ArabBert-LSTM: improving Arabic sentiment analysis based on transformer model and Long Short-Term Memory.

Sentiment analysis also referred to as opinion mining, plays a significant role in automating the identification of negative, positive, or neutral sentiments expressed in textual data. The proliferation of social networks, review sites, and blogs has rendered these platforms valuable resources for mining opinions. Sentiment analysis finds applications in various domains and languages, including English and Arabic. However, Arabic presents unique challenges due to its complex morphology characterized by inflectional and derivation patterns. To effectively analyze sentiment in Arabic text, sentiment analysis techniques must account for this intricacy. This paper proposes a model designed using the transformer model and deep learning (DL) techniques. The word embedding is represented by Transformer-based Model for Arabic Language Understanding (ArabBert), and then passed to the AraBERT model. The output of AraBERT is subsequently fed into a Long Short-Term Memory (LSTM) model, followed by feedforward neural networks and an output layer. AraBERT is used to capture rich contextual information and LSTM to enhance sequence modeling and retain long-term dependencies within the text data. We compared the proposed model with machine learning (ML) algorithms and DL algorithms, as well as different vectorization techniques: term frequency-inverse document frequency (TF-IDF), ArabBert, Continuous Bag-of-Words (CBOW), and skipGrams using four Arabic benchmark datasets. Through extensive experimentation and evaluation of Arabic sentiment analysis datasets, we showcase the effectiveness of our approach. The results underscore significant improvements in sentiment analysis accuracy, highlighting the potential of leveraging transformer models for Arabic Sentiment Analysis. The outcomes of this research contribute to advancing Arabic sentiment analysis, enabling more accurate and reliable sentiment analysis in Arabic text. The findings reveal that the proposed framework exhibits exceptional performance in sentiment classification, achieving an impressive accuracy rate of over 97%.

Large Ascites in a Cirrhotic Patient Reveal an Isolated and Late Metastasis of Ductal Breast Cancer: A Case Study.

Breast cancer is the most prevalent cancer in women worldwide, and its prevalence has increased since the introduction of screening programs. Most cases are discovered at an early stage; however, despite effective treatment, some cases progress to metastasis. The most common breast cancer metastatic locations are the bone, liver, and lungs. Ascites malignant due to peritoneal involvement is a rare manifestation of metastatic breast cancer. After 8 years of well-controlled breast cancer, we report a 54-year-old woman who presents with malignant ascites and is known to have cirrhosis of the liver.

Diagnosis of COVID-19 Using Chest X-ray Images and Disease Symptoms Based on Stacking Ensemble Deep Learning.

The COVID-19 virus is one of the most devastating illnesses humanity has ever faced. COVID-19 is an infection that is hard to diagnose until it has caused lung damage or blood clots. As a result, it is one of the most insidious diseases due to the lack of knowledge of its symptoms. Artificial intelligence (AI) technologies are being investigated for the early detection of COVID-19 using symptoms and chest X-ray images. Therefore, this work proposes stacking ensemble models using two types of COVID-19 datasets, symptoms and chest X-ray scans, to identify COVID-19. The first proposed model is a stacking ensemble model that is merged from the outputs of pre-trained models in the stacking: multi-layer perceptron (MLP), recurrent neural network (RNN), long short-term memory (LSTM), and gated recurrent unit (GRU). Stacking trains and evaluates the meta-learner as a support vector machine (SVM) to predict the final decision. Two datasets of COVID-19 symptoms are used to compare the first proposed model with MLP, RNN, LSTM, and GRU models. The second proposed model is a stacking ensemble model that is merged from the outputs of pre-trained DL models in the stacking: VGG16, InceptionV3, Resnet50, and DenseNet121; it uses stacking to train and evaluate the meta-learner (SVM) to identify the final prediction. Two datasets of COVID-19 chest X-ray images are used to compare the second proposed model with other DL models. The result has shown that the proposed models achieve the highest performance compared to other models for each dataset.

Ensemble Learning Based on Hybrid Deep Learning Model for Heart Disease Early Prediction.

Many epidemics have afflicted humanity throughout history, claiming many lives. It has been noted in our time that heart disease is one of the deadliest diseases that humanity has confronted in the contemporary period. The proliferation of poor habits such as smoking, overeating, and lack of physical activity has contributed to the rise in heart disease. The killing feature of heart disease, which has earned it the moniker the "silent killer," is that it frequently has no apparent signs in advance. As a result, research is required to develop a promising model for the early identification of heart disease using simple data and symptoms. The paper's aim is to propose a deep stacking ensemble model to enhance the performance of the prediction of heart disease. The proposed ensemble model integrates two optimized and pre-trained hybrid deep learning models with the Support Vector Machine (SVM) as the meta-learner model. The first hybrid model is Convolutional Neural Network (CNN)-Long Short-Term Memory (LSTM) (CNN-LSTM), which integrates CNN and LSTM. The second hybrid model is CNN-GRU, which integrates CNN with a Gated Recurrent Unit (GRU). Recursive Feature Elimination (RFE) is also used for the feature selection optimization process. The proposed model has been optimized and tested using two different heart disease datasets. The proposed ensemble is compared with five machine learning models including Logistic Regression (LR), Random Forest (RF), K-Nearest Neighbors (K-NN), Decision Tree (DT), Naïve Bayes (NB), and hybrid models. In addition, optimization techniques are used to optimize ML, DL, and the proposed models. The results obtained by the proposed model achieved the highest performance using the full feature set.

Lipid Signaling During Gamete Maturation.

Cell lipids are differentially distributed in distinct organelles and within the leaflets of the bilayer. They can further form laterally defined sub-domains within membranes with important signaling functions. This molecular and spatial complexity offers optimal platforms for signaling with the associated challenge of dissecting these pathways especially that lipid metabolism tends to be highly interconnected. Lipid signaling has historically been implicated in gamete function, however the detailed signaling pathways involved remain obscure. In this review we focus on oocyte and sperm maturation in an effort to consolidate current knowledge of the role of lipid signaling and set the stage for future directions.

Heterogeneous Ensemble Deep Learning Model for Enhanced Arabic Sentiment Analysis.

Sentiment analysis was nominated as a hot research topic a decade ago for its increasing importance in analyzing the people's opinions extracted from social media platforms. Although the Arabic language has a significant share of the content shared across social media platforms, its content's sentiment analysis is still limited due to its complex morphological structures and the varieties of dialects. Traditional machine learning and deep neural algorithms have been used in a variety of studies to predict sentiment analysis. Therefore, a need of changing current mechanisms is required to increase the accuracy of sentiment analysis prediction. This paper proposed an optimized heterogeneous stacking ensemble model for enhancing the performance of Arabic sentiment analysis. The proposed model combines three different of pre-trained Deep Learning (DL) models: Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU) in conjunction with three meta-learners Logistic Regression (LR), Random Forest (RF), and Support Vector Machine (SVM) in order to enhance model's performance for predicting Arabic sentiment analysis. The performance of the proposed model with RNN, LSTM, GRU, and the five regular ML techniques: Decision Tree (DT), LR, K-Nearest Neighbor (KNN), RF, and Naive Bayes (NB) are compared using three benchmarks Arabic dataset. Parameters of Machine Learning (ML) and DL are optimized using Grid search and KerasTuner, respectively. Accuracy, precision, recall, and f1-score were applied to evaluate the performance of the models and validate the results. The results show that the proposed ensemble model has achieved the best performance for each dataset compared with other models.

Lipidomic Analyses Reveal Specific Alterations of Phosphatidylcholine in Dystrophic Mdx Muscle.

In Duchenne muscular dystrophy (DMD), lack of dystrophin increases the permeability of myofiber plasma membranes to ions and larger macromolecules, disrupting calcium signaling and leading to progressive muscle wasting. Although the biological origin and meaning are unclear, alterations of phosphatidylcholine (PC) are reported in affected skeletal muscles of patients with DMD that may include higher levels of fatty acid (FA) 18:1 chains and lower levels of FA 18:2 chains, possibly reflected in relatively high levels of PC 34:1 (with 16:0_18:1 chain sets) and low levels of PC 34:2 (with 16:0_18:2 chain sets). Similar PC alterations have been reported to occur in the mdx mouse model of DMD. However, altered ratios of PC 34:1 to PC 34:2 have been variably reported, and we also observed that PC 34:2 levels were nearly equally elevated as PC 34:1 in the affected mdx muscles. We hypothesized that experimental factors that often varied between studies; including muscle types sampled, mouse ages, and mouse diets; may strongly impact the PC alterations detected in dystrophic muscle of mdx mice, especially the PC 34:1 to PC 34:2 ratios. In order to test our hypothesis, we performed comprehensive lipidomic analyses of PC and phosphatidylethanolamine (PE) in several muscles (extensor digitorum longus, gastrocnemius, and soleus) and determined the mdx-specific alterations. The alterations in PC 34:1 and PC 34:2 were closely monitored from the neonate period to the adult, and also in mice raised on several diets that varied in their fats. PC 34:1 was naturally high in neonate's muscle and decreased until age ∼3-weeks (disease onset age), and thereafter remained low in WT muscles but was higher in regenerated mdx muscles. Among the muscle types, soleus showed a distinctive phospholipid pattern with early and diminished mdx alterations. Diet was a major factor to impact PC 34:1/PC 34:2 ratios because mdx-specific alterations of PC 34:2 but not PC 34:1 were strictly dependent on diet. Our study identifies high PC 34:1 as a consistent biochemical feature of regenerated mdx-muscle and indicates nutritional approaches are also effective to modify the phospholipid compositions.