Water is the elixir of life. It is a vital component of human survival. Water should be purified for better and healthy style life of all living and non-living things. The quality of water plays an important role for all living beings. Water used for drinking purpose should be colourless, odourless and free from excess salts. Detecting such a variety of contamination from the drinking water becomes a challenging task. Feature selection acts as a significant role in identifying irrelevant features and redundant features from large dataset. Feature selection is a preprocessing course of action universally used for large amount of data. Feature selection concepts instruct us, to pick a subset of features or catalog of attribute or variables which helps to build an efficient model for describing the selected subset. Other than selecting the subset, it also congregate some other purposes, such as dimensionality reduction, compact the amount of data which are required for learning process, progress in predictive accuracy and increasing the constructed models. The main aim of this work is to investigate about the concept of feature selection, various criterions of feature selection methods and some existing methods are discussed from 1997 till 2014 and address the issues and challenges of feature selection.

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Background: Lung cancer has become a major cause of cancer-related deaths. Detection of potentially malignant lung nodules is essential for the early diagnosis and clinical management of lung cancer. In clinical practice, the interpretation of Computed Tomography (CT) images is challenging for radiologists due to a large number of cases. There is a high rate of false positives in the manual findings. Computer aided detection system (CAD) and computer aided diagnosis systems (CADx) enhance the radiologists in accurately delineating the lung nodules. Objectives: The objective is to analyze CAD and CADx systems for lung nodule detection. It is necessary to review the various techniques followed in CAD and CADx systems proposed and implemented by various research persons. This study aims at analyzing the recent application of various concepts in computer science to each stage of CAD and CADx. Methods: This review paper is special in its own kind because it analyses the various techniques proposed by different eminent researchers in noise removal, contrast enhancement, thorax removal, lung segmentation, bone suppression, segmentation of trachea, classification of nodule and nonnodule and final classification of benign and malignant nodules. Results: A comparison of the performance of different techniques implemented by various researchers for the classification of nodule and non-nodule has been tabulated in the paper. Conclusion: The findings of this review paper will definitely prove to be useful to the research community working on automation of lung nodule detection.

Information explosion along with the tremendous growth in software and hardware technology are moving industries, educational institutions, organizations towards "paperless" environment. This change has a direct impact on the amount of information digitized, thus increasing the need for efficient storage mechanisms. Digitized information mostly consist of compound images, which are a combination of different data types like text, graphics, line art, photographs, etc. Layer based compression is one of the frequently used solution to compress these kind of images. This paper proposes a novel layer based compound image compression technique. Care is taken to produce lossless compression for text part of the image and to improve the quality of the image as a whole after decompression. Experimental results were conducted to analyze the performance of the proposed compressor.

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