Arsenic is a natural element of the earth’s crust and is commonly distributed all over the environment in the air, water and land. It is extremely poisonous in its inorganic form. Arsenic (As) contamination is one of the leading issues in the south Asian countries, ground water is major sources of drinking water. The highest risk to public health from arsenic originates from polluted groundwater. Arsenic is naturally present at high levels in the groundwater of south Asian countries. Pakistan also one of them which is highly affected by this toxic element, especially rural areas of Sindh Pakistan, where Ground water is the only source of drinking. Due to climates changes day by day value of arsenic is increased in Ground water, that effects the human health in form of many diseases like skin cancer, blood cancer. The purpose of this study is to figure out the increasing level of Arsenic and Cancer rate in Tando Muhamad Khan Sindh Pakistan for next coming five years. For this we have developed model using Microsoft Azure Machine learning Techniques and algorithms including Bayesian Linear Regression (BLR), support vector machine (SVM), Linear Regression (LR), Boosted Decision tree (BDT), exponential smoothing ETS, Autoregressive Integrated Moving Average (ARIMA). Developed model will help us to forecast the increasing rate of Arsenic and its effects on human health in form of cancer.

Traffic Analysis has been a problem that city planners have dealt with for years. Smarter ways are being developed to analyze traffic and streamline the process. Analysis of traffic may account for the number of vehicles in an area per some arbitrary time period and the class of vehicles. People have designed such mechanism for decades now but most of them involve use of sensors to detect the vehicles i.e. a couple of proximity sensors to calculate the direction of the moving vehicle and to keep the vehicle count. Even though over the time these systems have matured and are highly effective, they are not very budget friendly. The problem is such systems require maintenance and periodic calibration. Therefore, this study has purposed a vision based vehicle counting and classification system. The system involves capturing of frames from the video to perform background subtraction in order detect and count the vehicles using Gaussian Mixture Model (GMM) background subtraction then it classifies the vehicles by comparing the contour areas to the assumed values. The substantial contribution of the work is the comparison of two classification methods. Classification has been implemented using Contour Comparison (CC) as well as Bag of Features (BoF) and Support Vector Machine (SVM) method.