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Contemporary Study for Detection of COVID-19 Using Machine Learning with Explainable AI

computers, Materials & Continua 2024年第7期80卷 1075-1104页

作者： Saad Akbar Humera Azam Sulaiman Sulmi Almutairi Omar Alqahtani Habib Shah Aliya Aleryani Department of Computer Science University of KarachiKarachi75270Pakistan Department of Computing Faculty of Engineering Science and TechnologyHamdard UniversityKarachi75540Pakistan Department of Health Informatics College of Applied Medical SciencesQassim UniversityQassim51452Saudi Arabia Department of Computer Science College of Computer ScienceKing Khalid UniversityAbha61421Saudi Arabia

The prompt spread of COVID-19 has emphasized the necessity for effective and precise diagnostic *** this article,a hybrid approach in terms of datasets as well as the methodology by utilizing a previously unexplored dataset obtained from a private hospital for detecting COVID-19,pneumonia,and normal conditions in chest X-ray images(CXIs)is proposed coupled with Explainable Artificial Intelligence(XAI).Our study leverages less preprocessing with pre-trained cutting-edge models like InceptionV3,VGG16,and VGG19 that excel in the task of feature *** methodology is further enhanced by the inclusion of the t-SNE(t-Distributed Stochastic Neighbor Embedding)technique for visualizing the extracted image features and Contrast Limited Adaptive Histogram Equalization(CLAHE)to improve images before extraction of ***,an AttentionMechanism is utilized,which helps clarify how the modelmakes decisions,which builds trust in artificial intelligence(AI)*** evaluate the effectiveness of the proposed approach,both benchmark datasets and a private dataset obtained with permissions from Jinnah PostgraduateMedical Center(JPMC)in Karachi,Pakistan,are *** 12 experiments,VGG19 showcased remarkable performance in the hybrid dataset approach,achieving 100%accuracy in COVID-19 *** classification and 97%in distinguishing normal ***,across all classes,the approach achieved 98%accuracy,demonstrating its efficiency in detecting COVID-19 and differentiating it fromother chest disorders(Pneumonia and healthy)while also providing insights into the decision-making process of the models.

关键词： COVID-19 detection deep neural networks support vector machine CXIs InceptionV3 VGG16 VGG19 t-SNE embedding CLAHE attention mechanism XAI

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HoloStream: a GPU-powered high-speed user interface for holographic microscopy imaging

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Optics Express 2025年第7期33卷 15648-15660页

作者： Morales, Johan Obando-Vásquez, Sofia Doblas, Ana Trujillo, Carlos Applied Optics Group School of Applied Science and Engineering EAFIT University Medellin Colombia Optical Imaging Research Lab Electrical and Computer Engineering Department University of Massachusetts Dartmouth DartmouthMA United States

This paper presents HoloStream, a GPU-powered high-speed user interface designed for holographic microscopy imaging. The platform reconstructs quantitative phase images rapidly for off-axis digital holographic microscopy (DHM) systems operating in the telecentric regime. By optimizing critical computational procedures, the HoloStream app reconstructs phase maps of 1280 × 960 pixels up to 11 frames per second (FPS) in live visualization and 90 fps for pre-recorded holographic videos. This real-time phase compensation is performed by upgrading the semi-heuristic phase compensation (SHPC) algorithm through GPU acceleration via PyCUDA. The interface also offers a user-friendly experience with flexible control over acquisition modes, visualization formats, and 3D tracking functionalities. Experimental validation demonstrates the platform’s capability to achieve fast and accurate phase imaging for live microscopy applications. The HoloStream app advances DHM technology by combining high-speed processing with ease of use, making it suitable for a wide range of DHM imaging applications. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： computer graphics equipment

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Real-Time Safety Monitoring for Construction Sites Using RFID and Visual Recognition Technologies 1

Real-Time Safety Monitoring for Construction Sites Using RFI...

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1st International Conference on AIML-Applications for engineering and Technology, ICAET 2025

作者： Thaleeparambil, Noeal Rajeev Biju, Abiya Krishna, Deva Prathap, Boppuru Rudra Rekha, V. Reddy, Vandana Department of Computer Science and Engineering Bengaluru India M. S. Ramaiah University of Applied Sciences Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350355611

The integrated automated safety monitoring system for construction sites utilizes RFID, Wi-Fi, and vision-based recognition systems to enhance worker safety and ensure adherence to safety regulations. This system combines sophisticated components such as RFID tags and Raspberry Pi, solenoid locks, servo motors, and PIR sensors to provide an exhaustive solution. RFID technology is applied to assign unique tags to each worker, facilitating accurate tracking and identification The Wi-Fi and visual recognition components improve the system's functionalities, enabling wireless connectivity instantaneous data transmission, and verification of appropriate safety gear application. Solenoid locks and servo motors ensure regulated access to hazardous areas, responding to authenticated safety compliance records. PIR sensors sense motion, differentiating between authentic presence and mere nearness. The methodology outlines the necessary hardware and software criteria, procedures for system initialization, evaluation phases, server connectivity setup, access control enactment, and session closure protocols. It details the seamless integration and verification of hardware components, backend connectivity, identity and safety adherence verification, data encoding, and session termination processes. This research aims to upgrade safety surveillance in construction environments, boosting productivity, accuracy, and security. It also underscores the need for further adaptability to various construction settings to advance greater uptake and continuous improvement in workplace safety protocols. © 2025 IEEE.

关键词： Radio frequency identification (RFID)

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A QoS-based technique for load balancing in green cloud computing using an artificial bee colony algorithm

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Journal of Experimental and Theoretical Artificial Intelligence 2025年第2期37卷 307-342页

作者： Tabagchi Milan, Sara Jafari Navimipour, Nima Lohi Bavil, Hamed Yalcin, Senay Department of Computer Engineering Tabriz Branch Islamic Azad University Tabriz Iran Department of Computer Engineering Faculty of Engineering and Natural Sciences Kadir Has University Istanbul Turkey Future Technology Research Center National Yunlin University of Science and Technology Douliou Taiwan Department of Computer Engineering University of Applied Sciences & Technology Tabriz Iran Department of Computer Engineering Nisantasi University Istanbul Turkey

Nowadays, high energy amount is being wasted by computing servers and personal electronic devices, which produce a high amount of carbon dioxide. Thus, it is required to decrease energy usage and pollution. Many applications are utilised by green computing to save energy. Scheduling of tasks acts as an important process to reach the mentioned goals. It is worth stating that the vital characteristic of task scheduling in green clouds is the load balancing of tasks on virtual machines. Efficient load balancing moves tasks from overloaded to underloaded virtual machines to maintain the Quality of Service (QoS). This issue is an NP-complete problem, so this research suggests a new technique based on the behavioural structure of artificial bee behaviour. This method aims to improve QoS while lowering energy usage in green computing. In addition, the honey bees are considered the removed tasks from overloaded virtual machines and a candidate for migrating selected tasks with the lowest priority. The CloudSim testing findings demonstrate that the technique is successful in QoS, makespan, and energy usage compared to other ways. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Cloud computing

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xCViT:Improved Vision Transformer Network with Fusion of CNN and Xception for Skin Disease Recognition with Explainable AI

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computers, Materials & Continua 2025年第4期83卷 1367-1398页

作者： Armughan Ali Hooria Shahbaz Robertas Damaševicius Department of Electrical Engineering Wah Engineering CollegeUniversity of WahWah Cantt47040Pakistan Department of Computer Science HITEC UniversityTaxila47080Pakistan Department of Applied Informatics Vytautas Magnus UniversityKaunas44309Lithuania

Skin cancer is the most prevalent cancer globally,primarily due to extensive exposure to Ultraviolet(UV)*** identification of skin cancer enhances the likelihood of effective treatment,as delays may lead to severe tumor *** study proposes a novel hybrid deep learning strategy to address the complex issue of skin cancer diagnosis,with an architecture that integrates a Vision Transformer,a bespoke convolutional neural network(CNN),and an Xception *** were evaluated using two benchmark datasets,HAM10000 and Skin Cancer *** the HAM10000,the model achieves a precision of 95.46%,an accuracy of 96.74%,a recall of 96.27%,specificity of 96.00%and an F1-Score of 95.86%.It obtains an accuracy of 93.19%,a precision of 93.25%,a recall of 92.80%,a specificity of 92.89%and an F1-Score of 93.19%on the Skin Cancer ISIC *** findings demonstrate that the model that was proposed is robust and trustworthy when it comes to the classification of skin *** addition,the utilization of Explainable AI techniques,such as Grad-CAM visualizations,assists in highlighting the most significant lesion areas that have an impact on the decisions that are made by the model.

关键词： Skin lesions vision transformer CNN Xception deep learning network fusion explainable AI Grad-CAM skin cancer detection

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Feature-oriented Digital Twins for Life Cycle Phases Using the Example of Reliable Museum Analytics 8th

Feature-oriented Digital Twins for Life Cycle Phases Using t...

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8th International Symposium on Reliability engineering and Risk Management, ISRERM 2022

作者： Luther, W. Auer, E. Sacher, D. Baloian, N. Department of Computer Science and Applied Cognitive Science University of Duisburg-Essen Germany Electrical Engineering and Computer Science Wismar University of Applied Sciences Germany Department of Computer Science University of Chile Santiago Chile

ISBN: (纸本)9789811851841

In this paper, we consider partial, feature-oriented digital twins of several virtual museums and formulate an approach to assessing them from the viewpoint of their reliability. Although the formulation specifically concerns the chosen application, it can be extended to any kind of feature-oriented digital twin. The features of a virtual museum that define its scope and focus fit into three broad categories: content, communication and collaboration. Moreover, any virtual museum needs to be risk-informed, that is, seek to support the communication and perception of different types of risks from a variety of threat categories. At the same time, appropriate algorithms should identify and analyze different types of risks for museums’ buildings, visitors, exhibitions, and pieces of art with the help of sensor data, operating rules and standards, in this way supporting stakeholders’ decision making. Using comprehensible quality criteria and their metrics, formal assessment can be carried out with the help of formulated requirements, ground truth data and related standards from the involved professional associations. Additionally, empirical assessment based on user surveys can be conducted during the museum’s lifetime. Aside from various realizations or interactive learning systems, the use cases we base our analysis on are a joint project with the Museo de Arte Contemporâneo (MAC, Santiago de Chile);a digital representation of largely destroyed works by German-Jewish sculptor Leopold Fleischhacker, a joint project initiated by the Salomon Ludwig Steinheim-Institute in Essen, presented onsite at the Düsseldorf Memorial to the Victims of Persecution;and the virtual Khachkar Museum dedicated to endangered Armenian cross stones in cooperation with the American University of Armenia. © 2022 ISRERM Organizers. Published by Research Publishing, Singapore.

关键词： Risk assessment

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Coordinated Motion Planning: Multi-Agent Path Finding in a Densely Packed, Bounded Domain 35

Coordinated Motion Planning: Multi-Agent Path Finding in a D...

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35th International Symposium on Algorithms and Computation, ISAAC 2024

作者： Fekete, Sándor P. Kosfeld, Ramin Kramer, Peter Neutzner, Jonas Rieck, Christian Scheffer, Christian Department of Computer Science TU Braunschweig Germany Department of Electrical Engineering and Computer Science Bochum University of Applied Sciences Germany

ISBN: (纸本)9783959773546

We study Multi-Agent Path Finding for arrangements of labeled agents in the interior of a simply connected domain: Given a unique start and target position for each agent, the goal is to find a sequence of parallel, collision-free agent motions that minimizes the overall time (the makespan) until all agents have reached their respective targets. A natural case is that of a simply connected polygonal domain with axis-parallel boundaries and integer coordinates, i.e., a simple polyomino, which amounts to a simply connected union of lattice unit squares or cells. We focus on the particularly challenging setting of densely packed agents, i.e., one per cell, which strongly restricts the mobility of agents, and requires intricate coordination of motion. We provide a variety of novel results for this problem, including (1) a characterization of polyominoes in which a reconfiguration plan is guaranteed to exist;(2) a characterization of shape parameters that induce worst-case bounds on the makespan;(3) a suite of algorithms to achieve asymptotically worst-case optimal performance with respect to the achievable stretch for cases with severely limited maneuverability. This corresponds to bounding the ratio between obtained makespan and the lower bound provided by the max-min distance between the start and target position of any agent and our shape parameters. Our results extend findings by Demaine et al. [13, 14] who investigated the problem for solid rectangular domains, and in the closely related field of Permutation Routing, as presented by Alpert et al. [6] for convex pieces of grid graphs. © Sándor P. Fekete, Ramin Kosfeld, Peter Kramer, Jonas Neutzner, Christian Rieck, and Christian Scheffer.

关键词： Motion planning

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Artificial Intelligence Enabled Future Wireless Electric Vehicles with Multi-Model Learning and Decision Making Models

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Tsinghua science and Technology 2024年第6期29卷 1776-1784页

作者： Gajula Ramesh Anil Kumar Budati Shayla Islam Louai A.Maghrabi Abdullah Al-Atwai Department of Computer Science and Engineering Gokaraju Rangaraju Institute of Engineering&TechnologyHyderabad 500090India Institute of Computer Science and Digital Innovation(ICSDI) UCSI UniversityKuala Lumpur 56000Malaysia also with Department of ECE KoneruLakshmaiah Education FoundationHyderabad 500090India ICSDI UCSI UniversityKuala Lumpur 56000Malaysia Department of Software Engineering College of EngineeringUniversity of Business and TechnologyJeddah 21448Kingdom of Saudi Arabia Department of Computer Science Applied CollegeUniversity of TabukTabuk 47512Kingdom of Saudi Arabia

In the contemporary era,driverless vehicles are a reality due to the proliferation of distributed technologies,sensing technologies,and Machine to Machine(M2M)***,the emergence of deep learning techniques provides more scope in controlling and making such vehicles energy *** existing methods,it is understood that there have been many approaches found to automate safe driving in autonomous and electric vehicles and also their energy ***,the models focus on different aspects *** is need for a comprehensive framework that exploits multiple deep learning models in order to have better control using Artificial Intelligence(AI)on autonomous driving and energy *** this end,we propose an AI-based framework for autonomous electric vehicles with multi-model learning and decision *** focuses on both safe driving in highway scenarios and energy *** deep learning based framework is realized with many models used for localization,path planning at high level,path planning at low level,reinforcement learning,transfer learning,power control,and speed *** reinforcement learning,state-action-feedback play important role in decision *** simulation implementation reveals that the efficiency of the AI-based approach towards safe driving of autonomous electric vehicle gives better performance than that of the normal electric vehicles.

关键词： wireless vehicles deep learning multi-model learning reinforcement learning Artificial Intelligence(Al)

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Problems in Video Conferencing-Mediated Teaching: Experiences of Three User Roles of a University in Bangladesh

Problems in Video Conferencing-Mediated Teaching: Experience...

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2024 International Conference on Advances in Computing, Communication, Electrical, and Smart Systems, iCACCESS 2024

作者： Mansoor, Nafees Rozario, Anatte Kibria, Muhammad Golam Khalid, Md Saifuddin University of Liberal Arts Department of Computer Science and Engineering Bangladesh Department of Applied Mathematics and Computer Science Kongens Lyngby Denmark

ISBN: (纸本)9798350350289

Video-conferencing systems (VCS) became a defacto tool for online and hybrid teaching during COVID-19 and the gradual return to physical classrooms. As part of the EdViCon Erasmus+ project, towards defining the scenarios for improving teaching with additional portable tools by university teachers, this study reports users' problems in the context of the University of Liberal Arts Bangladesh (ULAB). Classroom observations, problem-tree analysis, interviews, and focus-group discussions with academics, e-learning and IT consultants, and students provide different perspectives on the central problems, underlying causes, and the effects of VCS use during live teaching sessions. While teachers emphasized the lack of resources and technical barriers, students' problem statement focused on the reduced quality of interactions due to the differences between VCS-mediated and physical classrooms, and the IT & e-learning professionals emphasized technological issues (audio, tone, lack of facial expression etc.), lack of collaboration, and differences in expectations among participants. The central contribution of this paper is three diagrams showing problem tree analysis from the perspectives of the three roles. Each of the causes, effects, and problem statements will be considered as point-of-view statements for generating ideas for supporting hardware and software tools and training on technology and pedagogy, © 2024 IEEE.

关键词： Students

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Arbitrary style transfer system with split-and-transform scheme

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Multimedia Tools and Applications 2024年第23期83卷 62497-62517页

作者： Tu, Ching-Ting Lin, Hwei Jen Tsai, Yihjia Lin, Zi-Jun Department of Applied Mathematics National Chung Hsing University Taichung Taiwan Department of Computer Science and Information Engineering Tamkang University New Taipei Taiwan

For the subject of arbitrary image style transfer, there have been some proposed architectures that directly compute the transformation matrix of the whitening and coloring transformation (WCT) to obtain more satisfactory transformation results. However, calculating the transformation matrix of WCT is time-consuming. Li et al. trained a linear transformation module to generate a WCT transformation matrix for any pair of images, i.e., content image and style image, to avoid complex calculations and improves time efficiency. In this work, we introduce a flexible arbitrary image style transfer framework based on the LST, which uses deep neural networks to train a linear transformation matrix as the standard matrix for WCT. For the first part, inverse relationship between the Whitening matrix and the Coloring matrix w.r.t. the same image is enforced during the training of the linear transformation matrix, so that the resulting matrix will be more accurate and closer to the standard matrix of WCT. For the second part, a split-and-transform scheme is proposed. Unlike LST, which transforms the block of feature maps as a whole, the split-and-transform scheme divides the feature block into several smaller blocks and transforms them individually, so that the transformation is more localized, and the more the number of divided blocks, the more localized. In addition, the proposed split-and-transform scheme allows users to determine the number of divided blocks to flexibly control the locality of the transformations. Experimental results demonstrate the effectiveness and flexibility of the proposed framework by the high-quality stylized images and adjustable balance between globality and locality of transformations. The use of the split-and-transform scheme can reduce the computational time while preserving or even improving the stylization results. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Linear transformations

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