Bitcoins are cryptocurrencies that make use of blockchain technology, which consists of network nodes and permanent ledgers of events. Theft and illicit actions are referred to as abnormalities in the financial networ...
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Internet of Medical Things (IoMT) networks are high precision wireless interfaces that require design of accurate sensing, actuation, and processing devices. These devices include wireless electrocardiograph (ECG) sen...
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ISBN:
(纸本)9781665484251
Internet of Medical Things (IoMT) networks are high precision wireless interfaces that require design of accurate sensing, actuation, and processing devices. These devices include wireless electrocardiograph (ECG) sensors, electroencephalograph (EEG) sensors, wireless blood pressure monitoring devices, etc. Due to direct patient interface, these devices are required to have superior performance in terms of sensing accuracy, processing efficiency, and communication quality of service (QoS) parameters. A wide variety of models are proposed by IoMT researchers to perform this task, but each of these models vary in terms of network size, deployment complexity, cost of deployment, processing delay, etc. These models include machine learning routing techniques, blockchain based security methods, privacy preservation methods, high-precision sensor design methods, high-performance communication interfaces, etc. Due to such a wide variation in performance, IoMT network design requires continuous validation, which increases time-to-market, thereby increasing deployment cost. In order to reduce number of validations, a statistical survey of models for IoMT network design is discussed in this text. This discussion is focussed towards evaluating various characteristics, advantages, limitations, and future research scopes in existing models. readers would be able to identify best performing model(s) for a given IoMT application. It is followed by a statistical analysis of the reviewed IoMT network design models in terms of end-to-end delay, communication QoS, network security, scalability, computational complexity, cost of deployment, and application of use. This statistical performance evaluation will further assist readers to statistically compare the reviewed methods, and identify best performing model(s) their combinations for context-specific network deployments. Due to this, readers would be able to reduce network design validation delay, which will further assist in reduci
In certain emergencies, patients must be continuously monitored and cared for. However, visiting the hospital to do such activities is difficult because of time constraints. To modernize the healthcare sector, the stu...
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technology grows at a rapid rate and trust has involved in a risk judgment between different parties. Authentication and Authorization generally determines the trust while dealing with transactions of data over differ...
ISBN:
(数字)9781728141428
ISBN:
(纸本)9781728141435
technology grows at a rapid rate and trust has involved in a risk judgment between different parties. Authentication and Authorization generally determines the trust while dealing with transactions of data over different channels. Blockchain has become a digital backbone due to its security, time-management, and transparency on data decentralization ledger. This survey paper studies the existing blockchain structure, analyses the research gaps and proposes a new theoretical approach based on neighbor monitoring and updating model. This model may help the patients to decentralize real data and results in the blockchain network. This work discusses in detail about the various security challenges in block chain against 51% attack and double-spend attack by proposing a theoretical framework for the problem.
Increasing numbers of higher education institutions see themselves as service providers, catering primarily to the needs of its students. The improvement of student performance is a top priority for universities. It i...
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Increasing numbers of higher education institutions see themselves as service providers, catering primarily to the needs of its students. The improvement of student performance is a top priority for universities. It is critical to first assess the present situation of the students before designing a program to improve their performance. Higher education administrators face a significant problem in predicting a student's future success. The goal of this study is to learn what factors influence college students' decision on a major. It will be possible to forecast students' behavior, attitudes, and performance with the use of predictive tools and procedures. Predicting student performance ahead of time makes it possible to take proactive measures to raise achievement levels. To obtain a high education standard, several attempts have been made to forecast student performance. However the accuracy of these predictions falls short of the desired level of excellence. Machine learning approaches including Artificial Neural Network, Nave Bayes, and SVM are being studied. A university Data Set from UCI Machinery is used in the experimental investigation.
作者：
Janakiraman MoorthyRangin LahiriNeelanjan BiswasDipyaman SanyalJayanthi RanjanKrishnadas NanathPulak Ghosh(Coordinator) Director and Professor of Marketing at the Institute of Management Technology
Dubai. Earlier he was Professor of Marketing at the IIM Calcutta and IIM Lucknow. He received his PhD from IIM Ahmedabad. His recent research papers were published in the leading scholarly ournals such as Marketing Science British Food Journal Journal of Information Technology Case and Application Research Journal of Database Marketing & Customer Strategy Management. He has wide experience in the banking and investment industry. He was earlier the Global Research and Project Director of the Institute for Customer Relationship Management Atlanta USA. He was the Convener of the prestigious CAT Exam 2011. e-mail: Practice Director
leading Atos India's CRM practice while supporting Strategic Business Development for North American Market. With an experience of more than 15 years Rangin has worked extensively as a Business Consultant in Information Technology (Sales Automation Marketing & Service Management area) Customer Data Management and CRM Analytics. e-mail: Business Consultant at Atos with extensive experience in Business Analysis
Risk Management Analytics Business Development Presales Solution Ideation on Enterprise Data Management Enterprise Reference Data and Master Data Management area. e-mail: founder and CEO of dono consulting
a boutique quantitative analytics and investment research firm. He has worked for leading financial firms in New York and India including Dow Jones Blackstone Sorin Capital (VP Quantitative Modeling) and Thomson Reuters (Head of Real Estate Analytics). A CFA charter holder and Commonwealth Scholar Deep has an MS (Applied Economics) from University of Texas Dallas and an MA (Economics) from Jadavpur University e-mail: Professor in the Information Systems Group of the Institute of Management Technology
Ghaziabad. Her PhD is in the field of data mining from Jamia Millia Islamia Central University India. She has published five edited books. She is serving on the editorial b
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