AI-driven precision agriculture applications can benefit from the large data source that remote sensing provides, as it can gather agricultural monitoring data at various scales throughout the year. Numerous advantages for sustainable agricultural applications, including yield prediction, crop monitoring, and climate change adaptation, can be obtained from remote sensing and artificial intelligence. In this work, we proposed a fully automated Optimized Self-Attention Fused Convolutional Neural Network (CNN) architecture for land use and land cover classification using remote sensing (RS) data. A new contrast enhancement equation has been proposed and utilized in the proposed architecture for the data augmentation. After that, a fused Self-Attention CNN architecture was proposed. The proposed architecture initially consists of two custom models named IBNR-65 and Densenet-64. Both models have been designed based on the inverted bottleneck residual mechanism and Dense Blocks. After that, both models were fused using a depth-wise concatenation and append a Self-Attention layer for deep features extraction. After that, we trained the model and performed classification using neural network (NN) classifiers. The results obtained from the NN classifiers are insufficient; therefore, we implemented a Bayesian Optimization and fine-tuned the hyperparameters of NN. In addition, we proposed a Quantum Hippopotamus Optimization Algorithm for the best feature selection. The selected features are finally classified using fine-tuned NN classifiers and obtained improved accuracy of 98.20, 89.50, and 91.70%, and the highest precision rate is 98.23, recall is 98.20, and f1-score is 98.21 respectively, for SIRI-WHU, EuroSAT, and NWPU datasets. Moreover, a detailed ablation study was conducted, and the performance was compared with SOTA. The proposed architecture shows improved accuracy, sensitivity, precision and computational time performance.

Over the past few decades, credit scoring has become an important tool in the financial sector. It enables banks and financial institutions to assess the creditworthiness of individuals and reduce the risk of default. As a result of significant advances in artificial intelligence techniques. Machine learning (ML) has made it possible to improve credit scoring by distinguishing between people with good creditworthiness and those with poorer creditworthiness. In this article, we propose a systematic literature review of ML-based financial credit scoring methods published between 2018 and 2024. A total of 330 research papers were extracted from four different online databases and digital libraries. After the study selection procedure, 63 research papers were selected for this systematic review. This paper aims to identify the major ML methods used in credit scoring, assess their strengths and limitations, and highlight notable trends and advancements. In addition, the review addresses the critical challenges faced in the adoption of ML models for credit scoring. This study not only contributes to the understanding of effective ML techniques used for credit scoring but also guides future research by highlighting the promising avenues in ML-based credit scoring efforts.

Background Phishing attacks pose a significant threat to Industrial Internet of Things (IoT) networks, with a documented 350% increase in IoT-targeted phishing attempts since 2020. This alarming trend necessitates robust security measures to protect critical infrastructure. Methods We propose a novel methodology integrating a CNN-LSTM model for initial phishing detection and a sophisticated LLM for comprehensive analysis. Our approach combines spatial feature extraction via CNN layers with temporal pattern recognition through LSTM, enhanced by explainable AI techniques for transparency. Results Our CNN-LSTM model achieves 93.26% accuracy on the Web URL dataset and the DistilBERT model achieves an accuracy of 98.64% on text and email dataset. The addition of explainable AI improves transparency by 41% compared to black-box approaches, which is crucial for IoT security operations. Conclusion This approach not only improves phishing detection accuracy but also contributes to adaptive security measures tailored to evolving cybersecurity threats. By reducing false positives by 41% compared to traditional methods and providing human-interpretable security insights through LLM integration, our framework ensures the resilience of IoT networks while maintaining operational efficiency.

The dynamic Metaverse, a hybrid of virtual reality and immersive simulations, has transcended its origins in gaming and has become ingrained in many facets of everyday life. This innovation has an interesting prospect for the integration of the Metaverse, as it is affecting the intricate sector of intelligent transportation systems. However, as there are a variety of implementation strategies in this ecosystem, there are intricate roadblocks that must be carefully considered while making decisions. In this paper, the Pythagorean fuzzy (PyF) with CRITIC-AROMAN evaluation model provides cutting-edge for comprehensively rank alternative implementation approaches. CRITIC (criteria importance through intercriteria correlation) is one of the decision-making models that is used to find importance of each criterion within the context of Metaverse integration. This abstract, which emphasizes of the PyF with CRITIC-AROMANmodel and the critical role of CRITIC in enhancing decision-making precision, provides a major new contribution to the subject. With intelligent transportation systems playing a major role in the Metaverse movement, this research offers policymakers crucial insights into this field. Through the complex web of human experiences, it seeks to assist people in making informed decisions.

In our data-driven world, the healthcare sector faces significant challenges in the early detection and management of Non-Communicable Diseases (NCDs). The COVID-19 pandemic has further emphasized the need for effective tools to predict and treat NCDs, especially in individuals at risk. This research addresses these pressing concerns by proposing a comprehensive framework that combines advanced data mining techniques, feature selection, and meta-heuristic optimization. The proposed framework introduces novel hybrid algorithms, including the Hierarchical Genetic Multiple Reduct Selection Algorithm (H-GMRA) and the Customized Function-based Particle Swarm Optimization with Rough Set Theory for NCD Feature Selection (CPSO-RST-NFS). These algorithms aim to address the challenges of feature selection, computational complexity, and disease classification accuracy. H-GMRA outperforms traditional methods by identifying minimal feature sets with high dependency ratios. CPSO-RST-NFS combines meta-heuristic optimization with feature selection, resulting in improved efficiency and accuracy. Through extensive experimentation on diverse NCD datasets, this research demonstrates the framework’s ability to select informative features, improve classification accuracy, and contribute to better patient outcomes. By bridging the gap between computational efficiency and disease classification accuracy, this work offers valuable insights for healthcare practitioners and data analysts, ultimately advancing the field of NCD research. The proposed framework presents a significant step towards enhancing the early detection and management of NCDs, offering hope for more precise clinical predictions and improved patient care.

Maternal health risk prediction is a critical aspect of public health. This paper proposes a new parameter optimization method to improve maternal health risk prediction using the Mud Ring Algorithm (MRA). The proposed method is implemented in two stages to achieve the desired improvement. In the first stage, the MRA is used to optimize the parameter of Support Vector Machine (SVM) classifier. The MRA-optimized SVM (MRA-SVM) is then evaluated on thirteen real-world datasets to compare its performance with other state-of-the-art optimization algorithms. Subsequently, the performance of the MRA-SVM is specifically compared using a maternal health risk dataset. The maternal health risk dataset, being a medical dataset, faces a significant issue of imbalance. To address this problem, the second stage comes in turn, and the crossover oversampling technique is first employed. Furthermore, other classifiers such as Random Forest and K-Nearest Neighbor are also used for improving the prediction of the maternal health risk dataset. Experimental results indicate that our proposed method delivers highly competitive results compared to six well-known optimization algorithms, evaluated based on Accuracy, G-mean, F-Measure, MCC and Kappa metrics. Moreover, the results demonstrate that using a crossover oversampling method as a preprocessing step, along with MRA to optimize the parameters of SVM (MRA-SVM), Random Forest (MRA-RF), and K-Nearest Neighbor (MRA-KNN), increases prediction accuracy of maternal health risk dataset by 11.8%, 9.11%, and 17.08% respectively, compared to using the three classifiers without MRA and crossover oversampling. In sum, utilizing the crossover oversampling method, combined with the MRA to optimize the parameter of Random Forest classifier, leads to higher prediction performance compared to other recently published algorithms.