One of the most famous algorithms in the field of focused exploration of data mining correlation rules is the Apriori algorithm and its many developed versions. But what can be raised as a major challenge in this field is the proper application of this algorithm in the distributed environments of today's world. In this research, a parallelization-based approach is proposed to improve the performance of the Apriori algorithm in the process of exploring recurring patterns on network topologies. The proposed approach includes two major features: (1) combining the node centrality criterion and the Apriori algorithm to identify frequent patterns, (2) using the mapping/reduction method in order to create parallel processing and achieve optimal values in the shortest time. Also, this approach pursues three main goals: reducing the temporal and spatial complexity of the Apriori algorithm, improving the process of extracting dependency rules and identifying recurring patterns, comparing the performance of the proposed approach on different network topologies in order to determine the advantages and disadvantages of each topology. To prove the superiority of the proposed method, a comparison has been made between our approach and the basic Apriori algorithm. The evaluation results of the methods prove that the proposed approach provides an acceptable performance in terms of execution time criteria compared to other methods. تفاصيل المقالة

This study introduces a combined approach using deep learning and optimization to accurately and efficiently classify plant leaves based on disease and health. By optimizing hyper parameters with a whale optimization algorithm and utilizing a convolutional neural network for disease classification, the model achieves high accuracy. The Plant Village dataset is used, and data augmentation is applied to improve the model's performance. The optimized network achieves a classification accuracy of 95.22% for the test set and 99.57% for the training set, with precision and recall values of 95.24% and 95.22% respectively. The performance and efficiency of the proposed method are proven to be superior when compared to other models and pre-trained networks. This study has potential applications in various image classification tasks and can be valuable in agriculture, horticulture, and plant disease identification. Furthermore, the proposed network achieves higher accuracy with fewer trainable parameters and computations compared to similar works. تفاصيل المقالة