Cancer is a life-threatening disease with high mortality rates. In the Indian subcontinent, women have a higher possibility to be diagnosed with cancer than men. The most common cancers identified in Indian women are Breast Cancer and Cervical Cancer. Both these cancers have high survival rates in case of early prediction. This paper reviews the attributes which are used in the existing datasets for prediction of these two cancers. The paper also proposes new attributes to overcome the limitations of existing ones, which will further increase the effectiveness of cancer prediction systems. The efficiency of existing and proposed attributes is compared by processing datasets through data mining algorithms using WEKA tool. The algorithms used for this study are – J48 (Decision Tree), Na?ve Bayes, Random Forest, Random Tree, KStar and Bagging Algorithm. The empirical analysis done in the paper reported improvement in the efficiency of cancer prediction over existing prediction systems.

Adequate information about climate change helps farmers to prepare and helps boost crop yield. Over the years, crops prediction was performed by manually considering farmer's experience on the particular crop in relation to the weather. This method was Inadequate, depends on the farmer's unreliable memory and grossly inaccurate. There is a need to introduce computational means to study and predict optimal climatic factors for improved crop growth and yield. The aim of this research work is to study the impact of climatic changes on the yield production of roots and tubers crops. K-means classification algorithm, Multiple Linear Regression, Python programming language, Flask Framework, Python machine learning packages numpy, matplotlib, Scikit-learn are the methodology used. While the obtained results show that CO2 Emission and Temperature does not really play a key role on how climate impact yield of root and tubers, rainfall plays more role; therefore, the study concludes that the three variables (temperature, rainfall, and CO2 Emission) are not enough to predict agricultural yield. It is therefore recommended that further research should be carried out to determine how other climatic factors such as soil type; humidity, sunlight etc. affect the yield of crops. The objective of this research is to study climatic change using data mining techniques, to design a predictive model using multiple linear regression to find the most optimal temperature and rainfall for effective crop yield and to simulate the multiple linear regression model design that achieve a high accuracy and a high generality in terms of climate change to crop yield.

Water automation is all about controlling, monitoring and even billing of water usage in different places like hotel, house, irrigation land and industry. The researchers done water automation based on different purposes using different types of hardware and technologies. This paper develops Automated Water Management System (WMS) which can monitor water tank by measuring the water flow, water level, water temperature, cut ON/OFF water supply and send notifications to the user through mobile messaging. All of the things are connected through an android application that is much more efficient and easier to control the whole process.

In this day and age of developing cutting edge innovations, the traditional voting technique can be changed to a more up to date and powerful approach termed as electronic voting system. The electronic voting system gives a helpful, simple and proficient approach to cast a ballot eliminating the shortcomings of traditional approach. The point of this research work is to exhibit an electronic voting system (E-Voting) to be connected to organization constituent body. A software application was developed utilizing web API’s and the concept of Dynamic systems development method (DSDM) used with object-oriented methodology for the development of the application.

The purpose of this paper is to establish the memory effect in an inventory model. In this model, price dependent demand is considered during the shortage period. Primal geometric programming is introduced to solve the minimized total average cost and optimal ordering interval. And finally we have taken a numerical example to justify the memory effect of this type inventory system. From the result it is clear that the model is suitable for short memory affected business i.e. newly started business.