Land Cover (LC) classification is an effective technique that categorizes the Earth's surface into urban, forest, and agricultural land classes by utilizing remote sensing data. Nevertheless, the hyperspectral remote sensing images are afflicted with a lack of labeled data, spectral variability, and the curse of dimensionality, which limit their competence in remote sensing applications. Hence, this research presents a Spatial Split Attention-enabled Distributed Learning-based Encoder Generative Bidirectional Network (S²A-DL-EGBNet) model for accurate LC classification using hyperspectral images (HSIs). The model integrates a distributed learning module to process large datasets, and training is done in parallel by reducing complexity while improving scalability. Also, a Generative Adversarial Network (GAN)-based data balancing is employed to address the class imbalance problem and enables the model's effectiveness. Thereafter, the parallel Bidirectional Long Short-Term Memory (BiLSTM) is integrated to speed up the training process and minimize computation time. The research applies multiple feature extraction techniques, capturing complex features, scaling variations in photographic distortions, and illumination changes from the aspects of input data. Notably, the S²A-DL-EGBNet model is validated using the Hyperspectral Remote Sensing Scenes dataset, and the S²A-DL-EGBNet model shows remarkable performance by achieving 98.44% sensitivity, 98.84% accuracy, and 99.24% sensitivity on the Indian Pines dataset for training data of 90%.

Intrusion detection distinguishes unauthorized system access by monitoring network activities, where identifiable patterns and behaviors are difficult to recognize due to factors such as false positives and negatives. The inability to work on large and complex networks, poor generalizability, high computation time, and the adoption of new threats that emerge over time are major concerns. Therefore, we established a model for handling the aforementioned challenges using the Echo whale optimization-incentive learning-based Bidirectional Long Short-Term Memory (EWO-IL-BiLSTM) model, which was proposed to improve the accuracy of intrusion detection. The EWO algorithm was incorporated to effectively tune the best parameters of the BiLSTM model to improve detection accuracy while increasing convergence speed and reducing false errors. Through incentive learning integration, the system demonstrated an award mechanism during training, which effectively captures the temporal dependencies in the network data and prompts the BiLSTM classifier to reach the best results. This research demonstrated that the benefits of the EWO and the Incentive Learning mechanism-based BiLSTM are a major advancement that improves the accurate intrusion detection, thereby promoting the network security and robustness in the ever-changing cyber-threat environments. The evaluation findings indicated that the proposed EWO-IL-BiLSTM model demonstrated superior performance with a high accuracy of 97.06%, F1-Score of 96.78%, precision of 97.59%, and recall of 95.98% at 80% training using the WSN-BFSF dataset.