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The wireless sensor network is gaining significant attention because of its ubiquitous deployability nature. In general, the role of the wireless sensor network is remarkable in smart city applications for data sensing, collecting, and transmitting. Advanced metering infrastructure is an automatic system for the reading of electricity consumption by individual users. The reading of data from meters normally communicated over a wireless medium. Apart from collecting and sensing, routing is one of the major attributes in the AMI network. Successful communication is possible when the link and node are in the proper state. In this paper, we propose a fault-tolerant routing mechanism named Grid Topological Routing scheme to provide an efficient route in case of encountering a faulty path. The simulation shows better performance against the Routing Protocol for Low-Power and Lossy Networks and Ad-Hoc On-Demand Vector algorithm in terms of power consumption and packet delivery ratio. This model is explicitly designed for the smart grid in India. [ABSTRACT FROM AUTHOR]

In this study, an automated panicle blast grading methodology using support vector machine based on deep features of small convolutional neural network (CNN) is suggested. MobileNetV2 and ShuffleNet are the two small and powerful architecture among CNN. The transfer learning and deep learning approach are adapted for MobileNetV2 and ShuffleNet for classifying the panicle into three levels; healthy, less blast and high blast. The experimental results reveal the support vector machine (SVM) using deep features perform better compared to its counterpart i.e., transfer learning approach. In addition, the comparative analysis based on the accuracy score of MobileNetV2 & ShuffleNet in transfer learning & deep learning approach with the traditional image classification methods for panicle blast grading is performed. In over all, the ShuffleNet plus SVM is the best classification model for grading of panicle blast with accuracy of 89.37%, sensitivity 89.37%, specificity 94.68% and computational time 14.936 second. [ABSTRACT FROM AUTHOR]

Recognition of handwritten characters has been a challenging task so far. There exist thousands of official languages across the globe which are used to communicate through documentation. Optical character recognition (OCR) being the challenging domain in such context where images of such documents are to be recognized either offline or online. Online and offline recognition of documents refer to the approaches whereby the basic operation of character recognition is performed during documentation itself and recognizing characters from stored documents, respectively. Numerous applications from a range of fields like medical transcription, digitization of ancient manuscripts, language translations, etc. are solely dependent on the task of OCR. In this work, an efficient framework is presented for the purpose of handwritten character recognition that can be well utilized for both offline and online processes. The proposed work takes the handwritten character images as input. It applies set of pre-processing such that the samples become suitable for the feature extraction task. The novelty lies in the process of feature extraction whereby three distinct types of feature are considered based on the shape primitives of the images. These features are global to the sample. Subsequently, local shape features are further extracted out of this shape features after suitable quantization process. These local features are the evidences which can be generically used to recognize test samples. These local feature vectors are dubbed as evidences and are preserved into a glossary dubbed as evidence glossary. The efficiency of the proposed scheme is well justified as it utilizes only a fraction of the feature vector and still it can recognize the characters. Other advantages of the proposed work are scale invariance and rotations invariance. Suitable datasets from two distinct languages, namely Odia and English are utilized for evaluating the efficiency of the framework. Comparison of the performance of the framework with six distinct state-of-the-art machine learning models is conducted whereby it outclass the competent in terms of several performance metrics. [ABSTRACT FROM AUTHOR]

This paper considers the prediction of currency exchange rate, volatility, and momentum prediction by exploring the capabilities of Support Vector Machine (SVM) and k-Nearest Neighbor (k-NN). In this work, the parameters such as penalty C and kernel γ of SVM have been tuned with few optimization techniques such as random search, grid search, genetic algorithm, particle swarm optimization, ant colony optimization, firefly optimization, and BAT optimization algorithm. The final prediction has been obtained using k-NN by searching the neighborhood elements for either profit or loss. The performance of the proposed system has been tested with the Indian rupees with dollar (USD), British Pound (GBP), and Euro (EUR) for daily, weekly, and monthly in advance for prediction of currency exchange rate, volatility, and momentum in the currency market. The model BAT-SVM-k-NN has been found with the best forecasting ability based on performance measures such as mean absolute percentage error, root mean square error, mean squared forecast error, root mean squared forecast error, and mean absolute forecast error in comparison with other optimization techniques mentioned above. [ABSTRACT FROM AUTHOR]

• This study evaluates the performance of 13 CNN models for rice disease identification in transfer learning and deep features plus SVM approach. • The statistical analysis results, deep features of resnet50 and SVM classification model is superior compared to other models. • A comparative analysis is carried out among all classification models based on CNN and traditional methods. • The performance of mobilenetv2 plus SVM is comparable enough with the resnet50 plus SVM. • In addition, the study is carried four varieties of rice leaf diseases with a dataset of 5932 on-field images. Features are the vital factor for image classification in the field of machine learning. The advancement of deep convolutional neural network (CNN) shows the way for identification of rice diseases using deep features with the expectation of high returns. This paper introduced 5932 on-field images of four types of rice leaf diseases, namely bacterial blight, blast, brown spot and tungro. In addition, the performance evaluation of 11 CNN models in transfer learning approach and deep feature plus support vector machine (SVM) was carried out. The simulation results show the deep feature plus SVM perform better classification compared to transfer learning counterpart. Also, the performance of small CNN models such as mobilenetv2 and shufflenet was examined. The performance evaluation was carried out in terms of accuracy, sensitivity, specificity, false positive rate (FPR), F1 Score and training time. Again, the statistical analysis was performed to choose the better classification model. The deep feature of ResNet50 plus SVM performs better with F1 score of 0.9838. The fc6 layer of vgg16, vgg19 and AlexNet have better contribution towards classification compared to fc7 and fc8. Further, the F1 score of CNN classification models was compared with other traditional image classification models such as bag-of-feature, local binary patterns (LBP) plus SVM, histogram of oriented gradients (HOG) plus SVM and Gray Level Co-occurrence Matrix (GLCM) plus SVM. [ABSTRACT FROM AUTHOR]