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IEEE/ACM International Symposium on Big Data Computing (BDC)

作者： Yuto Inokuchi Pedro Henrique González Masayoshi Aritsugi Israel Mendonça Graduate School of Science and Technology Kumamoto University Kumamoto Japan System Engineering and Computer Science Program Federal University of Rio de Janeiro Rio de Janeiro Brazil Faculty of Advanced Science and Technology Kumamoto University Kumamoto Japan

ISBN: (数字)9798350367300

ISBN: (纸本)9798350367317

In this paper, we aim to reduce the number of nodes from Graph Neural Networks (GNNs), thereby simplifying models and reducing computational costs. GNNs are highly effective for various tasks, such as prediction, classification, and clustering, due to their ability to learn node and edge attributes and relationships, and they have been utilized for intelligent transportation systems recently by converting sensor networks into graph structures. Deep spatio-temporal neural networks, including Spatio-Temporal Graph Convolutional Networks (STGCNs), capture spatial and temporal dependencies, making them suitable for traffic speed forecasting, traffic demand prediction, and travel time estimation. Despite their success, GNNs face challenges in industrial applications due to significant memory usage and time consumption. In this paper, we propose a new approach to node reduction that outperforms existing methods in computational efficiency. Our experiments on two real-world traffic datasets demonstrate that using the heuristic and edge information to reduce nodes can cut computation time of optimization up to 95% and, by eliminating noise, can even enhance prediction accuracy.

关键词： Graph convolutional networks Noise Memory management Estimation Computational efficiency Forecasting Optimization Intelligent transportation systems Faces Genetic algorithms

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Efficient Hyperparameter Optimization Using Deep Q-Network and BRKGA

Efficient Hyperparameter Optimization Using Deep Q-Network a...

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Congress on Evolutionary Computation

作者： Kosei Kobayashi Masayoshi Aritsugi Pedro Henrique González Israel Mendonça Graduate School of Science and Technology Kumamoto University Kumamoto Japan Faculty of Advanced Science and Technology Kumamoto University Kumamoto Japan System Engineering and Computer Science Program Federal University of Rio de Janeiro Rio de Janeiro Brazil

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

Hyperparameter optimization (HPO) is paragon to maximize performance when designing machine learning models. Among different HPO methods, Genetic Algorithm (GA) based optimization is considered effective because it allows a wide and diverse range of solutions to be explored. However, GA's exploratory nature makes this type of algorithm to evaluate many solutions that do not improve the overall performance. This is specially costly when the objective function to be evaluated is time-consuming, like in the HPO field. In this paper, we propose an efficient hybrid algorithm that is able to reduce computational cost by combining deep reinforcement learning with the Biased Random Key Genetic Algorithm (BRKGA), a variant of genetic algorithms. Our reinforcement learning agent has a decision-making role during the population's fitness calculation, in which it filters out chromosomes that would not improve the overall fitness of the population. The agent uses small amounts of pre-trained data to identify trends in potentially good solutions, and carry out its decision process. We conduct experiments on eight different datasets to assess the effectiveness of the proposed method, and the results show that the proposed method can significantly reduce the computation time of hyperparameter search using BRKGA (up to 44% reduction in computational time) without compromising the quality of the solution (no statistically difference in results).

关键词： Machine learning algorithms Filters Sociology Evolutionary computation Hyperparameter optimization Market research Linear programming

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Agile Assessment Methods: Current State of the Art

arXiv

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arXiv 2022年

作者： Telemaco, Ulisses Alencar, Paulo Cowan, Donald Oliveira, Toacy David R. Cheriton School of Computer Science University of Waterloo WaterlooON Canada System Engineering and Computing Program Federal University of Rio de Janeiro Rio de Janeiro Brazil

Agility Assessment (AA) comprises tools, assessment techniques, and frameworks that focus on indicating how a company or a team is applying agile techniques and eventually pointing out problems in adopting agile practices at a project-level, organization-level or individual-level. There are many approaches for AA such as agility assessment models, agility checklists, agility surveys, and agility assessment tools. This report presents the state of the art approaches that support agility assessment. © 2022, CC BY.

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Deep learning-based real time detection for cardiac objects with fetal ultrasound video

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Informatics in Medicine Unlocked 2023年 36卷

作者： Iriani Sapitri, Ade Nurmaini, Siti Naufal Rachmatullah, Muhammad Tutuko, Bambang Darmawahyuni, Annisa Firdaus, Firdaus Rini, Dian Palupi Islami, Anggun Intelligent System Research Group Faculty of Computer Science Universitas Sriwijaya Palembang 30139 Indonesia Doctoral Program Faculty of Engineering Universitas Sriwijaya Palembang 30139 Indonesia Department of Informatics Engineering Faculty of Computer Science Universitas Sriwijaya Palembang 30139 Indonesia

Fetal cardiac anatomical structure interpretation by ultrasound (US) is a key part of prenatal assessment. Unfortunately, the numerous speckles in US video, the small size of fetal cardiac structures, and unfixed fetal positions make manual detection processes difficult. To alleviate such problems, a deep learning model was developed to fully automate the processing of fetal echocardiographs with 2-dimensional cross-sectional images. The weakness of such an approach is that a physician immediately interprets US video with 3-dimensional objects to diagnose patients in clinical practice. To improve fetal cardiac anatomical structure interpretation via US for accurate and real time diagnosis, this paper proposes a real-time fetal cardiac substructure detection using US video with the You Only Look Once (YOLO) framework. In YOLO, an end-to-end neural network makes predictions for cardiac substructure objects, boxes, and class probabilities simultaneously. To achieve reliable performance, 40 fetal echocardiography videos were trained with the new YOLOv7 architecture and fine-tuned to work optimally and run efficiently. We conducted with nine fetal cardiac substructure objects such as the left atrium, right atrium, left ventricle, right ventricle, tricuspid valve, pulmonary valve, mitral valve, aortic valve, and aorta. The results yielded the highest mean average precision of 82.10%, reaching 17 frames per second (FPS) for nine cardiac substructure objects in 0.3 ms. The main finding of our study is that with even a limited number of US videos, YOLOv7 can detect fetal cardiac substructure objects in real-time. Such a network can work efficiently to detect small fetal cardiac objects automatically in a rapid phase with the help of proper fine-tuning. This work mainly assists medical experts in the fetal cardiac anatomy diagnostic process. © 2022 The Authors

关键词： computer vision Fetal echocardiography Object detection YOLOv7

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Deep Learning Networks for Handwritten Bangla Character Recognition

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IAENG International Journal of Applied Mathematics 2023年第4期53卷 1页

作者： Begum, H. Islam, M.M. Eva, H.S. Emon, N.H. Siddique, F.A. Department of Electrical and Electronic Engineering East West University A/2 Aftabnagar Dhaka Bangladesh Department of Electrical Engineering and Computer Science South Dakota State University United States Engineering and Design Department Reverie Power and Automation Engineering Limited Bangladesh Program in Smart System Integrated Solutions Aalto University Finland

In recent years, deep convolutional networks (DCNN) have gained popularity for different classification (or recognition) tasks. In this paper, three well known DCNN structures were used, i.e., AlexNet, SqueezeNet and GoogLeNet, and their classification performances in recognizing handwritten Bangla isolated characters were compared. These networks have simpler structures compared to the recent DCNNs. Experiments on a standard Bangla database revealed that the overall performance of GoogLeNet is slightly better than the other two networks. Further analysis using saliency maps of the test samples revealed the important features that are learned by the networks for classifying characters. This information led us to understand why classification of some samples fail and how to rectify these. © (2023), (International Association of Engineers). All Rights Reserved.

关键词： Classification (of information)

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Digital Technology in Health Education Trend Mapping through Bibliometrics Analysis

Digital Technology in Health Education Trend Mapping through...

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International Conference on Informatics, Multimedia, Cyber and Information system (ICIMCIS)

作者： Fairuz Iqbal Maulana Dian Lestari Vandha Pradwiyasma Widartha Nirwana Haidar Wahyu Dirgantara Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Faculty of Medicine Airlangga University Surabaya Indonesia Information System of Industrial Engineering Faculty Telkom University Bandung Indonesia Informatics Engineering Study Program University of Madura Pamekasan Indonesia Department of Electrical Engineering University of Merdeka Malang Malang Indonesia

ISBN: (纸本)9781665473286

This study examines the mapping of research data on digital technology in the field of health education using bibliometric analysis method. Data was collected by identifying keywords in the Scopus database and sorting them out to sort the studies from the last 10 years (2012–2021). Through this step, a total of 1482 documents were obtained (articles, journals, proceedings, books, and others). The data is then processed using the VOSViewer instrument to obtain a visualization of the mapping analysis. This study also analyzes the network types of authors and co-authors through the VOSViewer instrument. The result of this study indicates that the most document types published within ten years are Articles (66.5%), Review (22.9 % ), Conference Paper (4.2 % ), and others. The most studied subjects are Medicine (55.7%), Nursing (9.0%), Health Professions (8.2%), Social sciences (7.8%), engineering (3.9%), computer science (2.6 % ), Environmental science (2.6 % ), Biochemistry-Genetics and Molecular Biology (2.2%), Psychology (1.6%), and Dentistry (1.3%). This study offers a written communication process and the nature and direction of developing descriptive means of counting and analyzing the various phases of communication as well as recognizing the authorship and direction of its symptoms in documents on the subject of digital technology in the health sector.

关键词： Training Pediatrics Instruments Education Bibliometrics Transportation Psychology

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Correction: Thiazole-fused androstenone and ethisterone derivatives: potent β- and γ-actin cytoskeleton inhibitors to treat melanoma tumors

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RSC medicinal chemistry 2025年第4期16卷 2025 Apr 3页

作者： Sanjay Adhikary Subrata Roy Shailesh Budhathoki Siam Chowdhury Abbey Stillwell Alexei G Basnakian Alan Tackett Nathan Avaritt Mohamed Milad Mohammad Abrar Alam Department of Chemistry and Physics College of Sciences and Mathematics Arkansas State University Jonesboro Arkansas 72467 USA malam@astate.edu. Environmental Sciences Program College of Sciences and Mathematics Arkansas State University Jonesboro AR 72467 USA. Molecular Biosciences Program College of Sciences and Mathematics Arkansas State University Jonesboro AR 72467 USA. Computer Science The College of Engineering and Computer Science Arkansas State University Jonesboro AR 72468 USA. Department of Pharmacology and Toxicology University of Arkansas for Medical Sciences 4301 W. Markham St Little Rock AR 72205 USA. Central Arkansas Veterans Healthcare System W. 7th St Little Rock AR 72205 USA. Department of Biochemistry and Molecular Biology University of Arkansas for Medical Sciences Little Rock AR 72205 USA. The Department of Mathematics and Statistics Arkansas State University Jonesboro AR 72467 USA. Arkansas Biosciences Institute Arkansas State University Jonesboro AR 72467 USA.

[This corrects the article DOI: 10.1039/D4MD00719K.].

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Exploring Low Rate Dos Attack Detection Methods: A Bibliometric Analysis and Roadmap for Further Research

Exploring Low Rate Dos Attack Detection Methods: A Bibliomet...

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International Conference on Data and Software engineering (ICODSE)

作者： Anteng Widodo Adian Fatchur Rochim Budi Warsito Doctoral Program of Information System School of Postgraduated Studies Diponegoro University Semarang Indonesia Department of Information System Faculty of Engineering Muria Kudus University Kudus Indonesia Department of Computer Engineering Faculty of Engineering Diponegoro University Semarang Indonesia Department of Statistics Faculty of Science and Mathematics Diponegoro University Semarang Indonesia

Low-Rate Denial of Service (LDoS) attacks, an emerging breed of DoS attacks, present a formidable challenge in terms of their detectability. Within the realm of network security, these attacks cast a substantial shadow over the availability and integrity of services. This study seeks to delve into the intricate mechanics of LDoS attacks and explore the landscape of developed detection methodologies. Employing bibliometric analysis, we employ a multifaceted approach to gather research insights pertaining to LDoS attack detection. This approach encompasses the examination of evolving research trends, scrutiny of widely cited scientific publications within the same domain, identification of prolific researchers shaping the field, and a judicious selection of reputable publishers for dependable information acquisition. Furthermore, our study undertakes a meticulous categorization and classification of previously proposed LDoS detection methods documented in well-established scientific literature. This endeavor not only provides a more profound comprehension of the evolutionary trajectory and thematic thrusts within LDoS attack detection research, but it also facilitates the identification of gaps and directions for future exploration. The culmination of these analytical efforts culminates in a comprehensive framework. This contribution manifests through structured categorization, classification, and a forward-looking research roadmap, which collectively serve as valuable tools for both current and aspiring researchers engaged in LDoS attack detection. In summary, this research not only delves into the complexities of LDoS attacks and their mechanisms but also augments the current understanding of detection capabilities. Through its nuanced contributions, this paper extends a helpful guide to the dynamic realm of LDoS attack detection, thus catalyzing advancements and insights for the future.

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Non-invasive biopsy diagnosis of diabetic kidney disease via deep learning applied to retinal images: a population-based study

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The Lancet Digital Health 2025年第5期7卷 100868页

作者： Meng, Ziyao Guan, Zhouyu Yu, Shujie Wu, Yilan Zhao, Yaoning Shen, Jie Lim, Cynthia Ciwei Chen, Tingli Yang, Dawei Ran, An Ran He, Feng Hamzah, Haslina Singh, Sarkaaj Abd Raof, Anis Syazwani Lee-Boey, Jian Wen Samuel Lim, Soo-Kun Sun, Xufang Ge, Shuwang Xu, Gang Su, Hua Cheng, Yang Lu, Feng Liao, Xiaofei Jin, Hai Deng, Chenxin Ruan, Lei Zhang, Cuntai Wu, Chan Dai, Rongping Jin, Yixiao Wang, Wenxiao Li, Tingyao Liu, Ruhan Li, Jiajia Shu, Jia Lu, Yuwei Wang, Xiangning Wu, Qiang Qin, Yiming Tang, Jin Sheng, Xiaohua Jiao, Qiong Yang, Xiaokang Guo, Minyi McKay, Gareth J Hogg, Ruth E Liew, Gerald Chee, Evelyn Yi Lyn Hsu, Wynne Lee, Mong Li Szeto, Simon Luk, Andrea O Y Chan, Juliana C N Cheung, Carol Y Tan, Gavin Siew Wei Tham, Yih-Chung Cheng, Ching-Yu Sabanayagam, Charumathi Lim, Lee-Ling Jia, Weiping Li, Huating Sheng, Bin Wong, Tien Yin Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases Department of Computer Science and Engineering School of Electronic Information and Electrical Engineering Institute for Proactive Healthcare Shanghai Jiao Tong University Department of Endocrinology and Metabolism Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai Diabetes Institute Shanghai Clinical Centre for Diabetes Shanghai Key Laboratory of Diabetes Mellitus Shanghai China MOE Key Laboratory of AI School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Beijing Tsinghua Changgung Hospital Eye Center School of Clinical Medicine Tsinghua Medicine Tsinghua University Beijing China Medical Records and Statistics Office Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China Department of Renal Medicine Singapore General Hospital SingHealth-Duke Academic Medical Centre Singapore Singapore Department of Ophthalmology Shanghai Health and Medical Center Wuxi China Department of Ophthalmology and Visual Sciences The Chinese University of Hong Kong Hong Kong Special Administrative Region China Singapore Eye Research Institute Singapore National Eye Centre Singapore Department of Medicine Faculty of Medicine Universiti Malaya Kuala Lumpur Malaysia Department of Ophthalmology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Hubei Wuhan China Department of Nephrology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China Department of Nephrology Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China Department of Ophthalmology Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China National Engineering Research Centre for Big Dat

Background: Improving the accessibility of screening diabetic kidney disease (DKD) and differentiating isolated diabetic nephropathy from non-diabetic kidney disease (NDKD) are two major challenges in the field of diabetes care. We aimed to develop and validate an artificial intelligence (AI) deep learning system to detect DKD and isolated diabetic nephropathy from retinal fundus images. Methods: In this population-based study, we developed a retinal image-based AI-deep learning system, DeepDKD, pretrained using 734 084 retinal fundus images. First, for DKD detection, we used 486 312 retinal images from 121 578 participants in the Shanghai Integrated Diabetes Prevention and Care system for development and internal validation, and ten multi-ethnic datasets from China, Singapore, Malaysia, Australia, and the UK (65 406 participants) for external validation. Second, to differentiate isolated diabetic nephropathy from NDKD, we used 1068 retinal images from 267 participants for development and internal validation, and three multi-ethnic datasets from China, Malaysia, and the UK (244 participants) for external validation. Finally, we conducted two proof-of-concept studies: a prospective real-world study with 3 months' follow-up to evaluate the effectiveness of DeepDKD in screening DKD;and a longitudinal analysis of the effectiveness of DeepDKD in differentiating isolated diabetic nephropathy from NDKD on renal function changes with 4·6 years' follow-up. Findings: For detecting DKD, DeepDKD achieved an area under the receiver operating characteristic curve (AUC) of 0·842 (95% CI 0·838–0·846) on the internal validation dataset and AUCs of 0·791–0·826 across external validation datasets. For differentiating isolated diabetic nephropathy from NDKD, DeepDKD achieved an AUC of 0·906 (0·825–0·966) on the internal validation dataset and AUCs of 0·733–0·844 across external validation datasets. In the prospective study, compared with the metadata model, DeepDKD could detect DKD wit

关键词： Deep learning

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Balancing collaboration and discipline in software development processes

Balancing collaboration and discipline in software developme...

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International Conference on Software engineering (ICSE)

作者： Andrea Magalhaes Magdaleno NP2Tec-Research and Practice Group in Information Technology UNIRIO Brazil COPPE/PESC-System Engineering and Computer Science Program UFRJ Rio de Janeiro Rio de Janeiro Brazil

Neither traditional, agile or free/open software development models can be effective to all projects contexts. We claim that collaboration and discipline can be the driver to tailor software development processes to meet projects and organizations needs. This work proposes that process tailoring can be conducted through a context management approach.

关键词： Context Collaboration programming Software Social network services Organizations Context modeling

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