An Integrated Model of Emotional Intelligence, Leadership, and Game Playability in Enhancing Team Effectiveness in MMOGs

Massively multiplayer online games (MMOGs) are becoming more and more significant places for people to work together online. Researchers still do not know much about how individual traits affect how teams work together in games.  Studies on players and game playability are still in the early stages. This study performed a quantitative analysis of data from 1,038 Chinese online players to investigate the interrelations between emotional intelligence (EI), transformational leadership (TL), team effectiveness (TE), and playability (PB). Drawing on the Input–Process–Output (IPO) model, social identity theory, and user experience theory, the research focused on how individual characteristics shape team outcomes and player experiences. The findings of this study indicate that both EI and TL have a positive impact on TE, which acts as a key mediating variable influencing the impact of both on PB. The findings also show that EI has a stronger link to PB than TL does. Notably, EI demonstrates a stronger association with PB than TL, highlighting its crucial role in enhancing team coordination and overall game enjoyment. This research augments current theories on virtual team collaboration and offers pragmatic guidance for game developers to improve player engagement and team coordination in online settings

Securing IoT-Cloud Applications with AQ-KGMO-DMG Enhanced SVM for Intrusion Detection

In contemporary society, the Internet has evolved into an indispensable facet of daily life, serving myriad functions across various domains. Intrusion detection, as a cornerstone of information security, plays a pivotal role in fortifying networks against potential threats, emphasizing the necessity for robust and reliable methods capable of discerning and mitigating network vulnerabilities effectively. In this work, a pioneering network intrusion detection model is introduced, leveraging Adaptive Quantum-Inspired KGMO with Dynamic Molecular Grouping (AQ-KGMO-DMG) for feature selection and employing Simplified Support Vector Machines (SVM) for the classification of intrusion data. The utilization of the UNSW-NB15 dataset serves as the litmus test for evaluating the efficacy of the developed intrusion detection model. Notably, this approach enhances the accuracy in categorizing classes with minimal instances while concurrently mitigating the false alarm rate (FAR). A notable innovation in this methodology involves the transformation of raw traffic vector data into a visual representation, thereby reducing computational costs significantly. To reduce the computation cost further, the raw traffic vector data is converted into picture format. The experimental findings showed that the proposed model performed better than conventional techniques in terms of FAR, accuracy, and computation cost

Bandwidth Optimization of Spline-Based Planar Sensor Using GA, PSO, and CMA-ES for EMC Testing and Wireless Communications

The expansion of communication technology and the increasing usage of the frequency spectrum drive the need for compatible device testing. Wideband antennas play a crucial role in supporting modern communication systems and applications, including those used as the sensors in electromagnetic compatibility (EMC) testing. Optimization techniques, such as genetic algorithm (GA), particle swarm optimization (PSO), and covariance matrix adaptation–evolution strategy (CMA-ES), are widely applied to enhance the bandwidth of electromagnetic devices. However, most studies focus on individual algorithms or limited comparisons, resulting in a lack of systematic evaluation within a unified framework. This paper fills that gap by directly comparing GA, PSO, and CMA-ES on the same planar sensor design, assessing their effectiveness in achieving the widest bandwidth. The planar sensor had a basic spline-based configuration using quadratic Bezier equation. A performance comparison based on a simulation showed that the planar sensor configuration with the best bandwidth was 17.77 GHz, spanning a frequency range from 2.23 GHz to 20 GHz, which was limited by the highest observation frequency of the available measuring instrument. Furthermore, verification of the realized planar sensor showed that the bandwidth reached 17.86 GHz, from 2.14 GHz to 20 GHz, with a geometric bandwidth of 273%

Enhancing Security of Databases through Anomaly Detection in Structured Workloads

In today’s world, the protection of databases in any global organization has become paramount due to the rapid growth of data and the new generations of cyber threats. This highlights the need for more enhanced security precautions to secure these databases containing sensitive information. One of the most advanced ways of enhancing database security is using an anomaly detection system, especially for structured workloads. Structured workloads typically exhibit predictable patterns of data access and usage, making them susceptible to displaying anomalies that may indicate unauthorized access, data manipulation, or other security breaches. Anomaly detection methods can identify patterns that are unusual, an indication of malicious activity, or a data security breach. The present research utilized the Isolation Forest algorithm to detect outliers in high-dimensional data sets. The main contribution and novelty of this research lies in leveraging the Isolation Forest algorithm for structured database workloads to proactively identify and mitigate potential security threats. Our study showed that the proposed model, with an accuracy of 85%, outperformed various state-of-the-art methods. Furthermore, anomaly detection systems powered by advanced algorithms and machine learning enable real-time database activities analysis, addressing challenges like preprocessing, model training and scalability

Foundations of Domain-specific Large Language Models for Islamic Studies: A Comprehensive Review

Large language models (LLMs) have undergone rapid evolution and are highly effective in tasks such as text generation, question answering, and context-driven analysis. However, the unique requirements of Islamic studies, where textual authenticity, diverse jurisprudential interpretations, and deep semantic nuances are critical, present challenges for general LLMs. This article reviews the evolution of neural language models by comparing the historical progression of general LLMs with emerging Islamic-specific LLMs. We discuss the technical foundations of modern Transformer architectures and examine how recent advancements, such as GPT-4, DeepSeek, and Mistral, have expanded LLM capabilities. The paper also highlights the limitations of standard evaluation metrics like perplexity and BLEU in capturing doctrinal, ethical, and interpretative accuracy. To address these gaps, we propose specialized evaluation metrics to assess doctrinal correctness, internal consistency, and overall reliability. Finally, we outline a research roadmap aimed at developing robust, ethically aligned, and jurisprudentially precise Islamic LLMs

Performance Enhancement of Video Data Transmission in Vehicular Adhoc Networks

The transmission of video frames among vehicles is a crucial global requirement in current road traffic conditions. Video data in vehicles may serve passengers with information as well as entertainment and, crucially, provide the drivers with dynamic information about the traffic conditions on the road ahead, assisting in reaching their destination safely at the earliest convenience using an optimal route. Intelligent Transportation System through its subsystem Vehicular Ad-hoc Networks (VANETs) make these necessities a reality. Inter-sessions carrying video data that exhibit multi-hop scenarios are encountered in the real world when people in a place of emergency communicate with the fire station/doctors/police. In such situations, the earlier the communication can occur, the smaller the damage. This work aimed to reduce the end-to-end delay (EED) experienced in VANETs when unicasting video streams among vehicles involved in inter-sessions by considering the packet delivery ratio (PDR) as a supporting parameter. The technique Network Coding was applied in VANETs and the possible recoder behaviors were simulated. An algorithm – Dynamic Recoder Recommender (DRR) – was designed and implemented to select an appropriate recoder on the fly in a path decided by the Adhoc On-Demand Vector routing protocol. This implementation outperformed the conventional network coding in terms of EED and PDR when applied with random recoders

A System Dynamics Model of 5G Low-Band Spectrum Management

The fifth-generation (5G) mobile communication system represents a major advancement in wireless technology, relying on effective radio spectrum management to ensure optimal performance. Among the available frequency ranges, the 5G low-band spectrum provides extensive coverage but limited capacity, making its efficient management a critical challenge. This study presents a predictive model based on the system dynamics approach to analyze the management of the 5G low-band spectrum. The model captures the interrelationships between technical and economic variables that influence spectrum allocation and service adoption over time. Three simulation scenarios—low, medium, and high allocation rates—were developed to examine allocation patterns and their effects on 5G service diffusion. The results revealed that spectrum management in 5G exhibits goal-seeking behavior constrained by spectrum scarcity, with service adoption showing a growth-to-saturation pattern. The findings demonstrate that appropriate low-band spectrum management can significantly enhance 5G deployment efficiency. The proposed model serves as a decision-support tool for policymakers and regulators, enabling evaluation of alternative management strategies prior to policy implementation and promoting evidence-based decision-making in future 5G spectrum policies

Enhancing Natural Language Inference Performance with Knowledge Graph for COVID-19 Automated Fact-Checking in Indonesian Language

Automated fact-checking is a key strategy to overcome the spread of COVID-19 misinformation on the internet. These systems typically leverage deep learning approaches through natural language inference (NLI) to verify the truthfulness of information based on supporting evidence. However, one challenge that arises in deep learning is performance stagnation due to a lack of knowledge during training. This study proposes using a knowledge graph (KG) as external knowledge to enhance NLI performance for automated COVID-19 fact-checking in the Indonesian language. The proposed model architecture comprises three modules: a fact module, an NLI module, and a classifier module. The fact module processes information from the KG, while the NLI module handles semantic relationships between the given premise and hypothesis. The representation vectors from both modules are concatenated and fed into the classifier module to produce the final result. The model was trained using the generated Indonesian COVID-19 fact-checking dataset and the COVID-19 KG Bahasa Indonesia. Our study demonstrates that incorporating KGs can significantly improve NLI performance in fact-checking, achieving a maximum accuracy of 0.8616. This suggests that KGs are a valuable component for enhancing NLI performance in automated fact-checking

Smart Campus Framework: Definition, Model, Measurement from Anthropocentric, Systemic and Technological Perspectives

This study developed a smart campus framework to help higher education institutions (HEIs) define and assess their smartness level. As HEIs faces growing demands for efficiency and competitiveness, implementing smart systems has become increasingly essential. A comprehensive framework is needed to support and improve the chances of successful adoption. This research addressed the question: how can a framework be created to measure campus smartness? The proposed framework encompasses a smart campus definition, an ideal model of smart system-based services, and a model for measuring smartness. The Design Science Research Methodology (DSRM) guided the development of the framework. Its evaluation was conducted in Indonesian HEIs to assess current smartness levels. The measurement model was validated through reliability testing (Cronbach’s Alpha = 0.883) and validity testing (Pearson Product Moment), both of which yielded strong results. Expert judgment from 10 specialists provided qualitative validation. The framework was applied across 10 campuses, involving 9,961 respondents. The results indicated that anthropocentric smartness (human-focused) was at levels 3 and 4 across all campuses, while systemic and technological smartness were mainly at level 2. Ten university leaders confirmed that the model effectively reflects actual campus conditions. The framework is built upon three perspectives of smartness: anthropocentric, systemic, and technological

AI-enhanced Cybersecurity Risk Assessment with Multi-Fuzzy Inference

The pace and complexity of modern cyber-attacks expose the limits of traditional ‘impact × likelihood’ risk matrices, which compress uncertainty into coarse categories and miss inter-dependent threat dynamics. We propose a three-layer multi-fuzzy inference system (MFIS) that models general infrastructure vulnerabilities and access-control weaknesses separately, then fuses them into a single, continuous 0-25 risk score. The framework was validated on three representative scenarios—catastrophic/continuous, serious/frequent, and minor/few attacks—encompassing sixteen threat criteria. Compared with a crisp 5 × 5 matrix, MFIS cut mean-absolute error and root-mean-square error by 90 to 99% and reproduced expert-panel judgments to within 0.55 points across all scenarios. Nine independent practitioners rated the prototype highly on usability (100% agreement), credibility (100%) and actionability (100%), with 78% willing to recommend adoption. These results demonstrate that MFIS delivers fine-grained, expert-aligned assessments without adding operational complexity, making it a viable drop-in replacement for time- or resource-constrained organizations. By capturing partial memberships and cross-domain interactions, MFIS offers a more faithful, adaptive and explainable basis for prioritizing cyber-defense investments and can be extended to emerging threat domains with modest rule-base updates