To investigate the effect of deep neural networks with transfer learning on MR images for tumor classification and improve the classification metrics by building image-level, stratified image-level, and patient-level models. Three thousand sixty-four T1-weighted magnetic resonance (MR) imaging from two hundred thirty-three patient cases of three brain tumors types (meningioma, glioma, and pituitary) were collected and it includes coronal, sagittal and axial views. The average number of brain images of each patient in three views is fourteen in the collected dataset. The classification is performed in a model of cross-trained with a pre-trained InceptionV3 model. Three image-level and one patient-level models are built on the MR imaging dataset. The models are evaluated in classification metrics such as accuracy, loss, precision, recall, kappa, and AUC. The proposed models are validated using four approaches: holdout validation, 10-fold cross-validation, stratified 10-fold cross-validation, and group 10-fold cross-validation. The generalization capability and improvement of the network are tested by using cropped and uncropped images of the dataset. The best results for group 10-fold cross-validation (patient-level) are obtained on the used dataset (ACC=99.82). A deep neural network with transfer learning can be used to classify brain tumors from MR images. Our patient-level network model noted the best results in classification to improve accuracy.

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<p>The concept of machine learning generate best results in health care data, it also reduce the work load of health care industry. This algorithm potentially overcome the issues and find out the novel knowledge for development of medical date in health care industry. In this paper propose a new algorithm for finding the outliers using different datasets. Considering that medical data are analytic of mutually health problems and an activity. The proposed algorithm is working based on supervised and unsupervised learning. This algorithm detects the outliers in medical data. The effectiveness of local and global data factor for outlier detection for medical data in real time. Whatever, the model used in this scenario from their training and testing of medical data. The cleaning process based on the complete attributes of dataset of similarity operations. Experiments are conducted in built in various medical datasets. The statistical outcome describe that the machine learning based outlier finding algorithm given that best accurateness.</p>

Recommender systems is a big breakthrough for the field of e-commerce. Product recommendation is challenging task to e-commerce companies. Traditional Recommender Systems provided the solutions in recommending the products. This in turn help companies to generate good revenue. Now a day Deep Learning is using in every domain. Deep Learning techniques in the field of Recommender Systems can be directly applied. Deep Learning has ample number of algorithms. These algorithms can be used to give recommendations to users to purchase products. In this paper performance of Traditional Recommender Systems and Deep Learning-based Recommender Systems are compared.

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