G. Malathi

Bio: G. Malathi is an academic researcher from VIT University. The author has contributed to research in topics: Image quality & Image restoration. The author has an hindex of 4, co-authored 9 publications receiving 72 citations. Previous affiliations of G. Malathi include Mother Teresa Women's University & Anna University.

Emotion Recognition for Challenged People Facial Appearance in Social using Neural Network

Abstract: Human communication is the vocal and non verbal signal to communicate with others. Human expression is a significant biometric object in picture and record databases of surveillance systems. Face appreciation has a serious role in biometric methods and is good-looking for plentiful applications, including visual scrutiny and security. Facial expressions are a form of nonverbal communication; recognizing them helps improve the human machine interaction. This paper proposes an idea for face and enlightenment invariant credit of facial expressions by the images. In order on, the person's face can be computed. Face expression is used in CNN classifier to categorize the acquired picture into different emotion categories. It is a deep, feed-forward artificial neural network. Outcome surpasses human presentation and shows poses alternate performance. Varying lighting conditions can influence the fitting process and reduce recognition precision. Results illustrate that dependable facial appearance credited with changing lighting conditions for separating reasonable facial terminology display emotions is an efficient representation of clean and assorted moving expressions. This process can also manage the proportions of dissimilar basic affecting expressions of those mixed jointly to produce sensible emotional facial expressions. Our system contains a pre-defined data set, which was residential by a statistics scientist and includes all pure and varied expressions. On average, a data set has achieved 92.4% exact validation of the expressions synthesized by our technique. These facial expressions are compared through the pre-defined data-position inside our system. If it recognizes the person in an abnormal condition, an alert will be passed to the nearby hospital/doctor seeing that a message.

Prophylactic Effect of Cucumis melo on Chromium Vi-Induced Male Albino Rats

Abstract: The production of reactive oxygen species (ROS), which induce oxidative stress in humans, is linked to the negative effects of Chromium 6.The protective action of Cucumis melo L. fruit extracts was evaluated in this study in an animal model of hematological and biochemical parameters, which was induced by chromium VI (K2 Cr2 O7). The purpose of this study was to analyse the efficacy of Cucumis melo L. on chromium VI (K2 Cr2 O7)-induced rats. For 42 days, male albino rats (160–20 g) were given the stated oral LD50 dosage of chromium VI (K2Cr2O7) (10 mg/kg body weight). After 42 days, chromium-induced rats were administered with two different concentrations of Cucumis melo L. and ascorbic acid (250 mg/kg, 500 mg/kg body weight) for 7 days. Following therapy, blood was drawn and analysed for a variety of biochemical markers.The results revealed that ingestion of either plant extract, ascorbic acid, or their combination on chromium 6 induced rats significantly increase the activity of Alanine Aminotransferase (ALT), Aspartate Aminotransferase (AST) and decreased the activity of Gamma Glutamyl transferase (γ-GT) was recorded. This study has proven that fruit extract particularly its combination with ascorbic acid has a potential prophylactic effect. Cucumis melois vital for modifying the Chromium (VI) induced toxicity on male albino rats. Indeed, the recommended fruits should be consumed to the Chromium (VI) deposited harmful region since they may protect cells from environmental stress.

Image Processing

Abstract: MUCKE aims to mine a large volume of images, to structure them conceptually and to use this conceptual structuring in order to improve large-scale image retrieval. The last decade witnessed important progress concerning low-level image representations. However, there are a number problems which need to be solved in order to unleash the full potential of image mining in applications. The central problem with low-level representations is the mismatch between them and the human interpretation of image content. This problem can be instantiated, for instance, by the incapability of existing descriptors to capture spatial relationships between the concepts represented or by their incapability to convey an explanation of why two images are similar in a content-based image retrieval framework. We start by assessing existing local descriptors for image classification and by proposing to use co-occurrence matrices to better capture spatial relationships in images. The main focus in MUCKE is on cleaning large scale Web image corpora and on proposing image representations which are closer to the human interpretation of images. Consequently, we introduce methods which tackle these two problems and compare results to state of the art methods. Note: some aspects of this deliverable are withheld at this time as they are pending review. Please contact the authors for a preview.