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28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2020

作者： Lee, Gin Chong Loo, Chu Kiong Liew, Wei Shiung Wermter, Stefan Multimedia University Faculty of Engineering and Technology Jalan Ayer Keroh Lama Melaka75450 Malaysia University of Malaya Faculty of Computer Science and Information Technology Lembah Pantai Kuala Lumpur50603 Malaysia University of Hamburg Department of Informatics Vogt-Koelln-Str.30 Hamburg22527 Germany

ISBN: (纸本)9782875870742

We propose a deterministic initialization of the Echo State Network reservoirs to ensure that the activation of its internal echo state representations reflects similar topological qualities of the input signal which should lead to a self-organizing reservoir. Human actions encoded as a multivariate time series signal are clustered before using the clustered nodes and interconnectivity matrices for initializing the S-ConvESN reservoirs. The capability of S-ConvESN is evaluated using several 3Dskeleton-based action recognition datasets. © ESANN 2020 - Proceedings, 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning.

关键词： Machine learning

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Swin-textural: A novel textural features-based image classification model for COVID-19 detection on chest computed tomography

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Informatics in Medicine Unlocked 2023年 36卷 101158页

作者： Tuncer, Ilknur Barua, Prabal Datta Dogan, Sengul Baygin, Mehmet Tuncer, Turker Tan, Ru-San Yeong, Chai Hong Acharya, U. Rajendra Elazig Governorship Interior Ministry Elazig Turkey School of Business (Information System) University of Southern Queensland Toowoomba 4350 QLD Australia Faculty of Engineering and Information Technology University of Technology Sydney Sydney 2007 NSW Australia Department of Digital Forensics Engineering College of Technology Firat University Elazig Turkey Department of Computer Engineering Faculty of Engineering Ardahan University Ardahan Turkey Department of Cardiology National Heart Centre Singapore Singapore Duke-NUS Medical School Singapore School of Medicine Faculty of Health and Medical Sciences Taylor's University Subang Jaya 47500 Malaysia Ngee Ann Polytechnic Department of Electronics and Computer Engineering 599489 Singapore Department of Biomedical Engineering School of Science and Technology SUSS University Singapore Department of Biomedical Informatics and Medical Engineering Asia University Taichung Taiwan

Background: Chest computed tomography (CT) has a high sensitivity for detecting COVID-19 lung involvement and is widely used for diagnosis and disease monitoring. We proposed a new image classification model, swin-textural, that combined swin-based patch division with textual feature extraction for automated diagnosis of COVID-19 on chest CT images. The main objective of this work is to evaluate the performance of the swin architecture in feature engineering. Material and method: We used a public dataset comprising 2167, 1247, and 757 (total 4171) transverse chest CT images belonging to 80, 80, and 50 (total 210) subjects with COVID-19, other non-COVID lung conditions, and normal lung findings. In our model, resized 420 × 420 input images were divided using uniform square patches of incremental dimensions, which yielded ten feature extraction layers. At each layer, local binary pattern and local phase quantization operations extracted textural features from individual patches as well as the undivided input image. Iterative neighborhood component analysis was used to select the most informative set of features to form ten selected feature vectors and also used to select the 11th vector from among the top selected feature vectors with accuracy >97.5%. The downstream kNN classifier calculated 11 prediction vectors. From these, iterative hard majority voting generated another nine voted prediction vectors. Finally, the best result among the twenty was determined using a greedy algorithm. Results: Swin-textural attained 98.71% three-class classification accuracy, outperforming published deep learning models trained on the same dataset. The model has linear time complexity. Conclusions: Our handcrafted computationally lightweight swin-textural model can detect COVID-19 accurately on chest CT images with low misclassification rates. The model can be implemented in hospitals for efficient automated screening of COVID-19 on chest CT images. Moreover, findings demonstrate tha

关键词： Computed tomography images COVID-19 classification Swin Swin-textural Textural feature extraction

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A hybrid facial recognition approach based on grasshopper optimization method

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AIP Conference Proceedings 2023年第1期2979卷

作者： Dyala R. Ibrahim Hisham A. Shehadeh Mohammad A. Aladaileh Kamal Alieyan Ghaith M. Jaradat We’am Telfah Xiaopeng Wang Cyber Security Department Amman Arab University Amman Jordan Computer Science and Computer Information System departments Amman Arab University Amman Jordan School of Computer Sciences Universiti Sains Malaysia Penang Malaysia Faculty of Electrical Engineering and Computer Science VŠB-Technical University of Ostrava Ostrava Czech Republic

This paper introduces a new “Facial Recognition (FR)” approach based on a new relatively optimization method. This new FR approach is used to mitigate the limitations of recent FR approaches, which are low accuracy and efficiency that caused by high dimensionality of facial images according to unconstrained conditions, such as expression, occlusion, and noise. The “Grasshopper Optimization Algorithm (GOA)” is selected in this work, which outperformed thirteen optimization methods compared early in the literature. In this paper, the efficacy of a hybrid extraction method in conjunction with the K-nearest neighbor classifier is investigated. When compared to other recently developed FR approaches, the performance evaluation of the hybrid “Facial Recognition system (FRS)” depends on the “Grasshopper Optimization Algorithm (GOA)” indicates a near-ideal recognition rate with optimization. In comparisons with the other approaches of recent proposed facial recognition techniques, the proposed algorithm achieved a significant accuracy.

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Identifying reusable services from collaborative activities using activity theory (AT): The activity-based service identification framework (ASIF)

TechRxiv

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TechRxiv 2021年

作者： Ayalew, Sertse Abebe Twinomurinzi, Hossana Sudan University of Science and Technology Khartoum Sudan Bahir Dar University Bahir Dar Institute of Technology Computing Faculty Bahir Dar Ethiopia Department of Applied Information System School of Computer Intelligence and Information Systems University of Johannesburg South Africa

Service-Orientation (SO) is a paradigm for developing collaboration platforms that promote services as reusable components. Service identification (SI) occurs earlier to identify patterns of a business process (BP) or model that can become software services. Although there exist several SI strategies, most are devised within the context of organizations with mature business processes and formalized structures. There is therefore a need for SI strategies that cater for the emerging heterogeneous and collaborative environments such as exists for small and micro enterprises (SMEs). This paper develops the Activity-based SI Framework (ASIF) from Activity-Theory using design science research (DSR) methods. Six collaboration attributes were elicited for SME;domain-oriented collaboration activity, actions of stakeholders towards the collaboration objective, heterogeneous stakeholders in a problem domain, autonomous actions of a participant, norms(rules) and structures(DOL), and interactive processes of stakeholders during collaboration. The framework is demonstrated using peer-to-peer collaborative lending activities of SMEs in Ethiopia called ‘EQUIB’. The findings show that the framework is promising in accurately guiding the identification of reusable and composable services in SME heterogeneous and collaborative contexts. © 2021, CC BY.

关键词： Process design

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Correction: Explainable lung cancer classification with ensemble transfer learning of VGG16, Resnet50 and InceptionV3 using grad-cam

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BMC medical imaging 2024年第1期24卷 195页

作者： Yogesh Kumaran S J Jospin Jeya Mahesh T R Surbhi Bhatia Khan Saeed Alzahrani Mohammed Alojail Department of Computer Science & Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bengaluru 562112 India. Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India. Department of Computer Science & Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bengaluru 562112 India. trmahesh.1978@***. School of science engineering and environment University of Salford Manchester UK. Management Information System Department College of Business Administration King Saud University Riyadh Saudi Arabia.

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An Empirical Study of Security Practices for Microservices systems

arXiv

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arXiv 2021年

作者： Ali, Rezaei Nasab Mojtaba, Shahin Ali, Hoseyni Raviz Seyed Peng, Liang Amir, Mashmool Valentina, Lenarduzzi School of Computer Science Wuhan University Wuhan430072 China School of Computing Technologies RMIT University Melbourne3000 Australia Department of Computer Science Bioengineering Robotics and System Engineering University of Genoa Genoa16126 Italy Faculty of Information Technology and Electrical Engineering University of Oulu OuluFI-90014 Finland

Despite the numerous benefits of microservices systems, security has been a critical issue in such systems. Several factors explain this difficulty, including a knowledge gap among microservices practitioners on properly securing a microservices system. To (partially) bridge this gap, we conducted an empirical study. We first manually analyzed 861 microservices security points, including 567 issues, 9 documents, and 3 wiki pages from 10 GitHub open-source microservices systems and 306 Stack Overflow posts concerning security in microservices systems. In this study, a microservices security point is referred to as "a GitHub issue, a Stack Overflow post, a document, or a wiki page that entails 5 or more microservices security paragraphs". Our analysis led to a catalog of 28 microservices security practices. We then ran a survey with 74 microservices practitioners to evaluate the usefulness of these 28 practices. Our findings demonstrate that the survey respondents affirmed the usefulness of the 28 practices. We believe that the catalog of microservices security practices can serve as a valuable resource for microservices practitioners to more effectively address security issues in microservices systems. It can also inform the research community of the required or less explored areas to develop microservices-specific security practices and tools. © 2021, CC BY.

关键词： Open systems

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Development of heterogeneous database synchronization algorithm based on entity relationship model approach

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AIP Conference Proceedings 2023年第1期2484卷

作者： Ayu Helinda Zuhar Musliyana Teuku Yuliar Arif Zalfie Ardian Zuriana Abu Bakar Zulaida Rahmi Mutiawati 1Informatic Engineering Department Faculty of Computer Science Ubudiyah Indonesia University Indonesia 2Information System Department Faculty of Computer Science Ubudiyah Indonesia University Indonesia 3Electrical and Computer Engineering Department Faculty of Engineering Syiah Kuala University Indonesia 4Faculty of Ocean Engineering Technology and Informatics Universiti Malaysia Terengganu Malaysia 5Faculty of Education Science Ubudiyah Indonesia University Indonesia

Nowadays, database technology keeps evolving and available on various platforms. However, the use of heterogeneous database had a limitation because each database only provides a synchronization method for similar types of databases. Therefore, the utilization of various types of databases becoming a problem since the entire data among the databases need to be integrated. Previous research had examined heterogeneous database synchronization algorithms on mobile devices using message digest. Nevertheless, this algorithm requires a longer synchronization time because necessity to check the hash value of each data record. Thus, this study investigates the development of a multi-platform database synchronization algorithm using the Entity Relationship (ER) model. This model is based on the data relationships in each table. ER model implementation is executed by creating a temporary table that stores the primary key of each data record in a table that has changed or consist new data. The synchronization process flow is determined based on the type of transaction by selecting data from the source table based on the relationship from the temporary table data to the destination database. The results of this study indicates that the proposed ER model could be a new method in the heterogeneous database synchronization process and is able to shorten the synchronization process time.

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Suitable parameters to optimize the information and communication technology resources in a public company of ecuador

Suitable parameters to optimize the information and communic...

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2020 Asia Conference on computers and Communications, ACCC 2020

作者： Toapanta, Segundo Moises T. Gallegos, Luis Enrique Mafla De Los, Angeles Ureta M. Estefania Solis, Maximo Prado Information Technology Consultant Management of Technologies for World 'Gtm' Quito Ecuador Faculty of Engineering System Quito Ecuador Department Computer Science Guayaquil Ecuador Universidad Técnica Luis Vargas Torres of Esmeraldas Career Social Sciences Esmeraldas Ecuador

ISBN: (纸本)9781728189673

The problems to optimize the resources of information and communication technologies in a public company in Ecuador are evident, it is necessary to improve the management of the use of ICT to provide services effectively and efficiently to users. The objective of this research is to analyze the appropriate information technology parameters to optimize management in a public company. The deductive method and exploratory research were used to analyze the information. It turned out that the implementation of the architecture integrated by COBIT and ITIL are options to establish the conditions to generate spaces for communicability, generate common benefits, improve public management, use of technological resources and reduce costs. It was concluded that the use of the COBIT and ITIL models are alternatives to improve the management of public companies, optimize the quality of services, generate trust in users, reduce risks in projects, improve company administration and reduce costs. © 2020 IEEE.

关键词： information management

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Research on Data Hiding Schemes for AMBTC Compressed Images

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International Journal of Network Security 2022年第6期24卷 1114-1123页

作者： Anggriani, Kurnia Wu, Nan-I Hwang, Min-Shiang Department of Computer Science & Information Engineering Asia University 500Lioufeng Rd. WufengTaichung41354 Taiwan Faculty of Engineering University of Bengkulu Indonesia Department of Digital Multimedia Lee-Ming Institute of Technology No.2-2Lijhuan Rd.Taishan TownshipTaipei County 243 Taiwan Department of Medical Research China Medical University Hospital China Medical University Taiwan

In this paper, the AMBTC compressed image-based datahiding research is summarized and introduced to present the current status of the research topic. We detailed explain the embedding procedures and the illustration of each survey paper to compare and evaluate the effectiveness of each method in the surveyed paper. Moreover, we summarize the experimental results and present them in tables and graphs to provide a precise performance comparison. In future work, it is highly desirable to develop a method for optimizing the structure of a compressed AMBTC image to achieve higher hiding capacity and image quality. © 2022, International Journal of Network Security. All Rights Reserved.

关键词： Hamming distance

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Efficient MedSAMs: Segment Anything in Medical Images on Laptop

arXiv

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arXiv 2024年

作者： Ma, Jun Li, Feifei Kim, Sumin Asakereh, Reza Le, Bao-Hiep Nguyen-Vu, Dang-Khoa Pfefferle, Alexander Wei, Muxin Gao, Ruochen Lyu, Donghang Yang, Songxiao Purucker, Lennart Marinov, Zdravko Staring, Marius Lu, Haisheng Dao, Thuy Thanh Ye, Xincheng Li, Zhi Brugnara, Gianluca Vollmuth, Philipp Foltyn-Dumitru, Martha Cho, Jaeyoung Mahmutoglu, Mustafa Ahmed Bendszus, Martin Pflüger, Irada Rastogi, Aditya Ni, Dong Yang, Xin Zhou, Guang-Quan Wang, Kaini Heller, Nicholas Papanikolopoulos, Nikolaos Weight, Christopher Tong, Yubing Udupa, Jayaram K. Patrick, Cahill J. Wang, Yaqi Zhang, Yifan Contijoch, Francisco McVeigh, Elliot Ye, Xin He, Shucheng Haase, Robert Pinetz, Thomas Radbruch, Alexander Krause, Inga Kobler, Erich He, Jian Tang, Yucheng Yang, Haichun Huo, Yuankai Luo, Gongning Kushibar, Kaisar Amankulov, Jandos Toleshbayev, Dias Mukhamejan, Amangeldi Egger, Jan Pepe, Antonio Gsaxner, Christina Luijten, Gijs Fujita, Shohei Kikuchi, Tomohiro Wiestler, Benedikt Kirschke, Jan S. de la Rosa, Ezequiel Bolelli, Federico Lumetti, Luca Grana, Costantino Xie, Kunpeng Wu, Guomin Puladi, Behrus Martín-Isla, Carlos Lekadir, Karim Campello, Victor M. Shao, Wei Brisbane, Wayne Jiang, Hongxu Wei, Hao Yuan, Wu Li, Shuangle Zhou, Yuyin Wang, Bo AI Collaborative Centre University Health Network Department of Laboratory Medicine and Pathobiology University of Toronto Vector Institute Toronto Canada Peter Munk Cardiac Centre University Health Network Toronto Canada Toronto General Hospital Research Institute University Health Network Department of Computer Science University of Toronto University Health Network Vector Institute Toronto Canada University of Science Vietnam National University Ho Chi Minh City Viet Nam Institute of Computer Science University of Freiburg Freiburg Germany School of Medicine and Health Harbin Institute of Technology Harbin China Division of Image Processing Department of Radiology Leiden University Medical Center Leiden Netherlands Department of System and Control Engineering School of Engineering Institute of Science Tokyo Formerly Tokyo Institute of Technology Tokyo Japan Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe Germany School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China School of Electrical Engineering and Computer Science University of Queensland Brisbane Australia School of Cyberspace Hangzhou Dianzi University Hangzhou China Division for Computational Radiology and Clinical AI The Department of Neuroradiology University Hospital Bonn Germany Division for Computational Radiology and Clinical AI The Department of Neuroradiology University Hospital Bonn Germany Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany Division for Computational Radiology and Clinical AI Department of Neuroradiology University Hospital Bonn Germany School of Biomedical Engineering Shenzhen University Shenzhen China School of Biological Science and Medical Engineering Southeast University Nanjing China Department of Urology Cleveland Clinic Cleveland United States Department of Computer Science University of Minnesota Minneapolis United St

Promptable segmentation foundation models have emerged as a transformative approach to addressing the diverse needs in medical images, but most existing models require expensive computing, posing a big barrier to their adoption in clinical practice. In this work, we organized the first international competition dedicated to promptable medical image segmentation, featuring a large-scale dataset spanning nine common imaging modalities from over 20 different institutions. The top teams developed lightweight segmentation foundation models and implemented an efficient inference pipeline that substantially reduced computational requirements while maintaining state-of-the-art segmentation accuracy. Moreover, the post-challenge phase advanced the algorithms through the design of performance booster and reproducibility tasks, resulting in improved algorithms and validated reproducibility of the winning solution. Furthermore, the best-performing algorithms have been incorporated into the open-source software with a user-friendly interface to facilitate clinical adoption. The data and code are publicly available to foster the further development of medical image segmentation foundation models and pave the way for impactful real-world applications. © 2024, CC BY-NC-SA.

关键词： Open source software

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