08211nam a2200697 i 45000010014000000030004000140050017000180060019000350070015000540080041000690200025001100200024001350200025001590200027001840240035002110350025002460350022002710400040002930500025003330600029003580720015003870720023004022450128004252640098005533000071006513360021007223370026007433380032007694900027008014900069008284900034008975000043009315040041009745052465010155060075034805200887035555210153044425300029045955380036046245380078046605451941047385880061066796500058067406500039067986500036068376500041068736500045069146500059069596500041070187000037070597000035070967000042071317100061071737760049072348300031072838300077073148300034073918560059074259420012074849990017074969780750359641IOP20251030124832.0m    eo  d        cr cn |||m|||a241213s2024    enka   fob    000 0 eng d  a9780750359641qebook  a9780750359634qmobi  z9780750359627qprint  z9780750359658qmyPrint7 a10.1088/978-0-7503-5964-12doi  a(CaBNVSL)thg00084067  a(OCoLC)1478800750  aCaBNVSLbengerdacCaBNVSLdCaBNVSL 4aR855.3b.A685 2024eb 4aW 26.55.A7bAR791 2024eb 7aMF2bicssc 7aMED0180002bisacsh  aArtificial intelligence :ba tool for effective diagnostics /cedited by Smith K. Khare, Sachin Taran, Ankush D. Jamthikar. 1aBristol [England] (Temple Circus, Temple Way, Bristol BS1 6HG, UK) :bIOP Publishing,c[2024]  a1 online resource (various pagings) :billustrations (some color).  atext2rdacontent  aelectronic2isbdmedia  aonline resource2rdacarrier1 a[IOP release $release]1 aIOP series in artificial intelligence in the biomedical sciences1 aIOP ebooks. [2024 collection]  a"Version: 20241101"--Title page verso.  aIncludes bibliographical references.  a1. Introduction to AI-driven diagnostics and human-machine interfaces / Smith K. Khare, Ankush Jamthikar and Sachin Taran -- 2. Recent advancements in emerging technology for healthcare management systems / Bhawna Sachdeva, Jeebananda Panda and Sachin Taran -- 3. The role of high-performance computing in processing electronic healthcare records / Govind Prasad, Ayan Banerjee and Khushboo Rani -- 4. Detection of attention deficit hyperactivity disorder using electroencephalogram signals : a review / Joseph Nixon Kiro, Bam Bahadur Sinha, Madhu Kumari and Shalini Mahato -- 5. Artificial neural network-based classification of eye states using electroencephalogram signals : a comparative analysis of algorithms and artifact removal techniques / Pranjali Gajbhiye, Vineet Singh, Sandeep Saharan and Shyam Joshi -- 6. Hybrid reptile search algorithm-snake optimizer and rational wavelet filter banks for Alzheimer's disease detection / Digambar V. Puri, Pramod H. Kachare, Sandeep B. Sangle and Sanjay L. Nalbalwar -- 7. Mother tree optimization for early detection of focal seizure using entropy-based features / Hesam Akbari and Wael Korani -- 8. Automatic detection of seizure activity using EEG signals / Hesam Akbari and Wael Korani -- 9. Prediction of rhythm-based abnormalities in electrocardiograms using time-frequency representations / Sandeep Kumar Singh, Anirvina Sharma, Sunil Athawale, Annapureddy Srija Reddy, Ashwin Kamble and Ankush Jamthikar -- 10. Real-time implementation of ECG beat identification using Hilbert transform and artificial neural network / Vikas Kumar Sinha and Govind Prasad -- 11. Simulation and review of blood smear image-based leukemia classification using machine learning methods / Gopal Singh Tandel, Nitin Kumar Mishra and Vivek Sharma -- 12. Subject-independent emotion classification using galvanic skin response and electroencephalogram data / Tanishqa Tyagi, Anukul Pandey, Sachin Taran and Amit Kumar Dwivedi -- 13. Speech emotion recognition using empirical wavelet transform and cubic support vector machine / Mehrab Hosain, Anukul Pandey, Sachin Taran, Ravi, Asmar Hafeez and Nikhil -- 14. Spectral and spatial analysis of EEG signals for imagined speech recognition / Ashwin Kamble and Pradnya Ghare -- 15. Classifying human attention states in EEG-based brain-computer interfacing using singular spectrum analysis / Ankit Baisoya, Ujjwal Kumar Upadhyay, Vansh Singh, Sachin Taran and Vikram Singh Kardam.  aFull-text restricted to subscribers or individual document purchasers.3 aThe book explores the application of artificial intelligence across various human-machine interfaces, addressing areas such as human attention, emotions, seizures, Alzheimer's disease, focal and non-focal disorders, electrocardiogram rhythms, abnormal heartbeats, and leukemia. It provides a thorough examination of techniques for analyzing and processing both physiological and physical signals, as well as smear blood images. Physiological signals discussed include electroencephalograms (EEGs), electrocardiograms (ECGs), and electronic health records (EHRs), while physical signals encompass human speech. Serving as a comprehensive guide, the book delves into advanced signal processing techniques and the use of machine learning and deep learning for automated signal pre-processing and classification. Part of IOP Series in Artificial Intelligence in the Biomedical Sciences.  aResearchers in healthcare analytics, medical big data analysis, medical adaptive signal processing and machine learning applied to time series data.  aAlso available in print.  aMode of access: World Wide Web.  aSystem requirements: Adobe Acrobat Reader, EPUB reader, or Kindle reader.  aSmith Khare is an Assistant Professor in the SDU Applied AI and Data Science, The Maersk Mc-Kinney Miller Institute, University of Southern Denmark, Denmark, and worked as a Postdoctoral researcher in the Aarhus University, Denmark. He received his doctoral degree in Electronics and Communication Engineering at the Indian Institute of Information Technology, Design and Manufacturing Jabalpur (IIITDMJ), India in 2022. He has authored more than 50+ research papers in various reputed international Journals such as IEEE Transactions. Smith is listed in the top 2% Scientists in the World (2023, 2024), according to Elsevier. Sachin Taran is an Assistant Professor in the Department of Electronics and Communication Engineering at Delhi Technical University, New Delhi, India. He has done postdoc research at the Nanyang Technological University (NTU) Singapore. His research interests include artificial intelligence, signal processing and time-frequency analysis. He is a fellow member of IETE, member of IANG and Associate Editor of Frontiers in Signal Processing. Since 2020, he has been continuously awarded by Commendable Research Award in Delhi Technological University. He has authored more than 55+ research papers in various reputed international Journals and conferences. Ankush D. Jamthikar is a postdoctoral research associate in the Division of Cardiovascular Disease and Hypertension at Rutgers University, Robert Wood Johnson Medical School, New Jersey. He has authored over 50 international journal papers, conference proceedings, and book chapters, focusing on cardiovascular disease (CVD) and stroke risk stratification, as well as artificial intelligence. He serves as an editorial board member for AI in Health and a guest editor for the MDPI journal, while also acting as a peer reviewer for several high-impact journals. Jamthikar has an h-index of 24 and an i-index of 34, with over 1,300 citations to his work.  aTitle from PDF title page (viewed on December 13, 2024).  aArtificial intelligencexMedical applications.965209  aDiagnosisxData processing.99712112aArtificial Intelligence.95844122aDiagnosis, Computer-Assisted.99712222aMedical Informatics Applications.962913 7aPre-clinical medicine - basic sciences.2bicssc996521 7aMEDICAL / Diagnosis.2bisacsh9960971 aKhare, Smith K.,eeditor.9971231 aTaran, Sachin,eeditor.9969951 aJamthikar, Ankush D.,eeditor.9971242 aInstitute of Physics (Great Britain),epublisher.917702 8iPrint version:z9780750359627z9780750359658 0aIOP (Series).pRelease 24.  aIOP series in artificial intelligence in the biomedical sciences.996536  aIOP ebooks.p2024 collection.4 uhttps://iopscience.iop.org/book/edit/978-0-7503-5964-1  cE-Books  c68521d68521