Sixth Generation (6G) wireless networks with ultra-low latency, high reliability, and massive connectivity require intelligent and privacy-concerned infrastructure optimization. This work presents a federated autoencoder platform combined with Explainable AI (XAI) for performance optimization of 6G-IoT systems. The method integrates traditional machine learning algorithms (Decision Tree, Random Forest, Logistic Regression, AdaBoost, Gradient Boosting) with a Variational Autoencoder (VAE) for dimensionality reduction and feature extraction. Federated Learning (FL) is utilized to maintain data privacy among distributed edge nodes, and SHAP and LIME explainers are utilized for explaining model decisions at the local and global levels. The framework points out key QoS parameters like latency and throughput as major optimization levers. Experimental outcomes on the 6G-IoT dataset indicate that Random Forest with highest accuracy for 80:20 split and Gradient Boosting has a 99.8% accuracy in a 10-fold validation, and FL gets a ROC-AUC value of 0.999 with robust privacy guarantees. XAI enhances transparency and regulatory compliance by making attribution of predictions to contributing features. As a whole, the proposed approach provides an interpretable, privacy-conscientious, and scalable tool for intelligent 6G-IoT infrastructure management.

Natural disasters are one of the biggest challenges for response operations. Their detection may need advanced and accurate detection technologies. Therefore, a novel UAV‐based multiclass natural disaster classification system with the integration of FusionNet‐4 architecture and water wheel‐guided walrus optimization (WWGWO) algorithm is proposed. The goal is to have a comprehensive and adaptive framework that may be used in identifying and classifying disaster scenarios accurately. The system has six major phases, which include image acquisition, preprocessing, segmentation, feature extraction, feature selection, and classification. The key innovation is the FusionNet‐4 ensemble‐based model, which employs ResNet‐50, DenseNet‐121, VGG‐19, and EfficientNet CNN architectures with the functionalities of multilevel feature extraction to increase the accuracy of disaster classification. The study proposes a method for automated natural disaster classification using UAV imagery, utilizing advanced deep learning and metaheuristic optimization techniques for swift and precise disaster response. Furthermore, an optimized UNet segmentation strategy, fine‐tuned using the hybrid WWGWO algorithm to achieve exploration and exploitation for efficient feature selection and superior segmentation quality, is proposed. Experimental testing on high‐resolution disaster datasets, such as RescueNet and xView2, has validated the proposed model. FusionNet‐4 architecture performs better than conventional CNNs, with an MSE of 0.0135 for an 80:20 training‐to‐testing data‐split ratio at a learning rate of 0.001, giving it better accuracy of 98.93% in classification and adaptability. Optimal feature selection has been ensured through the integration of the WWGWO algorithm, reducing computational complexity and improving overall efficiency.

Abstract The swift societal evolution and ceaseless advancement of human value of life have been set forth for reliability as well as rapidity of railway transportation. Latest advances in machine learning approaches as well as surging accessibility of numerous information sources is produced state‐of‐the‐art probabilities for significant, precise train delay identification. In this method called, Barzilai Borwein Incremental Grey Polynomial Regression (BBI‐GPR) is introduced for predicting train arrival/departure delays, which utilized for later delay management in an accurate manner with this method comprised into three sections such as, pre‐processing, feature selection and classification. First, with the raw ETA train delay dataset as input, Barzilai–Borwein Feature Rescaling‐based Pre‐processing is applied to model computationally efficient feature rescaled and normalized values. Second with processed features as input, Incremental Maximum Relevance Minimum Redundant‐based Feature Selection is applied to select error minimized optimal features. Finally, with optimal features selected as input, Grey Polynomial Regression‐based Prediction algorithm is employed to analyse train delay. For confirming proposed BBI‐GPR, as well as analyse its performance, compare standard train delay prediction method with existing machine learning‐based regression method. Results show that new variants outperform existing train delay prediction method by minimizing train delay prediction time, error rate by 25% and 27% respectively, with improved accuracy rate of 7%, therefore paving ways for efficient train delay prediction.

Cloud computing is a vast revolution in information technology (IT) that inhibits scalable and virtualized sources to end users with low infrastructure cost and maintenance. They also have much flexibility and these resources are supervised by various management organizations and provided over the Internet by known standards, formats, and networking protocols. Legacy protocols and underlying technologies consist of vulnerabilities and bugs which open doors for intrusion by network attackers. Attacks as distributed denial of service (DDoS) are one of most frequent attacks, which impose heavy damage and affect performance of the cloud. In this research work, DDoS attack detection is easily identified in an optimized way through a novel algorithm, namely, the proposed gradient hybrid leader optimization (GHLBO) algorithm. This optimized algorithm is responsible to train a deep stacked autoencoder (DSA) that detects the attack in an efficient manner. Here, fusion of features is carried out by deep maxout network (DMN) with an overlap coefficient, and augmentation of data is carried out by the oversampling process. Furthermore, the proposed GHLBO is generated by integrating the gradient descent and hybrid leader‐based optimization (HLBO) algorithm. Also, this proposed method is assessed by various performance metrics, such as the true positive rate (TPR), true negative rate (TNR), and testing accuracy with values attained as 0.909, 0.909, and 0.917, accordingly.

In today’s world, one of the most severe attacks that wireless sensor networks (WSNs) face is a Black-Hole (BH) attack which is a type of Denial of Service (DoS) attack. This attack blocks data and injects infected programs into a set of sensors in a group to capture packets before reached to the target. Therefore, raw data in the BH region is thwarted and is unable to reach its destination. The network is susceptible to various types of attacks as it is accessible to all types of users and minimizing the energy depletion without compromising the network lifetime is an NP-hard problem. Even though numerous protocols came into effect to overcome the BH attack and to enhance the security of packet delivery in WSNs, Simulated Annealing Black-hole attack Detection (SABD) based Enhanced Gravitational Search Algorithm (EGSA) is yet another implemented strategy to reduce the BH attacks. EGSA-SABD detects and isolates the BH infectors in WSNs. Initially, sensor nodes are hierarchically clustered using similar residual energy to reduce energy consumption. Then, the BH attack possibility in a deployed node is evaluated to find the existence of BH nodes in the region. In the end, EGSA-SABD is employed to detect and quarantine BH attackers in WSNs. The performance of EGSA-SABD is evaluated with certain metrics such as BH attack detection probability rate (BHatt_Prate), energy consumption (Ec), Duration of BH attack detection (Attduration), Packet delivery ratio (Pdr). Based on the experimental observations, the EGSA-SABD outperforms the BHatt_Prate by 13% and also reduces the energy consumption by 21%.