Sea level rise (SLR) is one of the most pressing challenges of climate change and has drawn noticeable research interest over the past few decades. Factors induced by global climate change, such as temperature increase, have resulted in both direct and indirect changes in sea levels at different spatial scales. Various climatic and non-climatic events contribute to sea level changes, posing risks to coastal and low-lying areas. Nevertheless, changes in sea level are not uniformly distributed globally due to several regional factors such as wave actions, storm surge frequencies, and tectonic land movement. The high exposure to those factors increases the vulnerability of subjected areas to SLR impacts. The impacts of events induced by climate change and SLR are reflected in biophysical, socioeconomic, and environmental aspects. Different indicator-based and model-based approaches are used to assess coastal areas’ vulnerabilities, response to impacts, and implementation of adaptation and mitigation measures. Various studies have been conducted to project future SLR impacts and evaluate implemented protection and adaptation approaches, aiding policymakers in planning effective adaptation and mitigation measures to reduce damage. This paper provides an overview of SLR and its key elements, encompassing contributing factors, impacts, and mitigation and adaptation measures, featuring a dedicated section on the Arabian Gulf, a semi-enclosed sea.

Global climate change in recent years has resulted in significant changes in sea levels at both global and local scales. Various oceanic and climatic factors play direct and indirect roles in influencing sea level changes, such as temperature, ocean heat, and Greenhouse gases (GHG) emissions. This study examined time series analysis models, specifically Autoregressive Moving Average (ARIMA) and Facebook’s prophet, in forecasting the Global Mean Sea Level (GMSL). Additionally, Vector Autoregressive (VAR) model was utilized to investigate the influence of selected oceanic and climatic factors contributing to sea level rise, including ocean heat, air temperature, and GHG emissions. Moreover, the models were applied to regional sea level data from the Arabian Gulf, which experienced higher fluctuations compared to GMSL. Results showed the capability of autoregressive models in long-term forecasting, while the Prophet model excelled in capturing trends and patterns in the time series over extended periods of time.

This study introduces an innovative all-in-one malware identification model that significantly enhances convenience and resource efficiency in classifying malware across diverse file types. Traditional malware identification methods involve the extraction of static and dynamic features, followed by comparisons with signature-based databases or machine learning-based classifiers. However, many malware detection applications that rely on transfer learning and image transformation suffer from excessive resource consumption. In recent years, transfer learning has emerged as a powerful tool for developing effective classifiers, leveraging pre-trained neural network models. In this research, we comprehensively explore various pre-trained network architectures, including compact and conventional networks, as well as series and directed acyclic graph configurations for malware classification. Our approach utilizes grayscale transform-based features as a standardized set of characteristics, streamlining malware classification across various file types. To ensure the robustness and generalization of our classification models, we integrate multiple datasets into the training process. Remarkably, we achieve an optimal model with 96% accuracy, while maintaining a modest 5 MB size using the SqueezeNet classifier. Overall, our model efficiently classifies malware across file types, reducing the computational load, which can be useful for cybersecurity professionals and organizations.

Climate change has significantly impacted vulnerable communities globally, with rising temperatures caused by greenhouse gas emissions accelerating global Sea Level Rise (SLR), threatening coastal infrastructure and ecosystems. This study evaluates statistical and deep learning models, including the Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) networks, for predicting SLR and visualizing potentially inundated areas in the United Arab Emirates (UAE) via an interactive web interface. Historical mean sea level (MSL) data from the National Oceanic and Atmospheric Administration (NOAA), spanning 1992 to 2024, were used for training and model evaluation. An interactive web platform was developed to visualize forecasted inundation areas and support decision-making. The enhanced LSTM model, integrated with a Squeeze-and-Excitation (SE) block, achieved the highest accuracy, with a Root Mean Square Error (RMSE) of 2.27, representing an improvement of 8.81% over the standalone LSTM (RMSE 2.47) and 13.66% over ARIMA (RMSE 2.58). The model forecasts sea level changes up to 2100, highlighting critical risks for low-lying coastal regions such as Umm Al Quwain, Abu Dhabi, and Dubai. The findings underscore the value of advanced AI-driven forecasting in enhancing climate resilience, assisting policymakers and urban planners in risk assessment, optimizing emergency response strategies, and implementing coastal adaptation measures. Future work should integrate additional environmental factors influencing MSL.

Sea Level Rise (SLR) is one of the most pressing challenges of climate change and has drawn noticeable research interest over the past few decades. Factors induced by global climate change such as temperature increase, have resulted in both direct and indirect changes in sea levels at different spatial scales. A number of climatic and non-climatic events drive the change in sea level and impose risk on coastal and low-lying areas. Nevertheless, changes in sea level are not uniformly distributed globally due to a number of regional factors such as wave actions, storm surge frequencies, and tectonic land movement. The high exposure to those factors increases the vulnerability of subjected areas to SLR impacts. The impacts of events induced by climate change and SLR are reflected in biophysical, socioeconomic, and environmental aspects. Different indicator-based and model-based approaches are used to assess coastal areas' vulnerabilities, response to impacts, and implementation of adaptation and mitigation measures. Various studies were made to project future SLR impacts and evaluate implemented protection and adaptation approaches to help policymakers plan effective adaptation and mitigation measures and reduce damage. This paper provides an overview of SLR and its key elements encompassing contributing factors, impacts, and mitigation and adaptation measures with a case study focus on the Arabian Gulf.