This paper presents the visual navigation method for determining the position and orientation of a ground robot using a diffusion map of robot images (obtained from a camera in an upper position—e.g., tower, drone) and for investigating robot s...
This study presents a novel technological framework that integrates web-based 3D modeling and digital prototyping into interdisciplinary design education. Addressing the gap between traditional theoretical assessment and modern industry demands, the...
Clustering is an unsupervised machine learning and pattern recognition method. In general, in addition to revealing hidden groups of similar observations and clusters, their number needs to be determined. Internal clustering validation indices estima...
This paper presents OrniSonix, a low-cost distributed edge–cloud acoustic monitoring system for continuous bird-sound detection and species classification. The system employs autonomous Raspberry Pi Zero 2W edge nodes equipped with acoustic sen...
Meta-learning has demonstrated significant advantages in small-sample tasks and has attracted considerable attention in wind turbine fault diagnosis. However, due to extreme operating conditions and equipment aging, the monitoring data of wind turbin...
This study proposes an implementation of an incremental neural network (INN) that was initially designed for affective computing tasks. INNs are a family of machine learning algorithms that combine prototype-based classifiers with neural networks. Th...
Nearest-neighbor classifiers are accurate and easy to deploy, but their memory footprint and inference time grow with the size of the reference set. This paper studies an evolutionary prototype selection strategy for k-nearest neighbor (K-NN) classif...
Agricultural machinery intelligence is the inevitable direction of agricultural machinery design, and the systems in these designs are important tools. In this paper, to address the problem of low processing power of traditional agricultural machiner...
Background/Objectives: The limited availability of labeled data, particularly in the medical domain, poses a significant challenge for training accurate diagnostic models. While deep learning techniques have demonstrated notable efficacy in image-bas...
Multimodal time-series anomaly detection incorporates textual data regarding external events to provide contextual information for numerical fluctuations; this facilitates the interpretation of sudden fluctuations in complex systems—such as tho...
Motor imagery electroencephalography (MI-EEG) decoding remains challenging in cross-subject scenarios due to pronounced inter-subject variability and signal non-stationarity, which often lead to performance degradation on unseen subjects. Existing pr...
The population of developed nations spends a significant amount of time indoors, and the implications of poor indoor air quality (IAQ) on human health are substantial. Many premature deaths attributed to exposure to indoor air pollutants result from...
Background: Intelligent decision support systems for colonoscopy can fail when encountering out-of-distribution surgical instruments such as snares or biopsy forceps, producing false-positive polyp masks that may contribute to automation bias and red...
Data streaming applications such as the Internet of Things (IoT) require processing or predicting from sequential data from various sensors. However, most of the data are unlabeled, making applying fully supervised learning algorithms impossible. The...
Detecting out-of-distribution (OOD) samples from limited labeled data is important for reliable recognition under semantic novelty. Existing few-shot approaches use synthetic or auxiliary unknowns, model only in-distribution (ID) data, or rely on lar...
We introduce a modeling framework for the investigation of on-line machine learning processes in non-stationary environments. We exemplify the approach in terms of two specific model situations: In the first, we consider the learning of a classificat...
Visual search can be guided and facilitated by a category-specific attentional template (CAT) when searching for a target embedded into a set of categories. While previous research has proven that CATs can be pre-activated, it has caused confusion du...
Sustainable product development demands components that last longer, consume less energy, and can be refurbished within circular supply chains. This study introduces a digital replica-based predictive prototyping workflow for industrial crusher blade...
This paper presents a novel hypersector-based method with Fuzzy Learning Vector Quantization (FLVQ) for the real-time classification of wind turbine blade defects using data acquired by unmanned aerial vehicles (UAVs). Unlike conventional prototype-b...
Lesion responses to chemoradiotherapy can differ within a patient, complicating prognostic assessment in locally advanced non-small-cell lung cancer. We developed AMMR-Surv to integrate prototype-based metabolic response learning, asymmetric CT compa...
Endangered species detection plays an important role in biodiversity conservation and is significant in maintaining ecological balance. Existing deep learning-based object detection methods are overly dependent on a large number of supervised samples...
With the recent intensification of geopolitical tensions, cyber-attacks have become increasingly sophisticated and dynamic. Traditional machine learning-based anomaly detection techniques, which rely on pre-trained data, often suffer from performance...
The assessment of building-level flood damage in low-relief floodplains is constrained by pronounced exposure heterogeneity and a lack of object-level damage data. This study proposes a similarity-based fuzzy modeling framework for direct material fl...
Background/Objectives: Detecting Alzheimer’s disease (AD) from normal aging using eyes-open (EO) EEG is challenging due to stimulus-driven nonstationarity and fragmented oscillatory responses. This study aims to determine whether prototype-base...
Reliable UAV-based inspection of wind turbine blades requires diagnostic models that maintain acceptable decision quality under image degradation and limited edge-computing resources. This study proposes a resource-adaptive neuro-fuzzy edge system fo...
Multi-label image classification (MLIC) is vulnerable to contextual bias, where models may exploit spurious label–context associations rather than object evidence, leading to degraded generalization under distribution shifts. To address this is...
With the rapid development of semiconductor manufacturing technology, methods to effectively control the production process, reduce variation in the manufacturing process, and improve the yield rate represent important competitive factors for wafer f...
Accurate detection and severity estimation of corrosion on metallic surfaces is essential for maintaining material integrity and ensuring operational safety in industrial systems. To address limitations in manual inspection methods, this study presen...
Whole slide imaging has transformed the field of pathology by enabling high-resolution digitization of histopathological slides. However, the large image size and variability in morphology, tissue processing, and imaging can pose challenges for robus...
Deep learning (DL) has transformed cancer imaging by enabling automated tumour detection, classification, and risk prediction. Despite impressive diagnostic performance, limited explainability and poor uncertainty calibration continue to restrict cli...
Enterprise financial risk prediction is a fundamental task in financial risk management, yet its performance is often hindered by severe class imbalance, cross-enterprise heterogeneity, and the limited availability of labeled risky samples. These cha...
Non-intrusive load monitoring (NILM) enables fine-grained energy awareness and demand-side energy management in residential and commercial buildings, but practical deployment requires load identification models to accommodate newly introduced applian...
Adversarial perturbations can alter neural-network predictions, motivating closer analysis of the geometry of learned feature representations. For a given local displacement in feature space, small-norm feature vectors can undergo larger first-order...
Image quality assessment (IQA) aims to measure the consistency between an objective algorithm output and a subjective perception measurement. This article focuses on this complex relationship in the context of color image scenarios—color image...
This paper presents a standard-oriented architecture for automating information security risk management (ISRM) using artificial intelligence. The study first evaluates eight international frameworks (including COBIT 2019, NIST SP 800-53, and ISO 310...
Aerial scene recognition has progressed substantially with deep learning methods for RGB and hyperspectral imagery; however, existing approaches typically operate on single modalities or rely on explicit multimodal fusion, limiting scalability, flexi...
Identifying essential proteins lays a foundation for dissecting disease mechanisms and mining therapeutic targets. Existing algorithms mostly adopt unimodal inputs and struggle to capture multi-level biological interactions from genomics, subcellular...
Video anomaly detection (VAD) faces significant challenges in multimodal semantic alignment and long-term temporal modeling within open surveillance scenarios. Existing methods are often plagued by modality discrepancies and fragmented temporal reaso...
Point cloud semantic segmentation is a core component in indoor scene understanding and autonomous driving. Under weak point-level supervision, only a small subset of points is annotated, making effective use of unlabeled points critical yet non-triv...
Accurate and efficient semantic segmentation of point cloud data is critical in many application areas involving indoor scene understanding. In particular, fine-grained object categories, high data density, and class imbalance in high-resolution indo...
The diagnosis of leaf diseases in medicinal and non-mainstream plants is often hindered by the scarcity of labeled samples, whereas existing deep learning methods generally rely on large-scale labeled datasets. Prototype-based few-shot methods are co...
Sentiment analysis is significant for exploiting countless opinion-rich data from social media. However, it faces known challenges, such as informal expression and data sparsity. While large language models (LLMs) excel at dynamic contextual disambig...
Composite-score estimation (or assessment auditing) and student profiling are two fundamental tasks in Educational Data Mining (EDM). However, existing studies often treat them as separate problems, typically adopting a sequential pipeline in which c...
Agrivoltaic systems co-locate photovoltaic (PV) arrays and crops, offering land-use efficiency and potential microclimate benefits, yet they introduce new challenges for computer-vision-based crop monitoring. PV structures produce strong, spatially v...
The rapid advancement of machine learning and artificial intelligence (AI) has created new opportunities to enhance diagnostic accuracy in dermatology, particularly within primary care settings. Computer-aided diagnosis (CAD) systems have demonstrate...
Deep learning has significantly advanced medical image analysis by enabling accurate, automated diagnosis across diverse clinical tasks such as lesion classification and disease detection. However, the practical deployment of these systems is still h...
Interpretable organization of latent spaces remains a central challenge in generative modeling. Most generative models rely on continuous latent variables, but the learned space often lacks an explicit structure that explains how samples are organize...
This paper highlights the importance of one-shot learning from prototype Stock Keeping Unit (SKU) images for efficient product recognition in retail and inventory management. Traditional methods require large supervised datasets to train deep neural...
In recent years sensor components have been extending classical computer-based support systems in a variety of applications domains (sports, health, etc.). In this article we review the use of sensors for the application domain of learning. For that...
In recent years, deep-learning-based WiFi fingerprinting has been intensively studied as a promising technology for providing accurate indoor location services. However, it still demands a time-consuming and labor-intensive site survey and suffers fr...
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