Detection of mold on the food surface using YOLOv5.

The study aimed to identify different molds that grow on various food surfaces. As a result, we conducted a case study for the detection of mold on food surfaces based on the "you only look once (YOLO) v5" principle. In this context, a dataset of 2050 food images with mold growing on their surfaces was created. Images were obtained from our own laboratory (850 images) as well as from the internet (1200 images). The dataset was trained using the pre-trained YOLOv5 algorithm. A laboratory test was also performed to confirm that the grown organisms were mold. In comparison to YOLOv3 and YOLOv4, this current YOLOv5 model had better precision, recall, and average precision (AP), which were 98.10%, 100%, and 99.60%, respectively. The YOLOv5 algorithm was used for the first time in this study to detect mold on food surfaces. In conclusion, the proposed model successfully recognizes any kind of mold present on the food surface. Using YOLOv5, we are currently conducting research to identify the specific species of the detected mold.

Evaluation of food safety knowledge, attitude, and self-reported practices of trained and newly recruited untrained workers of two baking industries in Dhaka, Bangladesh.

In Bangladesh, with the mounting esteem of bakery products, food safety issues in bakery industries are a paramount concern nowadays. In this regard, this current study was performed to evaluate food safety knowledge, attitude, and self-reported practices of two groups (160 trained and 55 new untrained) of workers from two popular baking industries in Dhaka, Bangladesh. A self-administrated questionnaire was used to acquire the data during the study. On food safety knowledge, attitude, and self-reported practices, trained workers' scores (33.01 ± 0.09, 14.86 ± 0.03, 10.66 ± 0.25, respectively) were significantly higher than the scores (9.82 ± 0.23, 10.44 ± 0.26, 5.91 ± 0.33, respectively) of newly appointed untrained workers. The quality assurance department displayed better knowledge, attitude, and self-reported practices scores than the rest of the departments of the industries. However, compared to knowledge and attitude, the self-reported practice was not up to a satisfactory level. According to the study, training can be proved effective for improving knowledge and attitude but does not always translate those into self-reported practice and behaviors. The results also reinforce the importance of conducting training for untrained workers and suggest further behavior-based food safety training for all employees.

A Systematic Review on the Use of AI and ML for Fighting the COVID-19 Pandemic.

Artificial intelligence (AI) and machine learning (ML) have caused a paradigm shift in healthcare that can be used for decision support and forecasting by exploring medical data. Recent studies have shown that AI and ML can be used to fight COVID-19. The objective of this article is to summarize the recent AI- and ML-based studies that have addressed the pandemic. From an initial set of 634 articles, a total of 49 articles were finally selected through an inclusion-exclusion process. In this article, we have explored the objectives of the existing studies (i.e., the role of AI/ML in fighting the COVID-19 pandemic); the context of the studies (i.e., whether it was focused on a specific country-context or with a global perspective; the type and volume of the dataset; and the methodology, algorithms, and techniques adopted in the prediction or diagnosis processes). We have mapped the algorithms and techniques with the data type by highlighting their prediction/classification accuracy. From our analysis, we categorized the objectives of the studies into four groups: disease detection, epidemic forecasting, sustainable development, and disease diagnosis. We observed that most of these studies used deep learning algorithms on image-data, more specifically on chest X-rays and CT scans. We have identified six future research opportunities that we have summarized in this paper. Impact Statement: Artificial intelligence (AI) and machine learning(ML) methods have been widely used to assist in the fight against COVID-19 pandemic. A very few in-depth literature reviews have been conducted to synthesize the knowledge and identify future research agenda including a previously published review on data science for COVID-19 in this article. In this article, we synthesized reviewed recent literature that focuses on the usages and applications of AI and ML to fight against COVID-19. We have identified seven future research directions that would guide researchers to conduct future research. The most significant of these are: develop new treatment options, explore the contextual effect and variation in research outcomes, support the health care workforce, and explore the effect and variation in research outcomes based on different types of data.