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Automatic detection and analysis of rice crop diseases is widely required in the farming industry, which can be utilized to avoid squandering financial and other resources, reduce yield losses, and improve treatment efficiency, resulting in healthier crop output. An automated approach was proposed for accurately detecting and classifying diseases from a supplied photograph. The proposed system for the recognition of rice plant diseases adopts a computer vision-based approach that employs the techniques of image processing, machine learning, and deep learning, reducing the reliance on conventional methods to protect paddy crops from diseases like bacterial leaf blight, false smut, brown leaf spot, rice blast, and sheath rot, the five primary diseases that frequently plague the Indian rice fields. Following image pre-processing, image segmentation is employed to determine the diseased section of the paddy plant, with the diseases listed above being identified purely on the basis of their visual contents. An integration of a support vector machine classifier and convolutional neural networks are used to recognize and classify specific varieties of paddy plant diseases. With ReLU and softmax functions, the suggested deep learning-based strategy attained the highest validation accuracy of 0.9145. Following recognition, a predictive remedy is recommended, which can assist agriculture-related individuals and organizations in taking suitable measures to combat these diseases.

There has been lot of research endeavours in detecting overlapping community structures in complex networks. This paper concentrates on one of the most popular complex networks of recent times—online social networks. Some of the existing methodologies in community detection for online social networks are discussed. The goal of the proposed algorithm is to solve the stability–plasticity dilemma and to suggest a new technique for detecting modular and granular overlaps in community detection. The stability–plasticity dilemma is solved using a Fuzzy ART-inspired algorithm for overlapping community detection for detecting modular and granular overlaps. The algorithm is designed by making use of network measures such as vertex betweenness, betweenness centrality, and split betweenness. The validation metrics used for testing the algorithm were sensitivity, specificity, accuracy and normalized mutual information. The algorithm has been tested and validated using benchmark datasets and real network datasets and gives a cumulative performance of 3.1/4.0.