Eiman Tamah Al-Shammari

Adaptive k-means clustering algorithm for MR breast image segmentation

Image segmentation is vital for meaningful analysis and interpretation of the medical images. The most popular method for clustering is k-means clustering. This article presents a new approach intended to provide more reliable magnetic resonance (MR) breast image segmentation that is based on adaptation to identify target objects through an optimization methodology that maintains the optimum result during iterations. The proposed approach improves and enhances the effectiveness and efficiency of the traditional k-means clustering algorithm. The performance of the presented approach was evaluated using various tests and different MR breast images. The experimental results demonstrate that the overall accuracy provided by the proposed adaptive k-means approach is superior to the standard k-means clustering technique.

Towards an error-free Arabic stemming

Stemming is a computational process for reducing words to their roots (or stems). It can be classified as a recall-enhancing or precision-enhancing component. Existing Arabic stemmers suffer from high stemming error-rates. Arabic stemmers blindly stem all the words and perform poorly especially with compound words, nouns and foreign Arabized words. The Educated Text Stemmer (ETS) is presented in this paper. ETS is a dictionary free, simple, and highly effective Arabic stemming algorithm that can reduce stemming errors in addition to decreasing computational time and data storage. The novelty of the work arises from the use of neglected Arabic stop-words. These stop-words can be highly important and can provide a significant improvement to processing Arabic documents. The ETS stemmer is evaluated by comparison with output from human generated stemming and the stemming weight technique.

Memetic multiobjective particle swarm optimization-based radial basis function network for classification problems

This paper presents a new multiobjective evolutionary algorithm applied to a radial basis function (RBF) network design based on multiobjective particle swarm optimization augmented with local search features. The algorithm is named the memetic multiobjective particle swarm optimization RBF network (MPSON) because it integrates the accuracy and structure of an RBF network. The proposed algorithm is implemented on two-class and multiclass pattern classification problems with one complex real problem. The experimental results indicate that the proposed algorithm is viable, and provides an effective means to design multiobjective RBF networks with good generalization capability and compact network structure. The accuracy and complexity of the network obtained by the proposed algorithm are compared with the memetic non-dominated sorting genetic algorithm based RBF network (MGAN) through statistical tests. This study shows that MPSON generates RBF networks coming with an appropriate balance between accuracy and simplicity, outperforming the other algorithms considered.

A novel Arabic lemmatization algorithm

Tokenization is a fundamental step in processing textual data preceding the tasks of information retrieval, text mining, and natural language processing. Tokenization is a language-dependent approach, including normalization, stop words removal, lemmatization and stemming. Both stemming and lemmatization share a common goal of reducing a word to its base. However, lemmatization is more robust than stemming as it often involves usage of vocabulary and morphological analysis, as opposed to simply removing the suffix of the word. In this work, we introduce a novel lemmatization algorithm for the Arabic Language. The new lemmatizer proposed here is a part of a comprehensive Arabic tokenization system, with a stop words list exceeding 2200 Arabic words. Currently, there are two Arabic leading stemmers: the root-based stemmer and the light stemmer. We hypothesize that lemmatization would be more effective than stemming in mining Arabic text. We investigate the impact of our new lemmatizer on unsupervised data mining techniques in comparison to the leading Arabic stemmers. We conclude that lemmatization is a better word normalization method than stemming for Arabic text.

Review article: Computational intelligence techniques in bioinformatics

Computational intelligence (CI) is a well-established paradigm with current systems having many of the characteristics of biological computers and capable of performing a variety of tasks that are difficult to do using conventional techniques. It is a methodology involving adaptive mechanisms and/or an ability to learn that facilitate intelligent behavior in complex and changing environments, such that the system is perceived to possess one or more attributes of reason, such as generalization, discovery, association and abstraction. The objective of this article is to present to the CI and bioinformatics research communities some of the state-of-the-art in CI applications to bioinformatics and motivate research in new trend-setting directions. In this article, we present an overview of the CI techniques in bioinformatics. We will show how CI techniques including neural networks, restricted Boltzmann machine, deep belief network, fuzzy logic, rough sets, evolutionary algorithms (EA), genetic algorithms (GA), swarm intelligence, artificial immune systems and support vector machines, could be successfully employed to tackle various problems such as gene expression clustering and classification, protein sequence classification, gene selection, DNA fragment assembly, multiple sequence alignment, and protein function prediction and its structure. We discuss some representative methods to provide inspiring examples to illustrate how CI can be utilized to address these problems and how bioinformatics data can be characterized by CI. Challenges to be addressed and future directions of research are also presented and an extensive bibliography is included.

Improving Arabic document categorization: Introducing local stem

Stemming is a fundamental step in processing textual data preceding the tasks of text mining, Information Retrieval (IR), and natural language processing (NLP). The common goal of stemming is to standardize words by reducing a word to its base (root or stem), thus can be also considered a feature reduction technique. This paper aims at presenting a new dictionary free, content-based Arabic stemmer and adopts it as a feature reduction (selection) mechanism to study its contribution in improving Arabic text categorization. We employed three stemming mechanisms (root-based, light, and our stemming technique and assessed their performance in text classification exercises for an Arabic corpus to compare and contrast the text mining effectiveness of these Arabic stemming algorithms. The experiments were conducted on a corpus consisting of 2,966 Arabic documents that fall into three categories: cultural, social, and general. The experiment results showed that our stemmer significantly improved text classification accuracy.

Genetic Algorithms for Multi-Objective Community Detection in Complex Networks

Community detection in complex networks has attracted a lot of attention in recent years. Communities play special roles in the structure–function relationship. Therefore, detecting communities (or modules) can be a way to identify substructures that could correspond to important functions. Community detection can be viewed as an optimization problem in which an objective function that captures the intuition of a community as a group of nodes with better internal connectivity than external connectivity is chosen to be optimized. Many single-objective optimization techniques have been used to solve the detection problem. However, those approaches have drawbacks because they attempt to optimize only one objective function, this results in a solution with a particular community structure property. More recently, researchers have viewed the community detection problem as a multi-objective optimization problem, and many approaches have been proposed. Genetic Algorithms (GA) have been used as an effective optimization technique to solve both single- and multi-objective community detection problems. However, the most appropriate objective functions to be used with each other are still under debate since many similar objective functions have been proposed over the years. We show how those objectives correlate, investigate their performance when they are used in both the single- and multi-objective GA, and determine the community structure properties they tend to produce.

A Novel Algorithm for Normalizing Noisy Arabic Text

In this paper, an algorithm to normalize noisy text, which only focuses on the Arabic language, is introduced. Although there have been many theories that discuss Arabic text processing, there has not been, so far, one theory that focuses on noisy Arabic texts. Additionally, this paper introduces a new similarity measure to stem Arabic noisy document. The need for such a new measure stems from the fact that the common rules applied in stemming cannot be applied on noisy texts, which do not conform to the known grammatical rules and have various spelling mistakes. Thus, the proposed normalization algorithm automatically group words after applying the similarity measure. In order to make sure of such a theory of algorithm, the new normalization technique is evaluated by the under-stemming errors reduction technique introduced by Paice.

Blind Watermark Approach for Map Authentication using Support Vector Machine

This paper presents a blind and robust watermark approach for authentication 2D Map based on polar coordinates mapping and support vector machine is presented. The proposed system is composed of three phases. Firstly, in the preprocessing phase, the proposed algorithm mapped all vertices into polar coordinate system. Then, in the support vector machine phase, the watermark portable points will be chosen using support vector machine to reduce the number of these points which increases the imperceptibility without any effect on the robustness of the watermark. Afterwards, in the watermarking algorithm phase, the watermark is embedded into the map vertices using the random table of the decimal valued of the polar coordinates through the digit substitution of the decimal part. Finally, in the theoretical analysis and experimental results shows that the presented approach is robust against a various attacks including rotation, scaling, and translation. The proposed approach attained high imperceptibility.

Comparative Study of Soft Computing Methodologies for Energy Input–Output Analysis to Predict Potato Production

In this study, an adaptive neuro-fuzzy inference system (ANFIS) was developed to predict potato production in Iran. Data related to potato yield from 2010 to 2011 was collected from 50 potato producers in Hamedan, Iran. The resulting ANFIS network has an input layer with eight neurons and an output layer with a single neuron (potato yield). The energy inputs were manual labor, diesel, chemical fertilizers, and manure from farm animals, chemicals, machinery, water, and seed. The most significant and influential inputs were selected from the eight initial inputs and the ANFIS network was used to choose the parameters that have the most influence on potato yield. A new ANFIS model was created after the three most influential parameters were selected. The new ANFIS model was then utilized to estimate yield using the three energy inputs. Next, the ANFIS model results were compared with the results from the support vector regression (SVR) technique. The end results revealed that ANFIS provided more accurate predictions and had the capacity to generalize. The Pearson correlation coefficient (r) for ANFIS potato yield prediction was 0.9999 in the training and testing phases, while the SVR model had a correlation coefficient of 0.8484 in training and 0.9984 in testing.ResumenEn este estudio se desarrolló un sistema de inferencia adaptativa de lógica difusa (ANFIS) para predecir la producción de papa en Irán. Se colectaron datos relacionados con el rendimiento de papa de 2010 a 2011 de 50 productores en Hamedan, Irán. La red ANFIS resultante tiene una capa de insumos con ocho neuronas y una capa de salidas con una única neurona (rendimiento de papa). Los insumos de energía fueron mano de obra, diésel, fertilizantes químicos y estiércol de animales de granja, químicos, maquinaria, agua y semilla. Se seleccionaron los insumos más significativos y de influencia de los ocho insumos iniciales, y se usó la red ANFIS para escoger los parámetros que tienen la mayor influencia en el rendimiento de papa. Se creó un nuevo modelo ANFIS después que se seleccionaron los tres parámetros de mayor influencia. Entonces se utilizó el nuevo modelo ANFIS para estimar rendimiento usando los tres insumos de energía. Después, los resultados del modelo ANFIS se compararon con los resultados de la técnica de regresión de vector de respaldo (SVR). Los resultados finales revelaron que ANFIS suministró predicciones más precisas y tuvo la capacidad de generalizar. El coeficiente de correlación de Pearson (r) para la predicción del rendimiento de papa por ANFIS fue 0.9999 en las fases de formación y de prueba, e el modelo SVR tuvo un coeficiente de correlación de 0.8484 en formación y 0.9984 en prueba.