Virtual Reality (VR) research has been widely applied in many fields. VR promises to deliver the experience that is beyond the user’s imagination. One of the advantages of VR is the feeling it gives of being there. VR can provide experiences impossible in the real world, such as flying, diving in deep water, exploring outer space, or living with dinosaurs. Despite the improvements in the software and hardware, the problem of motion sickness remains. We implement a deep learning model to train and predict motion sickness. A questionnaire is a well-known method to measure motion sickness. The weakness of the questionnaire is the measurement carried out after the user experiences motion sickness symptoms. By using the deep learning and EEG, the system will learn and classify motion sickness. The system learns the user’s EEG pattern when they begin to feel the sickness symptoms. The system will be trained using deep learning to identify the sickness patterns in the future. By the EEG patterns, the system can predict the sickness symptoms before it occurs. Our model outperforms traditional models in loss values, accuracy, and F-measure metrics in Roller Coaster. With other datasets, our model also performs well. Our model can achieve 82.83% accuracy from the dataset. We also found that the time steps to predict motion sickness during 5 minute periods is a suitable configuration.

Deep learning is a methodology applied across many fields. User comments are important for recommender systems because they include various types of emotional information that may influence the correctness or precision of the recommendation. Improving the accuracy of user ratings from obtained feasible recommendations is essential. In this paper, we propose a deep learning model to process user comments and to generate a possible user rating for user recommendations. First, the system uses sentiment analysis to create a feature vector as the input nodes. Next, the system implements noise reduction in the data set to improve the classification of user ratings. Finally, a deep belief network and sentiment analysis (DBNSA) achieves data learning for the recommendations. The experimental results indicated that this system has better accuracy than traditional methods.

Face mask detection is a technological application that employs computer vision methodologies to ascertain the presence or absence of a face mask on an individual depicted in an image or video. This technology gained significant attention and adoption during the COVID-19 pandemic, as wearing face masks became an important measure to prevent the spread of the virus. Face mask detection helps to enforce mask-wearing guidelines, which can significantly reduce the spread of respiratory illnesses, including COVID-19. Wearing masks in densely populated areas provides individuals with protection and hinders the spread of airborne particles that transmit viruses. The application of deep learning models in object recognition has shown significant progress, leading to promising outcomes in the identification and localization of objects within images. The primary aim of this study is to annotate and classify face mask entities depicted in authentic images. To mitigate the spread of COVID-19 within public settings, individuals can employ the use of face masks created from materials specifically designed for medical purposes. This study utilizes YOLOv8, a state-of-the-art object detection algorithm, to accurately detect and identify face masks. To analyze this study, we conducted an experiment in which we combined the Face Mask Dataset (FMD) and the Medical Mask Dataset (MMD) into a single dataset. The detection performance of an earlier research study using the FMD and MMD was improved by the suggested model to a “Good” level of 99.1%, up from 98.6%. Our study demonstrates that the model scheme we have provided is a reliable method for detecting faces that are obscured by medical masks. Additionally, after the completion of the study, a comparative analysis was conducted to examine the findings in conjunction with those of related research. The proposed detector demonstrated superior performance compared to previous research in terms of both accuracy and precision.

Travel pattern variations pose challenges in building a prediction model that accurately captures seasonal patterns or precision of BRT passenger numbers. An approach that integrates sophisticated prediction algorithms with high accuracy is needed to address the Transjakarta BRT passenger number prediction model problem. The proposed prediction model with the best accuracy is sought using deep learning on 8 models. The prediction model is used for short-term and long-term predictions, as well as looking for correlations in the prediction results of 13 Transjakarta corridors. The Python programming language with the Deep Learning Tensor Flow framework is run by Google Colaboratory used in the prediction simulation environment. The combination of BiLSTM-CNN was found to have the best accuracy of the evaluation value (SMAPE = 15.9387, MAPE = 0.598, and MSLE = 0.0425), although it has the longest time (134 seconds). Fluctuations in short-term predictions of passenger numbers evenly occur simultaneously across all corridors. Fluctuations in long-term predictions evenly occur simultaneously across all corridors, except in February. There is no negative correlation in the 13 prediction results and there are 8 corridors that have a close positive correlation. The prediction results can be used by transportation operators and the government to optimize resource planning and transportation policies to support sustainable community and economic mobility.

This paper presents an optimized Automatic License Plate Recognition (ALPR) system designed for resource-constrained devices, leveraging YOLOv8 for real-time object detection and Optical Character Recognition (OCR) to extract license plate information under challenging conditions such as low-light, motion blur, and occlusions. Unlike traditional ALPR systems that rely on high computational resources, our approach balances detection accuracy, processing speed, and efficiency. The system is evaluated on three benchmark datasets: the Chinese City Parking Dataset (CCPD) with 250,000 images under diverse conditions, the UFPR-ALPR Dataset (Universidade Federal do Paraná, Brazil) containing 4,500 real-world traffic images, and the RodoSol-ALPR Dataset with 20,000 highway surveillance images for high-speed license plate recognition. Among various YOLOv8 variants tested, YOLOv8-s achieved the best performance, with a mean Average Precision (mAP) of 99.3% while sustaining over 30 Frames Per Second (FPS), making it suitable for real-time ALPR applications. Furthermore, image sharpening and contour segmentation techniques improved OCR recognition accuracy by 5.1% under low-light conditions, improving robustness. Comparative analysis against state-of-the-art OCR-based ALPR methods (EasyOCR, FastOCR, and CR-NET) demonstrated that our approach surpasses existing models in both recognition rate and computational efficiency. These findings establish YOLOv8 as a highly effective and deployable solution for intelligent transportation, surveillance, and law enforcement applications requiring real-time license plate recognition with minimal computational overhead.

The lagoon is a natural system protected from the sea by a dune barrier creating energy from the movement of rising and falling tides, thus providing a sustainable option for extracting energy from tidal currents. The energy that can be extracted is one of the most potential renewable energy...