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Improved Twin Support Vector Machine Algorithm and Applications in Classification Problems

China Communications 2024年第5期21卷 261-279页

作者： Sun Yi Wang Zhouyang Beijing University of Posts and Telecommunications School of Computer Science(National Pilot Software Engineering School) Beijing University of Posts and Telecommunications International School

The distribution of data has a significant impact on the results of *** the distribution of one class is insignificant compared to the distribution of another class,data imbalance *** will result in rising outlier values and ***,the speed and performance of classification could be greatly *** the above problems,this paper starts with the motivation and mathematical representing of classification,puts forward a new classification method based on the relationship between different classification *** with the vector characteristics of the actual problem and the choice of matrix characteristics,we firstly analyze the orderly regression to introduce slack variables to solve the constraint problem of the lone *** we introduce the fuzzy factors to solve the problem of the gap between the isolated points on the basis of the support vector *** introduce the cost control to solve the problem of sample ***,based on the bi-boundary support vector machine,a twostep weight setting twin classifier is *** can help to identify multitasks with feature-selected patterns without the need for additional optimizers,which solves the problem of large-scale classification that can’t deal effectively with the very low category distribution gap.

关键词： fuzzy ordered regression(OR) relaxing variables twin support vector machine

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Redundant Transmission Control Algorithm for Information-Centric Vehicular IoT Networks

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computers, Materials & Continua 2023年第8期76卷 2217-2234页

作者： Abdur Rashid Sangi Satish Anamalamudi Mohammed SAlkatheiri Murali Krishna Enduri Anil Carie Mohammed AAlqarni Department of Computer Science College of Science and TechnologyWenzhou-Kean UniversityWenzhou325060China Department of Computer Science Engineering SRM University APAmaravati522502India Department of Cybersecurity College of Computer Science&EngineeringUniversity of JeddahJeddahSaudi Arabia Department of Software Engineering College of Computer Science&EngineeringUniversity of JeddahJeddahSaudi Arabia

Vehicular Adhoc Networks(VANETs)enable vehicles to act as mobile nodes that can fetch,share,and disseminate information about vehicle safety,emergency events,warning messages,and passenger ***,the continuous dissemination of information fromvehicles and their one-hop neighbor nodes,Road Side Units(RSUs),and VANET infrastructures can lead to performance degradation of VANETs in the existing hostcentric IP-based ***,Information Centric Networks(ICN)are being explored as an alternative architecture for vehicular communication to achieve robust content distribution in highly mobile,dynamic,and errorprone *** ICN-based Vehicular-IoT networks,consumer mobility is implicitly supported,but producer mobility may result in redundant data transmission and caching inefficiency at intermediate vehicular *** paper proposes an efficient redundant transmission control algorithm based on network coding to reduce data redundancy and accelerate the efficiency of information *** proposed protocol,called Network Cording Multiple Solutions Scheduling(NCMSS),is receiver-driven collaborative scheduling between requesters and information sources that uses a global parameter expectation deadline to effectively manage the transmission of encoded data packets and control the selection of information *** results for the proposed NCMSS protocol is demonstrated to analyze the performance of ICN-vehicular-IoT networks in terms of caching,data retrieval delay,and end-to-end application *** end-to-end throughput in proposed NCMSS is 22%higher(for 1024 byte data)than existing solutions whereas delay in NCMSS is reduced by 5%in comparison with existing solutions.

关键词： Caching data dissemination redundancy control ICN-vehicular IoT networks

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Layered Temporal Spatial Graph Attention Reinforcement Learning for Multiplex Networked Industrial Chains Energy Management

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Tsinghua science and Technology 2025年第2期30卷 528-542页

作者： Yuanshuang Jiang Kai Di Xingyu Wu Zhongjian Hu Fulin Chen Pan Li Yichuan Jiang School of Computer Science and Engineering Southeast UniversityNanjing 211189China School of Software Southeast UniversityNanjing 211189China School of Cyber Science and Engineering Southeast UniversityNanjing 211189

Demand response has recently become an essential means for businesses to reduce production costs in industrial ***,the current industrial chain structure has also become increasingly complex,forming new characteristics of multiplex networked industrial *** in real-time electricity prices in demand response propagate through the coupling and cascading relationships within and among these network layers,resulting in negative impacts on the overall energy management ***,existing demand response methods based on reinforcement learning typically focus only on individual agents without considering the influence of dynamic factors on intra and inter-network *** paper proposes a Layered Temporal Spatial Graph Attention(LTSGA)reinforcement learning algorithm suitable for demand response in multiplex networked industrial chains to address this *** algorithm first uses Long Short-Term Memory(LSTM)to learn the dynamic temporal characteristics of electricity prices for ***,LTSGA incorporates a layered spatial graph attention model to evaluate the impact of dynamic factors on the complex multiplex networked industrial chain *** demonstrate that the proposed LTSGA approach effectively characterizes the influence of dynamic factors on intra-and inter-network relationships within the multiplex industrial chain,enhancing convergence speed and algorithm performance compared with existing state-of-the-art algorithms.

关键词： demand response multiplex networked industrial chains multiagent reinforcement learning

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Deploying Hybrid Ensemble Machine Learning Techniques for Effective Cross-Site Scripting(XSS)Attack Detection

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computers, Materials & Continua 2024年第10期81卷 707-748页

作者： Noor Ullah Bacha Songfeng Lu Attiq Ur Rehman Muhammad Idrees Yazeed Yasin Ghadi Tahani Jaser Alahmadi School of Cyber Science and Engineering Huazhong University of Science and TechnologyWuhan430073China Department of Computer Science and Engineering University of Engineering and TechnologyLahore54000Pakistan Department of Computer Science and Software Engineering Al Ain UniversityAl Ain12555Abu Dhabi Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia

Cross-Site Scripting(XSS)remains a significant threat to web application security,exploiting vulnerabilities to hijack user sessions and steal sensitive *** detection methods often fail to keep pace with the evolving sophistication of cyber *** paper introduces a novel hybrid ensemble learning framework that leverages a combination of advanced machine learning algorithms—Logistic Regression(LR),Support Vector Machines(SVM),eXtreme Gradient Boosting(XGBoost),Categorical Boosting(CatBoost),and Deep Neural Networks(DNN).Utilizing the XSS-Attacks-2021 dataset,which comprises 460 instances across various real-world trafficrelated scenarios,this framework significantly enhances XSS attack *** approach,which includes rigorous feature engineering and model tuning,not only optimizes accuracy but also effectively minimizes false positives(FP)(0.13%)and false negatives(FN)(0.19%).This comprehensive methodology has been rigorously validated,achieving an unprecedented accuracy of 99.87%.The proposed system is scalable and efficient,capable of adapting to the increasing number of web applications and user demands without a decline in *** demonstrates exceptional real-time capabilities,with the ability to detect XSS attacks dynamically,maintaining high accuracy and low latency even under significant ***,despite the computational complexity introduced by the hybrid ensemble approach,strategic use of parallel processing and algorithm tuning ensures that the system remains scalable and performs robustly in real-time *** for easy integration with existing web security systems,our framework supports adaptable Application Programming Interfaces(APIs)and a modular design,facilitating seamless augmentation of current *** innovation represents a significant advancement in cybersecurity,offering a scalable and effective solution for securing modern web applications against evolving threats.

关键词： Cross-site scripting machine learning XSS detection stacking ensemble learning hybrid learning

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Is GPT-4 Conscious?

Journal of Artificial Intelligence and Consciousness

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Journal of Artificial Intelligence and Consciousness 2024年第1期11卷 1-16页

作者： Tait, Izak Bensemann, Joshua Wang, Ziqi Computer Science and Software Engineering Department Auckland University of Technology NAOInstitute The University of Auckland

GPT-4 (Generative Pre-Trained Transformer 4) is often heralded as a leading commercial AI offering, sparking debates over its potential as a steppingstone toward Artificial General Intelligence. But does it possess consciousness? This paper investigates this key question using the nine qualitative measurements of the Building Blocks theory. GPT-4's design, architecture, and implementation are compared to each of the building blocks of consciousness to determine whether it has achieved the requisite milestones to be classified as conscious or, if not, how close to consciousness GPT-4 is. Our assessment is that, while GPT-4 in its native configuration is not currently conscious, current technological research and development is sufficient to modify GPT-4 to have all the building blocks of consciousness. Consequently, we argue that the emergence of a conscious AI model is plausible in the near term. The paper concludes with a comprehensive discussion of the ethical implications and societal ramifications of engineering conscious AI entities. © 2024 World Scientific Publishing Company.

关键词： Artificial Intelligence Consciousness Ethics GPT-4 Philosophy of Mind Sentience

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Optimized COVID-19 detection using sparse deep learning models from multimodal imaging data

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Multimedia Tools and Applications 2025年第13期84卷 11451-11474页

作者： Moradi, MohammadMahdi Hassanzadeh, Alireza Haghanifar, Arman Ko, Seok Bum Faculty of Computer Science and Software Engineering Concordia University MontrealQC Canada Faculty of Electrical Engineering Shahid Beheshti University Tehran Tehran Iran Department of Electrical & Computer Engineering University of Saskatchewan SaskatoonSK Canada

This paper explores the global spread of the COVID-19 virus since 2019, impacting 219 countries worldwide. Despite the absence of a definitive cure, the utilization of artificial intelligence (AI) methods for disease diagnosis has demonstrated commendable effectiveness in promptly diagnosing patients and curbing infection transmission. The study introduces a deep learning-based model tailored for COVID-19 detection, leveraging three prevalent medical imaging modalities: computed tomography (CT), chest X-ray (CXR), and Ultrasound. Various deep Transfer Learning Convolutional Neural Network-based (CNN) models have undergone assessment for each imaging modality. For each imaging modality, this study has selected the two most accurate models based on evaluation metrics such as accuracy and loss. Additionally, efforts have been made to prune unnecessary weights from these models to obtain more efficient and sparse models. By fusing these pruned models, enhanced performance has been achieved. The models have undergone rigorous training and testing using publicly available real-world medical datasets, focusing on classifying these datasets into three distinct categories: Normal, COVID-19 Pneumonia, and non-COVID-19 Pneumonia. The primary objective is to develop an optimized and swift model through strategies like Transfer Learning, Ensemble Learning, and reducing network complexity, making it easier for storage and transfer. The results of the trained network on test data exhibit promising outcomes. The accuracy of these models on the CT scan, X-ray, and ultrasound datasets stands at 99.4%, 98.9%, and 99.3%, respectively. Moreover, these models’ sizes have been substantially reduced and optimized by 51.93%, 38.00%, and 69.07%, respectively. This study proposes a computer-aided-coronavirus-detection system based on three standard medical imaging techniques. The intention is to assist radiologists in accurately and swiftly diagnosing the disease, especially during the screen

关键词： COVID-19

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FIFAWC:a dataset with detailed annotation and rich semantics for group activity recognition

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Frontiers of computer science 2024年第6期18卷 271-272页

作者： Duoxuan PEI Di HUANG Yunhong WANG State Key Laboratory of Software Development Environment School of Computer Science and EngineeringBeihang UniversityBeijing 100191China Intelligent Recognition and Image Processing Lab. School of Computer Science and EngineeringBeihang UniversityBeijing 100191China

1 *** Activity Recognition(GAR),which aims to identify activities performed collectively in videos,has gained significant attention *** conventional action recognition centered on single individuals,GAR explores the c... 详细信息

关键词： has collective gained

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Review of cervical cell segmentation

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Multimedia Tools and Applications 2024年 1-40页

作者： Huang, Qian Zhang, Wei Chen, Yulin Chen, Junzhou Yang, Zheng College of Computer Science and Software Engineering Hohai University Nanjing China Nanjing Huiying Electronic Technology Corporation Nanjing China

Cervical cell segmentation is a significant task in medical image analysis and can be used for screening various cervical diseases. In recent years, substantial progress has been made in cervical cell segmentation techniques, leading to notable improvements in the performance of cervical cancer auxiliary diagnostic systems. This review summarizes and analyzes the recent research on cervical cell segmentation. The main contents include an introduction to cervical cell segmentation datasets, commonly used evaluation metrics, and various segmentation methods. Currently, mainstream segmentation methods can be classified into two categories: traditional and deep learning-based. Building upon this foundation, we unfold according to the context of segmentation objectives, evaluating the performance of each method in achieving specific segmentation objectives and exploring the relationships among different methods. Through this review, other researchers can clearly understand the development of cervical cell segmentation technology and future trends, and explore new methods and technologies based on integrating and sorting out existing technologies, so as to help cervical cancer auxiliary diagnosis systems achieve more accurate cell image segmentation. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Diagnosis

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Reliability of deep learning models for anatomical landmark detection: The role of inter-rater variability

Reliability of deep learning models for anatomical landmark ...

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Medical Imaging 2025: Image Processing

作者： Salari, Soorena Rivaz, Hassan Xiao, Yiming Computer Science and Software Engineering Concordia University Montreal Canada Electrical and Computer Engineering Concordia University Montreal Canada

ISBN: (纸本)9781510685901

Automated detection of anatomical landmarks plays a crucial role in many diagnostic and surgical applications. Progresses in deep learning (DL) methods have resulted in significant performance enhancement in tasks related to anatomical landmark detection. While current research focuses on accurately localizing these landmarks in medical scans, the importance of inter-rater annotation variability in building DL models is often overlooked. Understanding how inter-rater variability impacts the performance and reliability of the resulting DL algorithms, which are crucial for clinical deployment, can inform the improvement of training data construction and boost DL models' outcomes. In this paper, we conducted a thorough study of different annotation-fusion strategies to preserve inter-rater variability in DL models for anatomical landmark detection, aiming to boost the performance and reliability of the resulting algorithms. Additionally, we explored the characteristics and reliability of four metrics, including a novel Weighted Coordinate Variance metric to quantify landmark detection uncertainty/inter-rater variability. Our research highlights the crucial connection between inter-rater variability, DL-models performances, and uncertainty, revealing how different approaches for multi-rater landmark annotation fusion can influence these factors. © 2025 SPIE.

关键词： Deep learning

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Optimized Autonomous Computing with Trusted Resilient Data Management for IoT Emerging Networks

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IEEE Sensors Journal 2025年第11期25卷 20778-20785页

作者： Shafique, Ayesha Cheng, Benmao Zhao, Bo Alwakid, Ghadah Naif Haq, Muhammad Inaam Ul Laboratory of Intelligent IOT Technology and Applications in Universities China Jouf University College of Computer and Information Sciences Department of Computer Science Al Jowf Sakaka72341 Saudi Arabia Quanzhou University of Information Engineering School of Computer Science and Software Engineering Quanzhou362000 China

The innovative city network integrates numerous computational and physical components to develop real-time systems. These systems can capture sensor data and distribute it to end stations. Most solutions have been presented based on the centralized computing paradigm, which effectively and systematically increases data flow;however, distributed wireless technologies and heterogeneous network services continue to raise significant research problems. These challenges lower the optimization criteria and reflect communication structure around the network edges. In this research, we proposed a sustainable development for smart networks using efficient big data management with collaborative decisions for network devices. It differs from most existing work in the mentioned aspect. It applies computational lightweight intelligence for forwarding collected data using mobile collectors and reduces the congestion flow between devices on the limited bandwidth of wireless links. Moreover, the energy load is efficiently managed with edge-driven methods, and the incorporation of trusted devices ensures the integrity of the smart network. It also tackles potential communication threats smartly. Based on the experiments conducted in Network Simulator (NS-3), the proposed model enhances the efficacy of smart networks for performance metrics with reliability and effective management of resources in Internet of Things (IoT) network. © 2025 IEEE.

关键词： Mathematical models Computational modeling Intelligent sensors Data aggregation Internet of Things Security Routing Load modeling Silicon Data models

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