Review - Magic Sets and Other Strange Ways to Implement Logic Programs.

Obstructive sleep apnea (OSA) is a well-known sleep ailment. OSA mostly occurs due to the shortage of oxygen for the human body, which causes several symptoms (i.e., low concentration, daytime sleepiness, and irritability). Discovering the existence of OSA at an early stage can save lives and reduce the cost of treatment. The computer-aided diagnosis (CAD) system can quickly detect OSA by examining the electrocardiogram (ECG) signals. Over-serving ECG using a visual procedure is challenging for physicians, time-consuming, expensive, and subjective. In general, automated detection of the ECG signal’s arrhythmia is a complex task due to the complexity of the data quantity and clinical content. Moreover, ECG signals are usually affected by noise (i.e., patient movement and disturbances generated by electric devices or infrastructure), which reduces the quality of the collected data. Machine learning (ML) and Deep Learning (DL) gain a higher interest in health care systems due to its ability of achieving an excellent performance compared to traditional classifiers. We propose a CAD system to diagnose apnea events based on ECG in an automated way in this work. The proposed system follows the following steps: (1) remove noise from the ECG signal using a Notch filter. (2) extract nine features from the ECG signal (3) use thirteen ML and four types of DL models for the diagnosis of sleep apnea. The experimental results show that our proposed approach offers a good performance of DL classifiers to detect OSA. The proposed model achieves an accuracy of 86.25% in the validation stage.

Diagnosis of Obstructive Sleep Apnea from ECG Signals Using Machine Learning and Deep Learning Classifiers

Software fault prediction (SFP) is a challenging process that any successful software should go through it to make sure that all software components are free of faults. In general, soft computing and machine learning methods are useful in tackling this problem. The size of fault data is usually huge since it is obtained from mining software historical repositories. This data consists of a large number of features (metrics). Determining the most valuable features (i.e., Feature Selection (FS) is an excellent solution to reduce data dimensionality. In this paper, we proposed an enhanced version of the Whale Optimization Algorithm (WOA) by combining it with a single point crossover method. The proposed enhancement helps the WOA to escape from local optima by enhancing the exploration process. Five different selection methods are employed: Tournament, Roulette wheel, Linear rank, Stochastic universal sampling, and random-based. To evaluate the performance of the proposed enhancement, 17 available SFP datasets are adopted from the PROMISE repository. The deep analysis shows that the proposed approach outperformed the original WOA and the other six state-of-the-art methods, as well as enhanced the overall performance of the machine learning classifier.

/pdf/boosted-whale-optimization-algorithm-with-natural-selection-2iwsql5qrb.pdf

Boosted Whale Optimization Algorithm With Natural Selection Operators for Software Fault Prediction

Harris Hawks Optimization Algorithm: Variants and Applications

Advances in nature-inspired metaheuristic optimization for feature selection problem: A comprehensive survey

Designing a solution for an optimization problem requires two main aspects; the optimization technique (e.g., search strategy) and the evaluation criteria (i.e., objective function). In this paper, an enhanced binary version of a recent metaheuristic algorithm, the Harris Hawk Optimization algorithm (EBHHO), is presented to find a (near) optimal solution for the Feature Selection (FS) problem. Moreover, three different classifiers called K-nearest neighbors (kNN), Decision Trees (DT), and Linear Discriminant Analysis (LDA) were used as evaluation criteria to formulate the objective function. In addition to reducing the dimensionality of the dataset using the FS technique, the Adaptive Synthetic (ADASYN) oversampling technique was used to enhance the quality of the learning algorithm by re-balancing the dataset. A set of well-known datasets in the field of Software Fault Prediction (SFP) were used to validate the efficiency of the proposed approach. The obtained results showed that EBHHO is superior over the basic HHO as well as proved the ability of the EBHHO algorithm to produce the best result among a set of well-known optimization methods.

Efficient Multi-Swarm Binary Harris Hawks Optimization as a Feature Selection Approach for Software Fault Prediction

Abstract Automated software engineering has attracted a large amount of research efforts. The use of object-oriented methods for software systems development has made it necessary to develop approaches that automate the construction of different Unified Modeling Language (UML) models in a semiautomated approach from textual user requirements. UML use case models represent an essential artifact that provides a perspective of the system under analysis or development. The development of such use case models is very crucial in an object-oriented development method. The main principles used in obtaining these models are described. A natural language processing tool is used to parse different statements of the user requirements written in Arabic to obtain lists of nouns, noun phrases, verbs, verb phrases, etc., that aid in finding potential actors and use cases. A set of steps that represent our approach for constructing a use case model are presented. Finally, the proposed approach is validated using an experiment involving a group of graduate students who are familiar with use case modeling.

Generating Use Case Models from Arabic User Requirements in a Semiautomated Approach Using a Natural Language Processing Tool

Extracting synonyms from dictionaries or corpora is gaining a special attention as synonyms play an important role in improving NLP application performance. This paper presents a survey of the different approaches and trends used in automatically extracting the synonyms. These approaches can be divided into four main categories. The first approach is to find the Synonyms using a translation graph, The second approach is to discover new transition pairs such as (Arabic – English) (English – France) then (Arabic- France). The third approach is to construct new WordNets by exploring synonymy graphs, and the fourth approach is to find similar words from corpora using Deelp Learning methods, such as word embeddings and recently BERT models. The papers also presents comparative analysis between these approoaches, and highlights potential adaptation to generate synonyms automatically in the Arabic language as future work.

pdf/current-trends-and-approaches-in-synonyms-extraction-58bo73gu0g.pdf

Current Trends and Approaches in Synonyms Extraction: Potential Adaptation to Arabic

Automated software engineering has attracted a large amount of research efforts. The use of object-oriented methodologies for software systems development has made it necessary to develop approaches that automate the construction of different UML models in a semi-automated approach from textual user requirements. UML use case models represent an essential artifact that provide a perspective of the system under analysis or development The development of such use case models is very crucial in an object-oriented development methodology. The main principles used in obtaining these models are described. A natural language processing tool is used to parse different statements of the user requirements written in Arabic to obtain lists of nouns, noun phrases, verbs, verb phrases, etc. that aid in finding potential actors and use cases. A set of steps that represent our approach for constructing a use case model is presented.

http://ieeexplore.ieee.org/iel7/6908290/6916540/06916558.pdf

Constructing use case models from Arabic user requirements in a semi-automated approach

/pdf/normalizer-a-case-tool-to-normalize-relational-database-5p8x9iwcae.pdf

Normalizer: A Case Tool to Normalize Relational Database Schemas

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