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The rapid development of urbanization, population growth and the rapid development of economy resulted in the rapid increase in the total number of motor vehicles in the modern cities of India. Consequently, the importance of forecasting of the travel demand model has been increased in the recent years. Forecasting of the travel demand model involves various stages of trip generation and distribution, mode choice and traffic assignment. Among these stages, the mode choice analysis is a prominent stage as it considers the travelers mode to reach their destination. Further, study of mode choice criteria has become a vital area of research as individual and household sociodemographics exert a strong influence on travel mode choice decisions. There is a huge literature on travel model choice modeling to predict the range of trade-offs of transportation of commuters considering travel time and travel cost. In such literature intercity mode choice behavior has gained significant attention by several authors. In this study an attempt has made in order to calculate the model share of the different modes between the circle to the circle, and it is found that the modal share of 2-wheeler is 70 %, bus is about 23 % and car is about 7% of the total trips.

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Abstract: Alzheimer's is a neurodegenerative disease which can eventually leads to dementia. Mostly occurring in elderly people over the age of 65, it is hard to detect and diagnose correctly. OASIS dataset. We will use the aforementioned algorithms on the dataset and compare the accuracies achieved to find an optimal. Most common symptoms include memory loss and slow deterioration of cognitive functions. Given that these symptoms are seen often in old people, this hinders the detection of Alzheimer's disease (AD). Alzheimer's is currently incurable, but detection of the disease during its early stage is often beneficial to the patient, since there are treatments which can considerably improve the quality of life of the patient. However this can only be done if the patient has been diagnosed at a stage before any permanent brain damage has been done. Most of the current methods for detecting and diagnosing AD are not good enough. It is the need of the hour to develop better and early diagnostic tools. With the improvements in the field of machine learning, we now have the tools needed to drastically improve detection of Alzheimer's. We examine various machine learning methods and algorithms to find a method which can boost the chances of detecting the disease. We will use the following algorithms: Decision Tree, SVM, Random Forest and Adaboost. The dataset being used is the longitudinal MRI data available included in the

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