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作者： Gaur, Loveleen Bhatia, Ujwal Jhanjhi, N.Z. Muhammad, Ghulam Masud, Mehedi Amity International Business School Amity University Noida India School of Computer Science and Engineering SCE Taylor’s University Subang Jaya Malaysia Research Chair of Pervasive and Mobile Computing King Saud University Riyadh11543 Saudi Arabia Computer Engineering Department College of Computer and Information Sciences King Saud University Riyadh11543 Saudi Arabia Department of Computer Science College of Computers and Information Technology Taif University P.O. Box 11099 Taif21944 Saudi Arabia

The demand for automatic detection of Novel Coronavirus or COVID-19 is increasing across the globe. The exponential rise in cases burdens healthcare facilities, and a vast amount of multimedia healthcare data is being explored to find a solution. This study presents a practical solution to detect COVID-19 from chest X-rays while distinguishing those from normal and impacted by Viral Pneumonia via Deep Convolution Neural Networks (CNN). In this study, three pre-trained CNN models (EfficientNetB0, VGG16, and InceptionV3) are evaluated through transfer learning. The rationale for selecting these specific models is their balance of accuracy and efficiency with fewer parameters suitable for mobile applications. The dataset used for the study is publicly available and compiled from different sources. This study uses deep learning techniques and performance metrics (accuracy, recall, specificity, precision, and F1 scores). The results show that the proposed approach produced a high-quality model, with an overall accuracy of 92.93%, COVID-19, a sensitivity of 94.79%. The work indicates a definite possibility to implement computer vision design to enable effective detection and screening measures. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： COVID-19

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computing Challenges of UAV Networks: A Comprehensive Survey

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computers, Materials & Continua 2024年第11期81卷 1999-2051页

作者： Altaf Hussain Shuaiyong Li Tariq Hussain Xianxuan Lin Farman Ali Ahmad Ali AlZubi School of Computer Science&Technology Chongqing University of Posts and TelecommunicationsChongqing400065China Key Laboratory of Industrial Internet of Things and Networked Control Ministry of EducationChongqing University of Posts and TelecommunicationsChongqing400065China School of Computer Science and Technology Zhejiang Gongshang UniversityHangzhou310018China School of Artificial Intelligence Nanjing University of Information Science&TechnologyNanjing210044China Department of Computer Science and Engineering School of ConvergenceCollege of Computing and InformaticsSungkyunkwan UniversitySeoul03063Republic of Korea Department of Computer Science Community CollegeKing Saud UniversityRiyadh11495Saudi Arabia

Devices and networks constantly upgrade,leading to rapid technological ***-dimensional(3D)point cloud transmission plays a crucial role in aerial computing terminology,facilitating information *** network types,including sensor networks and 5G mobile networks,support this ***,Flying Ad hoc Networks(FANETs)utilize Unmanned Aerial Vehicles(UAVs)as nodes,operating in a 3D environment with Six Degrees of Freedom(6DoF).This study comprehensively surveys UAV networks,focusing on models for Light Detection and Ranging(LiDAR)3D point cloud compression/*** topics covered include autonomous navigation,challenges in video streaming infrastructure,motivations for Quality of Experience(QoE)enhancement,and avenues for future ***,the paper conducts an extensive review of UAVs,encompassing current wireless technologies,applications across various sectors,routing protocols,design considerations,security measures,blockchain applications in UAVs,contributions to healthcare systems,and integration with the Internet of Things(IoT),Artificial Intelligence(AI),Machine Learning(ML),and Deep Learning(DL).Furthermore,the paper thoroughly discusses the core contributions of LiDAR 3D point clouds in UAV systems and their future prediction along with mobility *** also explores the prospects of UAV systems and presents state-of-the-art solutions.

关键词： Autonomous vehicles UAV systems UAV transmission UAV in healthcare future of UAV system security measures

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An Approach of Analyzing Classroom Student Engagement in Multimodal Environment by Using Deep Learning

An Approach of Analyzing Classroom Student Engagement in Mul...

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IEEE International WIE Conference on Electrical and computer engineering (WIECON-ECE)

作者： Nishat Sultana Nuha Tanjim Mahmud Noortaz Rezaoana Mohammad Shahadat Hossain Karl Andersson Department of Computer Science and Engineering University of Chittagong Chittagong Bangladesh Department of Computer Science and Engineering Rangamati Science and Technology University Rangamati Bangladesh Pervasive and Mobile Computing Laboratory Luleå University of Technology Sweden

Student engagement refers to the level of involve-ment, interest, attention, and active participation that students demonstrate during their learning experiences. As the engage-ment rate increases the learning rate, teachers always try to mea-sure the engagement rate in the classroom in various ways. This paper introduces a multimodal approach for detecting student engagement, a vital aspect of effective learning. Leveraging Convolutional Neural Networks (CNNs), video and audio data are integrated to capture comprehensive insights into engagement levels. The objective is to classify student engagement into three categories: highly engaged, engaged, and not engaged. Separate unimodal models are employed for speech and video data., achieving notable accuracies of 92.87 % and 94.77 %, respectively, in recognizing engagement levels. The combined model, achieved through feature-level fusion, yields a competitive accuracy of 69.36%, benefiting from the strengths of both modalities. Al-though the combined model exhibits slightly lower test accuracy compared to the individual speech and video models, its enhanced interpretability and comprehensive insights make it a promising avenue for future research.

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Explainable AI-Based Humerus Fracture Detection and Classification from X-Ray Images 26

Explainable AI-Based Humerus Fracture Detection and Classifi...

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

作者： Barua, Koushick Mahmud, Tanjim Barua, Anik Sharmen, Nahed Basnin, Nanziba Islam, Dilshad Hossain, Mohammad Shahadat Andersson, Karl Hossain, Sazzad Rangamati Science and Technology University Department of Computer Science and Engineering Rangamati4500 Bangladesh Chattogram Maa-O-Shishu Hospital Medical College Dept. of Obstetrics and Gynecology Chittagong4100 Bangladesh Leeds Beckett University Department of Computer Science and Engineering United Kingdom Chattogram Veterinary and Animal Sciences University Dept. of Physical and Mathematical Sciences Chittagong4202 Bangladesh University of Chittagong Department of Computer Science and Engineering Chittagong4331 Bangladesh Luleå University of Technology Pervasive and Mobile Computing Laboratory Luleå971 87 Sweden Department of Computer Science and Engineering Dhaka1207 Bangladesh

ISBN: (纸本)9798350359015

The human skeletal framework relies heavily on bones, and one such crucial component is the 'Humerus.' Positioned in the upper arm, extending from the shoulder to the elbow junction, the Humerus provides essential structural support for muscles and facilitates upper-body movement, particularly in the arms and hands. Consequently, Humerus fractures significantly impact daily life, causing disruptions and limitations. This paper presents a thorough exploration of an Explainable AI-based Humerus Fracture Detection and Classification system, employing various deep learning models. Leveraging a dataset of 1266 X-ray images, encompassing fractured and non-fractured humerus bones from the publicly available 'MURA' dataset, our research evaluates the effectiveness of Convolutional Neural Networks (CNN), VGG16, VGG19, DenseNet121, and DenseNet169 in detecting fractures. After 30 epochs of training, we assessed their performance using critical metrics: accuracy, precision, recall, and F1 score. Notably, DenseNet121 and DenseNet169 exhibited superior accuracy, precision, and recall, laying a robust foundation for automated humerus fracture diagnosis. We also introduced two ensemble models, "Ensemble-1 (VGG16 and VGG19)"and "Ensemble-2 (DenseNet121 and DenseNet169),"which delivered substantial improvements in accuracy, precision, recall, and F1 score, showcasing the potential of ensemble techniques in clinical settings. Furthermore, we enhanced model interpretability and transparency by incorporating Saliency Maps and GRAD-CAM (Gradient-weighted Class Activation Mapping) for Explainable AI (XAI). This visualization allowed us to identify regions of interest in X-ray images contributing to the model's predictions, providing valuable insights for medical practitioners. © 2023 IEEE.

关键词： Convolutional neural network Explainable AI (XAI) Humerus X-ray diagnosis

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Modular Extremely Large-Scale Array Communication:Near-Field Modelling and Performance Analysis

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China Communications 2023年第4期20卷 132-152页

作者： Xinrui Li Haiquan Lu Yong Zeng Shi Jin Rui Zhang National Mobile Communications Research Laboratory Frontiers Science Center for Mobile Information Communication and SecuritySoutheast UniversityNanjing 210096China Purple Mountain Laboratories Nanjing 211111China The Chinese University of Hong Kong Shenzhen Research Institute of Big DataShenzhen 518172China Department of Electrical and Computer Engineering National University of SingaporeSingapore 117583Singapore

This paper investigates the wireless communication with a novel architecture of antenna arrays,termed modular extremely large-scale array(XLarray),where array elements of an extremely large number/size are regularly mounted on a shared platform with both horizontally and vertically interlaced *** module consists of a moderate/flexible number of array elements with the inter-element distance typically in the order of the signal wavelength,while different modules are separated by the relatively large inter-module distance for convenience of practical *** accurately modelling the signal amplitudes and phases,as well as projected apertures across all modular elements,we analyse the near-field signal-to-noise ratio(SNR)performance for modular XL-array *** on the non-uniform spherical wave(NUSW)modelling,the closed-form SNR expression is derived in terms of key system parameters,such as the overall modular array size,distances of adjacent modules along all dimensions,and the user's three-dimensional(3D)*** addition,with the number of modules in different dimensions increasing infinitely,the asymptotic SNR scaling laws are ***,we show that our proposed near-field modelling and performance analysis include the results for existing array architectures/modelling as special cases,e.g.,the collocated XL-array architecture,the uniform plane wave(UPW)based far-field modelling,and the modular extremely large-scale uniform linear array(XL-ULA)of *** simulation results are presented to validate our findings.

关键词： modular extremely large-scale array practical deployment projected apertures non-uniform spherical wave near-field modelling

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Detecting human-object interaction with multi-level pairwise feature network

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Computational Visual Media 2021年第2期7卷 229-239页

作者： Hanchao Liu Tai-Jiang Mu Xiaolei Huan Key Laboratory of Pervasive Computing Ministry of EducationBNRistDepartment of Computer Science and TechnologyTsinghua UniversityBeijing 100084China College of Information Sciences and Technology Pennsylvania State UniversityUniversity ParkPA 16802USA.

Human–object interaction(HOI)detection is crucial for human-centric image understanding which aims to infer human,action,object triplets within an *** studies often exploit visual features and the spatial configuration of a human–object pair in order to learn the action linking the human and object in the *** argue that such a paradigm of pairwise feature extraction and action inference can be applied not only at the whole human and object instance level,but also at the part level at which a body part interacts with an object,and at the semantic level by considering the semantic label of an object along with human appearance and human–object spatial configuration,to infer the *** thus propose a multi-level pairwise feature network(PFNet)for detecting human–object *** network consists of three parallel streams to characterize HOI utilizing pairwise features at the above three levels;the three streams are finally fused to give the action *** experiments show that our proposed PFNet outperforms other state-of-the-art methods on the VCOCO dataset and achieves comparable results to the state-of-the-art on the HICO-DET dataset.

关键词： human–object interaction detection pairwise feature network deep learning multi-level object instance

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Explainable AI for Tomato Leaf Disease Detection: Insights into Model Interpretability

Explainable AI for Tomato Leaf Disease Detection: Insights i...

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International Conference on computer and information Technology (ICCIT)

作者： Tanjim Mahmud Koushick Barua Anik Barua Nanziba Basnin Sudhakar Das Mohammad Shahadat Hossain Karl Andersson Department of Computer Science and Engineering Rangamati Science and Technology University Rangamati Bangladesh Department of Computer Science and Engineering Leeds Beckett University UK Department of Computer Science and Engineering University of Chittagong Chittagong Bangladesh Pervasive and Mobile Computing Laboratory Luleå University of Technology Luleå Sweden

Plant diseases pose a significant threat to global food security by limiting access to safe and abundant food sources while impacting agricultural productivity and food safety. To address this challenge, innovative disease detection techniques are crucial. This paper presents a novel approach to tomato leaf disease detection grounded in deep transfer learning. We employ convolutional neural networks (CNNs) to recognize and categorize diseases, leveraging pre-trained models such as EfficientNetB3, Xception, and mobileNetV2. Our experimental results highlight the efficacy of these models, with the CNN achieving an accuracy of 0.92, mobileNetV2 at 0.93, Xception at 0.94 and EfficientNetB3 at 0.993. By combining these machine learning techniques with image processing methods, we create a system capable of diagnosing tomato leaf diseases promptly. The system employs image processing techniques to facilitate disease recognition, enabling automated disease identification as soon as symptoms manifest on leaves or plants. This empowers farmers with timely information and appropriate treatment options, ultimately leading to precise disease management. Furthermore, our research delves into model interpretability by employing Saliency maps and Grad-CAM techniques, providing valuable insights into the decision-making process of our deep learning models. This transparency enhances the trustworthiness of the AI-driven disease detection system, fostering its practical application in agriculture.

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On the System Performance of mobile Edge computing in an Uplink NOMA WSN With a Multiantenna Access Point Over Nakagami-m Fading

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IEEE/CAA Journal of Automatica Sinica 2022年第4期9卷 668-685页

作者： Van-Truong Truong Van Nhan Vo Dac-Binh Ha Chakchai So-In the Faculty of Electrical-Electronic Engineering Duy Tan University the Institute of Research and Development Duy Tan University the Faculty of Information Technology Duy Tan University IEEE the Applied Network Technology(ANT)Laboratory Department of Computer ScienceCollege of ComputingKhon Kaen University

In this paper,we study the system performance of mobile edge computing(MEC)wireless sensor networks(WSNs)using a multiantenna access point(AP)and two sensor clusters based on uplink nonorthogonal multiple access(NOMA).Due to limited computation and energy resources,the cluster heads(CHs)offload their tasks to a multiantenna AP over Nakagami-m *** proposed a combination protocol for NOMA-MEC-WSNs in which the AP selects either selection combining(SC)or maximal ratio combining(MRC)and each cluster selects a CH to participate in the communication process by employing the sensor node(SN)*** derive the closed-form exact expressions of the successful computation probability(SCP)to evaluate the system performance with the latency and energy consumption constraints of the considered *** results are provided to gain insight into the system performance in terms of the SCP based on system parameters such as the number of AP antennas,number of SNs in each cluster,task length,working frequency,offloading ratio,and transmit power ***,to determine the optimal resource parameters,i.e.,the offloading ratio,power allocation of the two CHs,and MEC AP resources,we proposed two algorithms to achieve the best system *** approach reveals that the optimal parameters with different schemes significantly improve SCP compared to other similar *** use Monte Carlo simulations to confirm the validity of our analysis.

关键词： mobile edge computing(MEC) Nakagami-m fading offloading selection combining(SC)/maximal ratio combining(MRC) successful computation probability(SCP) uplink nonortho-gonal multiple access(NOMA) wireless sensor networks(WSNs)

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A Diffusion Model Translator for Efficient Image-to-Image Translation

arXiv

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arXiv 2025年

作者： Xia, Mengfei Zhou, Yu Yi, Ran Liu, Yong-Jin Wang, Wenping MOE-Key Laboratory of Pervasive Computing Department of Computer Science and Technology Tsinghua University Beijing100084 China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Department of Computer Science and Computer Engineering Texas A&M University TX United States

Applying diffusion models to image-to-image translation (I2I) has recently received increasing attention due to its practical applications. Previous attempts inject information from the source image into each denoising step for an iterative refinement, thus resulting in a time-consuming implementation. We propose an efficient method that equips a diffusion model with a lightweight translator, dubbed a Diffusion Model Translator (DMT), to accomplish I2I. Specifically, we first offer theoretical justification that in employing the pioneering DDPM work for the I2I task, it is both feasible and sufficient to transfer the distribution from one domain to another only at some intermediate step. We further observe that the translation performance highly depends on the chosen timestep for domain transfer, and therefore propose a practical strategy to automatically select an appropriate timestep for a given task. We evaluate our approach on a range of I2I applications, including image stylization, image colorization, segmentation to image, and sketch to image, to validate its efficacy and general utility. The comparisons show that our DMT surpasses existing methods in both quality and efficiency. Code will be made publicly available. Copyright © 2025, The Authors. All rights reserved.

关键词： Image denoising

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Total and Minimum Energy Efficiency Tradeoff in Robust Multigroup Multicast Satellite Communications

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Space(science & Technology) 2023年第1期3卷 389-401页

作者： Bin Jiang Yingchun Yan Jingjing Zhao Xiang Xiao Li You Di Zhang Jizhao Lei Kezhi Wang Wenjin Wang Xiqi Gao National Mobile Communications Research Laboratory Southeast UniversityNanjing 210096China Purple Mountain Laboratories Nanjing 211100China Department of Electronic Information Engineering Beihang UniversityBeijing 100191China School of Information Engineering Zhengzhou UniversityZhengzhou 450001China China Aerospace Science and Technology Corporation Beijing 100094China Department of Computer Science Brunel University LondonUxbridgeMiddlesexUB83PHUK.

Satellite communication is an indispensable part of future wireless communications given its global coverage and long-distance *** satellite communication systems,channel acquisition and energy consumption are two critical *** this end,we investigate the tradeoff between the total energy efficiency(TEE)and minimum EE(MEE)for robust multigroup multicast satellite communication systems in this ***,under the total power constraint,we investigate the robust beamforming aimed at balancing the TEE-MEE,so as to achieve the balance between the fairness and total performance on the system *** this optimization problem,we first model the balancing problem as a nonconvex problem while deriving its approximate closed-form average user ***,the nonconvex problem is handled by solving convex programs sequentially with the help of the semidefinite relaxation and the concave-convex *** addition,depending on the solution rank value,Gaussian randomization and eigenvalue decomposition method are applied to generate the feasible ***,simulation results illustrate that the proposed approach can effectively achieve the balance between the TEE and MEE,thus realizing a tradeoff between fairness and system EE *** is also indicated that the proposed robust approach outperforms the conventional baselines in terms of EE performance.

关键词： convex eigenvalue sequentially

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