The high data rates detail that internet-connected devices have been increasing exponentially. Cognitive radio (CR) is an auspicious technology used to address the resource shortage issue in wireless IoT networks. Resource optimization is considered a non-convex and nondeterministic polynomial (NP) complete problem within CR-based Internet of Things (IoT) networks (CR-IoT). Moreover, the combined optimization of conflicting objectives is a challenging issue in CR-IoT networks. In this paper, energy efficiency (EE) and spectral efficiency (SE) are considered as conflicting optimization objectives. This research work proposed a hybrid tabu search-based stimulated algorithm (HTSA) in order to achieve Pareto optimality between EE and SE. In addition, the fuzzy-based decision is employed to achieve better Pareto optimality. The performance of the proposed HTSA approach is analyzed using different resource allocation parameters and validated through simulation results.
Agriculture is the backbone of each country, and almost 50% of the population is directly involved in farming. In Pakistan, several kinds of fruits are produced and exported the other countries. Citrus is an important fruit, and its production in Pakistan is higher than the other fruits. However, the diseases of citrus fruits such as canker, citrus scab, blight, and a few more impact the quality and quantity of this Fruit. The manual diagnosis of these diseases required an expert person who is always a time-consuming and costly procedure. In the agriculture sector, deep learning showing significant success in the last five years. This research work proposes an automated framework using deep learning and best feature selection for citrus diseases classification. In the proposed framework, the augmentation technique is applied initially by creating more training data from existing samples. They were then modifying the two pre-trained models named Resnet18 and Inception V3. The modified models are trained using an augmented dataset through transfer learning. Features are extracted for each model, which is further selected using Improved Genetic Algorithm (ImGA). The selected features of both models are fused using an array-based approach that is finally classified using supervised learning classifiers such as Support Vector Machine (SVM) and name a few more. The experimental process is conducted on three different datasets-Citrus Hybrid, Citrus Leaf, and Citrus Fruits. On these datasets, the best-achieved accuracy is 99.5%, 94%, and 97.7%, respectively. The proposed framework is evaluated on each step and compared with some recent techniques, showing that the proposed method shows improved performance.
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