The most widely used method for detecting Coronavirus Disease 2019 (COVID-19) is real-time polymerase chain reaction. However, this method has several drawbacks, including high cost, lengthy turnaround time for results, and the potential for false-negative results due to limited sensitivity. To address these issues, additional technologies such as computed tomography (CT) or X-rays have been employed for diagnosing the disease. Chest X-rays are more commonly used than CT scans due to the widespread availability of X-ray machines, lower ionizing radiation, and lower cost of equipment. COVID-19 presents certain radiological biomarkers that can be observed through chest X-rays, making it necessary for radiologists to manually search for these biomarkers. However, this process is time-consuming and prone to errors. Therefore, there is a critical need to develop an automated system for evaluating chest X-rays. Deep learning techniques can be employed to expedite this process. In this study, a deep learning-based method called Custom Convolutional Neural Network (Custom-CNN) is proposed for identifying COVID-19 infection in chest X-rays. The Custom-CNN model consists of eight weighted layers and utilizes strategies like dropout and batch normalization to enhance performance and reduce overfitting. The proposed approach achieved a classification accuracy of 98.19% and aims to accurately classify COVID-19, normal, and pneumonia samples.

Accurate very short-term wind power forecasting is critical for the reliable integration of renewable energy into modern power systems. However, the inherent variability and non-linearity of wind power data pose significant challenges. To address these, this study proposes a novel hybrid deep learning framework, IAPO-LSTM, which combines Convolutional Neural Networks (CNNs) for spatial feature extraction and Gated Recurrent Units (GRUs) for temporal sequence modeling. The model is optimized using an enhanced Artificial Protozoa Optimizer (IAPO) augmented with an Adaptive Environmental Response Mechanism (AERM), which dynamically adjusts exploration and exploitation strategies based on the problem landscape to improve convergence and hyperparameter tuning efficiency. The proposed IAPO-LSTM model was evaluated on four real-world datasets—NREL WIND, EMD WIND, WWSIS, and ERCOT GRID—and benchmarked against six state-of-the-art forecasting models. Results demonstrate that IAPO-LSTM achieved the lowest forecasting errors across all datasets, with Mean Absolute Error (MAE) as low as 2.78, Root Mean Square Error (RMSE) of 4.50, and Theil’s Inequality Coefficient (TIC) of 0.0292 on the ERCOT dataset. Additionally, the model demonstrated faster inference times and better statistical significance (p < 0.005) compared to baseline methods. These outcomes confirm that IAPO-LSTM is not only highly accurate but also efficient and robust for real-time wind power forecasting applications.

Wildfires pose significant challenges globally, including in Iran. This study analyzes wildfire occurrences in Iran from 2001 to 2022, using NASA FIRMS’ active fire detections MCD14DL data. To enhance the reliability of this satellite-based dataset, particularly in data-scarce regions like Iran, we applied a multi-sensor cross-validation framework before modeling. We also aim to examine the country’s wildfire dynamics over two decades, employing k-means clustering to categorize wildfires into ten clusters, delineating fire zones. Two random forest regression models explore the relationships between annual CO ₂ emissions, indicative of human activities, and average temperature, a proxy for climate variability, with wildfire occurrence. Our findings reveal a notable escalation in the frequency and intensity of wildfires across Iran during the study period. Specifically, the western and southwest regions, designated as Zone 05, emerge as highly affected areas, recording 162,734 fires despite their smaller size. The years 2015–2018 stand out as critical, marked by heightened wildfire activity and rapid annual fluctuations. Interestingly, the regression analysis shows a strong correlation between CO ₂ emissions and wildfire activity, which highlights the significant influence of human activities. In contrast, the weaker link with the average temperature suggests that climate variability plays a comparatively smaller role in shaping wildfire patterns in Iran during the study period . This study provides insights into Iran’s wildfire patterns, revealing that the wildfire regime in Iran is evolving mainly through event frequency rather than fire intensity. These results emphasize the need for stakeholders to understand these dynamics thoroughly for effective mitigation strategies against the environmental and economic challenges posed by wildfires in the region.

OAuth2.0 is a Single Sign-On (SSO) approach that helps to authenticate and authorize users to log into multiple applications without re-entering the credentials. In this model, the data are stored in a central repository completely controlled by the OAuth service provider, which may lead to third-party fraud and identity theft. This paper proposes a decentralized authentication framework comprised of a blockchain-based decentralized identifier and private distributed storage via an interplanetary filesystem (IPFS), permitting the user to have end-to-end data control. Furthermore, we have added a secret sharing mechanism that allows secure storage of data in our proposed model. We implemented our proposed framework in Hyperledger Indy(permissioned blockchain) and Ethereum (permissionless blockchain) to compare the efficiency, scalability, and privacy of the data stored in the blockchain ledger.