Nowadays, with the improvement in communication through social network services, a massive amount of data is being generated from user's perceptions, emotions, posts, comments, reactions, etc., and extracting significant information from those massive data, like sentiment, has become one of the complex and convoluted tasks. On other hand, traditional Natural Language Processing (NLP) approaches are less feasible to be applied and therefore, this research work proposes an approach by integrating unsupervised machine learning (Self-Organizing Map), dimensionality reduction (Principal Component Analysis) and computational classification (Adam Deep Learning) to overcome the problem. Moreover, for further clarification, a comparative study between various well known approaches and the proposed approach was conducted. The proposed approach was also used in different sizes of social network data sets to verify its superior efficient and feasibility, mainly in the case of Big Data. Overall, the experiments and their analysis suggest that the proposed approach is very promissing.

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Due to the advances in information and communication technologies, the usage of the internet of things (IoT) has reached an evolutionary process in the development of the modern health care environment. In the recent human health care analysis system, the amount of brain tumor patients has increased severely and placed in the 10th position of the leading cause of death. Previous state-of-the-art techniques based on magnetic resonance imaging (MRI) faces challenges in brain tumor detection as it requires accurate image segmentation. A wide variety of algorithms were developed earlier to classify MRI images which are computationally very complex and expensive. In this paper, a cost-effective stochastic method for the automatic detection of brain tumors using the IoT is proposed. The proposed system uses the physical activities of the brain to detect brain tumors. To track the daily brain activities, a portable wrist band named Mi Band 2, temperature, and blood pressure monitoring sensors embedded with Arduino-Uno are used and the system achieved an accuracy of 99.3%. Experimental results show the effectiveness of the designed method in detecting brain tumors automatically and produce better accuracy in comparison to previous approaches.

Smartphone becomes one of the most popular devices in last few years due to the integration of powerful technologies in it. Now-a-days a smartphone can provide different services as like as a computer provides. Smartphone holds our important personal information such as photos and videos, SMS, email, contact list, social media accounts etc. Therefore, the number of security and privacy related threats are also increasing relatively. Our research aims at evaluating how much the smartphone users are aware about their security and privacy. In this study, firstly we have taken a survey for smartphone users to access the level of smartphone security awareness displayed by the public. We also determine whether a general level of security complacency exists among smartphone users and measure the awareness of android users regarding their privacy. From survey result we have found that, most of the people are not aware about their smartphone security and privacy. Secondly, based on survey results, we have shown a method to measure the level of awareness (LOA) for the smartphone users. By using this method, a user can easily measure his/her smartphone security and privacy related level of awareness.