Hatred and trolling detection transliteration framework using hierarchical LSTM in code-mixed social media text

The paper describes the usage of self-learning Hierarchical LSTM technique for classifying hatred and trolling contents in social media code-mixed data. The Hierarchical LSTM-based learning is a novel learning architecture inspired from the neural learning models. The proposed HLSTM model is trained to identify the hatred and trolling words available in social media contents. The proposed HLSTM systems model is equipped with self-learning and predicting mechanism for annotating hatred words in transliteration domain. The Hindi–English data are ordered into Hindi, English, and hatred labels for classification. The mechanism of word embedding and character-embedding features are used here for word representation in the sentence to detect hatred words. The method developed based on HLSTM model helps in recognizing the hatred word context by mining the intention of the user for using that word in the sentence. Wide experiments suggests that the HLSTM-based classification model gives the accuracy of 97.49% when evaluated against the standard parameters like BLSTM, CRF, LR, SVM, Random Forest and Decision Tree models especially when there are some hatred and trolling words in the social media data.

Levenberg–Marquart logistic deep neural learning based energy efficient and load balanced routing in MANET

As the advent of new technologies grows, the deployment of mobile ad hoc networks (MANET) becomes increasingly popular in many application areas. In addition, all the nodes in MANET are battery operated and the node mobility affects the path stability and creates excessive traffic leads to higher utilization of energy, data loss which degrades the performance of routing. So, in this paper we propose Levenberg–Marquardt logistic deep neural learning based energy efficient and load balanced routing (LLDNL-EELBR) which is a machine learning method to deeply analyze the mobile nodes to calculate residual load and energy and it also uses logistic activation function to select the mobile node having higher residual energy and residual load to route the data packet. Experimental evaluations of three methods (LLDNL-EELBR, multipath battery and mobility-aware routing scheme (MBMA-OLSR) and opportunistic routing with gradient forwarding for MANETs (ORGMA)) were done and the result reveals that LLDNL-EELBR method is able to increase the through put and minimizes the delay and energy consumption in MANET when compared to works under consideration.<br /><div> </div>

Drone-assisted automated plant diseases identification using spiking deep conventional neural learning

Detection and diagnosis of the plant diseases in the early stage significantly minimize yield losses. Image-based automated plant diseases identification (APDI) tools have started to been widely used in pest managements strategies. The current APDI systems rely on images captured in laboratory conditions, which hardens the usage of the APDI systems by smallholder farmers. In this study, we investigate whether the smallholder farmers can exploit APDI systems using their basic and cheap unmanned autonomous vehicles (UAVs) with standard cameras. To create the tomato images like the one taken by UAVs, we build a new dataset from a public dataset by using image processing tools. The dataset includes tomato leaf photographs separated into 10 classes (diseases or healthy). To detect the diseases, we develop a new hybrid detection model, called SpikingTomaNet, which merges a novel deep convolutional neural network model with spiking neural network (SNN) model. This hybrid model provides both better accuracy rates for the plant diseases identification and more energy efficiency for the battery-constrained UAVs due to the SNN’s event-driven architecture. In this hybrid model, the features extracted from the CNN model are used as the input layer for SNNs. To assess our approach’s performance, firstly, we compare the proposed CNN model inside the developed hybrid model with well-known AlexNet, VggNet-5 and LeNet models. Secondly, we compare the developed hybrid model with three hybrid models composed of combinations of the well-known models and SNN model. To train and test the proposed neural network, 32022 images in the dataset are exploited. The results show that the SNN method significantly increases the success, especially in the augmented dataset. The experiment result shows that while the proposed hybrid model provides 97.78% accuracy on original images, its success on the created datasets is between 59.97%–82.98%. In addition, the results shows that the proposed hybrid model provides better overall accuracy in the classification of the diseases in comparison to the well-known models and LeNet and their combination with SNN.