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International eConference on computer and Knowledge Engineering (ICCKE)

作者： Hananeh Sadat Madinei Mohammad Reza Keyvanpour Seyed Vahab Shojaedini Department of Computer Engineering Data Mining Laboratory Faculty of Engineering Alzahra University Tehran Iran Department of Electrical Engineering Iranian Research Organization for Science and Technology Tehran Iran

ISBN: (数字)9798331511272

ISBN: (纸本)9798331511289

In recent years, time series prediction has become a highly interesting topic in various applied areas, including clinical time series analysis. Hospitals and other clinical healthcare systems collect Electronic Health Records. These records contain important information that needs to be mined. This information provides a solid basis for predicting hospital readmission, helping to improve healthcare, enhance patient outcomes, and optimize hospital resources. This study introduces a novel deep learning attention-based approach for predicting 30-day ICU readmissions using clinical time series information extracted from the MIMIC III dataset. This approach incorporates time-aware positional encoding into the attention mechanism to enhance predictive performance by capturing the temporal representation of data and considering the temporal order of clinical time series data. This model is compared with cutting-edge models. The significant enhancements in the model performance achieved by the proposed method highlight the effectiveness of incorporating time-aware positional encoding in capturing temporal dependencies and improving prediction outcomes. These results underscore the potential of advanced attention mechanisms in clinical time series analysis.

关键词： Deep learning Analytical models Attention mechanisms Hospitals Computational modeling Time series analysis Predictive models Encoding data models Electronic medical records

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DDoS Detection and Defense Based on FLAD and SDN

DDoS Detection and Defense Based on FLAD and SDN

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International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Jie Dong Wenyu Fang Wanling Zheng Jinkun Liu Yanhua Liu College of Computer and Data Science Fuzhou University Fuzhou China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou China

ISBN: (数字)9798350356328

ISBN: (纸本)9798350356335

In order to achieve more efficient and accurate DDoS detection while ensuring data privacy, this paper proposes a DDoS detection method based on FLAD. Firstly, this paper uses the FLAD algorithm to train a global DDoS detection model without leaving local traffic data, protecting the privacy and security of traffic data between different hosts, and improving aggregation efficiency by dynamically adjusting the aggregation weights to adapt to different sub-dataset increments. Secondly, a DDoS traffic detection method based on the integration of LSTM and CNN is proposed, which extracts and analyzes the temporal correlation of traffic data by calculating the statistical characteristics of traffic data within a time period, to achieve real-time detection of traffic feature data. Again, combined with the concept of SDN, real-time defense against DDoS based on ODL-API is implemented, and precise matching of DDoS detection results with network entity information is achieved, realizing the technology of real-time and precise issuance of multiple flow rules, effectively blocking DDoS malicious attack traffic, protecting important entities in the topology, and maintaining stable traffic in the topology. This paper focuses on solving the detection problem of DDoS traffic data increments and uneven data distribution through the FLAD algorithm. Experimental results show that the proposed method improves the accuracy of DDoS attack detection by more than 4% and the F1 Score by more than 7% compared to the FedAvg aggregation algorithm.

关键词： Training data privacy network topology Denial-of-service attack Feature extraction Real-time systems Topology Labeling computer crime Long short term memory

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EyeUnderstand: Dashboard for Gaze and Deep-Learning Driven Comprehension Estimation in Online Lectures

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IEEE Access 2025年 13卷 102220-102233页

作者： Ko Watanabe Gitesh Gund Jayasankar Santhosh Haruka Sakagami Yuki Matsuda Andreas Dengel Shoya Ishimaru Smart Data and Knowledge Services German Research Center for Artificial Intelligence (DFKI) GmbH Kaiserslautern Germany Department of Computer Science RPTU Kaiserslautern-Landau Kaiserslautern Germany Graduate School of Science and Technology Nara Institute of Science and Technology (NAIST) Nara Japan Faculty of Environmental Life Natural Science and Technology Okayama University Okayama Japan Graduate School of Informatics Osaka Metropolitan University Sakai Japan

Online videos are a potent tool for educators to disseminate knowledge widely to diverse student audiences. However, collecting student feedback remains a significant challenge for lecturers, particularly in the absence of feedback. Understanding students’ subjective comprehension levels during online video lectures with sensor technology is yet to be thoroughly researched. This study uses eye-tracking technology to predict self-reported comprehension levels during video lectures. We recruited 20 participants from Germany and Japan who were invited to watch 50-minute lecture videos in three domains. The participants self-annotate the time segment in each lecture video where they dropout using open-source LabelStudio and answer the survey. We applied Long-Short-Term Memory (LSTM) to the preprocessed dataset and achieved an F1 Score of 0.886 for predicting binary self-annotated comprehension levels. We also introduce EyeUnderstand, the web-based application for visualizing the results of the comprehension estimation. We recruited 28 participants for the user study. As a result, 89.3% of the students and 92.9% of the lecturers confirmed that our application is practical.

关键词： Videos Estimation Gaze tracking Real-time systems Temperature measurement Education data visualization Vocabulary Surveys Pupils

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Securing ML-based Android Malware Detectors: A Defensive Feature Selection Approach against Backdoor Attacks

Securing ML-based Android Malware Detectors: A Defensive Fea...

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Cluster, Cloud and Internet Computing Workshops (CCGridW), IEEE/ACM International Symposium on

作者： Bartłomiej Marek Kacper Pieniążek Filip Ratajczak Wojciech Adamczyk Bartosz Bok Mateusz Krzysztoń Faculty of Information and Communication Technology Wroclaw University of Science and Technology Wrocław Poland Department of Distributed Systems Research and Academic Computer Network (NASK-PIB) Warsaw Poland

ISBN: (数字)9798350377514

ISBN: (纸本)9798350377521

This work investigates the vulnerability of ML-based Android malware detectors to backdoor attacks, and proposes a novel feature selection method to reduce system vulnerability. We present a realistic attack scenario and enhance the state-of-the-art genetic algorithm for trigger generation. The algorithm is used to design a feature selection method that prioritises attributes less prone to exploitation by attackers. The solution was evaluated using a dataset from the Koodous platform. The results show that the proposed improvements to the trigger selection algorithm were effective, resulting in a 10 percentage point increase. Furthermore, the proposed feature selection method significantly reduced the vulnerability of the ML system by 30%, without affecting the quality of the malicious application detection task execution.

关键词： Training Measurement Operating systems Computational modeling Neural networks Machine learning Predictive models Feature extraction Malware Genetic algorithms

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Capability Level of SPBE Application and Infrastructure Audit Tools Using COBIT 2019 6

Capability Level of SPBE Application and Infrastructure Audi...

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6th International Conference on Informatics, Multimedia, Cyber and Information System, ICIMCIS 2024

作者： Irzavika, Nindy Mahda, Friyogi Resvy Universitas Pembangunan Nasional Veteran Jakarta Faculty of Computer Science Jakarta Indonesia Badan Riset dan Inovasi Nasional Research Center of Data and Information Sciences Jakarta Indonesia

ISBN: (纸本)9798331533243

SPBE, an electronic government system, uses IT infrastructure and applications to deliver services. IT audits, conducted using tools like 'SPBE Audit Tools,' ensure effective service delivery. COBIT 2019 can improve governance and align IT with business goals. This study aims to evaluate its capability level using COBIT 2019. The research flow involves problem identification, literature review, data analysis, goal alignment, design, and recommendation. This process ensures that the governance system effectively supports the organization's strategic goals and addresses the identified research problem. Gap analysis is conducted to indicate areas where improvements are necessary to align with desired capabilities. It compares the current capability levels to the target levels. The domains APO02, APO04, and BAI03 are below respective targets. Based on the gap analysis results, recommendations will be formulated to minimize the gap and improve the capability levels. © 2024 IEEE.

关键词： Recommender systems

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A NovelMethod for Determining Tourism Carrying Capacity in a Decision-Making Context Using q−Rung Orthopair Fuzzy Hypersoft Environment

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computer Modeling in Engineering & sciences 2024年第2期138卷 1951-1979页

作者： Salma Khan Muhammad Gulistan NasreenKausar Seifedine Kadry Jungeun Kim Department of Mathematics and Statistics Hazara UniversityMansehra21120Pakistan Department of Mathematics Faculty of Arts and SciencesYildiz Technical UniversityEsenlerIstanbul34210Turkey Department of Applied Data Science Noroff University CollegeKristiansand4612Norway Artificial Intelligence Research Center(AIRC) Ajman UniversityAjman346United Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityP.O.Box 13-5053ByblosLebanon Department of Software and CMPSI Konju National UniversityCheonan31080Korea

Tourism is a popular activity that allows individuals to escape their daily routines and explore new destinations for various reasons,including leisure,pleasure,or business.A recent study has proposed a unique mathematical concept called a q−Rung orthopair fuzzy hypersoft set(q−ROFHS)to enhance the formal representation of human thought processes and evaluate tourism carrying *** approach can capture the imprecision and ambiguity often present in human *** the advanced mathematical tools in this field,the study has also incorporated the Einstein aggregation operator and score function into the q−ROFHS values to supportmultiattribute decision-making *** implementing this technique,effective plans can be developed for social and economic development while avoiding detrimental effects such as overcrowding or environmental damage caused by tourism.A case study of selected tourism carrying capacity will demonstrate the proposed methodology.

关键词： q−Rung orthopair fuzzy hypersoft set decision-making tourism carrying capacity aggregation operator

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Multiscale and Auto-Tuned Semi-Supervised Deep Subspace Clustering and Its Application in Brain Tumor Clustering

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computers, Materials & Continua 2024年第6期79卷 4741-4762页

作者： Zhenyu Qian Yizhang Jiang Zhou Hong Lijun Huang Fengda Li Khin Wee Lai Kaijian Xia School of Artificial Intelligence and Computer Science Jiangnan UniversityWuxi214122China Department of Medical Imaging The Changshu Affiliated Hospital of Soochow UniversitySuzhou215500China Department of Neurosurgery The Changshu Affiliated Hospital of Soochow UniversityChangshu215500China Department of Scientific Research The Changshu Affiliated Hospital of Soochow UniversitySuzhou215500China Changshu Key Laboratory of Medical Artificial Intelligence and Big Data Suzhou215500China Department of Biomedical Engineering Faculty of EngineeringUniversity of MalayaKuala Lumpur50603Malaysia

In this paper,we introduce a novel Multi-scale and Auto-tuned Semi-supervised Deep Subspace Clustering(MAS-DSC)algorithm,aimed at addressing the challenges of deep subspace clustering in high-dimensional real-world data,particularly in the field of medical *** deep subspace clustering algorithms,which are mostly unsupervised,are limited in their ability to effectively utilize the inherent prior knowledge in medical *** MAS-DSC algorithm incorporates a semi-supervised learning framework that uses a small amount of labeled data to guide the clustering process,thereby enhancing the discriminative power of the feature ***,the multi-scale feature extraction mechanism is designed to adapt to the complexity of medical imaging data,resulting in more accurate clustering *** address the difficulty of hyperparameter selection in deep subspace clustering,this paper employs a Bayesian optimization algorithm for adaptive tuning of hyperparameters related to subspace clustering,prior knowledge constraints,and model loss *** experiments on standard clustering datasets,including ORL,Coil20,and Coil100,validate the effectiveness of the MAS-DSC *** results show that with its multi-scale network structure and Bayesian hyperparameter optimization,MAS-DSC achieves excellent clustering results on these ***,tests on a brain tumor dataset demonstrate the robustness of the algorithm and its ability to leverage prior knowledge for efficient feature extraction and enhanced clustering performance within a semi-supervised learning framework.

关键词： Deep subspace clustering multiscale network structure automatic hyperparameter tuning semi-supervised medical image clustering

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Capability Level of SPBE Application and Infrastructure Audit Tools Using COBIT 2019

Capability Level of SPBE Application and Infrastructure Audi...

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

作者： Nindy Irzavika Friyogi Resvy Mahda Faculty of Computer Science Universitas Pembangunan Nasional Veteran Jakarta Jakarta Indonesia Research Center of Data and Information Sciences Badan Riset dan Inovasi Nasional Jakarta Indonesia

ISBN: (数字)9798331533243

ISBN: (纸本)9798331533250

SPBE, an electronic government system, uses IT infrastructure and applications to deliver services. IT audits, conducted using tools like “SPBE Audit Tools,” ensure effective service delivery. COBIT 2019 can improve governance and align IT with business goals. This study aims to evaluate its capability level using COBIT 2019. The research flow involves problem identification, literature review, data analysis, goal alignment, design, and recommendation. This process ensures that the governance system effectively supports the organization's strategic goals and addresses the identified research problem. Gap analysis is conducted to indicate areas where improvements are necessary to align with desired capabilities. It compares the current capability levels to the target levels. The domains APO02, APO04, and BAI03 are below respective targets. Based on the gap analysis results, recommendations will be formulated to minimize the gap and improve the capability levels.

关键词： data analysis Multimedia systems Informatics Systematic literature review Electronic government Business

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Feature selection in linear support vector machines via a hard cardinality constraint: A scalable conic decomposition approach

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European Journal of Operational research 2025年

作者： Bomze, Immanuel D'Onofrio, Federico Palagi, Laura Peng, Bo Faculty of Mathematics and Research Network Data Science University of Vienna Oskar-Morgenstern-Platz 1 Wien1090 Austria Department of Computer Control and Management Engineering Sapienza University of Rome Via Ariosto 25 Rome00185 Italy VGSCO University of Vienna Oskar-Morgenstern-Platz 1 Wien1090 Austria

In this paper, we study the embedded feature selection problem in linear Support Vector Machines (SVMs), in which a cardinality constraint is employed, leading to an interpretable classification model. The problem is NP-hard due to the presence of the cardinality constraint, even though the original linear SVM amounts to a problem solvable in polynomial time. To handle the hard problem, we first introduce two mixed-integer formulations for which novel semidefinite relaxations are proposed. Exploiting the sparsity pattern of the relaxations, we decompose the problems and obtain equivalent relaxations in a much smaller cone, making the conic approaches scalable. To make the best usage of the decomposed relaxations, we propose heuristics using the information of its optimal solution. Moreover, an exact procedure is proposed by solving a sequence of mixed-integer decomposed semidefinite optimization problems. Numerical results on classical benchmarking datasets are reported, showing the efficiency and effectiveness of our approach. © 2025

关键词： Mixed-integer linear programming

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Implementation of expert systems in potassium deficiency in cocoa plants using forward chaining method 7

Implementation of expert systems in potassium deficiency in ...

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7th International Conference on computer science and Computational Intelligence, CSCI 2022

作者： Hafizal, Muhammad Titan Putra, Dian Pratama Wirianata, Herry Nugraha, Nanda Satya Suparyanto, Teddy Hidayat, Alam Ahmad Pardamean, Bens Faculty of Agriculture STIPER Institute of Agriculture Yogyakarta55282 Indonesia Faculty of Forestry STIPER Institute of Agriculture Yogyakarta55282 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia

As one of the largest exporters in the world, cocoa (Theobroma cacao L.) production in Indonesia provides an important contribution to the plantation sector that can, directly and indirectly, attribute to the national economic development. However, recent data shows a decline in cocoa productivity in Indonesia. This is arguably caused by various factors, including poor agricultural management and practices in cocoa plantations, that may lead to lower fertility in the soil and an increased risk of diseases and pests. Potassium deficiency is a major contributing factor to the low soil fertility that affects cocoa yields. Therefore, in this work, we implement an application with an expert system utilizing the forward chaining method to detect potassium deficiency in cocoa plants and then give a fertilization-based recommendation based on the plants' condition. The system employs a set of rules to identify symptoms related to the deficiency on the sample photo of a cocoa leaf according to the channels of red, green, and blue of the image. The sample images of cocoa leaves are submitted to the application with an easy-To-use interface that can show the scanning result and proceed to display the suggested quantity of fertilizers to prevent potassium deficiency. Implementing the system can contribute constructive impacts to improve current practices in the overall cropping system of cocoa plants. © 2022 Elsevier B.V.. All rights reserved.

关键词： Expert systems

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