Dish understanding from digital media is an interesting problem, but it also contains a big challenge. The challenge comes from the complexity of ingredients in the dish. With the development of deep learning, several effective tools can solve the problem partially. In this work, the task of dish recognition is considered. A novel dish recognition method based on EfficientNet architecture and transfer learning is proposed. First, we modify the EfficientNet-B0 by adding several important layers. Second, we use transfer learning to utilize optimal parameters obtained from pretraining the model on ImageNet, and then retrain it on a new dataset of dish images, i.e., UEH-VDR dataset. The UEH-VDR dataset contains images about Vietnamese dishes collected from various sources. Experimental results show that the proposed method can achieve an accuracy of 92.33% for the task of recognizing a dish. It also works more effectively than other models based on popular convolutional neural networks such as VGG and ResNet. In addition, a mobile application is also developed based on the trained data to serve visitors who want to discover the Vietnamese culinary culture.

The explosive growth of the social media community has increased many kinds of misinformation and is attracting tremendous attention from the research community. One of the most prevalent ways of misleading news is cheapfakes. Cheapfakes utilize non-AI techniques such as unaltered images with false context news to create false news, which makes it easy and “cheap” to create and leads to an abundant amount in the social media community. Moreover, the development of deep learning also opens and invents many domains relevant to news such as fake news detection, rumour detection, fact-checking, and verification of claimed images. Nevertheless, despite the impact on and harmfulness of cheapfakes for the social community and the real world, there is little research on detecting cheapfakes in the computer science domain. It is challenging to detect misused/false/out-of-context pairs of images and captions, even with human effort, because of the complex correlation between the attached image and the veracity of the caption content. Existing research focuses mostly on training and evaluating on given dataset, which makes the proposal limited in terms of categories, semantics and situations based on the characteristics of the dataset. In this paper, to address these issues, we aimed to leverage textual semantics understanding from the large corpus and integrated with different combinations of text-image matching and image captioning methods via ANN/Transformer boosting schema to classify a triple of (image, caption₁, caption₂) into OOC (out-of-context) and NOOC (no out-of-context) labels. We customized these combinations according to various exceptional cases that we observed during data analysis. We evaluate our approach using the dataset and evaluation metrics provided by the COSMOS baseline. Compared to other methods, including the baseline, our method achieves the highest Accuracy, Recall, and F1 scores.

Many real-life problems present multiple options necessitating modern researchers to identify the most suitable choice or decision. In this context, fuzzy sets have proven to be valuable tools for addressing such problems. Picture fuzzy sets (PFSs), due to their increased flexibility and broader domain as compared to other extensions of fuzzy sets (FSs) have been extensively used in solving various decision-making problems. To achieve optimal decisions, various fuzzy graphs and fuzzy hypergraph structures are employed. Picture fuzzy directed hypergraphs (PFDHGs) offer greater utility in addressing problems with multiple options or uncertainties. In this study, we introduce novel concepts of PFDHGs, along with their applications in decision-making theory and hazardous chemicals management. Initially, we present the foundational concepts of PFDHGs, including essential terminologies such as hyper-edge and height. Subsequently, we explore various types of PFDHGs, such as simple PFDHGs and support simple PFDHGs. Additionally, we introduce the concept of (α,β,γ)-level hyperarcs and elucidate the fundamental sequence in PFDHGs based on these arcs. The study further examines elementary PFDHGs, partial PFDHGs, and ordered PFDHGs, investigating the relationships among them. Structural properties of PFDHGs are analyzed through different types of homomorphisms of PFDHGs. We also define the order and size of PFDHGs. Finally, we present the applications of PFDHGs in decision-making and managing hazardous chemicals, emphasizing the innovative contributions and practical implications of our work.

Smart E-Learning is an education form, in which information technology plays an important role. Smart E-Learning utilizes the achievements of modern technologies such as artificial intelligence (AI). So, it can help to gain the ability of connectivity, sharing, convenience, and flexibility for learners. All activities including teaching, learning, discussing, testing, and grading, are well done over the internet environment. However, some potential risks can cause participants, especially, about data privacy. The issue is a big challenge for developing applications for smart E-Learning. Therefore, the goal to combine E-Learning with Blockchain technology to improve security for smart E-Learning is necessary. In this paper, we discuss potential and challenge for applying AI and Blockchain for developing applications for smart E-Learning in Vietnam. We also propose to combine independent testing models integrated with AI and Blockchain into the same smart E-Learning system.

Image edge detection is an important task in image processing and pattern recognition. Edges in digital images signify image discontinuities and traditionally gradient information is utilized in finding possible edge pixels. In this work, we consider a fusion approach using multiscale gradient maps along with non-parametric Fisher information which is recently used in edge detection. By using multiscale gradient maps we obtain better edge localization and robust edge maps and local thresholding with Fisher information helps obtain better detection. Experimental results on a variety of digital images and performance evaluation undertaken in comparison with edge detectors from the literature show the advantage of the proposed approach.