Diabetes is one of the most serious illnesses and many people today suffer from it. Diabetes is caused by high blood sugar in the human body. At the same time, it is due to age, obesity, lifestyle, high blood pressure, etc. Hospitals typically perform various tests and treatments to collect the information needed to diagnose diabetes. Untreated diabetes can cause some major problems in people such as heart-related illnesses, blood pressure, kidney problems, eye damage, and in many forms it can affect the organs of the body. Diabetes can be controlled with early prediction and treatment. To achieve this goal, the project will apply a variety of machine learning techniques to develop a complete system for predicting diabetes in the human body or in patients.
Machine learning plays an important role in the medical industry. The healthcare industry has a large dataset. With the help of machine learning, you can study these datasets, gain knowledge from hidden patterns, data, and find information to predict outcomes accordingly. Machine learning is promising in the future, providing better predictive results by building models and applying algorithms directly to data collected from patients. This task uses machine learning techniques in the dataset to predict diabetes. Techniques include K-nearest neighbor method (KNN), support vector machine (SVM), logistic regression (LR), random forest (RF), decision tree classifier, and many more. By comparison, each model has different accuracy and can prove help worthy to predict accurately.

This study sought to investigate the effect of cost reduction techniques on the social performance of food production companies in Nigeria. A survey questionnaire was used to collect data from 15 different food production companies drawn from Nigeriagallories.com. The respondents interviewed were the accountants, supervisors, marketing and sales, managers, and finance. Descriptive and inferential statistics were adopted. The inferential include the Pearson's correlation and multiple linear regression. The study revealed that budgetary costing, standard costing, and activity-based costing had a positive significant effect on social performance. The result also revealed that value analysis and target costing showed no significant effect on social performance. The findings, however, revealed that cost reduction techniques had a significant effect on the social performance of food production companies in Nigeria and thereby recommended that a standard and budgetary costing reference for food production companies in Nigeria in terms of implementing cost reduction strategies as this would help them to achieve their aims and objectives as sustainable performance.

It is considered that India’s inability to achieve the high figure GDP is due to the lack of infrastructural facilities. No government in the world can alone meet the infrastructural requirements of its country. This is where the role of private parties comes to play. Private capital within the country and abroad is to be mobilized and channelized towards this end in view. Government of India has been encouraging private sector investment and participation in all sectors of infrastructure. It is in this context that Public Private Partnerships (PPP) are fast emerging as a way for India to grow. The study concluded that PPPs model adopted in Indian aviation sector is successful both in term of profitability and passenger traffic.

There has been proposed a mathematical model which, under certain conditions, will allow the development of a toothed tool with such a shape of the working surface that will satisfy the agro technical, technological and economic parameters during soil cultivation.
The operating tool is made in the form of a block of teeth. The valley and the protrusion of the tooth in the horizontal plane are made along a logarithmic spiral, the protrusions are made in the form of the fourth degree parabola.
The toothed tool equation is presented in the form of a combination of rotation, displacement and compression matrices.
The working surface is given kinematically as the trajectory of motion of the points of the generating logarithmic spiral.
Equations obtained describe the surface of the operating tool in the areas of valleys and protrusions.

Worldwide road crashes pose significant threat to social and economical life. WHO report – 2015 on road safety mentioned that the situation was more dangerous in the developing countries because of lack of proper enforcements and techniques in improving road user behavior and decreasing injury severity outcomes. In India as the urbanization is increasing, high speed road facilities have promoted the motorcyclists and other vulnerable road users (VRUs) to select these facilities. This has led to the increase in high VRUs road crashes. Lack of proper crash reporting system and complex nature of crash in heterogeneous traffic flow conditions have worsened the problem of road safety, particularly in the Patna, capital city of Bihar where crash severity in 2015 was 39.20 as compared to 29.10 of India.
The objective of this study was to identify the explanatory variables affecting the crash severity of motorized two wheelers in the Patna with the help of data mining. Two years (2014- 2015) of crash data, collected from police FIR reports, was used in the analysis. Total 17 categorical and numerical attributes such as time of a day, traffic signs, street lights, gap in medians and roadside features were used as independent variables. Roadways were divided into homogeneous segments in terms of land use pattern and vehicle mix of that area. Crash severity was divided into fatal, sever (incapacitating injury) and minor (Non- incapacitating injury). Decision tree models (J48 and random forest) were generated in the analysis of crash data because it can identify and easily explain the complex patterns associated with crash risk and do not need to specify a functional form. J48 and a Random Forest model were used using default parameters of Weka using 80% percentage split. The advantage of tree-based methods is that they are non-linear and non-parametric data mining tools for supervised classification and regression problems. They do not require a priori probabilistic knowledge about the phenomena under studying and consider conditional interactions among input data.
Both J48 and Random forest models were effective in predicting the crash severity with classification accuracies of 54 and 59 % respectively & having kappa statistics values of 0.32 and 0.37 respectively which falls in the fair agreement range.. Time of a day, no. of access/km, median openings, land use, on-street parking and street lights were found to be significant in predicting the injury severity levels in the city.