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This volume consitutes selected papers presented during the First Pan-African Conference on Artificial Intelligence, PanAfriCon AI 2022, held in Addis Ababa, Ethiopia, in October 2022. The 16 papers were thoroughly reviewed and selected from the 49 qualified submissions. The papers are organized in the following topical sections: AI in public health; agriculture; algorithmic optimization; human-machine interaction; economy and security.
This volume covers a wide array of topics related to research, technology and sustainability for technology researchers and educators. Chapter 1 explores the detection of fake news in a distributed environment. Material science is covered in Chapter 2, which explains the influence of MOS2, B4C, and graphite on the mechanical and dry sliding wear behavior of aluminum 7075 hybrid matrix composites. Chapter 3 focuses on sensors and antennas for smart sensor networks. Chapters 4 to 8 delve into various aspects of electrical and computer engineering, including induction motor condition monitoring, automatic conversion of building plans to graphs for robot navigation, and analysis of defects in mi...
This book constitutes the proceedings of the Second International Conference on Information and Communication Technology for Development for Africa, ICT4DA 2019, held in Bahir Dar, Ethiopia, in May 2019. The 29 revised full papers presented were carefully reviewed and selected from 69 submissions. The papers address the impact of ICT in fostering economic development in Africa. In detail they cover the following topics: artificial intelligence and data science; wireless and mobile computing; and Natural Language Processing.
This book constitutes the proceedings of the First International Conference on Information and Communication Technology for Development for Africa, ICT4DA 2017, held in Bahir Dar, Ethiopia, in September 2017. The 31 revised full papers presented were carefully reviewed and selected from 72 submissions. The papers address the impact of ICT in fostering economic development in Africa. In detail they cover the following topics: e-services, natural language processing, intelligent systems, mobile and wireless communication, privacy and security.
This book presents cutting-edge research and applications of deep learning in a broad range of medical imaging scenarios, such as computer-aided diagnosis, image segmentation, tissue recognition and classification, and other areas of medical and healthcare problems. Each of its chapters covers a topic in depth, ranging from medical image synthesis and techniques for muskuloskeletal analysis to diagnostic tools for breast lesions on digital mammograms and glaucoma on retinal fundus images. It also provides an overview of deep learning in medical image analysis and highlights issues and challenges encountered by researchers and clinicians, surveying and discussing practical approaches in general and in the context of specific problems. Academics, clinical and industry researchers, as well as young researchers and graduate students in medical imaging, computer-aided-diagnosis, biomedical engineering and computer vision will find this book a great reference and very useful learning resource.
In this work, the authors integrate three major basic themes of neuroscience to serve as an introduction and review of the subject.
The accelerating power of deep learning in diagnosing diseases will empower physicians and speed up decision making in clinical environments. Applications of modern medical instruments and digitalization of medical care have generated enormous amounts of medical images in recent years. In this big data arena, new deep learning methods and computational models for efficient data processing, analysis, and modeling of the generated data are crucially important for clinical applications and understanding the underlying biological process. This book presents and highlights novel algorithms, architectures, techniques, and applications of deep learning for medical image analysis.
This book provides detailed practical guidelines on how to develop an efficient pathological brain detection system, reflecting the latest advances in the computer-aided diagnosis of structural magnetic resonance brain images. Matlab codes are provided for most of the functions described. In addition, the book equips readers to easily develop the pathological brain detection system further on their own and apply the technologies to other research fields, such as Alzheimer’s detection, multiple sclerosis detection, etc.