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Extracting useful information from the datasets of high dimension and representing the learnt knowledge in an efficient way is a challenge in knowledge discovery and data mining. Although many pattern recognition, knowledge discovery and data mining techniques are available in literature, there is a need for techniques that represent the high dimensional data in a low dimension by preserving useful information for supervised learning. In this work, we design a novel model which effectively captures both inter-feature and intrafeature relationships in the sample space for knowledge discovery by performing dimensionality reduction, using a modified version of multi-factor dimensionality reduction based algorithm. The model uses the learnt knowledge to quantify the similarity of a test sample with respect to a specific class. The evaluation of the model on Fisher's IRIS dataset containing 50 samples each from three types of IRIS species-setosa, versicolor and verginica, shows that the designed model explores the data set for useful information and assigns test samples to a specific class with measurable similarity indices.

A brain tumor is defined by the proliferation of aberrant brain cells, some of which may progress to malignancy. A brain tumor is usually diagnosed via a magnetic resonance imaging (MRI) examination. These images demonstrate the recently observed aberrant brain tissue proliferation. Several academics have examined the use of machine learning and Deep Learning (DL) algorithms to diagnose brain tumors accurately A radiologist may also profit from these forecasts, which allow them to make more timely decisions. The VGG-16 pre-trained model is employed to detect the brain tumor in this study. Using the outcomes of training and validation, the model is completed by employing two critical metrics: accuracy and loss. Normal people confront numerous challenges in scheduling a doctor's appointment (financial support, work pressure, lack of time). There are various possibilities for bringing doctors to patients' homes, including teleconferencing and other technologies. This research creates a website that allows people to upload a medical image and have the website predict the ailment. The Google Cloud Platform (GCP) will be utilized to install the DL model due to its flexibility and compatibility. The customized brain tumor detection website is then constructed utilizing HTML code.