The increase of mobile apps has led to an exponential growth of user-generated reviews, which are often noisy, informal, and linguistically diverse, thereby posing significant challenges for automated analysis in requirements engineering. This study evaluates whether back-translation (BT) can normalize informal reviews while preserving meaning, and which model (Google Translate vs Facebook M2M100_418M) offers better semantic preservation, grammatical quality, and lexical alignment. We collected 323 Google Play reviews (667 sentences) from three Malaysian government apps. Texts were cleaned, expanded for colloquial forms, and then BT was applied using Malay as an intermediate language. Evaluation used four metrics which are semantic similarity (Sentence-BERT), grammar error count (LanguageTool), BLEU (NLTK), and perplexity (GPT-2). Models differences were tested with paired t-tests and Wilcoxon signed- rank tests, while paired scatterplots showed distributional patterns. Google was significantly better on semantic similarity (t(322)=5.38, p<.001), grammar errors (t(322)=3.66, p<.001), and BLEU (t(322)=2.99, p=.003); effect sizes were small to moderate. Perplexity differences were not significant, indicating comparable sentence-level fluency. Visualizations confirmed Google’s steadier performance with fewer extreme outliers. BT is a practical normalization step for noisy reviews. For the English–Malay pipeline studied here, Google provides more reliable semantic preservation and grammatical quality, while both systems are similar in fluency. However, the generalizability of these results are constrained by the relatively modest sample size (323 reviews, 667 sentences), and future work should validate results on large datasets and explore hybrid strategies combining strengths of both models.

In the age of 5G, seamless mobility handovers are vital, especially in densely populated areas like Malaysia, to prevent disruptions and resource inefficiencies. A proposed solution involves a Digital Twin Network mirroring Malaysia's 5G infrastructure, integrating real-time data and user behaviors to optimize energy consumption during handovers. Emphasis is placed on energy-efficient protocols and algorithms to enhance network performance. The research follows the format of Systematic Literature Review (SLR). The algorithms predict and manage handovers proactively, enabling adaptive resource allocation for improved efficiency. The Digital Twin Network aims to significantly enhance mobility handover efficiency through predictive handovers and adaptive resource allocation, supported by energy-efficient protocols and edge computing for sustainability. This research offers a tailored solution to Malaysia's 5G mobility handover challenges, promising seamless connectivity and sustainability. It introduces a customized Digital Twin Network focusing on energy efficiency, evaluated against practical applications in information retrieval. Evaluation standards gauge effectiveness, supplemented by in-depth analysis of methods and performance metrics, concluding with insights, limitations, and recommendations for future research.

The advanced economies have recognized the educational usefulness of handheld devices and have resolutely integrated them into their educational settings at all levels to strengthen instruction and learning. The blending of mobile technology with education has advanced the conventional methods of instruction and learning activities by allowing access to educational possibilities at any time or location. This technology will inevitably be used in higher education if people must be suitably prepared for their duties and responsibilities in contemporary society, which places a high value on digital skills and teamwork, and the development of skills necessary for lifelong learning, among other abilities. However, the majority of African nations, including Nigeria, are having difficulty incorporating contemporary technology into their educational systems, while other countries on the continent have persisted in using antiquated techniques that put students in disadvantaged positions of learning that encourage memorization of facts and knowledge. The primary objective of this research is to examine the factors that enable efficient mobile technology incorporation into higher education in Nigeria, with the aim of promoting successful attainment of learning outcomes. 180 higher education instructors from institutions of higher education in four states in the country participated in a cross-sectional study that combined documentary and survey research. A validated questionnaire that was self-administered was used to collect data, and multiple regression was used to analyze the data. Out of the ten (10) factors identified for the experiment, the study found that educators' involvement in the choice to adopt innovation, internet connectivity, technological skills, mobile technology instructional material, orientation for mobile learning, and technical support are significantly associated with effective integration of mobile technology into higher education in Nigeria.

This study introduces a novel approach utilizing digital image processing techniques to analyze concrete surface images for categorizing concrete strength levels based on two-dimensional RGB digital photographs. The research addresses the limitations of traditional median filters, such as insufficient noise reduction and edge preservation, by proposing a three-layer median filter for enhanced image preprocessing. The methodology involves three main phases. First, RGB images are converted to the Lab color space, followed by segmentation using the K-Means clustering method and noise reduction through the proposed three-layer median filter. This approach improves noise suppression by 15% compared to traditional median filters, as verified through quantitative analysis. Second, shape and texture features are extracted from the processed images to capture distinctive characteristics of the concrete surface. Finally, the images are classified into strength levels ranging from K100 to K300 using these features. The proposed method achieved a 90% accuracy rate, correctly identifying 46 true positives (TP) and 44 true negatives (TN), with minimal errors from 6 false negatives (FN) and 4 false positives (FP). This represents a significant improvement over conventional methods. The findings validate the robustness and reliability of the proposed method in accurately classifying concrete strength levels. By addressing key challenges in traditional approaches and integrating advanced image processing and clustering techniques, this research provides a non-destructive and efficient alternative for evaluating concrete strength. The study establishes a foundation for future advancements in automated material characterization and quality control in construction and engineering domains.

A mobile application (mobile app) is a software designed to operate on mobile devices such as smartphones and tablets, allowing users to access information or complete tasks quickly and easily without referring to traditional notes like books. In education, mobile apps function as interactive teaching tools that can be accessed easily and quickly by students. One of the initiatives under Continuous Quality Improvement in Polytechnics is the development of a Teaching Aid Tool to enhance students' understanding of the DFC10033-Introduction to Computer System (DFC10033) course. After a Continuous Quality Improvement meeting a held with the course lecturers and coordinators, a decision was taken on improving the teaching of DFC10033 Course. This made the need for additional teaching aid tools inevitable his led to the development of ICS Flashcard-Based Mobile App, which includes graphic elements and explanatory notes to improve students' understanding. This app is available for free on the Google Playstore.A study was then conducted to evaluate the usability and effectiveness of the app for students of the Diploma in Information Technology (Digital Technology) at Polytechnic Sultan Abdul Halim Mu'adzam Shah (Polimas), Jitra, Kedah, who are taking the DFC10033 course from the Department of Information Technology. The development of the app followed the ADDIE model, which consists of five phases: Analysis, Design, Development, Implementation, and Evaluation. This quantitative study involved 171 male and female students aged between 18 and 25 years. Questionnaires were distributed through Google Forms to collect data, which was then analyzed to assess the objectives and effectiveness of the developed application. The results of the study indicate that the ICS Flashcard Mobile App made a positive contribution, with a mean value of Section B=3.77 and Section C=4.65, which, according to the mean score interpretation, indicates acceptance. The app effectively helped students to understand the course concepts more efficiently, aligning with the Continuous Quality Improvement objectives of developing the DFC10033 Teaching Aid Tool application and testing its effectiveness.

Intelligent system-assisted UAV-based observer platforms could achieve various complex observing tasks over traditional methods. However, due to the complexity of their algorithms, UAV’s first-flight route is still challenging to deploy quickly and minimise energy consumption in an emergency. Another challenge is that the UAV-based observer platform severely requires an efficient classifier with high processing speed for higher observing efficiency. As the first research objective, this paper artificially evaluated seven UAV first-flight routes by simulation and real-world flighting environments to identify one proper first-flight route that could be deployed quickly. Secondly, a new integrated UAV-based observer platform, including a new three-colour channel-based online fuzzy classifier, is proposed for quickly detecting abnormal objectives in practical observing tasks. Simulation and real-world flighting experiments identified that the square helix with smooth turn consumes the most miniature battery and can cover the observing area among seven different first-flight routes. The results also proved the proposed integrated observer platform’s feasibility in detecting abnormal objectives while UAVs fly in a real-time, real-world environment. Most importantly, the proposed observer platform has good interpretability because it employs an actual image stream to train its classifier during flighting.

This study addresses the challenge of real-time plant disease identification on resource-constrained embedded platforms, a critical need for improving agricultural productivity. Using the NVIDIA Jetson Orin Developer Kit and the PlantVillage dataset, the research evaluates Residual Neural Networks (ResNets), focusing on ResNet-50, ResNet-101, and ResNet-152. The study highlights the balance between model depth, batch size, accuracy, and computational efficiency. ResNet-101, optimized with a batch size of 64, achieved 90.62% accuracy and an average identification time of 17.6 milliseconds, emerging as the most effective configuration. These findings demonstrate the feasibility of deploying deep learning models on embedded devices and provide insights into optimizing architectures for real-time agricultural applications.

The challenge of classifying imbalanced data persists in machine learning, particularly in critical applications such as medical diagnosis, fraud detection, and anomaly identification, where detecting the minority class is essential. Conventional classifiers like Support Vector Machine (SVM) tend to favor the majority class, leading to reduced sensitivity in identifying minority instances. This study introduces Posterior Probability and Correlation-Support Vector Machine (PC-SVM), a novel approach that integrates posterior probability estimation with correlation analysis to enhance SVM’s performance on imbalanced datasets. Unlike traditional SVM models, which struggle with class imbalance and require additional data balancing techniques, PC-SVM dynamically adjusts classification thresholds using posterior probability values and correlation-weighted features, simplifying the classification process while improving its effectiveness. The effectiveness of PC-SVM was evaluated using multiple imbalanced datasets from KEEL, UCI, and Kaggle repositories. Results demonstrate that PC-SVM achieves 100% recall for the minority class, significantly outperforming traditional SVM, which attained only 80% recall on average. This 20% improvement in recall underscores PC-SVM’s ability to mitigate the imbalance issue without relying on oversampling or cost-sensitive adjustments. Furthermore, PC-SVM exhibits consistent performance across various evaluation metrics, including accuracy, precision, recall, and F1-score, ensuring robust classification results. By improving the detection of minority classes, PC-SVM offers a transformative solution for real-world applications that demand high sensitivity in identifying rare but crucial instances. Its ability to maintain classification integrity without additional balancing techniques positions it as a valuable model for industries such as healthcare, finance, and cybersecurity, where accurate minority class recognition is critical.

The African Buffalo Optimization (ABO) is a swarm optimization technique developed in 2015 by Odili and Kahar. It obtained its inspiration from the movement of cape buffalos across the wide African rainforests and savannah using basically two sounds: /waaa/ and /maaa/. Since its development, the algorithm has been applied to a wide range of applications ranging from numerical function optimization, tuning of the Peripheral, Integral, Derivative parameters of Automatic Voltage Regulators, Travelling salesman problem etc. Despite the enormous potential benefits of combining stochastic optimization algorithms and Digital Humanities (DH), there is a lack of research exploring this intersection. DH data, such as historical texts, images, and artifacts etc. often require complex analysis and visualization techniques. Traditional methods may not effectively capture the nuances and complexities of these data. In this study, the relationship between the African Buffalo Optimization and Digital Humanities is explored with the aim of attracting digital humanities researchers’ attention to the enormous potentials available to the research community in swarm optimization algorithms like the ABO algorithm. Deploying Java programming language, this study implements the ABO algorithm to the various aspects of DH such as text analysis, digital pedagogy, cultural analytics, digital preservation, digital scholarship, digital museums and curation as well as digital edition and publishing. It was discovered that the ABO is good in optimizing topic modelling for literary analysis, sentiment analysis for historical text analysis and entity recognition for cultural trend analysis. In addition, the algorithm is effective in digital cultural analytics, digital pedagogy and scholarship. The successful implementation in this study proved to be a good contribution as it unravels the close relationship between the two disciplines that seemed to have nothing in common. In view of our findings, we recommend that global researchers in DH should explore the diverse opportunities inherent in swarm optimization algorithms to further DH scholarship and research.

Requirement Elicitation plays a great role in the successful completion of a software project. Elicitation is a productive effort to extricate project-associated information from the stakeholders. To gather complete, concise, and clear requirements, the concept of requirement elicitation allows various analytics and techniques. Requirement elicitation is significant due to the lack of efficiency in accurately articulating their demands towards the intended system. Therefore, requirement engineers realize the need to perform elicitation to ensure that the requirements produced are easily accessible, useable, and applicable according to the client’s demands. A software project can be considered successful if it satisfies all the requirements of the client. In certain situations, changes in requirements from the client side may arise during the development of the intended software. This research focuses on the challenges of this context mentioned as follows-many stakeholders are unable to clearly express their needs toward the intended system and the client-side requirements may change often. In this case, traceability plays a vital role in making the necessary changes in the components of the corresponding requirements. Therefore, the impact of any change can be easily visible & the changes can be applied correspondingly. After all, a project’s success can be achieved by ensuring traceability among the components used in eliciting requirements. So, this research proposes an effective approach for eliciting functional and non-functional requirements and ensuring traceability among the corresponding components used in eliciting requirements. In addition, a tool has been designed to ensure traceability among functional requirements, non-functional requirements, and other corresponding components. If any change request arises to any of the requirements, all the corresponding components associated with that requirement can be traced with the help of this tool. In overall, this proposed approach has been applied successfully to a case study of an Automated Teller Machine using this tool.