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Enhanced Neutrosophic Set and Machine Learning Approach for Kidney Disease Prediction

Neutrosophic Sets and Systems 2025年 80卷 465-477页

作者： Al-Doori, Humam M. Alkellezli, Tareef S. Abdelhafeez, Ahmed Eassaa, Mohamed Sawah, Mohamed S. El-Douh, Ahmed A. Cybersecurity Sciences Department College of Science Ashur University Baghdad Iraq Cybersecurity Engineering Department College of Engineering Ashur University Baghdad Iraq Computer Science Department Faculty of Information System and Computer Science October 6 University Giza12585 Egypt Applied Science Research Center Applied Science Private University Amman Jordan Department of Computer Science Faculty of Information Technology Ajloun National University P.O.43 Ajloun26810 Jordan Cybersecurity Technology Engineering Department College of Engineering Technology Ashur University Baghdad Iraq

Kidney disease (KD) is a gradually increasing global health concern. It is a chronic illness linked to higher rates of morbidity and mortality, a higher risk of cardiovascular disease and numerous other illnesses, and expensive medical expenses. The machine learning (ML) models are applied for KD prediction with higher accuracy and precision. The KD dataset has uncertainty and vague information, so, we used the neutrosophic set (NS) to deal with vague and uncertainty information in the KD dataset. The KD dataset is converted into the N-KD dataset with three membership functions: truth, indeterminacy, and falsity. Three ML models are used in this study such as logistic regression (LR), support vector machine (SVM), and k-nearest neighbor (KNN). These ML models are applied to the N-KD dataset. The results show the LR has higher accuracy and precision on the N-KD dataset than the original KD dataset. © 2025, University of New Mexico. All rights reserved.

关键词： Logistic regression

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A globally shared resource paradigm for encoded storage systems in the public cloud

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Fundamental research 2024年第3期4卷 642-650页

作者： Zhiyue Li Guangyan Zhang Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China Beijing National Research Center for Information Science and Technology Tsinghua UniversityBeijing 100084China

Public clouds favor sharing of storage resources,in which many tenants acquire bandwidth and storage capacity from a shared storage *** provide high availability,data are often encoded to provide fault tolerance with low storage *** this,efficiently organizing an encoded storage system for shared I/Os is critical for application *** is usually hard to achieve as different applications have different stripe configurations and fault tolerance *** this paper,we first study the block trace from the Alibaba cloud,and find that I/O patterns of modern applications prefer the resource sharing *** on this,we propose a globally shared resource paradigm for encoded storage system in the public *** globally shared resource paradigm can provide balanced load and fault tolerance for numerous disk pool sizes and arbitrary application stripe ***,we demonstrate with two case studies that our theory can help address the device-specific problems of HDD and SSD RAID arrays with slight modifications:comparing the existing resource partition and resource sharing methods,our theory can promote the rebuild speed of the HDD RAID arrays by 2.5,and reduce the P99 tail latency of the SSD arrays by up to two orders of magnitude.

关键词： Public cloud Encoded storage Load balancing RAID reconstruction Tail latency

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CMCF-Net: An End-to-End Context Multiscale Cross-Fusion Network for Robust Copy-Move Forgery Detection

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IEEE Transactions on Multimedia 2024年 26卷 6090-6101页

作者： Xiong, Lizhi Xu, Jianhua Yang, Ching-Nung Zhang, Xinpeng Nanjing University of Information Science and Technology School of Computer Science Nanjing210044 China Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology Engineering Research Center of Digital Forensics Ministry of Education Nanjing210044 China Nanjing University of Information Science and Technology Software Engineering Nanjing210044 China National Dong Hwa University Department of Computer Science and Information Engineering Hualien974301 Taiwan Fudan University School of Computer Science Shanghai200433 China

Image copy-move forgery detection (CMFD) has become a challenging problem due to increasingly powerful editing software that makes forged images increasingly realistic. Existing algorithms that directly connect multiple scales of features in the encoder part may not effectively aggregate contextual information, resulting in poor performance. In this paper, an end-to-end context multiscale cross-fusion network (CMCF-Net) is proposed to detect image copy-move forgery. The proposed network consists of a multiscale feature extraction fusion (MSF) module and a multi-information fusion decoding (MFD) module. Multiscale information is efficiently extracted and fused in the MSF module utilizing stacked-scale feature fusion, which improves the network's forgery localization ability on objects of different scales. The MFD module employs contextual information combination and weighted fusion of multiscale information to guide the network in obtaining relevant clues from correlated information at multiple different scales. Experimental results and analysis have demonstrated that the proposed CMCF-Net achieves the best localization results with higher robustness. © 1999-2012 IEEE.

关键词： Feature extraction

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Ghost Module Based Residual Mixture of Self-Attention and Convolution for Online Signature Verification

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computers, Materials & Continua 2024年第4期79卷 695-712页

作者： Fangjun Luan Xuewen Mu Shuai Yuan Department of Computer Technology School of Computer Science and EngineeringShenyang Jianzhu UniversityShenyang110168China Liaoning Province Big Data Management and Analysis Laboratory of Urban Construction Shenyang Jianzhu UniversityShenyang110168China Shenyang Branch of National Special Computer Engineering Technology Research Center Shenyang Jianzhu UniversityShenyang110168China

Online Signature Verification (OSV), as a personal identification technology, is widely used in various ***, it faces challenges, such as incomplete feature extraction, low accuracy, and computational heaviness. Toaddress these issues, we propose a novel approach for online signature verification, using a one-dimensionalGhost-ACmix Residual Network (1D-ACGRNet), which is a Ghost-ACmix Residual Network that combines convolutionwith a self-attention mechanism and performs improvement by using Ghost method. The Ghost-ACmix Residualstructure is introduced to leverage both self-attention and convolution mechanisms for capturing global featureinformation and extracting local information, effectively complementing whole and local signature features andmitigating the problem of insufficient feature extraction. Then, the Ghost-based Convolution and Self-Attention(ACG) block is proposed to simplify the common parts between convolution and self-attention using the Ghostmodule and employ feature transformation to obtain intermediate features, thus reducing computational ***, feature selection is performed using the random forestmethod, and the data is dimensionally reducedusing Principal Component Analysis (PCA). Finally, tests are implemented on the MCYT-100 datasets and theSVC-2004 Task2 datasets, and the equal error rates (EERs) for small-sample training using five genuine andforged signatures are 3.07% and 4.17%, respectively. The EERs for training with ten genuine and forged signaturesare 0.91% and 2.12% on the respective datasets. The experimental results illustrate that the proposed approacheffectively enhances the accuracy of online signature verification.

关键词： Online signature verification feature selection ACG block ghost-ACmix residual structure

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On Construction of Precise Positioning System via IEEE 802.11ax

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IEEE Internet of Things Journal 2023年第14期10卷 12951-12960页

作者： Tsung, Chen-Kun Yang, Chao-Tung Liu, Jung-Chun Hsiung, Chun Chang, Shih-Kuang Hsu, Ming-Shang National Chin-Yi University of Technology Department of Computer Science and Information Engineering Taichung41170 Taiwan Tunghai University Department of Computer Science and Research Center for Smart Sustainable Circular Economy Taichung407224 Taiwan Tunghai University Department of Computer Science Taichung40704 Taiwan Krystal Technology Company Ltd. Network Department Taichung42756 Taiwan

Wireless network stability is critical for organizations. Most companies rely on a solid Internet connection for at least part of their day-to-day activities. It is essential to show the fast and high capacity of Wi-Fi. Wireless network management also significantly supplies the user with quality service and helps the system administrator manage and track the network infrastructure. The user must be able to stay connected. The Wi-Fi connection engine was applied in this article using the analysis and locations engine (ALE). ALE provides the information, including MAC address, location, floor information, and the building where the device belongs. From this data, we presented the Web visualization of the Wi-Fi network monitoring system on the map using Cesium. Except for the common information about Wi-Fi users, this work presents the vertical position visualization in the buildings to provide more details about user usage. This work analyzes the user experience of using the proposed system, and latency is the appropriate metric to measure the user experience. The experiment results show that the optimized number of cluster partitions is four, and the latency is less than 1.3 s for 4-MB data. Therefore, the system provides a visualization platform with acceptable system response, and Internet managers would consider the proposed system as the management tool. © 2014 IEEE.

关键词： Engines

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A Study on Cypher Assignment Based on Secrecy Capacity

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International Journal of Network Security 2024年第2期26卷 167-172页

作者： Yang, Cheng-Ying Chen, Jong-Shin Yao, Jenq-Foung J F Hwang, Min-Shiang Department of Computer Science University of Taipei Taipei Taiwan Department of Information and Communication Engineering Chaoyang University of Science and Technology Taiwan Computer Science Georgia College and State University Milledgeville United States Department of Computer Science & Information Engineering Asia University Taiwan Fintech and Blockchain Research Center Asia University Taiwan 500 Lioufeng Rd. Wufeng Taichung41354 Taiwan

Accessing wireless services is convenient but not secure. The issue of privacy and security is highly demanded on the internet. The information is sent with the undesired threats of eavesdroppers. Secure communication enables the desired receiver to correctly and successfully receive the information without interruption. In the meantime, a secure system protects the transmitted information from the eavesdroppers. Traditionally, encryption, authorization, and authentication schemes could be employed to protect from eavesdroppers. However, the unanticipated attacks have been developing. According to Shannon’s perfect communication, the undesired receivers, such as eavesdroppers, could receive an error-free cryptogram. Based on the physical-layer security, it could be expected that secure communication could be implemented without complex computing at the higher level. The physical-layer security coding scheme aims to achieve an extremely low decoding error probability and obtain a critical security level against attackers. The positive secrecy capacity (secrecy rate) could provide secure communication. Hence, a positive secrecy capacity is the primary concern in the system. Besides, physical-layer security coding is not only to help the system achieve a critical level of security but also to obtain an extremely low decoding error probability. However, it decreases the bandwidth efficiency and suffers the system performance. Hence, the tradeoff between the security level and the coding rate is important to lead a secure communication. © (2023), (Femto Technique Co.). All Rights Reserved.

关键词： Bandwidth

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Enhancing Email Spam Detection Using Advanced AI Techniques

Enhancing Email Spam Detection Using Advanced AI Techniques

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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Al-Dmour, Nidal A. Kousar, Shagufta Khan, Areeba Ihsan, Anaum Abbas, Tahir Saeed, Ali Q College of Engineering Department of Computer Engineering Mutah University Jordan Applied Science Research Center Applied Science Private University Amman Jordan The TIMES Institute Department of Computer Science Multan60000 Pakistan Lahore Pakistan Northern Technical University Computer Center Ninevah Iraq

ISBN: (纸本)9798350369106

This review paper investigates the advances of artificial intelligence (AI) in the field of email spam detection. The study addresses AI-based techniques used for email spam filtering by classifying threats such as keywords, content-based, sender IDs, and phrases. Email spam remains one of the most pressing issues today, with spammers sending malicious links in junk folders to compromise confidential information. As spam filtering becomes increasingly complex, AI and machine learning techniques like Naïve Bayes, Support Vector Machine (SVM), and Random Forest are commonly used to mitigate these threats. This study proposes a novel framework that combines traditional machine learning models with deep neural networks to develop a robust and flexible spam detection system. The framework is designed to enhance the detection capabilities of the classifier by applying a series of key sequential steps, each intended to improve the system's ability to distinguish between legitimate emails and spam. The paper details the methodology, providing a step-by-step explanation of each phase to demonstrate how the framework advances email spam detection. © 2024 IEEE.

关键词： Deep learning Email spam detection machine learning naive-Bayes SVC

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Soft-GNN:towards robust graph neural networks via self-adaptive data utilization

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Frontiers of computer science 2025年第4期19卷 1-12页

作者： Yao WU Hong HUANG Yu SONG Hai JIN National Engineering Research Center for Big Data Technology and System Service Computing Technology and System LabCluster and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China College of Information and Communication National University of Defense TechnologyWuhan 430019China Department of Computer Science and Operations Research Universitéde MontréalMontreal H3C 3J7Canada

Graph neural networks(GNNs)have gained traction and have been applied to various graph-based data analysis tasks due to their high ***,a major concern is their robustness,particularly when faced with graph data that has been deliberately or accidentally polluted with *** presents a challenge in learning robust GNNs under noisy *** address this issue,we propose a novel framework called Soft-GNN,which mitigates the influence of label noise by adapting the data utilized in *** approach employs a dynamic data utilization strategy that estimates adaptive weights based on prediction deviation,local deviation,and global *** better utilizing significant training samples and reducing the impact of label noise through dynamic data selection,GNNs are trained to be more *** evaluate the performance,robustness,generality,and complexity of our model on five real-world datasets,and our experimental results demonstrate the superiority of our approach over existing methods.

关键词： graph neural networks node classification label noise robustness

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Socially Aware V2X Localized QoS

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IEEE Internet of Things Journal 2024年第15期11卷 25925-25938页

作者： Kaliski, Rafael Han, Yue-Hua National Sun Yat-sen University Department of Computer Science and Engineering Kaohsiung804 Taiwan Academia Sinica Research Center for Information Technology Innovation Taipei115 Taiwan National Taiwan University of Science and Technology Department of Computer Science and Information Engineering Taipei106 Taiwan

Vehicle to Everything (V2X) is a core 5G technology. V2X and its enabler, Device-to-Device (D2D), are essential for the Internet of Things (IoT) and the Internet of Vehicles (IoV). V2X enables vehicles to communicate with other vehicles (V2V), networks (V2N), and infrastructure (V2I). While V2X enables ubiquitous vehicular connectivity, the impact of bursty data on the network's overall Quality of Service (QoS), such as when a vehicle accident occurs, is often ignored. In this work, we study both 4G and 5G V2X utilizing Evolved Universal Terrestrial Radio Access New Radio (E-UTRA-NR) and propose the use of socially aware 5G NR dual connectivity (en-DC) for traffic differentiation. We also propose localized QoS, wherein high-priority QoS flows traverse 5G road side units (RSUs) and normal-priority QoS flows traverse 4G base station (BS). We formulate a max-min fair QoS-aware nonorthogonal multiple access (NOMA) resource allocation scheme, QoS reclassify. QoS reclassify enables localized QoS and traffic steering to mitigate bursty network traffic's impact on the network's overall QoS. We then solve QoS reclassify via integer linear programming (ILP) and derive its approximation. We demonstrate that both optimal and approximation QoS reclassify resource allocation schemes in our socially aware QoS management methodology outperform socially unaware legacy 4G V2X algorithms (no localized QoS support, no traffic steering) and socially aware 5G V2X (no localized QoS support, yet utilizes traffic steering). Our proposed QoS reclassify scheme's QoS flow end-to-end latency requires only approx 15% of the time legacy 4G V2X requires. © 2014 IEEE.

关键词： Quality of service

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ACSarF:a DRL-based adaptive consortium blockchain sharding framework for supply chain finance

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Digital Communications and Networks 2025年第1期11卷 26-34页

作者： Shijing Hu Junxiong Lin Xin Du Wenbin Huang Zhihui Lu Qiang Duan Jie Wu School of Computer Science Fudan UniversityShanghai 200438China Shanghai Blockchain Engineering Research Center Fudan UniversityShanghai 200438China Information Sciences and Technology Department Pennsylvania State UniversityAbingtonPA 19001USA

Blockchain technologies have been used to facilitate Web 3.0 and FinTech ***,conventional blockchain technologies suffer from long transaction delays and low transaction success rates in some Web 3.0 and FinTech applications such as Supply Chain Finance(SCF).Blockchain sharding has been proposed to improve blockchain ***,the existing sharding methods either use a static sharding strategy,which lacks the adaptability for the dynamic SCF environment,or are designed for public chains,which are not applicable to consortium blockchain-based *** address these issues,we propose an adaptive consortium blockchain sharding framework named ACSarF,which is based on the deep reinforcement learning *** proposed framework can improve consortium blockchain sharding to effectively reduce transaction delay and adaptively adjust the sharding and blockout strategies to increase the transaction success rate in a dynamic SCF ***,we propose to use a consistent hash algorithm in the ACSarF framework to ensure transaction load balancing in the adaptive sharding system to further improve the performance of blockchain sharding in dynamic SCF *** evaluate the proposed framework,we conducted extensive experiments in a typical SCF *** obtained experimental results show that the ACSarF framework achieves a more than 60%improvement in user experience compared to other state-of-the-art blockchain systems.

关键词： Web 3.0 Consortium blockchain Blockchain sharding Supply chain finance FinTech regulation

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