For changing semantics, ontological and information presentation, as well as computational linguistics for Asian social networks, are one of the most essential platforms for offering enhanced and real-time data mapping, as well as huge data access across diverse big data sources on the web architecture, information extraction mining, statistical modeling and data modeling, database control, and so on. The concept of opinion or sentiment analysis is often used to predict or classify the textual data, sentiment, affect, subjectivity, and other emotional states in online text. Recognizing the message's positive and negative thoughts or opinions by examining the author's goals will aid in a better understanding of the text's content in terms of the stock market. An intelligent ontology and knowledge Asian social network solution can improve the effectiveness of a company's decision making support procedures by deriving important information about users from a wide variety of web sources. However, ontology is concerned primarily with problem-solving knowledge discovery. The utilization of Internet-based modernizations welcomed a significant effect on the Indian stock exchange. News related to the stock market in the most recent decade plays a vital role for the brokers or users. This article focuses on predicting stock market news sentiments based on their polarity and textual information using the concept of ontological knowledge-based Convolution Neural Network (CNN) as a machine learning approach. Optimal features are essential for the sentiment classification model to predict the stock's textual reviews' exact sentiment. Therefore, the swarm-based Artificial Bee Colony (ABC) algorithm is utilized with the Lexicon feature extraction approach using a novel fitness function. The main motivation for combining ABC and CNN is to accelerate model training, which is why the suggested approach is effective in predicting emotions from stock news.

Machine learning is increasingly being employed in cancer detection and diagnosis. Cancer prediction will become quite easy in the future and we can predict it without the need of going to the hospitals. As we can see many technologies are being used and tested in the medical field. So, by this we can say that this will make us easier in the future to detect cancer. We are testing which algorithm will give us good result among CART, SVM AND KNN. We are making a cancer prediction using machine learning, in which we are including three types of cancer they are breast cancer, lungs cancer and prostate cancer. In breast cancer, we are using SVM algorithm and for lung and prostate we are using Random forest algorithm. We are going to give different attributes for three cancer system where the user has to enter data to get result. For breast cancer we are considering attributes like clump thickness, uniform cell size, uniform cell shape etc. and the prediction result will be whether the cancer is malignant or benign. For lung cancer, we are considering smoking, yellow fingers, anxiety, peer pressure etc. In prostate cancer, we are considering are radius, texture, perimeter, area etc. and the result for both cancer is likelihood of being affected by the cancer.

The objective of this paper is to give a critical survey of the present status within the field of control of a reactive distillation column. Control of a reactive distillation column is a challenging task due to process nonlinearity and complex interactions between the vapor-liquid equilibrium and chemical reactions. There are different types of control methodologies, which have been studied in the reactive distillation, ranging from a simple proportional-integral (PI) controller to advanced model predictive controllers (MPC) such as dynamic matrix control (DMC), quadratic dynamic matrix control (QDMC), robust multivariable predictive control technology (RMPCT), generalized predictive control (GPC), and other advanced control techniques. With the goals of optimal performance, energy conservation and cost effectiveness of process operations in industries, the design of optimal controllers and controller performance assessment have received great attention in both industries and academia. The main objective of control is to maintain the product purity within the desired range.