A Novel Rank Aggregation-Based Hybrid Multifilter Wrapper Feature Selection Method in Software Defect Prediction

The high dimensionality of software metric features has long been noted as a data quality problem that affects the performance of software defect prediction (SDP) models. This drawback makes it necessary to apply feature selection (FS) algorithm(s) in SDP processes. FS approaches can be categorized into three types, namely, filter FS (FFS), wrapper FS (WFS), and hybrid FS (HFS). HFS has been established as superior because it combines the strength of both FFS and WFS methods. However, selecting the most appropriate FFS (filter rank selection problem) for HFS is a challenge because the performance of FFS methods depends on the choice of datasets and classifiers. In addition, the local optima stagnation and high computational costs of WFS due to large search spaces are inherited by the HFS method. Therefore, as a solution, this study proposes a novel rank aggregation-based hybrid multifilter wrapper feature selection (RAHMFWFS) method for the selection of relevant and irredundant features from software defect datasets. The proposed RAHMFWFS is divided into two stepwise stages. The first stage involves a rank aggregation-based multifilter feature selection (RMFFS) method that addresses the filter rank selection problem by aggregating individual rank lists from multiple filter methods, using a novel rank aggregation method to generate a single, robust, and non-disjoint rank list. In the second stage, the aggregated ranked features are further preprocessed by an enhanced wrapper feature selection (EWFS) method based on a dynamic reranking strategy that is used to guide the feature subset selection process of the HFS method. This, in turn, reduces the number of evaluation cycles while amplifying or maintaining its prediction performance. The feasibility of the proposed RAHMFWFS was demonstrated on benchmarked software defect datasets with Naïve Bayes and Decision Tree classifiers, based on accuracy, the area under the curve (AUC), and F-measure values. The experimental results showed the effectiveness of RAHMFWFS in addressing filter rank selection and local optima stagnation problems in HFS, as well as the ability to select optimal features from SDP datasets while maintaining or enhancing the performance of SDP models. To conclude, the proposed RAHMFWFS achieved good performance by improving the prediction performances of SDP models across the selected datasets, compared to existing state-of-the-arts HFS methods

Unanswered issues on decarbonizing the aviation industry through the development of sustainable aviation fuel from microalgae

Concerns have been raised about the effects of fossil fuel combustion on global warming and climate change. Fuel consumer behavior is also heavily influenced by factors such as fluctuating fuel prices and the need for a consistent and reliable fuel supply. Microalgae fuel is gaining popularity in the aviation industry as a potential source of energy diversification. Microalgae can grow in saltwater or wastewater, capture CO2 from the atmosphere and produce lipids without requiring a large amount of land. As a result, the production of oil from microalgae poses no threat to food availability. The low carbon footprint of microalgae-derived fuels has the potential to mitigate the impact of traditional aviation fuels derived from petroleum on climate change and global warming. Therefore, aviation fuels derived from microalgae have the potential to be a more environmentally friendly and sustainable alternative to conventional fuels. Gathering microalgal species with a high lipid content, drying them, and turning them into aviation fuel is an expensive process. The use of biofuels derived from microalgae in the aviation industry is still in its infancy, but there is room for growth. This study analyses the potential routes already researched, their drawbacks in implementation, and the many different conceptual approaches that can be used to produce sustainable aviation fuel from microalgal lipids. Microalgae species with fast-growing rates require less space and generate lipids that can be converted into biofuel without imperiling food security. The key challenges in algal-based aviation biofuel include decreased lipid content, harvesting expenses, and drying procedure that should be enhanced and optimized to increase process viability