Wahiba Ben Abdessalem Karaa

MEDLINE Text Mining: An Enhancement Genetic Algorithm Based Approach for Document Clustering

MEDLINE is the largest biomedical literature database. It is updated daily with 200–4,000 citations. This permanent growth induces the need of a good MEDLINE abstract clustering to accelerate the procedure of research and information retrieval. Several works have been developed in this context, but clustering MEDLINE abstracts are still an area where researchers are trying to propose new approaches to better clustering. Over the last few years, evolutionary algorithms have been widely applied to clustering problems because of their ability to avoid local optimal solutions and converge to a global one. In this article, a new approach is proposed for clustering MEDLINE abstracts based on an extension of an evolutionary algorithm which is the genetic algorithm combined with a Vector Space Model and an agglomerative algorithm.

Evolutionary framework for coding area selection from cancer data

AbstractCancer data analysis is significant to detect the codes that are responsible for cancer diseases. It is significant to find out the coding regions from diseases infected biological data. The infected data will be helpful to design proper drugs and will be supportable in laboratory assessments. Codes bear specific meaning on various features as well as symptoms of diseases. Coding of biological data is a key area to get exact information on animals to discover the desired medicine. In the current work, four different machine learning approaches such as support vector machine (SVM), principal component analysis (PCA) technique, neural mapping skyline filtering (NMSF) and Fisher’s discriminant analysis (FDA) were applied for data reduction and coding area selection. The experimental analysis established that the SVM outperforms PCA and FDA. However, due to the mapping facility, NMSF outperforms SVM. Thus, the NMSF achieved the preeminent results among the four techniques. Matthews’s correlation coefficient was used to evaluate the accuracy, specificity, sensitivity, F-measures and error rate of the four methods that are used to determine the coding area. Detailed experimental analysis included comparison study among the four classifiers for the deoxyribonucleic acid dataset.

Performance Evaluation of Different Cost Functions in Motion Vector Estimation

Video is an important medium in terms of information sharing in this present era. The tremendous growth of video use can be seen in the traditional multimedia application as well as in many other applications like medical videos, surveillance video etc. Raw video data is usually large in size, which demands for video compression. In different video compressing schemes, motion vector is a very important step to remove the temporal redundancy. A frame is first divided into small blocks and then motion vector for each block is computed. The difference between two blocks is evaluated by different cost functions (i.e. mean absolute difference (MAD), mean square error (MSE) etc).In this paper the performance of different cost functions was evaluated and also the most suitable cost function for motion vector estimation was found.

Automatic builder of class diagram ABCD: an application of UML generation from functional requirements

Software development life cycle is a structured process, including the definition of user requirements specification, the system design, and programming. The design task comprises the transfer of natural language specifications into models. The class diagram of Unified Modeling Language has been considered as one of the most useful diagrams. It is a formal description of users requirements and serves as inputs to the developers. The automated extraction of UML class diagram from natural language requirements is a highly challenging task. This paper explains our vision of an automated tool for class diagram generation from user requirements expressed in natural language. Our new approach amalgamates the statistical and pattern recognition properties of natural language processing techniques. More than 1000 patterns are defined for the extraction of the class diagram concepts. Once these concepts are captured, an XML Metadata Interchange file is generated and imported with a Computer‐Aided Software Engineering tool to build the corresponding UML class diagram. Copyright

Effect of demons registration on biomedical content watermarking

In the field of medical diagnosis and therapeutics, patients valuable information is protected and authenticated by digital watermarking. Medical video contents are also needed to be watermarked. Image registration is a process of overlaying various images of the same scene, taken at different times, to locate a particular object in it and thus it is useful in diagnosis from medical video contents. The current work proposes a system which successfully broke a biomedical video into multiple image frames and those frames were used as a cover to hide the binary watermark. Our system also observed the effect of Demons algorithm based image registrations effect on the watermarked image frames. In order to observe the effects of image registration, we performed a comparative study between extracted binary watermarks from registered and non registered images. Later the visualization of the difference image between both of the extracted watermarks, helped to establish the effect of registration. The correlation between extracted binary watermark image from registered and non-registered frame also played a major role to observe the effect of image registration. In the post-processing of the obtained correlation, it was observed that the highest correlation was obtained for the target frame.

1-D group cellular automata based image encryption technique

In this paper, an image encryption system using one dimensional cellular automata (group cellular automata) has been proposed. The whole encryption system is based on symmetric key cryptography technique. As group cellular automata rules show some cyclic nature, it is rather easy for encryption and decryption. The whole idea and corresponding results has been developed on Matlab R2010a.

Bayesian information extraction network for Medline abstract

Biomedical is a huge domain that combines a variety of research areas. MEDELINE is one of the largest biomedical databases. Thereby, the searching of pertinent information through Medline has become a difficult task. Thats why; we need to develop information extraction systems in order to facilitate the treatment and the representation of data according to the users need. This paper applies Bayesian Networks to support information extraction based on ontological annotation from Medline. We present a tool developed that combines between semantic and probabilistic reasoning techniques.

Survey of works that transform requirements into UML diagrams

In this paper, we aim to cover works that are related to the process of transforming requirements into UML diagrams, from the first works which were manual techniques in 1976, to automatic tools in 2015. In this context, we try to exhibit different approaches and to indicate their strength as well as their shortcomings. This work will help us to evaluate existing approaches and propose other alternatives for Requirement Engineering. The objective of this paper is to present an overview of various works dedicated to requirement analysis and a comparative study of these works. Also, we tried to discuss the combination of Artificial Intelligence with Requirement Engineering.

Gene-Disease-Food Relation Extraction from Biomedical Database

Through the past years, an incredible increase in the biomedical data amount presented on the web is enlarged due to the increased data volume in the medical and biological domains. Hence, the search for documents and information on the internet became increasingly complicated. In the current work, a new approach for information extraction using the Natural Language Processing (NLP) tools and ontology was proposed. It described a system to extract relations between the concepts from biomedical texts using morphological analysis and information extraction techniques. In the first step, the system segmented the input text into sentences. Each sentence is then segmented into words that were tagged with part-of-speech labels and concept classes (food, drug, and gene). A set of relation extraction rules (regular expression patterns) are applied on the annotated sentences. If a pattern matches, the concepts and relations are extracted. The system has been tested on a set of 700 MEDLINE abstracts. For performance evaluation, the precision, recall and F-score were calculated. The proposed approach created by information retrieval from MEDLINE to gather a set of abstracts related to a given domain. Then, these texts were annotated using an automaton and ontology via recognizing interesting concepts for morphological analysis. After the annotation step, some rules were summarizing in an automaton that help gene-disease-food relationships discovery. This work proposed an approach for identifying relations between medical concepts using NLP tools. An evaluation experiment reported good effectiveness results.