The ability of AI models to analyse dynamic real-time datasets is a major advancement in stock market forecasting, driven by two important factors: (i) it allows for discerning non-linear relationships and uncovering latent factors that may elude traditional analytical approaches, and (ii) it enables capturing a realistic view of market dynamics by the assimilation of diverse data sources, such as social media sentiment, news articles, and historical market trends. Two leading contributions in this field provide an exhaustive review of feature extraction and selection methods for stock market prediction [15; 16]. The first work examines 32 research articles published from 2011 to 2022 which addressed the combination of feature study and ML approaches in multiple stock market applications. The achieved results show that correlation criteria, random forest, principal component analysis, and autoencoder are the feature selection and extraction techniques with the best prediction accuracy. The second contribution presents a complete literature review of the data preprocessing strategies, feature extraction and selection techniques, prediction models, and prospects in stock market forecasting. Additionally, after identifying and disserting the major advantages and flaws of the collected studies, the authors propose a novel structured methodology to flexibilize and improve the stock price prediction process. This Special Issue brings together six papers showing different feature selection and extraction methodologies applied to five different topics: (i) identification of malicious URLs, (ii) recognition and classification of music genres from audio data, (iii) assessment of the impact of land mining activities, (iv) feature reduction applied to high-dimensional datasets, and (v) knowledge base construction from the scientific literature. Particularly, the first study [17] demonstrates the effectiveness of the DA-BiGRU model in detecting malicious URLs and suggests that future research should be focused on optimising the model for real-life applications. This study also emphasises the positive impact of the Word2 Vec training and the BiGRU model with a regularisation mechanism and an attention mechanism. The document provides a comprehensive overview of the proposed method and its potential implications for future research and real-world applications. The work of [PERSON] et al. [18] discusses the significance of deep learning in music classification, particularly the effectiveness of CNN and RNN in extracting features and handling sequential data temporal dependencies. It highlights the limitations of traditional techniques and the potential of deep learning in automatically extracting unbiased features for music classification. In conclusion, this study demonstrates the effectiveness of the proposed hybrid model in music classification and highlights the potential for future experiments on other datasets for music classification, instrument recognition, and artist recognition. Following this, the authors of [19] assess the impact of land mining for 30 years (1990 to 2020) on Emalahleni (South Africa) using satellite imagery. The random forest algorithm was applied for land categorisation, achieving five different classes: settlement, water, mining area, vegetation, and bare land. The achieved results demonstrate that land mining harms land cover transformations, emphasising the need for sustainable land management strategies that balance economic activities with environmental protection for future generations. In summary, the data obtained provide a basis for further research and a foundation for establishing a policy for future land-use decisions in the region. Regarding the reduction in high-dimensional datasets, two works were published [D20, D21]. The authors of [20] propose a new method for selecting and ranking group features based on their importance, with a focus on high-dimensional datasets with limited samples.