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2023 IEEE 11th International Conference on Information, Communication and Networks, ICICN 2023

作者： Chen, Jie Yang, Shuai Chan, Tse-Tin Pan, Haoyuan Shenzhen University College of Computer Science and Software Engineering Shenzhen China The Education University of Hong Kong Department of Mathematics and Information Technology Hong Kong Hong Kong

ISBN: (纸本)9798350314014

This paper investigates semantic communication to enhance the information freshness of status update systems, using time division multiple access (TDMA) as an example. Age of information (AoI) is used to characterize information freshness, defined as the time elapsed since the generation of the last successfully received status update. Prior works on analyzing and optimizing AoI have focused on the conventional bit-oriented communication paradigm, aiming to efficiently and reliably deliver each bit of the update message. When a large number of users take turns transmitting their update messages over a wireless channel to a common access point (AP), conventional bit-oriented TDMA systems may result in a high average AoI because users have to wait a long time for the next update opportunity. To address this problem, we propose a knowledge graph-aided semantic communication for TDMA systems, referred to as KGSC-TDMA, to achieve higher information freshness. With the aid of the shared knowledge graph, KGSC-TDMA transmits the meaning of update messages in the form of triplets instead of exact bits, thus reducing the amount of information transmitted and the time to receive update messages. Furthermore, an update in KGSC-TDMA is successful, even with bit errors, as long as the AP can recover the meaning of the message received. Experimental results indicate that KGSC-TDMA outperforms conventional bit-oriented TDMA in average AoI, thanks to shorter packet durations and robust semantic recovery. © 2023 IEEE.

关键词： Time division multiple access

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Advancements in 5G and 6G Technologies Shaping the Future of Global Connectivity and Communication 5

Advancements in 5G and 6G Technologies Shaping the Future of...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Pachar, Sunita Reddy, Satti Sudha Mohan Kompally, Gouthami Srinivasagopalan, Lakshmi Narasimhan Sangore, Rohidas Balu Katta, Sivakoteswararao GLA University IBM Department India SRKR Engineering College Department of ECE India Department of CSE-IOT India Software Development Enterprise Architecture PlanoTX United States Computer Science and Engineering MIT Art Design Technology University India KIETW Department of Mechanical Kakinada India

ISBN: (纸本)9798331530013

The evolution of wireless communication is poised for a major shift with the transition from 5G to 6G, a leap characterized by breakthroughs in speed, ultra-reliable low-latency communication, and AI-driven network optimization. This paper examines these advancements, focusing on sub-millisecond latency, terahertz spectrum utilization, and enhanced network capabilities tailored to industries such as smart infrastructure, healthcare, and autonomous vehicles. While 6G promises revolutionary improvements, it also presents new challenges, including increased computational complexity and energy demands, data privacy concerns, and efficient spectrum allocation. The proposed model aims to address these issues by integrating AI and machine learning for optimized resource management, resulting in a high-performance, cost-effective solution for real-time, data-intensive applications. To assess the model's effectiveness, comprehensive simulations and real-world trials will be conducted, measuring critical metrics such as network throughput, latency, reliability, and energy efficiency. This study offers an all-encompassing guide to understanding and harnessing the transformative potential of 6G technology, laying the groundwork for future wireless communication systems. © 2024 IEEE.

关键词： Terahertz spectroscopy

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CD-FL:Cataract Images Based Disease Detection Using Federated Learning

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computer Systems science & engineering 2023年第11期47卷 1733-1750页

作者： Arfat Ahmad Khan Shtwai Alsubai Chitapong Wechtaisong Ahmad Almadhor Natalia Kryvinska Abdullah Al Hejaili Uzma Ghulam Mohammad Department of Computer Science College of ComputingKhon Kaen UniversityKhon Kaen40002Thailand College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz UniversityAlkharjSaudi Arabia School of Telecommunication Engineering Suranaree University of TechnologyNakhon Ratchasima30000Thailand Department of Computer Engineering and Networks College of Computer and Information SciencesJouf UniversitySakaka72388Saudi Arabia Information Systems Department Faculty of ManagementComenius University in BratislavaOdbojárovBratislava440Slovakia Faculty of Computers&Information Technology Computer Science DepartmentUniversity of TabukTabuk71491Saudi Arabia Department of Computer Science and Software Engineering International Islamic UniversityIslamabad44000Pakistan

A cataract is one of the most significant eye problems worldwide that does not immediately impair vision and progressively worsens over *** cataract prediction based on various imaging technologies has been addressed recently,such as smartphone apps used for remote health monitoring and eye *** recent years,advances in diagnosis,prediction,and clinical decision support using Artificial Intelligence(AI)in medicine and ophthalmology have been *** to privacy concerns,a lack of data makes applying artificial intelligence models in the medical field *** address this issue,a federated learning framework named CDFL based on a VGG16 deep neural network model is proposed in this *** study collects data from the Ocular Disease Intelligent Recognition(ODIR)database containing 5,000 patient *** significant features are extracted and normalized using the min-max normalization *** the federated learning-based technique,the VGG16 model is trained on the dataset individually after receiving model updates from two *** transferring the attributes to the global model,the suggested method trains the local *** global model subsequently improves the technique after integrating the new *** client analyses the results in three rounds to decrease the over-fitting *** experimental result shows the effectiveness of the federated learning-based technique on a Deep Neural Network(DNN),reaching a 95.28%accuracy while also providing privacy to the patient’s *** experiment demonstrated that the suggested federated learning model outperforms other traditional methods,achieving client 1 accuracy of 95.0%and client 2 accuracy of 96.0%.

关键词： Privacy-preserving cataract disease federated learning fundus images healthcare smartphone applications machine learning

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Exploiting the sequential nature of genomic data for improved analysis and identification

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computers in Biology and Medicine 2024年 183卷 109307-109307页

作者： Nawaz, M. Saqib Nawaz, M. Zohaib Junyi, Zhang Fournier-Viger, Philippe Qu, Jun-Feng College of Computer Science and Software Engineering Shenzhen University China Faculty of Computing and Information Technology Department of Computer Science University of Sargodha Pakistan School of Computer Engineering Hubei University of Arts and Science Hubei Xiangyang China

Genomic data is growing exponentially, posing new challenges for sequence analysis and classification, particularly for managing and understanding harmful new viruses that may later cause pandemics. Recent genome sequence classification models yield promising performance. However, the majority of them do not consider the sequential arrangement of nucleotides and amino acids, a critical aspect for uncovering their inherent structure and function. To overcome this, we introduce GenoAnaCla, a novel approach for analyzing and classifying genome sequences, based on sequential pattern mining (SPM). The proposed approach first constructs and preprocesses datasets comprising RNA virus genome sequences in three formats: nucleotide, coding region, and protein. Then, to capture sequential features for the analysis and classification of viruses, GenoAnaCla extracts frequent sequential patterns and rules in three forms and in codons. Eight classifiers are utilized, and their effectiveness is assessed by employing a variety of evaluation metrics. A performance comparison demonstrates that the suggested approach surpasses the current state-of-the-art genome sequence classification and detection techniques with a 3.18% performance increase in accuracy on average. © 2024 Elsevier Ltd

关键词： Genome

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The Design of Continuous Surgical Robotic Arms with Variable Stiffness Material and Magnetic Guidance 5

The Design of Continuous Surgical Robotic Arms with Variable...

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5th International Conference on Signal Processing and Machine Learning, CONF SPML 2025

作者： Zhang, Yixin Department of Computer Science and Software Engineering Hebei University of Technology Hebei Langfang065000 China

ISBN: (纸本)9781837243235

In recent years, with the aging population and the increasing incidence of natural cavity diseases and cancer tumors, the demand for minimally invasive surgery has risen. This, in turn, requires robotic arms with greater flexibility and precision. However, traditional rigid surgical robotic arms make it difficult to meet these needs. This paper includes an illustration of the invention related to a continuous surgical robotic arm which is combined with magnetic guidance and a variable stiffness material. This paper aims to explore the advantages of continuum robots based on Phase Change Materials (PCMs) in surgical applications, especially through the stiffness adjustability of the material and the combination of magnetic materials to improve positioning accuracy. In this study, low-melting-point alloys were chosen as the variable stiffness material. This paper analyzed a flexible and precise surgical robotic arm by summarizing and combining previous experiments on the properties of low-melting alloys under different conditions and simulating magnetic navigation in complex environments. Through this design, the robots can enhance the safety, versatility and collaboration ability among the continuous robots, opening up new ways for endocavitary robotic surgery. © The Institution of engineering & Technology 2025.

关键词： Robotic arms

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Self-Supervised Learning Using Noisy-Latent Augmentation

Self-Supervised Learning Using Noisy-Latent Augmentation

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Khosrowshahli, Rasa Rahnamayan, Shahryar Bidgoli, Azam Asilian Makrehchi, Masoud Department of Electrical Computer and Software Engineering Ontario Tech University OshawaON Canada Engineering Department Brock University St. CatharinesON Canada Faculty of Science Wilfrid Laurier University WaterlooON Canada Lab Canada

ISBN: (纸本)9798350337020

Generally speaking, labeled data is difficult and expensive to provide for applications in machine learning and data mining. One of the earliest approaches to tackle this problem is semi-supervised self-training to take advantages of labeled and unlabeled data to create pseudo-labeled data. However, reaching a high level of confidence to predict the unseen data by a classifier with a limited number of labeled samples is a challenging task. Generating pseudo-labeled data can collaboratively improve the self-training to increase its confidence for further predictions. This paper proposes a collaborative framework between augmentation and self-training to accurately train a model with very limited labeled data. Our framework includes two components where the first component is an unsupervised technique to augment labeled data and feed to the second component which is self-training. The first component uses a custom variational autoencoder architecture (VAE) to generate new samples by adding randomly generated noise to encoded latent representation. As a result, the augmentation component can generate the unique and unexplored images with respect to limited input data distribution. We evaluated the proposed framework on handwritten MNIST image dataset. The conducted experiment shows that the generative component can be helpful in overcoming the problem of inaccurate self-training prediction when sufficient labeled data is not accessible. © 2023 IEEE.

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Metaverse with Mobile Twin Edge Computing: Empowering Optimal Efficiency and Accuracy

Metaverse with Mobile Twin Edge Computing: Empowering Optima...

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2024 International Conference on engineering and Computing, ICECT 2024

作者： Bibi, Asma Noor, Umara Rashid, Zahid International Islamic University Faculty of Computing and Information Technology Department of Software Engineering Islamabad Pakistan Kohsar University Department of Computer Science Murree Pakistan Technology Management Economics and Policy Program College of Engineering Seoul National University Seoul08826 Korea Republic of

ISBN: (纸本)9798350349719

The concept of Digital Twin has been widely used by researchers to represent physical entities in computer-generated reality in the metaverse. In this research, a novel concept of 'Mobile Twin' is coined. Mobile Twin serves as a replica of a mobile device in the virtual environment enabling communication. The purpose of introducing Mobile Twin in the metaverse is to bring mobility and benefits similar to tangible mobile devices while eliminating the need for individual energy sources, storage limitations, resource usage, and efficiency. The true benefits of a mobile twin can be achieved with an architecture where computing nodes are decentralized, data processing is near the network's edge with faster response times and reduced latency. For this purpose, the mobile twin-based applications of metaverse are built over edge computing-based network architecture. This idea introduced the aspect of 'Revolutionary Ultra-low latency (RULL)' in the metaverse. © 2024 IEEE.

关键词： Electric power utilization

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Optimizing Performance of Cloud Computing Management Algorithm for High-Traffic Networks 2

Optimizing Performance of Cloud Computing Management Algorit...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Gorantla, Venkata Ashok K Sriramulugari, Shiva Kumar Gorantla, Bhargavi Yuvaraj, N. Singh, Kiran Solution Architect United States Sr. Lead Software Engineer United States Sri Shakthi Institute of Engineering and Technology Department of Cse Coimbatore India G.L. Bajaj Institute of Management Department of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350371055

Cloud computing has revolutionized the way people use technology, enabling a variety of virtual services and applications in the corporate world. The improved cost-efficiency and flexibility of cloud computing have made it an attractive option for companies that require high-traffic networks. However, this has also increased the need for optimizing network performance in order to ensure reliable service and a pleasing experience for users. To this end, various algorithms have been proposed for cloud computing management. However, these algorithms often conflict with each other, leading to a decrease in performance. Thus, there is a need for a unified approach that can take into account different methods in a holistic manner. The optimization algorithms developed for cloud computing management are targeted towards improving resource utilization, failure handling, and load balancing. By properly allocating and balancing resources, the overhead associated with these tasks is greatly reduced. Furthermore, the intelligent use of techniques such as fault tolerance, capacity planning, and quality of service guarantees ensures that the users' experience within the cloud is of optimal quality. Parametric results from research on Cloud Computing management algorithm performance showed that, when correctly implemented, the algorithms can provide significant improvements to the management and performance of high-traffic networks. Specifically, performance improvements up to 40% were observed in resource allocation speed and energy consumption. © 2024 IEEE.

关键词： Load Balancing Network Traffic Resource Allocation Scalability Traffic Management

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Self-adaptive Bat Algorithm With Genetic Operations

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IEEE/CAA Journal of Automatica Sinica 2022年第7期9卷 1284-1294页

作者： Jing Bi Haitao Yuan Jiahui Zhai MengChu Zhou H.Vincent Poor School of Software Engineering Faculty of Information TechnologyBeijing University of TechnologyBeijing 100124China Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewark NJ 07102 USA School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100191China Department of Electrical Engineering Princeton UniversityPrinceton NJ 08544 USA IEEE

Swarm intelligence in a bat algorithm(BA)provides social *** operations for reproducing individuals in a genetic algorithm(GA)offer global search ability in solving complex optimization *** integration provides an opportunity for improved search ***,existing studies adopt only one genetic operation of GA,or design hybrid algorithms that divide the overall population into multiple subpopulations that evolve in parallel with limited interactions *** from them,this work proposes an improved self-adaptive bat algorithm with genetic operations(SBAGO)where GA and BA are combined in a highly integrated ***,SBAGO performs their genetic operations of GA on previous search information of BA solutions to produce new exemplars that are of high-diversity and *** by these exemplars,SBAGO improves both BA’s efficiency and global search *** evaluate this approach by using 29 widely-adopted problems from four test *** is also evaluated by a real-life optimization problem in mobile edge computing *** results show that SBAGO outperforms its widely-used and recently proposed peers in terms of effectiveness,search accuracy,local optima avoidance,and robustness.

关键词： Bat algorithm(BA) genetic algorithm(GA) hybrid algorithm learning mechanism meta-heuristic optimization algorithms

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Teachers Perspectives on Transition to Online Teaching Deaf and Hard-of-Hearing Students during the COVID-19 Pandemic: A Case Study 23

Teachers Perspectives on Transition to Online Teaching Deaf ...

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16th Innovations in software engineering Conference, ISEC 2023

作者： Aljedaani, Wajdi Aljedaani, Mona Mkaouer, Mohamed Wiem Ludi, Stephanie Department of Computer Science and Engineering University of North Texas United States King Abdulaziz University Saudi Arabia Department of Software Engineering Rochester Institute of Technology United States University of North Texas United States

ISBN: (纸本)9798400700644

Education has faced various challenges due to the COVID- 19 pandemic. These challenges were even more dramatic for deaf and hard-of-hearing students as they transitioned to the online setting. This paper aims to explore the teachers' perspectives on transitioning to educating computer science deaf students at a technical college in the Kingdom of Saudi Arabia (KSA). Therefore, the paper presents the results of 10 surveys and 5 interviews with faculty to identify the main obstacles that teachers have faced during the transition. This study outcomes reveal several challenges related to poor accessibility of learning tools, issues with the online availability of materials, no updates on the teaching pedagogy to accommodate the new settings and communication problems between teachers and students. Our study outlines important takeaways to provide deaf and hard-of-hearing students with a better learning experience. © 2023 ACM.

关键词： COVID-19

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