Top-performing computer vision models are powered by convolutional neural networks (CNNs). Training an accurate CNN highly depends on both the raw sensor data and their associated ground truth (GT). Collecting such GT is usually done through human la...
Spam reviews are increasingly appearing on the Internet to promote sales or defame competitors by misleading consumers with deceptive opinions. This paper proposes a co-training approach called CoSpa (Co-training for Spam review identification) to id...
With the rapid development of electronic commerce, spam reviews are rapidly growing on the Internet to manipulate online customers’ opinions on goods being sold. This paper proposes a novel approach, called CoFea (Co-training by Features), to identif...
Automatic recognition of visual objects using a deep learning approach has been successfully applied to multiple areas. However, deep learning techniques require a large amount of labeled data, which is usually expensive to obtain. An alternative is...
Automatic crack segmentation plays an essential role in maintaining the structural health of buildings and infrastructure. Despite the success in fully supervised crack segmentation, the costly pixel-level annotation restricts its application, leadin...
Facing the problem of massive unlabeled data and limited labeled samples, semi-supervised learning is favored, especially co-training. Standard co-training requires sufficiently redundant and conditionally independent dual views; however, in fact, fe...
Semi-supervised learning is a technique that utilizes a limited set of labeled data and a large amount of unlabeled data to overcome the challenges of obtaining a perfect dataset in deep learning, especially in medical image segmentation. The accurac...
In recent years, a forward-looking subfield of machine learning has emerged with important applications in a variety of scientific fields. Semi-supervised learning is increasingly being recognized as a burgeoning area embracing a plethora of efficien...
Sentiment classification of forum posts of massive open online courses is essential for educators to make interventions and for instructors to improve learning performance. Lacking monitoring on learners’ sentiments may lead to high dropout rat...
Recently, convolutional neural networks (CNNs) have shown significant advantages in the tasks of image classification; however, these usually require a large number of labeled samples for training. In practice, it is difficult and costly to obtain su...
The theory of three-way decision has been widely utilized across various disciplines and fields as an efficient method for both knowledge reasoning and decision making. However, the application of the three-way decision theory to partially labeled da...
Due to the limited number of air quality monitoring stations, the data collected are limited. Using supervised learning for air quality fine-grained analysis, that is used to predict the air quality index (AQI) of the locations without air quality mo...
As the fastest growing trend in big data analysis, deep learning technology has proven to be both an unprecedented breakthrough and a powerful tool in many fields, particularly for image segmentation tasks. Nevertheless, most achievements depend on h...
Multimodal image fusion and semantic segmentation play pivotal roles in autonomous driving and robotic systems, yet their inherent interdependence remains underexplored. To address this gap and overcome performance bottlenecks, we propose SC-CoSF, a...
During natural disasters, social media platforms, such as X (formerly Twitter), become a valuable source of real-time information, with eyewitnesses and affected individuals posting messages about the produced damage and the victims. Although this in...
A large-scale and high-quality training dataset is an important guarantee to learn an ideal classifier for text sentiment classification. However, manually constructing such a training dataset with sentiment labels is a labor-intensive and time-consu...
Semantic image segmentation is a core task for autonomous driving, which is performed by deep models. Since training these models draws to a curse of human-based image labeling, the use of synthetic images with automatically generated labels together...
Lately, deep learning technology has been extensively investigated for accelerating dynamic magnetic resonance (MR) imaging, with encouraging progresses achieved. However, without fully sampled reference data for training, the current approaches may...
To provide more external knowledge for training self-supervised learning (SSL) algorithms, this paper proposes a maximum mean discrepancy-based SSL (MMD-SSL) algorithm, which trains a well-performing classifier by iteratively refining the classifier...
This paper studies the classification problem of hyperspectral image (HSI). Inspired by the great success of deep neural networks in Artificial Intelligence (AI), researchers have proposed different deep learning based algorithms to improve the perfo...
For decades, soft sensors have been extensively renowned for their efficiency in real-time tracking of expensive variables for advanced process control. However, despite the diverse efforts lavished on enhancing their models, the issue of label spars...
Sentiment Analysis (SA) is an active research area. SA aims to classify the online unstructured user-generated contents (UUGC) into positive and negative classes. A reliable training data is vital to learn a sentiment classifier for textual sentiment...
Visible and near infrared (VIS-NIR) spectroscopy has been applied to estimate soil organic carbon (SOC) content with many modeling strategies and techniques, in which a crucial and challenging problem is to obtain accurate estimations using a limited...
The scarcity and incompleteness of well-log data pose a critical challenge to deep learning-based reservoir prediction. To address this small-sample problem and improve prediction quality, we propose a novel semi-supervised asymmetric deep co-trainin...
Since manually labeling aerial images for pixel-level classification is expensive and time-consuming, developing strategies for land cover mapping without reference labels is essential and meaningful. As an efficient solution for this issue, domain a...
This paper proposes an extended semi-supervised regression approach to enhance the prediction accuracy of housing prices within the geographical information science field. The method, referred to as co-training geographical weighted regression (COGWR...
The failure of bearings can have a significant negative impact on the safe operation of equipment. Recently, deep learning has become one of the focuses of RUL prediction due to its potent scalability and nonlinear fitting ability. The supervised lea...
Participatory design (PD) and co-creation (Co-C) approaches to building Artificial Intelligence (AI) systems have become increasingly popular exercises for ensuring greater social inclusion and fairness in technological transformation by accounting f...
Edge devices execute pre-trained Artificial Intelligence (AI) models optimized on large Graphical Processing Units (GPUs); however, they frequently require fine-tuning when deployed in the real world. This fine-tuning, referred to as edge learning, i...
Background: Age-related hearing loss (ARHL) is associated with decreased communication, reduced social engagement, cognitive decline and an increased risk of dementia globally. Although increasing studies report the benefits of combing auditory and c...
7 July 2022
Tinnitus treatment, diagnosis and management across Europe varies significantly. The lack of national clinical guidelines for tinnitus management in most European countries and the absence of a common language across all disciplines involved is refle...
In the realm of medical image analysis, the cost associated with acquiring accurately labeled data is prohibitively high. To address the issue of label scarcity, semi-supervised learning methods are employed, utilizing unlabeled data alongside a limi...
With increasing concerns on transportation decarbonization, fuel cell hybrid trains (FCHTs) attract many attentions due to their zero carbon emissions during operation. Since fuel cells alone cannot recover the regenerative braking energy (RBE), ener...
With Transformers achieving outstanding performance on individual remote sensing (RS) tasks, we are now approaching the realization of a unified model that excels across multiple tasks through multi-task learning (MTL). Compared to single-task approa...
Semi-supervised classification is one of the core methods to deal with incomplete tag information without manual intervention, which has been widely used in various real problems for its excellent performance. However, the existing algorithms need to...
Wireless local area network (WLAN) fingerprint positioning is an indoor localization technique with high accuracy and low hardware requirements. However, collecting received signal strength (RSS) samples for the fingerprint database is time-consuming...
Medical image segmentation has made significant progress when a large amount of labeled data are available. However, annotating medical image segmentation datasets is expensive due to the requirement of professional skills. Additionally, classes are...
Recommendation systems offer an effective solution to information overload, finding widespread application across e-commerce, news platforms, and beyond. By analyzing interaction histories, these systems automatically filter and recommend items that...
Investigation shows that train travel has a lower pollution impact on the environment than flight travel or car travel. A stated preferences (SP) survey can effectively obtain the data of the commuter’s response to the hypothetical train price...
Existing photovoltaic (PV) power prediction methods suffer from insufficient data samples, poor model generalization ability, and the inability to share power data. In this paper, a hybrid prediction model based on federated learning (FL) is proposed...
Accurate citrus leaf disease detection requires a balance between visual discrimination and computational efficiency for camera-based monitoring in digital agriculture. This study proposes CoMATE-DETR, an efficient RT-DETR-based detector that coordin...
Athlete development depends on many factors that need to be balanced by the coach. The amount of data collected grows with the development of sensor technology. To make data-informed decisions for training prescription of their athletes, coaches coul...
Background:Mycobacterium bovis Bacillus Calmette-Guérin (BCG) is a live attenuated vaccine mainly administered to newborns and used for over 100 years to prevent the disease caused by Mycobacterium tuberculosis (M. tb). This vaccine can induce...
The key objective of this article is to present the importance of human capital in the training process co-financed by the European Social Fund (ESF) in shaping sustainable economic development attitudes from the perspective of the labour market comp...
Massive Open Online Courses (MOOCs) have expanded global access to education but continue to struggle with high attrition rates. This study presents a comparative analysis of self-labeled Semi-Supervised Learning (SSL) algorithms for predicting learn...
Background: Multiple Sclerosis (MS) is a chronic autoimmune disease of the central nervous system with a propensity to inflict severe neurological disability. Accurate and early prediction of MS progression is extremely crucial for its management and...
Post hoc explanations for black-box machine learning models have been criticized for potentially inaccurate surrogate models and computational burden at prediction time. We propose pre hoc and co hoc explainability frameworks that integrate interpret...
20 December 2025
Background: Rehabilitation technologies, including assistive devices, adaptive software, and robotic systems, are increasingly integral to contemporary rehabilitation practice. Yet, ensuring that training in their use is inclusive and accessible rema...
As the global aviation sector expands to accommodate increasing air travel demand, the subsequent rise in flights exacerbates carbon dioxide (CO2) emissions, challenging the sector’s environmental sustainability. Targeting net-zero emissions by...
Individuals with Autism Spectrum Disorder often experience distress or discomfort when facing changes in daily routines, particularly during major transitions such as beginning attendance at a Day Activity Center. To address this challenge, we design...
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