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Nowadays, human health, as well as environment, are at risk due to uncontrolled usage on natural resources. Groundwater is one of the crucial natural resource, excessively used and contaminated by human beings. It is, majorly contaminated by anthropogenic activities. Such contaminated water, when used for drinking purpose, may cause serious effects on human health. Therefore, it is necessary to precisely estimate the quality of groundwater. Conventionally, the WQI, a weighted arithmetic index method, is commonly used to estimate groundwater quality by researchers. As time rolled on, some drawbacks, such as uncertainty of data, of WQI came into light. So researchers tried to find a new approach to mitigate the problems associated with WQI. In this direction, fuzzy logic has been used and proven by researchers to eliminate the ambiguity involved in qualitative and quantitative researches. The paper aims to do a comparative study between fuzzy logic and WQI to estimate groundwater quality. The objective is to represent complex groundwater data into clear and simple data that can be easily interpreted by the general public and policymakers.

A biometric system is a pattern recognition tool for efficient human recognition based upon biological and behavioral traits. The fingerprint is one of the most promising and widely used biometric with almost 48% market share. The images for fingerprint systems may be captured in multispectral bands which needs special techniques for efficient working of the overall system. Multispectral Fingerprint biometric system has recently become quite popular as it provides high security and recognition. Multispectral images are of high dimensions, the high dimensionality of multispectral data requires robust compression technique. In this Paper a novel approach using Huffman coding and wavelet decomposition for multispectral fingerprint biometric system has been presented. The technique promises high security to templates in database as well as compressed templates. The compressed templates results in faster matching during authentication phase of the fingerprint biometric system. Moreover, the presented technique results in low revocability but high security to mitigate the effect of masquerade attacks.

LU/LC (land-use and land-cover) change detection information is essential for monitoring, planning, decision making, and socio-economic development. In the current study, multispectral Landsat-8 OLI, Landsat-7 ETM+, and Landsat-5 TM satellite imagery data has used in the Haridwar region for investigating the utility and possibility of remote sensing (RS) in LU/LC change assessment. After ensuring a ground survey with GPS, discussions with the individuals, and satisfactory literature-review, it has been found that RS can play a vital character in assessing the timely temporary changes in LU/LC of the Haridwar region. The satellite data of the different years 1996, 2003, 2010 and 2017 downloaded from USGS Earth Explorer. The satellite images of the selected study areas are categorized into seven different land use classes, viz. orchards, vegetation, agricultural land, rangeland, urban land, water bodies, and watershed. The pixel-based supervised classification method has performed 81.55%, 86.86%, 80.67%, and 93.00% accuracy using the Maximum Likelihood Classifier (MLC). The result of change detection of the LU/LC proved that the orchards area has been decreased very rapidly by 11806.65 ha (9.82%) and converted permanently in urban and agricultural land due to urbanization and industrialization in Haridwar region from 1996 to 2017 year. While the remaining area of land use such as urban land, rangeland, and watershed have increased up to 3022.11 ha (2.51%), 10088.82 ha (8.39%), and 5191.47 ha (4.32%). The vegetation land and water body have been decreasing since 1996 up to 12856.41 ha (10.70%) and 802.63 ha (0.66%) respectively in 21 years (1996-2017). The overall results presented by this research may be useful for orchard monitoring and socio-economic development of the Haridwar region.

The Face Recognition (FR) is growing as a major research area because of the broad choice of applications in the fields of commercial and law enforcement. Traditional FR methods based on Visible Spectrum (VS) are facing challenges like object illumination, pose variation, expression changes, and facial disguises. Unfortunately these limitations decrease the performance in object identification and verification. To overcome all these limitations, the Infrared Spectrum (IRS) may be used in human FR. So it leads and encourages the researchers for continuous research in this area of FR. Simultaneously, the present study emphasizes the use of three dimensional cubic dataset i.e. Multi/ Hyperspectral Imagery Data in FR. The IR based Multi/ Hyperspectral Imaging System can minimize the several limitations arise in the existing and classical FR system because the skin spectra derived with cubic dataset depicts the unique features for an individual. Multi/ Hyperspectral Imaging System provides valuable discriminants for individual appearance that cannot be obtained by additional imaging system that's why this may be the future of human FR. This paper also presents a detailed and time to time review of the literature on FR in IRS.