In the last few decades, Intrusion Detection System (IDS), in particular, machine learning‐based anomaly detection, has gained importance over Signature Detection Systems (SDSs) in the novel attacks detection. Herein, a novel approach called T‐Distributed Stochastic Neighbour Embedding and Random Forest Algorithm (T‐SNERF) is presented for the classification of cyber‐attacks. The approach consists of three different steps. First, the examination of feature correlations is provided. Second, the T‐Distributed Stochastic Neighbour Embedding (T‐SNE) data dimensional reduction technique is used. Third, Random Forest (RF) technique is utilised to evaluate the complications in the accuracy and False‐Positive Rate (FPR). The proposed approach has been tested on various well‐known datasets, namely, UNSW‐NB 15, CICIDS‐2017, and phishing datasets. The proposed novel approach achieved significant results compared with existing approaches, achieving 100% accuracy, and 0% FPR for the UNSW‐NB15 dataset, and achieving high accuracy rates, up to 99.7878%, and 99.7044%, for CICIDS‐2017 and Phishing datasets respectively.

In this article, we explore the usage of long short-term memory neural network (NN) in generating music pieces and propose an approach to do so. Bach’s musical style has been selected to train the NN to make it able to generate similar music pieces. The proposed approach takes midi files, converting them to song files and then encoding them to be as inputs for the NN. Before inputting the files into the NNs, an augmentation process which augments the file into different keys is performed then the file is fed into the NN for training. The last step is the music generation. The main objective is to provide the NN with an arbitrary note and then the NN starts amending it gradually until producing a good piece of music. Various experiments have been conducted to explore the best values of parameters that can be selected to obtain good music generations. The obtained generated music pieces are accepted in terms of rhythm and harmony; however, some other problems exist such as in certain cases the tone stops or in some other cases getting short melodies that do not change.

The integration of artificial intelligence (AI) in the financial sector has seen a rapid increase over the past few years, offering new possibilities to streamline processes while ensuring profitability for lending institutions. With its data-driven capability, predicting the creditworthiness of applicants has demonstrated strong predictive performance, particularly for thin-file clients. Despite these advances, growing concerns regarding AI’s fairness, explainability, and regulatory accountability have increasingly limited its adoption in high-stakes credit decision-making. This paper presents a synthesis derived from a systematic literature review (SLR) of 43 peer-reviewed studies published between 2020 and 2025, focusing on AI-based credit scoring and addressing at least one of the performance, fairness, or explainability dimensions. Eligible studies were limited to peer-reviewed journal and conference articles (2020–2025) retrieved from IEEE Xplore, Scopus, Web of Science, and ScienceDirect (last searched: 30 September), examining AI-driven credit scoring in consumer or lending decision contexts. Guided by the Relevance, Rigor, Reproducibility, and Quality (3Rs&Q) appraisal framework, the review analyzes how existing approaches navigate the interplay among performance, fairness, and explainability under regulatory and human oversight considerations. The findings indicate that these dimensions are predominantly addressed in isolation, with limited attention to their joint treatment in regulated deployment settings. By consolidating empirical and conceptual evidence, this review provides actionable guidance for designing and deploying credit scoring models in practice.

Artificial Neural Network (ANN) has played a significant role in many areas because of its ability to solve many complex problems that mathematical methods failed to solve. However, it has some shortcomings that lead it to stop working in some cases or decrease the result accuracy. In this research the authors propose a new approach combining particle swarm optimization algorithm (PSO) and genetic algorithm (GA), to increase the classification accuracy of ANN. The proposed approach utilizes the advantages of both PSO and GA to overcome the local minima problem of ANN, which prevents ANN from improving the classification accuracy. The algorithms start with using backpropagation algorithm, then it keeps repeating applying GA followed by PSO until the optimum classification is reached. The proposed approach is domain independent and has been evaluated by applying it using nine datasets with various domains and characteristics. A comparative study has been performed between the authors' proposed approach and other previous approaches, the results show the superiority of our approach.

With industrial and technological development and the increasing demand for electric power, wind energy has gradually become the fastest-growing and most environmentally friendly new energy source. Nevertheless, wind power generation is always accompanied by uncertainty due to the wind speed's volatility. Wind speed forecasting (WSF) is essential for power grids' dispatch, stability, and controllability, and its accuracy is crucial to effectively using wind resources. Therefore, this study proposes a novel WSF framework for stationary data based on a hybrid decomposition method and the Bidirectional Long Short-term Memory (BiLSTM) to achieve high forecasting accuracy for the Dumat Al-Jandal wind farm in Al-Jouf, Saudi Arabia. The hybrid decomposition method combines the Wavelet Packet Decomposition (WPD) and the Seasonal Adjustment Method (SAM). The SAM method eliminates the seasonal component of the decomposed subseries generated by WPD to reduce forecasting complexity. The BiLSTM is applied to forecast all the deseasonalized decomposed subseries. Five years of hourly wind speed observations acquired from a location in the Al-Jouf region were used to prove the effectiveness of the proposed model. The comparative experimental results, including 27 other models, demonstrated the proposed model's superiority in single and multiple WSF with an overall average mean absolute error of 0.176549, root mean square error of 0.247069, and R-squared error of 0.985987.