Mohamed H. Haggag

A novel chaotic salp swarm algorithm for global optimization and feature selection

Salp Swarm Algorithm (SSA) is one of the most recently proposed algorithms driven by the simulation behavior of salps. However, similar to most of the meta-heuristic algorithms, it suffered from stagnation in local optima and low convergence rate. Recently, chaos theory has been successfully applied to solve these problems. In this paper, a novel hybrid solution based on SSA and chaos theory is proposed. The proposed Chaotic Salp Swarm Algorithm (CSSA) is applied on 14 unimodal and multimodal benchmark optimization problems and 20 benchmark datasets. Ten different chaotic maps are employed to enhance the convergence rate and resulting precision. Simulation results showed that the proposed CSSA is a promising algorithm. Also, the results reveal the capability of CSSA in finding an optimal feature subset, which maximizes the classification accuracy, while minimizing the number of selected features. Moreover, the results showed that logistic chaotic map is the optimal map of the used ten, which can significantly boost the performance of original SSA.

Low Intensity Laser Irradiation Influence Proliferation of Mesenchymal Stem Cells: Comparison of Experimental Data to Intelligent Agent-Based Model Predictions

Over the past several decades, evidences have shown that low intensity laser can stimulate a number of biological processes, including stem cell proliferation. In order to fully utilize stem cells in research and medical studies, understanding these processes is essential. However, for gaining this fundamental understanding in a rapid and cost-effective manner, model predictions and computer simulations are required as they may yield useful information and represent powerful supportive tools. This chapter provides some of the experiments employed to measure influence of low intensity laser on proliferation of mesenchymal stem cells which can vary considerably according to many parameters and biological conditions such as laser nature of emission, irradiation time, wavelength, and energy density. These experiments were compared to intelligent agent-based model predictions and detailed information about the model description and comparison results are provided. The model was capable of predicting the data for the scenarios fairly well although a few were somewhat problematic. This study recommends a wave length ranging from 600 to 680 nm, and an energy density ranging from 0.3 to 4.0 J/\( \mathrm cm^{2}\) for enhancing proliferation of mesenchymal stem cells.

Ontology-Based Textual Emotion Detection

Emotion Detection from text is a very important area of natural language processing. This paper shows a new method for emotion detection from text which depends on ontology. This method is depending on ontology extraction from the input sentence by using a triplet extraction algorithm by the OpenNLP parser, then make an ontology matching with the ontology base that we created by similarity and word sense disambiguation. This ontology base consists of ontologies and the emotion label related to each one. We choose the emotion label of the sentence with the highest score of matching. If the extracted ontology doesn’t match any ontology from the ontology base we use the keyword-based approach. This method doesn’t depend only on keywords like previous approaches; it depends on the meaning of sentence words and the syntax and semantic analysis of the context.

Frame Semantics Evolutionary Model for Emotion Detection

Emotions play a significant role in identifying attitude, state, condition or mode of a particular circumstance. Textual data, in particular, involves emotional state and affective communication beside its informative contents. Emotion extraction from text has been potentially studied to stimulate and elicit articulation features. In this study, a machine learning emotion detection model is proposed for textual emotion recognition. A frame semantics approach is identified to extract knowledge from the text in an evolutionary process that improves the detection capabilities. Emotion detection process is controlled by a rule base; each of its entries is generated by pre-invoking event, action and resulting emotion state. Frame entities semantically collaborated to evaluate the frame emotion. Individual entities may arbitrary substituted by their synonyms or opposites if a candidate frame doesn’t match any of the knowledge set. The proposed model proves considerable capability of recognizing emotions by referencing their semantic relations. Results showed better detection accuracy for the proposed model compared with variety of emotion approaches including keyword spotting, knowledge-based ANN and supervised machine learning models. Experiments indicated encouraging results over both binary emotion and multiple labels classifiers.

Semantic Text Summarization Based on Syntactic Patterns

Text summarization is machine based generation of a shortened version of a text. The summary should be a non-redundant extract from the original text. Most researches of text summarization use sentence extraction instead of abstraction to produce a summary. Extraction is depending mainly on sentences that already contained in the original input, which makes it more accurate and more concise. When all input articles are surrounding a particular event, extracting similar sentences would result in producing a highly repetitive summary. In this paper, a novel model for text summarization is proposed based on removing the non-effective sentences in producing an extract from the text. The model utilizes semantic analysis by evaluating sentences similarity. This similarity is provided by evaluating individual words similarity as well as syntactic relationships between neighboring words. These relationships addressed throughout the model as syntactic patterns. Word senses and the correlating part of speech for the word within context are provided in the semantic processing of matched patterns. The introduction of syntactic patterns knowledge supports text reduction by mapping the matched patterns into summarized ones. In addition, syntactic patterns make use of sentence relatedness evaluation in defining which sentences to keep and which to drop. Experiments proved that the model presented throughout the paper is well performing in results evaluation of compression rate, accuracy, recall and other human criteria like correctness, novelty, fluency and usefulness.

Semantic Role Labeling Approach for Evaluation of Text Coherence

Detection of semantic roles associated with linguistic elements is important to the textual classification of communicative context into specific identities. In this paper, a new model for semantically identifying sentences is presented through contextual patterns. The proposed contextual pattern originated its structure from a labeling process of the semantic roles provided by constituents of a sentence within a semantic frame. Semantic roles of the pattern elements are properly identified through word sense disambiguation and accordingly the entire patterns sense is evaluated. Such semantic identification of text sentences is a generic semantic role labeling approach that could support many computational linguistic applications. A utilization of the proposed semantic labeling approach is introduced in the paper through a novel algorithm for text coherence evaluation. Coherence evaluation is provided by a matching task to individual semantic patterns and their relations to each other as well as patterns organization within the text segments. Results proved good capability of the modelling of contextual pattern, addressing semantic roles, to accurately evaluate text coherence. It has been shown that both contextual patterns labeling and coherence evaluation algorithm proposed here are generic, topic free and semantically arbitrated by the global concept within context.

Chronological Ordering Based on Context Overlap Detection

Context processing plays an important role in different Natural Language Processing applications. Sentence ordering is one of critical tasks in text generation. Following the same order of sentences in the row sources of text is not necessarily to be applied for the resulted text. Accordingly, a need for chronological sentence ordering is of high importance in this regard. Some researches followed linguistic syntactic analysis and others used statistical approaches. This paper proposes a new model for sentence ordering based on sematic analysis. Word level semantics forms a seed to sentence level sematic relations. The model introduces a clustering technique based on sentences senses relatedness. Following to this, sentences are chronologically ordered through two main steps; overlap detection and chronological cause-effect rules. Overlap detection drills down into each cluster to step through its sentences in chronological sequence. Cause-effect rules forms the linguistic knowledge controlling sentences relations. Evaluation of the proposed algorithm showed the capability of the proposed model to process size free texts, non-domain specific and open to extend the cause-effect rules for specific ordering needs.