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international conference on advanced research on computer science and information engineering, CSIE 2011

ISBN: (纸本)9783642214011

The proceedings contain 159 papers. The topics discussed include: the existence of analytic solutions of a functional differential equation with state dependent argument;design and analysis of a sun tracking system;research and understanding to grid's architecture;a novel compensation strategy for dynamic sag corrector;a brief study on resource management of self-study center based on web;empirical research on yield curve of china bond market;a new multi-scale texture analysis with structural texel;illumination invariant texture classification with pattern co-occurrence matrix;research on algorithm of image segmentation based on color features;a new web services filtrating mechanism with latent semantic analysis;vulnerability assessment methods - a review;collaborative polling scheme to detect and isolate the colluding packet droppers in mobile ad hoc networks;and design of secure chaotic hash function based on logistic and tent maps.

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advanced research on computer science and information engineering - international conference, CSIE 2011, Proceedings

Advanced Research on Computer Science and Information Engine...

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international conference on advanced research on computer science and information engineering, CSIE 2011

ISBN: (纸本)9783642214103

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22nd international conference on Software engineering research and Practice, SERP 2024, and 23rd international conference on e-Learning, e-Business, Enterprise information Systems, and e-Government, EEE 2024, held as part of the World Congress in computer science, computer engineering and Applied Computing, CSCE 2024

22nd International Conference on Software Engineering Resear...

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22nd international conference on Software engineering research and Practice, SERP 2024, and 23rd international conference on e-Learning, e-Business, Enterprise information Systems, and e-Government, EEE 2024, held as part of the World Congress in computer science, computer engineering and Applied Computing, CSCE 2024

ISBN: (纸本)9783031866432

The proceedings contain 21 papers. The special focus in this conference is on Software engineering research and Practice. The topics include: MetaPix: A Data-Centric AI Development Platform for Efficient Management and Utilization of Unstructured computer Vision Data;A Novel Architecture That Examines Network Activity in a Docker-Based Multitenant to Verify Zero Trust Container Architecture (ZTCA) Compliance;VENUS: Designing a Validation Engine for User Stories;a Framework for Requirements Modeling of Safety Critical Systems: A Continuous Glucose Monitoring System Case Study;plan-Based and Agile Companies: A Comparison of Project Management Approaches;Service Availability Ratio (SAR): An Availability Metric for Microservice;Development of a Desktop Agent System Using GPT;navigating Challenges in E-Participation: A Comprehensive Meta-Analysis;The Shifting Landscape of Cybersecurity: The Impact of Remote Work and COVID-19 on Data Breach Trends;Farmchain: Empowering Smallholder Farmers Through Blockchain and DAOs: Cryptournomic Approach;privacy Strategies for Police Personnel: Co-designing a Self-assessment Tool;cybersecurity Threats: An Analysis of the Rise and Impacts of State Sponsored Cyber Attacks;moore’s Law: What Comes Next?;use of Emerging Technologies in Africa;using Survey to Investigate the Integration of Artificial Intelligence in e-Learning;students Satisfaction with the Distance Education During Covid-19 Pandemic;Improving Student Success in Math Courses Using WeBWorK;fast Food Review Online;a Systems Approach to Improving E-Learning Using Theory of Constraints.

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Bibliometric and Content Analysis of Large Language Models research in Software engineering: The Potential and Limitation in Software engineering

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international Journal of advanced computer science and Applications 2025年第4期16卷 344-356页

作者： Damayanti, Annisa Dwi Gani, Hamdan Zhipeng, Feng Gani, Helmy Zuhriyah, Sitti Nurani Djabir, St. Nurhayati Wardani, Nur Ilmiyanti Department of Environmental Engineering Faculty of Engineering Hasanuddin University Makassar Indonesia Department of Machinery Automation System ATI Makassar Polytechnic Makassar Indonesia School of Culture Creativity and Media Hangzhou Normal University Zhejiang Hangzhou China Department of Industrial Hygiene Faculty of Public Health Occupational Health and Safety Makassar College of Health Sciences Indonesia Department of Computer System Universitas Handayani Makassar Makassar Indonesia Department of Information Systems and Technology Institut Teknologi dan Bisnis Nobel Indonesia Jl. Sultan Alauddin No.212 Makassar90221 Indonesia Department of Informatics Engineering Universitas Handayani Makassar Makassar Indonesia

Large Language Models (LLM) is a type of artificial neural network that excels at language-related tasks. The advantages and disadvantages of using LLM in software engineering are still being debated, but it is a tool that can be utilized in software engineering. This study aimed to analyze LLM studies in software engineering using bibliometric and content analysis. The study data were retrieved from Web of science and Scopus. The data were analyzed using two popular bibliometric approaches: bibliometric and content analysis. VOS Viewer and Bibliometrix software were used to conduct the bibliometric analysis. The bibliometric analysis was performed using science mapping and performance analysis approaches. Various bibliometric data, including the most frequently referenced publications, journals, and nations, were evaluated and presented. Then, the synthetic knowledge method was utilized for content analysis. This study examined 235 papers, with 836 authors contributing. The publications were published in 123 different journals. The average number of citations per publication is 1.44. Most publications were published in Proceedings international conference on Software engineering and ACM international conference Proceeding Series, with China and the United States emerging as the leading countries. It was discovered that international collaboration on the issue was inadequate. The most often used keywords in the publications were "software design," "code (symbols)," and "code generation." Following the content analysis, three themes emerged: 1) Integration of LLM into software engineering education, 2) application of LLM in software engineering, and 3) potential and limitation of LLM in software engineering. The results of this study are expected to provide researchers and academics with insights into the current state of LLM in software engineering research, allowing them to develop future conclusions. © (2025), (science and information Organization). All Rights Res

关键词： Software design

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Enhancing Electric Vehicle Security with Face Recognition: Implementation Using Raspberry Pi

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international Journal of advanced computer science and Applications 2025年第4期16卷 728-738页

作者： Alsayaydeh, Jamil Abedalrahim Jamil Yi, Chin Wei Bacarra, Rex Rashid, Fatimah Abdulridha Herawan, Safarudin Gazali Melaka76100 Malaysia Department of Computer Engineering University of Al-Iraqia Baghdad Iraq Department of General Education and Foundation Rabdan Academy Abu Dhabi United Arab Emirates Industrial Engineering Department Faculty of Engineering Bina Nusantara University Jakarta11480 Indonesia

Facial identification has emerged as a key research area due to its potential to enhance biometric security. This research proposes an advanced security system for electric vehicles (EVs) based on facial identification, implemented using Raspberry Pi. The system comprises two main modules: Face Detection and Face Recognition. For face detection, the researchers propose using the Viola-Jones algorithm, which leverages Haar-like features to detect and extract unique facial features, such as the eyes, nose, and mouth. MATLAB will be used as the development tool for this module. For face recognition, the proposed approach integrates Principal Component Analysis (PCA) with Support Vector Machine (SVM). PCA is used to extract the most relevant facial information and construct a computational model, while SVM enhances classification accuracy. The system’s performance is evaluated using accuracy and the Receiver Operating Characteristic (ROC) curve, with results demonstrating a face recognition accuracy of 95% and an average execution time of 2.32 seconds, meeting real-time operational requirements. These findings confirm the proposed method’s reliability in offering advanced and efficient biometric protection for modern electric vehicles. © (2025), (science and information Organization). All rights reserved.

关键词： Principal component analysis

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Investigating the Impact of Hyper Parameters on Intrusion Detection System Using Deep Learning Based Data Augmentation

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international Journal of advanced computer science and Applications 2025年第4期16卷 523-532页

作者： Iftikhar, Umar Ali, Syed Abbas Department of Computer & Information System Engineering NED University of Engineering & Technology Karachi Pakistan

The effects of changing learning rates, data augmentation percentage and numbers of epochs on the performance of Wasserstein Generative Adversarial Networks with Gradient Penalties (WGAN-GP) are evaluated in this study. The purpose of this research is to find out how they affect the data augmentation to enhance stability during training. In this research, the degree of system performance is measured using the Classification Model Utility approach. For this reason, this study aims to determine the interaction between learning rate, augmentation percentage and epoch value when using WGAN-GP to generate synthetic data for the recognition of the system performance. The results will provide the indications on how some of the hyper parameters can be adjusted up or down for having positive or negative consequences on the generation process for further research and use of WGAN-GP. It also provides insights into how the generative model is trained, and how that affects stability and quality of the result in various settings such as image synthesis or other generative tasks. © (2025), (science and information Organization). All rights reserved.

关键词： Deep learning

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BC-SBOM: Blockchain-based SBOM Management System 27

BC-SBOM: Blockchain-based SBOM Management System

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27th international conference on advanced Communications Technology, ICACT 2025

作者： Song, Ahyun Seo, Euiseong Kim, Heeyoul Department of Computer Science and Engineering Sungkyunkwan University Korea Republic of Division of Computer Science and Engineering Kyonggi University Korea Republic of

ISBN: (纸本)9791188428137

This paper presents BC-SBOM, a novel blockchainbased system designed to enhance the management of Software Bills of Materials (SBOMs). By leveraging blockchain technology, BC-SBOM ensures secure storage and sharing of SBOMs, while providing a comprehensive global view of dependencies among software components. The system also supports rapid propagation of alerts for newly discovered vulnerabilities, thereby increasing responsiveness to potential threats. Offering superior reliability, transparency, and availability compared to traditional SBOM tools, BC-SBOM aims to significantly improve the management of complex software systems and contribute to the advancement of software security practices. Copyright 2025 Global IT research Institute (GIRI). All rights reserved.

关键词： Blockchain

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Studying on design and manufacture of the customized cam using artificial intelligence 4

Studying on design and manufacture of the customized cam usi...

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4th international conference on advanced Manufacturing Technology and Electronic information, AMTEI 2024

作者： Zhao, Yan Dong, Guangcheng Hao, Wenyue School of Information Engineering Baise University Guangxi Autonomous Region Baise533000 China School of Material Science and Engineering Baise University Guangxi Autonomous Region Baise533000 China

ISBN: (数字)9781510688247

ISBN: (纸本)9781510688230

A virtual product creation technology was explored, focusing on the customized cam with a rise-dwell-fall-dwell (RDFD) motion program taking high and low speeds into account using artificial intelligence which is necessary to transform the related data of the product development (represented by CAD models) into the product. The digital solution which is the manufacturing process of numbers and can completely describe the working components to eventually realize the physical shape of the product was mentioned. The database carrying different interpolation functions for designing some special curvilinear paths meeting special kinematic requirements of the two types of cams was set up. The entire process starts with computer-Aided Process Planning (CAPP) and then there is computer-Aided Manufacturing with Numeric Control (NC). A further discussion for customized parts design and manufacturing in specialized fields has been conducted based on digital modeling technology and expert systems with the development of artificial intelligence in engineering applications. © 2025 SPIE.

关键词： computer aided manufacturing

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Intelligent Guitar Chord Recognition Using Spectrogram-Based Feature Extraction and AlexNet Architecture for Categorization

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international Journal of advanced computer science and Applications 2025年第4期16卷 758-767页

作者： Korade, Nilesh B. Salunke, Mahendra B. Bhosle, Amol A. Sangve, Sunil M. Joshi, Dhanashri M. Asalkar, Gayatri G. Kadu, Sujata R. Sarwade, Jayesh M. Department of Computer Engineering JSPM’s Rajarshi Shahu College of Engineering Tathawade Maharashtra Pune411033 India Department of Computer Engineering PCET’s Pimpri Chinchwad College of Engineering and Research Ravet Maharashtra Pune412101 India Department of Computer Science and Engineering School of Computing MIT Art Design and Technology University Loni Kalbhor Maharashtra Pune412201 India Department of Artificial Intelligence and Data Science Vishwakarma Institute of Technology Bibwewadi Pune411037 India Department of Computer Engineering Terna Engineering College Maharashtra Mumbai India Department of Information Technology JSPM’s Rajarshi Shahu College of Engineering Tathawade Maharashtra Pune411033 India

Chord prediction plays a key role in the advancement of musical technological innovations, such as automatic music transcription, real-time music tutoring, and intelligent composition tools. Accurate chord prediction can assist musicians, educators, and developers in constructing tools that help in learning, playing, and composing music. Background noise and audio distortions may have an impact on chord prediction accuracy, particularly in real-world situations. Chords can have distinct voicings or finger positions on the guitar, resulting in slight variations in audio representation. This study focuses on the classification of guitar chords using techniques of deep learning. There are eight major and minor guitar chords in the dataset. They have been turned into spectrograms, chromagrams, and Mel Frequency Cepstral Coefficients (MFCC) so that features can be extracted. Various deep learning architectures, including CNN, ResNet50, AlexNet, and VGG, were employed to classify the chords. Experimental results demonstrated that the spectrogram-based AlexNet model outperforms others, achieving good accuracy and robustness in chord classification. The proposed study demonstrates the efficiency of spectrograms and advanced deep learning models for audio signal processing in music applications. By automating chord detection, this study provides beneficial resources for music learners as well as educators, enabling more efficient learning and real-time feedback during practice sessions. © (2025), (science and information Organization). All Rights Reserved.

关键词： Deep learning

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uFOIL: An Unsupervised Fusion of Image Processing and Language Understanding

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IEEE Access 2025年 13卷 31683-31700页

作者： Rahman, Md. Abdur Hasan, Md. Tanzimul Farooq Howlader, Umar Kader, Md. Abdul Islam, Md. Motaharul Hung Pham, Phuoc Mehedi Hassan, Mohammad United International University Department of Computer Science and Engineering Dhaka1212 Bangladesh Providence College Department of Mathematics and Computer Science ProvidenceRI02918 United States King Saud University College of Computer and Information Sciences Department of Information Systems Riyadh11543 Saudi Arabia

In academic institutions, processing and evaluating documents such as exam scripts remains a labor-intensive process susceptible to human error. Traditional digitization systems face significant challenges in handling the complexities of mixed handwritten and printed text and varying document structures. These challenges are exacerbated by the absence of annotated datasets due to privacy concerns, particularly in contexts involving sensitive exam evaluations. To address these issues, this study introduces uFOIL, an unsupervised ensemble-based framework that integrates advanced image and language processing techniques to automate the extraction and validation of key information. The framework employs a majority voting mechanism that combines four state-of-the-art optical character recognition systems. Furthermore, a transformer architecture is incorporated to enhance contextual understanding and the structured formatting of extracted text that follows a post-processing confidence scoring mechanism. The proposed framework achieves high performance, with accuracies of 95.77% and 96.48% for student names and IDs, respectively;and 95.07% for total mark validation based on a dataset of exam script samples. Additionally, experiments on the benchmark ICDAR2013 dataset suggest the framework’s strong applicability achieving precision, recall, and F1-scores of 95.89%, 97.86%, and 96.87%, respectively. © 2025 The Authors.

关键词： Optical character recognition

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