http://localhost:8080/xmlui/handle/123456789/7480 Sun, 12 Apr 2026 22:46:05 GMT 2026-04-12T22:46:05Z http://localhost:8080/xmlui/handle/123456789/8136 Survey on Life Cycle Management and Version Control in Oracle Application Express Virpura, Digvijay; Swaminarayan, Priya As we are moving ahead in the Oracle Application Express technology, this paper is specifically focus on the possibilities and other aspects of implementing Application Life Cycle Management (ALM) and Version Control System (VCS) in Oracle Application Express. This paper also focuses on the options, Open source technologies available to implement ALM and VCS in Oracle Application Express technology. As Oracle APEX provides concurrent application development and it add more advantages to have multiple versions for the product. Wed, 01 Mar 2017 00:00:00 GMT http://localhost:8080/xmlui/handle/123456789/8136 2017-03-01T00:00:00Z http://localhost:8080/xmlui/handle/123456789/7493 Elimination of Angular Problem in Face Recognition Solanki, Kamini Face Recognition is considered to be the most suitable technique for the real-time application. This technique is commonly used for the security purposes of authentication in computerized fields. Previously various algorithms and techniques were used for the purpose of security and authentication, but they were having several pitfalls like time consumption, pose and illumination problem along with age differences. Keeping these things in mind along with the available literature review a hybrid technique for face recognition is proposed in this work. In the proposed method, face recognition is done by combining two most commonly used techniques and making it hybrid in nature which would combine their advantages and reduce the false matching rate along with fast key generation time. Fri, 01 Dec 2017 00:00:00 GMT http://localhost:8080/xmlui/handle/123456789/7493 2017-12-01T00:00:00Z http://localhost:8080/xmlui/handle/123456789/7492 STUDY OF SEMANTIC WEB BAS`ED E-RECRUITMENT SYSTEM : REVIEW Soni, Hina H; Swaminarayan, Priya R Data retrieval of existing online recruitment systems on the basis of exact match finding techniques of user’s stored profiles and the recruiter’s requirement is an addressable issue nowadays. Despite of eligibility criteria all the exact details of an applicant does not match the recruiter’s online form leading to zero results. Several Ontologies have been popular in the field of knowledge management and knowledge sharing, especially after the evolution of the Semantic Web. Ontology defines the terms and concepts (meaning) used to describe and represent an area of knowledge. The aim of this research is to review the existing Ontologies developed for e – recruitment. In this paper I have done a review and analysis of semantic web based e – recruitment systems with their different concepts undertaken for their development. It involves study of different job portals for domain terminology, different semantic technology used so far for similar purpose, development of Ontology, Development and testing of SPARQL queries for accurate searching, Deployment over the Internet to conduct practical search. I have explained the e-recruitment process and methods listing them through several sections and drawn the conclusion. Wed, 01 Nov 2017 00:00:00 GMT http://localhost:8080/xmlui/handle/123456789/7492 2017-11-01T00:00:00Z http://localhost:8080/xmlui/handle/123456789/7490 Sentiment analysis for Feature extraction using Dependency tree and Named Entities Rathod, Dharmendrasinh Gajendrasinh; Bohara, Mohammed Husain There has recently been growing interest in valence and emotion sensing using a variety of signals. Text, as a communication channel, gathers a substantial amount of interest for recognizing its underlying sentiment (valence or polarity), affect or emotion (e.g. happy, sadness).We consider recognizing the valence of a sentence as a prior task to emotion sensing. In this paper, we discuss our approach to classify sentences in terms of emotional valence. Our supervised Algorithm performs syntactic and semantic analysis for feature extraction. Our Algorithm processes the interactions between words in sentences using dependency parse trees, and it can identify the current polarity of named-entities based on the- fly topic modeling. We compared the performance of three rule-based approaches and two supervised approaches (i.e. Naive Bayes and Maximum Entropy).We trained our Algorithm using the NLTK and Python 3.5.2 for affective text dataset, which contains news headlines extracted from news websites. Sun, 01 Jan 2017 00:00:00 GMT http://localhost:8080/xmlui/handle/123456789/7490 2017-01-01T00:00:00Z