No abstract

No abstract

No abstract

No abstract

Growing science and medical technologies have produced a massive amount of knowledge on different scales of biological systems. By processing various amounts of medical data, these technologies will increase the quality of disease detection and enhance the usability of health information systems. The integration of machine learning in computer-based diagnostic systems facilitates the early detection of diseases, enabling more productive treatments and prolonged survival rates. The slime mould algorithm (SMA) may have drawbacks, such as being trapped in minimal local regions and having an unbalanced exploitation and exploration phase. To overcome these limitations, this paper proposes ISMA, an improved version of the slime mould algorithm (SMA) hybridized with the opposition-based learning (OBL) strategy based on the k-nearest neighbor (kNN) classifier for the classification approach. Opposition-based learning improves global exploratory ability while avoiding premature convergence. The experimental results revealed the superiority of the proposed ISMA-kNN in various classification evaluation metrics, including accuracy, sensitivity, specificity, precision, F-score, G-mean, computational time, and feature selection (FS) size compared with the tunicate swarm algorithm (TSA), the marine predators algorithm (MPA), the chimp optimization algorithm (ChOA), the moth-flame optimization (MFO) algorithm, the whale optimization algorithm (WOA), the sine cosine algorithm (SCA), and the original SMA algorithm. Performance tests were run on the same maximum number of function evaluations (FEs) on nine UCI benchmark disease data sets with different feature sizes.

Text summarization (TS) is considered one of the most difficult tasks in natural language processing (NLP). It is one of the most important challenges that stand against the modern computer system’s capabilities with all its new improvement. Many papers and research studies address this task in literature but are being carried out in extractive summarization, and few of them are being carried out in abstractive summarization, especially in the Arabic language due to its complexity. In this paper, an abstractive Arabic text summarization system is proposed, based on a sequence-to-sequence model. This model works through two components, encoder and decoder. Our aim is to develop the sequence-to-sequence model using several deep artificial neural networks to investigate which of them achieves the best performance. Different layers of Gated Recurrent Units (GRU), Long Short-Term Memory (LSTM), and Bidirectional Long Short-Term Memory (BiLSTM) have been used to develop the encoder and the decoder. In addition, the global attention mechanism has been used because it provides better results than the local attention mechanism. Furthermore, AraBERT preprocess has been applied in the data preprocessing stage that helps the model to understand the Arabic words and achieves state-of-the-art results. Moreover, a comparison between the skip-gram and the continuous bag of words (CBOW) word2Vec word embedding models has been made. We have built these models using the Keras library and run-on Google Colab Jupiter notebook to run seamlessly. Finally, the proposed system is evaluated through ROUGE-1, ROUGE-2, ROUGE-L, and BLEU evaluation metrics. The experimental results show that three layers of BiLSTM hidden states at the encoder achieve the best performance. In addition, our proposed system outperforms the other latest research studies. Also, the results show that abstractive summarization models that use the skip-gram word2Vec model outperform the models that use the CBOW word2Vec model.

The electrocardiogram (ECG) is a non-invasive tool used to diagnose various heart conditions. Arrhythmia is one of the primary causes of cardiac arrest. Early ECG beat classification plays a significant role in diagnosing life-threatening cardiac arrhythmias. However, the ECG signal is very small, the anti-interference potential is low, and the noise is easily influenced. Thus, clinicians face challenges in diagnosing arrhythmias. Thus, a method to automatically identify and distinguish arrhythmias from the ECG signal is invaluable. In this paper, a hybrid approach based on marine predators algorithm (MPA) and convolutional neural network (CNN) called MPA-CNN is proposed to classify the non-ectopic, ventricular ectopic, supraventricular ectopic, and fusion ECG types of arrhythmia. The proposed approach is a combination of heavy feature extraction and classification techniques; hence, outperforms other existing classification approaches. Optimal characteristics were derived directly from the raw signal to decrease the time required for and complexity of the computation. Precision levels of 99.31%, 99.76%, and 99.47% were achieved by the proposed approach on the MIT-BIH,EDB, and INCART databases, respectively.

No abstract