Amal Elsayed Aboutabl’s research while affiliated with Helwan University and other places

The broad availability and use of the internet have dramatically amplified the already existing problem of false information, particularly when it comes to health-related news. This phenomenon became a major concern during the COVID-19 pandemic, where false and fraudulent information spread rapidly, posing a significant threat to public health. The COVID-19 pandemic served as a powerful illustration of this phenomenon, as false and fraudulent health information spread rapidly across online platforms, creating confusion and jeopardizing public health efforts. This highlights the urgent need for solutions to address the spread of fake information, particularly in the area of health, where the consequences of false information can have a direct and potentially devastating impact on individuals and communities. To solve this issue, we propose an ensemble and stacking model that leverages Support Vector Machines (SVM), Arabic Bidirectional Encoder Representations from Transformers (AraBERT), Bidirectional Long Short-Term Memory (Bi-LSTM), and Logistic Regression (LR). Our model leverages the strengths of different machine learning techniques to mitigate the spread of harmful false information. To tackle this issue, we use the largest manually annotated Arabic dataset, ArCovidVac, focusing on the COVID-19 vaccination discourse, and we leverage an additional dataset for COVID-19 to generate additional training data for our fake news detection model. The experimental results demonstrate that our framework performs better than previous state-of-the-art approaches. Our proposed model achieved an accuracy, exceeding the performance of AraBERT.

In recent years, more people have been using the internet and social media to express their opinions on various subjects, such as institutions, services, or specific ideas. This increase highlights the importance of developing automated tools for accurate sentiment analysis. Moreover, addressing sarcasm in text is crucial, as it can significantly impact the efficacy of sentiment analysis models. This paper aims to provide a comprehensive overview of the conducted research on sentiment analysis and sarcasm detection, focusing on the time from 2018 to 2023. It explores the challenges faced and the methods used to address them. It conducts a comparison of these methods. It also aims to identify emerging trends that will likely influence the future of sentiment analysis and sarcasm detection, ensuring their continued effectiveness. This paper enhances the existing knowledge by offering a comprehensive analysis of 40 research works, evaluating performance, addressing multilingual challenges, and highlighting future trends in sarcasm detection and sentiment analysis. It is a valuable resource for researchers and experts interested in the field, facilitating further advancements in sentiment analysis techniques and applications. It categorizes sentiment analysis methods into ML, lexical, and hybrid approaches, highlighting deep learning, especially Recurrent Neural Networks (RNNs), for effective textual classification with labeled or unlabeled data.

Convolutional Neural Networks (CNN) with different architectures have shown promising results in skin cancer diagnosis. However, CNN has a high computational cost, which makes the need for a light version of CNN a desirable step. This version can be used on small devices, such as mobile phones or tablets. A light version can be created using pruning techniques. In this study, iterative magnitude pruning (IMP) is utilized. This method depends on pruning the network iteratively. The IMP method is applied on AlexNet with transfer learning (TL) and data augmentation. The proposed IMP AlexNet with TL is applied on three different skin cancer datasets which are PAD-UFES-20, MED-NODE, and PH2 dataset. The datasets used are a combination of smartphone, dermoscopic, and non-dermoscopic images. Different CNN versions are applied on the same datasets for comparison with IMP AlexNet. The CNNs used are VGG-16, ShuffleNet, SqueezNet, DarkNet-19, DarkNet-53, and Inception-v3. The proposed IMP AlexNet achieved accuracies of 97.62%, 96.79%, and 96.75%, with accuracy losses of 1.53%, 2.3%, and 2.2%, respectively, compared to the original AlexNet. In addition, the proposed IMP AlexNet requires less running time and memory usage than the traditional AlexNet. The average running time for IMP AlexNet is 0.45 min, 0.28 min, and 0.3 min, for PAD-UFES-20, MED-NODE, and PH2 datasets, respectively. The average RAM usage with IMP AlexNet is 1.8 GB, 1.6 GB, and 1.7 GB, respectively. IMP AlexNet accelerates the average running time by approximately 15 times that of the traditional AlexNet and reduces the average RAM used by 40%.

span>Ongoing loan fraud is a source of concern for financial institutions, as it has a direct financial impact and also scares off customers. This pattern, which can be traced to the development of modern technology, the introduction of novel ideas, and the quickening pace of international connections, makes the detection of fraud an expensive endeavour. This article proposes a novel framework for enhancing the fraud detection of loan banking using data mining algorithms. The framework extracts a number of predictive analysis techniques for identifying loan fraud. Several methods employing a wide range of pipeline architectures have been tried in order to select the optimal champion model. Autotuning has also been used to find the best possible setting for the model’s hyperparameters. The results of the evaluation show that autoencoder with gradient boosting outperformed the other classification algorithms with an accuracy of 98.62%. The proposed framework has the potential to significantly improve the fraud detection process of loan banking, which can ultimately lead to better faster fraud detects rates by combining data mining techniques with dimensionality reduction strategies in the feature space.</span

Cyberbullying has spread like a virus on social media platforms and is getting out of control. According to psychological studies on the subject, the victims are increasingly suffering, sometimes to the point of committing suicide among the victims. The issue of cyberbullying on social media is spreading around the world. Social media use is growing, and it can have useful and negative implications when you take into account how social media platforms are abused through different forms of cyberbullying. Although there is a lot of cyberbullying detection in English, there are few studies in the Arabic language. Data Mining techniques are often used to solve and detect this problem. In this study, different data mining algorithms were used to detect cyberbullying in Arabic texts.. Our study was conducted The Bullying datasets consisted of 26,000 comments written in Arabic and were collected from kaggle.com, the Cyber_2021 dataset consisted of 13,247 comments collected via github.com, and the Data 2022 dataset consisted of 47,224 comments collected via Instagram. Various extraction features CountVectorizer and Tf-Idf were used Accuracy, precision, recall, and the F1 score were used to evaluate classifier performance. In the study, Bagging Classifier achieve high results of Bullying dataset from Kaggle Accuracy 96.04, F1-Score 95.98, Recall 96.04, Precision 95.95, SVC model gave the highest results of Cyber_2021 dataset from Github an Accuracy 98.49, F1-Score 98.49, Recall 98.49, Precision 98.50, while Data 2022 dataset from (Instagram) achieving an Accuracy of 77.51, F1-Score 76.60, Recall 77.51, and Precision 77.24. Were achieved for Tf-Idf Vectorizer. Tf-Idf Vectorizer the best to all results than count Vectorizer .

Incorrect snake identification from the observable visual traits is a major reason of death resulting from snake bites. The classification of snake species has a significant role in determining the appropriate treatment without any delay, the delay may cause dangerous complications or lead to the death of the victim. The difficulty of classifying snakes by human lies in the variations of snake pattern based on geographic variation and age, the intraclass variance is high for specific classes and the interclass variance is low among others, and there may be two remarkably similar types in shape, with one being toxic and the other not. The limitation of the experts’ number in the herpetology and their geographical distribution leads us to the importance of using deep learning in the snake species classification. A model to classify snake species accately is proposed in this study. It is divided into two main processes, detecting the salient object by applying Salient Object Detection (SOD) model based on VGG16 architecture is the first process, the presence of snakes in places with a complex background led to the necessity of separating the salient object, then the classification model is applied with use of image augmentations parameters which improved the results. Four CNN models were used in the classification process including VGG16, ResNet50, MobileNetV2, and DenseNet121. Different experiments on 5,10,16,20, 22, and 45 number of classes and different models were conducted, and the model achieved unprecedented results. The results indicated that the VGG16, DenseNet121, and MobileNetV2 have achieved superior results in the same order from highest to lowest accuracy. The best accuracy is achieved using VGG16 architecture with accuracy 97.09% when using 45 number of classes.