International Journal of Reasoning-based Intelligent Systems (14 papers in press)
Improving Post-Processing Optical Character Recognition (OCR) Documents with Arabic Language Using Spelling Error Detection and Correction
by Iyad Abu Doush, Ahmed Al-trad
Abstract: The large amount of documents printed on papers throughout the world leads to accumulate vast amounts of data that needs to be converted into electronic format. The optical character recognition (OCR) was developed for doing this job, but the effectiveness of its results was not perfect due to some issues. One of these issues was its inability to check the spelling of the text correctly. The problem increased when working on Arabic text because of the complexity of Arabic language. This research aims to explore the ways of improving OCR spell checking effectiveness by proposing a post-Processing Arabic OCR system based on three different approaches: Microsoft Office Word with Google online suggestion system, Ayaspell spell checker with Google online suggestion system, and using Google online suggestion system alone. We have used precision and recall in order to evaluate the effectiveness of our proposed post-Processing Arabic OCR system by comparing the precision and recall values that have been returned using the three different approaches. We have used three different types of datasets, namely Arabic Recognized Text (ART) generated by KFUPM University, a set of pages from different books and some computer generated documents with random errors. The results show that using Microsoft Office Word with Google gives outperform other approaches with accuracy of (0.49), while using Ayaspell with Google gives accuracy of (0.28). Finally, using Google online suggestion system alone gives accuracy of (0.37).
Keywords: Post-Processing Arabic OCR; Arabic Optical Character Recognition; Arabic Spell checker
Combining graph decomposition techniques and
metaheuristics for solving PCSPs. Application to
by Sadeg Lamia
Abstract: This paper presents a study towards a framework for solving discrete
optimization problems modelled as Partial Constraint Satisfaction Problems (PCSPs).
These studies follow two approaches, namely a Bottom-Up, and a Top-Down one. Three
decomposition methods and an Adaptive Genetic Algorithm (AGA) are associated with
these approaches. The experimental results obtained for MI-FAP problems show a good
trade-off between the quality of the solution and the execution time of the different
Keywords: Optimization problems; Partial Constraint Satisfaction Problem (PCSP);
Frequency Assignment Problem (FAP); Graph Decomposition; Adaptive Genetic
Modeling of Multi Agent Coordination using Crocodile Predatory Strategy
by Pragyan Nanda, Sritam Patnaik, Srikanta Patnaik
Abstract: Multi Agent Systems (MAS) are essential solutions for large-scale, complex problems as single agents are having limited capabilities and computing resources. In Multi Agent Systems, a group of individual agents usually work together to achieve a common goal, that is beyond individual agents capabilities. In order to achieve the goal, the agents need to cooperate, communicate and collaborate with each other. In real-time problems, the agents have to take dynamic decisions during run-time and coordinate their actions accordingly. In such scenarios conflicts between agents may arise while coordinating and collaborating dynamic action among them to achieve the goal and even after achieving the goal. To resolve such issues and conflicts in coordination and collaboration we propose a model in this paper which is motivated by the cooperative hunting and feeding behavior of the crocodiles. The proposed model exhibits multi-level coordination in multi agent systems using the predatory strategy of crocodiles for resolving conflicts in coordination and collaboration. This proposed modeled can be applied to real-time systems.
Keywords: Multi Agent System, Agents, coordination, collaboration, crocodile‟ predatory strategy.
A fuzzy knowledge-based inference system for detecting human skin color
by Mehdi Neshat, Ghodrat Sepidname
Abstract: Abstract: One of the most important applications of intelligent systems in medicine is using expert systems to help with the diagnostic-treatment decision making process. Identification of a human has many various methods that one of the best ways is detecting the type of human skin. The processing speed and having high accuracy play an important role in this way. Designing a new Fuzzy Expert System is aimed at for detecting human skin from other materials intelligently. Skin detection is a challenging problem, because certain factors such as brightness change, a complicated background, and objects with colors similar to that of human skin represent the obstacles thereof. The dataset used in this research was adopted from the UCI accredited database having 245057 records, with each record having four fields. The skin is made up of R, G, and B color space segmentation obtained from the facial images of both genders in different age and racial groups. The results achieved show that this system has managed to detect human skin with 80% accuracy using the help of experts knowledge on human skin detection such as dermatologists and digital image processing experts.
Keywords: knowledge-based system; fuzzy logic; intelligent decision; human skin detection.
A Hybrid Fuzzy Knowledge-Based System for Forest Fire Risk Forecasting
by mehdi neshat, Masoud Tabatabi, Ebrahim Zahmati, Mohhammad Shirdel
Abstract: Fire is one of the most important factors destroying forest ecosystems which can result in negative economic and social consequences. Quick detection can be an effective factor in controlling this destructive phenomenon. This research was aimed at designing a hybrid fuzzy expert system in order to predict the size of forest fires effectively and accurately. The data were taken from the authentic Dataset named Forest Fire in University of California (UCI). In fact, the proposed system is a hybrid of six fuzzy inference systems with acceptable performances, according to their results. The accuracy of predicting the size of fire was 81.2%.
Keywords: Fuzzy Inference System; Hybrid System; Forest Fire; Risk Estimation; Fire Intensity; Modelling.
A Semi-Supervised Rough Set and Random Forest Approach for Pattern Classification of Gene Expression Data
by Pradeep Kumar Mallick, Debahuti Mishra, Srikanta Patnaik, Kailash Shaw
Abstract: In this paper, we present a semi-supervised rough set based random forest gene selection method for classification of data patterns. The proposed method tries to find the genes of interest known as significant genes and maximize the accuracy of the model with reduction percentage. The advantage of this approach is analyzed by experimental results on three benchmark datasets such as Leukemia, Colon Cancer and SRBCT and results showed an improved accuracy over existing methods such as Support Vector Machine, k-Nearest Neighbor and Random Forest. Finally, the performance of those selected significant genes has been measured using classifier validity and statistical measures. The experimental results and performance measures proves the efficiency of the proposed hybridized technique over traditional random forest method.
Keywords: Gene Selection; Rough Set; Random Forest; Lower Approximation; Lower Approximation; Importance Score
Recognizing the Kind of Cloud Using a New Fuzzy Knowledge-Based System
by Mehdi Neshat, Mohhammad Ahmadi
Abstract: Nowadays, expert systems play a major role in better doing of complex tasks and giving advice to the experts because expertism is a specialized knowledge. Overall, expert systems are used to solve the problems for which there is not an accurate knowledge and a particular algorithm. Understanding the atmospheric phenomena and their role in human life are the most important and affecting issues in human societies. In meteorology, it is important to identify the type of clouds. By monitoring from the Earth's surface (seeing bottom view of the cloud) and using satellites (seeing top view of the cloud), we can identify the variety of clouds. A Fuzzy Inference System with the specialists' knowledge of meteorology is designed in this paper and its aims are detection of the cloud type through extracting knowledge from satellite images of the cloud upper portions. The used data are extracted from the reputable website of UCI called Cloud Data set. This dataset is gathered by Philip Collard in two ranges of IR and VISIBLE. Using the experts' knowledge, this system determines the type of cloud with an accuracy level of 86% and according to experts opinion; the results are suitable and acceptable.
Keywords: Cloud Data set; expert system; fuzzy logic; fuzzy expert system; knowledge base.
Multi-objective design optimization of four-bar mechanisms using a hybrid ICA-GA algorithm
by Nejlaoui Mohamed
Abstract: This work presents a novel approach to the multi-objective optimal design of four-bar mechanisms. Three conflicting objective functions are considered simultaneously, i.e., the tracking error (TE), the transmission angle deviation from 90° (TA) and the maximum angular velocity ratio (MAVR). To improve the convergence and the diversity of the results, an imperialist competitive algorithm is coupled with a genetic algorithm (ICA-GA) and it is used to solve this problem. A comparative study of the proposed ICA-GA shows that this method yields better results and more diverse results than other methods.
Keywords: Design mechanism, ICA-GA, Hybrid algorithm, imperialist competitive algorithm, Genetic algorithm.
Community Detection Using Intelligent Water Drops Optimization Algorithm
by Iyad Abu Doush, Saba ElMustafa, Ameera Jaradat, Nahed Mansour
Abstract: Community structure means the existence of densely connected subgroups in the networks. It is a surprising property that appears in complex and naturally constructed networks. We are proposing a novel heuristic approach to the community detection problem. In this paper, the community detection problem is solved using the intelligent water drop heuristic on a group of real life networks. The proposed heuristic succeeded in grouping the nodes in the network into sets of densely connected subgroups. Our approach uses the modularity value as an optimization criterion. The quality of the resulting division in the network was proven using measures like modularity and NMI. The experimental results verify that our algorithm is highly efficient at discovering quality community structure.
Keywords: Complex network ; community detection ; intelligent water drop ; modularity Q ; social network ; community structure ; metaheuristic.
Special Issue on: CIIA'2015 Advances in Computational Intelligence for Big Data
An Improved HBA Metaheuristic
by Bekaddour fatima, Chikh Amine
Abstract: As simple and effective optimization approach, HBA (Homogeneity Based Algorithm) is one of the recent metaheuristics, proposed to minimize the total misclassification cost of data mining approaches. However, one problem is that HBA does not adopt computational complexity of the used data mining technique. This is due to the way objective function is defined. So, in this paper, we have proposed an improved HBA (IHBA), which is utilizing a modified objective function that compute the computational complexity of the used classification method. We also test several clus-tering techniques as HBA parameters tuning, in order to enhance classifiers’ performance. We have tested IHBA on different benchmarks and the obtained results show the effectiveness of the pro-posed method.
Keywords: Metaheuristics; Performance; HBA; Optimization; Data Mining
Rotation-invariant method for texture matching using model based histograms and GLCM.
by Izem Hamouchene
Abstract: Nowadays, the research is interested in informatics systems that process automatically the information without human intervention. Image is an interesting research area due to the growth of the technologies. Thus, very large data are generated. This represents industrial and economic problem. Texture is one of the important and complex field of image processing. As all surfaces of objects are textured in nature, we have proposed a new texture analysis method. One of the key problem in image processing is the rotation. Therefore, the proposed method is robust against rotation. The goal of this study is to construct a model from each texture. After that, the system classifies the query texture based on the extracted texture models. In this work, we applied a recent and efficient feature extraction method called Rotation Invariant Neighborhood-based Binary Pattern (RINBP). The RINBP method extract relative and invariant patterns from the textured image. The proposed system combines between two parts. First, extract the RINBP model from the texture to describe the local variation of the texture. Second, we apply the GLCM method in order to extract statistical measures from the texture. Thus, efficient combination between the model histogram and statistical measures represents an efficient and robust feature descriptor of the texture. In the experiments, we have used the Brodats album database, which is a reference texture database. Experimental parts illustrates the efficiency and the robustness of the proposed system against rotation.
Keywords: Rotation invariance; Model based histograms; Texture matching; Feature extraction; Neighborhood-based binary pattern.
Special Issue on: ICEST'2015 Information, Communication and Energy Systems and Technologies
Experimental determination of soil electrical parameters for creation of a computer model of a grounding system for lightning protection
by Rositsa Dimitrova, Marinela Yordanova, Margreta Vasileva, Milena Ivanova
Abstract: The paper presents multifactor experimental studies for determining the apparent soil resistivity and the dielectric permittivity depending on the frequency of the electromagnetic field, the multilayered structure, moisture content and density of the soil. The gravimetric method for considering the soil moisture during the experimental researches was chosen. The received experimental results were statistically processed and a mathematical modeling of the controlled parameters was performed considering the specifics of the examined soil. These analytically obtained results of the dependences would contribute to more precise sizing of the grounding systems and could be used for creation of accurate simulation models for study of wave processes in them.
Keywords: soil electrical parameters; soil resistivity; dielectric permittivity; multilayered soil; gravimetric method; mathematical modelling; grounding system; grounding rod; lightning protection.
An approach to Transformation of Data into Knowledge for Power Control in Smart Homes
by Ivaylo Atanasov, Anastas Nikolov, Evelina Pencheva
Abstract: Internet of Things (IoT) encompasses information and networking technologies which allow connected devices gathering data from their environment to exchange information with network applications. The increased number of diverse devices and the variety of multimodal data make interoperability a challenging task. Synthesis of semantic information from raw IoT data enables sharing of common data models between different applications. The paper presents an approach to modeling semantic annotation for power control in smart homes and then converting it to knowledge. The approach includes context aware models as well as a knowledge base describing behavior of an autonomous agent. The context aware models representing remote device management are formalized and verified using the concept of bisimulation. Temporal logic is used for specifying the agent behavior and reasoning about power control of home appliances.
Keywords: Internet of Things, Semantic annotation, Remote device management, Formal model verification, Weak bisimulation, Autonomous service model, Temporal logic.
Solving Medical Classification Problems with RBF Neural Network and Filter Methods
by Jasmina Novakovic
Abstract: This paper evaluates classification accuracy of radial basis function (RBF) neural network and filter methods for feature selection in medical data sets. To improve the diagnostic procedure in the daily routine and to avoid wrong diagnosis, machine learning methods can be used. Diagnosis of tumors, heart disease, hepatitis, liver and Parkinson's diseases are a few of the medical problems which we have used in artificial neural networks. The main objective of this paper is to show that it is possible to improve the performance of the system for inductive learning rules with RBF neural network for medical classification problems, using the filter methods for feature selections. The aim of this research is also to present and compare different algorithm approach for the construction system that learns from experience and makes decisions and predictions and reduce the expected number or percentage of errors.
Keywords: medical classification problems, classification accuracy, feature selection, filter methods, machine learning, RBF neural network