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A Social Distancing Framework Based on GPS and Bluetooth Empowered by Feature-based Machine Learning Algorithm

IEIE Transactions on Smart Processing and Computing 2025年第1期14卷 118-127页

作者： Khan, Muhammad Adnan Rehman, Abdur Abbas, Sagheer Ali, Muhammad Nadeem Kim, Byung-Seo Department of Software Faculty of Artificial Intelligence & Software Gachon University Seongnam13120 Korea Republic of School of Computer Science National College of Business Administration and Economics Lahore54000 Pakistan Department of Computer Science Prince Mohammad Bin Fahd University Al Khobar Saudi Arabia Department of Software and Communications Engineering Hongik University Sejong Campus Korea Republic of

The current COVID-19 epidemic is responsible for causing a catastrophe on a global scale due to its risky spread. The community’s insecurity is growing as a result of a lack of appropriate remedial measures and immunization against the disease. In this case, social distancing is thought to be an effective barrier against the spread of the contagion virus as the risk of virus transmission can be reduced by avoiding direct contact with people. Thus, the goal of this research is to develop and improve an AI (Artificial Intelligence) system architecture for social distance monitoring. The framework could also use the GPS (Global Positioning System) to recognize human separation through cell phones. The transition learning framework is also applied to improve the consistency of the existing system. In this manner, the detection system uses a pre-trained technique that takes a Bluetooth dataset and location-sharing dataset to link to an additional level. In an attempt to approximate social distancing breaches among people, we used Bluetooth technology along with GPS distance estimation and set a threshold. To predict if the distance value exceeds the required social distance standard, a violation threshold is calculated and then it sends an alarm to every individual who is not maintaining social distancing. In response, the individual who breaks the social distance limit is also monitored using a detection approach. © 2025 Institute of Electronics Engineers of Korea. All rights reserved.

关键词： COVID-19

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Enhanced QoS Optimization of Opportunistic Networks using Decision Tree

Enhanced QoS Optimization of Opportunistic Networks using De...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Mathur, Mansi Sawanni, Ritu Kumar, Anuj Jain, Ayushi Pal, Atul Kumar Manwal, Manika Department Of Information Technology New Delhi India Graphic Era Hill University Department Of Computer Science And Engineering Uttarakhand Dehradun India Department Of Computer Science And Engineering Lucknow Kannauj India Graphic Era Deemed To Be University Department Of Computer Science And Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350366846

Opportunistic networks are a type of dynamic wireless network that lack a fixed infrastructure and rely on intermittent connections between nodes to facilitate communication. Quality of Service (QoS) is an essential factor for the efficient operation of opportunistic networks, as it ensures that data is delivered in a timely and reliable manner. This paper proposes an enhanced QoS optimization approach for opportunistic networks using a Decision Tree algorithm. The algorithm considers various network parameters such as node mobility, data transmission rate, and available storage capacity to predict the optimal routing path for data transmission. The Decision Tree algorithm uses a classification technique to build a model based on historical data to identify the most suitable routing path for each data transmission. This approach allows for dynamic adaptation to changing network conditions, improving the overall performance and QoS of the opportunistic network. By incorporating the Decision Tree algorithm into the routing decisions of the opportunistic network, we can achieve better QoS optimization than traditional routing protocols. Our experimental results show that the proposed approach outperforms existing routing protocols regarding data delivery ratio, end-to-end delay, and energy consumption, making it a promising solution for enhancing QoS in opportunistic networks. © 2024 IEEE.

关键词： Decision trees

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A Clique-Based Separator for Intersection Graphs of Geodesic Disks in R2 40

A Clique-Based Separator for Intersection Graphs of Geodesic...

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40th International Symposium on Computational Geometry, SoCG 2024

作者： Aronov, Boris de Berg, Mark Theocharous, Leonidas Department of Computer Science and Engineering Tandon School of Engineering New York University BrooklynNY United States Department of Mathematics and Computer Science TU Eindhoven Netherlands

ISBN: (纸本)9783959773164

Let d be a (well-behaved) shortest-path metric defined on a path-connected subset of R2 and let D = {D1, . . ., Dn} be a set of geodesic disks with respect to the metric d. We prove that G×(D), the intersection graph of the disks in D, has a clique-based separator consisting of O(n3/4+Ε) cliques. This significantly extends the class of objects whose intersection graphs have small clique-based separators. Our clique-based separator yields an algorithm for q-Coloring that runs in time 2O(n3/4+Ε), assuming the boundaries of the disks Di can be computed in polynomial time. We also use our clique-based separator to obtain a simple, efficient, and almost exact distance oracle for intersection graphs of geodesic disks. Our distance oracle uses O(n7/4+Ε) storage and can report the hop distance between any two nodes in G×(D) in O(n3/4+Ε) time, up to an additive error of one. So far, distance oracles with an additive error of one that use subquadratic storage and sublinear query time were not known for such general graph classes. © Boris Aronov, Mark de Berg, and Leonidas Theocharous.

关键词： Computational geometry

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Unraveling the Intersection of HCI and Neurodiversity in Bangladesh: Exploring ADHD, Technology-Based Interventions, and Cultural Perspectives 26

Unraveling the Intersection of HCI and Neurodiversity in Ban...

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26th International Conference on computer and Information Technology, ICCIT 2023

作者： Alam, Dewan Mahinur Jibon, Md. Mehedi Hasan Chowdhury, Farida Rabbi, Md Forhad Shahjalal University of Science and Technology Department of Computer Science and Engineering Sylhet Bangladesh BRAC University Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350359015

Attention-Deficit/Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental condition worldwide, including in Bangladesh. Children with ADHD encounter difficulties in sustaining attention, impaired executive functioning, and struggles in academic and social settings. These problems become very prevalent in the schooling context. In many cases, children are sent to special education schools. However, either there are not enough special schools in Bangladesh or the existing special schools lack resources. On the flip side, the traditional Bangladeshi schooling system requires more attention and focus, which ADHD children find difficult to adjust to. In this regard, we hypothesize that e-learning-based intervention can address both of these problems. Besides, there is a notable absence of research on e-learning-based interventions for ADHD children by breaking cultural barriers. This study explored Bangladeshi parental attitudes, beliefs, and social and cultural perceptions towards ADHD and its technology-based interventions. We conducted a semi-structured interview with parents and caregivers of children with ADHD. In this paper, after a thorough analysis, we have presented some design challenges of implementing an effective e-learning-based intervention for Bangladeshi children with ADHD. Our results reveal that using gamification, and reward systems in the intervention will provide a sense of achievement in children, and the inclusion of practical learning tasks along with animated learning content will foster their sustained engagement in the learning process. © 2023 IEEE.

关键词： Attention-Deficit/Hyperactivity Disorder Cultural Factors E-Learning System Human-computer Interaction Interaction Design Semi-Structured Interview Technological Intervention

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Integrated Crowd Counting System Utilizing IoT Sensors, OpenCV and YOLO Models for Accurate People Density Estimation in Real-Time Environments 1

Integrated Crowd Counting System Utilizing IoT Sensors, Open...

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1st International Conference on Cognitive, Green and Ubiquitous Computing, IC-CGU 2024

作者： Jayasingh, Suvendra Kumar Naik, Pintu Swain, Satyaprakash Patra, Kumar Janardan Kabat, Manas Ranjan Institute of Management and Information Technology BPUT Department of Computer Science and Engineering Odisha Cuttack India Indian Institute of Technology Department of Computer Science and Engineering Jammu India

ISBN: (纸本)9798350361780

Real-time crowd monitoring plays a pivotal role in effectively managing public spaces and ensuring safety. This study investigates the fusion of IoT devices and the YOLO object detection model to accurately count crowds. IoT devices facilitate the instantaneous collection of data from cameras, while the YOLO model adeptly identifies individuals within recorded video frames. The study rigorously assesses the performance of three YOLO variants: YOLO V5, YOLO V8 and YOLO V8 NAS. Findings reveal that YOLO V8 NAS surpasses YOLO V5 and YOLO V8 in mean average precision (mAP), achieving an exceptional mAP of 95.1%. This heightened precision is attributed to the integration of Neural Architecture Search (NAS) into the YOLO V8 NAS model, fine-tuning its architecture specifically for crowd counting tasks. It analyzes various networking models proposed in earlier studies for analyzing crowded scenes in public spaces. It emphasizes the potential of a hybrid model involving an IP camera module and a Deep Neural Network for effective crowd sensing. In this setup, the IP camera captures video footage, while the DNN detects individuals and assesses crowd density based on the count of people recognized. This approach presents an encouraging solution for real-time crowd monitoring and management in public environments. © 2024 IEEE.

关键词： Face recognition

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PIDSNeRF: pose interpolation depth supervision neural radiance fields for view synthesis from challenging input

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Multimedia Tools and Applications 2024年 1-21页

作者： Wang, Jianxin Shao, Haijian Deng, Xing Jiang, Yingtao School of Computer Jiangsu University of Science and Technology Jiangsu Zhenjiang212003 China Department of Electrical and Computer Engineering University of Nevada Las VegasNV89154 United States

Recently, Neural Radiance Fields(NeRF) have shown remarkable performance in the task of novel view synthesis through multi-view. The present study introduces an advanced optimization framework, termed Pose Interpolation Depth Supervision Neural Radiance Fields (PIDSNeRF), designed to address the challenges encountered by NeRF in novel view synthesis. These challenges manifest as artifacts, texture details loss, and geometric inconsistencies, particularly in scenes characterized by different lighting conditions, Textureless regions, and sparse images as inputs. The principle of PIDSNeRF involves interpolating virtual camera positions in the 3D space domain based on known cameras, Subsequently, the sparse point clouds formed during camera pose estimation is re-projected onto these virtual poses, employing an inverse angle weighting strategy, thereby generating depth-supervised rays. Furthermore, we propose a depth diffusion method that spreads depth-supervised rays along the pixel plane, forming Gaussian functions with different variances based on the diffusion distances. Finally, depth supervision during volume rendering is achieved by optimizing the Kullback-Leibler(KL) divergence between the weight distribution of sampled points along each ray and the probability density function of the corresponding Gaussian function along that ray. The above process enables a more comprehensive optimization of multi-resolution grid features that align with the sampled points along the rays. The experiments on the DTU, LLFF, and DL3DV-10K datasets demonstrate, PIDSNeRF can synthesize complete novel views within minutes, and the various metrics of the synthesized images achieve the best performance. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Probability density function

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Leveraging deep learning with sentiment analysis for Online Book reviews polarity classification model

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Multimedia Tools and Applications 2024年 1-20页

作者： Bharathi, R. Bhavani, R. Priya, R. Department of Computer Science & amp Engineering Annamalai University Annamalai Nagar Chidambaram TamilNadu Cuddalore India

Sentiment analysis (SA) identifies the opinions or feelings expressed as negative, positive or neutral. It exploits models namely natural language processing (NLP) and computational linguistics to detect personal data. Recently, SA gained significant interest and is highly related to text classification to determine the intent of the text posted by the user. On the other hand, online book reviews are considered as a recently developed customer feeder source. In the digital era, people can explore books and make choices based on online review sites. SA can be commonly utilized by methods based on lexicon, machine learning (ML), combined, or hybrid analysis. Deep learning (DL) models are presented to classify sentiments without requiring high-level attribute engineering. Therefore, this research develops an optimal deep learning-based sentiment analysis for the online book review classification technique. This research work aims to classify the sentiments of the online book reviews from Amazon Kindle. The data were pre-processed in different ways initially to transform the input book reviews into a meaningful format. Two approaches are used for feature extraction: N-grams and Global Vectors for Word Representation (Gloves). Besides, cascaded recurrent neural network (CRNN) and convolutional neural network (CNN) models are used for the review classification process. The experimental evaluation of this research work is validated by utilizing the Amazon-kindle-book-review dataset. The proposed Glove-CNN model illustrates superior accuracy, precision, recall, and F-score values of 98.00%, 96.95%, 96.13%, and 96.52% over other state-of-the-art techniques. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Convolutional neural networks

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Performance Evaluation of Diagnostic and Classification Systems Using Deep Learning on Apache Spark 1

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12th International Conference on Integrated Design and Production, CPI 2022

作者： Taib, Chaymae Abdoun, Otman Haimoudi, Elkhatir Computer Science Department Polydisciplinary Faculty Abdelmalek Essaadi University Larache Morocco Computer Science Department Faculty of Science Abdelmalek Essaadi University Tetouan Morocco

ISBN: (数字)9783031236150

ISBN: (纸本)9783031236143

Respiratory disorders endanger people’s lives. A novel Sars species was discovered for the first time in 2019 in Wuhan when it was isolated from three people suffering from respiratory sickness, indicating that early discovery can help save lives. The purpose of this study is to implement and evaluate six hybrid architectures for multiclass chest X-ray images classified into four classes (Covid19, Normal, Lung Opacity, and Viral Pneumonia) using an open dataset. For feature extraction, we employed three CNN approaches (VGG19, ResNet50, and InceptionV3), along with two machine learning algorithms as classifiers (Logistic Regression and Random Forest). According to the outcomes of this study, the design using Logistic Regression as a classifier and VGG19 as a feature extractor was the best-performing architecture, with an accuracy value of 94.12%. Aside from the great value-added by deploying Apache Spark, which allows for speedier training and testing time. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Extraction

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Enhancing User Experience and Integrating Passwordless FIDO-Based Authentication through Bluetooth Low Energy (BLE) 3

Enhancing User Experience and Integrating Passwordless FIDO-...

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3rd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Rathi, Snehal Oswal, Harsh D'Souza, Ansfred Pratham, Shaantanu Khedkar, Vijayshri BRACTs Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Department of Computer Science & Engineering Pune India

ISBN: (纸本)9798350354348

Traditional password-based authentication methods confront several issues such as security weaknesses, user inconvenience, and susceptibility to various attacks, e.g. phishing. These issues are solved with newly created industry standards such as Web Authentication, CTAP (Client to Authenticator Protocol), FIDO (Fast Identity Online), and W3C guidelines that deliver reliable and interoperable authentication solutions. This research explores how the FIDO, Web Authentication, CTAP, and W3C standards can be blended to revolutionize authentication practices. The proposed method should increase the security and usability of the authentication process without requiring passwords. Our technology facilitates this by establishing secure connections over Bluetooth, offering seamless UX - between a mobile device (in your pocket) and single desktop while maintaining the strongest security postures through structured authentication experiences. © 2024 IEEE.

关键词： Authentication Protocol

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Performance Analysis of Liver Disease Prediction Using Supervised Machine Learning Classification Models 1

Performance Analysis of Liver Disease Prediction Using Super...

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1st International Conference on Advanced Computing and Emerging Technologies, ACET 2024

作者： Singh, Ramveer Sharma, Rahul Chaudhary, Shalbha Yadav, Deepika Jain, Jaishree Kumar, Pawan Raj Kumar Goel Institute of Technology Department of Computer Science & Engineering UP Ghaziabad India Raj Kumar Garg Institute of Technology Department of Computer Science & Engineering UP Ghaziabad India R.D. Engineering College Department of Management Studies UP Ghaziabad India Bharati Vidyapeeth's College of Engineering Department of Computer Science & Engineering New Delhi India Teerthanker Mahaveer University Department of Computer Science & Engineering UP Moradabad India Satyug Darshan College of Engineering Department of Computer Science Faridabad India

ISBN: (纸本)9798350367720

Liver is most fundamental part of human body. It is highly dependable to performing several body functions such as helping in food digestion, waste filtering, enzyme activation, detoxification, nutrition and glycogen storage, carbohydrate metabolism, vitamin and mineral storage etc. It has been found from various survey reports that across the world, 58 million people are suffering from liver disorders and nearly 01 million liver disorder patients are present in India. The growth rate of liver disorder patients is increasing rapidly worldwide due to a sharp rise in obesity, eating more junk foods, more alcohol consumption and unhealthy lifestyles. People who have liver disorders problem need medical treatment at the correct time and early disease prediction helps to reduce the number of deaths. The research focuses on designing an intelligent system that helps for prediction liver disorder by using various classification models of supervised machine learning and it also helps to prevent becoming India a global hub of liver diseases by 2030. The research emphasis on comparative performance analysis of different classification algorithms and ensemble methods. It also helps to select best accurate model for prediction of the liver diseases. © 2024 IEEE.

关键词： Self-supervised learning

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