No abstract

Inhalation of emissions from petrol and diesel exhaust particulates is associated with potentially severe biological effects. In the present study, polycyclic aromatic hydrocarbons (PAHs) were identified from smokes released by the automobile exhaust from petrol and diesel. Intensive sampling of unleaded petrol and diesel exhaust were done by using 800-cm(3) motor car and 3,455-cm(3) vehicle, respectively. The particulate phase of exhaust was collected on Whatman filter paper. Particulate matters were extracted from filter paper by using Soxhlet. PAHs were identified from particulate matter by reverse phase high performance liquid chromatography using C(18) column. A total of 14 PAHs were identified in petrol and 13 in case of diesel sample after comparing to standard samples for PAH estimation. These inhalable PAHs released from diesel and petrol exhaust are known to possess mutagenic and carcinogenic activity, which may present a potential risk for the health of inhabitants.

In the finance world stock trading is one of the most important activities. Stock market prediction is an act of trying to determine the future value of a stock other financial instrument traded on a financial exchange. The technical and fundamental or the time series analysis is used by the most of the stockbrokers while making the stock predictions. The programming language is used to predict the stock market using machine learning is Python. In this project the prediction of stock market is done by the Support Vector Machine (SVM). In the project, we proposed the use of the data collected from different global financial markets with machine learning algorithms in order to predict the stock index movements. SVM algorithm works on the large dataset value which is collected from different global financial markets. Various machine learning based models are proposed for predicting the daily trend of Market stocks. The model generates higher profit compared to the selected benchmarks. Keywords: Stock Market, Machine Learning, Predictions, Support Vector Machine.