Abstract-Early diagnosis of mental health problems helps the professionals to treat it at an earlier stage and improves the patients' quality of life. 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. Machine learning Techniques are currently well suited for analyzing medical data and diagnosing the problem. 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. A data set consisting of sixty cases is collected for training and testing the performance of the techniques. Twenty-five attributes have been identified as important for diagnosing the problem from the documents. The attributes have been reduced by applying Feature Selection algorithms over the full attribute data set. The accuracy over the full attribute set and selected attribute set on various machine learning techniques have been compared. 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.

No abstract

No abstract

No abstract

This paper discusses the existence of defective items in a manufacturing process. A rework strategy is implemented to rectify the defective items. A rework cost function under fuzzy environment which includes both synchronous and asynchronous items. The paper develops fuzzy optimal total cost function and fuzzy production quantity using trapezoidal numbers and applying Lagrangian method. A numerical example follows to justify the solution procedure. Keywords Economic order quantity (EOQ), Fuzzy inventory, Graded mean integration and Lagrangian method.