International journal of artificial intelligence 

About: International journal of artificial intelligence is an academic journal. The journal publishes majorly in the area(s): Metaheuristic & Optimization problem. It has an ISSN identifier of 0974-0635. Over the lifetime, 350 publications have been published receiving 3107 citations.

Computer Vision and Image Processing: A Paper Review

Abstract: Computer vision has been studied from many persective. It expands from raw data recording into techniques and ideas combining digital image processing, pattern recognition, machine learning and computer graphics. The wide usage has attracted many scholars to integrate with many disciplines and fields. This paper provide a survey of the recent technologies and theoretical concept explaining the development of computer vision especially related to image processing using different areas of their field application. Computer vision helps scholars to analyze images and video to obtain necessary information, understand information on events or descriptions, and scenic pattern. It used method of multi-range application domain with massive data analysis. This paper provides contribution of recent development on reviews related to computer vision, image processing, and their related studies. We categorized the computer vision mainstream into four group e.g., image processing, object recognition, and machine learning. We also provide brief explanation on the up-to-date information about the techniques and their performance.

Model-Free Sliding Mode and Fuzzy Controllers for Reverse Osmosis Desalination Plants

Abstract: This paper presents model-free sliding mode controllers and Takagi-Sugeno fuzzy controllers for the flux and conductivity control of Reverse Osmosis Desalination Plants (RODPs). The RODPs are Multi Input-Multi Output processes and two separate controllers are designed in Single Input-Single Output control systems. The design of the model-free sliding mode controllers is done by Lyapunov’s stability theory. The Takagi-Sugeno fuzzy controllers are designed by considering linear matrix inequalities as constraints in an optimization problem solved by a Grey Wolf Optimizer algorithm. Simulation results are included.

Surrogate Model based Optimization of Traffic Lights Cycles and Green Period Ratios using Microscopic Simulation and Fuzzy Rule Interpolation

Abstract: In this paper, first the travel and delay times in a road ending in a traffic light are determined under different traffic flows and traffic light cycles using a microscopic traffic simulator. The obtained results are analyzed and compared with the results of other models reported in the literature. In addition, the optimal traffic light cycle times under different loads are determined for one road and a method is shown to obtain the optimal green period ratios of a traffic light as well as the cycle time in an intersection. Finally, the generation of a fuzzy model is presented and a methodology is suggested, where the fuzzy system is applied as a surrogate model for the determination of the optimal green period ratios and traffic light cycle times.

A machine Learning Approach for Opinion Holder Extraction in Arabic Language

Abstract: Opinion mining aims at extracting useful subjective information from reliable amounts of text. Opinion mining holder recognition is a task that has not been considered yet in Arabic Language. This task essentially requires deep understanding of clauses structures. Unfortunately, the lack of a robust, publicly available, Arabic parser further complicates the research. This paper presents a leading research for the opinion holder extraction in Arabic news independent from any lexical parsers. We investigate constructing a comprehensive feature set to compensate the lack of parsing structural outcomes. The proposed feature set is tuned from English previous works coupled with our proposed semantic field and named entities features. Our feature analysis is based on Conditional Random Fields (CRF) and semi-supervised pattern recognition techniques. Different research models are evaluated via cross-validation experiments achieving 54.03 F-measure. We publicly release our own research outcome corpus and lexicon for opinion mining community to encourage further research.

Indian Logic Ontology based Automatic Query Refinement

Abstract: The effectiveness of user query plays a vital role in retrieving highly relevant documents in keyword based search engine. Due to the lack of domain knowledge, users tend to post very short queries which do not express their information need clearly, which reduces the precision and re-call of the search. Indian logic Ontology based Automatic Query Refinement system has been proposed to overcome this problem in search engine. This system proposes an Indian logic ontology-based automatic Query Refinement model for the exploitation of full-fledged domain ontology to support semantic search in keyword based search engine. Indian logic based ontology has elaborate structure, which can reuse, enriched and detailed information about the concepts. This system formulates effective query using Indian logic Ontology in order to increase the relevancy of documents retrieved.