In modern networks, a Network Intrusion Detection System (NIDS) is a critical security device for detecting unauthorized activity. The categorization effectiveness for minority classes is limited by the imbalanced class issues connected with the dataset. We propose an Imbalanced Generative Adversarial Network (IGAN) to address the problem of class imbalance by increasing the detection rate of minority classes while maintaining efficiency. To limit the effect of the minimum or maximum value on the overall features, the original data was normalized and one-hot encoded using data preprocessing. To address the issue of the low detection rate of minority attacks caused by the imbalance in the training data, we enrich the minority samples with IGAN. The ensemble of Lenet 5 and Long Short Term Memory (LSTM) is used to classify occurrences that are considered abnormal into various attack categories. The investigational findings demonstrate that the proposed approach outperforms the other deep learning approaches, achieving the best accuracy, precision, recall, TPR, FPR, and F1-score. The findings indicate that IGAN oversampling can enhance the detection rate of minority samples, hence improving overall accuracy. According to the data, the recommended technique valued performance measures far more than alternative approaches. The proposed method is found to achieve above 98% accuracy and classifies various attacks significantly well as compared to other classifiers.

The growing necessity for sustainable agriculture in the face of escalating global food demands and climate change underscores the importance of optimizing crop water usage. Current methodologies for estimating crop water requirements, primarily relying on traditional remote sensing and empirical models, face limitations in precision, adaptability to diverse crop types, and dynamic environmental conditions. This paper introduces an innovative machine learning model designed to accurately estimate crop water requirements and optimize irrigation scheduling using remote sensing data samples. The model’s robustness stems from its sophisticated pre-processing technique, employing a Grey Wolf Optimized (GWO)-based Non-Local Means (NLM) algorithm for denoising images sourced from various remote sensing technologies. Subsequently, these refined images undergo a transformation into multidomain features through the integration of a Vision Transformer, Fourier, Entropy, Color Map, and Gabor Maps. This multi-faceted approach ensures a comprehensive analysis of the data, capturing intricate details pertinent to crop water requirements. A key innovation in our model is the implementation of the Dual Elephant Herding Optimization (DEHO) Process. This optimizer adeptly selects features by minimizing intra-class differences and maximizing inter-class variance, thereby enhancing the model’s discriminative capabilities. The selected features are then classified into distinct irrigation schedule classes using a Deep Dyna Q Graph Convolutional Network (DDQGCN). This network not only augments the efficiency of classifying images from diverse sources but also bolsters the model’s capability to adapt to varying crop types and environmental conditions. The efficacy of the proposed model is further augmented by incorporating vector autoregressive moving average with eXogenous inputs (VARMAx) algorithms. This addition enables the conversion of output into temporal features, facilitating precise predictions of soil quality and the dynamic adjustment of irrigation schedules. Empirical testing of this model across different geographies on mango, rice, and cotton crops demonstrated significant improvements over existing methods. The model exhibited an 8.5% increase in precision, 8.3% in accuracy, 4.9% in recall, 5.5% in Area Under the Curve (AUC), and 4.5% in specificity, alongside a 3.5% reduction in response delays. Particularly noteworthy is the model’s enhanced pre-emption capabilities, reflected in a 2.9% higher precision of pre-emption, 2.5% greater accuracy, 1.5% increased recall, 2.4% higher AUC of pre-emption, 1.5% improved specificity, and a 1.9% reduction in delay compared to existing methodologies.

With developing technologies, network security is critical, predominantly active, and distributed ad hoc in networks. An intrusion detection system (IDS) plays a vital role in cyber security in detecting malicious activities in network traffic. However, class imbalance has triggered a challenging issue where many instances of some classes are more than others. Therefore, traditional classifiers suffer in classifying malicious activities and result in low robustness to unidentified glitches. This paper introduces a novel technique based on a modified conditional generative adversarial network (MCGAN) to address the class imbalance problem. The proposed MCGAN handles the class imbalance issue by generating oversamples to balance the minority and majority classes. Then, the Bi-LSTM technique is incorporated to classify the multi-class intrusion efficiently. This formulated model is experimented on using the NSL-KDD+ dataset with the aid of accuracy, precision, recall, FPR, and F-score to validate the efficacy of the proposed system. The simulation results of the proposed method are associated with other existing models. It achieved an accuracy of 95.16%, precision of 94.21%, FPR of 2.1%, and F1-score of 96.7% for the NSL-KDD+ dataset with 20 selected features.