This paper addresses the problem of responding to user queries by fetching the most relevant object from a clustered set of objects. It addresses the common drawbacks of cluster-based approaches and targets fast, high-quality ...

This work aims to provide a novel hybrid architecture to suggest appropriate hashtags to a collection of orpheline tweets. The methodology starts with defining the collection of batches used in the convolutional neural ...

This research presents a new generic deep learning framework for anomaly detection in the Internet of Everything (IoE). It combines decomposition methods, deep neural networks, and evolutionary computation to better detect ...

This paper introduced a new deep learning framework for fault diagnosis in electrical power systems. The framework integrates the convolution neural network and different regression models to visually identify which faults ...

Many research areas depend on group anomaly detection. The use of group anomaly detection can maintain and provide security and privacy to the data involved. This research attempts to solve the deficiency of the existing ...

Road safety is tackled and an intelligent deep learning framework is proposed in this work, which includes outlier detection, vehicle detection, and accident estimation. The road state is first collected, while an intelligent ...

This paper introduces a novel deep learning architecture for identifying outliers in the context of intelligent transportation systems. The use of a convolutional neural network with decomposition is explored to find ...

This paper presents a novel intelligent system based on graph convolutional neural networks to study road crack detection in intelligent transportation systems. The visual features of the input images are first computed ...

Recent anticipated advancements in ad hoc Wireless Mesh Networks (WMN) have made them strong natural candidates for Smart Grid’s Neighborhood Area Network (NAN) and the ongoing work on Advanced Metering Infrastructure ...

This research explores a new direction in power system technology and develops a new framework for pattern group discovery from large power system data. The efficient combination between the recurrent neural network and ...