LEADER 04665nam 22006735 450 001 996465460403316 005 20200704060910.0 010 $a3-030-37526-9 024 7 $a10.1007/978-3-030-37526-3 035 $a(CKB)4100000010770979 035 $a(DE-He213)978-3-030-37526-3 035 $a(MiAaPQ)EBC6152181 035 $a(PPN)243228767 035 $a(EXLCZ)994100000010770979 100 $a20200331d2020 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aInternet of Things Use Cases for the Healthcare Industry$b[electronic resource] /$fedited by Pethuru Raj, Jyotir Moy Chatterjee, Abhishek Kumar, B. Balamurugan 205 $a1st ed. 2020. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2020. 215 $a1 online resource (XII, 296 p. 79 illus., 59 illus. in color.) 311 $a3-030-37525-0 327 $aAI in Health Sector -- Real-Time Smart Healthcare Model using IoT -- A Fog Based Approach for Real-Time Analytics of IoT-Enabled Healthcare -- Applications of IoT in Indoor Air Quality Monitoring Systems -- CloudIoT for Smart Healthcare: Architecture, Issues and Challenges -- Impact of IoT on the Healthcare Producers: Epitomizing Pharmaceutical Drug Discovery Process -- Cyber-Security Threats in Medical Devices -- Smart Healthcare Use Cases and Applications -- IoT Use Cases and Applications -- Internet of Things for Ambient Assisted Living - An Overview -- Smart Health care Applications and Real Time Analytics through Edge Computing -- The Role of Blockchain for Medical Electronics Security -- Clinical Data Analysis using IoT Data Analytics Platforms -- Internet of Things - Tools and Technologies in Healthcare -- Clinical data analysis using IoT -- Security Issues in IoT and Healthcare Devices. 330 $aThis book explores potentially disruptive and transformative healthcare-specific use cases made possible by the latest developments in Internet of Things (IoT) technology and Cyber-Physical Systems (CPS). Healthcare data can be subjected to a range of different investigations in order to extract highly useful and usable intelligence for the automation of traditionally manual tasks. In addition, next-generation healthcare applications can be enhanced by integrating the latest knowledge discovery and dissemination tools. These sophisticated, smart healthcare applications are possible thanks to a growing ecosystem of healthcare sensors and actuators, new ad hoc and application-specific sensor and actuator networks, and advances in data capture, processing, storage, and mining. Such applications also take advantage of state-of-the-art machine and deep learning algorithms, major strides in artificial and ambient intelligence, and rapid improvements in the stability and maturity of mobile, social, and edge computing models. . 606 $aComputer communication systems 606 $aComputer engineering 606 $aInternet of things 606 $aEmbedded computer systems 606 $aHealth informatics 606 $aInput-output equipment (Computers) 606 $aComputer Communication Networks$3https://scigraph.springernature.com/ontologies/product-market-codes/I13022 606 $aCyber-physical systems, IoT$3https://scigraph.springernature.com/ontologies/product-market-codes/T24080 606 $aHealth Informatics$3https://scigraph.springernature.com/ontologies/product-market-codes/H28009 606 $aInput/Output and Data Communications$3https://scigraph.springernature.com/ontologies/product-market-codes/I12042 615 0$aComputer communication systems. 615 0$aComputer engineering. 615 0$aInternet of things. 615 0$aEmbedded computer systems. 615 0$aHealth informatics. 615 0$aInput-output equipment (Computers). 615 14$aComputer Communication Networks. 615 24$aCyber-physical systems, IoT. 615 24$aHealth Informatics. 615 24$aInput/Output and Data Communications. 676 $a610.28563 702 $aRaj$b Pethuru$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aChatterjee$b Jyotir Moy$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aKumar$b Abhishek$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aBalamurugan$b B$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996465460403316 996 $aInternet of Things Use Cases for the Healthcare Industry$92025671 997 $aUNISA LEADER 01898nas 2200517-a 450 001 996215915203316 005 20240413021530.0 035 $a(CKB)963018069661 035 $a(CONSER)---87656551- 035 $a(EXLCZ)99963018069661 100 $a19861113a19879999 --- a 101 0 $aeng 135 $aurunu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aCountry profile$iEgypt /$fEIU, the Economist Intelligence Unit 210 $aLondon, U.K. $cThe Unit$dc1986- 215 $a1 online resource 311 $aPrint version: Country profile. (DLC) 87656551 (OCoLC)14710875 0269-5227 517 3 $aEgypt 531 $aEGYPT COUNTRY PROFILE EIU 531 $aCOUNTRY PROFILE EGYPT ANNUAL SURVEY OF POLITICAL AND ECONOMIC BACKGROUND 531 $aECONOMIST INTELLIGENCE UNIT 531 0 $aCtry. profile, Egypt 606 $aEconomic history$2fast$3(OCoLC)fst00901974 606 $aPolitics and government$2fast$3(OCoLC)fst01919741 607 $aEgypt$xEconomic conditions$y1952-$vPeriodicals 607 $aEgypt$xPolitics and government$vPeriodicals 607 $aEgypt$xEconomic conditions$y1952-$xPeriodicals 607 $aÉgypte$xConditions économiques$y1952-$xPériodiques 607 $aÉgypte$xConditions économiques$y1952-$vPériodiques 607 $aÉgypte$xPolitique et gouvernement$vPériodiques 607 $aEgypt$2fast 607 $aEgypt$xEconomic conditions$y1952-$vPeriodicals$2nli 607 $aEgypt$xPolitics and government$vPeriodicals$2nli 608 $aPeriodicals.$2fast 615 7$aEconomic history. 615 7$aPolitics and government 676 $a330.962/005 712 02$aEconomist Intelligence Unit (Great Britain) 906 $aJOURNAL 912 $a996215915203316 920 $aexl_impl conversion 996 $aCountry profile$91886580 997 $aUNISA LEADER 04452nam 22006975 450 001 9911047658103321 005 20251120120405.0 010 $a981-9692-03-2 024 7 $a10.1007/978-981-96-9203-3 035 $a(MiAaPQ)EBC32422065 035 $a(Au-PeEL)EBL32422065 035 $a(CKB)43552579600041 035 $a(DE-He213)978-981-96-9203-3 035 $a(OCoLC)1555347783 035 $a(EXLCZ)9943552579600041 100 $a20251120d2025 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aRecent Trends in Artificial Intelligence and Data Sciences $eSelect Proceedings of the 15th International Conference?CONFLUENCE 2025 /$fedited by Sumit Kumar, Garima Aggarwal, Bhuvan Unhelkar, Raju Pal 205 $a1st ed. 2025. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2025. 215 $a1 online resource (714 pages) 225 1 $aLecture Notes in Electrical Engineering,$x1876-1119 ;$v1447 311 08$a981-9692-02-4 327 $aA Comparative Analysis of Existing and AI-Driven Frameworks for Industrial Interaction Practices -- A Comprehensive Review: Machine and Deep Learning Techniques for Sentiment Analysis on Datasets of Political Tweets -- Comparative Analysis of Feature Selection Techniques for Malware Detection in URLs -- Google and Flower Federated Learning Frameworks Comparison in Bug Prediction in terms of Flexibility and Technical Factors -- Exploring Sentiments in Stack Overflow Score and Discussion: A Dual Approach with Machine Learning Models and Expert Evaluation -- An AI-Enhanced Framework for Mental Health Management -- dvancing Diabetic Retinopathy Detection through Collaborative Vision Transformer and CNN Architectures Integrated with Explainable AI -- Diagnosis and Prediction of Multiple Sclerosis Disease Using Quantum Machine Learning Classifiers -- Predictive Modeling for Breast Cancer Diagnosis Using Machine Learning Algorithms -- Early Detection of Cardiovascular Disease through the Utilization of Multiple Machine Learning Techniques -- Early Prediction and Detection of Liver Disease Using Deep Learning -- Predictive Analytics based on Public Health Response using Epidemiological Models: A Data Driven Approach -- Prognosis, and Diagnosis of Diabetes through Minkowski Distance Metric -- Mental Health Reaction Based On Behavioral Analysis And Digital Device Usage Using Machine Learning Algortihm -- Automated Onset Seizure Detection Using EEG Signals by Machine Learning. 330 $aThis book presents original, peer-reviewed research papers from the International Conference on Recent Trends in Artificial Intelligence and Data Sciences?CONFLUENCE 2025. It highlights the latest advancements across diverse areas of data science and computational techniques. The volume focuses on artificial intelligence, machine learning, deep learning, soft computing, and other emerging methodologies. 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