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Revolutionizing anemia detection:integrative machine learning models and advanced attention mechanisms

Visual Computing for Industry,Biomedicine,and Art 2024年第1期7卷 183-195页

作者： Muhammad Ramzan Jinfang Sheng Muhammad Usman Saeed Bin Wang Faisal Z.Duraihem School of Computer Science and Engineering Central South UniversityChangsha 410017HunanChina Department of Mathematics College of ScienceKing Saud UniversityRiyadh 11451Saudi Arabia

This study addresses the critical issue of anemia detection using machine learning(ML)*** a widespread blood disorder with significant health implications,anemia often remains *** necessitates timely and efficient diagnostic methods,as traditional approaches that rely on manual assessment are time-consuming and *** present study explored the application of ML-particularly classification models,such as logistic regression,decision trees,random forest,support vector machines,Naïve Bayes,and k-nearest neighbors-in conjunction with innovative models incorporating attention modules and spatial attention to detect *** proposed models demonstrated promising results,achieving high accuracy,precision,recall,and F1 scores for both textual and image *** addition,an integrated approach that combines textual and image data was found to outperform the individual ***,the proposed AlexNet Multiple Spatial Attention model achieved an exceptional accuracy of 99.58%,emphasizing its potential to revolutionize automated anemia *** results of ablation studies confirm the significance of key components-including the blue-green-red,multiple,and spatial attentions-in enhancing model ***,this study presents a comprehensive and innovative framework for noninvasive anemia detection,contributing valuable insights to the field.

关键词： Anemia Noninvasive Multimodal Feature fusion Attention module

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GLAC-GCN: Global and Local Topology-Aware Contrastive Graph Clustering Network

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第6期6卷 1-12页

作者： Xu, Yuan-Kun Huang, Dong Wang, Chang-Dong Lai, Jian-Huang College of Mathematics and Informatics South China Agricultural University Guangzhou China School of Computer Science and Engineering Sun Yat-sen University Guangzhou China

Though many deep attributed graph clustering approaches have been developed in recent years, most still suffer from two limitations. First, in the input space, they primarily rely on the original topology structure as the input (to some graph network), lacking the ability to jointly leverage local and global topology information to refine the graph. Second, in the learning process, they usually employ a single graph learning pipeline (with a single input graph), overlooking the opportunities in the joint optimization of multiple graph learning pipelines (with multiple topology structures). In view of this, this paper presents a Global and Local Topology-Aware Contrastive Graph Clustering Network (GLAC-GCN) for attributed graph clustering. Specifically, the local topology structure and global semantic information are simultaneously utilized to refine the graph. Then a paralleled graph convolutional network (GCN) learning mechanism is designed, where (i) both the original graph and the globally and locally refined graph are treated as input graphs, and (ii) two pipelines of GCNs are jointly and interactively utilized. Furthermore, a self-adaptive learning mechanism is devised to ensure consistency between multiple learning pipelines via the Kullback-Leibler (KL)-divergence. Meanwhile, the contrastive learning is enforced by minimizing the mismatch of the cluster distributions obtained from different GCN pipelines. Extensive experiments are conducted on seven real-world datasets. Notably, GLAC-GCN achieves the best ACC (or NMI) scores on all (or five) of the seven datasets, demonstrating its superiority over the state-of-the-art approaches. Code available: https://***/xuyuankun631/GLAC-GCN. IEEE

关键词： Semantics

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Fast UAV path planning in urban environments based on three-step experience buffer sampling DDPG

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Digital Communications and Networks 2024年第4期10卷 813-826页

作者： Shasha Tian Yuanxiang Li Xiao Zhang Lu Zheng Linhui Cheng Wei She Wei Xie College of Computer Science South-Central Minzu UniversityWuhan430074China Hubei Provincial Engineering Research Center for Intelligent Management of Manufacturing Enterprises Wuhan430074China School of Computer Science Wuhan UniversityWuhan430072China School of Mathematics&Statistics South-Central Minzu UniversityWuhan430074China

The path planning of Unmanned Aerial Vehicle(UAV)is a critical issue in emergency communication and rescue operations,especially in adversarial urban *** to the continuity of the flying space,complex building obstacles,and the aircraft's high dynamics,traditional algorithms cannot find the optimal collision-free flying path between the UAV station and the ***,in this paper,we study the fast UAV path planning problem in a 3D urban environment from a source point to a target point and propose a Three-Step Experience Buffer Deep Deterministic Policy Gradient(TSEB-DDPG)*** first build the 3D model of a complex urban environment with buildings and project the 3D building surface into many 2D geometric *** transformation,we propose the Hierarchical Learning Particle Swarm Optimization(HL-PSO)to obtain the empirical ***,to ensure the accuracy of the obtained paths,the empirical path,the collision information and fast transition information are stored in the three experience buffers of the TSEB-DDPG algorithm as dynamic guidance *** sampling ratio of each buffer is dynamically adapted to the training ***,we designed a reward mechanism to improve the convergence speed of the DDPG algorithm for UAV path *** proposed TSEB-DDPG algorithm has also been compared to three widely used competitors experimentally,and the results show that the TSEB-DDPG algorithm can archive the fastest convergence speed and the highest *** also conduct experiments in real scenarios and compare the real path planning obtained by the HL-PSO algorithm,DDPG algorithm,and TSEB-DDPG *** results show that the TSEBDDPG algorithm can archive almost the best in terms of accuracy,the average time of actual path planning,and the success rate.

关键词： Unmanned aerial vehicle Path planning Deep deterministic policy gradient Three-step experience buffer Particle swarm optimization

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Advancing Web Browser Forensics: Critical Evaluation of Emerging Tools and Techniques

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SN computer science 2025年第4期6卷 1-28页

作者： Chand, Rishal Ravikesh Sharma, Neeraj Anand Kabir, Muhammad Ashad Department of Computer Science and Mathematics The University of Fiji Lautoka Fiji School of Computing Mathematics and Engineering Charles Sturt University Bathurst 2795 NSW Australia

As the use of web browsers continues to grow, the potential for cybercrime and web-related criminal activities also increases. Digital forensic investigators must understand how different browsers function and the critical areas to consider during web forensic analysis. Web forensics, a subfield of digital forensics, involves collecting and analyzing browser artifacts, such as browser history, search keywords, and downloads, which serve as potential evidence. While existing research has provided valuable insights, many studies focus on individual browsing modes or limited forensic scenarios, leaving gaps in understanding the full scope of data retention and recovery across different modes and browsers. This paper addresses these gaps by defining four browsing scenarios and critically analyzing browser artifacts across normal, private, and portable modes using various forensic tools. We define four browsing scenarios to perform a comprehensive evaluation of popular browsers—Google Chrome, Mozilla Firefox, Brave, Tor, and Microsoft Edge—by monitoring changes in key data storage areas such as cache files, cookies, browsing history, and local storage across different browsing modes. Overall, this paper contributes to a deeper understanding of browser forensic analysis and identifies key areas for enhancing privacy protection and forensic methodologies. © The Author(s) 2025.

关键词： Browser artifacts Browser forensics Cybercrime Digital forensics Forensics tool Web browser

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Machine Learning in Tennis Match Analysis: Predicting Score Point Victor and Momentum Shift 5

Machine Learning in Tennis Match Analysis: Predicting Score ...

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5th International Conference on Machine Learning and computer Application, ICMLCA 2024

作者： Hu, Jinming Yang, Xiaohua Huang, Zixuan Xie, Jinqi School of Computer and Communication Engineering Changsha University of Science & Technology Changsha China School of Mathematics and Computer Science Shanxi Normal University Taiyuan China

ISBN: (纸本)9798331530334

In tennis and other competitive sports, momentum plays a pivotal role, where actions like consecutive scoring can enhance a player's momentum and increase his chances of winning the match. Quantifying momentum and predicting match outcomes in real-time are critical for both scientific training and strategic decision-making in the sports industry. However, traditional forecasting approaches often overlook the cumulative nature of time-series data and lack quantitative analysis of the dynamic properties influencing match outcomes. In this study, we compare seven machine learning models across two key tasks: predicting the winner of the next point and identifying the critical moment of momentum transition. We introduce a novel momentum formula to quantify the win/loss trends throughout a match. Our experiments using data from the 2023 Wimbledon Championships, demonstrate up to 70% accuracy in predicting point winners using logistic regression (LR) and up to 97% accuracy in detecting momentum shifts via a light gradient boosting machine (LGBM). These findings underscore the significance of momentum in tennis and offer valuable insights for performance optimization and competitive strategy development. © 2024 IEEE.

关键词： Tennis

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A Hybrid Parallel Strategy for Isogeometric Topology Optimization via CPU/GPU Heterogeneous Computing

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computer Modeling in engineering & sciences 2024年第2期138卷 1103-1137页

作者： Zhaohui Xia Baichuan Gao Chen Yu Haotian Han Haobo Zhang Shuting Wang The State Key Lab of Digital Manufacturing Equipment and Technology Huazhong University of Science and TechnologyWuhan430074China School of Mathematics and Computer Science Wuhan Polytechnic UniversityWuhan430048China School of Mechanical Science and Engineering Huazhong University of Science and TechnologyWuhan430074China

This paper aims to solve large-scale and complex isogeometric topology optimization problems that consumesignificant computational resources. A novel isogeometric topology optimization method with a hybrid parallelstrategy of CPU/GPU is proposed, while the hybrid parallel strategies for stiffness matrix assembly, equationsolving, sensitivity analysis, and design variable update are discussed in detail. To ensure the high efficiency ofCPU/GPU computing, a workload balancing strategy is presented for optimally distributing the workload betweenCPU and GPU. To illustrate the advantages of the proposedmethod, three benchmark examples are tested to verifythe hybrid parallel strategy in this paper. The results show that the efficiency of the hybrid method is faster thanserial CPU and parallel GPU, while the speedups can be up to two orders of magnitude.

关键词： Topology optimization high-efficiency isogeometric analysis CPU/GPU parallel computing hybrid OpenMPCUDA

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Automated object recognition in high-resolution optical remote sensing imagery

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National science Review 2023年第6期10卷 38-41页

作者： Yazhou Yao Tao Chen Hanbo Bi Xinhao Cai Gensheng Pei Guoye Yang Zhiyuan Yan Xian Sun Xing Xu Hai Zhang School of Computer Science and Engineering Nanjing University of Science and Technology Aerospace Information Research Institute Chinese Academy of Sciences School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Key Laboratory of Network Information System Technology (NIST) Aerospace Information Research Institute Chinese Academy of Sciences Department of Computer Science and Technology Tsinghua University School of Computer Science and Engineering University of Electronic Science and Technology of China Pazhou Laboratory (Huangpu) School of Mathematics Northwest University

INTRODUCTION With the rapid development of remote sensing technology,high-quality remote sensing images have become widely *** automated object detection and recognition of these images,which aims to automatically locate objects of interest in remote sensing images and distinguish their specific categories,is an important fundamental task in the *** provides an effective means for geospatial object monitoring in many social applications,such as intelligent transportation,urban planning,environmental monitoring and homeland security.

关键词：

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Distributed stochastic model predictive control for energy dispatch with distributionally robust optimization

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Applied mathematics and Mechanics(English Edition) 2025年第2期46卷 323-340页

作者： Mengting LIN Bin LI C.C.ECATI School of Aeronautics and Astronautics Sichuan UniversityChengdu 610065China Department of Information Engineering Computer Science and MathematicsUniversity of L’AquilaL’Aquila 67100Italy

A chance-constrained energy dispatch model based on the distributed stochastic model predictive control(DSMPC)approach for an islanded multi-microgrid system is *** ambiguity set considering the inherent uncertainties of renewable energy sources(RESs)is constructed without requiring the full distribution knowledge of the *** power balance chance constraint is reformulated within the framework of the distributionally robust optimization(DRO)*** the exchange of information and energy flow,each microgrid can achieve its local supply-demand ***,the closed-loop stability and recursive feasibility of the proposed algorithm are *** comparative results with other DSMPC methods show that a trade-off between robustness and economy can be achieved.

关键词： distributed stochastic model predictive control(DSMPC) distributionally robust optimization(DRO) islanded multi-microgrid energy dispatch strategy

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A MODIFIED WEAK GALERKIN FINITE ELEMENTMETHOD FOR SINGULARLY PERTURBED PARABOLIC CONVECTION-DIFFUSION-REACTION PROBLEMS

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Journal of Computational mathematics 2023年第6期41卷 1246-1280页

作者： Suayip Toprakseven Fuzheng Gao Faculty of Engineering Department of Computer ScienceArtvin Coruh UniversityArtvin08100Turkey School of Mathematics Shandong UniversityJinan 250100China

In this work,a modified weak Galerkin finite element method is proposed for solving second order linear parabolic singularly perturbed convection-diffusion *** key feature of the proposed method is to replace the classical gradient and divergence operators by the modified weak gradient and modified divergence operators,*** apply the backward finite difference method in time and the modified weak Galerkin finite element method in space on uniform *** stability analyses are presented for both semi-discrete and fully-discrete modified weak Galerkin finite element *** order of convergences are obtained in suitable *** have achieved the same accuracy with the weak Galerkin method while the degrees of freedom are reduced in our *** numerical examples are presented to support the theoretical *** is theoretically and numerically shown that the method is quite stable.

关键词： The modified weak Galerkin finite element method Backward Euler method Parabolic convection-diffusion problems Error estimates

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Optimized Multi-Label Human Activity Recognition with Focal Loss and Attention-Enhanced LSTM Networks 29

Optimized Multi-Label Human Activity Recognition with Focal ...

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29th International computer Conference, computer Society of Iran, CSICC 2025

作者： Zohrevand, Ali Mirzaei, Sayeh Sajedi, Hedieh School of Mathematics Statistics and Computer Science College of Science University of Tehran Iran School of Engineering Science College of Engineering University of Tehran Iran

ISBN: (纸本)9798331523114

Recognizing human activity using artificial intelligence and deep learning methods has become increasingly important in various fields, including medicine, sports, security, and wearable technology. With the rise of data collection tools that utilize multidimensional sensors and the growing need for faster and more accurate analysis of human behaviors, deep neural networks can potentially improve traditional approaches, enhancing the accuracy and efficiency of activity detection systems. This research, which leverages deep learning and neural networks in the arena of human activity detection, plays a significant role in the advancement of sensor data and time series analysis models. Previous studies often converted time series data into structured formats;however, these methods mainly struggled with accuracy and efficiency in representing and analyzing complex features, which limited their practical applications. The current research aims to address this gap by focusing on deep neural network models. These models excel in processing multidimensional data and learning complex features, making them better suited for recognizing and classifying human behaviors. © IEEE.

关键词： Deep neural networks

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