Automatic Plant Image Identification of Vietnamese species using Deep Learning Models

Art in general and fine arts, in particular, play a significant role in human life, entertaining and dispelling stress and motivating their creativeness in specific ways. Many well-known artists have left a rich treasure of paintings for humanity, preserving their exquisite talent and creativity through unique artistic styles. In recent years, a technique called ’style transfer’ allows computers to apply famous artistic styles into the style of a picture or photograph while retaining the shape of the image, creating superior visual experiences. The basic model of that process, named ’Neural Style Transfer,’ has been introduced promisingly by Leon A. Gatys; however, it contains several limitations on output quality and implementation time, making it challenging to apply in practice. Based on that basic model, an image transform network was proposed in this paper to generate higher-quality artwork and higher abilities to perform on a larger image amount. The proposed model significantly shortened the execution time and can be implemented in a real-time application, providing promising results and performance. The outcomes are auspicious and can be used as a referenced model in color grading or semantic image segmentation, and future research focuses on improving its applications.

The natural ecosystem incorporates thousands of plant species and distinguishing them is normally manual, complicated, and time-consuming. Since the task requires a large amount of expertise, identifying forest plant species relies on the work of a team of botanical experts. The emergence of Machine Learning, especially Deep Learning, has opened up a new approach to plant classification. However, the application of plant classification based on deep learning models remains limited. This paper proposed a model, named PlantKViT, combining Vision Transformer architecture and the KNN algorithm to identify forest plants. The proposed model provides high efficiency and convenience for adding new plant species. The study was experimented with using Resnet-152, ConvNeXt networks, and the PlantKViT model to classify forest plants. The training and evaluation were implemented on the dataset of DanangForestPlant, containing 10,527 images and 489 species of forest plants. The accuracy of the proposed PlantKViT model reached 93%, significantly improved compared to the ConvNeXt model at 89% and the Resnet-152 model at only 76%. The authors also successfully developed a website and 2 applications called ‘plant id’ and ‘Danangplant’ on the iOS and Android platforms respectively. The PlantKViT model shows the potential in forest plant identification not only in the conducted dataset but also worldwide. Future work should gear toward extending the dataset and enhance the accuracy and performance of forest plant identification.

Due to the robust growth in the amount of data and Internet users, there has been a significant rise in information overload, hindering timely access to user demand. While information retrieval systems, such as Google, Bing, and Altavista have partially addressed this challenge, prioritization and personalization of information have yet to be fully implemented. Therefore, recommendation systems are developed to resolve the issue by filtering and segmenting important information from an enormous volume of data based on different criteria such as preferences, interests, and user behaviors. By collecting data on users' interests and purchased products, the system can predict whether a particular user would enjoy an item, thus delivering an appropriate suggestion strategy. However, the increased number of Internet users and items has resulted in sparseness in increasingly vast datasets, reducing the performance of recommendation algorithms. Therefore, this study developed a model integrating Convolutional Neural Network (CNN) and Matrix Factorization (MF) to add extra product and user information, extract contexts, and add bias to the observed ratings in the training process, attempting to enhance the recommendation accuracy and context understanding. This approach can take advantage of CNN to efficiently capture an image's or document's local features, with the combination of MF to create relationships between 2 main entities, users and items. The proposed model obtained the highest RMSE of 0.93 when predicting favorable movies for 4,000 users, with an ability to learn complex contextual features and suggest more relevant content. The results are promising and can act as a reference for developing context understanding in recommendation systems, and future work may focus on optimizing the performance and developing more text-processing techniques.

Keyphrase extraction has recently become a foundation for developing digital library applications, especially in semantic information retrieval techniques. From that context, in this paper, a keyphrase extraction model was formulated in terms of Natural Language Processing, applied explicitly in extracting information and searching techniques in tourism. The proposed process includes collecting and processing data from tourism sources such as Tripadvisor.com, Agoda.com, and vietnam-guide.com. Then, the raw data was analyzed and pre-processed with labeling keyphrase and fed data forward to Pretrained BERT model and Bidirectional Long Short-Term Memory with Conditional Random Field. The model performed the combination of Bidirectional Long Short-Term Memory with Conditional Random Field in order to solve keyphrase extraction tasks. Furthermore, the model integrated the Elasticsearch technique to enhance performance and time of looking up tourism destinations' information. The outcome extracted key phrases produce high accuracy and can be applied for extraction problems and textual content summaries.

In recent years, Magnetic Resonance Imaging (MRI) has emerged as a prevalent medical imaging technique, offering comprehensive anatomical and functional information. However, the MRI data acquisition process presents several challenges, including time-consuming procedures, prone motion artifacts, and hardware constraints. To address these limitations, this study proposes a novel method that leverages the power of generative adversarial networks (GANs) to generate multi-domain MRI images from a single input MRI image. Within this framework, two primary generator architectures, namely ResUnet and StarGANs generators, were incorporated. Furthermore, the networks were trained on multiple datasets, thereby augmenting the available data, and enabling the generation of images with diverse contrasts obtained from different datasets, given an input image from another dataset. Experimental evaluations conducted on the IXI and BraTS2020 datasets substantiate the efficacy of the proposed method compared to an existing method, as assessed through metrics such as Structural Similarity Index (SSIM), Peak Signal-to-Noise Ratio (PSNR) and Normalized Mean Absolute Error (NMAE). The synthesized images resulting from this method hold substantial potential as invaluable resources for medical professionals engaged in research, education, and clinical applications. Future research gears towards expanding experiments to larger datasets and encompassing the proposed approach to 3D images, enhancing medical diagnostics within practical applications.