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2023 IEEE International Workshop on Sport, Technology and Research, STAR 2023

作者： Lokas, Ivan Vasilj, Matea Skender, Sven Vucetic, Vlatko Mihaldinec, Hrvoje Dzapo, Hrvoje Ultrax Technologies Software Development Zagreb Croatia Human Performance Laboratory Faculty of Kinesiology Zagreb Croatia Hardware Development Ultrax Technologies Zagreb Croatia Faculty of Electrical Engineering and Computing Department of Electronic Systems and Information Processing Zagreb Croatia

ISBN: (纸本)9798350316056

Jump height estimation is well-studied problem in sports science with a variety of methods proposed over the years. This paper introduces a portable, simple-to-setup and a cost-effective video-based jump height estimation method, and compares it with existing traditional methods that use expensive and specialized equipment. To enable more accurate comparison between different systems, a custom-designed Inertial Measurement Unit (IMU) was used as an additional sensor input. The analysis was performed on eleven female physically active handball players. The dataset consists of three most common vertical jump variations: squat jump, counter-movement jump and counter-movement jump with free arms. The analysis show that our video-based approach achieves comparable results to the traditional methods. © 2023 IEEE.

关键词： Cost effectiveness

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Matrix-Based Ant Colony System for Traveling Salesman Problem

Matrix-Based Ant Colony System for Traveling Salesman Proble...

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

作者： Li, Xu Li, Jian-Yu Chen, Chun-Hua Zhan, Zhi-Hui Kwong, Sam Zhang, Jun School of Computer Science and Engineering South China University of Technology Guangzhou510006 China School of Software Engineering South China University of Technology Guangzhou510006 China Lingnan University Department of Computing and Decision Science Hong Kong College of Artificial Intelligence Nankai University Tianjin300350 China Hanyang University Ansan15588 Korea Republic of

ISBN: (纸本)9781665410205

Ant colony system algorithm (ACS), as an important evolutionary computation (EC) algorithm, has demonstrated significant advantages in solving complex optimization problems. However, traditional EC algorithms and traditional ACS algorithm often face the challenge of slow computational speed when dealing with large-scale problems. In recent years, matrix-based EC approaches have been proposed to accelerate the computational speed, which has obtained promising results in dealing with large-scale problems. However, most existing matrix-based EC algorithms are designed for continuous optimization problems, while the matrix-based approach integrated with ACS has not attracted enough attention, which will be efficient for solving large-scale discrete optimization problems. Therefore, in this paper, we propose a matrix-based ACS (MACS) algorithm and apply it to solve the traveling salesman problem (TSP). MACS is an innovative improvement over the traditional ACS algorithm, utilizing matrix operations to parallelly let ants select city and update pheromone. Experimental results show that the MACS algorithm has significantly better efficiency in accelerating computational speed while maintaining the remarkable problem-solving ability in solving large-scale TSP. © 2024 IEEE.

关键词： Ant colony optimization

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Case-based Explanation of Classification Models for the Detection of SQL Injection Attacks 31

Case-based Explanation of Classification Models for the Dete...

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Workshops at the 31st International Conference on Case-Based Reasoning, ICCBR-WS 2023

作者： Recio-Garcia, Juan A. Orozco-Del-Castillo, Mauricio G. Soladrero, Jose A. Tecnológico Nacional de México IT de Mérida Department of Systems and Computing Merida Mexico Association for the Advancement of Artificial Intelligence Mexico Department of Software Engineering and Artificial Intelligence Instituto de Tecnologías del Conocimiento Universidad Complutense de Madrid Spain

This paper investigates the interpretability of various machine learning models in the context of detecting SQL injection attacks. The models under investigation include decision trees, multi-layer perceptron, random forests, support vector machines, ADA Boost, naive Bayes, and quadratic discriminant analysis. Our main objective is to gain insights into how these models make decisions when classifying SQL commands as either legitimate or potentially malicious. To achieve this, we employ a set of commonly used features from the literature to train and evaluate the models. By prioritizing explainability, our aim is to uncover the underlying factors and decision rules that drive the models’ predictions. Through this research, we seek to bridge the gap between model accuracy and human understanding, thereby facilitating the practical application of machine learning models in real-world security scenarios. The findings of this study contribute to the development of a case-based explanation system that provides valuable insights for cybersecurity practitioners and researchers. © 2023 CEUR-WS. All rights reserved.

关键词： Decision trees

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Enhancing Blockchain Technology Education with Innovative Active Learning 2

Enhancing Blockchain Technology Education with Innovative Ac...

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2nd IEEE International Conference on Advanced Learning Technologies on Education and Research, ICALTER 2022

作者： Shi, Yong Shahriar, Hossain Lo, Dan Chi, Hongmei College of Computing and Software Engineering Kennesaw State University Marietta United States Florida A&m University Department of Computer and Information Sciences TallahasseeFL United States

ISBN: (数字)9781665456968

ISBN: (纸本)9781665456968

As an emerging technology that has already impacted various sectors including finance, energy, education, and more, blockchain provides a decentralized ledger system that ensures the integrity of the recorded transactions. With its wide application comes the huge demand for blockchain researchers and engineers, however, most schools have not offered courses related to blockchain research and development. To fill the gap between the increasing needs for blockchain professionals and shortage of blockchain curriculum, we propose a portable labware that covers the complete cycle of blockchain development. In this labware we apply active learning strategies to engage students in learning the blockchain knowledge more efficiently and effectively. © 2022 IEEE.

关键词： Blockchain

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Optimizing Secure Multi-User ISAC Systems With STAR-RIS: A Deep Reinforcement Learning Approach for 6G Networks

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IEEE Access 2025年 13卷 31472-31484页

作者： Kamal, Mian Muhammad Zain Ul Abideen, Syed Al-Khasawneh, M.A. Alabrah, Amerah Sohail Ahmed Larik, Raja Irfan Marwat, Muhammad Southeast University School of Electronic Science and Engineering Jiangning Jiangsu Nanjing211189 China Qingdao University College of Computer Science and Technology Qingdao266071 China Al-Ahliyya Amman University Hourani Center for Applied Scientific Research Amman19111 Jordan Skyline University College School of Computing University City Sharjah Sharjah United Arab Emirates King Saud University College of Computer and Information Science Department of Information Systems Riyadh11543 Saudi Arabia Ilma University Department of Computer Science Sindh Karachi75190 Pakistan University of Science and Technology Bannu Department of Software Engineering Bannu28100 Pakistan

The rapid evolution of wireless communication technologies and the increasing demand for multi-functional systems have led to the emergence of integrated sensing and communication (ISAC) as a key enabler for future 6G networks. Simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) have recently garnered significant attention for their ability to enhance signal coverage and improve system efficiency. This paper investigates a STAR-RIS-assisted ISAC system designed to secure communication for multiple legitimate users (LUs) while safeguarding against multiple eavesdroppers (Eves). By jointly optimizing the base station (BS) transmit beamforming, STAR-RIS transmission and reflection coefficients, and receive filters, the proposed framework aims to maximize the long-term average secrecy rate for all LUs. Constraints are imposed to ensure minimum echo signal-to-noise ratios (SNRs) for sensing and meet the achievable rate requirements of LUs. To address the inherent complexity of this non-convex problem, two deep reinforcement learning (DRL) algorithms are proposed. Numerical results demonstrate that the proposed system achieves significant improvements in secrecy rate compared to conventional RIS setups. This work provides a scalable and efficient approach for secure multi-user ISAC systems, making it highly relevant for future 6G networks, smart cities, and IoT applications. © 2025 The Authors.

关键词： Deep reinforcement learning

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A Comprehensive Survey of Techniques for Lung Cancer Diagnosis and Prediction

A Comprehensive Survey of Techniques for Lung Cancer Diagnos...

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Wireless and Optical Technologies (GCWOT), 2020 Global Conference on

作者： Danish Jamil Syed Mehr Ali Shah Fatma Mahfoodh Mohammed Al-Jarwani Department of Software Engineering Sir Syed University of Engineering & Technology Karachi Pakistan College of Computing and Information Sciences University of Technology and Applied Sciences Musandam Sultanate of Oman

ISBN: (数字)9798331534271

ISBN: (纸本)9798331534288

Despite significant advancements, lung cancer remains a formidable global health challenge, necessitating effective diagnostic and prognostic methodologies. This survey paper examines current literature to identify and evaluate methods and models utilized for lung cancer diagnosis and prognosis, aiming to enhance early detection strategies for improved patient outcomes. The research addresses critical questions: What are the current methods and models employed for lung cancer diagnosis and prognosis, their strengths, limitations, and potential for future improvement? It investigates specific applications of deep learning architectures such as CNN, GoogleNet, VGG-16, U-Net, and machine learning algorithms like XGBoost, SVM, KNN, ANN, Random Forest, and hybrid models in lung cancer detection. Methodologically, a systematic review across key databases using relevant keywords was conducted to synthesize findings. The study highlights the strengths and limitations of existing approaches and identifies gaps in model interpretability, real-world validation, and integration of diverse techniques. Conclusions underscore the transformative potential of advanced computational methods in enhancing lung cancer diagnosis and prognosis, proposing avenues for future research to address current challenges and improve patient care.

关键词： Surveys Deep learning Machine learning algorithms Accuracy Computational modeling Transfer learning Lung cancer Medical services Prognostics and health management Systematic literature review

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Effective Entry-Wise Flow for Molecule Generation 40

Effective Entry-Wise Flow for Molecule Generation

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40th IEEE International Conference on Data engineering, ICDE 2024

作者： Zhang, Qifan Yao, Junjie Yang, Yuquan Shi, Yizhou Gao, Wei Wang, Xiaoling School of Computer Science and Technology East China Normal University China Software Engineering Institute East China Normal University China Shanghai Key Laboratory of Trustworthy Computing China University of Massachusetts Department of Chemistry Boston United States University of Michigan Ann Arbor United States

ISBN: (纸本)9798350317152

Molecule generation is a critical process in the fields of drug discovery and materials science. Recently, generative models based on normalizing flows have demonstrated significant potential in this domain. These models are particularly suited for handling the symmetrical and complex chemical structures often encountered in molecular datasets. Despite their promising nature, normalizing flow-based models for molecule generation face considerable challenges. The complexity of molecule representation, the rigorous demands of optimization, and the scarcity of training labels in molecular datasets contribute to these difficulties. Additionally, adequately and comprehensively learning the distribution of molecular datasets remains a formidable task. In this paper, we delve into the intricate entry-wise modules in vanilla flows, introducing an effective variation of flow-based models. Our proposed approach innovatively encapsulates affine coupling transformations within normalizing flows. Furthermore, we deconstruct existing invertible flow models, integrating them with newly developed entry-wise transformations. Our experimental studies demonstrate that these proposed entry-wise modules, when incorporated into standard flow-based models, surpass other generative models in performance on various representative datasets and generation tasks. Notably, in the context of low-resourced molecular graph generation, our model achieves remarkable performance compared to its counterparts. © 2024 IEEE.

关键词： Complex networks

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Comparative analysis of various models for image classification on Cifar-100 dataset

Comparative analysis of various models for image classificat...

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作者： YuYu Zheng HaoXuan Huang JunMing Chen School of Optoelectronic Information and Computer Engineering University of Shanghai for Science and Technology Department of Computing and Software McMaster University Department of Mathematics University of CaliforniaLos Angeles

Nowadays,people developed various convolutional neural network(CNN) based models for computer *** famous models,such as GoogLeNet,Residual Network(ResNet),Visual Geometry Group(VGG),and You Only Look Once(YOLO),have different architecture and *** which model to use may be a troublesome problem for those just starting to study image *** solve this problem,we introduce the GoogLeNet,ResNet-18,and VGG-16 models,comparing their architecture,features,and *** we give our suggestions based on the test results to help beginners choose a suitable *** conducted experiments to train and test GoogLeNet,ResNet-18,and VGG-16 on the Cifar-100 datasets with the same *** on the test results(test accuracy,average test loss,training loss),we analyze the figures for trends,key points,increase rate,and other *** we combine the architecture of each model to make our *** experimental results show that ResNet-18 can be a good choice when training the model with the Cifar-100 datasets because it performs well after training and has a low time *** Net-18 also has the fastest convergence *** would be the second choice because it functions similarly to ResNet-18 and is even ***,training GoogLeNet is a time-consuming *** is not recommended in this experiment because it has the worst performance and similar training complexity compared with ResNet-18.

关键词： GoogLeNet ResNet VGG Image Classification Architecture Comparison

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R-TAP: A Cloud-Based Multi-Region Road Traffic Accident Severity Prediction Model

R-TAP: A Cloud-Based Multi-Region Road Traffic Accident Seve...

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IEEE Industrial Electronics and Applications Conference (IEACon)

作者： Albe Bing Zhe Chai Bee Theng Lau Mark Kit Tsun Tee Chris McCarthy Faculty of Engineering Computing & Science Swinburne University of Technology Sarawak Campus Kuching Malaysia Department of Computer Science & Software Engineering Swinburne University of Technology Melbourne Australia

ISBN: (数字)9798350382976

ISBN: (纸本)9798350382983

The tremendous impact of road traffic accidents (RTAs) remains a global concern, especially in low- and middle-income countries (LMICs). While most LMICs have limited budget to afford advanced traffic monitoring systems, it is necessary to develop feasible solutions for enhancing road safety in these regions. Recent studies have extensively explored the applicability of machine learning (ML) models to predict RTA severity in different regions. However, there is limited exploration of training and evaluating the performance of ML model from a multi-region perspective. Further investigation is also necessary to formulate a cost-effective implementation strategy for LMICs to deploy ML models for real-time prediction. Hence, this paper contributes by proposing R-TAP, which deploys a TabNet-based model on the cloud for multi-region RTA severity predictions. A web application is also developed to receive inputs and visualise the prediction results. The TabNet model is trained on a multi-region dataset consisting of 42 features, produced by combining the individual RTA datasets collected from Australia, Brazil, Ethiopia, New Zealand, the UK, and the US. Evaluation with random forest and multinomial logistic regression showed that TabNet can achieve performance comparable to random forest. It also has the highest area under the receiver operating characteristics at 0.8857. R-TAP is also simulated using testing samples as real-time input, with an overall execution time of 4.104s per prediction. It is considered satisfactory and is expected to be useful for emergency responders to make informed decisions. This solution is expected to support LMICs in their roadmap towards enhanced road safety. Future research is essential to improve the predictive power and include datasets from more regions for a more inclusive RTA severity prediction.

关键词： Training Visualization Receivers Predictive models Road traffic Road safety Real-time systems Random forests Accidents Testing

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A reinforcement learning and predictive analytics approach for enhancing credit assessment in manufacturing

Decision Analytics Journal

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Decision Analytics Journal 2025年 15卷

作者： Razaque, Abdul Beishenaly, Aliya Kalpeyeva, Zhuldyz Uskenbayeva, Raisa Nikolaevna, Moldagulova Aiman Department of Software Engineering & AI School of Computing Gachon University Seongnam-si South Korea Department of Software Engineering Institute of Automation and Information Technologies Satbayev University Kazakhstan Department of Computer Engineering International Information Technology University (IITU) Almaty Kazakhstan

The fundamental issue with a credit system for manufacturers and importers of commodities is inefficient credit assessment. Traditional techniques frequently produce inaccurate risk assessments and credit scores, resulting in financial losses for lenders, missing business growth possibilities, and less favorable client conditions. To overcome this issue, a comprehensive credit assessment scoring system should be implemented to increase importers’ confidence. The article proposes a predictive-based reinforcement learning (PRL) model to help manufacturers and importers acquire more accurate and dependable credit scores while avoiding default risk. Furthermore, the proposed PRL model enhances decision-making, system efficiency, and risk-tolerant financial conditions. To attain these cutting-edge objectives, the proposed PRL model combines three algorithms. Algorithm 1 collects and aggregates data to indicate areas for improvement if credit scoring is poor. Algorithm 2 uses reinforcement learning to validate and enhance bank scores. Algorithm 3 focuses on predictive modeling for bank scoring, ensuring that the credit decision-making system is operational and constantly improving. Furthermore, reinforcement learning leverages the features from local interpretable model-agnostic explanations (LIME) and shapely additive explanations (SHAP) to generate locally reliable explanations and attribute the contribution of each feature for determining the output of the model. The Python platform tests the proposed PRL to achieve the objectives. Based on the results, The PRL model markedly enhances credit assessment precision, achieving an accuracy of over 99.5%, which outstrips current methodologies such OCLA (96.12%), PSML (84.12%), and EMPCC (91.67%). Furthermore, the PRL model augments leverage ratios, rising from 2.75% in 2015 to 3.36% in 2024.5, and increases accounts receivable turnover from 4.38% in 2015 to 7.4% in 2024.5, surpassing alternative credit evaluation methodologi

关键词： Financial analytics Intelligent credit systems Machine learning Manufacturing Predictive analytics Reinforcement learning

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