WBAN Applications and Issues

In communications the area of coverage is very important, such that personal space or long range to send information. The distance refers to class of networks such as per-sonal range or wide area, while the protocols of communications refer to mode or type of networks, such as ad-hoc or self organization etc. Our aim is to provide a tutorial to introduce WBAN and its working knowledge as well as architecture. We will address Emergency health issues and suggest how it can be improved

Era of Deep Learning in Wireless Networking

This work deals with the use of emerging deep learning techniques in future wireless communication networks. It will be shown that data-driven approaches should not replace, but rather complement traditional design techniques based on mathematical models. Extensive motivation is given for why deep learning based on artificial neural networks will be an indispensable tool for the design and operation of future wireless communication networks, and our vision of how artificial neural networks should be integrated into the architecture of future wireless communication networks is present-ed. A thorough description of deep learning methodologies is provided, starting with the general machine learning paradigm, followed by a more in-depth discussion about deep learning and artificial neural networks, covering the most widely-used artificial neural network architectures and their training methods. Deep learning will also be connected to other major learning frameworks such as reinforcement learning and transfer learning. A thorough survey of the literature on deep learning for wireless communication networks is provided, followed by a detailed description of several novel case-studies wherein the use of deep learning proves extremely useful for net-work design. For each case-study, it will be shown how the use of (even approximate) mathematical models can significantly reduce the amount of live data that needs to be acquired/measured to implement data-driven approaches

WBAN Routing Protocols in Health care

The emergence of wireless body area network (WBAN) technology has brought hope and dawn to solve the problems of population aging, various chronic diseases, and medical facility shortage. The increasing demand for real-time applications in such networks stimulates many research activities. Our aim is to provide a tutorial to introduce WBAN routing protocols. We classify, and compare the advantages and disadvantages of various routing protocols. We also address Emergency health issues and suggest how it can be improved

WBAN Routing Protocols in Health care

The emergence of wireless body area network (WBAN) technology has brought hope and dawn to solve the problems of population aging, various chronic diseases, and medical facility shortage. The increasing demand for real-time applications in such networks stimulates many research activities. Our aim is to provide a tutorial to introduce WBAN routing protocols. We classify, and compare the advantages and disadvantages of various routing protocols. We also address Emergency health issues and suggest how it can be improved

WBAN Applications and Issues

In communications the area of coverage is very important, such that personal space or long range to send information. The distance refers to class of networks such as per-sonal range or wide area, while the protocols of communications refer to mode or type of networks, such as ad-hoc or self organization etc. Our aim is to provide a tutorial to introduce WBAN and its working knowledge as well as architecture. We will address Emergency health issues and suggest how it can be improved

Deep Reinforcement Learning in Health care systems

In computational science, particle swarm optimization (PSO) is a computational method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality. We classify, and compare the advantages and disadvantages of various routing proto-cols. We also address Emergency health issues and suggest how it can be improved

Artificial iIntelligence for Big Data: issues and challenges

Artificial intelligence (AI) concerns the study and development of intelligent ma-chines and software. The associated ICT research is highly technical and specialized, and its focal problems include the developments of software that can reason, gather knowledge, plan intelligently, learn, communicate, perceive and manipulate objects. AI also allows users of big data to automate and enhance complex descriptive and predictive analytical tasks that, when performed by humans, would be extremely la-bour intensive and time consuming. Thus, unleashing AI on big data can have a sig-nificant impact on the role data plays in deciding how we work, how we travel and how we conduct business. This paper explores how Artificial Intelligence, in conjunc-tion with Big Data technologies, can help organizations to bring about operational and business transformation.Deep learning will also be connected to other major learning frameworks such as reinforcement learning and transfer learning. A thorough survey of the literature on deep learning for wireless communication networks is provided, followed by a detailed description of several novel case-studies wherein the use of deep learning proves extremely useful for network design. For each case-study, it will be shown how the use of (even approximate) mathematical models can significantly reduce the amount of live data that needs to be acquired/measured to implement data-driven approaches

Wireless Communications and Mobile Computing using Machine learning

This work deals with the use of emerging deep learning techniques in future wireless communication networks. It will be shown that data-driven approaches should not re-place, but rather complement traditional design techniques based on mathematical models. Extensive motivation is given for why deep learning based on artificial neural networks will be an indispensable tool for the design and operation of future wireless communication networks, and our vision of how artificial neural networks should be integrated into the architecture of future wireless communication networks is present-ed. A thorough description of deep learning methodologies is provided, starting with the general machine learning paradigm, followed by a more in-depth discussion about deep learning and artificial neural networks, covering the most widely-used artificial neural network architectures and their training methods. Deep learning will also be connected to other major learning frameworks such as reinforcement learning and transfer learning. A thorough survey of the literature on deep learning for wireless communication networks is provided, followed by a detailed description of several novel case-studies wherein the use of deep learning proves extremely useful for net-work design. For each case-study, it will be shown how the use of (even approximate) mathematical models can significantly reduce the amount of live data that needs to be acquired/measured to implement data-driven approaches

Blockchain Wallets in Health care systems

The whole concept of Blockchain is fascinating, no doubt. However, there is a catch here. As cryptocurrencies are becoming the order of the day in financial markets, a lot of patience goes into understanding and managing them. We know that cryptocurrencies are digital money, so where do we store them? The answer to this question is Blockchain Wallets. We classify, and compare the advantages and disad-vantages of various routing protocols. We also address Emergency health issues and suggest how it can be improved