Mobile Language Learning Applications (MLLAs) are gaining widespread use in higher education because of the flexible nature of practice opportunities with languages. The majority have easy-to-use interfaces, but few enable long-term engagement, retention, and productive learning. There is much empirical work that isolates usability or outcomes singularly without noting how user experience, motivation, and pedagogy intersect in the mobile environment. This study investigates MLLA user experience among university students in Indonesia using a mixed-methods design. Structural Equation Modelling (SEM) was used to investigate the inter-plays of usability, acceptance, engagement, and retention. Qualitative data provided information about user views and context-based limitations. Findings suggest that while overall MLLAs are usable and widely accepted, they tend to lack instructional intensity and intrinsic motivation support. Motivation and perceived usefulness significantly impact engagement, but redundant repetition, superficial individualization, and minimal interaction reduce retention. With reference to TPACK, FRAME, and SDT, the study highlights learner-centered design featuring effective pedagogy, social interaction, and adaptive functionality as being vital. To transcend surface gamification, MLLAs must support deep, long-term language learning. These results provide actionable recommendations for developers and instructors seeking to optimize mobile language learning by synergizing technology, pedagogy, and learner psychology.

This study reviews state-of-the-art research on the application of the meta-analysis based Modified Unified Theory of Acceptance and Use of Technology model (Meta-UTAUT) to provide a comprehensive understanding of how the model has been applied across diverse contexts. From 1,881 citations of the original model, 36 Scopus-and Web of Science-indexed studies were analysed. Findings reveal that mobile payment is the most examined system, with financial technology as the predominant domain of application. Across studies, attitude consistently emerged as a strong determinant of behavioural intention and use behaviour, underscoring its central role in technology adoption. Behavioural intention and facilitating conditions were the most influential predictors of use behaviour, while performance expectancy and effort expectancy contributed substantially to shaping attitude. Many studies extended the Meta-UTAUT framework by incorporating external variables to enrich explanations of attitude, intention, and behaviour, deepening theoretical understanding of technology adoption. This review highlights recurring methodological limitations, including single-subject sampling, cross-sectional designs, and sampling methods, indicating the need for more rigorous approaches to strengthen theoretical refinement and improve generalisability. This study is the first to offer an extensive synthesis of Meta-UTAUT applications, providing valuable implications for researchers and guiding future inquiry toward more rigorous and contextually diverse investigations.

The use of relevant and structured instruments for measuring digital development is essential for policy-making in digitalization. The aim of the research is to compare structural adequacy of the global digital development indexes by means of multicriteria decision-making (MCDM). Theoretical contribution is to develop an evaluation framework and propose a novel methodological integration. Nine criteria were used to quantify six indexes: the Network Readiness Index (NRI), the E-Government Development Index (EGDI), the Digital Economy and Society Index (DESI), the ICT Development Index (IDI), the IMD World Digital Competitiveness Ranking (IMD) and the Global Digital Index (GDI). The criteria's objective weights were evaluated using the Method based on the Removal Effects of Criteria (MEREC) and the weights alterations effect was considered using the Shannon entropy method. The final prioritization was consolidated using five MCDMs scores: Combined Compromise Solution (CoCoSo), Measurement Alternatives and Ranking according to the Compromise Solution (MARCOS), Additive Ratio Assessment (ARAS), COmbinative Distance-based ASsessment (CODAS) and Evaluation based on Distance from Average Solution (EDAS). Practical contribution and originality are presented by proposing first time evaluation framework of digital development indexes based on a recently proposed MEREC and selecting the most appropriate index (NRI) in a neutral MCDM context.

The article discusses the optimization of batch processing in a public institution through the implementation of a proprietary centralized batch management system. The research is based on the analysis of processing log entries from 2018 to 2023 and the implementation of a proprietary information interface developed in C# and connected to an Oracle database. The study highlights the importance of operator roles, structured work orders, and socio-technical alignment between technology and organizational processes. The analysis confirms that advanced planning significantly reduces processing time, thereby improving operational efficiency. However, the impact on overall process success is limited, as reliability appears to depend on additional organizational and infrastructural factors.

A mobile agent system is a mobile computing approach where agents move autonomously among hosts to perform tasks. It offers advantages such as low latency, reduced bandwidth use, and cost efficiency. This paper proposes the Shared Knowledge Area Mechanism (SKAM) to improve mobile agent performance. SKAM uses a shared knowledge database that stores classification rules based on agents’ travel experiences. Each rule is an IF–THEN statement linking service combinations to host locations. We extract these rules using support, confidence, and lift to ensure reliability. Before starting a task, an agent queries the database to select hosts based on the most relevant rules. This reduces unnecessary host visits and shortens travel time. SKAM is implemented within the Secure Mobile Agent Generator (SMAG), a platform used to simulate mobile agent behavior. SKAM also applies rule prioritization to support accurate itinerary planning. Experimental results show that SKAM reduces average task completion time from 41,146.5 ms to 23,445.5 ms—a 43% improvement. This gain is statistically significant (p < 0.05) and consistent across all agents. It confirms that SKAM lowers both search overhead and travel time. These results highlight SKAM’s effectiveness and practical value for real-time, large-scale mobile agent systems.

Encrypting communications and data over the Internet becomes essential in ensuring the privacy of communications and protecting the data from increasing threats. Hence, majority of Internet traffic and networked communications are encrypted now. However, encryption also provides a means for attackers to hide them behind encrypted communications and conduct malicious activities. Analyzing the unencrypted communications is relatively easy. The same task is highly challenging due to the presence of encryption in network communication. Conventional network analysis methods fail to analyze encrypted communications. There are methods like flow monitoring that are available to detect encrypted traffic and analyze traffic flow related features. By using traditional analysis methods, we could not achieve accurate detection and classification of encrypted network packets in various types of network traffic such as VoIP, Text, Audio, Video, VPN traffic. In our work, we have proposed the Bootstrap Forest model to analyze and classify encrypted network traffic. Bootstrap Forest model accurately classifies the encrypted network traffic using statistical and time-based features. The performance of the proposed model is evaluated and compared with the performance of other machine learning models under various performance metrics. The three publicly available datasets such as UNSW-NB15, ISCXTor 2016 and ISCXVPN 2016 datasets were used in our experimentations and evaluations. The experimental results show that our proposed model provides the best performance for classifying encrypted network traffic while comparing the F1 score with other methods.

Purpose: The author proposes sixteen Shortest Job First - Machine Learning (SJF-ML) hybrid algorithms, combining the cloud's SJF scheduling algorithm with four ML algorithm categories, with cloud evolution through ML intelligence as the primary objective. The four categories include: SJF-CA, SJF-ELA, SJF-PM, and SJF-RA. The developed SJF-ML algorithms by the author perform pattern recognition of the tasks that are to be computed, to improve decision-making during task computations in the cloud. These sixteen SJF-ML algorithms include: SJF-ADAB, SJF-BAY, SJF-DT, SJF-KNN, SJF-LAS, SJF-LDA, SJF-LGB, SJF-LN, SJF-MLP, SJF-NAV, SJF-PLY, SJF-RDG, SJF-RF, SJF-RBST, SJF-SVM, and SJF-XGB. Performance Metrics: Cost, Time, Energy, and LB are utilized to compare the developed algorithms with baseline SJF, along with comparing them within their respective SJF-ML categories. Dataset: The real-time Google Big Data Task (BDT) dataset, comprising tasks ranging from one hundred to one thousand across nineteen files, was computed using the SJF-ML and SJF algorithms. Experiment: Open-source CloudSim simulator with VM counts of 20, 40, 60, 80, and 100 were utilized to compute the BDTs, outputting results across the considered metrics. Results: The algorithms SJF-XGB and SJF-LN provided the best results, with SJF-DT, SJF-LAS, and SJF-LDA providing poor results. Findings: Hybridization of the cloud's scheduling algorithms with ML provides improved intelligence and performance, resulting in the evolution of the cloud.

Active, technology-supported learning accelerated during and after COVID-19, yet evidence from non-programming computer science courses remains limited. This paper contributes (i) a focused review of flipped classroom (FC) studies in CS program (2020-2024) and (ii) a three-year case study of how the flipped classroom enhances the teaching of IT Service Management (ITSM) as a discipline in the computer science program in an online university environment, during and after the COVID-19 pandemic. The FC design combined pre-lecture micro-videos and auto graded quizzes with in-classroom clarification and post classroom activities (project). Using LMS telemetry, course outcomes, and an end of semester survey across three academic years (2021/22-2023/24), we examined engagement-achievement links with non-parametric, rank based correlations (Spearman ρ), regularized logistic regression, and comparisons across empirically defined engagement tertiles. Results show consistent, practically meaningful associations between quality weighted engagement (quiz participation and performance) and both passing and final grades, with survey perceptions aligning to the behavioral signals. While strictly non-causal, the pattern is robust across methods and suggests actionable uses: early identification of at-risk students and design guidance that emphasizes short, well scaffolded videos and steady formative assessment.

Despite continued efforts to digitize public services, many local government websites in emerging contexts still underperform in delivering satisfactory user experiences. This study develops an integrated evaluation framework that combines the ISO 25010 software quality model with the Technology Acceptance Model (TAM) to jointly assess system quality and user acceptance. We analyzed survey data from 524 users in Lombok Tengah, Indonesia, using Partial Least Squares Structural Equation Modeling (PLS-SEM) and Importance Performance Map Analysis (IPMA). The results indicate that functional suitability, usability, and reliability significantly shape perceived usefulness, whereas reliability, security, and performance efficiency drive perceived ease of use. Both perceived usefulness and perceived ease of use positively influence user satisfaction and behavioral intention, with satisfaction emerging as the strongest predictor. IPMA highlights performance efficiency and security as priority areas for improvement. The study contributes to e-government literature by proposing a dual layer model that links system level attributes to user-level perceptions and outcomes, and by translating statistical effects into actionable priorities for local governments seeking to enhance the quality and adoption of digital public services in semi urban developing regions.

The rapid growth of the Internet of Medical Things (IoMT) has introduced critical cybersecurity challenges, highlighting the need for robust and accurate intrusion detection systems (IDS). This study presents a hybrid machine learning (ML) framework to strengthen intrusion detection in IoMT networks using the CIC-IoMT2024 dataset. The framework combines Information Gain (IG) and Principal Component Analysis (PCA) for feature selection and dimensionality reduction, while SMOTEENN and SMOTETomek are applied to address severe class imbalance. The processed data are classified using Random Forest (RF), K-Nearest Neighbors (KNN), XGBoost (XGB), Multi-Layer Perceptron (MLPC), and Logistic Regression (LR), with hyperparameters optimized through Bayesian Optimization. Performance is evaluated using Accuracy, Precision, Recall, F1-Score, and AUC. Experimental results reveal that the optimized XGB classifier with SMOTEENN achieves a peak accuracy of 99.811%. This top-tier performance surpasses several existing benchmarks, validating the effectiveness of integrating IG-PCA with advanced resampling and optimization strategies. This work contributes a lightweight, scalable, and highly accurate IDS, offering a practical and efficient solution for enhancing security in resource-constrained, next-generation medical IoT systems.