Sunil Kumar Chaudhary – författare

This book reviews that narrate the development of current technologies under the theme of the emerging concept of healthcare, specifically in terms of what makes healthcare more efficient and effective with the help of high-precision algorithms. The mechanism that drives it is machine learning, deep learning, big data, and Internet of Things (IoT)—the scientific field that gives machines the ability to learn without being strictly programmed. It has emerged together with big data technologies and high-performance computing to create new opportunities to unravel, quantify, and understand data-intensive processes in healthcare operational environments.

This book offers comprehensive coverage of the most essential topics, including:

Introduction to e-monitoring for healthcare Case studies based on big data and healthcare Intelligent learning analytics in healthcare sectors using machine learning and IoT Identifying diseases and diagnosis using machine learning and IoT Deep learning architecture and framework for healthcare using IoT Knowledge discovery from big data of healthcare-related processing Big data and IoT in healthcare Role of IoT in sustainable healthcare A heterogeneous IoT-based application for remote monitoring of physiological and environmental parameters

This book reviews that narrate the development of current technologies under the theme of the emerging concept of healthcare, specifically in terms of what makes healthcare more efficient and effective with the help of high-precision algorithms. The mechanism that drives it is machine learning, deep learning, big data, and Internet of Things (IoT)—the scientific field that gives machines the ability to learn without being strictly programmed. It has emerged together with big data technologies and high-performance computing to create new opportunities to unravel, quantify, and understand data-intensive processes in healthcare operational environments.

This book offers comprehensive coverage of the most essential topics, including:

Introduction to e-monitoring for healthcare Case studies based on big data and healthcare Intelligent learning analytics in healthcare sectors using machine learning and IoT Identifying diseases and diagnosis using machine learning and IoT Deep learning architecture and framework for healthcare using IoT Knowledge discovery from big data of healthcare-related processing Big data and IoT in healthcare Role of IoT in sustainable healthcare A heterogeneous IoT-based application for remote monitoring of physiological and environmental parameters

This book reviews that narrate the development of current technologies under the theme of the emerging concept of healthcare, specifically in terms of what makes healthcare more efficient and effective with the help of high-precision algorithms. The mechanism that drives it is machine learning, deep learning, big data, and Internet of Things (IoT)—the scientific field that gives machines the ability to learn without being strictly programmed. It has emerged together with big data technologies and high-performance computing to create new opportunities to unravel, quantify, and understand data-intensive processes in healthcare operational environments.This book offers comprehensive coverage of the most essential topics, including:Introduction to e-monitoring for healthcareCase studies based on big data and healthcareIntelligent learning analytics in healthcare sectors using machine learning and IoTIdentifying diseases and diagnosis using machine learning and IoTDeep learning architecture and framework for healthcare using IoTKnowledge discovery from big data of healthcare-related processingBig data and IoT in healthcareRole of IoT in sustainable healthcareA heterogeneous IoT-based application for remote monitoring of physiological and environmental parameters

This book reviews that narrate the development of current technologies under the theme of the emerging concept of healthcare, specifically in terms of what makes healthcare more efficient and effective with the help of high-precision algorithms. The mechanism that drives it is machine learning, deep learning, big data, and Internet of Things (IoT)—the scientific field that gives machines the ability to learn without being strictly programmed. It has emerged together with big data technologies and high-performance computing to create new opportunities to unravel, quantify, and understand data-intensive processes in healthcare operational environments.This book offers comprehensive coverage of the most essential topics, including:Introduction to e-monitoring for healthcareCase studies based on big data and healthcareIntelligent learning analytics in healthcare sectors using machine learning and IoTIdentifying diseases and diagnosis using machine learning and IoTDeep learning architecture and framework for healthcare using IoTKnowledge discovery from big data of healthcare-related processingBig data and IoT in healthcareRole of IoT in sustainable healthcareA heterogeneous IoT-based application for remote monitoring of physiological and environmental parameters

Teaches Readers How to Apply a Structured Problem-Solving Methodology for Industrial Fields Based on Sound Scientific Principles As modern industrial processes have become increasingly complex, complicated multi-factor problems have emerged. These complex problems end up costing companies millions of dollars every day. Existing problem-solving techniques are only effective to a certain point. This book provides a solution to a myriad of industrial problems by using first principles and rigorous hypothesis testing. Key topics covered within the work include: How to use the latest research, advanced modeling, big data mining, analytical testing, and many other techniques to systematically create and test hypotheses surrounding why a process is malfunctioningHow to use scenario development to frame a team’s understanding of why a process is malfunctioningHow to approach today’s lack of experienced industrial workers, whose failure to approach problem solving from first fundamentals are causing myriad of inefficiencies in industryHow to use multiple methodologies together with an emphasis on first principles and mechanistic math modeling as a basis to industrial problem solvingEngineers of any discipline working in both research and development of manufacturing environments, along with professionals in any industrial discipline looking to reduce costs will be able to use this work to both understand and pragmatically solve the pressing issues we see in today’s industrial market.

Teaches Readers How to Apply a Structured Problem-Solving Methodology for Industrial Fields Based on Sound Scientific Principles

As modern industrial processes have become increasingly complex, complicated multi-factor problems have emerged. These complex problems end up costing companies millions of dollars every day. Existing problem-solving techniques are only effective to a certain point. This book provides a solution to a myriad of industrial problems by using first principles and rigorous hypothesis testing. Key topics covered within the work include:

How to use the latest research, advanced modeling, big data mining, analytical testing, and many other techniques to systematically create and test hypotheses surrounding why a process is malfunctioning How to use scenario development to frame a team’s understanding of why a process is malfunctioning How to approach today’s lack of experienced industrial workers, whose failure to approach problem solving from first fundamentals are causing myriad of inefficiencies in industry How to use multiple methodologies together with an emphasis on first principles and mechanistic math modeling as a basis to industrial problem solving

Engineers of any discipline working in both research and development of manufacturing environments, along with professionals in any industrial discipline looking to reduce costs will be able to use this work to both understand and pragmatically solve the pressing issues we see in today’s industrial market.

Teaches Readers How to Apply a Structured Problem-Solving Methodology for Industrial Fields Based on Sound Scientific Principles

As modern industrial processes have become increasingly complex, complicated multi-factor problems have emerged. These complex problems end up costing companies millions of dollars every day. Existing problem-solving techniques are only effective to a certain point. This book provides a solution to a myriad of industrial problems by using first principles and rigorous hypothesis testing. Key topics covered within the work include:

How to use the latest research, advanced modeling, big data mining, analytical testing, and many other techniques to systematically create and test hypotheses surrounding why a process is malfunctioning How to use scenario development to frame a team’s understanding of why a process is malfunctioning How to approach today’s lack of experienced industrial workers, whose failure to approach problem solving from first fundamentals are causing myriad of inefficiencies in industry How to use multiple methodologies together with an emphasis on first principles and mechanistic math modeling as a basis to industrial problem solving

Engineers of any discipline working in both research and development of manufacturing environments, along with professionals in any industrial discipline looking to reduce costs will be able to use this work to both understand and pragmatically solve the pressing issues we see in today’s industrial market.

Doctoral Thesis / Dissertation from the year 2021 in the subject Medicine - Dentistry, grade: A, , course: MDS( PUBLIC HEALTH DENTISTRY), language: English, abstract: The aim of the study is to assess the level of knowledge and fear amongst dental patients attending dental OPD in a dental institution. A cross- sectional, questionnaire study was conducted among dental patients attending dental OPD of K.D. Dental College & Hospital, Mathura. The study was conducted to assess the level of knowledge and fear amongst dental patient s using Knowledge assessment questionnaire and Fear of COVID-19 Scale, which was translated into Hindi. The observed data were coded, tabulated, and analyzed using IBM SPSS Version 20. Descriptive statistics were represented as frequency and percentages for categorical variables (gender, age group and socio-economic status) and Mean Standard Deviation for continuous variables (knowledge score, fear during lockdown score and fear after lockdown score). Comparison of gender with fear scores was done using independent test and comparison of age group and SES with fear scores was done using using one-way ANOVA test. Pearson s test was used to correlate between knowledge and fear scores. A p value of less than 0.05 was considered statistically significant. One of the emergent global challenges in managing infectious diseases is dealing with the novel coronavirus 2019 (COVID-19). With the extremely high infection rate and relatively high mortality, individuals naturally began worrying about the COVID-19. Apart from reducing the transmission rate, considering an individual s knowledge and fear is a vital aspect for their psychological wellbeing and may also largely influence the manner in which an individual may adhere to preventive measures and thereby determine the clinical outcome of COVID-19.