From the validation analysis of the DCCN, the following results were obtained: average throughput (bytes/Sec) for DCCN = 40.00%, DCell = 33.33% and BCube = 26.67%. The QoS metrics considered in the analysis are: the service delay time, resource availability, throughput and utilization. The validation of the DCCN performance was carried out using trace files from Riverbed Modeller 17.5 in order to ascertain the influence of virtualization on server resource pool. Virtualization services such as resource allocation and task scheduling were employed in heterogeneous server clusters. In this paper, the architectural decomposition of a proposed DCCN was carried out while exploring its functionalities for grid performance.
Such failure has been identified in the University of Nigeria datacenter network situated in the south eastern part of Nigeria. This is highly undesirable in Distributed Cloud Computing Networks (DCCNs) running mission critical services. Network failure in cloud datacenter could result from inefficient resource allocation scheduling and logical segmentation of physical machines (network constraints).
Keywords-Mental Health Diagnosis Machine Learning Prediction Feature Selection Basic Mental Health Problems
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It is evident from the results that the three classifiers viz., Multilayer Perceptron, Multiclass Classifier and LAD Tree produced more accurate results and there is only a slight difference between their performances over full attribute set and selected attribute set. The accuracy over the full attribute set and selected attribute set on various machine learning techniques have been compared. The attributes have been reduced by applying Feature Selection algorithms over the full attribute data set. Twenty-five attributes have been identified as important for diagnosing the problem from the documents. A data set consisting of sixty cases is collected for training and testing the performance of the techniques. This research has identified eight machine learning techniques and has compared their performances on different measures of accuracy in diagnosing five basic mental health problems. Machine learning Techniques are currently well suited for analyzing medical data and diagnosing the problem. So, there is an urgent need to treat basic mental health problems that prevail among children which may lead to complicated problems, if not treated at an early stage. Abstract-Early diagnosis of mental health problems helps the professionals to treat it at an earlier stage and improves the patients’ quality of life.
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