Generative Adversarial Networks (GANs) are capable of effectively synthesising new realistic images and estimating the potential distribution of samples utilising adversarial learning. Nevertheless, conventional GANs require a large amount of training data samples to produce plausible results. Inspired by the capacity for humans to quickly learn new concepts from a small number of examples, several meta-learning approaches for the few-shot datasets are presented. However, most of meta-learning algorithms are designed to tackle few-shot classification and reinforcement learning tasks. Moreover, the existing meta-learning models for image generation are complex, thereby affecting the length of training time required. Fast Adaptive Meta-Learning (FAML) based on GAN and the encoder network is proposed in this study for few-shot image generation. This model demonstrates the capability to generate new realistic images from previously unseen target classes with only a small number of examples required. With 10 times faster convergence, FAML requires only one-fourth of the trainable parameters in comparison baseline models by training a simpler network with conditional feature vectors from the encoder, while increasing the number of generator iterations. The visualisation results are demonstrated in the paper. This model is able to improve few-shot image generation with the lowest FID score, highest IS, and comparable LPIPS to MNIST, Omniglot, VGG-Faces, and mini ImageNet datasets. The source code is available on https://github.com/phaphuang/FAML .

Railway track defects pose significant safety risks and can lead to accidents, economic losses, and loss of life. Traditional manual inspection methods are either time-consuming, costly, or prone to human error. This paper proposes RailTrack-DaViT, a novel vision transformer-based approach for railway track defect classification. By leveraging the Dual Attention Vision Transformer (DaViT) architecture, RailTrack-DaViT effectively captures both global and local information, enabling accurate defect detection. The model is trained and evaluated on multiple datasets including rail, fastener and fishplate, multi-faults, and ThaiRailTrack. A comprehensive analysis of the model’s performance is provided including confusion matrices, training visualizations, and classification metrics. RailTrack-DaViT demonstrates superior performance compared to state-of-the-art CNN-based methods, achieving the highest accuracies: 96.9% on the rail dataset, 98.9% on the fastener and fishplate dataset, and 98.8% on the multi-faults dataset. Moreover, RailTrack-DaViT outperforms baselines on the ThaiRailTrack dataset with 99.2% accuracy, quickly adapts to unseen images, and shows better model stability during fine-tuning. This capability can significantly reduce time consumption when applying the model to novel datasets in practical applications.

Antimicrobial peptides (AMPs) provide a robust alternative to conventional antibiotics, combating escalating microbial resistance through their diverse functions and broad pathogen-targeting abilities. While current deep learning technologies enhance AMP generation, they face challenges in developing multifunctional AMPs due to intricate amino acid interdependencies and limited consideration of diverse functional activities. To overcome this challenge, we introduce a novel de novo multifunctional AMP design framework that enhances a Feedback Generative Adversarial Network (FBGAN) by integrating a global quantitative AMP activity regression module and a multifunctional-attribute integrated prediction module. This integrated approach not only facilitates the automated generation of potential AMP candidates, but also optimizes the network’s ability to assess their multifunctionality. Initially, by integrating an effective pre-trained regression and classification model with feedback-loop mechanisms, our model can not only identify potential valid AMP candidates, but also optimizes computational predictions of Minimum Inhibitory Concentration (MIC) values. Subsequently, we employ a combinatorial predictor to simultaneously identify and predict five multifunctional AMP bioactivities, enabling the generation of multifunctional AMPs. The experimental results demonstrate the efficiency of generating AMPs with multiple enhanced antimicrobial properties, indicating that our work can provide a valuable reference for combating multi-drug-resistant infections.

Purpose: The study's goal is to better understand how older and younger generations in Phnom Penh, Cambodia, behave when it comes to using digital payments. Investigating the choice of mobile payment application in the Phnom Penh region is another objective of this study. Here, it is also investigated how usage, economic situation, and adoption of mobile payments are related. Theoretical framework: One of the most well-known research models on consumer behavior among younger and older generations and the adoption of new technical systems emphasizes the influence of geography, socioeconomic status, and specific traits on consumer acceptance. The concept considers factors including social effects, credibility requirements, effort expectations, and performance requirements when determining the best digital payment application. Design/Methodology/Approach: The Unified Theory of Acceptance and Use of Technology (UTAUT) model and K-mean clustering for distance and economic status studies were used in this research paper to select 150 locals in Phnom Penh from the target population in order to study consumer behavioral intention to accept digital payment method and application. Findings: The result we aimed to show and analyses the Digital payment methods, payments sharing, receiving, storing evidences, that older generation started understanding the digital payment by Khmer language and accepting the digital transactions from known customers. The current generation using Sathapana application and English Language in wide ranges through the Phom Penh region. The study also trying to exhibit the purpose of the digital payment systems in and across the e-commerce activities, through the older and younger generation. Research, Practical & Social implications: The elder generation Z handled digital payments for regional transactions like banking, home tasks, loan repayment, peer lending, travel, and local marketplaces in Khmer. The subsequent generations of millennials also incorporated cross-border payments in English and Khmer. Originality/Value: Bringing up a Digital Platform shall make a positive change in economy and circulation of economy to the people and country. Unless the people trust the digital payments, they cant share their money for the business or basic operations to run the government. Thy the Digital Payment applications shall understand the behavior of the customers and share the services through the digital payment systems.