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A novel Modified Deep Fuzzy Clustering (MDFC) based classification model involves four major phases. They are preprocessing, segmentation, feature extraction and finally, detection and classification phase. To reduce noise and smooth the edges of the input image of the cotton crop, bilateral filtering is first used as a preprocessing approach. Next, a modified deep fuzzy clustering is suggested for the segmentation procedure that creates a collection of segments from the preprocessed image. The segmented image is then processed to extract relevant characteristics by using an enhanced Pyramid of Histogram Orientation Gradient (PHOG), Local Directional Ternary Pattern (LDTP), and statistical‐based features. In order to detect and classify cotton crop diseases more effectively, this paper proposes a hybrid system. Here, the features are put through a detection phase, after which the extracted features are trained in the Bidirectional Gated Recurrent Unit (Bi‐GRU) model to determine whether or not the cotton crop is infected. Once it is detected to be diseased, the type of disease is classified via an improved Recurrent Neural Network (RNN). In terms of several performance metrics, the proposed model is validated in comparison with the traditional approaches. The MDFC‐based classification model outperforms existing models with a specificity of 0.9687 at a learning rate of 90. In contrast, other models achieve lower specificities: Bi‐GRU (0.8436), RNN (0.8359), CNN (0.8654), LSTM (0.8769), SVM (0.7983), VGG16 (0.8619), DCNN (0.8725), BI‐RNN + BI‐LSTM (0.7869), and NN + CNN (0.85478). [ABSTRACT FROM AUTHOR]

The popularity of Network Function Virtualization (NFV) increases due to offering flexible and efficient methods for placing the chains of Virtual Network Functions (VNFs). The majority of recent studies consider consistent (immutable) bandwidth and flow processing while implementing the NFV-enabled network. The consistent flow processing and bandwidth requirement has become crucial in NFV-enabled network, where the network behaviour is highly dynamic. Due to the regular fluctuation in network traffic, the bandwidth demand, flow processing requirements, and the available Point-of-Presence (PoP) the network changes continuously. This article presents an adaptive model for the placement of VNF chains in order to mitigate the limitations mentioned above. The objective of re-arranging the prior placement of service chain is to maintain the performance of flow processing despite the variation in flow processing and resource requirements. Meanwhile, the proposed adaptive model minimizes the number of changes (i.e., re-allocation of VNFs) in the network. The simulation results show that the proposed model delivers the network services without disruption. Moreover, it also maintains the performance of flow processing and minimizes the 12-25% modifications in the previously placed VNFs. [ABSTRACT FROM AUTHOR]

Investigates the effect of solar features on geospheric conditions leading to geomagnetic storms (GMS) using interplanetary magnetic field, solar wind plasma and solar geophysical data during the period 1979 to 1999. Role of sunspots in major solar geomagnetic disturbances; Basic phenomena that occur during magnetic reconnection in the flare site; Possible association of GMS with solar features.