Four deep learning frameworks consisting of Yolov5m and Yolov5m combined with ResNet50, ResNet-101, and EfficientNet-B0, respectively, are proposed for classifying tomato fruit on the vine into three categories: ripe, immature, and damaged. For a training dataset consisting of 4500 images and a training process with 200 epochs, a batch size of 128, and an image size of 224 × 224 pixels, the prediction accuracy for ripe and immature tomatoes is found to be 100% when combining Yolo5m with ResNet-101. Meanwhile, the prediction accuracy for damaged tomatoes is 94% when using Yolo5m with the Efficient-B0 model. The ResNet-50, EfficientNet-B0, Yolov5m, and ResNet-101 networks have testing accuracies of 98%, 98%, 97%, and 97%, respectively. Thus, all four frameworks have the potential for tomato fruit classification in automated tomato fruit harvesting applications in agriculture.

This paper focuses on the application of artificial intelligence in education that artificial intelligence technology was utilized to reduce the instructor's workload as well as enhance learner engagement in online learning platforms. The idea of this paper is to use text-to-speech and speech-driven-face to automatically create a video lecture with the instructor's voice and face without recording the video. This approach not only gives the best display of lesson content of video lectures which contains slides (pdf format), instructor's voice, and face but also enables the modification of video lectures without recapture video. Furthermore, this approach allows learners to choose a video lecture with their desired instructor's voice and face. The experimental results validated our approach.

This paper focuses on developing autonomous waste management which helps efficient and optimal collecting and recycling of garbage. Optimized waste management and green energy-based approach lead to less fuel consumption, which eventually cuts off the carbon footprint, making the waste management process eco-friendlier. Specifically, an autonomous truck is utilized to automatically collect garbage from smart bins to a transfer station by means of solar-powered sensor networks, trajectory planning, self-driving car, RFID recognition, etc. To build an autonomous truck and an autonomous bin, we use hardware including Jetson Nano, Arduino, STM32F4 and camera and open source software including OpenCV, TensorFlow, Arduino IDE, Keil C.

Hepatitis B (HB) affects millions of people around the world every year. According to the World Health Organization (WHO), around 2 billion people have been infected with HB virus (HBV) historically, and the annual chronic HBV infection rate and death toll around the world are $\sim 296$ million and 820,000, respectively.¹ HBV is transmitted from one person to another via the exchange of body fluids and represents a serious health danger to both the individuals involved and the entire local population.² HBV comprises dual-stranded DNA and DNA polymerase enclosed by an exterior layer of HBsAg protein.^{3 – 5} The gold standard tests for HBV diagnosis include polymerase chain reaction (PCR) and enzyme-linked immunoassay (ELISA). However, PCR is time-consuming and expensive, whereas ELISA sometimes produces false positives (FPs) and false negatives (FNs).⁶ Consequently, there is an urgent need for cheaper, faster, and more reliable techniques for detecting HBV at early stage.

Large Language Models (LLMs) have received significant attention due to their potential to transform the field of education and assessment through the provision of automated responses to a diverse range of inquiries. The objective of this research is to examine the efficacy of three LLMs-ChatGPT, BingChat, and Bard-in relation to their performance on the Vietnamese High School Biology Examination dataset. This dataset consists of a wide range of biology questions that vary in difficulty and context. By conducting a thorough analysis, we are able to reveal the merits and drawbacks of each LLM, thereby providing valuable insights for their successful incorporation into educational platforms. This study examines the proficiency of LLMs in various levels of questioning, namely Knowledge, Comprehension, Application, and High Application. The findings of the study reveal complex and subtle patterns in performance. The versatility of ChatGPT is evident as it showcases potential across multiple levels. Nevertheless, it encounters difficulties in maintaining consistency and effectively addressing complex application queries. BingChat and Bard demonstrate strong performance in tasks related to factual recall, comprehension, and interpretation, indicating their effectiveness in facilitating fundamental learning. Additional investigation encompasses educational environments. The analysis indicates that the utilization of BingChat and Bard has the potential to augment factual and comprehension learning experiences. However, it is crucial to acknowledge the indispensable significance of human expertise in tackling complex application inquiries. The research conducted emphasizes the importance of adopting a well-rounded approach to the integration of LLMs, taking into account their capabilities while also recognizing their limitations. The refinement of LLM capabilities and the resolution of challenges in addressing advanced application scenarios can be achieved through collaboration among educators, developers, and AI researchers.