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Mental illnesses are a significant and growing public health concern. They have the potential to tremendously affect a person’s life. Depression, in particular, is one of the major reasons for suicide. In recent times, the popularity of social media websites has burgeoned as they are platforms that facilitate discussion and free-flowing conversation about a plethora of topics. Information and dialogue about subjects like mental health, which are still considered as a taboo in various cultures, are becoming more and more accessible. The objective of this paper is to review and comprehensively compare various previously employed Natural Language Processing techniques for the purpose of classification of social media text posts as those written by depressed individuals. Furthermore, pros, cons, and evaluation metrics of these techniques, along with the challenges faced and future directions in this area of research are also summarized.

In recent, the wireless networking technologies have witnessed monumental growth. The discovery of IEC (Integrated Electronic Circuits) which provide minute, intelligent sensory nodes that are being embedded within the human body has resulted in the formation of WBANs - Wireless Body Area Networks. The technology of WSN (wireless sensor networks) aids in uninterrupted monitoring of an individual’s health and the patient’s diabetes practice period with no involvement in patient’s mundane activities on local servers. There is a central sink node present within the sensor network, which supervises wireless transmission, optimizes the lifetime of the network and picks out the optimal path. The present research work proposes the framework of DCDH-SRP (dynamic channel direction and hop count based secure routing protocol) also termed as a secure communication monitoring system for transmitting and receiving the sensitive data in a secured way form the server to the network users. In the enhanced EAODV protocols, each node possesses the transmission rate as TX and receiving rate being RX. TX and RX rely upon the chosen optimal path. ACO - Ant Colony Optimization which is founded on the ODMRP-ACO (optimized dynamic, secure multipath routing protocol) is adapted for transmission of data in the wireless sensor networks. ACO determines the shortest path amidst the source and the destination node. The protocol recommended is DCDH-SRP which aids in data transmission and determining of shortest path relies upon the direction of dynamic channel node and minimum hop count in wireless sensor networks. For analyzing the performance related to the path duration and hop count metric, a network (n) model for DCDH-SRP is being formulated. A comparison is made between the proposed system and the improvised (Enhanced -Ad Hoc On-Demand Distance Vector) EAODV and (Optimized Dynamic Multi-path Routing Protocol- Ant Colony Optimization) ODMRP-ACO (of an earlier work). According to the output generated, the performance is enhanced using the proposed protocol than the other existing systems. In order to verify the mathematical models and perform extensive testing of the proposed protocol, the NS2 simulator is adopted. Remarkable and improvised performance is revealed by the DCDH-SR Protocols.

Overall academic performance and social compulsion have created a pressurized psychological state for students. Earlier, the competition level used to be less. As the science evolves, the knowledge and workload of the faculty and the school students have conjointly been evolving to view with the society. Students find it tough at times after they had completed their standards in SSLC (Secondary School Leaving Certificate) and HSC (Higher Secondary School Certificate). Associating with the NEET exam, the result influences a crucial-end on countless students' life. Naive Bayes technique is an emerging discipline, used to analyze the accurate facts and helps in organizing the most stress-causing factors based on the probabilistic parameters. Abstract Reference Object characteristics and correlation are the two main attributes based on which the stress accuracy level is predicted. Information gathered from the survey is analyzed using Bayes classifier and the outcomes are inferred using visualization tools. Based on the output, the socioeconomic and physical stress causing factors have been encoded and prioritized. Frequently stated stress factors should be reduced to help the students to excel in their academic performance and activate the social activities, thereby decreasing individual health issues like migraine headaches, spectacle- wearing and so on.