2,113 Results Found

A vehicle ad hoc network (VANET) is a solution for road safety, congestion management, and infotainment services. Integration of cognitive radio (CR), known as CR-VANET, is needed to solve the spectrum scarcity problems of VANET. Several research eff...

X-ray CT imaging provides a 3D view of a sample and is a powerful tool for investigating the internal features of porous rock. Reliable phase segmentation in these images is highly necessary but, like any other digital rock imaging technique, is time...

We seek the development and evaluation of a fast, accurate, and consistent method for general-purpose segmentation, based on interactive machine learning (IML). To validate our method, we identified retrospective cohorts of 20 brain, 50 breast, and 5...

Canopy closure is a critical indicator reflecting forest structure, biodiversity, and ecological balance. This study proposes an estimation method integrating U-Net segmentation with machine learning, significantly improving accuracy through multi-so...

Introduction: We developed a computed tomography (CT)-based tool designed for automated segmentation of deltoid muscles, enabling quantification of radiomic features and muscle fatty infiltration. Prior to use in a clinical setting, this machine lear...

Panoramic radiographs, also known as orthopantomograms, are routinely used in most dental clinics. However, it has been difficult to develop an automated method that detects the various structures present in these radiographs. One of the main reasons...

The incorporation of building information modeling (BIM) has brought about significant advancements in civil engineering, enhancing efficiency and sustainability across project life cycles. The utilization of advanced 3D point cloud technologies such...

Lingual ultrasound imaging is essential in linguistic research and speech recognition. It has been used widely in different applications as visual feedback to enhance language learning for non-native speakers, study speech-related disorders and remed...

In the era of digitalization, the biomedical sector has been affected by the spread of artificial intelligence. In recent years, the possibility of using deep and machine learning methods for clinical diagnostic and therapeutic interventions has been...

There is no doubt that brain tumors are one of the leading causes of death in the world. A biopsy is considered the most important procedure in cancer diagnosis, but it comes with drawbacks, including low sensitivity, risks during biopsy treatment, a...

In recent years, a number of different procedures have been proposed for segmentation of remote sensing images, basing on spectral information. Model-based and machine learning strategies have been investigated in several studies. This work presents...

In the cultural heritage field, point clouds, as important raw data of geomatics, are not only three-dimensional (3D) spatial presentations of 3D objects but they also have the potential to gradually advance towards an intelligent data structure with...

When it comes to forest management and protection, knowledge is key. Therefore, forest mapping is crucial to obtain the required knowledge towards profitable resource exploitation and increased resilience against wildfires. Within this context, this...

A brain Magnetic resonance imaging (MRI) scan of a single individual consists of several slices across the 3D anatomical view. Therefore, manual segmentation of brain tumors from magnetic resonance (MR) images is a challenging and time-consuming task...

Objective: This paper explores machine learning methods for exercise-induced laryngeal obstruction (EILO) diagnostics. Traditional diagnostic approaches like CLE scoring face subjectivity, limiting precise objective assessments. Machine learning is i...

Brain image segmentation is one of the most time-consuming and challenging procedures in a clinical environment. Recently, a drastic increase in the number of brain disorders has been noted. This has indirectly led to an increased demand for automate...

This paper presents a novel approach using machine learning methods for the automated segmentation of acceleration signals measured during ball burnishing (BB) operations performed on a computer numerical controlled (CNC) milling machine. The study a...

Photoacoustic (PA) imaging can be used to monitor high-intensity focused ultrasound (HIFU) therapies because ablation changes the optical absorption spectrum of the tissue, and this change can be detected with PA imaging. Multi-wavelength photoacoust...

Skin lesions are essential for the early detection and management of a number of dermatological disorders. Learning-based methods for skin lesion analysis have drawn much attention lately because of improvements in computer vision and machine learnin...

Objective: Skin diseases constitute a widespread health concern, and the application of machine learning and deep learning algorithms has been instrumental in improving diagnostic accuracy and treatment effectiveness. This paper aims to provide a com...

The use of machine learning algorithms and modern technologies for automatic segmentation of brain tissue increases in everyday clinical diagnostics. One of the most commonly used machine learning algorithms for image processing is convolutional neur...

Bolus tracking is a critical component of swallowing analysis, as the speed, course, and integrity of bolus movement from the mouth to the stomach, along with the presence of residue, serve as key indicators of potential abnormalities. Existing machi...

Landslides represent a severe hazard in many areas of the world. Accurate landslide maps are needed to document the occurrence and extent of landslides and to investigate their distribution, types, and the pattern of slope failures. Landslide maps ar...

Since not all suppliers are to be managed in the same way, a purchasing strategy requires proper supplier segmentation so that the most suitable strategies can be used for different segments. Most existing methods for supplier segmentation, however,...

With non-invasive and high-resolution properties, optical coherence tomography (OCT) has been widely used as a retinal imaging modality for the effective diagnosis of ophthalmic diseases. The retinal fluid is often segmented by medical experts as a p...

The fixed-size non-overlapping sliding window (FNSW) and fixed-size overlapping sliding window (FOSW) approaches are the most commonly used data-segmentation techniques in machine learning-based fall detection using accelerometer sensors. However, th...

Machine learning models for automated magnetic resonance image segmentation may be useful in aiding glioma detection. However, the image differences among facilities cause performance degradation and impede detection. This study proposes a method to...

Shield tunnel segment uplift is a common phenomenon in construction. Excessive and unstable uplift will affect tunnel quality and safety seriously, shorten the tunnel life, and is not conducive to the sustainable management of the tunnel’s enti...

A brain tumor is essentially a collection of aberrant tissues, so it is crucial to classify tumors of the brain using MRI before beginning therapy. Tumor segmentation and classification from brain MRI scans using machine learning techniques are widel...

In the era of precision medicine, increasing importance is given to machine learning (ML) applications. In breast cancer, advanced analyses, such as the radiomic process, characterise tumours and predict therapy responses. Breast magnetic resonance i...

Rapid advancements and the escalating necessity of autonomous algorithms in medical imaging require efficient models to accomplish tasks such as segmentation and classification. However, there exists a significant dependency on the image quality of d...

Currently, neural networks are being widely implemented for the diagnosis of various diseases, including cancer of various localizations and stages. The vast majority of such solutions use supervised or unsupervised convolutional neural networks, whi...

Artificial intelligence (AI) transforms image data analysis across many biomedical fields, such as cell biology, radiology, pathology, cancer biology, and immunology, with object detection, image feature extraction, classification, and segmentation a...

The precise identification and classification of tree species in young forests during their early development stages are vital for forest management and silvicultural efforts that support their growth and renewal. However, achieving accurate geolocat...

Dynamic wireless charging (DWC) has emerged as a viable approach to mitigate range anxiety by ensuring continuous and uninterrupted charging for electric vehicles in motion. DWC systems rely on the length of the transmitter, which can be categorized...

A critical issue in image analysis for analyzing animal behavior is accurate object detection and tracking in dynamic and complex environments. This study introduces a novel preprocessing algorithm to bridge the gap between computational efficiency a...

In the EU project SHAREWORK, methods are developed that allow humans and robots to collaborate in an industrial environment. One of the major contributions is a framework for task planning coupled with automated item detection and localization. In th...

Landslides are a critical geological phenomenon with devastating and catastrophic consequences. With the recent advancements in the geoinformation domain, landslide documentation and inventorization can be achieved with automated workflows using aeri...

This study proposes a comprehensive framework for the segmentation and identification of vertebrae in CT scans using a combination of deep learning and traditional machine learning techniques. The Res U-Net architecture is employed to achieve a high...

The object of this study was to demonstrate the ability of machine learning (ML) methods for the segmentation and classification of diabetic retinopathy (DR). Two-dimensional (2D) retinal fundus (RF) images were used. The datasets of DR—that is...

Cardiovascular diseases remain a leading cause of mortality worldwide, emphasizing the importance of early diagnosis. Heart sound analysis offers a non-invasive avenue for detecting cardiac abnormalities. This study systematically evaluates the effec...

Phenomics is a technology-driven approach with promising future to obtain unbiased data of biological systems. Image acquisition is relatively simple. However data handling and analysis are not as developed compared to the sampling capacities. We pre...

Road dust is a mixture of fine and coarse particles released into the air due to an external force, such as tire–ground friction or wind, which is harmful to human health when inhaled. Continuous dust emission from the road surfaces is detrimental to...

Automated Machine Learning (Auto-ML) has primarily been used to optimize network hyperparameters or post-processing parameters, while the most critical component for training a high-quality model, the dataset, is usually left untouched. In this paper...

Cutaneous leishmaniasis is a parasitic disease that poses significant diagnostic challenges due to the variability of results and reliance on operator expertise. This study addresses the development of a system based on machine learning algorithms to...

The unique challenges of polar ecosystems, coupled with the necessity for high-precision data, make Unmanned Aerial Vehicles (UAVs) an ideal tool for vegetation monitoring and conservation studies in Antarctica. This review draws on existing studies...

This paper presents an advanced hierarchical classification framework using the Random Forest (RF) algorithm to segment and classify large-scale point clouds of heritage buildings. By integrating the Uniclass classification system into a multi-resolu...

Wildfires are one of the major disasters among many and are responsible for more than 6 million acres burned in the United States alone every year. Accurate, insightful, and timely wildfire detection is needed to help authorities mitigate and prevent...

Profiting from the powerful feature extraction and representation capabilities of deep learning (DL), aerial image semantic segmentation based on deep neural networks (DNNs) has achieved remarkable success in recent years. Nevertheless, the security...

Accurate tumor segmentation is crucial for cancer diagnosis and treatment planning. We developed a hybrid framework combining complementary convolutional neural network (CNN) models and advanced post-processing techniques for robust segmentation. Mod...