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Understanding the capabilities of data quality measurement and monitoring
No full textBackground of Study: Ensuring high-quality data is essential for organizations that depend on analytics, automation, and regulatory compliance.
Aims and Scope of Paper: This paper explores the foundational concepts and evolving practices of two interrelated capabilities: data quality measurement and data quality monitoring. While measurement focuses on quantifying attributes such as accuracy, completeness, consistency, and timeliness, monitoring emphasizes the continuous detection and alerting of anomalies during data operations.
Methods: This paper examines the application of frameworks like Total Data Quality Management (TDQM), ISO 8000, and Data Management Association Data Management Body of Knowledge (DAMA DMBOK), alongside emerging tools such as rule-based engines, metadata-driven platforms, and AI-driven anomaly detection systems.
Results: Findings reveal a persistent gap in systems that integrate both measurement and monitoring effectively, hindering long-term data governance. This paper discusses a case study of the Data Quality framework implementation in the Healthcare sector. It was found that the healthcare organization implemented the Total Data Quality Management (TDQM) framework and Apache Griffin to ensure the accuracy, completeness, consistency, timeliness, and validity of clinical and IoT data through continuous monitoring, automated validation, and anomaly detection. Governance mechanisms aligned with ISO 8000 and HIPAA standards ensured full compliance, traceability, and accountability across all data quality and auditing processes. This study contributes to a deeper understanding of how integrated data quality practices can support digital transformation and operational resilience across industries.
Conclusion: The paper concludes by recommending the adoption of continuous quality measurement practices aligned with governance policies and supported by both human expertise and automation, arguing that data quality must be embedded as a dynamic and strategic function within the digital enterprise. While measurement emphasizes the quantification of data attributes such as accuracy, completeness, consistency, and timeliness, monitoring focuses on the continuous detection and alerting of data anomalies during data operations
Prototype development of Web AI-based decision support system: insights and recommendations for satellite anomaly identification
No full textSatellites are vital for various applications, including communication, navigation, earth observation and any other research. Satellite reliability is important for mission continuity and space sustainability, as anomalies can cause costly failures, loss of operational capacity, and loss of sustainability in outer space. It results in crowded orbit and poses a risk to other active or future missions. Traditional methods of identifying and resolving anomalies are often reactive and limited in their ability to handle the complexity and volume of satellite data. Hence, this paper proposes a Web AI-based Decision Support System (DSS) designed to enhance the identification and resolution of satellite anomalies. The proposed Web AI-based DSS framework integrates Machine Learning (ML) based Trade-Space Exploration (TSE) as the model base and Generative AI as the knowledge base to offer insights and recommendations for anomaly prevention and decision making prior to the launch of the satellite into orbit. The system architecture includes a statistical data module for satellite anomaly identification and a decision support application. The Seradata database is utilized as the main source data for satellite anomalies which consist of around 4050 data since 1957. This system aims to identify anomalies to prevent any failures that mostly occur during satellite orbit and provide appropriate recommendations for counteractive actions. A detailed literature review highlights the current state of satellite anomaly identification and the application of the Web AI-based DSS in various fields. The review identifies gaps in existing research, emphasizing the need for a specialized DSS in satellite anomaly identification. The proposed framework addresses these gaps by incorporating state-of-the-art artificial intelligence (AI) and a decision-making system. This paper also summarizes the key findings of the case studies and discusses the benefits of using a Web AI-based DSS for satellite anomaly identification management. It also addresses potential challenges and limitations, such as the need for continuous updates to the model and the integration of diverse data sources. The contribution of this paper can be summarized as (i) outlines Web AI-based DSS for satellite anomaly identification, (ii) present comprehensive taxonomy of the Web AI-Based DSS methods applied to space situational awareness (SSA), which addresses the insight of factors leading to the loss of a satellite and its recommendation to prevent satellite failure during their operational. In a nutshell, the proposed Web AI-based DSS framework provides a robust solution to manage satellite anomalies, improves operational efficiency, and mitigates the risk of orbital satellite failure. The paper outlines prospective research directions that include the advancement of more sophisticated anomaly identification algorithms and the incorporation of additional data sources, such as data cost, to further improve system performance
Enhanced obstacle detection using bilateral vision-aided transformer neural network for visually impaired persons
No full textObstacle detection remains vital in autonomous navigation and assistive technologies, especially for visually impaired individuals. This work introduces an enhanced obstacle detection framework based on a Bilateral Vision Transformer and Convolution Kernel Neural Network (BViT-CKNN). The system incorporates stereo vision data and applies a bilateral filter to reduce noise while preserving edge details. A Vision Transformer (ViT) model is then used for global feature extraction, and a Convolution Kernel Neural Network (CKNN) captures fine-grained local features. Evaluated using the COCO dataset, the proposed BViT-CKNN achieves superior performance in precision (0.93), recall (0.91), F1-score (0.92), and Mean Absolute Error (MAE) reduction (3.16%) compared to existing method
Enhancing healthcare services through machine learning and artificial intelligence applications
No full textThe rapid growth of digital technologies has positioned Machine Learning (ML) and Artificial Intelligence (AI) as key drivers in modernizing healthcare. This study explores recent advancements in applying ML and AI to improve disease diagnosis, predictive analytics, treatment personalization, and patient monitoring. Using a systematic literature review of peer-reviewed studies from IEEE Xplore, PubMed, and Scopus, the research prioritizes clinical relevance, methodological rigor, and innovation. Findings show that deep learning models, notably convolutional and recurrent neural networks, enhance diagnostic accuracy and chronic disease prediction. AI tools also support real-time decision-making, remote monitoring, and early detection of complications, especially in resource-limited settings. Despite their potential, challenges remain in data privacy, model transparency, and interdisciplinary collaboration. This study highlights the transformative role of AI/ML in patient-centered care and efficiency, stressing the need for ethical standards and strong data governance to guide implementation across diverse health systems
100 years of thiosemicarbazone: a bibliometric study using Scopus database
No full textThiosemicarbazones (TSC) have received much attention in the scientific community due to their potential therapeutic applications, particularly in cancer and infectious disease treatment. This study aims to provide a comprehensive analysis of the global research trends, key contributors, and collaboration networks in TSC research. The bibliometric analysis utilizes a refined dataset of 6287 articles sourced from the Scopus database, covering the extensive period from 1922 to 2022. Based on the collected data, it can be determined that significant growth has occurred for the past century in TSC-related publications, especially in recent decades, with India, China, and the United States have emerged as major contributors to a substantial portion of the research output. In brief, this study provides valuable insights into global research dynamics, highlighting major contributors and emerging trends. Three most emerging trends discovered by this analysis are shift toward multifunctional therapeutic applications; development of metal complexes for enhanced bioactivity and globalization of research with growing contributions and collaborations. The implications of these findings underscore the importance of strategic partnerships and interdisciplinary approaches in propelling TSC research forward in the coming years. This study provides the first comprehensive, century-long analysis of global research trends on thiosemicarbazone (TSC), revealing its growing importance in therapeutic applications such as cancer and antimicrobial treatment. It identifies key contributors, emerging topics, and international collaborations, offering a valuable roadmap for future research. By visualizing data through bibliometric tools, the study supports evidence based decision making for researchers and institutions
Pengenalan linguistik
No full textLinguistics is a study of language, so the introduction to this knowledge is very important. Knowledge of linguistics is related to the characteristics of language studies, the nature of language, reflections on the thoughts of linguistic figures and the history of the development of linguistics; external factors or paralinguistics, language variations, paradigmatic rules, stylistics and scientific language. In this regard, this book Introduction to Malay Linguistics describes in detail the field of linguistics and the nature of language; the history of the development of linguistics; linguistic structure, external factors, language variations, and paradigmatic rules; stylistic elements in prose and poetry; and the characteristics of the scientific Malay language. This book provides students with a general overview of the basics of language and linguistics, focusing on the nature of language, the basic concepts of the sound system (phonology), word formation and structure (morphology), sentence structure (syntax), the meaning of words and expressions (semantics). It will also discuss child language acquisition, dialects, social aspects of language and language change. This book contains 14 chapters to help students understand linguistic concepts with the help of various illustrations in the form of diagrams and related tables
Feeding the mind: the connection between diet, drugs, and mental health volume 2 innovations and specialized approaches in nutritional Neuroscience
No full textThe Islamic ethical principles and Maqasid al-Shariah to enhance digital competency among adolescents
No full textModern advancements, including computers, smart devices, high-speed internet and generative artificial intelligence,
have made technology an integral part of adolescents' daily lives. The digital age has significantly transformed how
they engage with technology, reshaping the ways they learn, communicate and interact. As digital environment
become essential to their social and educational development, the need for digital competency has grown. However,
digital competency should go beyond technical skills, incorporating ethical decision-making, identities and
responsible online behavior. This article explores the integration of the Islamic ethical and Maqasid al-Shariah (the
objectives of Islamic law) framework to enhance digital competency among adolescents. By focusing on the core
Islamic principles such as adab (proper conduct), amanah (trustworthiness), ihsan (excellence) and niyyah (intention).
The article demonstrates how these values guide adolescents in engaging with digital technologies responsibly. Furthermore, the objectives of the Maqasid al-Shariah focus on the preservation of religion (din), life (nafs), intellect (aql), lineage (nasl) and property (mal) will provide a holistic approach that ensures digital practices promote the well-being of individuals and society. This framework encourages adolescents to use digital platforms ethically, protect their personal integrity, rights, responsibilities, well-being and contribute positively to the community. The article
highlights how aligning digital behavior with these Islamic values fosters both technical proficiency and a sense of
social responsibility. Ultimately, holistic Islamic ethical principles and Maqasid al-Shariah into digital literacy education supports the personal, social and spiritual growth of adolescents. Thus, promoting a balanced approach to digital engagement that contributes to the broader goals of Islamic ethics and societal well-being
