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E-Book, Englisch, Band 2258, 444 Seiten
Abdelgawad / Ahmad A New Direction for Informatics and Problem-Solving
Erscheinungsjahr 2026
ISBN: 978-3-032-39783-6
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark
Selected Papers from the International Conference on Informatics and Problem Solving (ICIPS 2025)
E-Book, Englisch, Band 2258, 444 Seiten
Reihe: Lecture Notes in Networks and Systems
ISBN: 978-3-032-39783-6
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark
This book brings together peer-reviewed research that bridges competitive programming, artificial intelligence, data analytics, and modern computing education with practical applications and emerging technologies. It highlights innovative approaches for developing problem-solving skills, advancing trustworthy AI, enhancing learning through intelligent systems, and addressing real-world challenges through interdisciplinary research. Covering topics ranging from programming education and large language models to knowledge graphs, machine learning, human–machine interaction, and student well-being, this book reflects current research directions presented at ICIPS 2025. It is intended for researchers, academics, graduate students, and practitioners in computer science, artificial intelligence, and educational technologies. This book serves as a valuable reference for research, teaching, curriculum development, and future innovations in intelligent computing and problem solving.
Zielgruppe
Research
Autoren/Hrsg.
Weitere Infos & Material
Evaluating the Skills Required for ICPC Programming Contest in Palestine: An Analytical Study.- Problem Solving Training Impact on Employment Ratio for IT Students in Palestine: PCPC Case Study (2012–2025).- Expanding Competitive Programming Education in Palestine: A Multi-Level Training Model to Develop Problem-Solving Skills Across Universities.- A Mobile Application for Early Detection of Skin Cancer.- Developing a VR Framework for Robot Arm Teleoperation.- DRASS-Net: A Hybrid Multi-Scale State-Space Framework for Classification of Forest Tree Species Using Environmental Attributes.
