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"An edge-aided parallel evolutionary privacy-preserving algorithm for I" by Akbar Telikani, Asadollah Shahbahrami et al.

Data sanitization in the context of Internet of Things (IoT) privacy refers to the process of permanently and irreversibly hiding all sensitive information from vast amounts of streaming data. Taking into account the dynamic and real-time characteristics of streaming IoT data, we propose a parallel evolutionary Privacy-Preserving Data Mining (PPDM), called High-performance Evolutionary Data Sanitization for IoT (HEDS4IoT), and implement two mechanisms on a Graphics Processing Units (GPU)-aided p...
Privacy Preserving Data Mining High Performance Evolutionary Data Sanitization Graphics Processing Units Parallel Indexing Engine Parallel Fitness Function Engine Big Data
Source: uow.edu.au

"Pumps-as-Turbines' (PaTs) performance prediction improvement using evo" by Akbar Telikani, Mosé Rossi et al.

Energy production from clean sources is mandatory to reduce pollutant emissions. Among different options for hidden hydropower potential exploitation, Pump-as-Turbine (PaT) represents a viable solution in pico- and micro-hydropower applications for its flexibility and low-cost. Pumps are widely available in the global market in terms of both sizes and spare parts. To date, there are several PaTs’ performance prediction models in the literature, but very few of them use optimization algorithms ...
Artificial Neural Networks Anns Artificial Neural Networks Best Efficiency Point Adaptive Differential Evolution Artificial Neural Network Evolutionary Algorithms
Source: uow.edu.au

"Industrial IoT intrusion detection via evolutionary cost-sensitive lea" by Akbar Telikani, Jun Shen et al.

Cyber-attacks and intrusions have become the major obstacles to the adoption of the Industrial Internet of Things (IIoT) in critical industries. Imbalanced data distribution is a common problem in IIoT environments that negatively influence machine learning-based intrusion detection systems. To address this issue, we introduce EvolCostDeep, a hybrid model of stacked auto-encoders (SAE) and convolutional neural networks (CNNs) with a new cost-dependent loss function. The loss function aims to opt...
Industrial Internet Class Imbalance Computational Modeling Convolutional Neural Networks Cost Sensitive Learning Deep Learning
Source: uow.edu.au

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