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Asian Network for Scientific Information is a leading service provider to the publishers of Science, Technology and Medicine (STM) in Asia. Currently Asian Network for Scientific Information is serving more than 37 peer-reviewed journals covering a wide range of academic disciplines to foster communication among scientists, researchers, students and professionals - enabling them to work more efficiently and intelligently, thereby advancing knowledge and learning.

Journal of Artificial Intelligence
eISSN: 2077-2173
pISSN: 1994-5450

Editor-in-Chief:  Dr. Santosh Kumar Nanda
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Review Article
Emotional Intelligence Models as Generators of Business Management Change in the Human Talent Area
Zlata Borsic Laborde, Karol Benítez Burbano, Verónica Gallardo Reinoso, Manjunatha Bangeppagari, Sikandar I. Mulla and Mariadoss Selvanayagam
Emotional intelligence models helps in designing strategies that allow the development in human talent area. The objective of this study was to perform a critical analysis on the evolution of construct and emotional intelligence models in the human talent area. An intellectual quotient approach based on social and emotional intelligence is also carried out. Social intelligence develops the ability of individuals to perform in the human relations area, self-awareness and contact with others. Emotional intelligence due to its role in the organizational structure, is able to identify emotions, feelings, self-esteem and emotional management. This article describes emotional intelligence models focused on the company and its contribution in increasing business productivity, adaptation and change as a generator of interpersonal relationships, which facilitates proper management, efficient evaluation of staff and better productivity of organizations. Finally, a brief discussion is presented regarding the future development of the intelligence theory in the administrative area as a manager of human talent.
Research Article
A Two-Phase Pattern Matching-parse Tree Validation Approach for Efficient SQL Injection Attacks Detection
Randa Osman Morsi and Mona Farouk Ahmed
Background and Objective: Data is one of the most valuable assets as it is the core for any organization website. SQL Injection Attack (SQLIA) is the way by which hackers gain access to data. An approach was proposed in this paper to efficiently detect SQLIA. Methodology: One of the most powerful algorithms, Parsing Tree validation (PT), depends only on accurate detection but takes much time so combining it with a fast dynamic algorithm with the purpose of learning and storing the malicious input patterns to compare with the next coming inputs will be a great achievement. An algorithm was proposed that is based on the combination of two of the existing detection algorithms: pattern matching algorithm using Aho-Corasick (AC) and PT. Results: Experiments showed that the proposed approach guarantees high accuracy of 99.9%, reasonable time which was 53.6% of PT's time and less memory usage. Conclusion: SQLIA is one of the most severe threats to the database. In general, the approaches that provide the best guard for the database against SQLIA are those that make use of a mix of primitive approaches as this leads to strengthening their merits and improving their weaknesses.

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