COVID‐19 is the disease evoked by a new breed of coronavirus called the severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2). Recently, COVID‐19 has become a pandemic by infecting more than 152 million people in over 216 countries and territories. The exponential increase in the number of infections has rendered traditional diagnosis techniques inefficient. Therefore, many researchers have developed several intelligent techniques, such as deep learning (DL) and machine learning (ML), which can assist the healthcare sector in providing quick and precise COVID‐19 diagnosis. Therefore, this paper provides a comprehensive review of the most recent DL and ML techniques for COVID‐19 diagnosis. The studies are published from December 2019 until April 2021. In general, this paper includes more than 200 studies that have been carefully selected from several publishers, such as IEEE, Springer and Elsevier. We classify the research tracks into two categories: DL and ML and present COVID‐19 public datasets established and extracted from different countries. The measures used to evaluate diagnosis methods are comparatively analysed and proper discussion is provided. In conclusion, for COVID‐19 diagnosing and outbreak prediction, SVM is the most widely used machine learning mechanism, and CNN is the most widely used deep learning mechanism. Accuracy, sensitivity, and specificity are the most widely used measurements in previous studies. Finally, this review paper will guide the research community on the upcoming development of machine learning for COVID‐19 and inspire their works for future development. This review paper will guide the research community on the upcoming development of ML and DL for COVID‐19 and inspire their works for future development.

Many techniques and algorithms for automatic text categorization had been devised and proposed in the literature. However, there is still much space for researchers in this area to improve existing algorithms or come up with new techniques for text categorization (TC). Polynomial Networks (PNs) were never used before in TC. This can be attributed to the huge datasets used in TC, as well as the technique itself which has high computational demands. In this paper, we investigate and propose using PNs in TC. The proposed PN classifier has achieved a competitive classification performance in our experiments. More importantly, this high performance is achieved in one shot training (noniteratively) and using just 0.25%-0.5% of the corpora features. Experiments are conducted on the two benchmark datasets in TC: Reuters-21578 and the 20 Newsgroups. Five well-known classifiers are experimented on the same data and feature subsets: the state-of-the-art Support Vector Machines (SVM), Logistic Regression (LR), the k-nearest-neighbor (kNN), Naive Bayes (NB), and the Radial Basis Function (RBF) networks.

Although the Arab world has an estimated number of 250 million Arabic speakers, there has been little research on Arabic speech recognition when compared to other languages of similar importance (e.g. Mandarin). Due to the lack of diacritic Arabic text and the lack of Pronunciation Dictionary (PD), most of previous work on Arabic Automatic Speech Recognition has been concentrated on developing recognizers using Romanized characters i.e. let the system recognizes the Arabic word as an English one, then map it to Arabic word from lookup table that maps the Arabic word to its Romanized pronunciation.In this work, we introduce the first SPHINX-IV-based Arabic recognizer and propose an automatic toolkit, which is capable of producing (PD) for both Holly Qura'an and standard Arabic language. Three corpuses are completely developed in this work, namely the Holly Qura'an Corpus HQC-1 about 18.5 hours, the command and control corpus CAC-1 about 1.5 hours and Arabic digits corpus ADC less than one hour of speech. The building process is completely described. Fully diacritic Arabic transcriptions, for all the three corpuses were developed too.SPHINX-IV engine was customized and trained, for both the language model and the lexicon modules shown in the frame work architecture block diagram on next page.Using the three mentioned corpuses; the (PD) developed by our automatic tool with the transcripts, SPHINX-IV engine is trained and tuned in order to develop three acoustic models, one for each corpus. Training is based on an HMM model that is built on statistical information and random variables distributions extracted from the training data itself. New algorithm is proposed to add unlabeled data to the training corpus in order to increase the corpus size. This algorithm is based on Neural Network confidence scorer and then is used to annotate the decoded speech in order to decide whether the proposed transcript is accepted and can be added to the seed corpus or not.The model parameters were fine-tuned using simulated annealing algorithm; optimum values were tested and reported. Our major contribution is mainly using the open source SPHINX-IV model in Arabic speech recognition by building our own language and acoustic models without Romanization for the Arabic speech. The system is fine-tuned and data are refined for training and validation. Optimum values for number of Gaussian mixtures distributions and number of states in HMM's have been found according to specified performance measures. Optimum values for confidence 134 Int J Speech Technol (2006) 9: 133-150 scores were found for the training data. Although much more work need to be done to complete the work with this size, we consider the corpus used in our system is enough to validate our approach. SPHINX has never been used before in this manner for Arabic speech recognition. The work is an invitation for all open source speech recognition developers and groups to take over and capitalize on what we have started.

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In this review paper, JAYA algorithm, which is a recent population-based algorithm is intensively overviewed. The JAYA algorithm combines the survival of the fittest principle from evolutionary algorithms as well as the global optimal solution attractions of Swarm Intelligence methods. Initially, the optimization model and convergence characteristics of JAYA algorithm are carefully analyzed. Thereafter, the proposed versions of JAYA algorithm have been surveyed such as modified, binary, hybridized, parallel, chaotic, multi-objective and others. The various applications tackled using relevant versions of JAYA algorithm are also discussed and summarized based on several problem domains. Furthermore, the open sources code of JAYA algorithm are identified to provide enrich resources for JAYA research communities. The critical analysis of JAYA algorithm reveals its advantages and limitations in dealing with optimization problems. Finally, the paper ends up with conclusion and possible future enhancements suggested to improve the performance of JAYA algorithm. The reader of this overview will determine the best domains and applications used by JAYA algorithm and can justify their JAYA-related contributions.

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Selecting relevant feature subsets is vital in machine learning, and multiclass feature selection is harder to perform since most classifications are binary. The feature selection problem aims at reducing the feature set dimension while maintaining the performance model accuracy. Datasets can be classified using various methods. Nevertheless, metaheuristic algorithms attract substantial attention to solving different problems in optimization. For this reason, this paper presents a systematic survey of literature for solving multiclass feature selection problems utilizing metaheuristic algorithms that can assist classifiers selects optima or near optima features faster and more accurately. Metaheuristic algorithms have also been presented in four primary behavior-based categories, i.e., evolutionary-based, swarm-intelligence-based, physics-based, and human-based, even though some literature works presented more categorization. Further, lists of metaheuristic algorithms were introduced in the categories mentioned. In finding the solution to issues related to multiclass feature selection, only articles on metaheuristic algorithms used for multiclass feature selection problems from the year 2000 to 2022 were reviewed about their different categories and detailed descriptions. We considered some application areas for some of the metaheuristic algorithms applied for multiclass feature selection with their variations. Popular multiclass classifiers for feature selection were also examined. Moreover, we also presented the challenges of metaheuristic algorithms for feature selection, and we identified gaps for further research studies.