Matches in SemOpenAlex for { <https://semopenalex.org/work/W4225269757> ?p ?o ?g. }
- W4225269757 endingPage "138" @default.
- W4225269757 startingPage "115" @default.
- W4225269757 abstract "The internet of things (IoT) and machine learning (ML) are massive technologies that provide enhancement and better resolutions in our daily life, such as in industry, healthcare, space sector, defense, buildings, agriculture, traffic, and so on. The area of IoT has shown a boost over the past decades with the continuous development of the ML tools. The combination of IoT with ML tools has a high impact on medical devices. The application of ML and IoT can provide significant improvements in all parts of healthcare domain from diagnostics to treatment. It is generally believed that ML tools will simplify and boost human work. In this chapter, the authors present the overview of current advance integration IoT and ML application on healthcare care management listed with all benefits and uses. This chapter also supports healthcare professionals to detect and treat disease more efficiently and researchers for their understanding of growth in ML and IoT-based technology." @default.
- W4225269757 created "2022-05-04" @default.
- W4225269757 creator A5040720864 @default.
- W4225269757 creator A5083123100 @default.
- W4225269757 creator A5087699889 @default.
- W4225269757 date "2022-04-08" @default.
- W4225269757 modified "2023-10-16" @default.
- W4225269757 title "Integration of ML and IoT for Healthcare Systems" @default.
- W4225269757 cites W1124052577 @default.
- W4225269757 cites W1797580880 @default.
- W4225269757 cites W1904925675 @default.
- W4225269757 cites W1918839972 @default.
- W4225269757 cites W2008183828 @default.
- W4225269757 cites W2076587863 @default.
- W4225269757 cites W2083780116 @default.
- W4225269757 cites W2131414141 @default.
- W4225269757 cites W2134295053 @default.
- W4225269757 cites W2152183328 @default.
- W4225269757 cites W2423141470 @default.
- W4225269757 cites W2463638724 @default.
- W4225269757 cites W2468494111 @default.
- W4225269757 cites W2532801783 @default.
- W4225269757 cites W2560251073 @default.
- W4225269757 cites W2562505331 @default.
- W4225269757 cites W2610332124 @default.
- W4225269757 cites W2736380543 @default.
- W4225269757 cites W2749144149 @default.
- W4225269757 cites W2760784103 @default.
- W4225269757 cites W2763393827 @default.
- W4225269757 cites W2770987547 @default.
- W4225269757 cites W2777794149 @default.
- W4225269757 cites W2779565479 @default.
- W4225269757 cites W2782429987 @default.
- W4225269757 cites W2798895321 @default.
- W4225269757 cites W2802249969 @default.
- W4225269757 cites W2802878531 @default.
- W4225269757 cites W2807931621 @default.
- W4225269757 cites W2808420356 @default.
- W4225269757 cites W2883753518 @default.
- W4225269757 cites W2889296555 @default.
- W4225269757 cites W2900427548 @default.
- W4225269757 cites W2942776390 @default.
- W4225269757 cites W2954505781 @default.
- W4225269757 cites W2955525271 @default.
- W4225269757 cites W2956571997 @default.
- W4225269757 cites W2964248614 @default.
- W4225269757 cites W2976872795 @default.
- W4225269757 cites W2981778102 @default.
- W4225269757 cites W3024884693 @default.
- W4225269757 cites W3037431541 @default.
- W4225269757 cites W3090464903 @default.
- W4225269757 cites W3091074923 @default.
- W4225269757 cites W3099185017 @default.
- W4225269757 cites W3112071620 @default.
- W4225269757 cites W3117818584 @default.
- W4225269757 cites W3119836263 @default.
- W4225269757 cites W3156342878 @default.
- W4225269757 cites W3166737224 @default.
- W4225269757 cites W3204894386 @default.
- W4225269757 cites W3213576683 @default.
- W4225269757 cites W4238187091 @default.
- W4225269757 cites W4239510810 @default.
- W4225269757 cites W4249738683 @default.
- W4225269757 cites W4294215472 @default.
- W4225269757 doi "https://doi.org/10.4018/978-1-7998-9831-3.ch006" @default.
- W4225269757 hasPublicationYear "2022" @default.
- W4225269757 type Work @default.
- W4225269757 citedByCount "0" @default.
- W4225269757 crossrefType "book-chapter" @default.
- W4225269757 hasAuthorship W4225269757A5040720864 @default.
- W4225269757 hasAuthorship W4225269757A5083123100 @default.
- W4225269757 hasAuthorship W4225269757A5087699889 @default.
- W4225269757 hasConcept C110354214 @default.
- W4225269757 hasConcept C111919701 @default.
- W4225269757 hasConcept C127413603 @default.
- W4225269757 hasConcept C134306372 @default.
- W4225269757 hasConcept C160735492 @default.
- W4225269757 hasConcept C162324750 @default.
- W4225269757 hasConcept C18762648 @default.
- W4225269757 hasConcept C2522767166 @default.
- W4225269757 hasConcept C2778572836 @default.
- W4225269757 hasConcept C2988170871 @default.
- W4225269757 hasConcept C2989086416 @default.
- W4225269757 hasConcept C33923547 @default.
- W4225269757 hasConcept C36503486 @default.
- W4225269757 hasConcept C38652104 @default.
- W4225269757 hasConcept C41008148 @default.
- W4225269757 hasConcept C50522688 @default.
- W4225269757 hasConcept C78519656 @default.
- W4225269757 hasConcept C81860439 @default.
- W4225269757 hasConceptScore W4225269757C110354214 @default.
- W4225269757 hasConceptScore W4225269757C111919701 @default.
- W4225269757 hasConceptScore W4225269757C127413603 @default.
- W4225269757 hasConceptScore W4225269757C134306372 @default.
- W4225269757 hasConceptScore W4225269757C160735492 @default.
- W4225269757 hasConceptScore W4225269757C162324750 @default.
- W4225269757 hasConceptScore W4225269757C18762648 @default.
- W4225269757 hasConceptScore W4225269757C2522767166 @default.