Sheraz Aslam

Topological indices provide numerical descriptions of graph structure and support graph analysis in cheminformatics, network design, and graph mining. This study presents a reproducible computational framework that combines controlled cyclic-graph generation, structure-preserving transformations, exact computation of six classical topological indices, and multi-output graph neural network regression. The framework evaluates the Wiener, Merrifield–Simmons, Hosoya, first Zagreb, second Zagreb, and Randi’c indices for unicyclic and bicyclic graphs. It represents each graph using sparse connectivity and node-level features that encode degree, cycle membership, pendant connectivity, leaf status, and normalized eccentricity. A graph isomorphism network (GIN) jointly predicts the six indices and is compared with graph convolutional networks (GCNs), graph attention networks (GATs), and descriptor-based regression baselines. The controlled benchmark shows that nonlinear descriptor-based models achieve the lowest aggregate errors because the supplied graph-level descriptors contain strong prior information about graph size, degree structure, branching, and cycle complexity. Although GIN does not achieve the highest overall accuracy, it provides the strongest graph-native performance by learning directly from sparse connectivity and node-level features without requiring a fixed handcrafted graph-level descriptor vector. The proposed surrogate does not replace exact evaluation for isolated small graphs, where exact computation remains more appropriate. Instead, its practical value emerges through repeated evaluations of larger, more complex graph instances. To examine this setting, a computational stress experiment evaluates sparse multicyclic graphs under increasing cyclomatic complexity and measures exact computation time, timeout frequency, prediction accuracy, and the amortized break-even point. The results indicate that surrogate prediction becomes beneficial when combinatorial index computation becomes sufficiently expensive, and the trained model is reused across many structurally related graph queries. An external experiment on circulant graphs also demonstrates that the framework can extend beyond the original graph generators by modifying only the graph-construction stage.

Accurate, up to date, and quick information related to any disaster supports disaster management team/authorities to perform quick, easy, and cost-effective response to enhance rescue operations to alleviate the possible loss of lives, financial risks, and properties. Due to damaged infrastructure in disaster-affected areas, social media is the only way to share/ exchange real time information. Therefore, ‘X’ (formerly Twitter) has become a major platform for disseminating real-time information during disaster events or emergencies, i.e., floods and earthquake. Rapid identification of actionable content is critical for effective humanitarian response; however, the brief and noisy nature of tweets makes automated classification challenging. To tackle this problem, this study proposes a hybrid classification framework that integrates term frequency–inverse document frequency (TF-IDF) features with graph convolutional networks (GCNs) to enhance disaster-related tweet analysis. The proposed model performs three classification tasks: identifying disaster-related tweets (achieving 94.47% accuracy), categorizing disaster types (earthquake, flood, and non-disaster) with 91.78% accuracy, and detecting aid requests such as food, donations, and medical assistance (94.64% accuracy). By combining the statistical strengths of TF-IDF with the relational learning capabilities of GCNs, the model attains high accuracy while maintaining computational efficiency and interpretability. The results demonstrate the framework’s strong potential for real-time disaster response, offering valuable insights to support emergency management systems and humanitarian decision-making.

This correction addresses an issue identified in the original publication regarding the citation of references in the statement about Iran’s electricity consumption trend

Maritime transportation plays a critical role in global containerized cargo logistics, with seaports serving as key nodes in this system. Ports are responsible for container loading and unloading, along with inspection, storage, and timely delivery to the destination, all of which heavily depend on the performance of the container handling equipment (CHE). Inefficient maintenance strategies and unplanned maintenance of the port equipment can lead to operational disruptions, including unexpected delays and long waiting times in the supply chain. Therefore, the maritime industry must adopt intelligent maintenance strategies at the port to optimize operational efficiency and resource utilization. Towards this end, this study presents a machine learning (ML)-based approach for predicting faults in CHE to improve equipment reliability and overall port performance. Firstly, a statistical model was developed to check the status and health of the hydraulic system, as it is crucial for the operation of the machines. Then, several ML models were developed, including artificial neural networks (ANNs), decision trees (DTs), random forest (RF), Extreme Gradient Boosting (XGBoost), and Gaussian Naive Bayes (GNB) to predict inverter over-temperature faults due to fan failures, clogged filters, and other related issues. From the tested models, the ANNs achieved the highest performance in predicting the specific faults with a 98.7% accuracy and 98.0% F1-score.

Haze-induced image degradation in outdoor scenes, caused by atmospheric scattering and absorption, poses significant challenges for applications such as surveillance, autonomous navigation, and remote sensing. This research focuses on improving image dehazing by enhancing two key components: Dark Channel Prior (DCP) and Atmospheric Light Estimation (ALE). DCP exploits the statistical property that haze-free regions exhibit low minimum intensity values, aiding in accurate haze detection. ALE estimates atmospheric light from distant, less-scattered sources, ensuring precise haze removal. To further enhance dehazing performance, this study introduces a Minimum Fusion Technique, which selects the minimum intensity value for each pixel across multiple dehazed outputs, effectively preserving fine details and reducing artifacts. The proposed method is evaluated on the SOTS Outdoor dataset from the RESIDE benchmark, comprising 500 pairs of real-world hazy and clear images. By integrating optimized DCP, ALE, and the Minimum Fusion Technique, the approach achieves superior performance with a PSNR of 35.45 dB and an SSIM of 0.97, outperforming existing methods such as Scene-Specific DCP, TON, and FFA_NET. Additionally, it demonstrates reduced execution time and enhanced visual quality, making it a more efficient and reliable solution for real-world dehazing applications. These improvements establish the proposed method as a state-of-the-art approach for restoring clarity in outdoor images, contributing to more effective and accurate image reconstruction in challenging environments.

Maritime transportation plays a critical role in global containerized cargo logistics, with seaports serving as key nodes in this system. Ports are responsible for container loading and unloading along with inspection, storage, and timely delivery to the destination, all of which heavily depend on the performance of the container handling equipment (CHE). Inefficient maintenance strategies and unplanned maintenance of the port equipment can lead to operational disruptions, including unexpected delays and long waiting times in the supply chain. Therefore, the maritime industry must adopt intelligent maintenance strategies at the port to optimize operational efficiency and resource utilization. Towards this end, this study presents a machine learning (ML) based approach for predicting faults in CHE to improve equipment reliability and overall port performance. Firstly, a statistical model was developed to check the status and health of the hydraulic system, as it is crucial for the operation of the machines. Then, several ML models were developed, including artificial neural networks (ANNs), decision trees (DTs), random forest (RF), Extreme Gradient Boosting (XGBoost), and Gaussian naive bayes (GNB) to predict inverter over-temperature faults due to fan failures, clogged filters, and other related issues. From the tested models, the ANNs achieved the highest performance in predicting the specific faults with a 98.7% accuracy and 98.0% F1-score.