The World Health Organization (WHO) has stated that the spread of the coronavirus (COVID-19) is on a global scale and that wearing a face mask at work is the only effective way to avoid becoming infected with the virus. The pandemic made governments worldwide stay under lock-downs to prevent virus transmissions. Reports show that wearing face masks would reduce the risk of transmission. With the rise in population in cities, there is a greater need for efficient city management in today’s world for reducing the impact of COVID-19 disease. For smart cities to prosper, significant improvements to occur in public transportation, roads, businesses, houses, city streets, and other facets of city life will have to be developed. The current public bus transportation system, such as it is, should be expanded with artificial intelligence. The autonomous mask detection and alert system are needed to find whether the person is wearing a face mask or not. This article presents a novel IoT-based face mask detection system in public transportation, especially buses. This system would collect real-time data via facial recognition. The main objective of the paper is to detect the presence of face masks in real-time video stream by utilizing deep learning, machine learning, and image processing techniques. To achieve this objective, a hybrid deep and machine learning model was designed and implemented. The model was evaluated using a new dataset in addition to public datasets. The results showed that the transformation of Convolution Neural Network (CNN) classifier has better performance over the Deep Neural Network (DNN) classifier; it has almost complete face-identification capabilities with respect to people’s presence in the case where they are wearing masks, with an error rate of only 1.1%. Overall, compared with the standard models, AlexNet, Mobinet, and You Only Look Once (YOLO), the proposed model showed a better performance. Moreover, the experiments showed that the proposed model can detect faces and masks accurately with low inference time and memory, thus meeting the IoT limited resources.

Optimization problems can be found in many aspects of our lives. An optimization problem can be approached as searching problem where an algorithm is proposed to search for the value of one or more variables that minimizes or maximizes an optimization function depending on an optimization goal. Multi-objective optimization problems are also abundant in many aspects of our lives with various applications in different fields in applied science. To solve such problems, evolutionary algorithms have been utilized including genetic algorithms that can achieve decent search space exploration. Things became even harder for multi-objective optimization problems when the algorithm attempts to optimize more than one objective function. In this paper, we propose a hybrid genetic algorithm (HGA) that utilizes a genetic algorithm (GA) to perform a global search supported by the particle swarm optimization algorithm (PSO) to perform a local search. The proposed HGA achieved the concept of rehabilitation of rejected individuals. The proposed HGA was supported by a modified selection mechanism based on the K-means clustering algorithm that succeeded to restrict the selection process to promising solutions only and assured a balanced distribution of both the selected to survive and selected for rehabilitation individuals. The proposed algorithm was tested against 4 benchmark multi-objective optimization functions where it succeeded to achieve maximum balance between search space exploration and search space exploitation. The algorithm also succeeded in improving the HGA’s overall performance by limiting the average number of iterations until convergence.

In the coffee industry, "cupping" is the process of sensorial evaluation of coffee beans, also known as Sample Evaluation. This process is done for three major reasons: 1. to determine the actual sensory differences between coffee samples; 2. to describe the flavors of the samples; and 3. to determine preference of product. In totality, cupping targets the measurement of the coffee's quality which is expressed with a final numerical score. When cupping, the expert judge writes down the individual components' scores and ranks their intensities for reference. Fuzzy logic has been employed for sensory evaluation of chhana podo (a baked dairy product), also for mango pulp and litchi juice. Moreover, a similar work exists only to train the Honduran Coffee Cuppers. This paper introduces a fuzzy expert system, AI Cupper offering an intuitive way for representing the judge's knowledge by linguistically modeling his perception of the coffee attributes through sensorial evaluation, it is capable of training cuppers when evaluating coffees from several countries and even has the capacity to learn as cupping data flows through it. The system was tested and showed more than 95% of matching results compared with the experts' results.

The lack of cybersecurity awareness among everyday users is a significant issue that can have detrimental effects on individuals and organizations alike. Traditional training methods such as slideshows and presentations have proven to be ineffective and can cause trainees to feel overwhelmed, overloaded, confused, or bored. To address this issue, the development of an adaptive serious game that teaches cybersecurity in an effective, engaging, and personalized manner is proposed. Serious games provide an immersive and simulated experience that can help users determine how they might act in real-life scenarios. However, existing cybersecurity serious games often measure effectiveness outside of the game using surveys, tests, and interviews, which can lessen immersion and the simulated experience. Therefore, measuring improvement within the game itself can provide more meaningful data and derive truer conclusions about the usefulness of serious games in teaching cybersecurity. The goal of this research study is to develop such a game and measure its effectiveness in a way that can inform future cybersecurity training programs. By providing an engaging and personalized experience, serious games can improve cybersecurity awareness and reduce the risk of cyber threats. The results show that 79% of the participants admitted that they learned new things by playing the game, 84% said that they were engaged by the background story, 68% agreed that they had fun while playing the game, and 84% would recommend the game to others.

Plant diseases pose a significant threat to global food security, affecting crop yield, quality, and overall agricultural productivity. Traditionally, diagnosing plant diseases has relied on time-consuming visual inspections by experts, which can often lead to errors. Machine learning (ML) and artificial intelligence (AI), particularly Vision Transformers (ViTs), and Convolutional Neural Networks, offer a faster, automated alternative for identifying plant diseases through leaf image analysis. However, these models are often criticized for their “black box” nature, limiting trust in their predictions due to a lack of transparency. Our findings show that incorporating Explainable AI (XAI) techniques, such as Grad-CAM, Integrated Gradients, and LIME, significantly improves model interpretability, making it easier for practitioners to identify the underlying symptoms of plant diseases. This study not only contributes to the field of plant disease detection but also offers a novel perspective on improving AI transparency in real-world agricultural applications through the use of XAI techniques. With training accuracies of 100.00% for ViT, 96.88% for EfficientNetB7, 93.75% for EfficientNetB0, and 87.50% for ResNet50, and corresponding validation accuracies of 96.39% for ViT, 86.98% for EfficientNetB7, and 82.00% for EfficientNetB0, our proposed models outperform earlier research on the same dataset. This demonstrates a notable improvement in model performance while maintaining transparency and trustworthiness through interpretable and reliable decision-making.