Maneuvering extended target tracking, an important but challenging research field, has attracted increasing attention in the field of radar signal processing. Variable structure multiple-model (VSMM) estimation is the current mainstream tracking algo...
Background: The integration of Artificial Intelligence (AI) into Business Process Management Systems (BPMSs) has led to the emergence of AI-Augmented Business Process Management Systems (ABPMSs). These systems offer dynamic adaptation, real-time proc...
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...
This paper investigates the effectiveness of various data augmentation techniques for enhancing Arabic speech emotion recognition (SER) using convolutional neural networks (CNNs). Utilizing the Saudi Dialect and BAVED datasets, we address the challen...
The accurate detection of ancient artifacts is very crucial in recognizing and tracking the origin of these relics. The methodologies used in engraving characters onto these objects are different from the ones used in the modern era, prompting the ne...
After lung cancer, breast cancer is the second leading cause of death in women. If breast cancer is detected early, mortality rates in women can be reduced. Because manual breast cancer diagnosis takes a long time, an automated system is required for...
Misinformation poses a significant challenge in the digital age, requiring robust methods to detect fake news. This study investigates the effectiveness of using Back Translation (BT) augmentation, specifically transformer-based models, to improve fa...
The global poultry industry faces growing challenges from skeletal disorders, with Kinky Back (KB) significantly impacting broiler welfare and production. KB causes spinal deformities that reduce mobility, feed access, and increase mortality. It ofte...
We aimed to assess the effects of hyperparameter tuning and automatic image augmentation for deep learning-based classification of orthodontic photographs along the Angle classes. Our dataset consisted of 605 images of Angle class I, 1038 images of c...
Introduction: Dental segmentation in panoramic radiograph has become very relevant in dentistry, since it allows health professionals to carry out their assessments more clearly and helps them to define the best possible treatment plan for their pati...
Deep learning model training and achieved performance relies on available data. Diabetic foot ulcers and other image processing applications in the medical domain add another layer of complexity to training data collection. Data collection is trouble...
Mapping agricultural fields using high-resolution satellite imagery and deep learning (DL) models has advanced significantly, even in regions with small, irregularly shaped fields. However, effective DL models often require large, expensive labeled d...
This study investigates the parameters influencing the compatibility between cement and polycarboxylate ether (PCE) admixtures in cements produced with various types and dosages of grinding aids (GAs). A total of 29 cement types (including a control)...
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...
Data augmentation techniques have recently gained more adoption in speech processing, including speech emotion recognition. Although more data tend to be more effective, there may be a trade-off in which more data will not provide a better model. Thi...
Deep learning models are widely used for medical image analysis and require large datasets, while sufficient high-quality medical data for training are scarce. Data augmentation has been used to improve the performance of these models. The lack of tr...
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...
Image captioning is an important task for improving human-computer interaction as well as for a deeper understanding of the mechanisms underlying the image description by human. In recent years, this research field has rapidly developed and a number...
Rapid and accurate detection of Camellia oleifera fruit is beneficial to improve the picking efficiency. However, detection faces new challenges because of the complex field environment. A Camellia oleifera fruit detection method based on YOLOv7 netw...
This paper aims to investigate the use of a Romanian data source, different classifiers, and text data augmentation techniques to implement a fake news detection system. The paper focusses on text data augmentation techniques to improve the efficienc...
The torque ripples in a switched reluctance motor (SRM) are minimized via an optimal adaptive dynamic regulator that is presented in this research. A novel reinforcement neural network learning approach based on machine learning is adopted to find th...
The World Health Organization and Korea National Health Insurance assert that the number of alopecia patients is increasing every year, and approximately 70 percent of adults suffer from scalp problems. Although alopecia is a genetic problem, it is d...
Data augmentation is one of the most important problems in deep learning. There have been many algorithms proposed to solve this problem, such as simple noise injection, the generative adversarial network (GAN), and diffusion models. However, to the...
The incidence of diabetes in Mauritius is amongst the highest in the world. Diabetic retinopathy (DR), a complication resulting from the disease, can lead to blindness if not detected early. The aim of this work was to investigate the use of transfer...
Deep learning-based plant disease classification models often suffer from performance degradation when training data are limited. Hence, generative models offer a promising solution for model performance in plant disease classification. In this work,...
Although artificial neural networks are widely used in various fields, including mining and mineral processing, they can be problematic for appropriately choosing the model architecture and parameters. In this article, we describe a procedure for the...
As cryptographic implementations leak secret information through side-channel emissions, the Hamming weight (HW) leakage model is widely used in deep learning profiling side-channel analysis (SCA) attacks to expose the leaked model. However, imbalanc...
Gastric cancer has a high mortality rate worldwide, but it can be prevented with early detection through regular gastroscopy. Herein, we propose a deep learning-based computer-aided diagnosis (CADx) system applying data augmentation to help doctors c...
A conversational system is an artificial intelligence application designed to interact with users in natural language, providing accurate and contextually relevant responses. Building such systems for low-resource languages like Swahili presents sign...
The reliance on deep learning models for sensor-based material classification amplifies the demand for labeled training data. However, acquiring large-scale, annotated spectral data for applications such as near-infrared (NIR) reflectance spectroscop...
Image recognition has been applied to many fields, but it is relatively rarely applied to medical images. Recent significant deep learning progress for image recognition has raised strong research interest in medical image recognition. First of all,...
Human Gait Recognition (HGR) is a biometric technique that has been utilized for security purposes for the last decade. The performance of gait recognition can be influenced by various factors such as wearing clothes, carrying a bag, and the walking...
Deep learning models yield remarkable results in skin lesions analysis. However, these models require considerable amounts of data, while accessibility to the images with annotated skin lesions is often limited, and the classes are often imbalanced....
There are numerous applications for building dimension data, including building performance simulation and urban heat island investigations. In this context, object detection and instance segmentation methods—based on deep learning—are of...
Background: Preoperative, noninvasive prediction of meningioma grade is important for therapeutic planning and decision making. In this study, we propose a dual-level augmentation strategy incorporating image-level augmentation (IA) and feature-level...
The diagnosis of faults in induction motors, such as broken rotor bars, is critical for preventing costly emergency shutdowns and production losses. The complexity of this task lies in the diversity of induction motor operating regimes. Specifically,...
Maize, a globally important crop, is highly susceptible to aflatoxin contamination, posing a serious threat. Therefore, accurate detection of aflatoxin levels in maize is of critical importance. In this study, the Multi-Scale Feature Network with Eff...
Triply periodic minimal surface microarchitectures (TPMS) were developed by mathematicians and evolved in all kingdoms of living organisms. Renowned for their lightweight yet robust attributes, TPMS structures find application in diverse fields, such...
Early illness detection enables medical professionals to deliver the best care and increases the likelihood of a full recovery. In this work, we show that computer-aided design (CAD) systems are capable of using chest X-ray (CXR) medical imaging moda...
Introduction: Cone-beam computed tomography (CBCT) has been applied to implant dentistry. The increasing use of this technology produces a critical number of images that can be used for training artificial intelligence (AI). Objectives: To investigat...
Brain tumor detection and categorization of its subtypes are essential for early diagnosis and improving patient outcomes. This research presents a cutting-edge approach that employs advanced data augmentation and deep learning methodologies for brai...
The extensive utilization of drones has led to numerous scenarios that encompass both advantageous and perilous outcomes. By using deep learning techniques, this study aimed to reduce the dangerous effects of drone use through early detection of dron...
This study involves data augmentation modeling using Generative Adversarial Networks (GAN) on the tensile test data of austenitic stainless steel, which encompasses chemical compositions, heat treatments, and mechanical properties. The synthetic data...
We present a semi-automatic framework for transcribing foreign personal names into Lithuanian, aimed at reducing pronunciation errors in text-to-speech systems. Focusing on noisy, web-crawled data, the pipeline combines rule-based filtering, morpholo...
This comparative study evaluates the performance of three popular deep learning architectures, AlexNet, VGG-16, and VGG-19, on a custom-made dataset of GPR C-scans collected from several archaeological sites. The introduced dataset has 15,000 trainin...
Hand gesture recognition (HGR) is a challenging and fascinating research topic in computer vision with numerous daily life applications. In HGR, computers aim to identify and classify hand gestures. The limited diversity of the dataset used in HGR is...
Ten years after the Predictions in Ungauged Basins (PUB) initiative was put forward, known as the post-PUB era (2013 onwards), reducing uncertainty in hydrological prediction in ungauged basins still receives considerable attention. This integration...
The classical least squares (CLS) model and three augmented CLS models are adopted and validated for the analysis of pyridoxine HCl (PYR), cyclizine HCl (CYC), and meclizine HCl (MEC) in a quinary mixture with two related impurities: the CYC main imp...
Phishing is one of the most dangerous attacks targeting individuals, organizations, and nations. Although many traditional methods for email phishing detection exist, there is a need to improve accuracy and reduce false-positive rates. Our work inves...
Deep learning (DL) has been shown to obtain superior results for classification tasks in the field of remote sensing hyperspectral imaging. Superpixel-based techniques can be applied to DL, significantly decreasing training and prediction times, but...
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