Plastic waste issues emerged from the build-up of plastics that negatively impacts the environment. As a result, plastic waste detection is proposed in many research studies to tackle the problems. Therefore, this paper aims to review hyperspectral imaging techniques and machine learning in plastic waste detection. Hyperspectral imaging techniques are found to be effective in detecting plastic waste and microplastics as they were able to capture plastic reflectance spectral by using the near-infrared sensor. However, the review also shows that hyperspectral imaging techniques were less efficient in capturing the electromagnetic spectrum of black plastics due to carbon-black absorption properties. Carbon-black strongly absorbs light in the ultraviolet and infrared spectral range of the electromagnetic spectrum, therefore not detected by the near-infrared sensor. This paper also reviews how machine learning can alternatively detect and sort all types of waste, including plastics. Multiple studies show that the machine learning model achieved good accuracy in detecting all types of plastics based on the waste dataset. Finally, it can be seen that the spectral information of plastic can be used as feature extraction for machine learning models for better plastic detection. It is hoped that this study will contribute to more systematic research on the same topic.

In gastronomic tourism, food is viewed as the central tourist attraction. Specifically, indigenous food is known to represent the expression of local culture and identity. To promote gastronomic tourism, it is critical to have a model for the food business analytics system. This research undertakes an empirical evaluation of recent transfer learning models for deep learning feature extraction for a food recognition model. The VIREO-Food172 Dataset and a newly established Sabah Food Dataset are used to evaluate the food recognition model. Afterwards, the model is implemented into a web application system as an attempt to automate food recognition. In this model, a fully connected layer with 11 and 10 Softmax neurons is used as the classifier for food categories in both datasets. Six pre-trained Convolutional Neural Network (CNN) models are evaluated as the feature extractors to extract essential features from food images. From the evaluation, the research found that the EfficientNet feature extractor-based and CNN classifier achieved the highest classification accuracy of 94.01% on the Sabah Food Dataset and 86.57% on VIREO-Food172 Dataset. EFFNet as a feature representation outperformed Xception in terms of overall performance. However, Xception can be considered despite some accuracy performance drawback if computational speed and memory space usage are more important than performance.

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.

As cloud becomes the tool of choice for more data storage services, the number of service providers has also increased. With these choices, organisations have a wide selection of services available to move their data to the cloud. However, the responsibility to maintain the security of sensitive data stored therein remains paramount. This paper will discuss some of the challenges of securing a cloud storage and putting it into context by reviewing relevant literature. The challenges associated with the three important security aspects (confidentiality, integrity and availability) are discussed together with the vulnerabilities linked to them. It is important to look into these challenges as cloud storage is not only about technological evolution but involves security considerations. We aim to provide insights of security challenges and its solutions to enhance cloud storage implementation.

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 attempts to illustrate the phases of designing a flipped learning mobile application. It is worth noting that changes in students’ learning behavior should be met by changes in the classroom – particularly on the way a course should be delivered. Studies have shown that students who learn using the flipped learning method are less likely to fail as opposed to their counterparts in the traditional classroom setting. The rising importance and popularity of flipped learning necessitates the development of a mobile application that assists both students to learn and allow instructors to manage their course via their mobile devices, almost anywhere and anytime. The software development life cycle (SDLC) is divided into four distinct phases: 1) Preliminary study, 2) content design, 3) System design and development, and 4) System evaluation. The effectiveness of the application is tested using electroencephalography (EEG). The findings suggest effectiveness of the mobile application falls within the acceptable range. Improvements for the flipped learning mobile application is also presented.