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Proceedings of the 7th International Conference on Communication and Information Processing

作者： Grace U. Nneji Jingye Cai Jianhua Deng Happy N. Monday Edidiong C. James Bona D. Lemessa Abel Z. Yutra Yobsan B. Leta Saifun Nahar School of Information and Software Engineering University of Electronic Science and Technology of China China School of Computer Science and Engineering University of Electronic Science and Technology of China China School of Computer and Information System University of Missouri St.Louis USA

ISBN: (纸本)9781450385190

Chest X-ray has become a useful method in the detection of coronavirus disease-19 (COVID-19). Due to the extreme global COVID-19 crisis, using the computerized diagnosis method for COVID-19 classification upon CXR images could significantly decrease clinician workload. We explicitly addressed the issue of low CXR image resolution by using Super-Resolution Convolutional Neural Network (SRCNN) to effectively reconstruct high-resolution (HR) CXR images from low-resolution (LR) CXR correspondents. Then, the HRCXR images are fed into the modified capsule network to retrieve distinct features for the classification of COVID-19. We demonstrate the proposed model on a public dataset and achieve ACC of 97.3%, SEN of 97.8%, SPE of 96.9%, and AUC of 98.0%. This new conceptual framework is proposed to play a vital task in the issue facing COVID-19 and related ailments.

关键词： capsule network COVID-19 super-resolution chest x-ray Deep learning Convolutional Neural Network (CNN)

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Study of the Software Development Life Cycle and the Function of Testing

Study of the Software Development Life Cycle and the Functio...

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Interdisciplinary Humanitarian Conference for Sustainability (IIHC), International

作者： Sunil Gupta N Gayathri Department of Computer & System Sciences School of Computer & System Sciences Jaipur National University Jaipur India Department of Computer Science and Engineering Galgotias University Greater Noida Uttar Pradesh India

A construction investigation and configuration process that is imposed on the development of a software item is known as the software development life cycle. Testing is now an important and crucial phase in the software development life cycle in every organisation. Fundamentally important to the software development cycle is testing. Testing should begin early in the software development process to prevent potential bugs, reduce costs, and aid in determining requirements. Software testing is done with the intention of strengthening software and preventing problems. The testing phase of the software development life cycle is critical and vital. However, the manner in which it is finished varies from one association to the next. Software testing has evolved into a component of development, and it is wiser to start testing in the early phases in order to avoid problems by fixing bugs at the end. Additionally, the purpose of testing in the software development life cycle is to improve steadfast quality, execution, and other crucial components that may be characterized as SRS. The SDLC consist an explanatory plan that described to create, schedule, and maintain specific software. The organizational framework that explains the tasks carried out at each level of the software development procedure.

关键词： Software testing Schedules Costs computer bugs Windings Software Encoding

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Multiple-replica integrity auditing schemes for cloud data storage

Multiple-replica integrity auditing schemes for cloud data s...

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作者： Lin, Yangfei Li, Jie Jia, Xiaohua Ren, Kui Graduate School of System and Information Engineering University of Tsukuba Ibaraki Japan Department of Computer Science and Engineering Shanghai Jiaotong University Shanghai China Department of Computer Science City University of Hong Kong Hong Kong Department of Computer Science and Engineering State University of New York at Buffalo New YorkNY United States

Cloud computing has been an essential technology for providing on-demand computing resources as a service on the Internet. Not only enterprises but also individuals can outsource their data to the cloud without worrying about purchase and maintenance cost. The cloud storage system, however, is not fully trustable. Cloud data integrity auditing is crucial for defending against the security threats of data in the untrusted multicloud environment. Storing multiple replicas is a commonly used strategy for the availability and reliability of critical data. In this paper, we summarize and analyze the state-of-the-art multiple-replica integrity auditing schemes in cloud data storage. We present the system model and security threats of outsourcing data to the cloud with classification of ongoing developments. We also summarize the existing data integrity auditing schemes for multicloud data storage. The important open issues and potential research directions are addressed. © 2019 John Wiley & Sons, Ltd.

关键词： Cloud storage

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Evaluation of the Software Development Life Cycle Model with Value of Testing and Quality Control

Evaluation of the Software Development Life Cycle Model with...

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Interdisciplinary Humanitarian Conference for Sustainability (IIHC), International

作者： Vikram Singh S Annamalai Department of Computer & System Sciences School of Computer & System Sciences Jaipur National University Jaipur India Department of Computer Science and Engineering Galgotias University Greater Noida Uttar Pradesh India

One of the most noteworthy, essential, and important issues in the PC world is software project development. It is a portion of the life cycle for developing frameworks (SDLC). The goal of the SDLC is to reduce the likelihood of failure and disappointment while improving the quality of the software product. The structure that includes multiple tasks and assignments to be completed during the software development process is known as the SDLC. In order to examine existing models, have included five of these SDLC models in this paper: the cascade model, the angular model, the twisting model, and the iterative model. As a result, support and upgrades for the software should be based on current requirements for long-term and stable usage. This study illustrates the concept of testing and its role in quality assurance, experimentation and test levels, and the ongoing planning, execution, and observation of tests. This outline was created with the intention of focusing on the current SDLC sequence.. Approx 300 circulations were selected in addition to the crucial accentuation carried out with embeddings inquiry toward the open-source data collections.

关键词： Software testing Quality assurance Windings Process control Quality control Software Planning

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Poster: Data-Driven Studies of UAV-sharing in Parcel Delivery and Surveillance

Poster: Data-Driven Studies of UAV-sharing in Parcel Deliver...

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International Conference on Network Protocols

作者： Junhui Gao Yan Pan Zhigang Li Qingye Han School of Computer Science Northwestern Polytechnical University School of System Engineering National University of Defense Technology School of Management Science and Real Estate Chongqing University

ISBN: (数字)9781665482349

ISBN: (纸本)9781665482356

Parcel delivery and Point of Interest (PoI) surveillance are two fundaches were conducted for an isolated application, separately. UAV-sharing in the two heterogeneous applications bears significant but unexplored benefit potentials, similar to current sharingmental applications of Unmanned Aerial Vehicles (UAVs) in city. Traditional resear economy such as taxi sharing. However, the inconsistency of the two heterogeneous applications in both temporal and spatial domains would impact the sharing performance. This work illustrates the first quantified studies of the UAV-sharing performance in the two heterogeneous applications. Specifically, some critical constraints of the UAV-sharing process are first discussed. Thereafter, a data-driven evaluation is conducted to understand the sharing process of the UAVs with a delivery dataset and a traffic accident dataset obtained from Shanghai city. Some inspiring results verify the particularly excellent UAV-sharing performance in the two heterogeneous applications.

关键词： Protocols Surveillance Urban areas Autonomous aerial vehicles Public transportation Accidents

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One-Dimensional EEG Artifact Removal Network Based on Convolutional Neural Networks

Journal of Network Intelligence

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Journal of Network Intelligence 2024年第1期9卷 142-159页

作者： Xiong, Jun Meng, Xiang-Long Chen, Zhao-Qi Wang, Chuan-Sheng Zhang, Fu-Quan Grau, Antoni Chen, Yang Huang, Jing-Wei School of Computer and Data Science Minjiang University Fuzhou University Town No. 200 Xiyuangong Road Fuzhou China College of Electronic Engineering Shandong University of Science and Technology No. 579 Qianwangang Road Huangdao District Qingdao China College of Computer and Big Data Fuzhou University Fuzhou University Town No. 2 Wulongjiang North Road Fuzhou China Department of Automatic Control Technical Polytechnic University of Catalonia Autonomous Region of Catalonia Barcelona Spain Digital Media Art Key Laboratory of Sichuan Province Sichuan Conservatory of Music Fuzhou Technology Innovation Center of intelligent Manufacturing information System Minjiang University Fuzhou University Town No. 200 Xiyuangong Road Fuzhou China Fujian Province University No. 1 Campus New Village Longjiang Street Fuqing China School of Mechanical and Automotive Engineering Fujian University of Technology No. 33 Xuefu South Road University New District Fuzhou China

The electroencephalogram (EEG) serves as a significant tool in the realms of clinical medicine, cerebral investigation, and neurological disorders research. However, the EEG records we obtain are often easily contaminated by various artifacts, which can blur or distort the underlying EEG signals and make data interpretation difficult. Generally speaking, removing EEG artifacts is considered an essential step in brain signal analysis. Therefore, removing artifacts is crucial for obtaining accurate and reliable EEG signals for subsequent analysis. Recently, deep learning techniques have found widespread application across various domains for denoising tasks, including image denoising and EEG denoising. Many advanced algorithms have been developed in image denoising, which has achieved good results in enhancing low-quality images. Moreover, it has shown superior performance in EEG denoising. In contrast, few people have devoted themselves to studying EEG denoising, and existing convolutional neural network EEG denoising methods still have problems of overfitting and poor denoising effect in Electromyograph(EMG) and ElectroOculoGram(EOG) artifact removal. Therefore, this paper proposes a method called DWINet (De-artifacting with Image-based Network for EEG Signals) based on an image dehazing network DRHNet for removing artifacts from EEG signals. Specifically, our approach DWINet, addresses the de-artifacting issue in EEG signals by converting it as an image dehazing problem and utilizes the image dehazing capability of DRHNet to enhance the denoising performance of EEG signals. Experimental results demonstrate that the proposed method outperforms the compared algorithms in removing the ocular artifact in EEG signals and exhibits higher accuracy and robustness. © 2024, J. Network Intell. All rights reserved.

关键词： Image enhancement

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Analyses of Software Testing Approaches

Analyses of Software Testing Approaches

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Interdisciplinary Humanitarian Conference for Sustainability (IIHC), International

作者： Shalini Joshi Indra Kumari Department of Computer & System Sciences School of Computer & System Sciences Jaipur National University Jaipur India Department of Computer Science and Engineering Galgotias University Greater Noida Uttar Pradesh India

Software testing is an essential part of the Software Development Lifecycle because of its importance both before and after code is written. As such, it necessitates the use of well-developed, well-executed processes and procedures. This article attempts to discuss both existing testing techniques and possible enhancements to those procedures in the sake of higher quality assurance. Extensive testing, including validation and verification, is required to guarantee that the software satisfies all clients' requirements. As a result, software development processes and testing strategies are made. However, software testing strategies provide a well-thought-out sequence of actions that should reliably yield results, as opposed to software testing approaches, which focus on how to test a specific feature or function. In this analysis, the advantages and disadvantages of various software testing strategies are explored. Furthermore, it specifies the basic life cycles that must be observed throughout quality assurance testing.

关键词： Software testing Quality assurance Costs computer bugs Process control Quality control Software

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Graph Neural Network Meets Multi-Agent Reinforcement Learning: Fundamentals, Applications, and Future Directions

arXiv

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arXiv 2024年

作者： Liu, Ziheng Zhang, Jiayi Shi, Enyu Liu, Zhilong Niyato, Dusit Ai, Bo Shen, Xuemin The School of Electronic and Information Engineering Korea Republic of The Frontiers Science Center for Smart High-Speed Railway System Beijing Jiaotong University China Nanyang Technological University Singapore The Department of Electrical and Computer Engineering University of Waterloo Canada

Multi-agent reinforcement learning (MARL) has become a fundamental component of next-generation wireless communication systems. Theoretically, although MARL has the advantages of low computational complexity and fast convergence rate, there exist several challenges including partial observability, non-stationary, and scalability. In this article, we investigate a novel MARL with graph neural network-aided communication (GNNComm-MARL) to address the aforementioned challenges by making use of graph attention networks to effectively sample neighborhoods and selectively aggregate messages. Furthermore, we thoroughly study the architecture of GNNComm-MARL and present a systematic design solution. We then present the typical applications of GNNComm-MARL from two aspects: resource allocation and mobility management. The results obtained unveil that GNNComm-MARL can achieve better performance with lower communication overhead compared to conventional communication schemes. Finally, several important research directions regarding GNNComm-MARL are presented to facilitate further investigation. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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ConvoyLLM: Dynamic Multi-Lane Convoy Control Using LLMs

arXiv

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arXiv 2025年

作者： Lu, Liping He, Zhican Chu, Duanfeng Wang, Rukang Peng, Saiqian Zhou, Pan School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan430070 China Intelligent Transportation Systems Research Center Wuhan University of Technology Wuhan430070 China Hubei Key Laboratory of Distributed System Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China

This paper proposes a novel method for multi-lane convoy formation control that uses large language models (LLMs) to tackle coordination challenges in dynamic highway environments. Each connected and autonomous vehicle in the convoy uses a knowledge-driven approach to make real-time adaptive decisions based on various scenarios. Our method enables vehicles to dynamically perform tasks, including obstacle avoidance, convoy joining/leaving, and escort formation switching, all while maintaining the overall convoy structure. We design a Interlaced formation control strategy based on locally dynamic distributed graphs, ensuring the convoy remains stable and flexible. We conduct extensive experiments in the SUMO simulation platform across multiple traffic scenarios, and the results demonstrate that the proposed method is effective, robust, and adaptable to dynamic environments. The code is available at: https://***/chuduanfeng/ConvoyLLM . Copyright © 2025, The Authors. All rights reserved.

关键词： Autonomous vehicles

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Monkeypox Lesion Classification: A Transfer Learning Approach for Early Diagnosis and Intervention 7

Monkeypox Lesion Classification: A Transfer Learning Approac...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Al Noman, Abdullah Fardin, Hasib Chhabra, Gunjan Sultana, Shayma Haque, Rezaul Ahmed, Md. Redwan Hasibunnahar, Swarna Kaushik, Keshav Westcliff University Department of Information Technology United States Westcliff University Department of Engineering Management United States Graphic Era Hill University Department of Cse Uttarakhand Dehradun India Graphic Era Deemed to be University Uttarakhand Dehradun India International American University Department of Management Information System United States East West University Department of Computer Science and Engineering Bangladesh Huzhou University Department of Life Sciences China Amity University Amity School of Engineering and Technology Punjab Mohali India

ISBN: (纸本)9798350350067

Monkeypox is a viral disease found in Central and West Africa, and it presents notable public health challenges due to its clinical similarity to smallpox. Recent outbreaks in over 40 countries have underscored the urgent need for quick and accurate diagnosis, especially in settings with limited resources where traditional diagnostic methods may be inadequate. This study explores the application of deep learning techniques to classify four specific types of monkeypox skin lesions. Transfer learning was employed to evaluate the performance of different models using a dataset of 770 skin lesion images. Various methods, including data augmentation and image preprocessing, were utilized to improve the effectiveness of the models. The results indicate that the custom SqueezeNet model was the most successful, achieving an accuracy of 98.85% and an F1 score of 98.89%. These results surpass previous research benchmarks across four key evaluation measures. Additionally, the model was integrated into a web-based diagnostic tool capable of reliably detecting different forms of monkeypox. This study represents a significant advancement in utilizing artificial intelligence for disease detection, offering a practical approach for early and accurate monkeypox diagnosis. The proposed system has thex potential for seamless integration into clinical practice, aiding healthcare professionals in timely interventions. © 2024 IEEE.

关键词： Contrastive Learning

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