Globally, many research works are going on to study the infectious nature of COVID-19 and every day we learn something new about it through the flooding of the huge data that are accumulating hourly rather than daily which instantly opens hot research avenues for artificial intelligence researchers. However, the public’s concern by now is to find answers for two questions; (1) When this COVID-19 pandemic will be over? and (2) After coming to its end, will COVID-19 return again in what is known as a second rebound of the pandemic? In this work, we developed a predictive model that can estimate the expected period that the virus can be stopped and the risk of the second rebound of COVID-19 pandemic. Therefore, we have considered the SARIMA model to predict the spread of the virus on several selected countries and used it for predicting the COVID-19 pandemic life cycle and its end. The study can be applied to predict the same for other countries as the nature of the virus is the same everywhere. The proposed model investigates the statistical estimation of the slowdown period of the pandemic which is extracted based on the concept of normal distribution. The advantages of this study are that it can help governments to act and make sound decisions and plan for future so that the anxiety of the people can be minimized and prepare the mentality of people for the next phases of the pandemic. Based on the experimental results and simulation, the most striking finding is that the proposed algorithm shows the expected COVID-19 infections for the top countries of the highest number of confirmed cases will be manifested between Dec-2020 and Apr-2021. Moreover, our study forecasts that there may be a second rebound of the pandemic in a year time if the currently taken precautions are eased completely. We have to consider the uncertain nature of the current COVID-19 pandemic and the growing inter-connected and complex world, that are ultimately demanding flexibility, robustness and resilience to cope with the unexpected future events and scenarios.

The Internet of Things (IoT) is revolutionizing industries by enabling seamless interconnectivity across domains such as healthcare, smart cities, the Industrial Internet of Things (IIoT), and the Internet of Vehicles (IoV). However, IoT security remains a significant challenge due to vulnerabilities related to data breaches, privacy concerns, cyber threats, and trust management issues. Addressing these risks requires advanced security mechanisms, with machine learning (ML) emerging as a powerful tool for anomaly detection, intrusion detection, and threat mitigation. This survey provides a comprehensive review of ML-driven IoT security solutions from 2020 to 2024, examining the effectiveness of supervised, unsupervised, and reinforcement learning approaches, as well as advanced techniques such as deep learning (DL), ensemble learning (EL), federated learning (FL), and transfer learning (TL). A systematic classification of ML techniques is presented based on their IoT security applications, along with a taxonomy of security threats and a critical evaluation of existing solutions in terms of scalability, computational efficiency, and privacy preservation. Additionally, this study identifies key limitations of current ML approaches, including high computational costs, adversarial vulnerabilities, and interpretability challenges, while outlining future research opportunities such as privacy-preserving ML, explainable AI, and edge-based security frameworks. By synthesizing insights from recent advancements, this paper provides a structured framework for developing robust, intelligent, and adaptive IoT security solutions. The findings aim to guide researchers and practitioners in designing next-generation cybersecurity models capable of effectively countering emerging threats in IoT ecosystems.

Hate speech towards a group or an individual based on their perceived identity, such as ethnicity, religion, or nationality, is widely and rapidly spreading on social media platforms. This causes harmful impacts on users of these platforms and the quality of online shared content. Fortunately, researchers have developed different machine learning algorithms to automatically detect hate speech on social media platforms. However, most of these algorithms focus on the detection of hate speech that appears in English. There is a lack of studies on the detection of hate speech in Arabic due to the language’s complex nature. This paper aims to address this issue by proposing an effective approach for detecting Arabic hate speech on social media platforms, namely Twitter. Therefore, this paper introduces the Arabic BERT-Mini Model (ABMM) to identify hate speech on social media. More specifically, the bidirectional encoder representations from transformers (BERT) model was employed to analyze data collected from Twitter and classify the results into three categories: normal, abuse, and hate speech. In order to evaluate our model and state-of-the-art approaches, we conducted a series of experiments on Twitter data. In comparison with previous works on Arabic hate-speech detection, the ABMM model shows very promising results with an accuracy score of 0.986 compared to the other models.

Background/Objectives: Automatic classification of ECG signal arrhythmias plays a vital role in early cardiovascular diagnostics by enabling prompt detection of life-threatening conditions. Manual ECG interpretation is labor-intensive and susceptible to errors, highlighting the demand for automated, scalable approaches. Deep learning (DL) methods are effective in ECG analysis due to their ability to learn complex patterns from raw signals. Methods: This study introduces two models: a custom convolutional neural network (CNN) with a dual-branch architecture for processing ECG signals and demographic data (e.g., age, gender), and a modified VGG16 model adapted for multi-branch input. Using the PTB-XL dataset, a widely adopted large-scale ECG database with over 20,000 recordings, the models were evaluated on binary, multiclass, and subclass classification tasks across 2, 5, 10, and 15 disease categories. Advanced preprocessing techniques, combined with demographic features, significantly enhanced performance. Results: The CNN model achieved up to 97.78% accuracy in binary classification and 79.7% in multiclass tasks, outperforming the VGG16 model (97.38% and 76.53%, respectively) and state-of-the-art benchmarks like CNN-LSTM and CNN entropy features. This study also emphasizes interpretability, providing lead-specific insights into ECG contributions to promote clinical transparency. Conclusions: These results confirm the models’ potential for accurate, explainable arrhythmia detection and their applicability in real-world healthcare diagnostics.

A neuro-developmental disorder known as autism spectrum disorder (ASD) affects a significant portion of the global population. Those with ASD frequently struggle to interact and communicate with others and may engage in restricted or repetitive behaviors or interests. The symptoms of autism begin early in childhood and can continue into adulthood. Machine learning and deep learning (DL) models are employed in clinical research for the early identification and diagnosis of ASD. However, the majority of the existing models lack interpretability in their results for ASD diagnosis. The explainable artificial intelligence (XAI) concepts can be used to provide transparent and understandable explanations for models’ decisions. In this work, we present an explainable autism spectrum disorder model based on DL for autism disorder detection in toddlers and children. The primary objective of this study is to better understand and interpret the classification process and to discern the significant features that contribute to the prediction of ASD. The proposed model is divided into two distinct components. The first component employs a DL model for autism disorder detection. The second uses an XAI technique known as shapley additive explanations (SHAP) to emphasis key characteristics and explain the model’s outcomes. The model showed perfect performance on the training set, with an accuracy of 1 and a receiver operating characteristic score of 1. On the test set, the model achieved an accuracy score of 0.9886, indicating that it performed nearly as well as on the training set. The experimental results demonstrate that the proposed model has the capability to accurately predict and diagnose ASD while also providing explanatory insights into the obtained results. Furthermore, the results indicate that the proposed model performs competitively compared to the state-of-the-art models in terms of accuracy and F1-score. The results highlight the efficacy and potential of the proposed model in accurately predicting ASD in binary classification tasks.