Utilization of Machine Learning in Brain Tumor Classification

Since past few years data mining lot of attention related to knowledge like extracting methods in health care system like diabetes, cancer, CVS etc. There are lot of technique of data mining like decision tree, Naive base, KNN; J48 etc. are being used for prediction of diabetes. Diabetes is metabolic disorder related to poor absorption of insulin into body mussels or poor lowered secretion of insulin from pancreases. As this disease, this is main death causes disease in the world. So, prediction of these diseases with the help of data mining technique may help to protect many lives. In this study, we have to discuss various data mining technique, types of diabetes, application of these data mining technique. Prediction of diabetes or any other disease could play a significant role in health system. Data mining are very useful in the scenario. These techniques help in selection, understanding and designing of large size data to analysis the chances of diseases occurrence. Recently who has announced diseases a major cause of death worldwide. The prediction and identification early stage of diabetes can play major role to treat this disease significantly. Various data mining techniques like KNN, Decision tree, Naïve Bays etc. would be a significant asset for the researcher for gaining various data about diabetes, its causes, symptoms and possible treatment that have been using in the past and currently used by various physician. In this study we have briefly discussed various data mining techniques/models. Which have been currently used for diabetes prediction? Along with this discussion, we have also focused on performance and short coming of existing models/techniques time to time evaluated by researchers.

Superior advance research in the electro-discharge machining of Ti alloys: Review

Electro Discharge Machining (EDM) known as an advanced machining process that much used for machining material with any hardness and complexity geometry in a high level of the accuracy in the cases of demand of the industrial application. The conception of the EDM process is included in applying electro-thermal energy in the gap, by discharge electrical power on the breaking point of the dielectric without any contact between electrodes. Due to the existing ability for removing debris from the surface, there is a lot of metallurgy defect on it. To improve and optimize EDM performance, the machine’s operating parameters, Input, during& output parameters, need to be optimized. Another handy way to reform of the view metallurgical must take picture sub-structure from the initial and subsequent layer. At the end for catching special status, adjust the technical parameters with metallurgical defects. Studies in case of the EDM have indicated that the appropriate selection of the way to control of the process, material, and operating parameters had considerably improved the process performance and also causing to was better the quality machined surface. This paper made a comprehensive review of the research studies in a different section of the EDM of various grades of titanium and its alloys. This review presents the simulation, experimental and theoretical studies on EDM that helped to improve the process performance, including material removal rate, surface quality, and tool wear rate, the stability of process among others. However, here are collected most of the research are done surrounding of EDM of Ti alloys as the developments of EDM and are seen material for future.