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In WSN assisted IoT networks, the devices have to search the path up to the IoT hub for data transmission. Although the device's hardware capabilities are increasing day by day, the device's battery capacity is not increasing at the same rate. IoT devices have limited-power batteries that are not rechargeable. It is also challenging to replace their battery while the devices are in use. Moreover, we need advanced power-saving schemes to make these IoT technologies more adaptable. Due to the energy constraints and limited memory capacity of these IoT devices, we require more energy-efficient routing protocols to route data to the IoT hub. This paper has surveyed the most recent state-of-the-art routing protocol and highlighted their limits and path for future research. [ABSTRACT FROM AUTHOR]

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]

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]

There are different conditions where SPP play a vital role. However, there are various conditions, where we have to face with uncertain parameters such as variation of cost, time and so on. So to remove this uncertainty, Yang et al. [1] "[Journal of Intelligent & Fuzzy Systems, 32(1), 197-205"] have proposed the fuzzy reliable shortest path problem under mixed fuzzy environment and claimed that it is better to use their proposed method as compared to the existing method i.e., "[Hassanzadeh et al.; A genetic algorithm for solving fuzzy shortest path problems with mixed fuzzy arc lengths, Mathematical and Computer Modeling, 57(2013) 84-99" [2]]. The aim of this note is, to highlight the shortcoming that is carried out in Yang et al. [1] article. They have used some mathematical incorrect assumptions under the mixed fuzzy domain, which is not true in a fuzzy environment. [ABSTRACT FROM AUTHOR]

Researchers are interested in Facial Emotion Recognition (FER) because it could be useful in many ways and has promising applications. The main task of FER is to identify and recognize the original facial expressions of users from digital inputs. Feature extraction and emotion recognition make up the majority of the traditional FER. Deep Neural Networks, specifically Convolutional Neural Network (CNN), are popular and highly used in FER due to their inherent image feature extraction process. This work presents a novel method dubbed as EfficientNet-XGBoost that is based on Transfer Learning (TL) technique. EfficientNet-XGBoost is basically a cascading of the EfficientNet and the XGBoost techniques along with certain enhancements by experimentation that reflects the novelty of the work. To ensure faster learning of the network and to overcome the vanishing gradient problem, our model incorporates fully connected layers of global average pooling, dropout and dense. EfficientNet is fine-tuned by replacing the upper dense layer(s) and cascading the XGBoost classifier making it suitable for FER. Feature map visualization is carried out that reveals the reduction in the size of feature vectors. The proposed method is well-validated on benchmark datasets such as CK+, KDEF, JAFFE, and FER2013. To overcome the issue of data imbalance, in some of the datasets namely CK+ and FER2013, we augmented data artificially through geometric transformation techniques. The proposed method is implemented individually on these datasets and corresponding results are recorded for performance analysis. The performance is computed with the help of several metrics like precision, recall and F1 measure. Comparative analysis with competent schemes are carried out on the same sample data sets separately. Irrespective of the nature of the datasets, the proposed scheme outperforms the rest with overall rates of accuracy being 100%, 98% and 98% for the first three datasets respectively. However, for the FER2013 datasets, efficiency is less promisingly observed in support of the proposed work. [ABSTRACT FROM AUTHOR]