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A dangerous and potentially fatal condition is a brain tumor. Early detection of this disease is critical for determining the best course of treatment. Tumor detection and classification by human inspection is a time consuming, error-prone task involving huge amounts of data. Computer-assisted machine learning and image analysis techniques have achieved significant results in image processing. In this study, we use supervised and deep learning classifiers to detect and classify tumors using the MRI images from the BRATS 2020 dataset. At the outset, the proposed system classifies images as healthy or normal brains and brain having tumorous growth. We employ four supervised machine learning classifiers SVM, Decision tree, Naïve Bayes and Linear Regression, for the binary classification. Highest accuracy (96%) was achieved with SVM and DT, with SVM giving a better Recall rate of 98%. Thereafter, categorization of the tumor as Pituitary adenoma, Meningioma, or Glioma, is performed using supervised (SVM, DT) classifiers and a 6-layer Convolution Neural Network. CNN performs better than the other classifiers, with a 93% accuracy and 92% recall rate. The suggested system is employable as a powerful decision-support tool to assist radiologists and oncologists in clinical diagnosis without requiring invasive procedures like a biopsy.

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The main aim of the proposed study is to develop a hybrid temporal model that provides learning pattern for classifying the temporal data. These results are unusual, which is in contrast with the Hidden Markov Models (HMM). The system is evaluated in terms of the capabilities of a hybrid learning algorithm, which is applied over the temporal data. Performance of the hybrid algorithm depends entirely on the dynamic data, which is fed into the system. The data fitting is an important concern, to find, analyse and predict the future instance. Hence, the difficulty in making a hybrid algorithm to fit the dynamic data is increasing, however, the data fits in better proportion over the expert system. An expensive research is required to build the required module for data pre-processing, analyzing and prediction. Also comparing such systems’ performance with the conventional schemes is required to prove its effectiveness. The study aims at developing a most generic artificial neural network hybrid algorithm, which predicts well the stock market data without the knowledge of past outputs. Hence, the end user does not trouble the recognition system and that is regarded as the virtues of soft computing tools

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