Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

Introduction: Monitoring neurophysiological signals and neuroimaging helps the researchers to identify the relations between the major disorders in neurodevelopmental and behavioural changes. Specifically, the brain typical pattern that leads to Autism Spectrum Disorders (ASD) can be analyzed in children with the signals effectively. Objectives: Early identification of Autism Spectrum Disorders syndrome helps the parents to decide about the therapies and treatments required for improving their day to day activities. Materials and Methods: Different analyses performed by various researchers using machine learning algorithms using the EEG and ECG signals have been presented. Different techniques and devices used for acquiring the data through various experiments are also presented. Results: This paper presented a few techniques followed by various researchers; accuracy, sensitivity, recall achieved by the methodologies have been presented. Conclusion: Data collection plays a major role in analyzing the Autism Spectrum Disorders where most of the researches find it very difficult and early diagnosis also plays a major role in the improvement the Autism Spectrum Disorders children. © IJCRR.

Computer Science

School of Computer Science and Engineering

Chennai Campus

International Journal of Current Research and Review

Autistic disorder analysis among children and adult

Context aware systems are embedded in the environment and provide services anytime and everywhere. Context aware system is built upon context information gathered from the environment and the adaptation rule which describes what need to be performed for the context information gathered. Consistent behavior of the context aware system will be ensured by the complete and correct context information and adaptation rules. There may have a situation where conflicts may occur at context level and adaptation rule level. Many researchers presented different methods to detect and resolve conflict in context information level. In this work we proposed a procedure to detect and resolve conflicts in the adaptation. Two different types of conflicts are addressed in this work. We have discussed about the procedure to resolve these two types of conflicts in the adaptation rule.

International Journal of Recent Technology and Engineering 2

Detecting and Resolving Conflicts in Adaptation Rules for the Context Aware System

Context aware adaptive system need to have some model for their adaptive behavior. These models need to have effective adaptive mechanism to react to the changes in the environment. Specifying the adaptive behavior and ensuring the correctness of the adaptive behavior is the challenging task. Correctness of the adaptive behavior can be assured by designing the complete and consistent adaptive rules. While designing the adaptive rule, sometimes there may be situation where some environmental changes are not addressed. In this proposed work we generated the adaptive rules for all combinations of context values in the defined adaptive rules. We have discussed the priority based rule matching process for finding the decision classes for the newly generated rule. We have used Attribute effectiveness, Attribute Autonomy and Minimum value dependency criteria of RBDT-1 method for assigning priority to the attribute. This proposed method creates the new adaptation rules from the existing rule set for the undefined context situation which is influenced more than one context variable. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.

Wireless Networks

Dynamic rule generation and rule matching for composite environmental condition in context aware system

The motivation for developing computer vision is the human vision system which is the richest sense that we have. To us, vision seems an easy task ofjust seeing objects in daily life and identifying them, but in reality, our eyes along with the brain are processing information of around 50 images everysecond with millions of pixels in each image. Most of these images obtained are currently just looked at by people. The challenging task is to processimages from all these cameras and allow automation of tasks never before considered. Neural networks help us in making cameras intelligent enoughto understand the images it captures. Convolutional neural networks (CNN) are trained to give image classification results of good accuracy, with thechallenge to improve utilization of computing resources. Google Net is in its essence a deep CNN that uses inception architecture to attain leadingedge results for classification and detection problems. In this paper, a study was made on applications of computer vision techniques in retail andcustomer strategic projects. Further, it was analyzed that if cameras trained with CNN can work well enough to be deployed in retail market scenariosto automate sales and stock supervision.

VIT Unitversity

Fulltext

Asian Journal of Pharmaceutical and Clinical Research

A STUDY ON APPLICATION OF MACHINE LEARNING AND COMPUTER VISION FOR RETAIL PROJECTS

Concepts are the most basic units which act as building blocks in Artificial Intelligence research. They play a key role in Knowledge Representation (e. g., in semantic networks, conceptual graphs, description logics), All these approaches focus on other aspects of concepts, leading to different formalizations A concept is determined by a collection of objects (extent) which fall under the concept and a collection of attributes (intent) covered by the concepts. Here we focus on two formalizations, namely Ontologies and Formal Concept Analysis. In particular, we will discuss how Formal Concept Analysis can be used to support Ontology Engineering. Here we are focusing on the two main activities of ontological engineering: designing an ontology and merging of ontologies. In this chapter we have discussed the use of formal concept analysis in designing an ontology and merging of ontologies. © 2017, Indian Journal of Public Health Research and Development. All rights reserved.