Recently, revealing more valuable information except for quantity value for a database is an essential research field. High utility itemset mining (HAUIM) was suggested to reveal useful patterns by average-utility measure ...

The Industrial Internet of Things (IIoT) promises to deliver innovative business models across multiple domains by providing ubiquitous connectivity, intelligent data, predictive analytics, and decision-making systems for ...

According to the Paris agreement and the European climate law, carbon neutrality should be achieved by 2050. Therefore, the long-term planning of energy production and the development of green energy have become indispensable ...

This paper introduces a new model to identify collective abnormal human behaviors from large pedestrian data in smart cities. To accurately solve the problem, several algorithms have been proposed in this paper. These can ...

In this research, we propose a collaborative clustering method where the exchange of raw data is not required. The attention-based model is used with a federated learning framework. The edge devices compute the model updates ...

This research work introduces a new intelligent framework for infectious disease detection by exploring various emerging and intelligent paradigms. We propose new deep learning architectures such as entity embedding networks, ...

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 ...

Opportunistic networks utilize pocket switching for routing where each node forwards its messages to a suitable next node. The selection of the forwarder node is crucial for the efficient performance of a routing protocol. ...

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 ...

This paper proposes a framework that can be employed to mitigate adversarial evasion attacks on Android malware classifiers. It extracts multiple discriminating feature subsets from a single Android app such that each ...