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Automatic Eyewitness Identification During Disasters by Forming a Feature-Word Dictionary

computers, Materials & Continua 2022年第9期72卷 4755-4769页

作者： Shahzad Nazir Muhammad Asif Shahbaz Ahmad Hanan Aljuaid Shahbaz Ahmad Yazeed Ghadi Zubair nawaz Department of Computer Science National Textile UniversityFaisalabadPakistan Computer Sciences Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman University(PNU)P.O.Box 84428Riyadh 11671Saudi Arabia Department of Software Engineering and Computer Science Al Ain UniversityAbu DhabiUnited Arab Emirates Department of Data Science University of the PunjabPakistan

Social media provide digitally interactional technologies to facilitate information sharing and exchanging ***,in case of disasters,a massive corpus is placed on platforms such as *** accounts can benefit humanitarian organizations and agencies,but identifying the eyewitness Tweets related to the disaster from millions of Tweets is *** approaches have been developed to address this kind of *** recent state-of-the-art system was based on a manually created dictionary and this approach was further refined by introducing linguistic ***,these approaches suffer from limitations as they are dataset-dependent and not *** this paper,we proposed a method to identify eyewitnesses from *** experiment,we utilized 13 features discovered by the pioneer of this domain and can classify the tweets to determine the *** each feature,a dictionary of words was created with the Word Dictionary Maker algorithm,which is the crucial contribution of this *** algorithm inputs some terms relevant to a specific feature for its initialization and then creates the words ***,keyword matching for each feature in tweets is *** a feature exists in a tweet,it is termed as 1;otherwise,***,for 13 features,we created a file that reflects features in each *** classify the tweets based on features,Naïve Bayes,Random Forest,and Neural Network were *** approach was implemented on different disasters like earthquakes,floods,hurricanes,and Forest *** results were compared with the state-of-the-art linguistic rule-based system with 0.81 F-measure *** the same time,the proposed approach gained a 0.88 value of *** results were comparable as the proposed approach is not ***,it can be used for the identification of eyewitness accounts.

关键词： Word dictionary social media eyewitness identification disasters

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Automated Physical Distance Estimation and Crowd Monitoring Through Surveillance Video

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SN computer science 2023年第1期4卷 1-13页

作者： Junayed, Masum Shah Islam, Md Baharul Department of Computer Engineering Bahcesehir University Istanbul 34349 Turkey Department of Computer Science and Engineering Daffodil International University Dhaka 1207 Bangladesh College of Data Science and Engineering American University of Malta Bomla 1013 Malta

The contagious Corona Virus (COVID-19) transmission can be reduced by following and maintaining physical distancing (also known as COVID-19 social distance). The World Health Organisation (WHO) recommends preventing COVID-19 from spreading in public areas. On the other hand, people may not be maintaining the required 2-m physical distance as a mandated safety precaution in shopping malls and public places. The spread of the fatal disease may be slowed by an active monitoring system suitable for identifying distances between people and alerting them. This paper introduced a deep learning-based system for automatically detecting physical distance using video from security cameras. The proposed system introduced the TH-YOLOv5 for object detection and classification and Deepsort for tracking the detected people using bounding boxes from the video. TH-YOLOv5 included another prediction head to identify objects of varying sizes. The original prediction heads are then replaced with Transformer Heads (TH) to investigate the prediction capability of the self-attention mechanism. Then, we include the convolutional block attention model (CBAM) to identify attention areas in settings with dense objects. Pairwise L2 vectorized normalization was utilized to generate a three-dimensional feature space for tracking physical distances and the violation index, determining the number of individuals who follow the distance rules. We use the MS COCO and HumanCrowd, CityPersons, and Oxford Town Centre (OTC) data sets for training and testing. Experimental results demonstrate that the proposed system obtained a weighted mAP score of 89.5% and an FPS score of 29;both are computationally comparable. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： COVID-19 social distancing Crowd monitoring Distance measurement Human detection and tracking Video surveillance

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SmartDine: A Comprehensive System for Enhancing Customer Satisfaction and Optimizing Business Operations in the Restaurant Context 6

SmartDine: A Comprehensive System for Enhancing Customer Sat...

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6th International Conference on Advancements in Computing, ICAC 2024

作者： Kumarasiri, Kanishka Premarathne, Janitha Mendis, Navinga Bandhuratne, Monali Karunasena, Anuradha Weerasinghe, Lokesha Specializes in Data Science Sri Lanka Institute of Information Technology Malabe Department of Information Technology Sri Lanka

ISBN: (纸本)9798331517878

This research explores the integration of real-time video analysis, deep learning techniques, and advanced data processing tools to enhance customer satisfaction and operational efficiency in restaurant management. Leveraging CCTV footage, along with tools such as Python,PyTorch, OpenCV, and advanced models like YOLOv10 for object detection and YOLO-pose for key point extraction, the study focuses on optimizing key aspects such as food satisfaction analysis, customer engagement and wait time reduction, optimal use and planning of resources. Custom models, including an LSTM network, analyze detected key points to identify customer actions and estimate time spent on various activities. data augmentation techniques enhance model performance, with metrics like precision and recall used for evaluation. The study further aims to provide restaurant managers with a dynamic dashboard that tracks real-time insights, including table turnover rates, preferred menu items, and optimal resource allocation. This research contributes to data-driven restaurant management by offering a robust framework for real-time operational analysis, ultimately improving decision-making and enhancing the dining experience. © 2024 IEEE.

关键词： Sales

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DocFlow: A Visual Analytics System for Question-Based Document Retrieval and Categorization

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IEEE Transactions on Visualization and computer Graphics 2024年第2期30卷 1533-1548页

作者： Qiu, Rui Tu, Yamei Wang, Yu-Shuen Yen, Po-Yin Shen, Han-Wei The Ohio State University Department of Computer Science and Engineering ColumbusOH43210-1132 United States National Yang Ming Chiao Tung University Department of Computer Science HsinChu300 Taiwan Washington University School of Medicine Institute for Informatics St. LouisMO63110 United States

A systematic review (SR) is essential with up-to-date research evidence to support clinical decisions and practices. However, the growing literature volume makes it challenging for SR reviewers and clinicians to discover useful information efficiently. Many human-in-the-loop information retrieval approaches (HIR) have been proposed to rank documents semantically similar to users' queries and provide interactive visualizations to facilitate document retrieval. Given that the queries are mainly composed of keywords and keyphrases retrieving documents that are semantically similar to a query does not necessarily respond to the clinician's need. Clinicians still have to review many documents to find the solution. The problem motivates us to develop a visual analytics system, DocFlow, to facilitate information-seeking. One of the features of our DocFlow is accepting natural language questions. The detailed description enables retrieving documents that can answer users' questions. Additionally, clinicians often categorize documents based on their backgrounds and with different purposes (e.g., populations, treatments). Since the criteria are unknown and cannot be pre-defined in advance, existing methods can only achieve categorization by considering the entire information in documents. In contrast, by locating answers in each document, our DocFlow can intelligently categorize documents based on users' questions. The second feature of our DocFlow is a flexible interface where users can arrange a sequence of questions to customize their rules for document retrieval and categorization. The two features of this visual analytics system support a flexible information-seeking process. The case studies and the feedback from domain experts demonstrate the usefulness and effectiveness of our DocFlow. © 1995-2012 IEEE.

关键词： Information retrieval

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An Optimized CNN and Transfer Learning Approach for Pneumonia Detection 3

An Optimized CNN and Transfer Learning Approach for Pneumoni...

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3rd International Conference on Technological Advancements in Computational sciences, ICTACS 2023

作者： Verma, Sonia Ganesh Gopal, D. Kumar Sharma, Pankaj Srm Institute of Science and Technology Department of Computer Science and Engineering Uttar Pradesh Ghaziabad201204 India ABES Engineering College Department of Computer Science Ghaziabad201204 India

ISBN: (纸本)9798350342338

The most prevalent lung condition affecting people worldwide is pneumonia. Diagnosing pneumonia only from a chest X-ray (CXR) might be challenging. The study aims to simplify pneumonia infection detection for experts and novices alike. We suggest a deep learning (DL) approach for identifying pneumonia using transfer learning (TL). A residual network previously trained on ImageNet is used in the proposed method to recover image features, which is then fed into a CNN classifier for prediction. The performance of the suggested model displays the ability to diagnose pneumonia, showing that the ResNet152V2 model could effectively distinguish between normal and pneumonia X-rays, reducing the burden of radiologists. Using residual learning (ResNet152V2), a model that can determine whether or not a person has pneumonia is trained. Here, the outputs of five different models are compared. The model is executed or trained using GPU through the Google colab. Compared to the CPU's performance, the GPU can considerably speed up the detecting process. © 2023 IEEE.

关键词： convolutional neural network (CNN) lung Chest X-rays (CXR) machine learning(ML) pneumonia transfer learning (TL)

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Customer segmentation using various machine learning techniques

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International Journal of Business Intelligence and data Mining 2022年第4期20卷 480-496页

作者： Othayoth, Samyuktha Palangad Muthalagu, Raja Department of Computer Science Birla Institute of Technology and Science Pilani Dubai Campus Dubai United Arab Emirates

In the field of retail industry and marketing, customer segmentation is one of the most important tasks. A proper customer segmentation can help the managers to enhance the quality of products and provide better services for the targeting segments. Various machine learning algorithms-based customer segmentation techniques are used to get an insight about the customer’s behaviour and the potential customers that could be targeted to maximise profit. Based on the previous studies, this paper proposes improved machine learning models for customer segmentation in e-commerce. The agglomerative clustering algorithms have been implemented to segment the customers with the new metric for customer behaviour. Also, we have proposed a systematic approach for combining agglomerative clustering algorithm and filtering-based recommender system to improve customer experience and customer retention. In the experiment, the results were compared with K-means clustering model, and it was found that BLS greatly reduced training time while guaranteeing accuracy. © 2022 Inderscience Enterprises Ltd.

关键词： Sales

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Enhancing Harris Hawk Optimization for Use in Smart Building Applications 1

Enhancing Harris Hawk Optimization for Use in Smart Building...

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1st International Conference on Pioneering Developments in computer science and Digital Technologies, IC2SDT 2024

作者： Adharsh, R.J. Balaji, S. Sowmyan, R J Ajith Blodin Pvt. Ltd. Department of Technology Tamil Nadu Chennai India SRM Institute of Science and Technology Department of Electronics and Communication Chennai India SRM Institute of Science and Technology Department of Computer Science Chennai India

ISBN: (纸本)9798350365016

The paper proposes an enhanced Harris Hawk optimization model tailored for energy prediction in smart buildings and renewable energy systems. Leveraging advanced optimization techniques, the proposed model aims to accurately forecast energy consumption and generation, thus facilitating efficient energy management and resource utilization in sustainable infrastructures. The R-squared (R2) value of the proposed model is 0.9828, which clearly shows that it performs better than the existing optimization ML models. © 2024 IEEE.

关键词： Smart homes

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DNN-based Secure Remote Patient data Analysis Framework for Improving Human Life Expectancy in Healthcare 4.0 2

DNN-based Secure Remote Patient Data Analysis Framework for ...

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2nd International Conference on Machine Learning and data Engineering, ICMLDE 2023

作者： Darji, Krisha Ramoliya, Fenil Kakkar, Riya Gupta, Rajesh Tanwar, Sudeep Garg, Deepak Department of Computer Science and Engineering Institute of Technology Nirma University Ahmedabad India School of Computer Science and Artificial Intelligence SR University Telangana Warangal506371 India

The evolution of healthcare from its early beginnings to the recent healthcare 4.0 revolution has remarkably improved human life and living standards. The integration of emerging technologies has played a pivotal role in this progress. Notably, remote analysis of patient data has emerged as a promising approach, enabling telemedicine and remote patient monitoring enhancing healthcare accessibility and efficiency. However, the vulnerabilities of patient data to malicious network attacks pose critical challenges in the healthcare industry, potentially compromising patients' safety and undermining trust in the system. Thus, we have introduced a cutting-edge deep neural network (DNN) model to mitigate the risks associated with remote patient data transfer in healthcare 4.0 by bifurcating the data into malicious or non-malicious. The primary objective of the proposed framework is to ensure the secure and private communication of patient data, thereby fostering a more dependable and trustworthy healthcare ecosystem. Finally, the proposed framework is evaluated against various standard metrics such as accuracy, loss considering binary cross-entropy, receiver operating characteristic (ROC) curve, precision-recall curve and confusion matrix. The implications of the proposed framework offer data security to the healthcare system as it contributes to creating a more resilient and dependable ecosystem, thus promoting better patient care and ultimately elevating the overall life expectancy and well-being of individuals. © 2024 Elsevier B.V.. All rights reserved.

关键词： Deep neural networks

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VulnFusion: Exploiting Multimodal Representations for Advanced Smart Contract Vulnerability Detection 6

VulnFusion: Exploiting Multimodal Representations for Advanc...

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6th International Conference on Blockchain Computing and Applications, BCCA 2024

作者： Upadhya, Jiblal Sainju, Arpan Upadhyay, Kritagya Poudel, Samir Hasan, Md Nahid Poudel, Khem Ranganathan, Jaishree Department of Computer Science Middle Tennessee State University MurfreesboroTN37132 United States Department of Computational and Data Science Middle Tennessee State University MurfreesboroTN37132 United States

ISBN: (纸本)9798350351538

In the fast-evolving domain of blockchain technology, smart contracts have become instrumental, facilitating billions of dollars in transactions daily. However, the increasing prevalence of smart contract vulnerabilities poses significant risks, potentially undermining trust in this innovative technology. To address these security challenges, our research introduces a novel approach for detecting and mitigating vulnerabilities through a comprehensive analysis of four principal modalities: Solidity source code, bytecode, opcode, and intermediate representations. This multimodal analysis employs a Transformer architecture, VulnFusion, which uniquely integrates these modalities to provide a deeper understanding of both the semantic and syntactical nuances of smart contracts. We propose an interleaving-based data fusion technique that outperforms traditional fusion methods such as concatenation and cross-attention. Experimental evaluations demonstrate that our interleaving-based fusion technique achieves an impressive average F1 score of 89% across source code, opcode, and intermediate representations, demonstrating the effectiveness of our approach. © 2024 IEEE.

关键词： Smart contract

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Transforming Education with Innovative Virtual Learning Environments

Transforming Education with Innovative Virtual Learning Envi...

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2024 IEEE International Conference on Recent Advances in science and Engineering Technology, ICRASET 2024

作者： Shwetha, G.K. Shetty, Pratham S. Baliga, Samarth N. Rathod, Jayant Kumar A. Divya, C. Department of Computer Science and Engineering Karnataka Nitte India Alva's Institute of Engineering and Technology Department of Information Science and Engineering Karnataka Moodubidire India Kalpataru Institute of Technology Department of Information Science and Engineering Karnataka Tiptur India

ISBN: (纸本)9798350388602

There are several issues with traditional schooling, including poor student participation, rigid scheduling, and restricted accessibility. We require a cutting-edge virtual classroom management system that uses technology to create a dynamic and engaging learning environment in order to solve these problems. The Innovative Virtual Learning Environments is a modern web-based application developed using the MERN stack (MongoDB, ***, ***, ***). It offers a dynamic virtual learning environment, bridging the gap between traditional and remote education by providing features like live video lectures, chat messaging, assignment submissions. By enabling remote attendance, the system removes geographical constraints, enhances accessibility, and offers flexibility and convenience to educators and students. It aims to lower education costs, making high-quality education more affordable and accessible. Utilizing modern web development tools and real-time communication protocols like WebSocket's or WebRTC, the system ensures a interactive and engaging learning experience, promoting teamwork, personalized learning, and real-time communication. The objective is to foster an environment of active learning and continuous interaction, so equipping our users with the critical thinking, creativity, and problem-solving skills necessary to thrive in an increasingly digital and interconnected world. © 2024 IEEE.

关键词： Virtual environments

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