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9th International Conference on computer science and Computational Intelligence, ICCSCI 2024

作者： Kenneth, Christian Nirwantono, Rudi Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Biotechnology Department Faculty of Engineering Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Computer Science Program Bina Nusantara University Jakarta11480 Indonesia

As one of the cancer types with the highest incidence rates, colorectal cancer (CRC) would benefit from treatments with fewer side effects and reduced treatment-resistant potential. One of the options is to harness the anti-CRC potential of natural products. Previous studies have shown that Calamus draco exudate, dragon's blood, has anticancer activity in liver cancer and acute myeloid leukemia, but its bioactivity has not been studied in CRC. Here we conduct a bioinformatics study based on network pharmacology to explore the anti-CRC potential and mechanism of C. draco-derived compounds. The bioinformatics pipeline is composed of compound and target collection, biological network evaluation, and enrichment analysis. We found that there are 43 bioactive compounds from C. draco targeting 91 CRC-related targets, of which most compounds target MEN1, PTGS2, and IDH1. Further analyses show that the targets of C. draco are involved in the cellular response to hypoxia. By inhibiting those targets, C. draco bioactive compounds can potentially hinder angiogenesis and increase treatment response efficacy. © 2024 The Authors.

关键词： Lung cancer

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research on Optimization and Scheduling of MPTCP data networks in GNSS network Reference Stations

Research on Optimization and Scheduling of MPTCP Data Networ...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Hao, Fengqi Wang, Yuxin Kong, Hoiio Ma, Dexin Zhao, Xiyuan Jinan China City University of Macau Faculty of Data Science China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China Communication College Qingdao Agricultural University Qingdao266109 China

ISBN: (纸本)9781665410205

The Global Navigation Satellite System network Reference Stations (GNSS-NRS) are pivotal in modern po-sitioning and navigation applications. However, GNSS-NRS face significant communication challenges, including instability during rapid user movement and inconsistent coverage by different operators, which often results in data transmission interruptions and delays. Moreover, the conventional Multipath Transmission Control Protocol (MPTCP) scheduling strategy does not accommodate the diversity of the data types managed by GNSS-NRS. This default approach treats all data uniformly, even under deteriorating network conditions. To overcome these limitations, this paper introduces a 'Multiple Reception' strategy based on MPTCP that ensures timely and reliable GNSS data transmission. This novel strategy differentiates between observational and control data based on their real-time criticality and applies tailored transmission techniques. Under adverse conditions, it utilizes multiple transmission paths and selectively receives control data, thus prioritizing the transfer of critical information. We conducted simulation experiments using MiniNet to compare the 'Multiple Reception' method against conventional and redundant strategies, demonstrating that it significantly reduces data loss rates in GNSS-NRS. © 2024 IEEE.

关键词： Global positioning system

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Numerical Computation of SEIR Model for the Zika Virus Spreading

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computers, Materials & Continua 2023年第4期75卷 2155-2170页

作者： Suthep Suantai Zulqurnain Sabir Muhammad Asif Zahoor Raja Watcharaporn Cholamjiak Data Science Research Center Department of MathematicsFaculty of ScienceChiang Mai UniversityChiang Mai50200Thailand Research Group in Mathematics and Applied Mathematics Faculty of ScienceChiang Mai UniversityChiang Mai50200Thailand Department of Mathematics and Statistics Hazara UniversityMansehraPakistan Department of Computer Science and Mathematics Lebanese American UniversityBeirutLebanon Future Technology Research Center National Yunlin University of Science and Technology123 University RoadSection 3DouliouYunlinspan64002Taiwan School of Science University of PhayaoPhayao 56000Thailand

The purpose of this study is to present the numerical performancesand interpretations of the SEIR nonlinear system based on the Zika virusspreading by using the stochastic neural networks based intelligent computingsolver. The epidemic form of the nonlinear system represents the four dynamicsof the patients, susceptible patients S(y), exposed patients hospitalized inhospital E(y), infected patients I(y), and recovered patients R(y), i.e., SEIRmodel. The computing numerical outcomes and performances of the systemare examined by using the artificial neural networks (ANNs) and the scaledconjugate gradient (SCG) for the training of the networks, i.e., *** correctness of the ANNs-SCG scheme is observed by comparing theproposed and reference solutions for three cases of the SEIR model to solvethe nonlinear system based on the Zika virus spreading dynamics throughthe knacks of ANNs-SCG procedure based on exhaustive *** outcomes of the ANNs-SCG algorithm are found consistently in goodagreement with standard numerical solutions with negligible errors. Moreover,the procedure’s constancy, dependability, and exactness are perceived by usingthe values of state transitions, error histogram measures, correlation, andregression analysis.

关键词： SEIR nonlinear system Zika virus artificial neural networks scaled conjugate gradient statistical measures

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Feature Aggregation network for Memory-Based Industrial Anomaly Detection 9

Feature Aggregation Network for Memory-Based Industrial Anom...

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9th IEEE International Conference on data science in Cyberspace, DSC 2024

作者： Li, Min Han, Delong Shao, Rui He, Jinghui City University of Macau Faculty of Data Science China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350391367

In the process of large-scale industrial production, complex defects of different shapes and sizes can occur in industrial products and their components, and these defects seriously affect the performance of industrial products and their components, and being able to quickly and accurately identify defective industrial products and their components is an important task. Memory-based methods are usually used for anomaly detection, and these methods often rely on pre-trained models for feature extraction, but have high requirements on feature quality. In addition, patch-based methods mainly focus on local differences in feature embedding when detecting anomalies without explicitly exploiting broader contextual information, thus greatly affecting the robustness and accuracy of anomaly detection. Aiming at the above problems and requirements, this paper proposes a Feature Aggregation network for Memory-Based Industrial Anomaly Detection (FANet), which is based on the pre-extraction of anomalous features, attentional attention to key features, information aggregation of multi-level and multi-scale features, and the FANet provides advanced performance in industrial image anomaly detection and localisation by pre-extraction of anomalous features, attentional attention to key features, information aggregation of multi-layered and multi-scale features, and introduction of environmental information in the anomaly score computation to perform industrial anomaly detection tasks, and using core set subsampling to reduce storage requirements. On the MVTec AD, FANet has an image-level AUROC score of 99.1% and a pixel-level AUROC score of 98.2%. Similarly high detection results were achieved on the ViSA dataset, further demonstrating the robustness of FANet. © 2024 IEEE.

关键词： Anomaly detection

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PraK Tool: An Interactive Search Tool Based on Video data Services 30th

PraK Tool: An Interactive Search Tool Based on Video Data S...

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30th International Conference on MultiMedia Modeling, MMM 2024

作者： Lokoč, Jakub Vopálková, Zuzana Stroh, Michael Buchmueller, Raphael Schlegel, Udo SIRET Research Group Department of Software Engineering Faculty of Mathematics and Physics Charles University Prague Czech Republic Visual Computing Department of Computer Science Faculty of Natural Sciences Konstanz University Konstanz Germany Data Analysis and Visualization Department of Computer Science Faculty of Natural Sciences Konstanz University Konstanz Germany

ISBN: (纸本)9783031533013

This paper presents a tool relying on data service architecture, where technical details of all VBS datasets are completely hidden behind an abstract stateless data layer. The data services allow independent development of interactive search interfaces and refinement techniques, which is demonstrated by a smart front-end component. The component supports common search features and allows users to exploit content-based statistics for effective filtering. We believe that video data services might be a valuable addition to the open-source VBS toolkit, especially when available for the competition on a shared server with all VBS datasets, extracted features, and meta-data behind. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Video recording

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Assessment of Heart-Attack Prediction using Fuzzy Rule Based System 8

Assessment of Heart-Attack Prediction using Fuzzy Rule Based...

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8th IEEE International Conference on Computing, Communication, Control and Automation, ICCUBEA 2024

作者： Saratkar, Saniya Chaudhari, Aarti Thute, Trupti Raut, Rohini Thakre, Gayatri Kumar, Hemant Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Computer Science and Design Maharashtra Wardha442107 India

ISBN: (纸本)9798350391770

This paper evaluates the prediction of heart attacks with a fuzzy rule-based system (FRBS). Since heart disease is the world's leading cause of death, precise prediction models are crucial for prevention and early intervention. With its reputation for managing imprecise and ambiguous data, fuzzy logic presents a viable method for raising prediction accuracy in medical diagnostics. In order to capture the complexity and unpredictability of cardiovascular risk variables, fuzzy rules are used in this work to investigate the creation and assessment of a fuzzy rule-based system (FRBS) for heart attack prediction. The efficacy and dependability of the FRBS in identifying those at high risk of heart attack are shown via testing and comparison with conventional prediction models. The results highlight fuzzy logic's potential to improve cardiovascular predictive analytics. © 2024 IEEE.

关键词： Prediction models

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Mapping the Scientific Landscape of Integrated Scheduling Towards Industry 5.0 with A Bibliometric Analysis 6

Mapping the Scientific Landscape of Integrated Scheduling To...

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6th International Conference on Industry 4.0 and Smart Manufacturing, ISM 2024

作者： Putri Ardyanti, Anak Agung Ayu Xie, Zhiqiang Karnyoto, Andrea Stevens Pardamean, Bens Estiyanti, Ni Made School of Computer Science and Technology Harbin University of Science and Technology Harbin150000 China Faculty of Information Technology and Design Primakara University Bali 80226 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta 11480 Indonesia School of Management and Business University of Debrecen Debrecen Hungary

The industry is rapidly transitioning from the 4.0 era to the 5.0 era, prompting renewed interest among scholars in scheduling problems. They allow operations to process and assemble various components simultaneously. This bibliometric analysis provides a comprehensive understanding of the diverse research perspectives on the potential challenges of integrated scheduling within the framework. We identified 357 Scopus articles and analysed co-occurrence keyword (CNK) by VOSViewer tools and Global Citation Score (GCS) by Scopus tools. The research data obtained from 1993 to 2023 mainly in the subject areas of Engineering, computer science, Maths, and others. CNK identified six cluster keywords, and nine most cited articles based on normalised GCS. These results contribute to our understanding of the development of scheduling algorithms, shifting from an emphasis on manufacturing transfers to contemporary demand-centric research directions. Furthermore, this research provides a valuable perspective for policymakers, plant personnel, and manufacturing managers to make informed decisions. © 2025 The Authors. Published by Elsevier B.V.

关键词： Job shop scheduling

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Unleashing the Potential of Decentralized Federated Learning in Healthcare: A Comprehensive Survey on Insights and Challenges 9

Unleashing the Potential of Decentralized Federated Learning...

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9th IEEE International Conference on data science in Cyberspace, DSC 2024

作者： Cheng, Haibo Liu, Wenjian Qu, Youyang Fu, Kexue Wang, Qinyuan City University of Macau Faculty of Data Science China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China

ISBN: (纸本)9798350391367

Federated learning (FL) is a distributed learning method that reduces data transmission and privacy risks. However, the existence of a central server may become a target of attackers, resulting in an increased risk of user privacy leakage. The emergence of Decentralized Federated Learning (DFL) is posited to enhance data privacy and system reliability by reducing reliance on central servers and enhancing local data control, especially in the medical field, where personal health data is protected. This research investigates the application of (DFL) in the medical domain, focusing on its limitations. This study employs a systematic literature review, adhering to PRISMA guidelines, to comprehensively understand DFL's current landscape and challenges in healthcare. The key findings highlight four principal obstacles faced by DFL: security and privacy, communication efficiency, data and model heterogeneity, and incentive mechanisms. It also explores the potential for DFL to facilitate personalized healthcare treatments by large datasets distributed across various medical institutions. This research fills a significant void by systematically reviewing DFL technologies in healthcare. It provides insights into applications, challenges, and future directions for participants seeking secure and efficient healthcare data handling. © 2024 IEEE.

关键词： Federated learning

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Reversible data Hiding With Secret Encrypted Image Sharing and Adaptive Coding

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IEEE Internet of Things Journal 2025年第13期12卷 23933-23945页

作者： Fang, Guangtian Wang, Feng Zhao, Chenbin Qin, Chuan Chang, Ching-Chun Chang, Chin-Chen Fujian University of Technology College of Computer Science and Mathematics Fuzhou350118 China Fujian University of Technology College of Computer Science and Mathematics Key Laboratory of Big Data Mining and Applications Fuzhou350118 China Monash University Faculty of IT Department of Software Systems and Cybersecurity VIC3800 Australia Ministry of Education School of Cyber Science and Engineering Key Laboratory of Aerospace Information Security and Trusted Computing Wuhan430072 China University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai200093 China Feng Chia University Information and Communication Security Research Center Taichuang 40724 Taiwan Feng Chia University Department of Information Engineering and Computer Science Taichung40724 Taiwan

To ensure the security of image information and facilitate efficient management in the cloud, the utilization of reversible data hiding in encrypted images (RDHEI) has emerged as pivotal. However, most existing RDHEI schemes suffer from lower security and limited embedding capacity. To tackle these challenges, we propose a reversible data hiding with secret encrypted image sharing and adaptive coding scheme. Specifically, in the encryption phase, we introduce an improved secret sharing (SS) encryption method based on the chinese remainder theorem for polynomials (CRTP). This method not only improves the security of encrypted images but also vacates a larger room for embedding. In the embedding phase, we introduce an adaptive coding embedding approach using XOR preservation (XORP) and huffman coding, which provides high embedding capacity. Experimental results and security analysis demonstrate that our proposed encryption method achieves optimal values in security indicators for encrypted images, such as information entropy, histograms, NPCR and UACI. The proposed embedding method is superior to some state-of-the-art schemes in terms of embedding capacity. Furthermore, in datasets BOSSBase and BOWS2, the average embedding rates of the proposed embedding approach can reach 2.1745 bpp and 2.0656 bpp, respectively. © 2025 IEEE.

关键词： Cryptography Encryption Security data mining Adaptive coding Correlation Cloud computing Polynomials Image coding Streaming media

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Pushing AI to wireless network edge: an overview on integrated sensing, communication, and computation towards 6G

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science China(Information sciences) 2023年第3期66卷 7-25页

作者： Guangxu ZHU Zhonghao LYU Xiang JIAO Peixi LIU Mingzhe CHEN Jie XU Shuguang CUI Ping ZHANG Shenzhen Research Institute of Big Data Future Network of Intelligence Institute (FNii) The Chinese University of Hong Kong (Shenzhen) School of Science and Engineering (SSE) The Chinese University of Hong Kong (Shenzhen) State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Peking University Department of Electrical and Computer Engineering and Institute for Data Science and Computing University of Miami Peng Cheng Laboratory State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications

Pushing artificial intelligence(AI) from central cloud to network edge has reached board consensus in both industry and academia for materializing the vision of artificial intelligence of things(AIoT) in the sixth-generation(6G) era. This gives rise to an emerging research area known as edge intelligence, which concerns the distillation of human-like intelligence from the vast amount of data scattered at the wireless network edge. Typically, realizing edge intelligence corresponds to the processes of sensing, communication,and computation, which are coupled ingredients for data generation, exchanging, and processing, ***, conventional wireless networks design the three mentioned ingredients separately in a task-agnostic manner, which leads to difficulties in accommodating the stringent demands of ultra-low latency, ultra-high reliability, and high capacity in emerging AI applications like auto-driving and metaverse. This thus prompts a new design paradigm of seamlessly integrated sensing, communication, and computation(ISCC) in a taskoriented manner, which comprehensively accounts for the use of the data in downstream AI tasks. In view of its growing interest, this study provides a timely overview of ISCC for edge intelligence by introducing its basic concept, design challenges, and enabling techniques, surveying the state-of-the-art advancements, and shedding light on the road ahead.

关键词： sixth-generation (6G) edge intelligence artificial intelligence of things (AIoT) integrated sensing communication and computation (ISCC)

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