Developing a Shared Knowledge Area Mechanism for Multi-Mobile Agents to Improve Performance Using Machine Learning: Classification-Rule

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

Model Checking Access Control Protocol for Spreadsheets

Spreadsheets are one of the most used software systems in business and academia. Since the first introduction of electronic spreadsheets for personal computers in 1979, spreadsheets have significantly evolved. With recent technological advancements and new features added, spreadsheets have become powerful computing platforms capable of complex analysis and modelling. However, numerous publications over the years described cases of spreadsheet errors. In focus of this research paper are spreadsheet errors caused by unauthorized access and modifications of spreadsheets in multi-user environments. Specifically, this paper is structured around formal verification of the novel ABAC4S (Attribute Based Access Control for Spreadsheets) protocol designed for prevention or detection of unauthorized modifications to spreadsheets in multi-user environments. We utilized a model checking approach to verify ABAC4S protocol rules for correctness

The Impact of Generative AI on University Students’ Learning Experience: A Study on Cognitive and Affective Outcomes

Generative Artificial Intelligence (GenAI) is rapidly transforming higher education, yet its impact on learning experiences remains contested. Existing research often isolates either cognitive outcomes (e.g., comprehension, creativity) or affective outcomes (e.g., motivation, engagement), leaving a gap in integrated analyses that also account for heterogeneity across student groups. This study investigates both dimensions simultaneously by examining university students’ perceptions of GenAI, focusing on learning, creativity, motivation, and engagement, alongside perceived risks such as overreliance, ethical concerns, and difficulties in verifying accuracy. Data were collected from 93 students and analyzed through Spearman’s correlations and unsupervised clustering (k-means) with PCA visualization. Findings indicate low to moderate positive correlations between GenAI usage and learning outcomes, particularly problem-solving and motivation. Cluster analysis reveals diverse usage–perception profiles, including paradoxical cases where frequent users report limited cognitive benefit. These results align with Technology Acceptance Model (TAM) and UTAUT assumptions of perceived usefulness and performance expectancy, while also showing that digital literacy moderates these relationships, especially in critical thinking and responsible use. The study contributes by integrating cognitive and affective outcomes, revealing latent profiles beyond averages, and bridging adoption models with responsible AI frameworks. Practical implications highlight the need for AI literacy training, ethical policies, and instructional design to foster effective and responsible GenAI integration in higher education

Bootstrap Forest based method for Encrypted Network Traffic Analysis

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

Assessing User Satisfaction of Local Government Websites Through ISO 25010 and Technology Acceptance Model (TAM): A SmartPLS and IPMA-Based Study in Lombok Tengah

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

Evaluating the Impact of 5G and 4G Networks on the Performance of Real-Time Health Monitoring Systems

This paper investigates the performance of 5G networks compared to 4G LTE, WiFi, and BLE for transmitting real-time health monitoring data. Using Apple Watch Series 7 and Fitbit Sense devices connected to commercial 5G and 4G networks, our experimental analysis demonstrates that 5G technology offers significant advantages for healthcare monitoring applications. Results show a 62% reduction in latency (8.2ms versus 21.6ms), 83.4% improvement in throughput, and 75% reduction in packet loss compared to 4G LTE networks. The low latency achieved with 5G (8.2ms) is particularly critical for remote cardiac monitoring, where transmission delays directly impact clinical response time. Signal strength correlation analysis reveals that 5G networks maintain performance consistency across varying RSRP levels, with only 16% performance degradation at -110dBm compared to 42% for 4G networks. Our findings confirm that 5G networks provide the reliability and performance required for next-generation real-time health monitoring systems, especially for applications requiring continuous vital sign monitoring and immediate clinical feedback

Longitudinal Impacts of Job Insecurity on Life Satisfaction: Mediating Roles of Trust in Government and Hope in the European Union

This longitudinal study examines the relationships among job insecurity, life satisfaction, trust in government, and hope during the COVID-19 pandemic across 27 European Union countries. Using data from 8,750 participants collected via the PsyCorona Study, the analysis applies the PROCESS macro (model 6) with 5,000 bootstrapped samples to estimate indirect effects with 95% bias-corrected confidence intervals. Findings reveal that job insecurity significantly reduces life satisfaction, explaining 13.62% of the variance over time. Trust in government mediates this relationship in earlier waves, though its influence diminishes later. Conversely, hope consistently emerges as a strong mediator across all waves, accounting for 24.64% of the variance. Sequential mediation via trust and hope is significant early on but weakens by wave 22. These findings underscore the essential role of government trust and hope in buffering the negative effects of job insecurity and enhancing societal resilience during times of crisis

Validation of the TPACK Instrument in the Croatian Primary School Context

This study aimed to validate a generic TPACK questionnaire within the Croatian educational context, involving 609 in-service subject teachers from five regions. The survey-based research included exploratory and confirmatory factor analyses. The resulting structure diverged from the original seven-factor model, yielding five factors: pedagogical knowledge (PK), technological knowledge (TK), content knowledge (CK), technological content knowledge (TCK), and technological pedagogical content knowledge (TPACK). Some items were excluded to enhance validity. Pedagogical and pedagogical content knowledge merged, as did technological pedagogical and technological pedagogical content knowledge. The final five-factor model exhibited strong psychometric properties, characterized by high internal consistency and a good fit to the data. Evidence of convergent and discriminant validity supports the distinct yet interconnected nature of the identified knowledge domains. Significant positive correlations were found between all constructs, with the strongest correlations between TPACK and TCK, and also between TPACK and TK. The findings underscore the contextuality of the TPACK framework, and therefore, they were discussed in relation to recent national educational initiatives as well as international indicators on digital competencies. These results contribute to understanding the TPACK framework in the Croatian context, supporting future research on teacher competencies in integrating technology into education

Factors Affecting Students’ Acceptance of Learning Simulation Tools in Computing Education Courses from Social, Technology, and Personal Trait Perspectives

This study presents a theoretical model to explore the factors influencing students' acceptance of simulation tools in computing education. These factors include social influences, technology-related aspects, and personal characteristics. The term "simulation tools" refers to systems that can replicate complex processes and situations, providing students with realistic, hands-on experiences without the risks or costs associated with physical setups. To validate the proposed model, 312 responses from university students were collected. A cross-sectional online survey was conducted, and the participants were selected through purposive sampling. The findings indicated that subjective norms have the most significant direct effect on students' perceptions of usefulness, influencing their views on learning outcomes from using simulation tools in computing education courses. Additionally, social support and self-efficacy were also found to have significant effects. However, the impacts of fidelity and innovativeness were not supported. This study sets itself apart from previous research by using a comprehensive approach to explore the factors influencing student acceptance of simulation tools in computing education. Specifically, this research develops a theory based on the Technology Acceptance Model (TAM) and expands it by incorporating environmental factors and personal characteristics of students

Evaluating The Flipped Classroom Approach in Computer Science Curricula

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