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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Shankar, Amar Acharjya, Kalyan Saraswat, Nidhi School of Engineering and Technology Department of Food Technology Karnataka Bangalore India Maharishi University of Information Technology Uttar Pradesh Maharishi School of Engineering and Technology India Sanskriti University Department of Computer Science Engineering Uttar Pradesh Mathura India

ISBN: (纸本)9798350370249

This technical summary makes a specialty of the effects of channel contention in adaptive communication systems, and how this phenomenon influences communication performance. specifically, this summary examines how distinct channel rivalry techniques, including service experience more than one get admission to (CSMA), can be used to lessen interference and postpone in a wireless communication community. It's also explored how exclusive adaptive communique systems attempt to reduce the effects of channel contention. moreover, this summary examines how studies has been carried out on the way to examine the performance of various channel rivalry schemes, in addition to how the studies effects may be implemented to enhance conversation performance in adaptive communication systems. In the end, this abstract discusses the capability advantages and barriers related to exceptional types of channel rivalry and their effect on communique overall performance. © 2024 IEEE.

关键词： Communication Conversation Exclusive. Discusses Performance systems Techniques

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A comprehensive survey on 6G-security: physical connection and service layers

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Discover Internet of Things 2025年第1期5卷 1-30页

作者： Saeed, Mamoon M. Saeed, Rashid A. Hasan, Mohammad Kamrul Ali, Elmustafa Sayed Mazha, Tehseen Shahzad, Tariq Khan, Sunawar Hamam, Habib Sana00967 Yemen Department of Computer Engineering College of Computers and Information Technology Taif University P.O. Box 11099 Taif21944 Saudi Arabia Department of Electronics Engineering College of Electronics Engineering Sudan University of Science and Technology Omdurman Sudan Bangi43600 Malaysia Department of Electrical and Electronic Engineering Faculty of Engineering Red Sea University Port Sudan33312 Sudan School of Computer Science National College of Business Administration and Economics Lahore54000 Pakistan Faculty of Engineering Uni de Moncton MonctonNBE1A3E9 Canada Department of Computer Engineering COMSATS University Islamabad Sahiwal Campus Sahiwal57000 Pakistan Department of Computer Science and Software Engineering International Islamic University Islamabad44000 Pakistan School of Electrical Engineering University of Johannesburg Johannesburg2006 South Africa BP 1989 Av. Grandes Ecoles Libreville Gabon Bridges for Academic Excellence-Spectrum Tunis Tunisia Department of Computer Science School Education Department Government of Punjab Layyah31200 Pakistan Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura140401 India

After adopting 5G technology, businesses and academia have started working on sixth-generation wireless networking (6G) technologies. Mobile communications options are expected to expand in areas where previous generations could not do so. 6G networks are anticipated to be constructed using various diverse technologies. These encompass diverse cutting-edge advancements, such as distributed ledger systems like blockchain, visible light communications (VLC), post-quantum cryptography, edge computing, molecular communication, THz, and other advances. These advances necessitate a reassessment of previous security strategies from a security perspective. In the future, networks must adhere to stricter criteria for authentication, encryption, access control, connectivity, and detection of harmful activities. Ensuring privacy and dependability necessitates the implementation of supplementary security protocols. The essay explores the primary concerns and challenges related to the security of the 6G network. This paper describes the improvements in security in communications from 1G through 6G. This paper divides security in the sixth generation into three layers: physical, connection, and service. Each layer-by-layer discusses the standard technologies and security issues for each technology proposed in each sixth-generation security layer. All proposed solutions for each of the three layers are discussed in Sixth Generation Security. It also reviews all proposed solutions for each layer, indicating the proposed solution and its limitations. © The Author(s) 2025.

关键词： 5G mobile communication systems

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Zhang-Taylor Type Newton Iterations (NI) Handling Nonlinear Equation, Nonlinear Equation System, and Matrix Inverse, in Addition to Euler-Type NI

Zhang-Taylor Type Newton Iterations (NI) Handling Nonlinear ...

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2023 China Automation Congress, CAC 2023

作者： Wang, Guanquan Zhang, Yunong Qiu, Binbin School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China School of Intelligent Systems Engineering Sun Yat-sen University Shenzhen518107 China

ISBN: (纸本)9798350303759

Nonlinear equation, nonlinear equation system (NES), and matrix inverse are critical topics in contemporary research. In this paper, we utilize Zhang dynamics (ZD, or termed, Zhang neural dynamics, Zhang neural network) and Newton iterations (NI) for handling the nonlinear equation, NES, and matrix inverse. For more efficient computation and easier implementation in digital computer, we propose a novel Zhang-Taylor type (ZT-type) NI by using the ZT-type 1-step-ahead numerical differentiation rule to discretize the continuous ZD (CZD) model. Moreover, numerical experimental results show the feasibility and effectiveness of the ZT-type NI for handling nonlinear equation, NES, and matrix inverse. Besides, comparative experimental results show that the ZT-type NI is comparable with the Euler-type NI. © 2023 IEEE.

关键词： Nonlinear equations

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Randomized-enhanced Magnitude Channel Pruning for Radar Jamming Detection 7

Randomized-enhanced Magnitude Channel Pruning for Radar Jamm...

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7th IEEE International Conference on Electronic information and Communication Technology, ICEICT 2024

作者： Ni, Xun Wu, Hao Zhu, Xuan Shi, Zhaopeng Huazhong University of Science and Technology School of Computer Science and Technology Wuhan China Naval University of Engineering National Key Laboratory of Electromagnetic Energy Wuhan China Wuhan University School of Electrical Engineering and Automation Wuhan China Huazhong University of Science and Technology School of Electronic Information and Communications Wuhan China

ISBN: (纸本)9798350384437

With the rapid development of radar jamming technology, traditional detection techniques face significant limitations in complex electromagnetic environments. Recent interference detection methods based on deep learning struggle with processing speed and real-time performance. To address this, we propose a new pruning technique called randomized-enhanced magnitude channel pruning (RMP) to achieve faster detection speeds and more lightweight models. This study evaluates the performance of RMP compared to random channel pruning and magnitude channel pruning in radar jamming detection. To verify RMP's generalization, we also tested it on the VOC2007 [16] object detection task. Results show that RMP consistently reduces mAP@50 loss more effectively than other methods. In one iteration pruning, RMP reduced mAP@50 loss by an average of 0.8% and 1% in radar jamming detection, and by 0.4% and 3.1% in the VOC2007 dataset, compared to random and magnitude pruning, respectively. In multi-pass pruning, RMP reduced mAP@50 loss by an average of 0.3% in radar jamming detection, and by 0.7% and 1.6% in the VOC2007 dataset, compared to random and magnitude pruning, respectively. These findings confirm RMP's effectiveness in maintaining model accuracy and its practical value in fast-response scenarios like radar jamming detection. © 2024 IEEE.

关键词： Jamming

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The Effect of Buyer Churn Factors on Buyer’s Loyalty Through Telecommunication Service Providers in Egypt 3rd

The Effect of Buyer Churn Factors on Buyer’s Loyalty Throug...

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3rd World Conference on Internet of Things: Applications and Future, ITAF 2023

作者： Mohamed, Mohamed Hegazy Magdi, Dalia Ahmed Business Information Systems Department Helwan University Cairo Egypt Faculty of Computers and Information Sadat Academy for Management Sciences School of Computer Science Canadian International College Cairo Egypt

ISBN: (纸本)9789819947638

This study looked into how loyal Egyptian telecoms customers are affected by factors that contribute to customer churn. Descriptive language is used to describe this. The emails used were selected at random from 1500 different telecom service users who had previously used the services. The surveys were disseminated and self-administered for data collection, with a response rate of 25.6%. The responses were subjected to linear regression analyses. The most recent findings suggest that in order to gain a foothold in Egypt’s telecom sector, service providers ought to focus on improving the management of customer connections. The discoveries uncovered a genuinely critical connection between client stir counteraction components and client devotion to support degrees of consistency. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Sales

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Robust Vehicle Detection Based on Improved You Look Only Once

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computers, Materials & Continua 2023年第2期74卷 3561-3577页

作者： Sunil Kumar Manisha Jailia Sudeep Varshney Nitish Pathak Shabana Urooj Nouf Abd Elmunim Department of Computer Science Banasthali VidyapithRajasthan304022India Department of Computer Science&Engineering School of Engineering&TechnologySharda UniversityGreater Noida201310India Department of Information Technology Bhagwan Parshuram Institute of Technology(BPIT)GGSIPUNew Delhi110078India Department of Electrical Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityP.O.BOX 84428Riyadh11671Saudi Arabia

Vehicle detection is still challenging for intelligent transportation systems(ITS)to achieve satisfactory *** existing methods based on one stage and two-stage have intrinsic weakness in obtaining high vehicle detection *** to advancements in detection technology,deep learning-based methods for vehicle detection have become more popular because of their higher detection accuracy and speed than the existing *** paper presents a robust vehicle detection technique based on Improved You Look Only Once(RVD-YOLOv5)to enhance vehicle detection *** proposed method works in three phases;in the first phase,the K-means algorithm performs data clustering on datasets to generate the classes of the ***,in the second phase,the YOLOv5 is applied to create the bounding box,and the Non-Maximum Suppression(NMS)technique is used to eliminate the overlapping of the bounding boxes of the ***,the loss function CIoU is employed to obtain the accurate regression bounding box of the vehicle in the third *** simulation results show that the proposed method achieves better results when compared with other state-of-art techniques,namely LightweightDilated Convolutional Neural Network(LD-CNN),Single Shot Detector(SSD),YOLOv3 and YOLOv4 on the performance metric like precision,recall,mAP and *** simulation and analysis are carried out on PASCAL VOC 2007,2012 and MS COCO 2017 datasets to obtain better performance for vehicle ***,the RVD-YOLOv5 obtains the results with an mAP of 98.6%and Precision,Recall,and F1-Score are 98%,96.2%and 97.09%,respectively.

关键词： Image-processing K-means clustering CNN YOLOv5 loss function deep-learning

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From Deep Learning to Interpretable and Explainable Deep Learning in Medical Image Computing: Balancing Innovation with Ethics and Responsibilities 28th

From Deep Learning to Interpretable and Explainable Deep Lea...

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28th International Conference on Knowledge Based and Intelligent information and engineering systems, KES 2024

作者： Chehri, Abdellah Ahmed, Imran Jeon, Gwanggil School of Computing and Information Science Anglia Ruskin University Cambridge United Kingdom Canada Department of Embedded Systems Engineering Incheon National University Incheon Korea Republic of

The utilization of artificial intelligence (AI) and other cutting-edge techniques in the field of medical image analysis has exhibited significant potential. Nevertheless, a significant obstacle that impedes the extensive implementation of these models in the healthcare sector is their restricted interpretability. The concept of explainability is a subject of extensive discussion and debate within the context of utilizing artificial intelligence in the healthcare domain. Notwithstanding the empirical evidence demonstrating the superior performance of AI-driven systems compared to humans in certain analytical tasks, particularly in the field of medical image computing, these systems still encounter challenges due to their limited explainability. The present study provides a comprehensive assessment of the significance of explainability in the field of medical artificial intelligence and performs an ethical analysis of the influence of explainability on the incorporation of AI-driven tools in data engineering in medicine and health care. The paper examines various subjects including data security, confidentiality, privacy, fairness, and discrimination, among others. © 2024 The Authors.

关键词： Data Analytics Deep Learning Ethics Explainable AI Medical Imaging Responsibilities

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Gradual Domain Adaptation via Manifold-Constrained Distributionally Robust Optimization 38

Gradual Domain Adaptation via Manifold-Constrained Distribut...

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38th Conference on Neural information Processing systems, NeurIPS 2024

作者： Saberi, Amirhossein Najafi, Amir Behjati, Amin Emrani, Ala Zolfi, Yasaman Shadrooy, Mahdi Motahari, Abolfazl Khalaj, Babak H. Department of Electrical Engineering Sharif University of Technology Tehran Iran Department of Computer Engineering Sharif University of Technology Tehran Iran Sharif Center for Information Systems and Data Science Sharif University of Technology Tehran Iran

The aim of this paper is to address the challenge of gradual domain adaptation within a class of manifold-constrained data distributions. In particular, we consider a sequence of T ≥ 2 data distributions P1, ..., PT undergoing a gradual shift, where each pair of consecutive measures Pi, Pi+1 are close to each other in Wasserstein distance. We have a supervised dataset of size n sampled from P0, while for the subsequent distributions in the sequence, only unlabeled i.i.d. samples are available. Moreover, we assume that all distributions exhibit a known favorable attribute, such as (but not limited to) having intra-class soft/hard margins. In this context, we propose a methodology rooted in Distributionally Robust Optimization (DRO) with an adaptive Wasserstein radius. We theoretically show that this method guarantees the classification error across all Pis can be suitably bounded. Our bounds rely on a newly introduced compatibility measure, which fully characterizes the error propagation dynamics along the sequence. Specifically, for inadequately constrained distributions, the error can exponentially escalate as we progress through the gradual shifts. Conversely, for appropriately constrained distributions, the error can be demonstrated to be linear or even entirely eradicated. We have substantiated our theoretical findings through several experimental results. © 2024 Neural information processing systems foundation. All rights reserved.

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Understanding and improving fairness in cognitive diagnosis

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science China(information sciences) 2024年第5期67卷 99-114页

作者： Zheng ZHANG Le WU Qi LIU Jiayu LIU Zhenya HUANG Yu YIN Yan ZHUANG Weibo GAO Enhong CHEN Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and Technology& School of Data Science University of Science and Technology of China State Key Laboratory of Cognitive Intelligence School of Computer Science and Information Engineering Hefei University of Technology

Intelligent education is a significant application of artificial intelligence. One of the key research topics in intelligence education is cognitive diagnosis, which aims to gauge the level of proficiency among students on specific knowledge concepts(e.g., Geometry). To the best of our knowledge, most of the existing cognitive models primarily focus on improving diagnostic accuracy while rarely considering fairness issues; for instance, the diagnosis of students may be affected by various sensitive attributes(e.g., region). In this paper,we aim to explore fairness in cognitive diagnosis and answer two questions:(1) Are the results of existing cognitive diagnosis models affected by sensitive attributes?(2) If yes, how can we mitigate the impact of sensitive attributes to ensure fair diagnosis results? To this end, we first empirically reveal that several wellknown cognitive diagnosis methods usually lead to unfair performances, and the trend of unfairness varies among different cognitive diagnosis models. Then, we make a theoretical analysis to explain the reasons behind this phenomenon. To resolve the unfairness problem in existing cognitive diagnosis models, we propose a general fairness-aware cognitive diagnosis framework, FairCD. Our fundamental principle involves eliminating the effect of sensitive attributes on student proficiency. To achieve this, we divide student proficiency in existing cognitive diagnosis models into two components: bias proficiency and fair *** design two orthogonal tasks for each of them to ensure that fairness in proficiency remains independent of sensitive attributes and take it as the final diagnosed result. Extensive experiments on the Program for International Student Assessment(PISA) dataset clearly show the effectiveness of our framework.

关键词： fairness intelligent education cognitive diagnosis psychometrics adversarial learning

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Future Directions in Recommender System: Opportunities for Improving Recommender systems using Machine Learning and Deep Learning Techniques 2

Future Directions in Recommender System: Opportunities for I...

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2nd IEEE Global Conference on Communications and information Technologies, GCCIT 2024

作者： Ramachandra, H.V. Shilpa, Biradar Dr Ambedkar Institute of Technology Affiliated to Visvesvaraya Technological University Department of Computer Science and Engineering Karnataka Belagavi590 018 India Reva University School of Computer Science and Engineering Karnataka Bengaluru560064 India Dr. Ambedkar Institute of Technology Department of Information Science and Engineering Karnataka Bengaluru560056 India

ISBN: (纸本)9798350388916

Recommender systems (RS) are algorithms which provides the users with customized suggestions for the things that are most relevant to them. The vast expansion of internet content accessibility has left users with an excessive array of options. It is therefore crucial for web platforms to offer recommendations of items to each user, in order to increase user satisfaction and engagement. This paper offers a thorough overview of the main difficulties and issues encountered in the development of recommender systems, as well as a synopsis of recent research findings and solutions. Paper began with a basic overview of recommender systems, briefly outlining the many types of RSs and covered the various research projects completed to date by numerous researchers, along with their conclusions and summaries, in the related work chapter. In the next section, we conducted an exploratory analysis of the many issues and potential fixes pertaining to recommender systems. Finally, we have summed up the various metrics needed to assess recommender system quality. © 2024 IEEE.

关键词： Collaborative learning

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