The research proposed in this article presents a novel improved version of the widely adopted firefly algorithm and its application for tuning and optimising XGBoost classifier hyper-parameters for network intrusion detection. One of the greatest issues in the domain of network intrusion detection systems are relatively high false positives and false negatives rates. In the proposed study, by using XGBoost classifier optimised with improved firefly algorithm, this challenge is addressed. Based on the established practice from the modern literature, the proposed improved firefly algorithm was first validated on 28 well-known CEC2013 benchmark instances a comparative analysis with the original firefly algorithm and other state-of-the-art metaheuristics was conducted. Afterwards, the devised method was adopted and tested for XGBoost hyper-parameters optimisation and the tuned classifier was tested on the widely used benchmarking NSL-KDD dataset and more recent USNW-NB15 dataset for network intrusion detection. Obtained experimental results prove that the proposed metaheuristics has significant potential in tackling machine learning hyper-parameters optimisation challenge and that it can be used for improving classification accuracy and average precision of network intrusion detection systems.

Novel hybrid firefly algorithm: an application to enhance XGBoost tuning for intrusion detection classification

Research and application of a hybrid model for mid-term power demand forecasting based on secondary decomposition and interval optimization

Comprehensive review of load forecasting with emphasis on intelligent computing approaches

The customer churn is one of the key challenges for enterprises, and market saturation and increased competition to maintain business position has caused companies to make all attempts to identify customers who are likely to leave and end their relationship with a company in a particular period to become the customer of another company. In recent years, many methods have been developed including data mining for predicting the customer churn and manners that customers are likely to behave in the future and therefore, taking action early to prevent their leaving. This study proposes a hybrid system based on fuzzy entropy criterion selection algorithm with similar classifiers, grey wolf optimization algorithm, and artificial neural network to predict the customer churn of those companies that suffer losses from losing customers over time. The research results are evaluated by other methods in the criteria of accuracy, recall, precision, and F_measure, and it is declared that the proposed method is superior over other methods.

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Customer Churn Modeling via the Grey Wolf Optimizer and Ensemble Neural Networks

Industrial artificial intelligence based energy management system: Integrated framework for electricity load forecasting and fault prediction

Hybrid ensemble intelligent model based on wavelet transform, swarm intelligence and artificial neural network for electricity demand forecasting

Wind speed prediction has received reasonable attention recently because of its clean and promising source of renewable energy. Recent studies have shown that developing efficient model to predict wind speed is a challenging task because of its nonlinear and stochastic characteristics. This paper aims to propose a new hybrid model to predict wind speed. For this purpose, Discrete Wavelet Transform (DWT), Phase Space Reconstruction (PSR) of chaos theory, Aquila Optimization Algorithm (AOA) and Backpropagation Neural Network (BPNN) are hybridised and a novel DWT-PSR-AOA-BPNN is proposed. To ascertain the proposed DWT-PSR-AOA-BPNN model performance, different hybrid model variants (DWT-PSR-GA-BPNN, DWT-PSR-PSO-BPNN, PSR-PSO-BPNN and PSR-AOA-BPNN) were developed for comparison. The comparison was done using statistical model evaluators of Mean Average Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and model efficiency of Loague and Green (ELG). The statistical results showed that the proposed DWT-PSR-AOA-BPNN model performed better and is therefore considered efficient wind speed prediction tool when compared with DWT-PSR-GA-BPNN, DWT-PSR-PSO-BPNN, PSR-PSO-BPNN and PSR-AOA-BPNN hybrid models. That is, the proposed DWT-PSR-AOA-BPNN had the lowest MAE, RMSE and MAPE values for the model testing (MAE = 1.1490, RMSE = 1.4190 and MAPE = 0.2743) and validation (MAE = 0.8122, RMSE = 0.9771 and MAPE = 0.1953). The DWT-PSR-AOA-BPNN also achieved the highest ELG values of 0.9904 (testing) and 0.99738 (validation) respectively. It is therefore concluded that considering the DWT-PSR-AOA-BPNN results, the indication corroborates the fact that this model can be utilized for efficient grid operations. 

Eric Ofori-Ntow Jnr

Papers

Hybrid ensemble intelligent model based on wavelet transform, swarm intelligence and artificial neural network for electricity demand forecasting

A hybrid chaotic-based discrete wavelet transform and Aquila optimisation tuned-artificial neural network approach for wind speed prediction

A New Long-Term Photovoltaic Power Forecasting Model Based on Stacking Generalization Methodology

A Novel Three-Stage Short-Term Photovoltaic Prediction Approach Based on Neighborhood Component Analysis and ANN Optimized with PSO (NCA-PSO-ANN)