{"id":251518,"date":"2026-07-13T01:19:14","date_gmt":"2026-07-12T22:19:14","guid":{"rendered":"https:\/\/1kitap1.com\/en\/ai-and-knowledge-processing-decision-making-hemachandran-k\/"},"modified":"2026-07-13T01:19:14","modified_gmt":"2026-07-12T22:19:14","slug":"ai-and-knowledge-processing-decision-making-hemachandran-k","status":"publish","type":"post","link":"https:\/\/1kitap1.com\/en\/ai-and-knowledge-processing-decision-making-hemachandran-k\/","title":{"rendered":"AI And Knowledge Processing Decision – Making – Hemachandran K"},"content":{"rendered":"
\"<\/figure>\n

In the future, this system can be made accessible to everyone in the world, anywhere and anytime, by creating web applications and mobile applications. The system\u2019s accu- racy can be increased even more by working on large amounts of data. [1] C. R. Ferreira, C. D. M. van Karnebeek, J. Vockley and N. Blaue, \u201cA proposed nosology of inborn errors of metabolism\u201d, Genetic Medicine, vol. 21, no. 1, pp. 102\u2013106, 2019.<\/p>\n

[2] J. Tan, M. Wagner, S. L. Stenton, T. M. Storm, S. B. Wortmaan et al., \u201cLifetime risk of autosomal recessive mitochondrial disorders calculated from genetic databases\u201d, Lancet, vol. 54, pp. 111\u2013119, 2019. [3] Y. Park and M. Kellis, \u201cDeep learning for regulatory genomics\u201d, Nature Biotechnology, pp. 825\u2013826, 2015.<\/p>\n

[4] J. Menche, A. Sharma, M. Kitsak, S. Ghiassian, M. Vidal et al., \u201cUncovering disease- disease relationships through the incomplete human interactome\u201d, Science, vol. 347, no. 6224, pp. 1257601, 2015. [5] S. Won, H. Choi, S. Park, J. Lee, C. Park et al., \u201cEvaluation of penalized and nonpenal- ized methods for disease prediction with large-scale genetic data\u201d, BioMed Research International, p. 605891, 2015.<\/p>\n

[6] N.G, B. A et al., Cardiovascular Disease Prediction using Genetic Algorithm and Neu\u2011 ral Network. IEEE, 2012. [7] K. R. Gray et al., \u201cAlzheimer\u2019s disease neuroimaging initiative. Random forest-based similarity measures for multimodal classification of Alzheimer\u2019s disease\u201d, Neuro Image, vol. 65, pp. 167\u2013175, 2013. [8] Y. liu, D. A. Tennant, Z. Zhu, J. K. Health, X. Yao et al., \u201cDime: A scalable disease mod- ule identification algorithm with application to glioma progression\u201d, PloS One, vol. 9, no. 2, pp. 866\u2013876, 2014.<\/p>\n

[9] S. D. Ghiassian, J. Menche and A. L. Barabasi, \u201cA disease module detection (diamond) algorithm derived from a systematic analysis of connectivity patterns of disease proteins in the human interactome\u201d, PloS Computational Biology, vol. 11, no. 4, pp. 1004120, 2015. [10] W. Hoskins, Y. Zhang, Y. Guo, and J. Tang, Down syndrome prediction\/screening model based on deep learning and Illumina genotyping array. IEEE, 2017.<\/p>\n

\n

Artificial intelligence (AI) and knowledge processing play a vital role in various automation industries and their functioning in converting traditional industries to AI-based factories. This book acts as a guide and blends the basics of AI in various domains, which include machine learning, deep learning, artificial neural networks, and expert systems, and extends their application in all sectors. The book discusses the designing of new AI algorithms used to convert general applications to AI-based applications. It highlights different machine learning and deep learning models for various applications used in healthcare and wellness, agriculture, and automobiles.<\/p>\n

The book offers an overview of the rapidly growing and developing field of AI applications, along with knowledge of engineering and business analytics. Real-time case studies are included across several different fields such as image processing, text mining, healthcare, finance, digital marketing, and HR analytics. The book also introduces a statistical background and probabilistic framework to enhance the understanding of continuous distributions. Topics such as ensemble models, deep learning models, artificial neural networks, expert systems, and decision-based systems round out the offerings of this book. This multicontributed book is a valuable source for researchers, academics, technologists, industrialists, practitioners, and all those who wish to explore the applications of AI, knowledge processing, deep learning, and machine learning.<\/p>\n

Artificial Intelligence and Knowledge Processing Improved Decision-Making and Prediction Edited by Hemachandran K, Raul V. Rodriguez, Umashankar Subramaniam and Valentina Emilia Balas Design cover image: \u00a9 Shutterstock First edition published 2024 by CRC Press 2385 Executive Center Drive, Suite 320, Boca Raton, FL 33431 and by CRC Press 4 Park Square, Milton Park, Abingdon, Oxon, OX14 4RN CRC Press is an imprint of Taylor & Francis Group, LLC \u00a9 2024 selection and editorial matter, Hemachandran K., Raul V.<\/p>\n

Rodriguez, Umashankar Subramaniam, and Valentina Emilia Balas; individual chapters, the contributors Reasonable efforts have been made to publish reliable data and information, but the author and publisher cannot assume responsibility for the validity of all materials or the consequences of their use. The authors and publishers have attempted to trace the copyright holders of all material reproduced in this publication and apologize to copyright holders if permission to publish in this form has not been obtained. If any copyright material has not been acknowledged please write and let us know so we may rectify in any future reprint.<\/p>\n

Except as permitted under U.S. Copyright Law, no part of this book may be reprinted, reproduced, transmitted, or utilized in any form by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying, microfilming, and recording, or in any information storage or retrieval system, without written permission from the publishers.<\/p>\n<\/blockquote>\n

This is a short excerpt from the opening of “” by Unknown, quoted for review and introduction purposes. All rights belong to the copyright holders.<\/em><\/p>\n

\n
\n

Table of Contents<\/p>\nToggle<\/span><\/path><\/svg><\/svg><\/span><\/span><\/span><\/a><\/span><\/div>\n