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      1. Naturvetenskap och teknik
      2. Teknik och industri
      3. Biokemisk teknik

      Predictive Modeling in Biomedical Data Mining and Analysis

      AvSudipta Roy,Lalit Mohan Goyal

      Häftad, Engelska, 2022

      1 778 kr

      Beställningsvara. Skickas inom 10-15 vardagar. Fri frakt över 249 kr.

      Beskrivning

      Predictive Modeling in Biomedical Data Mining and Analysis presents major technical advancements and research findings in the field of machine learning in biomedical image and data analysis. The book examines recent technologies and studies in preclinical and clinical practice in computational intelligence. The authors present leading-edge research in the science of processing, analyzing and utilizing all aspects of advanced computational machine learning in biomedical image and data analysis. As the application of machine learning is spreading to a variety of biomedical problems, including automatic image segmentation, image classification, disease classification, fundamental biological processes, and treatments, this is an ideal reference.

      Machine Learning techniques are used as predictive models for many types of applications, including biomedical applications. These techniques have shown impressive results across a variety of domains in biomedical engineering research. Biology and medicine are data-rich disciplines, but the data are complex and often ill-understood, hence the need for new resources and information.



      • Includes predictive modeling algorithms for both Supervised Learning and Unsupervised Learning for medical diagnosis, data summarization and pattern identification
      • Offers complete coverage of predictive modeling in biomedical applications, including data visualization, information retrieval, data mining, image pre-processing and segmentation, mathematical models and deep neural networks
      • Provides readers with leading-edge coverage of biomedical data processing, including high dimension data, data reduction, clinical decision-making, deep machine learning in large data sets, multimodal, multi-task, and transfer learning, as well as machine learning with Internet of Biomedical Things applications

      Produktinformation

      • Utgivningsdatum:2022-08-26
      • Mått:191 x 235 x 25 mm
      • Vikt:700 g
      • Format:Häftad
      • Språk:Engelska
      • Antal sidor:344
      • Förlag:Elsevier Science
      • ISBN:9780323998642

      Utforska kategorier

      • Biokemisk teknik inom Naturvetenskap och teknik
      • Biomedicinsk teknik inom Medicin

      Mer om författaren

      Dr. Sudipta Roy received his Ph.D. in Computer Science & Engineering from the Department of Computer Science and Engineering, University of Calcutta. He is author of more than forty publications in refereed national / international journals and conferences. Dr. Roy holds a US patent in medical image processing, and filed an Indian patent in smart agricultural systems. Dr. Roy serves as an Associate Editor of IEEE Access, and IEEE and International Journal of Computer Vision and Image Processing (IJCVIP). His fields of research interest are biomedical image analysis, image processing, steganography, artificial intelligence, big data analysis, machine learning and big data technologies. Currently, he is a Research Associate at PRTTL, Washington University in St. Louis, Saint Louis, MO, USA Dr. Lalit Mohan Goyal has completed Ph.D. from Jamia Millia Islamia, New Delhi, in Computer Engineering; M.Tech (Honors) in Information Technology from Guru Gobind Singh Indraprastha University, New Delhi; and B.Tech (Honors) in Computer Engineering from Kurukshetra University, Kurukshetra. He has 17 years of teaching experience in the area of Theory of Computation, Parallel and Random algorithms, Distributed Data Mining & Cloud Computing. He has completed a project sponsored by the Indian Council of Medical Research, Delhi. He has published and communicated more than 40 research papers and attended many workshops, FDPs and Seminars. He has filed nine patents in the area of Artificial Intelligence and Deep Learning. He is the reviewer of many reputed journals, conferences book series. Presently, He is working in Department of Computer Engineering, J.C. Bose University of Science & Technology, YMCA, Faridabad. Dr. Valentina Emilia Balas is Full Professor in the Department of Automatics and Applied Software, Faculty of Engineering, Aurel Vlaicu University of Arad, Romania. She holds a PhD in Applied Electronics and Telecommunications from the Polytechnic University of Timisoara. Her research interests include intelligent systems, fuzzy systems, soft computing, smart sensors, information fusion, modeling, and simulation.Dr. Balas serves as Director of the Intelligent Systems Research Center and Director of International Relations, Programs and Projects at Aurel Vlaicu University of Arad. She is Editor-in-Chief of the International Journal of Advanced Intelligence Paradigms and International Journal of Computational Systems Engineering, and serves on the editorial boards of several scientific journals. She has led and contributed to numerous national and international research initiatives, including the European Union-funded BioCell-NanoART project on bio-inspired cellular nano-architectures. She is active in several professional societies and technical committees, including IEEE, the European Society for Fuzzy Logic and Technology, and the Society for Industrial and Applied Mathematics. Dr. Basant Agarwal works as an Assistant Professor at the Indian Institute of Information Technology Kota (IIIT-Kota), India, which is an Institute of National Importance. He holds a Ph.D. and M.Tech. from the Department of Computer Science and Engineering, Malaviya National Institute of Technology Jaipur, India. He has more than 9 years of experience in research and teaching. He has worked as a Postdoc Research Fellow at the Norwegian University of Science and Technology (NTNU), Norway, under the prestigious ERCIM (European Research Consortium for Informatics and Mathematics) fellowship in 2016. He has also worked as a Research Scientist at Temasek Laboratories, National University of Singapore (NUS), Singapore. His research interest include Artificial Intelligence, Cyber physical systems, Text mining, Natural Language Processing, Machine learning, Deep learning, Intelligent Systems, Expert Systems and related areas. Dr. Mamta Mittal works as Head and Associate Professor (Data Analytics and Data Science) in Delhi Skill & Entrepreneurship University (under Government of NCT Delhi), New Delhi. She received a PhD in Computer Science and Engineering from Thapar University, Patiala; MTech (Honors) in Computer Science & Engineering from YMCA, Faridabad; and B. Tech in Computer Science & Engineering from Kurukshetra University, Kurukshetra, in 2001. She has been teaching for the last 18 years with emphasis on Data Mining, Machine Learning, DBMS and Data Structure. Dr. Mittal is a lifetime member of CSI and published more than 80 research papers. She holds five patents, two of which have been granted copyrights, and three more published in the area of Artificial Intelligence, IoT and Deep Learning. Dr. Mittal has edited/authored many books with reputed publishers, and is working on DST approved Project “Development of IoT based hybrid navigation module for mid-sized autonomous vehicles”. Currently, she is guiding PhD scholars in Machine Learning, Computer Vision and Deep Learning areas. Dr. Mittal is Editorial Board member with Inder-Science, Bentham Science, Springer and Elsevier, handled Special issues, has chaired many Conferences.

      Innehållsförteckning

      • 1. Data mining with deep learning in biomedical data2. Applications of supervised machine learning techniques with the goal of medical analysis and prediction: a case study of breast cancer3. Medical decision support system using data mining4. Role of AI techniques in enhancing multi-modality medical image fusion results5. A comparative performance analysis of backpropagation training optimizers to estimate clinical gait mechanics6. High-performance medicine in cognitive impairment: Brain-computer interfacing for prodromal Alzheimer’s disease7. Machine learning in healthcare: Brain tumor classifications by gradient and XG boosting models8. Biofeedback method for human-computer interaction to improve elder caring: Eye gaze tracking9. Blood screening parameters prediction for preliminary analysis using neural networks10. Classification of hypertension using the improved unsupervised learning technique and image processing11. Biomedical data visualization and clinical decision-making in rodents using a multi-usage wireless brain stimulator using novel embedded design12. LSTM neural network-based classification of sensory signals for healthy and unhealthy gait assessment13. Addressing challenges and roadblocks in iomedical data using data-driven machine learning14. Multibjective evolutionary algorithm based on decomposition for feature selection in medical diagnosis15. Machine learning techniques in healthcare informatics: Showcasing prediction of type 2 diabetes mellitus disease using lifestyle data
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