lloyd Law College Greater Noida India

ISBN: (纸本)9798350330915

In order to analyze Electrocardiograms (ECGs) in real time, this research is the first to integrate Recurrent Neural Networks (RNNs) into a cloud-based healthcare system. By utilizing sophisticated computational algorithms and an interpretivist perspective, the research seeks to transform cardiac diagnosis. Though extremely useful, traditional ECG analysis techniques have processing speed as well as scalability issues. The suggested method instantaneously interprets ECG waveforms by utilizing RNNs' temporal modeling capabilities. This paradigm change makes it possible to identify cardiac problems in a timely manner, potentially improving patient outcomes. Setting up a scalable cloud system for effective data processing is part of the study methodology. The RNN model is trained, verified, and then incorporated into the cloud system using secondary ECG data. The system's efficacy is confirmed by performance evaluation criteria like processing speed, sensitivity, and accuracy. Scalability, safety of data, and interpretability issues in models are highlighted via critical analysis. Techniques for improved model openness, strict data protection policies, and thorough scalability testing are all included in the recommendations. © 2023 IEEE.

关键词： data analysis performance physiological Recurrent Neural Networks remote monitoring

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Secured Reversible Data Hiding Scheme with NMI Interpolation and Arnold Transformation

Secured Reversible Data Hiding Scheme with NMI Interpolatio...

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International Conference on Network Security and Blockchain Technology, ICNSBT 2023

作者： Jana, Manasi Jana, Biswapati Joardar, Shubhankar Jana, Sharmistha Lu, Tzu Chuen Department of Computer Applications Haldia Institute of Technology West Bengal Haldia India Department of Computer Science Vidyasagar University West Bengal West Midnapore India Department of Computer Science and Engineering Haldia Institute of Technology West Bengal Haldia India West Bengal Midnapore India Department of Information Management Chaoyang University of Technology Taichung Taiwan

ISBN: (纸本)9789819944323

A novel and efficient 2-level secure reversible data hiding (RDH) method has been proposed based on image interpolation and Arnold transformation. An input image is scaled-down to original image and then scaled up to cover image using NMI interpolation method. The gray values of pixels are classified into a set of ranges. A difference value is calculated from adjacent pixels and it is used to find out the lower bound and upper bound of the range interval to which it belongs to. Before embedding, the secret data is scrambled using Arnold transformation implementing the 1st level security of proposed method. The scrambled secret data again updated to modified secret message to implement 2nd level security of proposed scheme. The input image is taken as reference during data embedding phase to raise the quality of the stego image. The experimental analysis shows that the proposed method has relatively higher imperceptibility and data embedding capacity than other schemes. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Interpolation

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Evaluating the Effectiveness of Object Detection Algorithms for Autonomous Parking

Evaluating the Effectiveness of Object Detection Algorithms ...

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2024 International Conference on computer, Electronics, Electrical engineering and their applications, IC2E3 2024

作者： Sharma, Utkarsh Subramonian, Krishnan Dsouza, Aaron Sughosh, P. Savitha, G. Girisha, S. Manipal Institute of Technology Manipal Academy of Higher Education Department of Data Science and Computer Applications Manipal576104 India Manipal Institute of Technology Manipal Academy of Higher Education Department of Civil Engineering Manipal576104 India

ISBN: (纸本)9798350388534

In response to the challenges posed by increasing urbanization, traditional parking systems face numerous issues, such as inaccurate slot detection and manual tracking, which exacerbate traffic congestion and environmental strain. This paper presents an autonomous parking system as a solution to these challenges. By leveraging advanced object detection algorithms, including YOLO-NAS, YOLOv8, and DETR models, this system revolutionizes parking space identification, freeing drivers from the task of manual searching. Our research focused on identifying the most suitable object detection algorithm for our dataset and objectives. Utilizing the 'PKLot' Dataset, we trained and fine-tuned our models, ensuring consistency in hyperparameters across algorithms to isolate their impact on performance. We evaluated algorithmic efficacy using metrics like mean average precision (mAP) and latency, measuring accuracy in discerning occupied and vacant spaces alongside real-time processing efficiency. Our evaluation results highlight the performance differences among algorithms, guiding future research. This research advocates for an intelligent, autonomous approach to parking management, aligning with goals of optimizing operations and promoting sustainable urban development. © 2024 IEEE.

关键词： Traffic congestion

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Securing Neural Network in the Cloud: A Systematic Approach of Cloud Security Based on ANN-SVM Model 4

Securing Neural Network in the Cloud: A Systematic Approach ...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Arunarani, Ar Vijayagopal, V. Kartheesan, L. Dwivedi, Vijay Kumar Rajesh Kumar, A. Nishant, Neerav School of Computing College of Engineering Srm Institute of Science and Technology Department of Computational Intelligence Tamilnadu Chennai India Dr. M.G.R. Educational and Research Institute Faculty of Computer Applications Tamilnadu Chennai India School of Computing Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai India Vishwavidyalaya Engineering College Department of Mathematics Chhattisgarh Surguja India N.S.N. College of Engineering and Technology Department of Computer Science and Engineering Tamilnadu Karur India School of Engineering Babu Banarasi Das University Department of Computer Science and Engineering Lucknow India

ISBN: (纸本)9798331540364

Many services have made the transition to the cloud because of the increased reliability and efficiency of cloud computing. Due to their capacity to ease service access while also securing communications in the public network, the three-factor Mutual Authentication and Key Agreement (MAKA) protocols are seeing increased use in multi-server designs. A lack of formal security proof or high communication and computation costs makes the limited three-factor MAKA protocols that are currently available susceptible to a wide range of attacks. The proposed approach consists of three phases, which are preprocessing, feature selection, and model training. Text scheduling and normalization make up preprocessing. In feature selection, the PSO is an optimization method that uses a population-based approach to find optimal solutions. An ANN-SVM was employed throughout the model's training phase. With an average accuracy of 93.30%, the proposed technique surpasses ANN and SVM. © 2024 IEEE.

关键词： Cloud security

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Sentiments Analysis and Text Summarization of Medicine Reviews Using Deep Sequential Iterative Model with Attention Encoder

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Journal of Mobile Multimedia 2022年第4期18卷 1281-1300页

作者： Prabhu, B. Rajalaxmi Seema, S. Department of Computer Science and Engineering M.S. Ramaiah Institute of Technology Bengaluru India Department of Master of Computer Applications M.S. Ramaiah Institute of Technology Bengaluru India Department of Computer Science and Engineering NMAM Institute of Technology VTU Belagavi India

In this day and age of data innovation, vast amounts of data are being generated daily, much of it is useless to the general public unless it is correctly handled. This year has seen a huge increase in social networks' relevance, resulting in vast quantities of data provided by users. This vast amount of information is gathered from a variety of sources, including company websites, customer blogs, and item reviews. The text outline is probably the most significant point of view in daily life. Analysis of text sentiment is a method for assessing, summarising, and drawing conclusions about the most important content. In the field of sentiment analysis, attention methods have been crucial since they make use of sentiment lexicons to gather a huge amount of sentiment polarity *** an attention mechanism that acts as a link between linguistic information with a strong emotional component and deep learning algorithms, it may be possible to boost text sentiment significantly. In the LSTM model, word sequence addictions can be captured over the long term. For the first time, scientists have developed an attention model that combines LSTM with an incredibly deep RNN model to tackle the problem of sentiment analysis in the real world. The iterative method trains the first set of word embeddings using a Word to Vector technique. With the Word2Vec algorithm, text strings are converted into a numerical value vector and distance between words and comparative words based on meaning are calculated. Using the attention strategy has the added benefit of potentially enhancing machine learning's ability to learn sentiment representations. The attention model is more scalable and adaptable than previous approaches. The major objective of this work is to assess feelings and develop an abstracted content outline, decide on the semantic summarization of different materials and effectively process the data. © 2022 River Publishers.

关键词： attention model decoder encoder Polarity summarization

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IoT-Cloud Integration with Reinforcement Learning for Elderly Fall Detection 1

IoT-Cloud Integration with Reinforcement Learning for Elderl...

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1st International Conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

ISBN: (纸本)9798350330915

This project greatly contributes to the integration of IOT systems for the actual development of fall detection mechanisms with advanced RL algorithms. Hence the IOT system mainly prefers the healthcare system for elderly individuals. The individual responses are evaluated according to the major development of fall detection algorithms. A brief introduction is given here with a brief overview of IOT-cloud formation and its theoretical frameworks related to the maximum usage of RL algorithms. The methodology part shows the philosophy, approach, and design part of elderly fall detection. Hence the thematic analysis is provided to make a descriptive result of IOT-cloud integration with RL algorithms for elderly fall detection with an efficient mechanism of fall detection algorithms and alarm structures. © 2023 IEEE.

关键词： Alarming Cloud Infrastructure Elderly Individuals Fall Detection Algorithms IoT RL

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Graph Convolutional Networks for Disease Network Analysis in Healthcare 1

Graph Convolutional Networks for Disease Network Analysis in...

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1st International Conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Kumar, Rakesh Sharma, Manish Saravanan, V. Shalini, N. Yadav, Vijay Kumar Kumar, Navneet Gla University Department of Computer Engineering & Applications Mathura India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Institute of Electronics and Communication Engineering Chennai India Institute of Aeronautical Engineering Department of Computer Science and Engineering Telangana Hyderabad India Lloyd Institute of Engineering and Technology Greater Noida India Lloyd Law College Greater Noida India

ISBN: (纸本)9798350330915

The use of Graph Convolutional Networks (GCNs) in disease network analysis in the healthcare industry has been pioneered by this research. The adoption of GCNs is prompted by the limitations of current methods in collecting complex relationships within large datasets. Various healthcare datasets are used to develop and assess a PyTorch-based GCN model that makes use of an interpretivism philosophy and a deductive approach. The model's superiority over conventional approaches is demonstrated by the results, which also provide fresh perspectives on disease relationships. The interpretative analysis uncovers subtle patterns that advance our comprehensive knowledge of the dynamics of disease. Subsequent research will focus on improving GCNs for real-time applications, scalability, as well as multi-modal integration of information. This work promises to have revolutionary effects on healthcare analytics by utilizing cutting-edge deep-learning techniques to decipher complex disease networks. © 2023 IEEE.

关键词： Deep Learning in Healthcare Disease Network Analysis Graph Convolutional Networks (GCNs) Healthcare Analytics Interpretivist Philosophy

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Social-Aware Group Display Configuration in VR Conference 38

Social-Aware Group Display Configuration in VR Conference

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38th AAAI Conference on Artificial Intelligence, AAAI 2024

作者： Hsu, Bay-Yuan Shen, Chih-Ya Yuan, Hao Shan Lee, Wang-Chien Yang, De-Nian Department of Industrial Engineering and Engineering Management National Tsing Hua University China Department of Computer Science National Tsing Hua University China Institute of Information Systems and Applications National Tsing Hua University China Department of Computer Science and Engineering Pennsylvania State University United States Institute of Information Science Academia Sinica Taiwan Research Center of Information Technology Innovation Academia Sinica Taiwan

ISBN: (纸本)1577358872

Virtual Reality (VR) has emerged due to advancements in hardware and computer graphics. During the pandemic, conferences and exhibitions leveraging VR have gained attention. However, large-scale VR conferences, face a significant problem not yet studied in the literature - displaying too many irrelevant users on the screen which may negatively impact the user experience. To address this issue, we formulate a new research problem, Social-Aware VR Conference Group Display Configuration (SVGD). Accordingly, we design the Social Utility-Aware VR Conference Group Formation (SVC) algorithm, which is a 2-approximation algorithm to SVGD. SVC iteratively selects either the P-Configuration or S-Configuration based on their effective ratios. This ensures that in each iteration, SVC identifies and chooses the solution with the highest current effectiveness. Experiments on real metaverse datasets show that the proposed SVC outperforms 11 baselines by 75% in terms of solution quality. Copyright © 2024, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Virtual reality

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Review on Depression Detection Using Machine Learning Techniques 4th

Review on Depression Detection Using Machine Learning Techni...

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4th International Conference on Recent Trends in Machine Learning, IoT, Smart Cities, and applications, ICMISC 2023

作者： Vidya, S. Raju, G.T. Vinayaka Murthy, M. REVA University Bengaluru560064 India Department of MCA Bangalore Institute of Technology Bengaluru560004 India Department of Computer Science and Engineering Sri Jagadguru Chandrashekaranatha Swamiji Institute of Technology Karnataka Chickballapur562101 India School of Computer Science and Applications REVA University Bengaluru Kattigenahalli Yelahanka 560064 India

ISBN: (纸本)9789819994410

Machine learning has achieved notable advancements in the medical field by improving the accuracy, precision, and analysis of diagnostics while also reducing labour-intensive tasks. With strong evidence, machine learning has exhibited the capability of detecting mental distress including depression. It is fact that a large proportion of the population is currently experiencing depression, which is considered to be a global burden of disease. Hence there is a critical need to develop an early depression detection model. This article aims to improve our understanding the present state of depression prediction models by exploring issues and identifying difficulties. This review relies on the examination of audio, video, images, PHQ questions, Social media (Twitter and Facebook), EEG signal depression detection models using different machine learning approaches. This paper reviews indicators of depression and provides a summary evaluation of diverse research studies encompassing their performance parameters. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Diagnosis

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Resnet Transfer Learning For Enhanced Medical Image Classification In Healthcare 1

Resnet Transfer Learning For Enhanced Medical Image Classifi...

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1st International Conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Varshney, Neeraj Sharma, Manish Saravanan, V. Shalini, N. Yadav, Vijay Kumar Kumar, Navneet Gla University Department of Computer Engineering and Applications Mathura India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India Institute of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India Institute of Aeronautical Engineering Department of Computer Science and Engineering Telangana Hyderabad India Lloyd Institute of Engineering and Technology Greater Noida India Lloyd Law College Greater Noida India

ISBN: (纸本)9798350330915

This work overcomes the limitations of sparsely labeled data by optimizing ResNet transfer learning methods in medical classification of images. Using a deductive approach along with interpretive philosophy, we optimize ResNet for better diagnostic performance on healthcare data sets. Our team of technical approach includes preprocessing datasets, configuring model architectures, and fine-tuning hyperparameters using secondary data. The improved model performance as demonstrated by the results is confirmed by metrics such as precision, reliability, and recall. Analyses of comparisons demonstrate superiority over basic models. Upcoming tasks include working together to create standardized benchmarks, improving interpretability along with scalability, and verifying in actual clinical settings. © 2023 IEEE.

关键词： Hyperparameter Tuning Interpretivism Medical Image Classification ResNet Transfer Learning

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