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A neurological condition of the brain is Parkinson's disease. It causes the body to shake, the hands to shake, and it makes the body stiff. At this advanced level, there is still no viable treatment or cure. Only when treatment is initiated at the earliest stage of the disease is it effective. These could potentially save a life in addition to lowering the cost of the illness. The majority of ways can identify Parkinson's disease after it has advanced, which results in basal ganglia, which regulates movement of the body with a small quantity of dopamine, losing about 60% of their dopamine. The presence of diminutive precursors to chronic diseases, especially neurologically based ailments such as Parkinson’s can indicate and diagnose them in their earliest stages. Parkinson’s disease (PD) is characterized by symptoms such as spasms in the limbs, jaw, and head, rigidity in the limbs and trunk, slow movement, etc. It is important to notice these preliminary symptoms early on to avoid developing Parkinson's disease. This project proposes algorithms used on the dataset to predict PD. Two kinds of datasets are used; one voice dataset and another spiral drawing dataset, and algorithms are used in these datasets to predict the disease and to show the results a user-friendly Web-Application is developed. The algorithms used are K-Nearest Neighbours (KNN) on voice dataset implementation and Random Forest on spiral drawing implementation.

This paper presents a bottom-up methodology to estimate multi-pollutant hourly gridded on-road traffic emission using advanced traffic flow and speed data for Delhi. We have used the globally adopted COPERT (Computer Programme to Calculate Emissions from Road Transport) emission functions to calculate the emission as a function of speed for 127 vehicle categories. At first, the traffic volume and congestion (travel time delay) relation is applied to model the 24 h traffic speed and flow for all the major road links of Delhi. The modelled traffic flow and speed shows an anti-correlation behaviour having peak traffic and emissions in morning–evening rush hours. We estimated an annual emission of 1.82 Gg for PM (particulate matter), 0.94 Gg for BC (black carbon), 0.75 Gg for OM (organic matter), 221 Gg for CO (carbon monoxide), 56 Gg for NOx (oxides of nitrogen), 64 Gg for VOC (volatile organic compound), 0.28 Gg for NH3 (ammonia), 0.26 Gg for N2O (nitrous oxide) and 11.38 Gg for CH4 (methane) for 2018 with an uncertainty of 60 %–68 %. The hourly emission variation shows bimodal peaks corresponding to morning and evening rush hours and congestion. The minimum emission rates are estimated in the early morning hours whereas the maximum emissions occurred during the evening hours. Inner Delhi is found to have higher emission flux because of higher road density and relatively lower average speed. Petrol vehicles dominate emission share (>50 %) across all pollutants except PM, BC and NOx, and within them the 2W (two-wheeler motorcycles) are the major contributors. Diesel-fuelled vehicles contribute most of the PM emission. Diesel and CNG (compressed natural gas) vehicles have a substantial contribution in NOx emission. This study provides very detailed spatiotemporal emission maps for megacity Delhi, which can be used in air quality models for developing suitable strategies to reduce the traffic-related pollution. Moreover, the developed methodology is a step forward in developing real-time emission with the growing availability of real-time traffic data. The complete dataset is publicly available on Zenodo at https://doi.org/10.5281/zenodo.6553770 (Singh et al., 2022).

Our aim is to develop a Machine learning model and testing the model by using the data in relating to previous 6 months payment behaviour which is behavioural data and personal information which is demographic data as input of a client is used for this study. The research study is conducted using Random Forest Algorithm. Our aim is to identify that credit card customer is likely to default in the coming month.