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Baseline toxicity of neonicotinoid insecticides viz., thiamethoxam 25WG and sulfoxaflor 21.8SC was assessed in the field collected populations of the rice white backed planthopper Sogatella furcifera (Horvath). The major rice growing regions of Tamil Nadu when studied revealed that the field populations vary in their susceptibility. Coimbatore and Bhavani populations were more susceptible compared to the Nagapattinam one. Baseline toxicity of thiamethoxam 25WG was relatively high with narrow variation (LC50-0.3130 to 1.7882 ppm; LC95-1.0993 to 6.3840 ppm); LC50 of sulfoxaflor 21.8SC ranged from 1.3121 to 3.8392 ppm, while the LC95 values were 3.1507 to 5.2754 ppm. The resistance ratios varied among the populations viz., thiamethoxam 25WG (2.5 to 5.6 folds) and sulfoxaflor 21.8SC (1.8 to 2.9 folds). Thus, the population from Nagapattinam exhibited maximum resistance. [ABSTRACT FROM AUTHOR]

In the current era, as there is an abundant expansion of functionally similar web services, it becomes a prodigious issue for the web service discovery process. The service classification plays a significant role to greatly reduce the search space and retrieves the desirable service quickly and accurately. The classification is performed using the functional values. Recent research activities recommend RNN (Recurrent Neural Network) deep learning algorithms for efficient classification process. The state-of-the-art of GRU (Gated Recurrent Unit) one of the RNN model, provides a proficient classification process. However, the ratio of training and testing dataset, and hyperparameters namely neural network size, and batch size etc, affects the classification accuracy. The objective of the paper is to incorporate GRU model for efficient classification process. The novelty of the proposed model lies in implementing the GRU model for semantic web service classification. Furthermore, the genetic algorithm is used to predict the optimal ratio of training and testing dataset and optimal hidden neural Network units of GRU model in order to attain optimal classification. The experimental results exemplifies that the semantic web service classification is efficiently deliberated using the proposed GA-GRU model that outperforms the classification process as compared with the conventional semantic extraction using accuracy, precision, F-measure, recall and FDR (False Date Rate) rate. [ABSTRACT FROM AUTHOR]