Plastic waste is a growing environmental concern that poses a significant threat to onshore ecosystems, human health, and wildlife. The accumulation of plastic waste in oceans has reached a staggering estimate of over eight million tons annually, leading to hazardous outcomes in marine life and the food chain. Plastic waste is prevalent in urban areas, posing risks to animals that may ingest it or become entangled in it, and negatively impacting the economy and tourism industry. Effective plastic waste management requires a comprehensive approach that includes reducing consumption, promoting recycling, and developing innovative technologies such as automated plastic detection systems. The development of accurate and efficient plastic detection methods is therefore essential for effective waste management. To address this challenge, machine learning techniques such as the YOLOv5 model have emerged as promising tools for developing automated plastic detection systems. Furthermore, there is a need to study both visible light (RGB) and near-infrared (RGNIR) as part of plastic waste detection due to the unique properties of plastic waste in different environmental settings. To this end, two plastic waste datasets, comprising RGB and RGNIR images, were utilized to train the proposed model, YOLOv5m. The performance of the model was then evaluated using a 10-fold cross-validation method on both datasets. The experiment was extended by adding background images into the training dataset to reduce false positives. An additional experiment was carried out to fuse both the RGB and RGNIR datasets. A performance-metric score called the Weighted Metric Score (WMS) was proposed, where the WMS equaled the sum of the mean average precision at the intersection over union (IoU) threshold of 0.5 (mAP@0.5) × 0.1 and the mean average precision averaged over different IoU thresholds ranging from 0.5 to 0.95 (mAP@0.5:0.95) × 0.9. In addition, a 10-fold cross-validation procedure was implemented. Based on the results, the proposed model achieved the best performance using the fusion of the RGB and RGNIR datasets when evaluated on the testing dataset with a mean of mAP@0.5, mAP@0.5:0.95, and a WMS of 92.96% ± 2.63%, 69.47% ± 3.11%, and 71.82% ± 3.04%, respectively. These findings indicate that utilizing both normal visible light and the near-infrared spectrum as feature representations in machine learning could lead to improved performance in plastic waste detection. This opens new opportunities in the development of automated plastic detection systems for use in fields such as automation, environmental management, and resource management.

COVID-19, an infectious coronavirus disease, has triggered a pandemic that has claimed many lives. Clinical institutes have long considered computed tomography (CT) as an excellent and complementary screening method to reverse transcriptase-polymerase chain reaction (RT-PCR). Because of the limited dataset available on COVID-19, transfer learning-based models have become the go-to solutions for automatic COVID-19 detection. However, CT images are typically provided in grayscale, thus posing a challenge for automatic detection using pre-trained models, which were previously trained on RGB images. Several methods have been proposed in the literature for converting grayscale images to RGB (three-channel) images for use with pre-trained deep-learning models, such as pseudo-colorization, replication, and colorization. The most common method is replication, where the one-channel grayscale image is repeated in the three-channel image. While this technique is simple, it does not provide new information and can lead to poor performance due to redundant image features fed into the DL model. This study proposes a novel image pre-processing method for grayscale medical images that utilize Histogram Equalization (HE) and Contrast Limited Adaptive Histogram Equalization (CLAHE) to create a three-channel image representation that provides different information on each channel. The effectiveness of this method is evaluated using six other pre-trained models, including InceptionV3, MobileNet, ResNet50, VGG16, ViT-B16, and ViT-B32. The results show that the proposed image representation significantly improves the classification performance of the models, with the InceptionV3 model achieving an accuracy of 99.60% and a recall (also referred as sensitivity) of 99.59%. The proposed method addresses the limitation of using grayscale medical images for COVID-19 detection and can potentially improve the early detection and control of the disease. Additionally, the proposed method can be applied to other medical imaging tasks with a grayscale image input, thus making it a generalizable solution.

This study examines the response of users' feelings from a gender perspective toward interactive video mobile learning (IVML). An IVML prototype was developed for the Android platform allowing users to install and make use of the app for m-learning purposes. This study aims to measure the level of feelings toward the IVML prototype and examine the differences in gender perspectives, identify the most responsive feelings between male, and female users as prominent feelings and measure the correlation between user-friendly feeling traits as an independent variable in accordance with gender attributes. The feelings response could then be extracted from the user experience, user interface, and human-computer interaction based on gender perspectives using the Kansei engineering approach as the measurement method. The statistical results demonstrated the different emotional reactions from a male and female perspective toward the IVML prototype may or may not have a correlation with the user-friendly trait, perhaps having a similar emotional response from one to another.