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Journal articles on the topic 'Learning artifact'

Author: Grafiati

Published: 25 June 2021

Last updated: 29 July 2025

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1

Fahrenbach, Florian, Kate Revoredo, and Flavia Maria Santoro. "Valuing prior learning." European Journal of Training and Development 44, no. 2/3 (2019): 209–35. http://dx.doi.org/10.1108/ejtd-05-2019-0070.

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Purpose This paper aims to introduce an information and communication technology (ICT) artifact that uses text mining to support the innovative and standardized assessment of professional competences within the validation of prior learning (VPL). Assessment means comparing identified and documented professional competences against a standard or reference point. The designed artifact is evaluated by matching a set of curriculum vitae (CV) scraped from LinkedIn against a comprehensive model of professional competence. Design/methodology/approach A design science approach informed the development

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Yan, Jinlin, and Yu Chen. "Artifact Elimination of Eeg Signals Based on Deep Learning: Survey." International Journal of Research Publication and Reviews 03, no. 12 (2022): 1320–28. http://dx.doi.org/10.55248/gengpi.2022.31236.

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Electroencephalogram (EEG) plays an important role in measuring human status and activities. EEG signals come from weak currents and are very vulnerable to artifact pollution, which affects the performance of many EEG tasks. It is crucial to develop methods that can effectively identify and remove artifacts. In the past, researchers have proposed a variety of methods to eliminate artifacts, but there is still no method to achieve the best effect. With the rapid development of deep learning, the new method has made excellent progress in eliminating artifacts. Compared with traditional methods,

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He, Ruonan, Yi Chen, Yufei Jiang, et al. "Deep Learning Realizes Photoacoustic Imaging Artifact Removal." Applied Sciences 14, no. 12 (2024): 5161. http://dx.doi.org/10.3390/app14125161.

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Photoacoustic imaging integrates the strengths of optics and ultrasound, offering high resolution, depth penetration, and multimodal imaging capabilities. Practical considerations with instrumentation and geometry limit the number of available acoustic sensors and their “view” of the imaging target, which result in image reconstruction artifacts degrading image quality. To address this problem, YOLOv8-Pix2Pix is proposed as a hybrid artifact-removal algorithm, which is advantageous in comprehensively eliminating various types of artifacts and effectively restoring image details compared to exi

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Nachlieli, Hila, Hadas Kogan, Morad Awad, Doron Shaked, and Smadar Shiffman. "Learning Print Artifact Detectors." Conference on Colour in Graphics, Imaging, and Vision 6, no. 1 (2012): 81–85. http://dx.doi.org/10.2352/cgiv.2012.6.1.art00015.

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Huang, Mei, Gang Li, Rui Sun, et al. "Sparse-View Artifact Correction of High-Pixel-Number Synchrotron Radiation CT." Applied Sciences 14, no. 8 (2024): 3397. http://dx.doi.org/10.3390/app14083397.

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High-pixel-number synchrotron radiation computed tomography (CT) has the advantages of high sensitivity, high resolution, and a large field of view. It has been widely used in biomedicine, cultural heritage research, non-destructive testing, and other fields. The Nyquist sampling theorem states that when the detector’s pixels per row are increased, it requires more CT projections, resulting in a lengthened CT scan time and increased radiation damage. Sparse-view CT can significantly reduce radiation damage and improve the projection data acquisition speed. However, there is insufficient sparse

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Kromrey, M. L., D. Tamada, H. Johno, et al. "Reduction of respiratory motion artifacts in gadoxetate-enhanced MR with a deep learning–based filter using convolutional neural network." European Radiology 30, no. 11 (2020): 5923–32. http://dx.doi.org/10.1007/s00330-020-07006-1.

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Abstract Objectives To reveal the utility of motion artifact reduction with convolutional neural network (MARC) in gadoxetate disodium–enhanced multi-arterial phase MRI of the liver. Methods This retrospective study included 192 patients (131 men, 68.7 ± 10.3 years) receiving gadoxetate disodium–enhanced liver MRI in 2017. Datasets were submitted to a newly developed filter (MARC), consisting of 7 convolutional layers, and trained on 14,190 cropped images generated from abdominal MR images. Motion artifact for training was simulated by adding periodic k-space domain noise to the images. Origin

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Stalin, Shalini, Vandana Roy, Prashant Kumar Shukla, et al. "A Machine Learning-Based Big EEG Data Artifact Detection and Wavelet-Based Removal: An Empirical Approach." Mathematical Problems in Engineering 2021 (October 7, 2021): 1–11. http://dx.doi.org/10.1155/2021/2942808.

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The electroencephalogram (EEG) signals are a big data which are frequently corrupted by motion artifacts. As human neural diseases, diagnosis and analysis need a robust neurological signal. Consequently, the EEG artifacts’ eradication is a vital step. In this research paper, the primary motion artifact is detected from a single-channel EEG signal using support vector machine (SVM) and preceded with further artifacts’ suppression. The signal features’ abstraction and further detection are done through ensemble empirical mode decomposition (EEMD) algorithm. Moreover, canonical correlation analys

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Zou, Huachun, Zonghuo Wang, Mengya Guo, et al. "Metal artifact reduction combined with deep learning image reconstruction algorithm for CT image quality optimization: a phantom study." PeerJ 13 (June 4, 2025): e19516. https://doi.org/10.7717/peerj.19516.

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Background Aiming to evaluate the effects of the smart metal artifact reduction (MAR) algorithm and combinations of various scanning parameters, including radiation dose levels, tube voltage, and reconstruction algorithms, on metal artifact reduction and overall image quality, to identify the optimal protocol for clinical application. Methods A phantom with a pacemaker was examined using standard dose (effective dose (ED): 3 mSv) and low dose (ED: 0.5 mSv), with three scan voltages (70, 100, and 120 kVp) selected for each dose. Raw data were reconstructed using 50% adaptive statistical iterati

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Pancholi, Deepak, Rajeev Goyal, Paresh Rawat, Linda Elzubir Gasm Alsid, and Prince Jain. "Multi class EEG artifacts classification and removal using adaptive neural filter." Intelligent Decision Technologies 19, no. 2 (2024): 943–60. https://doi.org/10.1177/18724981241299612.

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Numerous adaptive filtering methods have been developed to enhance the quality of electroencephalogram (EEG) signals and thereby improve the efficacy of artifact removal and classification. The goal of this work is to categorize and remove artificial, eye blink, and muscle motion artifacts. An adaptive neural network (NN) based filter is proposed to simultaneously suppress all motion artifacts. To increase classification accuracy, an extended statistical feature set is employed. Using hybrid principal component analysis (PCA) and quadratic support vector machine (SVM) kernel combinations based

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Nishit Agarwal, Venkata Ramanaiah Chintha, Raja Kumar Kolli, Om Goel, and Raghav Agarwal. "Deep Learning for Real time EEG Artifact Detection in Wearables." International Journal for Research Publication and Seminar 13, no. 5 (2022): 402–33. http://dx.doi.org/10.36676/jrps.v13.i5.1510.

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Electroencephalography (EEG) has become a valuable tool for monitoring brain activity in both clinical and consumer applications. However, EEG signals collected from wearable devices are often disrupted by artifacts such as eye blinks, muscle movements, and external noise, which can severely compromise the accuracy of real-time analysis. Traditional methods for artifact detection and removal rely on manual techniques or simple filtering, making them unsuitable for continuous, real-time applications, particularly in mobile and wearable devices. This study explores the use of deep learning for r

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Cai, Yinan, Zhao Meng, and Dian Huang. "DHCT-GAN: Improving EEG Signal Quality with a Dual-Branch Hybrid CNN–Transformer Network." Sensors 25, no. 1 (2025): 231. https://doi.org/10.3390/s25010231.

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Electroencephalogram (EEG) signals are important bioelectrical signals widely used in brain activity studies, cognitive mechanism research, and the diagnosis and treatment of neurological disorders. However, EEG signals are often influenced by various physiological artifacts, which can significantly affect data analysis and diagnosis. Recently, deep learning-based EEG denoising methods have exhibited unique advantages over traditional methods. Most existing methods mainly focus on identifying the characteristics of clean EEG signals to facilitate artifact removal; however, the potential to int

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Hasasneh, Ahmad, Nikolas Kampel, Praveen Sripad, N. Jon Shah, and Jürgen Dammers. "Deep Learning Approach for Automatic Classification of Ocular and Cardiac Artifacts in MEG Data." Journal of Engineering 2018 (2018): 1–10. http://dx.doi.org/10.1155/2018/1350692.

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We propose an artifact classification scheme based on a combined deep and convolutional neural network (DCNN) model, to automatically identify cardiac and ocular artifacts from neuromagnetic data, without the need for additional electrocardiogram (ECG) and electrooculogram (EOG) recordings. From independent components, the model uses both the spatial and temporal information of the decomposed magnetoencephalography (MEG) data. In total, 7122 samples were used after data augmentation, in which task and nontask related MEG recordings from 48 subjects served as the database for this study. Artifa

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Hung, Alex Ling Yu, Edward Chen, and John Galeotti. "Weakly- and Semisupervised Probabilistic Segmentation and Quantification of Reverberation Artifacts." BME Frontiers 2022 (March 1, 2022): 1–15. http://dx.doi.org/10.34133/2022/9837076.

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Objective and Impact Statement. We propose a weakly- and semisupervised, probabilistic needle-and-reverberation-artifact segmentation algorithm to separate the desired tissue-based pixel values from the superimposed artifacts. Our method models the intensity decay of artifact intensities and is designed to minimize the human labeling error. Introduction. Ultrasound image quality has continually been improving. However, when needles or other metallic objects are operating inside the tissue, the resulting reverberation artifacts can severely corrupt the surrounding image quality. Such effects ar

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Muntean, Mihaela, and Florin Daniel Militaru. "Design Science Research Framework for Performance Analysis Using Machine Learning Techniques." Electronics 11, no. 16 (2022): 2504. http://dx.doi.org/10.3390/electronics11162504.

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We propose a methodological framework based on design science research for the design and development of data and information artifacts in data analysis projects, particularly managerial performance analysis. Design science research methodology is an artifact-centric creation and evaluation approach. Artifacts are used to solve real-life business problems. These are key elements of the proposed approach. Starting from the main current approaches of design science research, we propose a framework that contains artifact engineering aspects for a class of problems, namely data analysis using mach

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Graffieti, Gabriele, and Davide Maltoni. "Artifact-Free Single Image Defogging." Atmosphere 12, no. 5 (2021): 577. http://dx.doi.org/10.3390/atmos12050577.

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In this paper, we present a novel defogging technique, named CurL-Defog, with the aim of minimizing the insertion of artifacts while maintaining good contrast restoration and visibility enhancement. Many learning-based defogging approaches rely on paired data, where fog is artificially added to clear images; this usually provides good results on mildly fogged images but is not effective for difficult cases. On the other hand, the models trained with real data can produce visually impressive results, but unwanted artifacts are often present. We propose a curriculum learning strategy and an enha

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Lee, Seung-Bo, Hakseung Kim, Young-Tak Kim, et al. "Artifact removal from neurophysiological signals: impact on intracranial and arterial pressure monitoring in traumatic brain injury." Journal of Neurosurgery 132, no. 6 (2020): 1952–60. http://dx.doi.org/10.3171/2019.2.jns182260.

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OBJECTIVEMonitoring intracranial and arterial blood pressure (ICP and ABP, respectively) provides crucial information regarding the neurological status of patients with traumatic brain injury (TBI). However, these signals are often heavily affected by artifacts, which may significantly reduce the reliability of the clinical determinations derived from the signals. The goal of this work was to eliminate signal artifacts from continuous ICP and ABP monitoring via deep learning techniques and to assess the changes in the prognostic capacities of clinical parameters after artifact elimination.METH

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Abhishek, Parikh, and Anilkumar Suthar Dr. "DEVELOPMENT OF AN ACCURATE SEIZURE DETECTION SYSTEM USING RANDOM FOREST CLASSIFIER WITH ICA BASED ARTIFACT REMOVAL ON EEG DATA." Journal of Biomechanical Science and Engineering September, Theme 1 (2023): 1–15. https://doi.org/10.5281/zenodo.8385047.

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Abstract The creation of a reliable artifact removal and precise epileptic seizure identification system using Seina Scalp EEG data and cutting-edge machine learning techniques is presented in this paper. Random Forest classifier used for seizure classification, and independent component analysis (ICA) is used for artifact removal. Various artifacts, such as eye blinks, muscular activity, and environmental noise, are successfully recognized and removed from the EEG signals using ICA-based artifact removal, increasing the accuracy of the analysis that comes after. A precise distinction between

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Deepika, J., T. Senthil, C. Rajan, and A. Surendar. "Machine learning algorithms: a background artifact." International Journal of Engineering & Technology 7, no. 1.1 (2017): 143. http://dx.doi.org/10.14419/ijet.v7i1.1.9214.

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With the greater development of technology and automation human history is predominantly updated. The technology movement shifted from large mainframes to PCs to cloud when computing the available data for a larger period. This has happened only due to the advent of many tools and practices, that elevated the next generation in computing. A large number of techniques has been developed so far to automate such computing. Research dragged towards training the computers to behave similar to human intelligence. Here the diversity of machine learning came into play for knowledge discovery. Machine

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Wu, Chao, Xiaonan Zhao, Mark Welsh, et al. "Using Machine Learning to Identify True Somatic Variants from Next-Generation Sequencing." Clinical Chemistry 66, no. 1 (2019): 239–46. http://dx.doi.org/10.1373/clinchem.2019.308213.

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Abstract BACKGROUND Molecular profiling has become essential for tumor risk stratification and treatment selection. However, cancer genome complexity and technical artifacts make identification of real variants a challenge. Currently, clinical laboratories rely on manual screening, which is costly, subjective, and not scalable. We present a machine learning–based method to distinguish artifacts from bona fide single-nucleotide variants (SNVs) detected by next-generation sequencing from nonformalin-fixed paraffin-embedded tumor specimens. METHODS A cohort of 11278 SNVs identified through clinic

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Emmitt, Joshua, Sina Masoud-Ansari, Rebecca Phillipps, Stacey Middleton, Jennifer Graydon, and Simon Holdaway. "Machine learning for stone artifact identification: Distinguishing worked stone artifacts from natural clasts using deep neural networks." PLOS ONE 17, no. 8 (2022): e0271582. http://dx.doi.org/10.1371/journal.pone.0271582.

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Stone artifacts are often the most abundant class of objects found in archaeological sites but their consistent identification is limited by the number of experienced analysts available. We report a machine learning based technology for stone artifact identification as part of a solution to the lack of such experts directed at distinguishing worked stone objects from naturally occurring lithic clasts. Three case study locations from Egypt, Australia, and New Zealand provide a data set of 6769 2D images, 3868 flaked artifact and 2901 rock images used to train and test a machine learning model b

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Cirino de Mattos, Max, and Renata Maria Abrantes Baracho. "Transdisciplinary environments of learning: an initial proposal." Design e Tecnologia 14, no. 29 (2024): 01–11. https://doi.org/10.23972/det2024iss29pp01-11.

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Over the past decade, we have developed and implemented a simpler Design Science Research (DSR) approach for undergraduate and graduate courses in Administration, Information Science, and Architecture, exploring the potential of transdisciplinary learning environments in both the academic and professional spheres. We have named this approach Transdisciplinary Environments of Learning (TEL) and it is based upon the concept of a complex, presented by Bogdanov – a Russian philosopher highly concerned with the “language of science”. This paper presents the foundational concepts of TEL, a proposed

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Wajer, Róża, Adrian Wajer, Natalia Kazimierczak, Justyna Wilamowska, and Zbigniew Serafin. "The Impact of AI on Metal Artifacts in CBCT Oral Cavity Imaging." Diagnostics 14, no. 12 (2024): 1280. http://dx.doi.org/10.3390/diagnostics14121280.

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Objective: This study aimed to assess the impact of artificial intelligence (AI)-driven noise reduction algorithms on metal artifacts and image quality parameters in cone-beam computed tomography (CBCT) images of the oral cavity. Materials and Methods: This retrospective study included 70 patients, 61 of whom were analyzed after excluding those with severe motion artifacts. CBCT scans, performed using a Hyperion X9 PRO 13 × 10 CBCT machine, included images with dental implants, amalgam fillings, orthodontic appliances, root canal fillings, and crowns. Images were processed with the ClariCT.AI

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Shvarts, Anna, Rosa Alberto, Arthur Bakker, Michiel Doorman, and Paul Drijvers. "Embodied instrumentation in learning mathematics as the genesis of a body-artifact functional system." Educational Studies in Mathematics 107, no. 3 (2021): 447–69. http://dx.doi.org/10.1007/s10649-021-10053-0.

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AbstractRecent developments in cognitive and educational science highlight the role of the body in learning. Novel digital technologies increasingly facilitate bodily interaction. Aiming for understanding of the body’s role in learning mathematics with technology, we reconsider the instrumental approach from a radical embodied cognitive science perspective. We highlight the complexity of any action regulation, which is performed by a complex dynamic functional system of the body and brain in perception-action loops driven by multilevel intentionality. Unlike mental schemes, functional systems

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Su, Chunjie. "Deep learning based Metal Artifact Reduction in X-ray Computed Tomography." Academic Journal of Science and Technology 6, no. 3 (2023): 138–43. http://dx.doi.org/10.54097/ajst.v6i3.10656.

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Due to the presence of metal implants, computed tomography(CT) images of patients undergoing CT scans produce severe metal artifacts. In recent years, metal artifact reduction (MAR) algorithms have been developed at a high speed, and deep learning-based MAR algorithms have proved to be one of the very effective methods. However, based on the fact that most of the current deep learning-based solutions utilize simulated data for supervised training, these models are difficult to be directly applied in clinical settings. In addition, current MAR schemes still face considerable challenges in reduc

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Weiss, Dennis M. "Learning to be human with sociable robots." Paladyn, Journal of Behavioral Robotics 11, no. 1 (2020): 19–30. http://dx.doi.org/10.1515/pjbr-2020-0002.

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AbstractThis essay examines the debate over the status of sociable robots and relational artifacts through the prism of our relationship to television. In their work on human-technology relations, Cynthia Breazeal and Sherry Turkle have staked out starkly different assessments. Breazeal’s work on sociable robots suggests that these technological artifacts will be human helpmates and sociable companions. Sherry Turkle argues that such relational artifacts seduce us into simulated relationships with technological others that largely serve to exploit our emotional vulnerabilities and undermine au

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Busi, Matteo, Christian Kehl, Jeppe R. Frisvad, and Ulrik L. Olsen. "Metal Artifact Reduction in Spectral X-ray CT Using Spectral Deep Learning." Journal of Imaging 8, no. 3 (2022): 77. http://dx.doi.org/10.3390/jimaging8030077.

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Spectral X-ray computed tomography (SCT) is an emerging method for non-destructive imaging of the inner structure of materials. Compared with the conventional X-ray CT, this technique provides spectral photon energy resolution in a finite number of energy channels, adding a new dimension to the reconstructed volumes and images. While this mitigates energy-dependent distortions such as beam hardening, metal artifacts due to photon starvation effects are still present, especially for low-energy channels where the attenuation coefficients are higher. We present a correction method for metal artif

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Bedi, Pradeep, S. B. Goyal, Dileep Kumar Yadav, Sunil Kumar, and Monika Sharma. "Hybrid Learning Model for Metal Artifact Reduction." Journal of Physics: Conference Series 1714 (January 2021): 012021. http://dx.doi.org/10.1088/1742-6596/1714/1/012021.

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Parmaxi, Antigoni, and Panayiotis Zaphiris. "Emerging Technologies for Artifact Construction in Learning." Computers in Human Behavior 99 (October 2019): 366–67. http://dx.doi.org/10.1016/j.chb.2019.05.034.

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Dorri Giv, Masoumeh, Guluzar Ozbolat, Hossein Arabi, et al. "Optimizing Attenuation Correction in 68Ga-PSMA PET Imaging Using Deep Learning and Artifact-Free Dataset Refinement." Diagnostics 15, no. 11 (2025): 1400. https://doi.org/10.3390/diagnostics15111400.

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Background/Objectives: Attenuation correction (AC) is essential for achieving quantitatively accurate PET imaging. In 68Ga-PSMA PET, however, artifacts such as respiratory motion, halo effects, and truncation errors in CT-based AC (CT-AC) images compromise image quality and impair model training for deep learning-based AC. This study proposes a novel artifact-refinement framework that filters out corrupted PET-CT images to create a clean dataset for training an image-domain AC model, eliminating the need for anatomical reference scans. Methods: A residual neural network (ResNet) was trained us

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He, Mingxuan. "Recent Study of Artifact Elimination in EEG Signals." Highlights in Science, Engineering and Technology 74 (December 29, 2023): 455–61. http://dx.doi.org/10.54097/kzwt1x69.

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The progress of electroencephalography (EEG) has promoted this technology to be widely used in various fields such as computer science, medical engineering, and signal processing because of its non-invasiveness and low cost. However, the quality of EEG recordings may be degraded due to the introduction of artifacts, which has a non-negligible negative impact on subsequent operations. Artifacts are unwanted signals that vary in their source, such as muscular, ocular, and cardiac ones. Many studies have proposed methods to remove artifacts from EEG signals, from the classic ones including filter

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Dong, Guoya, Yutong He, Xuan Liu, Jingjing Dai, Yaoqin Xie, and Xiaokun Liang. "Better Cone-Beam CT Artifact Correction via Spatial and Channel Reconstruction Convolution Based on Unsupervised Adversarial Diffusion Models." Bioengineering 12, no. 2 (2025): 132. https://doi.org/10.3390/bioengineering12020132.

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Cone-Beam Computed Tomography (CBCT) holds significant clinical value in image-guided radiotherapy (IGRT). However, CBCT images of low-density soft tissues are often plagued with artifacts and noise, which can lead to missed diagnoses and misdiagnoses. We propose a new unsupervised CBCT image artifact correction algorithm, named Spatial Convolution Diffusion (ScDiff), based on a conditional diffusion model, which combines the unsupervised learning ability of generative adaptive networks (GAN) with the stable training characteristics of diffusion models. This approach can efficiently and stably

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Wang, Nicholas C., Douglas C. Noll, Ashok Srinivasan, Johann Gagnon-Bartsch, Michelle M. Kim, and Arvind Rao. "Simulated MRI Artifacts: Testing Machine Learning Failure Modes." BME Frontiers 2022 (November 1, 2022): 1–16. http://dx.doi.org/10.34133/2022/9807590.

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Objective. Seven types of MRI artifacts, including acquisition and preprocessing errors, were simulated to test a machine learning brain tumor segmentation model for potential failure modes. Introduction. Real-world medical deployments of machine learning algorithms are less common than the number of medical research papers using machine learning. Part of the gap between the performance of models in research and deployment comes from a lack of hard test cases in the data used to train a model. Methods. These failure modes were simulated for a pretrained brain tumor segmentation model that util

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Islind, Anna Sigridur, and Ulrika Lundh Snis. "Learning in home care: a digital artifact as a designated boundary object-in-use." Journal of Workplace Learning 29, no. 7/8 (2017): 577–87. http://dx.doi.org/10.1108/jwl-04-2016-0027.

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Purpose The aim of this paper is to understand how the role of an mHealth artifact plays out in home care settings. An mHealth artifact, in terms of a mobile app, was tested to see how the quality of home care work practice was enhanced and changed. The research question is: In what ways does an mHealth artifact re-shape a home care practice and how does this affect the interaction between caregivers and the elderly and learning opportunities for the caregivers? Design/methodology/approach An action research approach was taken and the study was conducted in a home care organization in a Swedis

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Wang, Junkongshuai, Yangjie Luo, Haoran Wang, et al. "FLANet: A multiscale temporal convolution and spatial-spectral attention network for EEG artifact removal with adversarial training." Journal of Neural Engineering 22, no. 1 (2025): 016021. https://doi.org/10.1088/1741-2552/adae34.

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Abstract Objective. Denoising artifacts, such as noise from muscle or cardiac activity, is a crucial and ubiquitous concern in neurophysiological signal processing, particularly for enhancing the signal-to-noise ratio in electroencephalograph (EEG) analysis. Novel methods based on deep learning demonstrate a notably prominent effect compared to traditional denoising approaches. However, those still suffer from certain limitations. Some methods often neglect the multi-domain characteristics of the artifact signal. Even among those that do consider these, there are deficiencies in terms of effic

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Walker, Caren M., Alexandra Rett, and Elizabeth Bonawitz. "Design Drives Discovery in Causal Learning." Psychological Science 31, no. 2 (2020): 129–38. http://dx.doi.org/10.1177/0956797619898134.

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We assessed whether an artifact’s design can facilitate recognition of abstract causal rules. In Experiment 1, 152 three-year-olds were presented with evidence consistent with a relational rule (i.e., pairs of same or different blocks activated a machine) using two differently designed machines. In the standard-design condition, blocks were placed on top of the machine; in the relational-design condition, blocks were placed into openings on either side. In Experiment 2, we assessed whether this design cue could facilitate adults’ ( N = 102) inference of a distinct conjunctive cause (i.e., that

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Athaya, Tasbiraha, and Sunwoong Choi. "An Efficient Fingertip Photoplethysmographic Signal Artifact Detection Method: A Machine Learning Approach." Journal of Sensors 2021 (October 4, 2021): 1–18. http://dx.doi.org/10.1155/2021/9925033.

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A photoplethysmography method has recently been widely used to noninvasively measure blood volume changes during a cardiac cycle. Photoplethysmogram (PPG) signals are sensitive to artifacts that negatively impact the accuracy of many important measurements. In this paper, we propose an efficient system for detecting PPG signal artifacts acquired from a fingertip in the public healthcare database named Multiparameter Intelligent Monitoring in Intensive Care (MIMIC) by using 11 features as the input of the random forest algorithm and classified the signals into two classes: acceptable and anomal

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Guerra Lopes, Arminda. "Unexpected Artifact – A Modding Interface Design." Interaction Design and Architecture(s), no. 37 (June 10, 2018): 130–42. http://dx.doi.org/10.55612/s-5002-037-006.

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Human Computer-Interaction (HCI) has been handled in different ways, which permittedtointerpret the theories, methods and practices according the chosen approach. Some considerations are presented about HCI approaches over the years, and the adopted pedagogical tactics to teach HCI disciplines is described. HCI was taught following the method of teaching / learning pointed to student-centered, supported by incentives for self-learning and integration of knowledge, preparing students for lifelong learning. The student was confronted with a set of theoretical and practical problems, based on rea

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Jiang, Hao, John M. Carroll, and Roderick Lee. "Extending the task-artifact framework with organizational learning." Knowledge and Process Management 17, no. 1 (2010): 22–35. http://dx.doi.org/10.1002/kpm.338.

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Romashchenko, Alexey R. "DIGITAL LEARNING FOOTPRINT AS A WAY OF FIXING SECONDARY EDUCATIONAL TEXTS AS A RESULT OF SEMANTIC READING BY SECONDARY SCHOOL STUDENTS." Russian Journal of Education and Psychology 15, no. 3 (2024): 157–79. http://dx.doi.org/10.12731/2658-4034-2024-15-3-583.

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Purpose. One of the main directions of teaching methodology is the creation of a personalized educational route. In the context of the development of the digital educational environment, it is necessary to explore the possibilities of combining individualization of learning with modern digital platforms. The key concept in the study of this topic is the "digital footprint". The article presents a model for displaying a digital footprint in the form of a digital profile, a universal diagnosis of the understanding of the educational text is compiled to create its own text field and it is recorde

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Seo, Youngmin, and Joongjin Kook. "DRRU-Net: DCT-Coefficient-Learning RRU-Net for Detecting an Image-Splicing Forgery." Applied Sciences 13, no. 5 (2023): 2922. http://dx.doi.org/10.3390/app13052922.

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In this paper, we propose a lightweight deep learning network (DRRU-Net) for image-splicing forgery detection. DRRU-Net is an architecture that combines RRU-Net for learning the visual content of images and image acquisition artifacts, and a JPEG artifact learning module for learning compression artifacts in the discrete cosine transform (DCT) domain. The backbone model of a network based on pre-training, such as CAT-Net, a representative network for image forgery detection, has a relatively large number of parameters, resulting in overfitting in a small dataset, which hinders generalization p

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Song, Yuyan, Tianyi Yao, Shengwang Peng, et al. "b-MAR: bidirectional artifact representations learning framework for metal artifact reduction in dental CBCT." Physics in Medicine & Biology, April 8, 2024. http://dx.doi.org/10.1088/1361-6560/ad3c0a.

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Abstract Objective. Metal artifacts in computed tomography (CT) images hinder diagnosis and treatment significantly. Specifically, dental cone-beam computed tomography (Dental CBCT) images are seriously contaminated by metal artifacts due to the widespread use of low tube voltages and the presence of various high-attenuation materials in dental structures. Existing supervised metal artifact reduction (MAR) methods mainly learn the mapping of artifact-affected images to clean images, while ignoring the modeling of the metal artifact generation process. Therefore, we propose the bidirectional ar

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Carrizales, Joshua W., Mattison J. Flakus, Dallin Fairbourn, et al. "4DCT image artifact detection using deep learning." Medical Physics, November 14, 2024. http://dx.doi.org/10.1002/mp.17513.

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AbstractBackgroundFour‐dimensional computed tomography (4DCT) is an es sential tool in radiation therapy. However, the 4D acquisition process may cause motion artifacts which can obscure anatomy and distort functional measurements from CT scans.PurposeWe describe a deep learning algorithm to identify the location of artifacts within 4DCT images. Our method is flexible enough to handle different types of artifacts, including duplication, misalignment, truncation, and interpolation.MethodsWe trained and validated a U‐net convolutional neural network artifact detection model on more than 23 000 c

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Reiley, Kathryn, and Marilyn DeLong. "The Student Learning Experience: A Case Study in Object-Based Learning." Clothing and Textiles Research Journal, October 5, 2022, 0887302X2211310. http://dx.doi.org/10.1177/0887302x221131035.

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Artifacts are a primary source of information for fashion history students participating in object-based learning through careful observation, analysis and interpretation. Object-based learning is an advantage that allows students to connect the course material with the physical artifact in-person. Due to the Covid-19 pandemic, classes at a Midwestern university moved midterm to an online format. Artifacts previously viewed in person were posted digitally, thus this was the first semester that artifact analysis included both in-person and online. Students evaluated their learning experience in

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haoran, yang. "(Artifact)." February 5, 2023. https://doi.org/10.5281/zenodo.7606488.

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Thopalle, Praveen Kumar. "A Unified Machine Learning Approach for Efficient Artifact Management in Jenkins CI/CD Pipelines." Journal of Artificial Intelligence & Cloud Computing, September 30, 2022, 1–6. http://dx.doi.org/10.47363/jaicc/2022(1)e191.

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In this paper, we provide an innovative way to manage artifacts in Jenkins-based CI/CD pipelines using a single flexible ML model. Managing artifacts effectively is a priority when teams grow. In this paper, we present a single ML model that can handle various artifact management problems such as retention prediction, compression optimization, artifact classification, cache optimization, and anomaly detection. With just one model deployed across these multiple functions, we are able to simplify things, reduce compute overhead, and provide a highly scalable solution for Jenkins deployments on l

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Madesta, Frederic, Thilo Sentker, Tobias Gauer, and René Werner. "Deep learning‐based conditional inpainting for restoration of artifact‐affected 4D CT images." Medical Physics, December 6, 2023. http://dx.doi.org/10.1002/mp.16851.

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AbstractBackground4D CT imaging is an essential component of radiotherapy of thoracic and abdominal tumors. 4D CT images are, however, often affected by artifacts that compromise treatment planning quality and image information reliability.PurposeIn this work, deep learning (DL)‐based conditional inpainting is proposed to restore anatomically correct image information of artifact‐affected areas.MethodsThe restoration approach consists of a two‐stage process: DL‐based detection of common interpolation (INT) and double structure (DS) artifacts, followed by conditional inpainting applied to the a

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Kim, Hojin, Sang Kyun Yoo, Dong Wook Kim, et al. "Metal artifact reduction in kV CT images throughout two-step sequential deep convolutional neural networks by combining multi-modal imaging (MARTIAN)." Scientific Reports 12, no. 1 (2022). http://dx.doi.org/10.1038/s41598-022-25366-0.

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AbstractThis work attempted to construct a new metal artifact reduction (MAR) framework in kilo-voltage (kV) computed tomography (CT) images by combining (1) deep learning and (2) multi-modal imaging, defined as MARTIAN (Metal Artifact Reduction throughout Two-step sequentIAl deep convolutional neural Networks). Most CNNs under supervised learning require artifact-free images to artifact-contaminated images for artifact correction. Mega-voltage (MV) CT is insensitive to metal artifacts, unlike kV CT due to different physical characteristics, which can facilitate the generation of artifact-free

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Wang-Nöth, Lu, Philipp Heiler, Hai Huang, et al. "How much data is enough? Optimization of data collection for artifact detection in EEG recordings." Journal of Neural Engineering, March 10, 2025. https://doi.org/10.1088/1741-2552/adbebe.

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Abstract Objective. Electroencephalography (EEG) is a widely used neuroimaging technique known for its cost-effectiveness and user-friendliness. However, the presence of various artifacts leads to a poor signal-to-noise ratio, limiting the precision of analyses and applications. The proposed work focuses on the Electromyography (EMG) artifacts, which are among the most challenging biological artifacts. The currently reported EMG artifact cleaning performance largely depends on the data used for validation, and in the case of machine learning approaches, also on the data used for training. The

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"An Efficient Motion and Noise Artifacts Removal Method using GAIT and Machine Learning Model." International Journal of Innovative Technology and Exploring Engineering 9, no. 2 (2019): 285–92. http://dx.doi.org/10.35940/ijitee.b6176.129219.

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obtaining an exact measurement of oxygen saturation (SpO2) using a finger-probe based pulse oximeter is dependent on both artifact-free infrared (IR) and red (R) Photoplethysmographic signals. However, in actual real-time environment condition, these Photoplethysmographic signals are corrupted due to presence of motion artifact (MA) signal that is produced due to the movement/motion from either hand or finger. To address this motion artifacts interference, the cause of the contamination of Photoplethysmographic signals by the motion artifacts signal is observed using GAIT. Motion and noise art

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Fu, Tianyu, Yan Wang, Kai Zhang, et al. "Deep-learning-based ring artifact correction for tomographic reconstruction." Journal of Synchrotron Radiation 30, no. 3 (2023). http://dx.doi.org/10.1107/s1600577523000917.

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X-ray tomography has been widely used in various research fields thanks to its capability of observing 3D structures with high resolution non-destructively. However, due to the nonlinearity and inconsistency of detector pixels, ring artifacts usually appear in tomographic reconstruction, which may compromise image quality and cause nonuniform bias. This study proposes a new ring artifact correction method based on the residual neural network (ResNet) for X-ray tomography. The artifact correction network uses complementary information of each wavelet coefficient and a residual mechanism of the

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