Multibeam echosounders are widely used for 3D bathymetric mapping, and increasingly for water column studies. However, they rapidly collect huge volumes of data, which poses a challenge for water column data processing that is often still manual and...
In this research, an improved automated medical prediction system, namely, the Neoplasm Medical Image Classification System (NMICS), is proposed. The proposed NMICS aims to robotically identify whether the given test magnetic resonance image (MRI) be...
Tea cultivation faces significant threats from red spider mite (RSM: Oligonychus coffeae) infestations, which reduce yields and economic viability in major tea-producing regions. Current automated detection methods rely on supervised deep learning mo...
Land use/land cover (LULC) change is one of the most important indicators in understanding the interactions between humans and the environment. Traditionally, when LULC maps are produced yearly, most existing remote-sensing methods have to collect gr...
Behavioural studies of elusive wildlife species are challenging but important when they are threatened and involved in human-wildlife conflicts. Accelerometers (ACCs) and supervised machine learning algorithms (MLAs) are valuable tools to remotely de...
We present a novel and innovative automated processing environment for the derivation of land cover (LC) and land use (LU) information. This processing framework named TWOPAC (TWinned Object and Pixel based Automated classification Chain) enables the...
Mixture tuned matched filtering (MTMF) image classification capitalizes on the increasing spectral and spatial resolutions of available hyperspectral image data to identify the presence, and potentially the abundance, of a given cover type or endmemb...
This paper focuses on an automated ANN classification system consisting of two modules: an unsupervised Kohonen’s Self-Organizing Mapping (SOM) neural network module, and a supervised Multilayer Perceptron (MLP) neural network module using the Backpr...
Interpretable-by-design architectures, such as Concept-Bottleneck Models (CBMs), are essential for trustworthy AI under regulations like the EU AI Act. While classical CBMs rely on costly expert labels, recent automated methods use vision–langu...
The automated classification of acid rock drainage (ARD) potential developed in this study is based on a manual ARD Index (ARDI) logging code. Several components of the ARDI require accurate identification of sulfide minerals that hyperspectral drill...
Image classification is a fundamental technique that has been broadly adopted in remote sensing, computer vision and related areas. High-quality annotated datasets are necessary for the development and reliable evaluation of classification and segmen...
The automated evaluation of machine conditions is key for efficient maintenance planning. Data-driven methods have proven to enable the automated mapping of complex patterns in sensor data to the health state of a system. However, generalizable appro...
Rheumatic heart disease (RHD) poses a significant global health challenge, necessitating improved diagnostic tools. This study investigated the use of self-supervised multi-task learning for automated echocardiographic analysis, aiming to predict ech...
Leukemia is the most frequent malignancy in children and adolescents, with acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML) as the most common subtypes. Minimal residual disease (MRD) measured by flow cytometry (FCM) has proven to...
Water quality in Peru is an increasing concern, particularly in the upper Huarmey watershed, which is affected by heavy metal contamination and untreated wastewater. This study proposes an automated classification approach using three supervised mach...
Microplastics have recently been discovered as remarkable contaminants of all environmental matrices. Their quantification and characterisation require lengthy and laborious analytical procedures that make this aspect of microplastics research a crit...
The Landsat archives have been made freely available in 2008, allowing the production of high resolution built-up maps at the regional or global scale. In this context, most of the classification algorithms rely on supervised learning to tackle the h...
Background/Objectives: Accurate and reliable automated dermoscopic lesion classification remains challenging. This is due to pronounced dataset bias, limited expert-annotated data, and poor cross-dataset generalization of conventional supervised deep...
The need for a novel automated mosquito perception and classification method is becoming increasingly essential in recent years, with steeply increasing number of mosquito-borne diseases and associated casualties. There exist remote sensing and GIS-b...
This paper reviews automated visual-based defect detection approaches applicable to various materials, such as metals, ceramics and textiles. In the first part of the paper, we present a general taxonomy of the different defects that fall in two clas...
This research introduces a novel method that integrates both unsupervised and supervised learning, leveraging SimCLR (Simple Framework for Contrastive Learning of Visual Representations) for self-supervised learning along with different pre-trained m...
Identifying calcifications in mammograms is crucial for early breast cancer detection, and semi-supervised learning, which utilizes a small dataset for supervised learning combined with deep learning, is anticipated to be an effective approach for au...
The exponential growth of Indicators of Compromise (IoCs) has overwhelmed manual triage processes in Security Operations Centers (SOCs), necessitating automated solutions for large-scale log analysis. This study proposes a hybrid machine learning fra...
This paper offers a comprehensive examination of the process involved in developing and automating supervised end-to-end machine learning workflows for forecasting and classification purposes. It offers a complete overview of the components (i.e., fe...
Optical coherence tomography (OCT) is a high-resolution and non-contact optical imaging and sensing modality that provides depth-resolved cross-sectional visualization of retinal microstructures. It plays an important role in the assessment of retina...
In the competitive landscape of steel-strip production, ensuring the high quality of steel surfaces is paramount. Traditionally, human visual inspection has been the primary method for detecting defects, but it suffers from limitations such as reliab...
Process automation, in general, enables the enhancement of productivity, product quality, and consistency alongside other production metrics. Liquor production on an industrial scale also follows the automation trend. However, small and medium produc...
Monitoring marine litter in the Arctic is crucial for environmental assessment, yet automated methods are needed to process large volumes of visual data. This study develops and compares two distinct machine learning approaches to automatically detec...
Automated electrocardiogram (ECG) arrhythmia classification remains challenging due to signal noise, inter-patient variability, and limited annotated data, which constrain the generalization of supervised learning approaches. This study presents a se...
Optical coherence tomography plays a critical role in diagnosing retinal diseases, yet automated deep learning classification is hindered by severe class imbalance in which rare pathologies are underrepresented and frequently misclassified, a limitat...
It is necessary to monitor, acquire, preprocess, and classify microseismic data to understand active faults or other causes of earthquakes, thereby facilitating the preparation of early-warning earthquake systems. Accordingly, this study proposes the...
At SAE Level 2 automation, the human driver retains full supervisory responsibility, making unobtrusive monitoring relevant for maintaining supervision under real-world driving conditions. Driver monitoring systems capable of operating robustly under...
The automated management of invasive weeds is critical for sustainable agriculture, yet the performance of deep learning models in real-world fields is often compromised by two factors: challenging environmental conditions and the high cost of data a...
Cotton root rot (CRR) is a persistent soilborne fungal disease that is devastating to cotton in the southwestern U.S. and Mexico. Research has shown that CRR can be prevented or at least mitigated by applying a fungicide at planting, but the fungicid...
There is a great demand to develop new acoustic techniques to efficiently map the seabed and automate the interpretation of acoustic, sedimentological, and imaging data sets, eliminating subjectivity. Here, we evaluate the potential, limitations and...
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...
When a populated area is inundated, the availability of a flood extent map becomes vital to assist the local authorities to plan rescue operations and evacuate the premises promptly. This paper proposes a novel automatic way to rapidly map the flood...
This study presents the development and validation of a robust semi-supervised learning framework specifically designed for the automated segmentation and classification of sandstone thin section images from the Yanchang Formation in the Ordos Basin....
The threat of plant diseases in economically significant crops of the Solanaceae family, especially tomatoes and potatoes, is a significant challenge to global food security, highlighting the necessity of fast and convenient diagnostic methods. This...
Automation in point cloud data processing is central in knowledge discovery within decision-making systems. The definition of relevant features is often key for segmentation and classification, with automated workflows presenting the main challenges....
In recent years, the use of 3D models in cultural and archaeological heritage for documentation and dissemination purposes is increasing. The association of heterogeneous information to 3D data by means of automated segmentation and classification me...
Land cover is one of the essential climate variables of the ESA Climate Change Initiative (CCI). In this context, the Land Cover CCI (LC CCI) project aims at building global land cover maps suitable for climate modeling based on Earth observation...
Crop type mapping is regarded as an essential part of effective agricultural management. Automated crop type mapping using remote sensing images is preferred for the consistent monitoring of crop types. However, the main obstacle to generating annual...
The increasing volume and diversity of scientific publications poses challenges for scalable and interpretable topic discovery and automated document categorization. This study proposes an integrated framework that combines probabilistic topic modeli...
Multivariate control charts (MCC) are widely used to detect out-of-control (OOC) situations in interrelated processes; however, they do not directly provide information about the source(s) of these signals. Although various methods for signal decompo...
Ship structural failure mode classification still relies heavily on subjective expert judgment, which is time-consuming and may introduce uncertainty in safety assessment. Although deep learning provides a promising avenue for automation, many existi...
Land use and land cover (LULC) mapping is a powerful tool for monitoring large areas. For the Amazon rainforest, automated mapping is of critical importance, as land cover is changing rapidly due to forest degradation and deforestation. Several resea...
Alzheimer’s disease (AD) is a progressive neurodegenerative disorder primarily impacting memory and cognitive functions. The hippocampus serves as a key biomarker associated with AD. In this study, we present an end-to-end automated approach fo...
Accurate plant-type (PT) detection forms an important basis for sustainable land management maintaining biodiversity and ecosystem services. In this sense, Sentinel-2 satellite images of the Copernicus program offer spatial, spectral, temporal, and r...
Live sheep export has become a public concern. This study aimed to test a non-contact biometric system based on artificial intelligence to assess heat stress of sheep to be potentially used as automated animal welfare assessment in farms and while in...
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