Al-Kindi Center for Research and Development (KCRD) (E-Journals)
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6248 research outputs found
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Safety Culture in The Construction Industry: A Proposed Enhanced Safety Management Program
No full textThe construction industry faces persistent challenges in workplace safety, with safety compliance being a key determinant of accident prevention. This study investigates the relationships among positive leadership, job competence, safety participation, and safety compliance behavior to identify actionable strategies for enhancing safety performance. Data were collected from 249 respondents across two major construction companies, CSCO (Phils) and SM Development Corporation, using structured surveys and analyzed through Pearson correlation and multiple regression methods. The findings revealed that positive leadership significantly influences safety compliance behavior, accounting for 22.8% of its variation, while safety participation emerged as a stronger predictor, explaining 33.6% of the variance. Conversely, job competence showed limited direct impact, suggesting a disconnect between technical proficiency and compliance adherence. The study highlights the importance of integrating leadership development with targeted safety training to address gaps in emergency preparedness and proactive safety engagement. Recommendations include fostering a safety-oriented culture through participative leadership, recognition programs, and leveraging technology for training and hazard reporting. The research provides a comprehensive framework for reducing workplace accidents and enhancing employee well-being by aligning leadership, participation, and competency strategies. These findings underscore the critical need for collaborative efforts among stakeholders to build safer, more efficient construction environments. Keywords: Positive leadership, safety compliance, job competence, safety participation, construction safety
From Green to Growth: A Critical Assessment of Legal Frameworks and Environmental Consequences in the PIK 2 Project, Tangerang
No full textThe conversion of protected forests into production forests in the Pantai Indah Kapuk (PIK) 2 project in Tangerang Regency raises significant legal and environmental implications. This study analyzes the legal aspects and environmental impacts of this policy using normative and empirical approaches. Legally, this land-use change is controversial due to the lack of involvement of key stakeholders and its potential violation of spatial planning regulations and good governance principles. Environmentally, the project threatens ecosystem balance, reduces biodiversity, and negatively impacts coastal communities that depend on natural resources in the area. Therefore, an in-depth and transparent assessment is necessary before making land-use conversion decisions. This study recommends a comprehensive evaluation involving public participation to ensure policies align with sustainable development principles and environmental justice
Advancing Renewable Energy Systems through Explainable Artificial Intelligence: A Comprehensive Review and Interdisciplinary Framework
No full textExplainable Artificial Intelligence (XAI) plays a pivotal role in advancing transparency, reliability, and informed decision-making in renewable energy systems. This review provides a comprehensive analysis of state-of-the-art XAI methodologies—including Shapley Additive Explanations (SHAP), Local Interpretable Model-agnostic Explanations (LIME), Deep Learning Important FeaTures (DeepLIFT), and rule-based models—by critically evaluating their applications, advantages, and limitations within renewable energy research. Despite notable progress, significant challenges persist, including computational inefficiencies, the absence of standardized evaluation metrics, and the inherent trade-off between model accuracy and interpretability. This study proposes a novel interdisciplinary framework that integrates domain-specific XAI methodologies, standardized benchmarking protocols, and collaborative efforts between AI researchers and energy experts. By addressing these challenges, this review aims to facilitate the broader adoption of interpretable and reliable AI-driven solutions for the sustainable advancement of renewable energy systems
Demystifying Dimensional Modeling for Modern Data Warehousing
No full textThis article demystifies dimensional modeling for data warehousing professionals by breaking down complex concepts into accessible components. It explores the foundational elements of dimensional design—fact tables, dimension tables, and star schemas—while delving into advanced topics like slowly changing dimensions, conformed dimensions, and hierarchical structures. The article examines implementation considerations, including surrogate keys versus natural keys, star versus snowflake schemas, and aggregation strategies that impact performance. It demonstrates how dimensional modeling principles remain relevant in modern data environments by illustrating real-world applications in retail and healthcare settings, integration with data lakes, and adaptation to cloud platforms. By translating theoretical concepts into practical implementation decisions, the article guides readers in understanding how dimensional modeling affects query performance, data integrity, and analytical capabilities in business environments
Strategic Implementation of Cloud Automation for Enhanced Scalability
No full textThe rapid evolution of cloud infrastructure demands effective automation strategies for optimal scalability. Organizations are increasingly adopting serverless computing, microservices architecture, and Infrastructure as Code (IaC) to enhance their cloud operations. The shift towards automated cloud management has revolutionized how businesses handle infrastructure deployment, resource optimization, and system maintenance. Through the implementation of machine learning-driven solutions and advanced automation frameworks, organizations can achieve significant improvements in operational efficiency, cost management, and security controls. The integration of these modern practices enables enterprises to build resilient, scalable systems while maintaining high performance and reliability across their cloud environments
Wearable AI for Cardiovascular Health Monitoring: Enabling Early Detection and Prevention
No full textDespite advancements in medicine, cardiovascular diseases (CVD) remain the leading cause of death in the world, highlighting the urgent need for continuous tracking and early detection. Wearable technology powered by artificial intelligence (AI) enables real-time, non-invasive monitoring of cardiovascular health. This study investigates the potential of wearable technology and artificial intelligence (AI)-based predictive analytics to revolutionize the early diagnosis and prevention of coronary vascular disease (CVD). Machine learning (ML) algorithms include decision trees, random forests, support vector machines, and deep neural networks that analyze medical data such as heart rate variability, activity levels, and sleep maintenance to detect subtle cardiovascular risk factors. These models can detect deviations earlier than conventional diagnostics, and an individualized data-driven therapy can also be designed for them. Wearable AI systems paired with imaging, genetics, and electronic health record (EHR) data provide a holistic view of patient health. However, challenges like data privacy, algorithmic bias, and clinical integration must be addressed to ensure responsible adoption. This study aims to maximize the potential of wearable AI to enable proactive health management by reviewing the current status of wearable AI, featuring recent advances, examples of use cases, and implementation methods in cardiovascular care
Stock Market Analysis Using Deep Learning
No full textThis research explores advanced transformer architectures for stock market prediction, focusing on TimeGPT and Spacetimeformer models. We implement sophisticated time-series transformers that leverage self-attention mechanisms and temporal pattern recognition to enhance prediction accuracy. Our methodology combines multi-layered transformer pipelines with specialized market-specific encodings and quantum-inspired computing elements. Testing across diverse market conditions demonstrates significant improvements over traditional approaches, achieving accuracy rates of 96.2% in short-term predictions and 94.8% in long-term forecasting. The system processes financial time series data through multi-head attention layers while maintaining sub-millisecond prediction times, establishing new benchmarks in market prediction performance. This work contributes novel techniques for handling market volatility and regime changes, with particular strength in adapting to extreme market events
Security Implications of Fully Autonomous Process Agents in Enterprise Workflows
No full textThe increasing adoption of Agentic Process Automation (APA) introduces significant security challenges as organizations transition from traditional Robotic Process Automation (RPA) to more advanced autonomous systems. This article examines the fundamental security implications of this evolution, highlighting how the autonomous nature of these agents—characterized by independent decision-making, continuous learning, and adaptive behaviors—creates an expanded attack surface with unique vulnerabilities. The investigation analyzes several critical security concerns, including adversarial AI attacks targeting machine learning models, data privacy and compliance risks stemming from extensive data access requirements, unauthorized access vulnerabilities, and process integrity threats. Drawing on recent studies and experimental evidence, the article proposes a comprehensive security-first design policy incorporating robust authentication mechanisms, continuous monitoring capabilities, adversarial defense strategies, and specialized data protection techniques. The article concludes by examining emerging security paradigms for future APA deployments, including agent-to-agent security protocols, federated learning protections, self-healing mechanisms, and evolving regulatory frameworks, emphasizing the importance of collaborative security development for these increasingly sophisticated autonomous systems
Demystifying the MITRE ATT&CK Framework: A Practical Guide to Threat Modeling
No full textThe MITRE ATT&CK Framework has emerged as a transformative approach to cybersecurity, shifting focus from traditional signature-based defenses to behavior-centric threat modeling. This article provides a comprehensive examination of the framework\u27s structure, applications, and real-world impact across the security landscape. The hierarchical organization of tactics, techniques, and procedures offers security practitioners a common language for understanding adversary behaviors based on empirical observations rather than theoretical vulnerabilities. When applied to endpoint security, the framework reveals concentrated patterns of attack techniques, enabling targeted defensive strategies with measurable operational improvements. Through systematic threat modeling, organizations can identify relevant adversaries, prioritize techniques based on potential impact, and map attack paths to implement proportionate countermeasures. Case studies including the SolarWinds compromise demonstrate the framework\u27s practical value, with ATT&CK-aligned organizations showing enhanced capabilities in early threat detection and incident response. By adopting this structured approach to security, organizations can transform their posture from reactive to proactive, focusing limited resources on the specific techniques most relevant to their threat landscape while developing adaptive capabilities that respond to evolving adversary behaviors
AI-Powered Virtual Agents in CRM: Revolutionizing Customer Interaction and Service Automation
No full textThis article examines the transformative impact of AI-powered virtual agents on Customer Relationship Management systems. It explores how these sophisticated software entities are fundamentally redefining customer interactions and service automation across industries. It traces the evolution of CRM systems through four distinct generations, culminating in today\u27s AI-driven solutions that leverage natural language processing, machine learning, and sentiment analysis to deliver personalized customer experiences. It details how virtual agents enhance omnichannel engagement, enable intelligent self-service, and deliver proactive assistance that anticipates customer needs. Beyond basic task automation, these systems manage complex processes, optimize service delivery through intelligent routing, and contribute to organizational learning through operational intelligence. The article analyzes implementation considerations including integration requirements, change management strategies, and ethical implications across diverse market contexts. It further explores emerging capabilities in emotional intelligence, augmented reality integration, and autonomous decision-making that will define the future direction of virtual agents in CRM environments