Naeem Ahmad – författare

Deep Learning Applications in Translational Bioinformatics, a new volume in the Advances in Ubiquitous Sensing Application for Healthcare series, offers a detailed overview of basic bioinformatics, deep learning, various applications of deep learning in translational bioinformatics including deep learning ensembles, deep learning in protein classification, detection of various diseases, prediction of antiviral peptides, identification of antibiotic resistance, computer aided drug design and drug formulation.This new volume helps researchers working in the field of machine learning and bioinformatics to foster future research and development in ensemble deep learning and inspire new bioinformatics applications that cannot be attained by using traditional machine learning models.- Addresses the practical application of deep learning algorithms to a wide range of bioinformatics challenges- Presents integrative and multidisciplinary approaches to ubiquitous healthcare- Includes case studies to illustrate the concepts discussed

Deep Learning Applications in Translational Bioinformatics, a new volume in the Advances in Ubiquitous Sensing Application for Healthcare series, offers a detailed overview of basic bioinformatics, deep learning, and various applications of deep learning in translational bioinformatics, including deep learning ensembles, deep learning in protein classification, detection of various diseases, prediction of antiviral peptides, identification of antibiotic resistance, computer aided drug design and drug formulation. This new volume helps researchers working in the field of machine learning and bioinformatics foster future research and development.

Addresses the practical application of deep learning algorithms to a wide range of bioinformatics challengesPresents integrative and multidisciplinary approaches to ubiquitous healthcareIncludes case studies to illustrate the concepts discussed

This edited book provides a comprehensive exploration of the latest advancements and methodologies in signal processing for intelligent systems. The book serves as a multidisciplinary reference, integrating theoretical foundations, algorithmic developments, and real-world applications across diverse domains, including healthcare, agriculture, environmental monitoring, and human activity recognition. By consolidating recent advancements and cross-domain insights, this book bridges the gap between theory and practice in sensor signal processing for intelligent systems. It fosters interdisciplinary collaboration and stimulates further research in the development of more efficient and scalable intelligent systems for diverse applications.

This comprehensive volume focuses on the latest advancements in Generative AI, including state-of-the-art techniques and models that are pushing the boundaries of what is possible. It covers recent developments in areas such as Generative AI models, transfer learning and Natural Language Processing (NLP) highlighting their potential to revolutionize content generation and creative applications including OpenAI, LangChain, NLTK and their practical implementations across diverse domains. The volume provides insights into emerging research areas, novel architectures, and innovative approaches in Generative AI, giving searchers a glimpse into the exciting future of the field. The aim is to offer readers a deep understanding of Generative AI and how it can be harnessed to tackle complex real-world challenges.