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Automated Classification and Identification of Non-Functional Requirements in Agile-Based Requirements Using Pre-Trained Language Models

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IEEE Access 2025年 13卷 87401-87417页

作者： Alhaizaey, Abdulrahim Al-Mashari, Majed College of Computer and Information Sciences King Saud University Department of Information Systems Riyadh Saudi Arabia King Khalid University Department of Information Systems Abha Saudi Arabia

Non-functional requirements (NFRs) are critical factors for software quality and success. A frequently reported challenge in agile requirements engineering is that NFRs are often neglected due to the focus on functional requirements (FRs) and the limited capability of agile requirements documented as user stories to represent NFRs. With the emergence of transfer learning and large pre-trained language models, various applications in requirements engineering have become feasible, alleviating several longstanding challenges. This study evaluates transformer-based models for the automated identification and classification of NFRs. We leveraged transfer learning with pre-trained transformer models to automate the identification and classification of NFRs in agile textual requirements documented as user stories. A dataset of over 10k user stories was collected and labeled, and pre-trained transformer models, including BERT, RoBERTa, XLNet, and DistilBERT, were fine-tuned to automate the identification of NFRs. We incorporated Focal Loss during training to mitigate the dominance of functionally driven requirements and class imbalances. In addition, thorough experiments on hyperparameter optimization were employed using Bayesian hyperparameter optimization to obtain the combination of hyperparameters that best correlated with the aim of enhancing each model’s performance. Our evaluation demonstrated that the finetuned pre-trained models significantly outperformed comparable prior approaches relying on rule-based techniques or traditional machine learning, with a fine-tuned BERT model achieving an F1 Score of 93.4 %. These findings highlight the potential of pre-trained language models in agile requirements engineering, enabling more efficient NFRs identification, reducing manual review burden, and facilitating a viable and efficient approach to address the neglect of NFRs in agile development processes. © 2013 IEEE.

关键词： Requirements engineering

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Permissions-based Android malware detection using machine learning

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Neural Computing and Applications 2025年第6期37卷 5255-5270页

作者： Alomar, Atheer AlJarullah, Asma Abu-Ghazalah, Sarah Department of Information Systems King Khalid University Alfara Abha61421 Saudi Arabia Department of Informatics and Computer Systems King Khalid University Alfara Abha61421 Saudi Arabia

Android applications are evolving quickly, and both consumers and developers are growing in number. This popularity increases Android system vulnerabilities and attacks. Researchers have proposed various approaches to detect Android malware. One of these approaches is to utilize machine learning to detect Android malware based on permissions. This study developed a permissions-based Android malware detection model using machine learning. It aims to identify the significant permissions list that can be used to distinguish between benign and malware apps. A dataset containing permissions for both types of apps was collected to compare feature selection algorithms (IG, RFE, CHI, GA) with machine learning algorithms (SVM, DS, RF, GB, XG) to identify the best prediction model. The results showed that the SVM model achieved the highest accuracy of 98.88% using RFE with 13 permissions. The model was able to complete the detection process in 12.00 milliseconds. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Android malware

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Interaction-aware Trust Management Scheme for IoT systems With Machine Learning-Based Attack Detection

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IEEE Internet of Things Journal 2025年第11期12卷 17169-17182页

作者： Farhat, Ali Eldosouky, Abdelrahman Ibnkahla, Mohamed Matrawy, Ashraf Carleton University Department of Systems and Computer Engineering OttawaON Canada Carleton University School of Information Technology OttawaON Canada

The recent Internet of Things (IoT) adoption has revolutionized various applications while introducing significant security and privacy challenges. Traditional security solutions are unsuitable for IoT systems due to their dynamicity, heterogeneity, and resource constraints. Trust-based solutions are emerging as promising alternatives due to their ability to track the dynamic behavior in IoT systems. However, existing trust management schemes are implemented at the device level, raising several challenges, including device modification that compromises certification and scalability, increased network overhead, and higher device resource utilization. To address these challenges, this paper proposes a novel trust management scheme that shifts its implementation to a higher layer in the IoT system, specifically to the IoT access layer (e.g., gateway). The proposed scheme establishes trust based on typical device interactions with the gateway without requiring additional information from the device. It relies on objective attributes spanning communication, security, and advanced dimensions to compute the trust value of an IoT device. Additionally, an Artificial Neural Network (ANN) is integrated to determine if the device acts maliciously or behaves normally. Simulation results demonstrate a notable improvement in the detection rate, primarily due to incorporating the proposed ANN, compared to the threshold-based approaches in the literature. Overall, the improvements highlight the significant advantage of the proposed scheme's robustness. © 2025 IEEE.

关键词： Gateways (computer networks)

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Deep Convolution Neural Networks for Image-Based Android Malware Classification

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computers, Materials & Continua 2025年第3期82卷 4093-4116页

作者： Amel Ksibi Mohammed Zakariah Latifah Almuqren Ala Saleh Alluhaidan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia College of Computer and Information Sciences King Saud UniversityRiyadhP.O.Box 11442Saudi Arabia

The analysis of Android malware shows that this threat is constantly increasing and is a real threat to mobile devices since traditional approaches,such as signature-based detection,are no longer effective due to the continuously advancing level of *** resolve this problem,efficient and flexible malware detection tools are *** work examines the possibility of employing deep CNNs to detect Android malware by transforming network traffic into image data ***,the dataset used in this study is the CIC-AndMal2017,which contains 20,000 instances of network traffic across five distinct malware categories:***,***,***,***,*** network traffic features are then converted to image formats for deep learning,which is applied in a CNN framework,including the VGG16 pre-trained *** addition,our approach yielded high performance,yielding an accuracy of 0.92,accuracy of 99.1%,precision of 98.2%,recall of 99.5%,and F1 score of 98.7%.Subsequent improvements to the classification model through changes within the VGG19 framework improved the classification rate to 99.25%.Through the results obtained,it is clear that CNNs are a very effective way to classify Android malware,providing greater accuracy than conventional *** success of this approach also shows the applicability of deep learning in mobile security along with the direction for the future advancement of the real-time detection system and other deeper learning techniques to counter the increasing number of threats emerging in the future.

关键词： Android malware detection deep convolutional neural network(DCNN) image processing CIC-AndMal2017 dataset exploratory data analysis VGG16 model

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Designing and Analyzing Green Networking Solutions in Data Centers Using AI for Energy Optimization 2

Designing and Analyzing Green Networking Solutions in Data C...

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2nd International Conference on Machine Learning and Autonomous systems, ICMLAS 2025

作者： Ebadinezhad, Sahar Shaalan Shaalan, Alwaled Khalid Omar Algailani, Noor Khattab Al-Hussein, Mohammed Yonmah, Charles Zulu Department of Computer Information Systems Nicosia99138 Cyprus Near East University Department of Computer Information Systems Nicosia99138 Cyprus

ISBN: (纸本)9798331505745

A significant obstacle in the world of information IT, especially in terms of energy efficiency, is the increasing demand for energy from data centers. These massive plants currently consume nearly 1% of the world's electricity, and this percentage is expected to increase significantly in the coming decades. Therefore, it has now become imperative to identify effective ways to maximize the energy use of data centers, which has led to the study of the use of artificial intelligence to come up with creative solutions. The purpose of this research is to use AI to optimize energy use in data centers by designing and analyzing green networking solutions. It entails using AI methods like neural networks and reinforcement learning to increase data centers' energy efficiency and lessen their environmental effect. The study specifically focuses on enhancing important characteristics, including cooling systems and Power Usage Effectiveness (PUE). This study aims to replace conventional, static energy management techniques with adaptive strategies that react to current facts and anticipated energy demands by utilizing artificial intelligence. It is anticipated that the suggested solutions will significantly increase energy efficiency, lessen their negative effects on the environment, and help data centers become more sustainable. © 2025 IEEE.

关键词： Reinforcement learning

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Prognostic Survival Analysis for AD Diagnosis and Progression Using MRI Data: An AI-Based Approach

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IEEE Access 2025年 13卷 89059-89078页

作者： Deenadayalan, T. Shantharajah, S.P. Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore India

Alzheimer’s Disease is a progressive neuro-degenerative disorder and a leading cause of dementia, marked by cognitive decline, memory loss, and behavioral changes. Despite advancements in medical imaging and Artificial Intelligence (AI), early detection and accurate prognostic modeling remain challenging, particularly in distinguishing overlapping disease stages. This study proposes a novel AI framework combining EfficientNetB0, a state-of-the-art deep learning model, with dual attention mechanisms for robust feature extraction and survival analysis using the Alzheimer’s MRI dataset hosted on the Kaggle platform. The model was trained and evaluated on a balanced dataset of 6,400 MRI images, classified into 4 AD stages: Non-Demented (NoD), very Mild-Demented (vMiD), Mild-Demented (MiD), and Moderate-Demented (MoD). The pre-processing steps, including resizing, normalization, and data augmentation, ensured robustness and generalizability. The framework achieved classification accuracies of 99. 93% (training), 93. 60% (validation) and 93. 59% (testing), with a perfect Concordance Index of 1.0 for survival predictions, demonstrating its ability to accurately rank survival times. By integrating survival analysis with deep learning, the framework addresses limitations in previous work, such as reliance on linear assumptions and limited classification capabilities, and provides actionable insights for clinical workflows, supporting personalized treatment and early intervention strategies. Future work will focus on integrating longitudinal data, exploring time-dependent co-variates, and enhancing explainability to promote adoption in diverse clinical settings. This approach represents a significant advancement in AI-driven diagnosis and progression modeling of AD, with the potential to transform patient care and outcomes. © 2013 IEEE.

关键词： Deep learning

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A decentralized and privacy-preserving framework for electronic health records using blockchain

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Alexandria Engineering Journal 2025年 126卷 196-203页

作者： Alahmari, Saad Alshardan, Amal Al-Wesabi, Fahd N. Sorour, Shaymaa Alghushairy, Omar Alsini, Raed Khadidos, Alaa O. Duhayyim, Mesfer Al Department of Computer Science Applied College Northern Border University Arar Saudi Arabia Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of Computer Science Applied College at Mahayil King Khalid University Saudi Arabia Department of Management Information Systems College of Business Administration King Faisal University Al-Ahsa31982 Saudi Arabia Department of Information Systems and Technology College of Computer Science and Engineering University of Jeddah Jeddah21589 Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Department of Computer Science College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Al-Kharj16273 Saudi Arabia

As healthcare services have become increasingly digitized, Electronic Health Records (EHRs) have become widely adopted, providing seamless data exchange among providers. Conventional EHRs, however, are extremely vulnerable to cyber threats because patients' sensitive data is centralized and transmitted electronically. The paper proposes a decentralized, privacy-preserving framework for managing EHRs on blockchains in order to address these security and privacy concerns. Using cryptographic techniques, such as homomorphic encryption and zero-knowledge proofs, the proposed system enhances security and ensures data integrity. Additionally, the model facilitates scalable, efficient, and secure access to patient records through the integration of cloud-based storage and blockchain. Using smart contracts, we also ensure compliance with healthcare regulations by regulating access control and authentication. As a result of performance evaluations, the proposed approach is demonstrated to be feasible, and the advantages it offers in terms of security, privacy, and efficiency are highlighted. © 2025 The Authors

关键词： Electronic health record

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Inverse kinematics solution for a six-degree-of-freedom upper limb rehabilitation robot using deep learning models

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Neural Computing and Applications 2025年 1-19页

作者： Shah, Muhammad Faizan Khan, Naveed Ahmad Jamwal, Prashant K. Chetty, Girija Goecke, Roland Hussain, Shahid School of Information Technology & Systems University of Canberra Canberra Australia Department of Electrical and Computer Engineering Nazarbayev University Astana Kazakhstan School of Systems and Computing University of New South Wales Canberra Australia

The inverse kinematics problem in serially manipulated upper limb rehabilitation robots implies the usage of the end-effector position to obtain the joint rotation angles. In contrast to the forward kinematics, there are no systematic approaches for solving the inverse kinematics problem. Furthermore, for some morphology of the upper limb rehabilitation robots, the inverse kinematics problem is particularly challenging to solve. Conventional methods to solve the inverse kinematics problem reported in the literature are computationally expensive. In the present work, we propose a deep learning-based model to acquire the joint angles for a given end-effector position. The proposed approach exhibits high efficacy in determining the joint angles for various target positions and can accurately predict the end-effector positions once trained, improving the ability of the upper limb rehabilitation robot to adapt to varying patient needs. Due to its improved capability and effectiveness to track positions, the proposed algorithm lays the foundation for the development of efficient controllers in future. © The Author(s) 2025.

关键词： Inverse kinematics

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Hybrid TCN-transformer model for predicting sustainable food supply and ensuring resilience

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Alexandria Engineering Journal 2025年 127卷 380-391页

作者： Alrashdi, Ibrahim Abd El-Aziz, Rasha M. Taloba, Ahmed I. Farsi, Mohammed Department of Computer Science College of Computer and Information Sciences Jouf University Al-Jouf Sakaka73211 Saudi Arabia Department of Information Systems College of Computer Science and Engineering Taibah University Yanbu966144 Saudi Arabia

Major concerns occur in maintaining a sustainable food supply due to population expansion, supply chain interruptions, and climate-related changes. Traditional forecasting models, such as ARIMA, LSTM, and GRU, fail to deal with the dynamic fluctuations and long-range correlations. These methods often have reduced accuracy, high computational cost, and poor generalization when applied to shifting patterns in the food supply system. Based on the abovementioned limitations, this study proposes a Hybrid TCN-Transformer Model by combining the strength of Temporal Convolutional Networks (TCN) with Transformer Attention to provide accurate and efficient food supply prediction. Thus, the project's key objective is to develop a deep learning system that effectively handles massive quantities of temporal data while preserving dependence in short and long durations. The proposed method is novel since it integrates TCN's causal convolutions for extracting sequential patterns. It uses a Transformer Model's self-attention mechanism to capture the complex interactions across time steps. Hybrid design enables faster training, increased interpretability, and better prediction accuracy than current methods. Results from experiments have revealed that the suggested model surpasses the performance of the stand-alone TCN, ARIMA, LSTM, and GRU models in terms of accuracy of predictions, efficiency of computations, and adaptability. Findings thus far suggest deep learning-based predictive analytics has helped improve the food supply chain management by mitigating food wastage, making it environmentally resilient. © 2025 The Authors

关键词： Deep learning

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AI-powered dining: text information extraction and machine learning for personalized menu recommendations and food allergy management

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International Journal of information Technology (Singapore) 2025年第4期17卷 2107-2115页

作者： Brahimi, Samiha Department of Computer Information Systems College of Computer Science and Information Technology Imam Abdulrahman Bin Faisal University Dammam 31441 Saudi Arabia

Individuals with food allergies face limitations in social events and restaurant dining. Artificial intelligence solutions should be offered to this category. In this paper, a recommender system is proposed for the benefit of people with food allergies. The system aims to identify convenient options for the user in a restaurant/hotel menu. The system collects user’s allergy information and the restaurant menu, it extracts dishes names using a machine learning model. Then it conducts search about the recipes of these dishes and identify allergen-free ones. The system has been implemented as a mobile application involving a Naïve Bayes classification model and a web search API. The performance of the classifier was significant (accuracy 87%). Yet, an enhancement approach was introduced to increase the accuracy to 90%. In addition, an expert-driven test has been conducted and 98.5% of the system allergen identification was accurate in comparison with the original recipes used by restaurants’ chefs. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： information retrieval Machine learning Menu recommendation Recommender systems

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