Recent deep models trained on large-scale RGB datasets lead to considerable achievements in visual detection tasks. However, the training examples are often limited for an infrared detection task, which may deteriorate the performance of deep detecto...
This paper aimed to increase accuracy of an Alzheimer’s disease diagnosing function that was obtained in a previous study devoted to application of decision roots to the diagnosis of Alzheimer’s disease. The obtained decision root is a di...
There are conflicts between the increasingly complex operational requirements and the slow rate of system platform upgrading, especially in the industry of railway transit-signaling systems. We attempted to address this problem by establishing a mode...
While Machine Learning has become the holy grail of modern-day computing, it has many security flaws that have yet to be addressed and resolved. Adversarial attacks are one of these security flaws, in which an attacker appends noise to data samples t...
Adversarial examples easily mislead vision systems based on deep neural networks (DNNs) trained with softmax cross entropy (SCE) loss. The vulnerability of DNN comes from the fact that SCE drives DNNs to fit on the training examples, whereas the resu...
State-of-the-art neural network models are actively used in various fields, but it is well-known that they are vulnerable to adversarial example attacks. Throughout the efforts to make the models robust against adversarial example attacks, it has bee...
In order to increase the effectiveness of model training, data reduction is essential to data-centric Artificial Intelligence (AI). It achieves this by locating the most instructive examples in massive datasets. To increase data quality and training...
This paper discusses the challenges associated with a class imbalance in medical data and the limitations of current approaches, such as machine multi-task learning (MMTL), in addressing these challenges. The proposed solution involves a novel hybrid...
Geotechnical investigation plays an indispensable role in site characterization and provides necessary data for various construction projects. However, geotechnical measurements are time-consuming, point-based, and invasive. Non-destructive geophysic...
Identification of the protein targets of hit molecules is essential in the drug discovery process. Target prediction with machine learning algorithms can help accelerate this search, limiting the number of required experiments. However, Drug-Target I...
Performance limitations of automotive sensors and the resulting perception errors are one of the most critical limitations in the design of Advanced Driver Assistance Systems and Autonomous Driving Systems. Ability to efficiently recreate realistic e...
During the last few years, supervised deep convolutional neural networks have become the state-of-the-art for image recognition tasks. Nevertheless, their performance is severely linked to the amount and quality of the training data. Acquiring and la...
Convolutional neural networks (CNNs) have been extensively used in numerous remote sensing image detection tasks owing to their exceptional performance. Nevertheless, CNNs are often vulnerable to adversarial examples, limiting the uses in different s...
The present article focuses on the efficient deployment of dual locomotives in regional rail freight transport considering the quantification of traction energy and energy savings. In the first part of the article, a categorization of dual locomotive...
An effective anomaly-based intelligent IDS (AN-Intel-IDS) must detect both known and unknown attacks. Hence, there is a need to train AN-Intel-IDS using dynamically generated, real-time data in an adversarial setting. Unfortunately, the public datase...
In the present work, we propose a novel method utilizing only a decoder for generation of pseudo-examples, which has shown great success in image classification tasks. The proposed method is particularly constructive when the data are in a limited qu...
Thanks to their capability to learn generalizable descriptors directly from images, deep Convolutional Neural Networks (CNNs) seem the ideal solution to most pattern recognition problems. On the other hand, to learn the image representation, CNNs nee...
Data-driven soft sensors have increasingly been applied for the quality measurement of industrial polymerization processes in recent years. However, owing to the costly assay process, the limited labeled data available still pose significant obstacle...
Black-box attacks against deep neural network (DNN) classifiers are receiving increasing attention because they represent a more practical approach in the real world than white box attacks. In black-box environments, adversaries have limited knowledg...
Many machine-learning-enabled approaches towards anomaly detection depend on the availability of vast training data. Our data are formed from power readings of cycles from domestic appliances, such as dishwashers or washing machines, and contain no k...
Certain fields present significant challenges when attempting to train complex Deep Learning architectures, particularly when the available datasets are limited and imbalanced. Real-time object detection in maritime environments using aerial images i...
In the last decade, the number of studies about machine learning algorithms applied to sports, e.g., injury forecasting and athlete performance prediction, have rapidly increased. Due to the number of works and experiments already present in the stat...
While Deep Neural Networks (DNNs) and Transfer Learning (TL) have greatly contributed to several medical and clinical disciplines, the application to multivariate physiological datasets is still limited. Current examples mainly focus on one physiolog...
Research on deep neural network (DNN)-based multi-dimensional data visualization has thoroughly explored cross-modal hash retrieval (CMHR) systems, yet their vulnerability to malicious adversarial examples remains evident. Recent work improves the ro...
Positive and unlabeled learning (PU learning) is a significant binary classification task in machine learning; it focuses on training accurate classifiers using positive data and unlabeled data. Most of the works in this area are based on a two-step...
Named entity recognition (NER) is a fundamental task in natural language processing. Recently, non-traditional nouns (known as complex NER) have increasingly emerged, including long noun phrases and ambiguous names, for example, Birds of Prey (and th...
Training a deep learning model requires highly variable data to permit reasonable generalization. If the variability in the data about to be processed is low, the interest in obtaining this generalization seems limited. Yet, it could prove interestin...
This study presents WoolGAN, a lightweight texture style transfer method based on a generative adversarial network (GAN), with wool felting texture as the primary example. Unlike conventional convolutional approaches, it requires only a small trainin...
This study presents a probabilistic prediction method for train-induced vibrations by combining a deep neural network (DNN) with the mixture density model in a cascade fashion, referred to as the DNN-RMDN model in this paper. A benchmark example is c...
Out-of-gauge trains are trains with loading freight that exceeds the loading limitation border. Considering collision avoidance, the out-of-gauge trains have speed restriction of their own, and the trains running on the parallel track. Therefore, it...
We explore unique considerations involved in fitting machine learning (ML) models to data with very high precision, as is often required for science applications. We empirically compare various function approximation methods and study how they scale...
The potential of machine learning (ML) models for predicting crystallographic symmetry information from single-phase powder X-ray diffraction (XRD) patterns is investigated. Given the scarcity of large, labeled experimental datasets, we train our mod...
This work is dedicated to the development of a system for generating artificial data for training neural networks used within a conveyor-based technology framework. It presents an overview of the application areas of computer vision (CV) and establis...
Influence-based data selection methods, such as TracIn, aim to estimate the impact of individual training samples on model predictions and are increasingly used for dataset curation and reduction. This study investigates whether selecting the most po...
The rapid development of information technology and Internet technology has a far-reaching impact on vocational education. It is possible to accurately and objectively evaluate the training of learners by recording the process data of learners’...
Wheat head detection is a core computer vision problem related to plant phenotyping that in recent years has seen increased interest as large-scale datasets have been made available for use in research. In deep learning problems with limited training...
This paper continues the proposed idea of stability training for legged robots with any number of legs and any size on a motion platform and introduces the concept of a learning-based controller, the global self-stabilizer, to obtain a self-stabiliza...
China’s landslide disasters are serious, and regional landslide disaster early-warning is one of the important means of disaster prevention and mitigation. The traditional regional landslide disaster early-warning model, however, is limited by...
Few-shot learning is a deep learning subfield that is the focus of research nowadays. This paper addresses the research question of whether a triplet-trained Siamese network, initially designed for multi-class classification, can effectively handle m...
Deep learning, neural networks and other data-driven processing techniques are increasingly used in the analysis of LiDAR point cloud data in forest environments due to the benefits offered in accuracy and adaptability to new environments. One of the...
To overcome the limitations of CMOS digital systems, emerging computing circuits such as memristor crossbars have been investigated as potential candidates for significantly increasing the speed and energy efficiency of next-generation computing syst...
Humans are able to quickly adapt to new situations, learn effectively with limited data, and create unique combinations of basic concepts. In contrast, generalizing out-of-distribution (OOD) data and achieving combinatorial generalizations are fundam...
This paper investigates the problem of spacing control between adjacent trains in train formation and proposes a distributed train-formation speed-convergence cooperative-control algorithm based on barrier Lyapunov function. Considering practical lim...
The generalizability of machine learning (ML) models for wearable monitoring in stroke rehabilitation is often constrained by the limited scale and heterogeneity of available data. Data augmentation addresses this challenge by adding computationally...
The most common forensic entomological application is the estimation of some portion of the time since death, or postmortem interval (PMI). To our knowledge, a PMI estimate is almost never accompanied by an associated probability. Statistical methods...
Small-object detection is a challenging task in computer vision due to the limited training samples and low-quality images. Transfer learning, which transfers the knowledge learned from a large dataset to a small dataset, is a popular method for impr...
Batch processes in the biopharmaceutical and chemical manufacturing industries often develop new products to meet changing market demands. When the dynamic models of these new products are trained, dynamic modeling with limited data for each product...
Traditional object detectors require extensive instance-level annotations for training. Conversely, few-shot object detectors, which are generally fine-tuned using limited data from unknown classes, tend to show biases toward base categories and are...
This paper presents signal filtering methods that can be effectively applied to train detection systems based on the axle counter systems that are currently in operation for train detection and provide information on the unoccupied status of railway...
The proliferation of deep learning has transformed artificial intelligence, demonstrating prowess in domains such as image recognition, natural language processing, and robotics. Nonetheless, deep learning models are susceptible to adversarial exampl...
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