eBook, PDF

Decision Mathematics, Statistical Learning and Data Mining (eBook, PDF)

Selected Contributions from ICMSCT2023, Manila, Philippines, September 20-21

Redaktion: Wan Yaacob, Wan Fairos; Mehmood, Obaid Ullah; Wah, Yap Bee

Beschreibung

This book is a collection of selected research papers presented at the Mathematics, Statistics and Computing Technology (ICMSCT2023), held at the UST Angelicum College, Philippines, from 20th to 21st September 2023. This biennial event is a result from collaborations of university partners in Malaysia, Thailand, Indonesia and Philippines.

Increasing investment in digital technologies is a challenge faced by most countries after the crisis caused by COVID-19 and the demand of technological revolution 4.0. Indirectly, regardless of their level of development, they take into account the importance of redesigning strategies for resilient and sustainable regional economic development, increasing regional resilience and minimizing recovery costs as a basis for development. In such situation, this book gather discussion, viewpoints and findings on the recent works of mathematical and computing technology applications in order to propose solutions to overcome adversity of digital resilience.

This book covers a wide range of topics on applied mathematics, which includes decision mathematics and also applied statistics covering statistical learning with applications. In addition, the book also highlight the latest application of statistical mining and data visualization, particularly on data mining, machine learning and data visualization. Editors believe this book will interest and influence researchers on the recent techniques, methodologies and applications to ensure digital resilience and support future research.

Technische Details

• Geräte: PC
• ohne Kopierschutz
• Größe: 20.49MB

Autor

WAN FAIROS WAN YAACOB is an associate professor at Mathematical Sciences Studies, College of Computing, Informatics and Mathematics, Universiti Teknologi MARA (UiTM), Malaysia. She is currently an Associate Research Fellow at Institute for Big Data Analytics and Artificial Intelligence (IBDAAI), Malaysia.  She had a basic statistics degree from Universiti Teknologi MARA, Master of Science in Statistics from Universiti Kebangsaan Malaysia and PhD in Statistics from Universiti Teknologi MARA. Her area of research interest focuses on statistical modelling in dengue disease and road accidents. In addition to her passion in modelling count data, her interest is also in data mining and machine learning. She had published papers in various well-known scientific journals. She has presented papers at conferences both locally and internationally. She is also a certified trainer of data analyst and machine learning master by RapidMiner, USA. She has received UiTM Top Talent Award for her contribution and achievements as successful researcher by UiTM. Her current work is a collaboration with researchers from North Texas University on machine learning dengue outbreak prediction in Malaysia. She was a visiting lecturer with the Fakultas Matematika dan Ilmu Pengetahuan Alam (FMIPA), Institut Teknologi Bandung, Indonesia. She has been invited as speaker for intensive courses on panel count model, data mining workshops and conferences from Institute Teknologi Bandung, PT Komatsu, Jakarta, Kasetsart University, UCSI, MIROS, ARI and IBDAAI. She is also a member of many organizations including IEEE and Institute of Statistics Malaysia.   YAP BEE WAH is Professor at UNITAR International University. She graduated with a BSc (Hons) (Mathematics Education) from University Sains Malaysia. She then obtained her Masters of Statistics degree from University of California, Riverside, USA, and her PhD (Statistics) from the University of Malaya. She recently joined UniTAR International University in 2021. Professor Dr. Yap Bee Wah was formerly a faculty member of the Center of Statistical and Decision Science Studies in Faculty of Computer and Mathematical Sciences (FSKM), Universiti Teknologi MARA. Her research interests are in Big Data Analytics, computational statistics and multivariate data analysis. Her research works are in healthcare, environment and business analytics. She was the head of Advanced Analytics Engineering Centre (AAEC) and a Research Interest Group in Faculty of Computer and Mathematical Sciences (2016 - 2020). In February 2021, AAEC became a Centre of Excellence of UiTM with the name Institute of Big Data Analytics and Artificial Intelligence (IBDAAI). She is now the principal fellow of IBDAAI, UiTM. She is supervising many PhD students. She is an active researcher and has published many papers. She is the conference chair of International Conference on Soft Computing in Data Science (2015-2019 & 2021). She is also one of the editors of the SCDS conference proceedings published in Springer CCIS series.   HAFIZ OBAID ULLAH MEHMOOD is an associate professor at COMSATS University Islamabad. He is a distinguished academic currently holding the position of tenured associate professor at COMSATS University Islamabad. With an illustrious career spanning multiple institutions, he previously served as an assistant professor at both COMSATS University Islamabad and Quaid-I-Azam University. Driven by a passion for interdisciplinary research, he earned his graduation honors from Universiti Teknologi Malaysia in 2013, where he was conferred with the Best Student Award. His academic journey has been marked by impactful mentorship, having supervised and co-supervised numerous PhD and MS students. Through his scholarly pursuits, Dr. Mehmood has contributed significantly to the scientific community, publishing a plethora of research articles, books and book chapters. His expertise lies at the intersection of mathematical biology, dynamics of suspensions, nano materials and non-Newtonian materials. His research methodologies encompass a range of analytical and numerical approaches.

Produktdetails

• Verlag: Springer Nature Singapore
• Seitenzahl: 381
• Erscheinungstermin: 26. Oktober 2024
• Englisch
• ISBN-13: 9789819734504
• Artikelnr.: 71996711