Lexical relations are one of the most important semantic relations in exploring the meanings of words in English language. They are mainly used to analysis the meanings of words in terms of their relations to each other within sentences. Those relations vary according to the kind of the relation that a word may have with another word or words. The current study aims at investigating this level of language by illustrating what lexical relations are and how they are manifested in language. In addition, the paper surveys the most important and the most basic kinds of lexical relations. Finally, it discusses in detail the importance of lexical relations in language use being an important linguistic source in the analysis, understanding and use of language.Copy Right, IJAR, 2017,. All rights reserved. …………………………………………………………………………………………………….... Introduction:-Lexical relations are one of the most important subfields of semantics which are entirely concerned with approaching the meanings of words through relating them to other words within English sentences. Such relations are manifested according to the type of the relation that a word may have with another word or words as when having two words with close meanings, two words with opposite ones and so on. They play major role in explaining the exact meaning of words in relation to other words and not in relation to the meaning of the word itself. The paper aims at exploring such types of semantic relations by showing the main features of lexical relations in addition to surveying their main types that are widely used in the explanation and analysis of the meanings of words. The study focuses in particular on synonyms, antonyms and hyponyms with various instances. Finally, it aims at showing the importance of lexical semantics in the use of language as well as the analysis of meanings.

Introduction:-Gait is one of the few biometric features that can be measured remotely without physical contact and proximal sensing, which makes it useful in surveillance applications. Such applications play a decisive role in monitoring high security areas including banks, airports, military bases and railway stations. In the real world, there are various factors, significantly affecting human gait including clothes, shoes, carrying objects, walking surfaces, walking speeds and observed views. A large number of gait recognition methods have been published recently, which can be roughly divided into two categories, model-based methods include "A new view-invariant feature for cross-view gait recognition" and appearance-based method include "Recognizing gaits across views through correlated motion co-clustering". These methods require a preprocessing of foreground/background segmentation (FG/BG) on a gait video, in order to extract shape contours, silhouettes, skeletons, or body joints for further gait analysis. The modelbased methods generally aim to model kinematics of human joints in order to measure physical gait parameters such as trajectories, limb lengths and angular speeds. The appearance-based methods typically analyze gait sequences without explicit modeling of human body structure. These methods have shown their effectiveness on human gait recognition under fixed view. However, they lack a proper methodology to address the problem of view change.

Image denoising is a very familiar technique which is used to remove the all unwanted noises from the original image. There are various methods to remove noise from digital images. In this paper we use Discrete Wavelet Transform for this purpose. In wavelet transform, there are two types of thresholding-Hard thresholding and Soft thresholding. We take a building image to describe the denoising process. First we add different types of noises in our image and then we apply the different thresholdings of DWT. We also use combination of both thresholdings in this paper to denoise the noisy image. To compare the denoised images with the noisy image, we take some performance parameters which are as follows; Signal to Noise Ratio (SNR), Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), Root Mean Square Error (RMSE), and Structural Similarity Index (SSIM). We use MATLAB for simulation purpose.

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Anorexia nervosa, a type of eating disorder is commonly seen in teenagers. An intense fear of gaining weight and distorted body image compels the teenagers to go on diet, which is resulting in eating less and skipping the meal and ultimately making the teenagers more prone to stress,anxiety,depression and other mental health issues. The study was taken with an intention to find the association of anorexia and mental health. A sample of 913 adolescents from junior college were selected as sample and a standardized test was administered. The findings showed that there is a positive correlation between eating disorder and mental health among adolescents.