The stock market prices of the company vary in a daily fashion. The social media pattern usage of the company can be determined to find the sentiment score values. The dependency factor between the social media tweet platform and the performance of an organization can have how much effect on the stock prices is determined. The historical data from the Yahoo Finance APIs are taken for the unique company ID and then the probability of stock being good or bad is determined. Also, the tweets related to the company are scanned and analyzed to find the positive and negative scores. The concentration value connected to growth, the intensity of capital expenditure, and the volume of promotion were among the factors utilized in the stock’s modeling. This paper also takes the yearly finances of the end-user based on LIC payments, medical insurance payments, and average rent and then performs a classification of the user. Based on the user classification, companies are recommended to the end-user based on descending order of stock value. The average volume, average price, average market index, average daily turnover, and sentiment discrepancy index are based on the tweets of a company and the predicted value of its performance. For the classification of the user, we make use of the support vector machine algorithm. For the sentiment analysis of the tweets, the naïve Bayes algorithm is made use of, and then stock classification is done based on mathematical modeling, which includes the sentiment analysis index.
The improper and excessive growth of brain cells may lead to the formation of a brain tumor. Brain tumors are the major cause of death from cancer. As a direct consequence of this, it is becoming more challenging to identify a treatment that is effective for a specific kind of brain tumor. The brain may be imaged in three dimensions using a standard MRI scan. Its primary function is to examine, identify, diagnose, and classify a variety of neurological conditions. Radiation therapy is employed in the treatment of tumors, and MRI segmentation is used to guide treatment. Because of this, we are able to assess whether or not a piece that was spotted by an MRI is a tumor. Using MRI scans, this study proposes a machine learning and medically assisted multimodal approach to segmenting and classifying brain tumors. MRI pictures contain noise. The geometric mean filter is utilized during picture preprocessing to facilitate the removal of noise. Fuzzy c-means algorithms are responsible for segmenting an image into smaller parts. The identification of a region of interest is facilitated by segmentation. The GLCM Grey-level co-occurrence matrix is utilized in order to carry out the process of dimension reduction. The GLCM algorithm is used to extract features from photographs. The photos are then categorized using various machine learning methods, including SVM, RBF, ANN, and AdaBoost. The performance of the SVM RBF algorithm is superior when it comes to the classification and detection of brain tumors.
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