One of the significant issues that the world has faced in recent decades has been the estimation of water quality and location where safe drinking water is available. Due to the unexpected nature of the mode of water contamination, it is not easy to analyze the quality and maintain it. Some machine-learning techniques are used for predicting contaminating factors but there is no technique that can predict the contamination using latitude, longitude, and elevation. The main aim of this paper is to put factors such as water body location and elevation, which are used as inputs, into the different machine-learning techniques that predict the contamination. The results are reviewed and analyzed according to groundwater contamination and the chemical composition of the groundwater location. Non-changeable factors such as latitude, longitude, and elevation are used to predict pH, temperature, turbidity, dissolved oxygen hardness, chlorides, alkalinity, and chemical oxygen demand. Such a study has not been conducted in the past where location-based factors are used to predict the water contamination of any area. This research focuses on creating a relationship between the location base factors affecting the water contamination in a given area.

In the software industry, where the quality of the output is based on human performance, fatigue can be a reason for performance degradation. Fatigue not only degrades quality, but is also a health risk factor. Sleep disorders, depression, and stress are all results of fatigue which can contribute to fatal problems. This article presents a comparative study of different techniques which can be used for detecting fatigue of programmers and data miners who spent lots of time in front of a computer screen. Machine learning can used for worker fatigue detection also, but there are some factors which are specific for software workers. One of such factors is screen illumination. Screen illumination is the light of the computer screen or laptop screen that is casted on the workers face and makes it difficult for the machine learning algorithm to extract the facial features. This article presents a comparative study of the techniques which can be used for general fatigue detection and identifies the best techniques.

The size of the Genetic data base is increasing incessantly. Hence the difficultyhas increased not only in storing and managing such huge database but also in extracting information from such gigantic databases. This brings in the need of genetic data compression. Standard compression algorithms fail to compress genetic data. Hence, we require special algorithms for compressing genetic databases. In this paper, we propose a new genetic compression algorithm which is going to explore inter chromosomal similarities and yield positive compression. This study will not only improve the compression of genetic sequences but will also help in studying the level of similarity between two different chromosomes of the same genome.

INTRODUCTION: Genetic data plays a key role in the healthcare area in specific, but they are typically very large in size. Many research shows that absence of DNA information at the right time is one of the major causes of error in the healthcare area. The more genomics information that analysts secure, the better the prospects for individual and general wellbeing. Persevering and retrieving genetic information in the right form within the given time is a big challenge in the field of Healthcare. Effectively, pre-birth DNA tests screen for formative variations from the norm. Before long, patients will have their blood sequenced to detect any nonhuman DNA that may flag an irresistible illness. Later on, somebody managing malignancy will most likely track the movement of the sickness by having the DNA and RNA of single cells from various tissues sequenced every day. DNA sequencing of whole population will give a complete and better prediction of population wellbeing.OBJECTIVES: Hereditary data is growing exponentially; hence it is hard to deal with the consistently developing hereditary database. The human genome in its base configuration occupies almost thirty terabyte of storage space. Computational assets are constrained. Not just storage, transmission abilities and run time memory is likewise constrained. Data Compression is a test when the hereditary information is exponentially expanding. It is critical to save the integrity of hereditary information while packing it. Hence the main objective of this paper is to develop a lossless DNA compression algorithm that not only gives better compression but also help in retrieval of Information for efficient use in the area of Healthcare.METHODS: In this paper a lossless hereditary data compression method is being proposed. The proposed calculation works in a horizontal mode and utilization a reference based substitution technique for compression. The principle thought of this paper is in the kind of similarity scanned. All the predominant hereditary Compression methods search for similarity within the chromosome. These calculations either pursue flat mode or vertical mode for accomplishing compression. But whichever method the existing genetic compression algorithms use, they are all based on searching similarities within the chromosome i.e. they exploit only inter chromosomal similarities. The current studies focus will show that compression ratio achieved by analyzing individual chromosome is always less than the method in which we analyze and compress intra chromosomal similarities.RESULTS: This study shows that by simply using exactly matching repeats amongst all the chromosomes of the same genome, not only the compression ratio is improving but also a detailed study of all the similarities and differences between two genomes of the same species can be conducted.CONCLUSION: In this study, a new compression algorithm is being proposed for compressing DNA. Along with Inter chromosomal similarities, Intra chromosomal similarities are considered for this method. The results clearly shows that intra chromosomal matches are bigger and more than inter chromosomal matches which helps us to achieve better compression ratio.

The occurrence of road accidents continues to be one of the prominent causes of deaths, disabilities and hospitalisation in the country. This makes traffic accident risk prediction important in order to minimise it and save lives. Several kinds of models have been proposed to achieve the same ranging from old statistical models to the new models motivated by the advent of machine learning. This paper presents a comparative study of a variety of these models in an effort to analyse and deduce a beneficial approach to traffic accident risk prediction. Since the drivers are the ones in control on the road the study aims to provide traffic accident risk prediction to the drivers by analysing the factors they would know of beforehand like vehicle type, age sex, time of the day and weather etc. Optimal Classification Trees is a model that would provide such results that make intuitive sense to the driver along with the use of Random Forest and Logistic Regression. Furthermore, the geolocation data analysis using K-means clustering algorithm can provide information regarding places that are more prone to accidents. Through the analysis of previously known factors using these algorithms the drivers can be equipped with traffic accident risk predictions that would help them make informed decisions to minimise the same.