Mental health is an indicator of emotional, psychological and social well-being of an individual. It determines how an individual thinks, feels and handle situations. Positive mental health helps one to work productively and realize their full potential. Mental health is important at every stage of life, from childhood and adolescence through adulthood. Many factors contribute to mental health problems which lead to mental illness like stress, social anxiety, depression, obsessive compulsive disorder, drug addiction, and personality disorders. It is becoming increasingly important to determine the onset of the mental illness to maintain proper life balance. The nature of machine learning algorithms and Artificial Intelligence (AI) can be fully harnessed for predicting the onset of mental illness. Such applications when implemented in real time will benefit the society by serving as a monitoring tool for individuals with deviant behavior. This research work proposes to apply various machine learning algorithms such as support vector machines, decision trees, naïve bayes classifier, K-nearest neighbor classifier and logistic regression to identify state of mental health in a target group. The responses obtained from the target group for the designed questionnaire were first subject to unsupervised learning techniques. The labels obtained as a result of clustering were validated by computing the Mean Opinion Score. These cluster labels were then used to build classifiers to predict the mental health of an individual. Population from various groups like high school students, college students and working professionals were considered as target groups. The research presents an analysis of applying the aforementioned machine learning algorithms on the target groups and also suggests directions for future work.
A mobile ad hoc network (MANET) is a self-organizing, self-configuring confederation of wireless systems. MANET devices join and leave the network asynchronously at will, and there are no predefined clients or server. The dynamic topologies, mobile communications structure, decentralized control, and anonymity creates many challenges to the security of systems and network infrastructure in a MANET environment. Consequently, this extreme form of dynamic and distributed model requires a revaluation of conventional approaches to security enforcements. In this paper, we propose a new routing mechanism to combat the common selective packet dropping attack. Associations between nodes are used to identify and isolate the malicious nodes. Simulation results show the effectiveness of our scheme compared with conventional scheme.
Problem Statement: Mobile adhoc networks (MANETs) were extensively used in defense and rescue applications. The dynamic topology of MANETs allows nodes to join and leave the network at any point of time. This dynamic property of MANET has rendered it vulnerable to various security attacks. Many trust establishment methods were proposed to increase the security in MANET. In this paper we propose a new trust based relationship among the nodes to combat the packet dropping attack. Approach: In the proposed scheme we considered the dynamic source routing protocol for simulation due to its common usage and flexible nature. Network simulator-2 was used for the simulation and the standard DSR and proposed relationship enhanced DSR were compared. Results: The result of the proposed scheme was compared with the standard DSR protocol. The performance metrics such as normalized throughput, packet delivery ratio, dropped data packets and ratio between the total drop and malicious drops were used for the comparison study. The results obtained prove that the proposed scheme outscores the traditional DSR protocol in all aspects. Conclusions/Recommendations: The proposed trust enhanced dynamic source routing protocol provides the solution for the possible packet dropping attack in an adhoc network. As the results show it has enhanced technique for encountering such type of attacks when compared to the traditional DSR protocol.
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