Authors - Mary Anne Q. Zheng, Gerry Paul C. Genove Abstract - This paper presents a global benchmarking analysis to evaluate the Philippines’ infrastructure adaptability to 5G, IoT, and AI. Based on a systematic literature review of 35 peer-reviewed studies, industry analyses, and government reports from 2018 to 2025, it evaluates the current state of deployment, identifies key barriers, and proposes actionable strategies for alignment with international best practices. Results indicate that 5G coverage is expanding in metropolitan areas, IoT applications are emerging in sectors such as utilities and agriculture, and AI integration is concentrated in enterprise and pilot projects. However, socioeconomic inequality, an archipelagic geography, bureaucratic inefficiencies, and fragmented governance emerge as primary constraints on broader adaptability and inclusivity. Comparative analysis against global leaders and selected ASEAN peers, including Japan, South Korea, the United States, Germany, Malaysia, Vietnam, Thailand, and Singapore, highlights the importance of regulatory coherence, coordinated infrastructure investment, and integration with socioeconomic strategies. Policy lessons from peer nations demonstrate how centralized spectrum management, regulatory sandboxes, rural inclusion programs, and digital upskilling initiatives can accelerate adaptation. The findings highlight the need for coherent policies, shared infrastructure models, and capacity-building frameworks to align the Philippines with international best practices.
Authors - Monica Pilco, Lisset Ugaz, Edgar Ramos, Donovan Fuqua Abstract - The volatility and complexity of the marketplace require planning approaches that balance efficiency and adaptability in engineer-to-order (ETO) manufacturing environments. This study brings together lean, agile (Leagile) principles and smart manufacturing planning to improve responsiveness without compromising operational performance. Using the Fuzzy Analytic Hierarchy Process (F-AHP) methodology, this study combines a comprehensive literature review with the evaluation of strategies and drivers in a manufacturing case study. The results highlight the importance of Digital Integration, Digital Value Stream, and Integrated Planning to achieve leagile and sustainable operations. Conclusively, this study offers practical suggestions for leveraging digital drivers to strengthen resilience and long-term sustainable smart manufacturing.
Authors - Wahyu Wastuti Abstract - This study conducts a Systematic Literature Review (SLR) of 30 Scopus-indexed articles published between 2016 and 2025 to examine the conceptualization and development of Islamic Social Enterprise (ISE) and Islamic Social Entrepreneurship (ISEn). The review reveals that ISE is predominantly framed as an organizational form integrating Islamic principles into governance, accountability, and social welfare structures, while ISEn emphasizes individual-driven innovation and entrepreneurial behavior aligned with sharī‘ah values. Despite their differences, both models converge in their pursuit of financial sustainability, social justice, and community empowerment. Thematic analysis identifies four recurring domains in the literature: governance, innovation, sustainability, and maqāṣid al-sharī‘ah. Governance studies highlight ac-countability, transparency, and stakeholder trust, while research on ISEn emphasizes digitalization, creative financing, and entrepreneurial intention. Sustainability is viewed not only in terms of economic viability but also as encompassing ethical and social commitments, with maqāṣid al-sharī‘ah providing a normative framework that guides decision-making and ensures alignment with faith-based objectives. The findings reveal that the conceptual boundaries be-tween ISE and ISEn remain blurred but complementary, suggesting their integration offers both institutional stability and entrepreneurial dynamism. This study contributes to the academic discourse by clarifying definitions, synthesizing thematic trends, and identifying research gaps. Practically, the results offer implications for policymakers in designing supportive ecosystems, for practitioners in building hybrid models, and for academics in advancing theoretical clarity.
Authors - Jaimin Dave, Chintan Shah, Premal Patel Abstract - Intrusion Detection Systems (IDS) have been instrumental in preventing emerging threats from compromising a network. One of the key challenges in the field is to design an IDS with high detection coverage and low false positives. This study investigates the potential of hybrid deep learning systems to address this challenge. We developed a CNN+LSTM model that leverages the spatial feature extraction power of CNN and the temporal sequence learning capacity of LSTM. Experimental results demonstrate that the proposed CNN-LSTM model consistently outperforms LightGBM and XGBoost, achieving the highest accuracy (92.7%), precision (89.5%), recall (81.8%), and F1-score (84.3%), thereby confirming its robustness and effectiveness for intrusion detection. Additionally, comprehensive assessments indicate that for more complex and hidden attack patterns, the CNN+LSTM model is more advanced in terms of detection availability and effectiveness. The relevance of this work emphasises the need to modernise IDS systems by using hybrid deep learning techniques. Furthermore, the combination of convolutional neural networks with LSTM improves detection capacity and supports feature-level detail for new hazards.
Authors - Lukas Herout, Arvind Panwar Abstract - This paper explores the adoption of Zero Trust Architecture (ZTA) in small and medium-sized enterprises (SMEs) in the Czech Republic, aiming to identify key barriers, enabling factors, and readiness levels for its implementation. Drawing on survey data from 230 SME respondents, we investigate aware-ness of ZTA principles, current security practices, and the perceived value of specific support measures. The study is guided by clearly defined research questions and hypotheses. We construct a composite readiness index based on technical and organizational measures and analyze its relationship with firm size, security maturity, and training frequency. Key findings indicate that while ZTA awareness is relatively low (only 43.4% had heard of the term), many companies have already implemented foundational elements aligned with ZTA. Security readiness is significantly higher among firms with formal security policies and regular employee training. Medium-sized enterprises show notably greater implementation levels than micro and small firms. The most frequently requested forms of support include practical implementation guides, financial aid, and consultations with experts. These preferences vary by firm size, with smaller firms emphasizing financial support and larger ones favoring professional services. The paper concludes with a roadmap for SME-oriented ZTA adoption, emphasizing the need for scalable, low-barrier solutions and targeted support. Our findings provide empirical insights to inform policy design, training programs, and vendor strategies for secure digital transformation in the SME sector.
Authors - Hasin Mahir, Tahfizul Hasan Zihan, Md. Shirazim Munir, M. M. Kamal, Mahady Hasan, Md. Tarek Habib Abstract - Accurate Indoor Air Quality (IAQ) forecasting can support affordable and privacy-conscious interventions in classrooms and workplaces. However, most existing systems depend on cloud-based inference, which introduces additional cost, network dependency, and reliability issues. In this work, we present a comparative evaluation of three ensemble tree models—Random Forest, XGBoost, and LightGBM—implemented directly on an ESP32-S3 microcontroller for edge-based IAQ prediction of CO2 and PM2.5. A unified feature extraction strategy was employed, combining temporal encodings, lagged variables, and short rolling aggregates. Models were trained offline, converted into C, and embedded into the device firmware. Their performance was assessed with respect to (i) forecasting accuracy, using MAPE and RMSPE, and (ii) resource efficiency, including flash storage, RAM usage, and inference latency. Experimental results indicate that boosted methods (XGBoost and LightGBM) provide higher accuracy than Random Forest while staying within the strict memory and timing limits of the ESP32. This demonstrates the feasibility of fully self-contained IAQ forecasting on ultra-low-cost hardware, without reliance on cloud resources. Furthermore, we outline trade-offs between model complexity and efficiency, and provide a reproducible toolchain for exporting tree ensembles into deployable ESP32 firmware, highlighting their practicality for resource-constrained IAQ monitoring.
Authors - Amogh Kotha Nagaraj, Rajesh Eswarawaka, Aadi B Kumar, Sujay D Abstract - This paper will examine the synthesis of fuzzy logic, neural network applications and hybrid computational techniques with quantum computing for data-driven system improvement. The intersection of these innovations will provide exciting directions for intelligent decision-making, adaptive learning, and effective data processing. Fuzzy logic offers a mechanism for dealing with uncertainty, whereas these hybrid techniques blend the powers of multiple paradigms of computation. Neural networks provide the capability of deep learning, and quantum computing provides new means for optimization and parallelism. In this work, the theoretical background, real-world applications, and prospects of quantum-assisted hybrid intelligence systems are presented. The results imply that combined approaches can greatly enhance performance in high-complexity data environments, opens up opportunities for future intelligent systems.
Authors - Pimal Khanpara, Prasham Shah, Sharada Valiveti, Gaurang Raval Abstract - This article examines recent advancements in log anomaly detection, a key factor in ensuring system reliability and security. Research is categorized into four main approaches like traditional machine learning, deep learning, transformer-based models, and other emerging methods leveraging large language models (LLMs). Evaluations across benchmark datasets such as HDFS, BGL, Thunderbird, and Spirit reveal a steady progression in detection capabilities, with LLM-based approaches generally outperforming earlier methods. Techniques like LogRAG, LLMeLog, and EagerLog achieve F1-scores above 99% on certain datasets, marking substantial improvements over traditional baselines. Current research trends include multimodal analysis, few-shot and zeroshot learning, explainable AI, lightweight models, and deeper semantic understanding of logs. These directions are increasingly important as modern systems generate massive, diverse log volumes that demand more scalable and intelligent anomaly detection solutions. However, persistent challenges like log parsing accuracy, effective feature extraction, and model adaptability to evolving log formats continue to limit performance in real-world applications. This review provides a structured overview of current methods, highlights the strengths of LLM-based techniques, and outlines promising areas for future research to advance the field of log anomaly detection.
Authors - Sudarshan Sharma, Amita Chauhan, Sakshi Kaushal Abstract - Unmanned Aerial Vehicles (UAVs) have revolutionized industries such as defense, agriculture, and logistics due to their adaptability and ease of deployment. However, their growing dependence on wireless communication protocols, including MAVLink, UAVCAN, and UranusLink, introduces significant security challenges. This research offers a comprehensive security evaluation of the MAVLink protocol through experimental simulations in Software-In- The-Loop (SITL) environments. The study simulates Distributed Denial-of-Service (DDoS) attacks at both the Transmission Control Protocol (TCP) and User Datagram Protocol (UDP) layers to identify protocol vulnerabilities. Captured network traffic is compiled into a structured dataset and analyzed using machine learning algorithms, specifically Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighbor (KNN) algorithms, to differentiate between normal and malicious packets. The findings highlight critical security weaknesses in MAVLink communications and introduce a robust simulation and classification pipeline designed to enhance the effective detection of attacks, thereby improving UAV security.
Authors - Suparna Ray, Sushruthan Babu, Niranjan Babu Abstract - Our study explores the transformative role of Blockchain Technology (BCT) in capital markets (CM) employing a mixed-method research design. We synthesize extant research and analyse global regulatory case studies, particularly focusing on the Indian context. Utilising both bibliometric analysis and qualitative case study research, our paper highlights the evolution of academic dis-course, publication trends, and practical applications of Distributed Ledger Technology (DLT) across equities, debt, and derivatives markets. Our findings indicate that while BCT promises efficiency, transparency, decentralization, and cost reduction, its large-scale adoption remains constrained by regulatory uncertainty, scalability concerns, and institutional resistance. This paper emphasizes the emerging regulatory frameworks in the US, EU, and Asia-Pacific, acknowledging diverse approaches ranging from innovation-friendly sandboxes to cautious restrictions. We find limited empirical evidence on systemic risk, tokenization, liquidity, and ESG-linked instruments; hence, we propose future research avenues that integrate finance, law, computer science, and public policy. Through coherent mapping of the global regulatory landscape and reviewing scholarly contributions, our paper contributes to understanding how BCT is reshaping CM infrastructure and provides pathways for policymakers, regulators, and market participants toward responsible and sustainable digital transformation.
