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In today's modern world, health care management system is becoming as an emerging research area. In health care, still manual work take a lot of time to measure multiple parameters of a patient. Also, in recent days there is an extensive demand for skilled workers in the field of health care. To reduce the manual work, a solution is proposed to automate all the patient monitoring activities through Blue Eyes Technology. The main idea is to use the BET for the betterment in health care management system and to monitor a patient with the help of BET. It measures various parameters that include emotions, mood variations, blood pressure, heart rate, skin temperature, and electrocardiogram. The major steps involved are process of giving sensing capacity to a device, so that it can sense some basic metrics of a human being, then, human emotion detection involves the process of observing different emotions of a human which includes happiness, sadness, fear, anger, surprise and disgust and finally, respond appropriately and properly if any critical situation occur

The clinical researchers found that, Magnetic Resonance Imaging is applied in various computer science applications. However, for detecting the brain tumor without human involvement is taken into account has a major space of analysis. As a result of the obtained brain tumor images need to be optimized segmentation rule that must be more silent towards noise and size of the cluster sensitivity problem with automatic region based segmentation property detection. Region based segmentation is used to segment the over segment and under segment of the tumor region with automatic ROI detection. After the successful detection of tumor parts that are going to classify the different classes of brain tumors such as benign and malignant. The Benign tumor and malignant tumor are classified as normal stage, medium stage and severe stage. For that different feature extraction and classification methods are used for identifying the above. Without human intercession the brain tumor should be detected and analyzed. The SVM algorithm is used to classify the tumor cell. The accurate classification and giving appropriate therapy to the Patient plays a vital role in Health Care Sector.

The first attempt to predict weather numerically has required a larger workforce. With the development of powerful computers and better modeling techniques, weather prediction has made a comeback to the early models in similarity. Then the simple-basic equations are used as the forecast equations in Weather Prediction. Since equations govern how meteorological variables change with time, if the initial condition of the atmosphere is known, equations can be deployed in our project to obtain new values of those variables during the later future for forecasting. The weather data at a particular station is obtained for a short range over a particular region is considered. The results obtained shows that, it can estimate the weather conditions more precisely and accurately.

Choosing an appropriate course is significant for understudies to accomplish their ideal objectives however the wealth obviously related data accessible makes it hard for understudies to choose a course that coordinates their inclinations, objectives and current information. The proposed framework prescribes the discretionary courses to understudies dependent on their inclinations by recommending courses that other comparable understudies have taken. The similitude between understudies is determined by the methods for pearson connection coefficient. It utilizes a memory based community oriented sifting way to deal with anticipate the scores of the understudy for each course and the courses with the most elevated anticipated scores are then suggested.

Skin cancer is a highly contagious disease that spreads over the human body very rapidly within a short period of time. Skin cancer and rashes may seem to be similar to each other in the eyes of a dermatologist so it is not easy to determine whether the skin is affected by rashes or skin cancer. Rashes are generic terms used by people and doctors to denote the changes in the skin such as skin infections, skin allergies, skin diseases. Dermatologist identifies skin cancer from rashes when skin does not heal over medication for a prolonged period of time and continues to spread rapidly to other parts of the skin meanwhile knowing the differences between rashes and skin cancer can help a person seek the necessary help or avoid anxiety about a noncancerous rash. The objective of the paper is to differentiate skin cancer from rashes and helps in detecting skin cancer in human skin. Pervious researches were successful in classifying the skin cancer types using image classification and this modal detects and differentiates skin cancer images from rashes images by using CNN[Convolutional neural network] and classify the images as skin cancer or rashes. The model is able to classify the image as skin cancer affected image or rashes image and obtained an average accuracy of 80.2% for 20 epochs.

Dengue is a mosquito borne endemic disease threatening the world now-a-days. If not treated on proper time, it will lead to death. World Health Organization (WHO) reported, dengue is prevalent in more than 80 countries all over the world. Globally, 50–100 million cases are affected with dengue every year. In order to reduce the mortality rate, there is an urgent need for early diagnosis. This research aims to develop a prediction framework that supports early diagnosis of disease. It summarizes the hidden causes for infection at an early stage, using decision tree classifiers. Decision tree classification is done using clinical dataset from a hospital at south TamilNadu is used for data analysis. The proposed system resulted in prediction accuracy of 86.13%. Number of true positives and true negatives are high. The proposed model identified critical values for symptoms that lead to illness.