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5th International Conference on Blockchain Computing and Applications, BCCA 2023

作者： Alja'Afreh, Mohammad Alouneh, Sahel Obaidat, Muath Karime, Ali Elsaddik, Abdulmotaleb United Arab Emirates Princess Sumaya University for Technology Amman Jordan Cybersecurity Program College of Engineering Al Ain University United Arab Emirates German Jordanian University Amman Jordan Computer Science-City University of New York United States Royal Military College of Canada Canada

ISBN: (纸本)9798350339239

As technology has advanced, people's lives have been transformed by the virtual world, which has been created by technologies such as the Internet, computers, artificial intelligence, and hardware. The metaverse, a new social ecology connecting the physical and virtual worlds, is rapidly expanding as the demand for virtual reality grows. Privacy, security, high synchronization, and low latency have all been challenged as data volumes and value have grown and the metaverse continues to evolve. As blockchain and intelligent networking technologies continue to evolve, these challenges can be addressed and the metaverse's needs for trusted construction, continuous data interaction, and computing can be satisfied. In order to provide immersive experiences in the metaverse, a comprehensive review of blockchain's role and benefits is essential. The purpose of this survey is to discuss the metaverse's development trend, architecture, and characteristics. This review paper is novel in providing an overview of existing blockchain research, including overviews, applications, and challenges. Furthermore, we summarized the metaverse's applications, emphasizing their significance and areas of development. Using the survey, we are able to discuss open issues, challenges, and future research directions. © 2023 IEEE.

关键词： Blockchain

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Ant Colony Optimization based Medical Image Preservation and Segmentation 2

Ant Colony Optimization based Medical Image Preservation and...

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2nd IEEE International Conference on Advanced Technologies in Intelligent Control, Environment, Computing and Communication Engineering, ICATIECE 2022

作者： Nandihal, Priya Pareek, Piyush Kumar De Albuquerque, Victor Hugo Costa Madhumala, R.B. Khanna, Ashish Sunil Kumar, V. Dayananda Sagar Academy of Technology and Management Dept of Computer Science and Design Bangalore India Graduate Program in Telecommunication Engineering Federal Institute of Education Science and Technology of Ceara Fortaleza Brazil Federal University of Ceara Department of Teleinformatics Engineering Brazil Maharaja Agrasen Institute of Technology GGSIPU CSE Dept India Nitte Meenakshi Institute of Technology Department of Artificial Intelligence & Machine Learning Bengaluru India

ISBN: (纸本)9781665493963

Image segmentation is an essential component in many different types of computer vision systems. Image segmentation is used in order to identify objects and boundaries within pictures. It is important to note that the success of recognition algorithms is heavily dependent on the quality of the picture segmentation, and there is yet no technique that can universally segment images. Boundary detection has a long history of being recognised as one of the most difficult challenges in the fields of image processing and pattern analysis, in particular, when it comes to applications in medical imaging. The clarity of the picture is being muddled by more information, which is the source of the problem's complexity. The creation of segmentation algorithms for medical pictures is an active field of study, and segmentation algorithms are meant to account for a variety of factors, including noise, intensity inhomogeneity, poor contrast between the lesion and the surrounding skin, and so on. There is currently no active contour model that combines the locally statistical property of being more noise-resistant, in which the intensities in the transformed domain have less overlapping in the statistics, and the locally image fitting property of extracting the local image information in order to be able to segment images with intensity inhomogeneity. This is because there is no such model. So the proposed work includes Ant colony optimization based double threshold segmentation in medical images. Performance of the proposed algorithm is more efficient, reliable and accurate than the existing algorithm. Performance parameters such as segmentation accuracy, error rate, and Dice co-efficient are used in order to conduct the evaluation of the proposed work. © 2022 IEEE.

关键词： Impulse noise

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Tracking Players in a Badminton Court by Two Cameras

arXiv

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arXiv 2023年

作者： Chou, Young-Ching Zhang, Shen-Ru Chen, Bo-Wei Chen, Hong-Qi Lin, Cheng-Kuan Tseng, Yu-Chee Department of Computer Science National Yang Ming Chiao Tung University Taiwan Undergraduate Degree Program of Systems Engineering and Technology National Yang Ming Chiao Tung University Taiwan Computer Science and Information Engineering Chung Cheng Institute of Technology NDU Taiwan

This study proposes a simple method for multi-object tracking (MOT) of players in a badminton court. We leverage two off-the-shelf cameras, one on the top of the court and the other on the side of the court. The one on the top is to track players’ trajectories, while the one on the side is to analyze the pixel features of players. By computing the correlations between adjacent frames and engaging the information of the two cameras, MOT of badminton players is obtained. This two-camera approach addresses the challenge of player occlusion and overlapping in a badminton court, providing player trajectory tracking and multi-angle analysis. The presented system offers insights into the positions and movements of badminton players, thus serving as a coaching or self-training tool for badminton players to improve their gaming strategies. Copyright © 2023, The Authors. All rights reserved.

关键词： Cameras

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Leveraging Advanced computer Vision for Hazardous Behavior Monitoring on Campus Safety Maintenance

Leveraging Advanced Computer Vision for Hazardous Behavior M...

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Electronic Communications, Internet of Things and Big Data (ICEIB), International Conference on

作者： Shang-Te Tsai Zong-Rong Wu Yu-Cheng Chang Wei-Hsin Lin Jia-Yi Lee Wei Chien Business School Yulin Normal University Yulin China Department of Technical and Vocational Program in Artificial Intelligence National Yunlin University of Science and Technology Yunlin Taiwan Department of Computer Science and Information Engineering National Taipei University of Technology Taipei Taiwan Department of Computer Information and Network Engineering Lunghwa University of Science and Technology Taoyuan Taiwan Department of International Business National Pingtung University Pintung Taiwan

There was an incident on a campus involving the casualty of students. It created social unrest and heightened concerns over campus security. Consequently, it becomes important to prevent such incidents. Thus, we devel... 详细信息

ISBN: (数字)9798350360721

ISBN: (纸本)9798350360738

关键词： computer vision Electric potential Neural networks Safety Maintenance Security Sparks

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Apache Spark Powered: Enhancing Network Intrusion Detection System Using Random Forest

Apache Spark Powered: Enhancing Network Intrusion Detection ...

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Novel Intelligent and Leading Emerging sciences Conference (NILES)

作者： Hesham Mamdouh Mohamed Tarek Abdelrahman Radwan Adel Saeed Ahmed Abdeen Monier Ashraf Khaled M. Fouad Ibrahim Abdelbaky School of Engineering and Applied Science (EAS) Nile University Giza Egypt CS program School of Information Technology and Computer Science (ITCS) Nile University Giza Egypt Faculty of Computer Science and Engineering New Mansoura University Artificial Intelligence Department Faculty of Computers and Artificial Intelligence Benha University Banha Egypt

ISBN: (数字)9798350378511

ISBN: (纸本)9798350378528

The increasing sophistication of cyber attacks necessitates effective intrusion detection systems. We propose a novel intrusion detection method integrating deep learning with big data management using Apache Spark. Leveraging the comprehensive CSE-CIC-IDS2018 dataset, we apply extensive data preprocessing, including handling missing and unreliable values, duplicates, and redundant columns. In addition, implementation of a Random Forest based feature importance approach is derived to prioritize the most impactful Features. Furthermore, stratified k-fold cross-validation is used for a model selection process on a class-imbalanced dataset. Our weighted Random Forest classifier achieves a remarkable weighted average F1-score of 0.999 and a test inference time of 0.673 seconds using only the top 34 features, outperforming previous studies without sampling techniques. The proposed architecture offers a scalable and accurate solution for intrusion detection in cloud architectures, demonstrating the effectiveness of combining deep learning and big data technologies for cybersecurity.

关键词： Deep learning Cloud computing Data preprocessing Network intrusion detection Cluster computing Forestry Big Data Random forests Cyberattack Testing

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Implementation of AI-Based Support Vector Machine Algorithm for Fraud Detection in Digital Payment Systems

Implementation of AI-Based Support Vector Machine Algorithm ...

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International Seminar on Research of Information technology and Intelligent Systems (ISRITI)

作者： Edy Prayitno Rikie Kartadie Uning Lestari Munengsih Sari Bunga Accounting Information Systems Faculty of Information Technology Universitas Teknologi Digital Indonesia Yogyakarta Indonesia Computer Engineering Study Program Faculty of Information Technology Universitas Teknologi Digital Indonesia Yogyakarta Indonesia Informatics Study Program Faculty of Science and Information Technology Universitas Akprind Indonesia Yogyakarta Indonesia Department of Computer Engineering Smart City Information Systems Politeknik Negeri Indramayu Indramayu Indonesia

ISBN: (数字)9798331519643

ISBN: (纸本)9798331519650

The increasing use of digital payment systems has led to a rise in fraudulent activities, presenting a significant challenge in ensuring secure transactions. This research focuses on implementing the Support Vector Machine (SVM) algorithm with a Radial Basis Function (RBF) kernel to detect fraud in digital payment systems. One of the main challenges addressed in this study is the severe class imbalance in the dataset, where fraudulent transactions account for only 0.17% of total transactions. To overcome this, the SMOTE (Synthetic Minority Over-sampling Technique) method was applied to balance the dataset, allowing the model to better recognize fraudulent patterns. The results indicate that the SVM model achieved an accuracy of 99.93%, with a precision of 86.23% and a recall of 75.51%. These results demonstrate that SVM, combined with SMOTE and RBF kernel, is highly effective in detecting fraudulent transactions while minimizing false positives. This research provides a strong foundation for improving fraud detection models in the context of digital payment systems, offering enhanced security and trust for users. Further research could explore hybrid models and real-time data analysis to improve performance.

关键词： Support vector machines Seminars Adaptation models Accuracy Data models Real-time systems Fraud Security Kernel Intelligent systems

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Information technology Challenges for Integrated Governance, Risk and Compliance (GRC) 1

Information Technology Challenges for Integrated Governance,...

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1st International Conference on Smart technology, Applied Informatics, and Engineering, APICS 2022

作者： Mahendra, Irfan Meyliana Prabowo, Harjanto Hidayanto, Achmad Nizar Bina Nusantara University Computer Science Program Jakarta11480 Indonesia Nusa Mandiri University Technology Information Faculty Jakarta11450 Indonesia Bina Nusantara University Information System Department Jakarta11480 Indonesia Bina Nusantara University Management Departement Jakarta11480 Indonesia Universitas Indonesia Faculty of Computer Science Depok16424 Indonesia

ISBN: (数字)9781665489850

ISBN: (纸本)9781665489850

Integrated GRC is an area of concern for organizations today. Integrated GRC provides benefits for increasing competitiveness and organizational excellence, realizing effectiveness, efficiency, and optimization, as well as supporting the prevention of corruption and fraud. Information technology has a very important role in determining the success of implementing an integrated GRC in the organization. However, based on the research conducted, adequate research on the application of information technology in the implementation of integrated GRC is still lacking. In this paper, a study will be conducted on trends and research gaps related to the application of information technology in the current integrated GRC implementation. The conclusion of this study states that there is a great opportunity for wider research on the opportunities and challenges of implementing the latest information technology such as blockchain, artificial intelligence, big data, and cloud computing to support the effectiveness and success of integrated GRC implementation. © 2022 IEEE.

关键词： Crime

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IIOF: Intra- and Inter-feature orthogonal fusion of local and global features for music emotion recognition

引用

Pattern Recognition 2024年 148卷

作者： Chang, Pei-Chun Chen, Yong-Sheng Lee, Chang-Hsing Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan Bachelor Program in Industrial Artificial Intelligence Ming Chi University of Technology New Taipei Taiwan

In this paper, we propose intra- and inter-feature orthogonal fusion (IIOF) of local and global features obtained from MS-SincResNet or MS-SSincResNet (a variant of MS-SincResNet) for music emotion recognition (MER). Given a raw waveform of music signal, MS-SincResNet/MS-SSincResNet is first used to learn several 2D representations having different receptive fields and obtain embeddings with time-frequency information from different layers. Then, local and global features are extracted from these embeddings. IIOF consisting of intra-feature OF and inter-feature OF is further employed to integrate both local and global features to obtain a discriminative descriptor for MER. The intra-feature OF is used to enhance the diversity of the global feature, and the inter-feature OF is utilized to reduce redundancies and produce complementary information between local and global features. The experimental results have demonstrated that the representation discriminability can be enhanced by IIOF considering the feature orthogonality. Furthermore, extensive experimental results have shown that the proposed method outperforms other state-of-the-art methods in terms of regression and classification tasks on the well-known MER datasets, including the DEAM dataset and the PMEmo dataset. The codes are available at https://***/PeiChunChang/MS-SSincResNet_with_IIOF. © 2023 Elsevier Ltd

关键词： Emotion Recognition

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Stochastic Dynamics and Probability Analysis for a Generalized Epidemic Model with Environmental Noise

arXiv

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arXiv 2024年

作者： Boukanjime, Brahim Maama, Mohamed Laboratory of Mathematical Modeling and Economic Calculations Hassan First University of Settat Morocco Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Saudi Arabia

In this paper we consider a stochastic SEIQR (susceptible-exposed-infected-quarantined-recovered) epidemic model with a generalized incidence function. Using the Lyapunov method, we establish the existence and uniqueness of a global positive solution to the model, ensuring that it remains well-defined over time. Through the application of Young’s inequality and Chebyshev’s inequality, we demonstrate the concepts of stochastic ultimate boundedness and stochastic permanence, providing insights into the long-term behavior of the epidemic dynamics under random perturbations. Furthermore, we derive conditions for stochastic extinction, which describes scenarios where the epidemic may eventually die out, and V-geometric ergodicity, which indicates the rate at which the system’s state converges to its equilibrium. Finally, we perform numerical simulations to verify our theoretical results and assess the model’s behavior under different parameters. Copyright © 2024, The Authors. All rights reserved.

关键词： Stochastic models

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Development of a healthcare monitoring diabetes mobile application for community

Development of a healthcare monitoring diabetes mobile appli...

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International Conference on Computational science and Computational Intelligence (CSCI)

作者： Kanyarat Bussaban Phanu Waraporn Pattarapan Roonrakwit Chanyapat Sangsuwan Kunyanuth Kularbphettong Faculty of Science and Technology Suan Sunandha Rajabhat University Bangkok Thailand Computer Science Program Suan Sunandha Rajabhat University Bangkok Thailand Faculty of Information and Communication Technology Silpakorn University Bangkok Thailand Computer Science Program Faculty of Science and Technology Suan Sunandha Rajabhat University Bangkok Thailand

The purpose of this project is to develop the mobile application, by applied Machine learning, for analyzing, collecting, monitoring, and retrieving information between patients with diabetes especially diabetes type 2 and village public health volunteers and to study the impact of using mobile application based on self-learning and self-management in diabetes information. This is a research and development mobile application and the sample consisted of 30 diabetes patients and 5 village health volunteers participated in this research. The project has demonstrated the effectiveness of using mobile application to support patients and village health volunteers. The results showed that user satisfaction has a high level.

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