Emotions are an essential part of daily human communication. The emotional states and dynamics of the brain can be linked by electroencephalography (EEG) signals that can be used by the Brain–Computer Interface (BCI), to provide better human–machine interactions. Several studies have been conducted in the field of emotion recognition. However, one of the most important issues facing the emotion recognition process, using EEG signals, is the accuracy of recognition. This paper proposes a deep learning-based approach for emotion recognition through EEG signals, which includes data selection, feature extraction, feature selection and classification phases. This research serves the medical field, as the emotion recognition model helps diagnose psychological and behavioral disorders. The research contributes to improving the performance of the emotion recognition model to obtain more accurate results, which, in turn, aids in making the correct medical decisions. A standard pre-processed Database of Emotion Analysis using Physiological signaling (DEAP) was used in this work. The statistical features, wavelet features, and Hurst exponent were extracted from the dataset. The feature selection task was implemented through the Binary Gray Wolf Optimizer. At the classification stage, the stacked bi-directional Long Short-Term Memory (Bi-LSTM) Model was used to recognize human emotions. In this paper, emotions are classified into three main classes: arousal, valence and liking. The proposed approach achieved high accuracy compared to the methods used in past studies, with an average accuracy of 99.45%, 96.87% and 99.68% of valence, arousal, and liking, respectively, which is considered a high performance for the emotion recognition model.

With the dramatic increase of the global population and with food insecurity increasing, it has become a major concern for both individuals and governments to fulfill the need for foods such as vegetables and fruits. Moreover, the desire for the consumption of healthy food, including fruit, has increased the need for applications in the field of agriculture that help to achieve better methods for fruit sorting and fruit disease prediction and classification. Automated fruit recognition is a potential solution to reduce the time and labor required to identify different fruits in situations such as retail stores during checkout, fruit processing centers during sorting, and orchards during harvest. Automating these processes reduces the need for human intervention, making them cheaper, faster, and immune to human error and biases. Past research in the field has focused mainly on the size, shape, and color features of fruits or employed convolutional neural networks (CNNs) for their classification. This study investigates the effectiveness of pre-trained deep learning models for fruit classification using two distinct datasets: Fruits-360 and the Fruit Recognition dataset. Four pre-trained models, DenseNet-201, Xception, MobileNetV3-Small, and ResNet-50, were chosen for the experiments based on their architecture and features. The results show that all models achieved almost 99% accuracy or higher with Fruits-360. With the Fruit Recognition dataset, DenseNet-201 and Xception achieved accuracies of around 98%. The good results exhibited by DenseNet-201 and Xception on both the datasets are remarkable, with DenseNet-201 attaining accuracies of 99.87% and 98.94%, and Xception attaining 99.13% and 97.73% accuracy, respectively, on Fruits-360 and the Fruit Recognition dataset.

A large number of intelligent models for masked face recognition (MFR) has been recently presented and applied in various fields, such as masked face tracking for people safety or secure authentication. Exceptional hazards such as pandemics and frauds have noticeably accelerated the abundance of relevant algorithm creation and sharing, which has introduced new challenges. Therefore, recognizing and authenticating people wearing masks will be a long-established research area, and more efficient methods are needed for real-time MFR. Machine learning has made progress in MFR and has significantly facilitated the intelligent process of detecting and authenticating persons with occluded faces. This survey organizes and reviews the recent works developed for MFR based on deep learning techniques, providing insights and thorough discussion on the development pipeline of MFR systems. State-of-the-art techniques are introduced according to the characteristics of deep network architectures and deep feature extraction strategies. The common benchmarking datasets and evaluation metrics used in the field of MFR are also discussed. Many challenges and promising research directions are highlighted. This comprehensive study considers a wide variety of recent approaches and achievements, aiming to shape a global view of the field of MFR.

Security attacks on legitimate websites to steal users’ information, known as phishing attacks, have been increasing. This kind of attack does not just affect individuals’ or organisations’ websites. Although several detection methods for phishing websites have been proposed using machine learning, deep learning, and other approaches, their detection accuracy still needs to be enhanced. This paper proposes an optimized stacking ensemble method for phishing website detection. The optimisation was carried out using a genetic algorithm (GA) to tune the parameters of several ensemble machine learning methods, including random forests, AdaBoost, XGBoost, Bagging, GradientBoost, and LightGBM. The optimized classifiers were then ranked, and the best three models were chosen as base classifiers of a stacking ensemble method. The experiments were conducted on three phishing website datasets that consisted of both phishing websites and legitimate websites—the Phishing Websites Data Set from UCI (Dataset 1); Phishing Dataset for Machine Learning from Mendeley (Dataset 2, and Datasets for Phishing Websites Detection from Mendeley (Dataset 3). The experimental results showed an improvement using the optimized stacking ensemble method, where the detection accuracy reached 97.16%, 98.58%, and 97.39% for Dataset 1, Dataset 2, and Dataset 3, respectively.

The following article investigates the Metaverse and its potential to bolster digital accessibility for persons with disabilities. Through qualitative analysis, we examine responses from eleven experts in digital accessibility, Metaverse development, disability advocacy, and policy formulation. This exploration uncovers key insights into the Metaverse’s current state, its inherent principles, and the challenges and opportunities it presents in terms of accessibility. The findings reveal a mixed state of inclusivity within the Metaverse, highlighting significant advancements along with notable gaps, especially in integrating assistive technologies and ensuring interoperability across different virtual environments. This study emphasizes the Metaverse’s potential to revolutionize experiences for individuals with disabilities, provided that accessibility is embedded in its foundational design. Ethical and legal considerations, such as privacy, non-discrimination, and evolving legal frameworks, are identified as critical factors that shape an inclusive Metaverse. We propose a comprehensive framework that emphasizes technological adaptation and innovation, user-centric design, universal access, social and economic considerations, and global standards. This framework aims to guide future research and policy interventions to foster an inclusive digital environment in the Metaverse. This paper contributes to the emerging discourse on the Metaverse and digital accessibility, offering a nuanced understanding of its complexities and a roadmap for future exploration and development. This underscores the necessity of a multi-faceted approach that incorporates technological innovation, user-centered design, ethical considerations, legal compliance, and continuous research to create an inclusive and accessible Metaverse.

This paper proposes an improved one-power-point (OPP) maximum power point tracking (MPPT) algorithm for wind energy conversion system (WECS) to overcome the problems of the conventional OPP MPPT algorithm, namely, the difficulty in getting a precise value of the optimum coefficient, requiring pre-knowledge of system parameters, and non-uniqueness of the optimum curve. The solution is based on combining the particle swarm optimization (PSO) and optimum-relation-based (ORB) MPPT algorithms. The PSO MPPT algorithm is used to search for the optimum coefficient. Once the optimum coefficient is obtained, the proposed algorithm switches to the ORB MPPT mode of operation. The proposed algorithm neither requires knowledge of system parameters nor mechanical sensors. In addition, it improves the efficiency of the WECS. The proposed algorithm is studied for two different wind speed profiles, and its tracking performance is compared with conventional optimum torque control (OTC) and conventional ORB MPPT algorithms under identical conditions. The improved performance of the algorithm in terms of tracking efficiency is validated through simulation using MATLAB/Simulink. The simulation results confirm that the proposed algorithm has a better performance in terms of tracking efficiency and energy extracted. The tracking efficiency of the PSO-ORB MPPT algorithm could reach up to 99.4% with 1.9% more harvested electrical energy than the conventional OTC and ORB MPPT algorithms. Experiments have been carried out to demonstrate the validity of the proposed MPPT algorithm. The experimental results compare well with system simulation results, and the proposed algorithm performs well, as expected.