Improving sentiment classification using a RoBERTa-based hybrid model

IntroductionSeveral attempts have been made to enhance text-based sentiment analysis’s performance. The classifiers and word embedding models have been among the most prominent attempts. This work aims to develop a hybrid deep learning approach that combines the advantages of transformer models and sequence models with the elimination of sequence models’ shortcomings.MethodsIn this paper, we present a hybrid model based on the transformer model and deep learning models to enhance sentiment classification process. Robustly optimized BERT (RoBERTa) was selected for the representative vectors of the input sentences and the Long Short-Term Memory (LSTM) model in conjunction with the Convolutional Neural Networks (CNN) model was used to improve the suggested model’s ability to comprehend the semantics and context of each input sentence. We tested the proposed model with two datasets with different topics. The first dataset is a Twitter review of US airlines and the second is the IMDb movie reviews dataset. We propose using word embeddings in conjunction with the SMOTE technique to overcome the challenge of imbalanced classes of the Twitter dataset.ResultsWith an accuracy of 96.28% on the IMDb reviews dataset and 94.2% on the Twitter reviews dataset, the hybrid model that has been suggested outperforms the standard methods.DiscussionIt is clear from these results that the proposed hybrid RoBERTa–(CNN+ LSTM) method is an effective model in sentiment classification

A Multitier Deep Learning Model for Arrhythmia Detection

Electrocardiograph (ECG) is employed as a primary tool for diagnosing cardiovascular diseases (CVD) in the hospital, which often helps in the early detection of such ailments. ECG signals provide a framework to probe the underlying properties and enhance the initial diagnosis obtained via traditional tools and patient-doctor dialogues. It provides cardiologists with inferences regarding more serious cases. Notwithstanding its proven utility, deciphering large datasets to determine appropriate information remains a challenge in ECG-based CVD diagnosis and treatment. Our study presents a deep neural network (DNN) strategy to ameliorate the aforementioned difficulties. Our strategy consists of a learning stage where classification accuracy is improved via a robust feature extraction. This is followed using a genetic algorithm (GA) process to aggregate the best combination of feature extraction and classification. The MIT-BIH Arrhythmia was employed in the validation to identify five arrhythmia categories based on the association for the advancement of medical instrumentation (AAMI) standard. The performance of the proposed technique alongside state-of-the-art in the area shows an increase of 0.94 and 0.953 in terms of average accuracy and F1 score, respectively. The proposed model could serve as an analytic module to alert users and/or medical experts when anomalies are detected in the acquired ECG data in a smart healthcare framework

CYTOTOXICITY OF IMIDACLOPRID AND MYCLOBUTANIL PESTICIDES ON THREE CANCER CELL LINES

Three cancer cell lines, i.e. HEpG-2 (human liver carcinoma), MCF-7 (human breast adeno-carcinoma), and PC3 (Prostatic Small Cell Carcinoma) were used to determine the cytotoxic effects of the neonicotinoid insecticide (imidacloprid) and conazole fungicide (myclobutanil). Cytotoxicity was measured by neutral red incorporation (NRI) assay. The lowest concentration of the tested pesticides (0.5 μg/ml) was toxic. With the increase of the concentration up to 80 μg/ml, the Department of plant protection, Faculty of Agric., Ain Shams University, shoubra  Elkheima, Cairo, Egypt Department of Genetics, Faculty of Agric., Ain Shams University, Cairo, Egypt damage degree of the cellular form and size was more serious. The midpoint cytotoxicity value, (NRI50) for imidacloprid and myclobutanil for HEpG-2, MCF-7, and PC3 cancer cell lines were 110.5, 67.7 and 67.6 μg/ml and 38.12, 41 and 27.5 μg/ml, respectively. In general, myclobutanil was very toxic in the three cancer cell lines compared with imidacloprid

C-Abl inhibition; a novel therapeutic target for Parkinson’s disease

Parkinson’s disease (PD) is the most prevalent movement disorder in the world. The major pathological hallmarks of PD are death of dopaminergic neurons and the formation of Lewy bodies. At the moment, there is no cure for PD; current treatments are symptomatic. Investigators are searching for neuroprotective agents and disease modifying strategies to slow the progress of PD. However, recently, due to the ignorance of the main pathological sequence of PD, many drug targets failed to provide neuroprotective effects in human trials. Currently, a huge amount of evidence suggests the involvement of C-Abelson (c-Abl) tyrosine kinase enzyme in the pathology of PD. C-abl plays a role in PD pathology on the levels of parkin activation, alpha synuclein aggregation, and impaired autophagy of toxic elements. Experimental studies showed that (1) c-abl activation is involved in neuronal death and (2) c-abl inhibition shows neuroprotective effects and prevents dopaminergic neurons’ death. Current evidence from experimental studies and the first in-human trial shows that c-abl inhibition holds the promise for neuroprotection against PD and therefore, justifies the movement towards larger clinical trials. In this review article, we discussed the role of c-abl in PD pathology and the findings of preclinical experiments and the first in-human trial. In addition, based on the lessons of the last decade and current preclinical evidence, we provide recommendations for future research in this area

The patterns of clinical presentations of cerebellar syndromes among adult Sudanese patients

Cerebellar syndromes are one of the commonest neurological diseases.Objectives: To study the patterns of clinical presentations of cerebellar syndromes and to identify the possible causes.Methods: This is a prospective hospital based, cross-sectional study. One hundred adult Sudanese patients with cerebellar syndromes were included in the study during the period from January 2006– January 2007.Results: The most common age group affected was 18 – 25 years. Male to female ratio was 1.5: 1 unsteadiness on walking was the most common symptom (83%). Gait-ataxia was the most common sign (83%). Cerebrovascular disease was the most common aetiology (25%).Conclusion: Cerebellar syndromes are not rare in Sudan. However, they were diagnosed more commonly at the central regions of the country probably because of more awareness of patients and better facilitiesfor diagnosis. The age of onset, the male predominance, the presentation and clinical findings were not different from reported literature. This also goes for the common causes apart from alcohol which is a strikingly rare as a cause in this study and could be accounted for the implementation of Elshariya (Islamic laws) Laws in Sudan.Keywords: ataxia, dysmetria, disdiadochokenesis, decomposition, nystagmus, dysarthria

Potential Dependent Frictional Schrodinger Equation

By treating particles as harmonic oscillator is obtained the friction energy related to the momentum. The energy and the corresponding Newtonian operator is found. This result in a new Schrodinger equation accounting for the effect of friction. This new equation shows that the energy and mass are quantized, if one treats particles as strings. The radioactive decay law and collision probability is also derived

Efficient Multimodal Deep-Learning-Based COVID-19 Diagnostic System for Noisy and Corrupted Images

Introduction: In humanity\u27s ongoing fight against its common enemy of COVID-19, researchers have been relentless in finding efficient technologies to support mitigation, diagnosis, management, contact tracing, and ultimately vaccination. Objectives: Engineers and computer scientists have deployed the potent properties of deep learning models (DLMs) in COVID-19 detection and diagnosis. However, publicly available datasets are often adulterated during collation, transmission, or storage. Meanwhile, inadequate, and corrupted data are known to impact the learnability and efficiency of DLMs. Methods: This study focuses on enhancing previous efforts via two multimodal diagnostic systems to extract required features for COVID-19 detection using adulterated chest X-ray images. Our proposed DLM consists of a hierarchy of convolutional and pooling layers that are combined to support efficient COVID-19 detection using chest X-ray images. Additionally, a batch normalization layer is used to curtail overfitting that usually arises from the convolution and pooling (CP) layers. Results: In addition to matching the performance of standard techniques reported in the literature, our proposed diagnostic systems attain an average accuracy of 98% in the detection of normal, COVID-19, and viral pneumonia cases using corrupted and noisy images. Conclusions: Such robustness is crucial for real-world applications where data is usually unavailable, corrupted, or adulterated

Narghile (water pipe) smoking among university students in Jordan: prevalence, pattern and beliefs

<p>Abstract</p> <p>Background and objectives</p> <p>Narghile is becoming the favorite form of tobacco use by youth globally. This problem has received more attention in recent years. The aim of this study was to investigate the prevalence and pattern of narghile use among students in three public Jordanian universities; to assess their beliefs about narghile's adverse health consequences; and to evaluate their awareness of oral health and oral hygiene.</p> <p>Methods</p> <p>The study was a cross-sectional survey of university students. A self-administered, anonymous questionnaire was distributed randomly to university students in three public Jordanian universities during December, 2008. The questionnaire was designed to ask specific questions that are related to smoking in general, and to narghile smoking in specific. There were also questions about oral health awareness and oral hygiene practices.</p> <p>Results</p> <p>36.8% of the surveyed sample indicated they were smokers comprising 61.9% of the male students and 10.7% of the female students in the study sample. Cigarettes and narghile were the preferred smoking methods among male students (42%). On the other hand, female students preferred narghile only (53%). Parental smoking status but not their educational level was associated with the students smoking status. Smokers had also significantly poor dental attendance and poor oral hygiene habits.</p> <p>Conclusion</p> <p>This study confirmed the spreading narghile epidemic among young people in Jordan like the neighboring countries of the Eastern Mediterranean region. Alarming signs were the poor oral health awareness among students particularly smokers.</p