The development of wireless sensors and wearable devices has led health care services to the new paramount. The extensive use of sensors, nodes, and devices in health care services generate an enormous amount of health data which is generally unstructured and heterogeneous. Many generous methods and frameworks have been developed for efficient data exchange frameworks, security protocols for data security and privacy. However, very less emphasis has been devoted to structuring and interpreting health data by fuzzy logic systems. The wireless sensors and device performances are affected by the remaining battery/energy, which induces uncertainties, noise, and errors. The classification, noise removal, and accurate interoperation of health data are critical for taking accurate diagnosis and decision making. Fuzzy logic system and algorithms were found to be effective and energy efficient in handling the challenges of raw medical data uncertainties and data management. The integration of fuzzy logic is based on artificial intelligence, neural network, and optimization techniques. The present work entails the review of various works which integrate fuzzy logic systems and algorithms for enhancing the performance of healthcare-related apps and framework in terms of accuracy, precision, training, and testing data capabilities. Future research should concentrate on expanding the adaptability of the reasoning component by incorporating other features into the present cloud architecture and experimenting with various machine learning methodologies.

Networks which are lacking in uninterrupted back-to-back links amongst their nodes due to node movement, controlled power sources or restricted data storage space are called DTNs. To rise above this irregular connectivity, DTN nodes stock up and hold the data packets they accept until they come into communication range of each other. In addition, they extend many copies of the same packet on the network to increase the deliverance prospect. In recent time, several routing protocols have been developed specifically for DTNs. These protocols differ in the number of copies they extend and the information they use to guide the packets to their destinations. This paper studies four recognized DTN routing protocols i.e. EPIDEMIC, Spray-and-Wait, PROPHET and MAXPROP. In this paper, DTN is studied along with its protocols and measured in environment of packet deliverance, delivery cost and average packet delay and finally finding out the best protocol under certain networking parameters.

Healthiness nowadays is unquestionably one of the essential apprehensions of humans and it is being validated by worldwide healthcare manufacturing, which is increasing exponentially with time. The technology that complements this recklessly developing business is artificial intelligence and its enactment with machine learning. Machine learning, along with artificial intelligence, has entered each facet of our daily lives and is leaving a positive impact on our lives. To speed up the entrenched machine learning as a part of a varied number of applications besides integrating it in real-world situations, automated machine learning stands as an important and emerging vertical. The foremost persistence in terms of automated machine learning is in providing unified incorporation of machine learning in numerous businesses, facilitating improved consequences in daily tasks. In terms of healthcare, automated machine learning is being applied to structured data like tabular laboratory information. Although there is a necessity of applying automated machine learning in terms of inferring medicinal transcript, which in turn is being produced at a very incredible rate, and for this purpose, an efficient and effective capable technique that inculcates various strategies of automated machine learning in terms of medical notes scrutiny becomes quite useful. Here, varied applications of automated machine learning in healthcare diligence besides effective expansions that are quite precise for medical situations are discussed. Furthermore, some of the existing challenges besides prospects are also discussed in addition to numerous gears and practices. <!----> <!----> <!----> <!---->