Personalized healthcare systems deliver e-health services to fulfill the medical and assistive needs of the aging population. Internet of Things (IoT) is a significant advancement in the Big Data era, which supports many real-time engineering applications through enhanced services. Analytics over data streams from IoT has become a source of user data for the healthcare systems to discover new information, predict early detection, and makes decision over the critical situation for the improvement of the quality of life. In this paper, we have made a detailed study on the recent emerging technologies in the personalized healthcare systems with the focus towards cloud computing, fog computing, Big Data analytics, IoT and mobile based applications. We have analyzed the challenges in designing a better healthcare system to make early detection and diagnosis of diseases and discussed the possible solutions while providing e-health services in secure manner. This paper poses a light on the rapidly growing needs of the better healthcare systems in real-time and provides possible future work guidelines.

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The concern for the environmental pollution and the prevention of resources has attracted researchers to develop new eco-friendly green materials based on sustainability principles. In this experimental study, there are six different composite samples were fabricated by using banana and carbon fibers with epoxy resin matrix. The mechanical properties such as tensile strength, flexural strength, impact strength, and water uptake properties of these composites have been evaluated. The composites reinforced with pure carbon fibers can hold the maximum tensile strength of 288.03 MPa, flexural strength of 3.12 kN, impact strength of 4.58 J and water intake percentage of 62.3%. Whereas the composites reinforced with carbon and banana fibers can withstand the maximum tensile strength of 277.06 MPa, flexural strength of 3.07 kN, impact strength of 4.36 J and water intake percentage of 70%. The finite element analysis has been carried out to predict the mechanical properties of the composites by using ANSYS 15.0. The experimental results are compared with the predicted values and have found that, there is a high correlation occurs between the results. Scanning electron microscopy (SEM) analysis is carried out to study the fiber matrix interfaces and analyse the structure of the fractured and water absorbed surfaces.

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Rapid growth of web and its applications has created a colossal importance for recommender systems. Being applied in various domains, recommender systems were designed to generate suggestions such as items or services based on user interests. Basically, recommender systems experience many issues which reflects dwindled effectiveness. Integrating powerful data management techniques to recommender systems can address such issues and the recommendations quality can be increased significantly. Recent research on recommender systems reveals an idea of utilizing social network data to enhance traditional recommender system with better prediction and improved accuracy. This paper expresses views on social network data based recommender systems by considering usage of various recommendation algorithms, functionalities of systems, different types of interfaces, filtering techniques, and artificial intelligence techniques. After examining the depths of objectives, methodologies, and data sources of the existing models, the paper helps anyone interested in the development of travel recommendation systems and facilitates future research direction. We have also proposed a location recommendation system based on social pertinent trust walker (SPTW) and compared the results with the existing baseline random walk models. Later, we have enhanced the SPTW model for group of users recommendations. The results obtained from the experiments have been presented.