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8th IEEE International Conference on Software Engineering and Computer Systems, ICSECS 2023

作者： Kustiawan, Yanche Ari Lim, Tek Yong Multimedia University Faculty of Computing and Informatics Cyberjaya Malaysia

ISBN: (纸本)9798350310931

A user story is commonly applied in requirement elicitation, particularly in agile software development. User story is typically composed in semi-formal natural language, and often follow a predefined template. The user story is used to elicit requirements from the users' perspective, emphasizing who requires the system, what they expect from it, and why it is important. This study aims to acquire a comprehensive understanding of user stories in requirement elicitation. To achieve this aim, this systematic review merged an electronic search of four databases related to computer science. 40 papers were chosen and examined. The majority of selected papers were published through conference channels which comprising 75% of total publications. This study identified 24 problems in user stories related to requirements elicitation, with ambiguity or vagueness being the most frequently occurring problem reported 18 times, followed by incompleteness reported 11 times. Finally, the model approach was the most popular approach reported in the research paper, accounting for 30% of the total approaches reported. © 2023 IEEE.

关键词： Software design

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An Ensemble-Based Framework to Estimate Software Project Effort 8

An Ensemble-Based Framework to Estimate Software Project Eff...

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8th IEEE International Conference on Software Engineering and Computer Systems, ICSECS 2023

作者： Haris, Mohammad Chua, Fang-Fang Lim, Amy Hui-Lan Multimedia University Faculty of Computing and Informatics Cyberjaya Malaysia

ISBN: (纸本)9798350310931

Effort estimation is essential for successful software project planning, budgeting, and risk identification. However, the techniques used to estimate effort are often inaccurate, outdated, and only consider technical factors while neglecting project management or stakeholder engagement. Expert estimation remains an important technique for leveraging human expertise in software estimation, but solely relying on this technique causes biased and subjective predictions. Machine learning (ML) techniques have shifted the direction of software project effort estimation towards computational intelligence. Nonetheless, there is a lack of deployment due to ambiguous outcomes and ineffective model-building approaches. This study presents an ensemble-based framework that can estimate software project effort more accurately with the incorporation of domain knowledge and experiences. To achieve this, six homogeneous classifier ensembles will be constructed using six distinct classifiers on the proposed USP05-FT dataset. The collected expert estimations will be integrated into the proposed dataset as an additional feature in the form of numerical values such as expert-provided software project effort estimations (in person hours) that provide additional insight and knowledge. Subsequently, the predictions of all six homogeneous classifier ensembles will be combined through majority voting to obtain a more accurate and reliable prediction with increased robustness against errors and uncertainties. The performance of the proposed framework will be evaluated using Recall, F-measure, Precision, and Accuracy. It is expected that the proposed ensemble-based framework for software project effort estimation will lead to more efficient and effective software project management, an improvement in resource allocation, empowering informed decision-making, and timely project delivery. © 2023 IEEE.

关键词： Forecasting

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Traffic Density Identification From UAV Images Using Deep Learning: A Preliminary Study 15

Traffic Density Identification From UAV Images Using Deep Le...

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15th International Conference on Software, Knowledge, Information Management and Applications, SKIMA 2023

作者： Mohd-Tajudin, Muhammad-Haziq Mat-Desa, Shahbe Abdullah, Junaidi Multimedia University Faculty of Computing and Informatics Cyberjaya Malaysia

ISBN: (纸本)9798350316551

This study presents a preliminary investigation into the application of deep learning techniques for the identification of traffic density from unmanned aerial vehicle (UAV) images. The primary objective is to categorize the traffic flows into three classes: low, moderate, and high. The study proposes a VGG16-based framework with the aim of achieving high classification rates. The experimental results, obtained from a curated dataset with a 70:30 data-splitting ratio, demonstrate a good accuracy at 95.67%. The initial findings regarding traffic density identification are notably satisfactory, especially considering the challenges posed by the UA V -based images utilized in the experiments. © 2023 IEEE.

关键词： Deep learning

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User Authentication with Keystroke Dynamics: Performance Evaluation in Neural Network 6

User Authentication with Keystroke Dynamics: Performance Eva...

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6th IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2024

作者： Kek, Xin-Jin Leau, Yu-Beng Tan, Soo Fun Cybersecurity Research Lab Faculty of Computing and Informatics Universiti Malaysia Sabah Sabah Malaysia Creative Advanced Machine Intelligence Research Centre Faculty of Computing and Informatics Universiti Malaysia Sabah Sabah Malaysia

ISBN: (纸本)9798350389692

In the current context of research, it investigates user authentication through keystroke dynamics using the neural network model which focuses on Convolutional Neural Network (CNN), Gated Recurrent Unit (GRU) and Long Short-Term (LSTM). As of above, four distinct experiments were conducted: (1) evaluating 3 different algorithms, (2) layering configuration for CNN and GRU fusion, (3) number of filters and neurons as hyperparameters, (4) assessing combinations of events counts and classes. In justification, the final outcome demonstrates an achieved accuracy of 87%. Based on the experiments that were carried out, optimal hyperparameters were identified: window size of 40, 256 for the number of filters and neurons and 2500 as the optimal number of events with 30 as the number of classes. These findings underscore a significance of considering diverse combinations of a number of events and classes by providing insights into the determination of optimal hyperparameters for keystroke dynamics in performance evaluation. © 2024 IEEE.

关键词： Convolutional neural networks

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Antisocial Behavior and the Dopamine Loop on Different Technological Platforms and Industries: An Overview 8th

Antisocial Behavior and the Dopamine Loop on Different Te...

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8th International Congress on Information and Communication Technology, ICICT 2023

作者： Stupavský, Igor Dakić, Pavle Faculty of Informatics and Information Technologies Institute of Informatics Information Systems and Software Engineering Slovak University of Technology in Bratislava Bratislava Slovakia Faculty of Informatics and Computing Singidunum University Belgrade Serbia

ISBN: (纸本)9789819932351

The aim of this review is to explore the relationship between the architecture of microservices and antisocial behavior. Microservice architecture is a software design approach that involves decomposing a system into small, independent services that can offer benefits such as flexibility and scalability. Expected result is to analyze dataset of statuses, which defines the length of time the status displays on the user’s device. Because people spend time on social networks at every opportunity, even for hours. When combined with the time-valued, we try to gain a deeper understanding of social networks and users behavior. They are forced into this behavior through the feedback loop mechanism of the neurotransmitter Dopamine. Main goal is to understand how to minimize the impact of antisocial behavior on social networks and what exactly makes users spend a disproportionate amount of time on these platforms. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Software design

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Extreme Learning Machine Optimization for Employee Satisfaction With Modified Metaheuristic 4

Extreme Learning Machine Optimization for Employee Satisfact...

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4th International Conference on Expert Clouds and Applications, ICOECA 2024

作者： Abdulla, Tasnim Jovanovic, Luka Radomirovic, Branislav Njegus, Angelina Zivkovic, Miodrag Bacanin, Nebojsa Singidunum University Faculty of Informatics and Computing Belgrade Serbia Singidunum University Faculty of Technical Sciences Belgrade Serbia

ISBN: (纸本)9798350385793

In the contemporary business landscape, the success of a company is intricately linked to the engagement and satisfaction of its workforce. This study analyzes the signifi-cance of developing a contented and engaged employee base, emphasizing the direct impact of workplace satisfaction on productivity, turnover rates, and overall organizational dynamics. Organizational culture emerges as a pivotal factor influencing the recruitment, retention, and satisfaction of talented employees. To address the complexities of identifying and mitigating employee dissatisfaction, this research work proposes a comprehensive solution harnessing the capabilities of cloud-based technologies, specifically text mining, Natural Language Processing (NLP), and modified metaheuristic techniques. The study explores the application of an extreme learning machine as a classifier for assessing employee satisfaction within a cloud computing framework. Acknowledging the critical role of hyperparameter selection in model performance, metaheuristic optimizers and cloud platforms implementation are employed to enhance accu-racy and effectiveness. Furthermore, a novel modification to a metaheuristic algorithm for satisfying the unique requirements of this research is introduced. This research study demonstrates the efficiency of the optimized models, achieving an accuracy rate surpassing 84%. By integrating cloud computing technologies into the proposed framework, organizations gain a powerful and scalable tool for proactively identifying and addressing employee dissatisfaction, ultimately contributing to the improved employee well-being and organizational success in the cloud era. © 2024 IEEE.

关键词： Job satisfaction

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Exploring Lightweight YOLOv8 Architectures for Circuit Board Defect Detection

Exploring Lightweight YOLOv8 Architectures for Circuit Board...

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2024 IEEE International Conference on Intelligent computing and Emerging Communication Technologies, ICEC 2024

作者： Jovanovic, Luka Jokic, Aleksandar Milovanovic, Marina Antonijevic, Milos Zivkovic, Miodrag Bacanin, Nebojsa Singidunum University Faculty of Technical Sciences Belgrade Serbia Singidunum University Faculty of Informatics and Computing Belgrade Serbia

ISBN: (纸本)9798331508432

Modern electronics relies heavily on circuit boards, which must be designed and produced using a variety of procedures in order to be filled with electrical components. This procedure includes drilling and plating holes, applying masks, painting and etching fiberglass-reinforced epoxy resin boards, and adding components to the boards. Because of variations in the materials and processes, mistakes might occur at any stage. Traditionally, conductivity, impedance, and visual testing are used in a multi-stage approach to identify and resolve these problems. With modest hardware requirements, computer vision, especially using convolutional networks, provides considerable promise to improve defect detection. Once implemented, these models may significantly increase error detection and lower board failure rates, despite the significant hardware requirements for training. Using a publicly accessible dataset, this study investigates the use of the YOLO model for fault identification in circuit board fabrication. The YOLOv8 model's lightweight design is examined;the medium-sized model achieves a mAP@50 score of 0.990, indicating that it considerably improves fault detection. © 2024 IEEE.

关键词： Digital storage

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Artificial Intelligence in Human-Computer Interaction: To Serve and Protect 16

Artificial Intelligence in Human-Computer Interaction: To Se...

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16th International Conference on Advanced Technologies, Systems and Services in Telecommunications, TELSIKS 2023

作者： Jevremovic, Aleksandar D. Singidunum University Faculty of Informatics and Computing Belgrade11000 Serbia

ISBN: (纸本)9798350347029

The rapid development and progress of artificial intelligence algorithms in the last decade has opened up many new possibilities and fields for its application. The field of human-computer interaction is not only not an exception, but it can also be considered a veteran. Among the general population, there is a certain level of apprehension when it comes to introducing artificial intelligence into new areas and aspects of human life. In this paper, we will attempt to shed new light on this issue and the current trend of villainizing AI, as well as present current trends in enhancing user security based on it. We will summarize the current conditions, trends and experiences in this area. © 2023 IEEE.

关键词： Human computer interaction

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Addressing Imbalance in Health Datasets: A New Method NR-Clustering SMOTE and Distance Metric Modification

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Computers, Materials & Continua 2025年第2期82卷 2931-2949页

作者： Hairani Hairani Triyanna Widiyaningtyas Didik Dwi Prasetya Afrig Aminuddin Department of Electrical Engineering and Informatics Faculty of EngineeringUniversitas Negeri MalangMalang65145Indonesia Department of Computer Science Universitas BumigoraMataram83127Indonesia Department of Computer Graphic and Multimedia Faculty of ComputingCollege of Computing and Applied SciencesUniversiti Malaysia Pahang Al-Sultan AbdullahPekan26600Malaysia

An imbalanced dataset often challenges machine learning, particularly classification methods. Underrepresented minority classes can result in biased and inaccurate models. The Synthetic Minority Over-Sampling Technique (SMOTE) was developed to address the problem of imbalanced data. Over time, several weaknesses of the SMOTE method have been identified in generating synthetic minority class data, such as overlapping, noise, and small disjuncts. However, these studies generally focus on only one of SMOTE’s weaknesses: noise or overlapping. Therefore, this study addresses both issues simultaneously by tackling noise and overlapping in SMOTE-generated data. This study proposes a combined approach of filtering, clustering, and distance modification to reduce noise and overlapping produced by SMOTE. Filtering removes minority class data (noise) located in majority class regions, with the k-nn method applied for filtering. The use of Noise Reduction (NR), which removes data that is considered noise before applying SMOTE, has a positive impact in overcoming data imbalance. Clustering establishes decision boundaries by partitioning data into clusters, allowing SMOTE with modified distance metrics to generate minority class data within each cluster. This SMOTE clustering and distance modification approach aims to minimize overlap in synthetic minority data that could introduce noise. The proposed method is called “NR-Clustering SMOTE,” which has several stages in balancing data: (1) filtering by removing minority classes close to majority classes (data noise) using the k-nn method;(2) clustering data using K-means aims to establish decision boundaries by partitioning data into several clusters;(3) applying SMOTE oversampling with Manhattan distance within each cluster. Test results indicate that the proposed NR-Clustering SMOTE method achieves the best performance across all evaluation metrics for classification methods such as Random Forest, SVM, and Naїve Bayes, compared t

关键词： SMOTE modification Clustering-SMOTE manhattan distance

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Forecasting Solar Energy Substation Voltages Using Metaheuristic Optimized GRU 32

Forecasting Solar Energy Substation Voltages Using Metaheuri...

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32nd Telecommunications Forum, TELFOR 2024

作者： Jovanovic, Luka Jovic, Nikola Petrovic, Aleksandar Budimirovic, Nebojsa Zivkovic, Miodrag Bacanin, Nebojsa Singidunum University Faculty of Informatics and Computing Belgrade Serbia Singidunum University Faculty of Technical Sciences Belgrade Serbia

ISBN: (纸本)9798350391053

As global energy demand grows, renewable sources offer a key alternative to fossil fuels. However, integrating these sources into power grids presents challenges, especially with supply unpredictability. This paper demonstrates how AI, specifically using a modified PSO algorithm to enhance GRU models, can predict solar energy output and improve grid balancing. Experiments on public datasets show that the optimized models achieve high accuracy, with the best models demonstrating a mean square error (MSE) of 0.024642 and an mean absolute percentage error (MAPE) of 0.000943 in hourly predictions, facilitating renewable energy integration. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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