The primary identification and prediction of type of the cancer ought to develop a compulsion in cancer study, in order to assist and supervise the patients. The significance of classifying cancer patients into high or low risk clusters needs commanded many investigation teams, from the biomedical and the bioinformatics area, to learn and analyze the application of machine learning (ML) approaches. Logistic Regression method and Multi-classifiers has been proposed to predict the breast cancer. To produce deep predictions in a new environment on the breast cancer data. This paper explores the different data mining approaches using Classification which can be applied on Breast Cancer data to build deep predictions. Besides this, this study predicts the best Model yielding high performance by evaluating dataset on various classifiers. In this paper Breast cancer dataset is collected from the UCI machine learning repository has 569 instances with 31 attributes. Data set is pre-processed first and fed to various classifiers like Simple Logistic-regression method, IBK, K-star, Multi-Layer Perceptron (MLP), Random Forest, Decision table, Decision Trees (DT), PART, Multi-Class Classifiers and REP Tree.  10-fold cross validation is applied, training is performed so that new Models are developed and tested. The results obtained are evaluated on various parameters like Accuracy, RMSE Error, Sensitivity, Specificity, F-Measure, ROC Curve Area and Kappa statistic and time taken to build the model. Result analysis reveals that among all the classifiers Simple Logistic Regression yields the deep predictions and obtains the best model yielding high and accurate results followed by other methods IBK: Nearest Neighbor Classifier, K-Star: instance-based Classifier, MLP- Neural network. Other Methods obtained less accuracy in comparison with Logistic regression method.

As of late, with the progression of AI and man-made brainpower, there has been a developing spotlight on versatile e-learning. As all ways to deal with e-learning lose their allure and the level of online courses builds, they move towards more customized versatile learning so as to collaborate with students and achieve better learning results. The schools focus on the examination, mindfulness, and arranging techniques that infuse innovation into the vision and educational program. E-learning issues are a standard examination issue for us all. The motivation behind this research analysis is to separate the potential outcomes of assessing e-learning models utilizing AI strategies such as Supervised, Semi Supervised, Reinforced Learning advances by investigating upsides and downsides of various methods organization. The literature review methodology is to review the cross sectional impacts of e-learning and Machine learning algorithms from existing literatures from the year 1993 to 2020 and to assess the essentialness of elearning features to optimize the e-learning models with available Machine learning techniques from peerinspected journals, capable destinations, and books. Second, it legitimizes the chances of e-learning structures introduction, and changes demonstrated through AI and Machine Learning algorithms. This examination assists in providing helpful new highlights to analysts, researchers and academicians. It gives an exhaustive structure of existing e-learning frameworks for the most recent innovations identified with learning framework capacities and learning tasks to envision ML research openings in appropriate spaces. The survey paper identifies and demonstrates the important role of different types of e-learning features such as Individual pertinent feature, Course pertinent feature, Context pertinent feature and Technology pertinent feature in framework performance tuning. The performance of Machine Learning algorithms to optimize the features of E-Learning models were reviewed in previous literatures and Support Vector Machine technique was found to be the one of the best to predict the input and output parameters of elearning models and it is found that Fuzzy C Means, Deep Learning algorithms are producing better results for Big Data sets.

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Rock classification plays significant role in determining the fluid flow movement inside the reservoir. With recent developments in computer vision of porous medium and artificial intelligence techniques, it is now possible to visualize unprecedented detail at the scale of individual grains, understand the patterns of contact angles and its direct connection to multiphase fluid movements within the porous media. The outcome of this work is a probabilistic rock classification model that provides a reliable and realistic description of the reservoir. As part of this work, 400 fully brine saturated 3D micro-CT images of Bentheimer and Clashach micro core plugs are utilized. Various three-dimension image analysis techniques are applied to quantify the rock properties (e.g. porosity, absolute permeability) and to extract pore structure information, such as pore throat distribution, pore connectivity and pore roughness from these images. The rock surface roughness is quantified as the local deviation from the plane (AlRatrout et al. 2018). The whole image dataset is divided into two separate subsets, 80% for training purpose and 20% for testing purpose. Both subsets are fed to an artificial intelligence-based model to verify and validate the results. To improve the accuracy of the model, k-fold validation technique is implemented. The accuracy of the developed model is validated using Root-Mean-Square Error (RMSE), coefficient of determination (R2) and relative error (RE). Blind test of comparing predicted results with second subset of experimental data have shown that the developed model is capable to predict rock type with a maximum error of 3.5%. The results of this study indicate that for the given dataset, pore surface roughness has dominant effect on rock classification. The accuracy of the developed model can be improved by incorporating additional information, for example rock mineralogy. However, the developed model is limited only aforementioned rock types, can be easily extended to other rock types provided enough micro CT images are available.