2025 October - 36 articles
In shearography-based tire testing, so-called “Mode Hops”, abrupt phase changes caused by laser mode changes, can lead to significant disturbances in the interference image analysis. These artifacts distort defect assessment, lead to rete...
Epilepsy, a chronic neurological disorder marked by recurrent and unpredictable seizures, poses significant risks of injury and compromises patient quality of life. The accurate forecasting of seizures is paramount for enabling timely interventions a...
Background: Knowledge distillation (KD) compresses deep neural networks by transferring knowledge from a high-capacity teacher model to a lightweight student model. However, conventional evaluation metrics such as accuracy, mAP, IoU, or RMSE focus ma...
In post-disaster search and rescue (SAR) operations, unmanned aerial vehicles (UAVs) are essential tools, yet the large volume of raw visual data often overwhelms human operators by providing isolated, context-free information. This paper presents an...
Staining-based assays are widely used for cell analysis but are invasive, alter physiology, and prevent longitudinal monitoring. Label-free, morphology-based approaches could enable real-time, non-invasive drug testing, yet detection of subtle and dy...
Modern semantic segmentation models require extensive pixel-level annotations, creating a significant barrier to practical deployment as labeling a single image can take hours of human effort. Active learning offers a promising way to reduce annotati...
Diabetic Macular Edema (DME) poses a significant threat to vision, often leading to permanent blindness if not detected and addressed swiftly. Existing manual diagnostic methods are arduous and inconsistent, highlighting the pressing necessity for au...
This study presents a novel approach to financial time series forecasting by introducing asymmetric loss functions. This is specifically designed to enhance directional accuracy across major stock indices (S&P 500, DJI, and NASDAQ Composite) over...
Multiple time series forecasting is critical in domains such as energy management, economic analysis, web traffic prediction and air pollution monitoring to support effective resource planning. Traditional statistical learning methods, including Vect...
We study motif-based optimization for sparse multilayer perceptrons (MLPs), where weights are shared and updated at the level of small neuron groups (‘motifs’) rather than individual connections. Building on Sparse Evolutionary Training (...
Few-shot image classification seeks to recognize novel categories from only a handful of labeled examples, but conventional metric-based methods that rely mainly on global image features often produce unstable prototypes under extreme data scarcity,...
Animal behavior recognition is an important research area that provides insights into areas such as neural functions, gene mutations, and drug efficacy, among others. The manual coding of behaviors based on video recordings is labor-intensive and pro...
Single-object vision–language tracking has become an important research topic due to its potential in applications such as intelligent surveillance and autonomous driving. However, existing cross-modal alignment methods typically rely on contra...
Atmospheric gravity waves (AGWs) are treated as density structure perturbations of the atmosphere and play an important role in atmospheric dynamics. Utilizing All-Sky Airglow Imagers (ASAIs) with OI-Filter 557.7 nm, AGW phase velocity and propagatio...
Cervical cytology processing involves the morphological analysis of cervical cells to detect abnormalities. In recent years, machine learning and deep learning algorithms have been explored to automate this process. This study investigates the use of...
Large Language Models (LLMs) exhibit remarkable generative capabilities but remain vulnerable to hallucinations—outputs that are fluent yet inaccurate, ungrounded, or inconsistent with source material. To address the lack of methodologically gr...
Public safety and security require an effective monitoring system to detect violence through visual, audio, and motion data. However, current methods often fail to utilize the complementary benefits of visual and auditory modalities, thereby reducing...
Artificial intelligence (AI), when integrated with photocatalysis, has demonstrated high predictive accuracy in optimizing photocatalytic processes for wastewater treatment using a variety of catalysts such as TiO2, ZnO, CdS, Zr, WO2, and CeO2. The p...
Data preprocessing and feature engineering play key roles in data mining initiatives, as they have a significant impact on the accuracy, reproducibility, and interpretability of analytical results. This review presents an analysis of state-of-the-art...
As artificial intelligence transitions from conversational agents to autonomous actors in high-stakes environments, a critical gap emerges: how to ensure AI prioritizes human safety when its core objectives conflict with human well-being. Current saf...
Artificial Intelligence (AI) applications in sports dentistry have the potential to improve early detection and diagnosis. We aimed to validate the diagnostic performance of AI-assisted software in detecting dental caries, periodontitis, and tooth we...
This study proposes the first ground motion prediction equation (GMPE) for the parameter INp, an intensity measure based on the spectral shape. A Machine Learning Algorithm based on Support Vector Machines (SVMs) was employed due to its robustness to...
Artificial intelligence (AI) offers several opportunities in wildfire management, particularly for improving short- and long-term fire occurrence forecasting, spread modeling, and decision-making. When properly adapted beyond research into real-world...
Robotics can help address the growing worker shortage challenge of the manufacturing industry. As such, machine tending is a task collaborative robots can tackle that can also greatly boost productivity. Nevertheless, existing robotics systems deploy...
Aviation safety remains a critical area of research, requiring accurate and efficient classification of incident reports to enhance risk assessment and accident prevention strategies. This study evaluates the performance of three deep learning models...
Various types of interfering signals are an integral part of ECGs recorded using wearable electronics, specifically during field monitoring, outside the controlled environment of a medical doctor’s office, or laboratory. The frequency spectrum...
Emotion detection in Brazilian Portuguese is less studied than in English. We benchmarked a large language model (Mistral 24B), a language-specific transformer model (BERTimbau), and the lexicon-based EmoAtlas for classifying emotions in Brazilian Po...
Creativity is central to innovation and problem-solving, yet scalable training solutions remain limited. This study evaluates Creativeable, an AI-powered creativity training program that provides automated feedback and adjusts creative story writing...
This paper presents an innovative end-to-end framework for conformal antenna array design and beam steering in Low Earth Orbit (LEO) satellite-based IoT communication systems. We propose a multi-stage learning architecture that integrates machine lea...
This paper provides the modeling, implementation, and simulation of fractional-order processes associated with the production of the enriched 13C isotope due to chemical exchange processes between carbamate and CO2. To demonstrate and simulate the pr...
Industrial trademark examination enables users to apply for and manage their trademarks efficiently, promoting industrial and commercial economic development. However, there still exist many challenges, e.g., how to customize a blockchain-based crowd...
With the rapid development of artificial intelligence technology in the field of intelligent manufacturing, convolutional neural networks (CNNs) have shown excellent performance and generalization capabilities in industrial applications. However, the...
The early detection of diabetes risk and effective management of patient data are critical for avoiding serious consequences and improving treatment success. This research describes a two-part architecture that combines an energy-efficient wireless c...
The growing energy demand has increased the number of wind turbines, raising the need to monitor blade health. Since blades are prone to damage that can cause severe failures, early detection is crucial. Machine learning-based monitoring systems can...
Electronic Health Records (EHRs) have enhanced access to medical information but have also introduced challenges for healthcare providers, such as increased documentation workload and reduced face-to-face interaction with patients. To mitigate these...
Background: This study presents a real-time mobile system for identifying mixed and partly covered foreign coins and banknotes using the You Only Look Once version 11 (YOLOv11) deep learning framework. The proposed system addresses practical challeng...
