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The segmentation, detection and extraction of the infected tumor from Magnetic Resonance Imaging (MRI) images are the key concerns for radiologists or clinical experts. But it is tedious and time consuming and its accuracy depends on their experience only. This paper suggest a new methodology segmentation, recognition, classification and detection of different types of cancer cells from both MRI and RGB (Red, Green, Blue) images are performed using supervised learning, Convolutional Neural Network (CNN) and morphological operations. In this methodology, CNN is used to classify cancer types and semantic segmentation to segment cancer cells. The system trained using the pixel labeled the ground truth where every image labeled as cancerous and non-cancerous. The system trained with 70% images and validated and tested with the rest 30%. Finally, the segmented cancer region is extracted and its percentage area is calculated. The research examined on the MATLAB platform on MRI and RGB images of the infected cell of BreCaHAD dataset for breast cancer, SN-AM Dataset for leukemia, Lung and Colon Cancer Histopathological Images dataset for lung cancer and Brain MRI Images for Brain Tumor Detection dataset for brain cancer.

This paper explores machine learning techniques and evaluates their performances when trained to perform against datasets consisting of features that can differentiate between a Phishing Website and a safe one. This capability of telling these sites apart from one another is vital in the modernday internet surfing. As more and more of our resources shift online, one vulnerability and a leak of sensitive information by someone could bring everything down in a connected network. This paper's objective through this research is to highlight the best technique for identifying one of the most commonly occurring cyberattacks and thus allow faster identification and blacklisting of such sites, therefore leading to a safer and more secure web surfing experience for everyone. To achieve this, we describe each of the techniques we look into in great detail and use different evaluation techniques to portray their performance visually. After pitting all of these techniques against each other, we have concluded with an explanation in this paper that Random Forest Classifier does indeed work best for Phishing Website Detection.

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The coronary artery disease (CAD) occurs from the narrowing and damaging of major blood vessels or arteries. It has become the most life-threatening disease in the world, especially in the South Asian region. Its detection and treatment involve expensive medical facilities. The early detection of CAD, which is a major challenge, can minimize the patients’ suffering and expenses. The major challenge for CAD detection is incorporating numerous factors for detailed analysis. The goal of this study is to propose a new Clinical Decision Support System (CDSS) which may assist doctors in analyzing numerous factors more accurately than the existing CDSSs. In this paper, a Rule-Based Expert System (RBES) is proposed which involves five different Belief Rules, and can predict five different stages of CAD. The final output is produced by combining all BRBs and by using the Evidential Reasoning (ER). Performance evaluation is measured by calculating the success rate, error rate, failure rate and false omission rate. The proposed RBES has higher a success rate and false omission rate than other existing CDSSs.

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