With the widespread adoption of smart farms and continuous advancements in IoT (Internet of Things) technology, acquiring diverse additional data has become increasingly convenient. Consequently, studies relevant to deep learning models that leverage...
Finger vein recognition has gained significant attention for its importance in enhancing security, safeguarding privacy, and ensuring reliable liveness detection. As a foundation of vein recognition systems, vein detection faces challenges, including...
Existing fault diagnosis methods assume the identical distribution of training and test data, failing to adapt to source–target domain differences in industrial scenarios and limiting generalization. They also struggle to explore inter-domain c...
Software effort estimation (SEE) serves as a cornerstone of effective software project management, and case-based reasoning (CBR) stands out as one of the most extensively adopted approaches within this domain. Nevertheless, CBR-based SEE models are...
Despite substantial progress in the field of deep learning, overfitting persists as a critical challenge, and data augmentation has emerged as a particularly promising approach due to its capacity to enhance model generalization in various computer v...
Graph Neural Networks (GNNs) have achieved promising performance in various graph learning tasks. Real-world graph learning often suffers from severe label scarcity, which hinders GNNs’ feature and topology learning and causes poor generalizati...
Mixup is a data augmentation technique that improves prediction accuracy in classification tasks by combining representations of training samples, which makes it particularly effective in settings with limited data and during fine-tuning for downstre...
Human Activity Recognition (HAR) is an essential area of research in Artificial Intelligence and Machine Learning, with numerous applications in healthcare, sports science, and smart environments. While several advancements in the field, such as atte...
In view of the time-consuming and laborious manual picking and sorting of strawberries, the direct impact of image recognition accuracy on automatic picking and the rapid development of deep learning(DL), a Faster Regions with Convolutional Neural Ne...
Alzheimer’s disease (AD) is an irreversible progressive cerebral disease with most of its symptoms appearing after 60 years of age. Alzheimer’s disease has been largely attributed to accumulation of amyloid beta (Aβ), but a complete...
Machine learning (ML) has revolutionized healthcare by enhancing diagnostic capabilities because of its ability to analyze large datasets and detect minor patterns often overlooked by humans. This is beneficial, especially in pain recognition, where...
The abuse of chemical agents in wastewater treatment is very universal. However, accurate predictive models capable of addressing this issue rely on precise and abundant data. Limitations in sampling frequency and equipment often lead to insufficient...
Deep neural network (DNN) models are usually built based on the i.i.d. (independent and identically distributed), also known as in-distribution (ID), assumption on the training samples and test data. However, when models are deployed in a real-world...
Stroke is a leading cause of long-term disability worldwide, necessitating efficient motor function assessment to guide personalized rehabilitation. The Fugl–Meyer Assessment for the Upper Extremity (FMA-UE) is a clinical gold-standard tool, bu...
Human-machine addressee detection (H-M AD) is a modern paralinguistics and dialogue challenge that arises in multiparty conversations between several people and a spoken dialogue system (SDS) since the users may also talk to each other and even to th...
Few-shot image classification aims to improve the performance of traditional image classification when faced with limited data. Its main challenge lies in effectively utilizing sparse sample label data to accurately predict the true feature distribut...
White blood cells (WBCs) in the human immune system defend against infection and protect the body from external hazardous objects. They are comprised of neutrophils, eosinophils, basophils, monocytes, and lymphocytes, whereby each accounts for a dist...
Pain recognition based on multimodal physiological signals remains a challenge, not only because of the limited training data, but also due to the varying responses of individuals. In this article, we present a randomized modality mixing technique (M...
Deep learning-based medical image analysis technology has been developed to the extent that it shows an accuracy surpassing the ability of a human radiologist in some tasks. However, data labeling on medical images requires human experts and a great...
Aflatoxin B1 (AFB1) is widely present in maize silage feed and poses strong toxicity, seriously threatening livestock production and food safety. To achieve efficient and accurate non-destructive detection of AFB1, this study proposes a quantitative...
Combinations of data augmentation methods and deep learning architectures for automatic pancreas segmentation on CT images are proposed and evaluated. Images from a public CT dataset of pancreas segmentation were used to evaluate the models. Baseline...
Modern data augmentation strategies such as Cutout, Mixup, and CutMix, have achieved good performance in image recognition tasks. Particularly, the data augmentation approaches, such as Mixup and CutMix, that mix two images to generate a mixed traini...
Data augmentation with mixup has been proven effective in various machine learning tasks. However, previous methods primarily concentrate on generating previously unseen virtual examples using randomly selected mixed samples, which may overlook the i...
Background/Objectives: Cytochrome P450 3A4 (CYP3A4) metabolizes approximately 30–50% of clinically used drugs; thus, accurate prediction of CYP3A4 inhibition is essential for early assessment of drug–drug interaction (DDI) risk and toxici...
Large-scale automatic speech recognition model has achieved impressive performance. However, huge computational resources and massive amount of data are required to train an ASR model. Knowledge distillation is a prevalent model compression method wh...
Reliable photovoltaic (PV) power forecasting based on deep learning typically requires large historical datasets to capture the high temporal and spatial variability of solar irradiance. However, in many real-world applications, data availability is...
In order to ensure the earthquake safety of existing buildings, retrofitting applications come to the fore in terms of being fast and cost-effective. Among these applications, fiber-reinforced polymer (FRP) composites are widely preferred thanks to t...
As the forms of cyber threats become increasingly severe, cybersecurity knowledge graphs (KGs) have become essential tools for understanding and mitigating these threats. However, the quality of the KG is critical to its effectiveness in cybersecurit...
Named Entity Recognition (NER) is at the core of natural language understanding. The quality and amount of datasets determine the performance of deep-learning-based NER models. As datasets for NER require token-level or word-level labels to be assign...
The design of cushioning packaging for flat-screen television (TV) products relies heavily on repeated simulations, resulting in high development costs and low design efficiency. In this study, we propose a hybrid framework integrating finite element...
Accurate estimation of the maize leaf area index (LAI) and biomass is of great importance in guiding field management and early yield estimation. Physical models and traditional machine learning methods are commonly used for LAI and biomass estimatio...
The task of charge prediction is to predict the charge based on the fact description. Existing methods have a good effect on the prediction of high-frequency charges, but the prediction of low-frequency charges is still a challenge. Moreover, there e...
Semi-supervised learning has been proven to be effective in utilizing unlabeled samples to mitigate the problem of limited labeled data. Traditional semi-supervised learning methods generate pseudo-labels for unlabeled samples and train the classifie...
Automated betel leaf disease recognition remains challenging under limited and imbalanced data conditions. This study investigates an Upper Confidence Bound (UCB)-guided adaptive augmentation framework for the classification of healthy leaves, leaf r...
SMD (Singapore Maritime Dataset) is a public dataset with annotated videos, and it is almost unique in the training of deep neural networks (DNN) for the recognition of maritime objects. However, there are noisy labels and imprecisely located boundin...
Reliable in-situ defect classification is essential for ensuring the mechanical performance of parts produced by material extrusion. However, machine learning and deep learning models often suffer from poor generalization because training datasets ty...
Unsupervised medical image translation remains challenging because model development often relies on unpaired training, whereas reliable evaluation requires well-matched reference images. PD-weighted and T2-weighted brain MR images provide a useful t...
Data augmentation (DA) is a widely used technique to improve generalization in Deep Learning. However, many existing mixing-based approaches apply uniform strategies or rely on computationally expensive saliency guidance. In this work, we propose Ada...
Nowadays, financial institutions increasingly leverage artificial intelligence to enhance decision-making and optimize investment strategies. A specific application is the automatic analysis of large volumes of unstructured textual data to extract re...
Accurate and efficient modeling of AlGaN/GaN HEMTs is essential for the design of next-generation power electronics. This study introduces a hybrid Auxiliary Classifier Generative Adversarial Network (ACGAN)–mixup data augmentation framework to...
To tackle phenotypic variability and detection accuracy issues of tea shoots in open-air gardens due to lighting and varietal differences, this study proposes Tea CycleGAN and a data augmentation method. It combines multi-scale image style transfer w...
Rotating machinery is a core component of modern industry, and its operational state directly affects system safety and reliability. In order to achieve intelligent fault diagnosis of bearings under complex working conditions, the health management o...
Research on neuromorphic computing has gained popularity in recent years. In particular, regularized embedded neural systems have been applied in several significant real-world situations, such as recommendation systems and transfer learning. This pa...
Sound event localization and detection (SELD) is a joint task that unifies sound event detection (SED) and direction-of-arrival estimation (DOAE). The task has become such a popular topic that it was introduced into the challenge on Detection and Cla...
Adversarial transferability makes black-box attacks practical and exposes weaknesses of deep neural networks for computer vision, image recognition, and visual understanding. Among various transferability-enhancing methods, input transformation is on...
Semi-supervised object detection (SSOD) has emerged as a promising technique to boost the performance of the detectors, utilizing both labeled and unlabeled data. However, in marine environments, SSOD faces formidable challenges posed by complex cond...
Background and Objectives: Computer-aided diagnostic systems have achieved remarkable success in the medical field, particularly in diagnosing malignant tumors, and have done so at a rapid pace. However, the generalizability of the results remains a...
Detecting banana pseudostems is an indispensable part of the intelligent management of banana cultivation, which can be used in settings such as counting banana pseudostems and smart fertilization. In complex environments, dense and occlusion banana...
Bird sound classification plays an important role in large-scale temporal and spatial environmental monitoring. In this paper, we investigate both transfer learning and training from scratch for bird sound classification, where pre-trained models are...
Respiratory diseases are leading causes of death worldwide, and failure to detect diseases at an early stage can threaten people’s lives. Previous research has pointed out that deep learning and machine learning are valid alternative strategies...
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