Financial markets are inherently unpredictable. They continue to change based on the performance of the company, past records, market value and are also dependent on news & timings. By carrying out trend analysis, one has the ability to prejudge stock prices. Machine Learning Techniques that are available, have the potential to forecast future stock prices. Each stock represents a different trend, so a singular machine learning model can’t be applicable to other stocks. Thus, one model giving a high degree of precision can’t guarantee working on another. Too many variables are involved while predicting stocks-physical factors vs. psychological, irrational and rational behaviour, etc. All of these factors combined indicate stock prices as capricious and difficult to foresee. In order to resolve this challenge, a comprehensive study with historical data on stock prices of listed firms was performed. The main premise behind this research was to illustrate how to apply machine learning algorithms such as Averaging, Linear Regression including advanced deep learning techniques such as Long-Term Short Memory and applying technical tools like the Modern Portfolio Theory and Bollinger bands.

Current and forthcoming Information Retrieval algorithms demand high mean average precision with contemporary high recall rates in the technical literature. Nevertheless, the existing state-of-the-art is still not optimized for speed, average query latency, and performance. The previous researchers presented various information retrieval models in the literature but the user search led to a ranking of documents that were hopeful to be relevant. In this paper, an evaluation of various information retrieval models is presented with a range of algorithms. The aim is to elaborate and review the current information retrieval function in the context of enterprise domain- specific search. Experiments were conducted on the OHSUMED benchmark data set from MEDLINE, a medical information database. The experimental results demonstrate that BM25F ranking function outperforms other extensively used ranking functions such as BM25, TFIDF, and Cosine on precision and recall measures.

Semantic Web is, without a doubt, gaining momentum in both industry and academia. The word "Semantic" refers to "meaning" - a semantic web is a web of meaning. In this fast changing and result oriented practical world, gone are the days where an individual had to struggle for finding information on the Internet where knowledge management was the major issue. The semantic web has a vision of linking, integrating and analysing data from various data sources and forming a new information stream, hence a web of databases connected with each other and machines interacting with other machines to yield results which are user oriented and accurate. With the emergence of Semantic Web framework the na\"ive approach of searching information on the syntactic web is clich\'e. This paper proposes an optimised semantic searching of keywords exemplified by simulation an ontology of Indian universities with a proposed algorithm which ramifies the effective semantic retrieval of information which is easy to access and time saving.