Artificial Intelligence and Machine Learning Models
Year: 2026
Type: book
Abstract: Artificial intelligence and machine learning have become among the most influential technological advances of the twenty-first century, transforming scientific research and practical applications across healthcare, engineering, agriculture, economics, environmental sciences, education, and the social sciences. Beyond processing vast amounts of information, intelligent systems are increasingly expected to generate reliable knowledge, support evidence-based decision-making, and provide meaningful solutions to complex scientific and societal challenges. Despite these remarkable advances, the success of artificial intelligence is determined not only by sophisticated algorithms but also by the quality of the underlying data and the scientific rigor of the analytical process. Reliable artificial intelligence requires well-designed studies, representative data, sound statistical methodologies, robust validation procedures, and transparent evaluation frameworks. Consequently, statistics and data science have become more essential today than at any previous time in scientific history. They are no longer supporting disciplines but fundamental pillars that ensure the credibility, reproducibility, and interpretability of modern artificial intelligence. While statistics has traditionally been associated with data analysis, it now plays a central role throughout the entire research lifecycle—from study design and data collection to feature engineering, model development, uncertainty quantification, performance evaluation, explainable artificial intelligence, and evidence-based decision-making. Likewise, data science has evolved into an interdisciplinary field that integrates statistical thinking, computational methods, and domain expertise to transform data into trustworthy scientific knowledge. The chapters cover a broad spectrum of contemporary topics, including artificial intelligence, machine learning, deep learning, data mining, big data analytics, natural language processing, computer vision, optimization techniques, and intelligent decision-support systems developed for diverse scientific disciplines. A common objective shared by all contributions is the development of artificial intelligence systems that are not only accurate but also reliable, transparent, explainable, and scientifically verifiable . As artificial intelligence continues to reshape scientific discovery, future progress will increasingly depend on the integration of advanced computational models with rigorous statistical reasoning and responsible data science practices. We express our sincere appreciation to all authors, reviewers, members of the scientific committee, and the organizing committee for their valuable contributions. We hope this volume will serve as a valuable reference for researchers, academics, graduate students, and professionals working in artificial intelligence, machine learning, data science, and applied statistics.
Source: Özgür Yayınları eBooks
Authors: Yalçın Tahtalı, Lütfi Bayyurt, İbrahim Demir, Alain Attalie Mwema, Güvenç Arslan, Ayça Gözel, Elif Çiğdem Keleş, Tugce Hacioglu, Tugba Ozkal, Muhammet Oğuzhan Yalçın, Mehmet Can Duru, Serpil Aydin, Tuğçe Ata, Harun Benli, Peri Güneş, Aynur Körükçüoğlu, Ferdi Güler, Melek Çetinkaya Ocak, Burak Yüksel, Ecem Güven, Samet Kaya, Esin Köksal Babacan, Batuhan Selamoglu, Şafak Yasun, Derviş Topuz, Selçuk Tekgöz, Necla İ. Küçükçolak, Gürsel Kona, Enes Faruk Keskin, Mustafa Hakan Saldı, Ayfer Göksu Bakır, Murat Gökşin Bakır
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7208728201