Innovations in Optimization and Machine Learning

Innovations in Optimization and Machine Learning

Projected Release Date: September, 2024|Copyright: © 2025 |Pages: 320
DOI: 10.4018/979-8-3693-5231-1
ISBN13: 9798369352311|ISBN13 Softcover: 9798369352328|EISBN13: 9798369352335
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Description & Coverage
Description:

In today's rapidly evolving world, businesses are confronted with the complex task of streamlining their operations, utilizing machine learning to their advantage, and maneuvering through the intricacies of artificial intelligence. It has become increasingly essential to allocate resources effectively, make informed decisions based on data, and capitalize on AI technologies. However, many organizations require assistance in understanding these disciplines' theoretical principles, practical implementations, and ethical implications.

Innovations in Optimization and Machine Learning serve as a comprehensive solution, offering a deep dive into optimization, machine learning, and AI. By unraveling the complexities and providing practical insights, it empowers researchers, practitioners, students, and enthusiasts to understand and contribute to advancing these fields. The book covers many topics, from evolutionary algorithms to ethical AI development, ensuring a thorough understanding of key concepts and their real-world implications.

By bridging the gap between theory and practice, this book equips readers with the knowledge and tools to address optimization, machine learning, and AI challenges. Whether you're looking to enhance operational efficiency, develop innovative solutions, or drive meaningful change, this book is your guide to unlocking the transformative potential of optimization, machine learning, and AI in today's dynamic landscape.

Coverage:

The many academic areas covered in this publication include, but are not limited to:

  • Bayesian Optimization
  • Bias and Fairness in Machine Learning Algorithms
  • Computer Vision and Image Processing
  • Convex Optimization
  • Deep Learning Architectures
  • Ethical Considerations in AI Development
  • Evolutionary Algorithms
  • Financial Optimization Models
  • Healthcare Optimization
  • Human-AI Collaboration and Augmentation
  • Metaheuristic Optimization Methods
  • Natural Language Processing
  • Neural Network Optimization Techniques
  • Reinforcement Learning
  • Wastewater Treatment Plants Optimization
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Editor/Author Biographies

Dr. Mzili Toufik holds the position of Assistant Professor in the Department of Computer Science at the Faculty of Science, Chouaib Doukkali University. Recognized as a distinguished researcher, he has made noteworthy contributions to the fields of metaheuristics, optimization, and scheduling problems. His scholarly impact is evident through numerous publications in esteemed Q1 journals. Additionally, Dr. Mzili Toufik has demonstrated his expertise as a peer reviewer for several prestigious journals, showcasing his commitment to advancing the academic discourse in his field.

A competent professional with nearly 20 years of experience in Research and Development / Teaching chemical engineering graduates and post graduates.

• Exercises judgment within generally defined practices in selecting methods & techniques for obtaining teaching solutions; ensuring compliance to quality measures and maintenance requisite documentation as well as records

• Skills in managing the administrative activities entailing student management, faculty appraisal/training and upholding of the institution's motto

• Proficiency in teaching Oil and Gas Pipeline and core chemical engineering subjects to Chemical Engineering Graduate and post graduate students.

• Experience in developing curriculum to accommodate different learning styles & maximize students' comprehension

• Received research awards

• Exposure in working with T.A.N.A.P. Gas Pipeline Project at Worley Parsons.

• Exposure in planning and organizing day-to-day research activities and resolving the procedural problem as appropriate to the timely accomplishment of research objectives

• An effective leader with proven skills in leading teams during the project phase, training & guiding team members and enabling knowledge sharing among the team

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