The effects of rehearsing actions by source (slideshow vs. story) and of test modality (picture vs. verbal) on source monitoring were examined. Seven- to 8-year-old children (N = 30) saw a slideshow event and heard a story about a similar event. One to 2 days later, they recalled the events by source (source recall), recalled the events without reference to source (no-source-cue recall), or engaged in no recall. Seven to 8 days later, all children received verbal and picture source-monitoring tests. Children in the source recall group were less likely than children in the other groups to claim they saw actions merely heard in the story. No-source-cue recall impaired source identification of story actions. The picture test enhanced recognition, but not source monitoring, of slide actions. Increasing the distinctiveness of the target events (Experiment 2) allowed the picture test to facilitate slideshow action discrimination by children in the no-recall group.
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http://dx.doi.org/10.1037/1076-898X.11.1.33 | DOI Listing |
Sci Rep
January 2025
Faculty of Engineering, Université de Moncton, Moncton, NB, E1A3E9, Canada.
Diabetes is a growing health concern in developing countries, causing considerable mortality rates. While machine learning (ML) approaches have been widely used to improve early detection and treatment, several studies have shown low classification accuracies due to overfitting, underfitting, and data noise. This research employs parallel and sequential ensemble ML approaches paired with feature selection techniques to boost classification accuracy.
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January 2025
Ministry of Higher Education, Mataria Technical College, Cairo, 11718, Egypt.
The current work introduces the hybrid ensemble framework for the detection and segmentation of colorectal cancer. This framework will incorporate both supervised classification and unsupervised clustering methods to present more understandable and accurate diagnostic results. The method entails several steps with CNN models: ADa-22 and AD-22, transformer networks, and an SVM classifier, all inbuilt.
View Article and Find Full Text PDFAm J Orthod Dentofacial Orthop
February 2025
Department of Orthodontics, Faculty of Dentistry, Çanakkale Onsekiz Mart University, Çanakkale, Turkey.
Introduction: This study aimed to assess the precision of an open-source, clinician-trained, and user-friendly convolutional neural network-based model for automatically segmenting the mandible.
Methods: A total of 55 cone-beam computed tomography scans that met the inclusion criteria were collected and divided into test and training groups. The MONAI (Medical Open Network for Artificial Intelligence) Label active learning tool extension was used to train the automatic model.
Food Chem Toxicol
January 2025
Western Growers Association.
As part of the U.S. Food and Drug Administration's (FDA's) Closer to Zero initiative, a toxicological reference value (TRV) for oral dietary exposure to Cd was established.
View Article and Find Full Text PDFBioengineering (Basel)
January 2025
School of Chemical Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea.
Breast cancer ranks as the second most prevalent cancer globally and is the most frequently diagnosed cancer among women; therefore, early, automated, and precise detection is essential. Most AI-based techniques for breast cancer detection are complex and have high computational costs. Hence, to overcome this challenge, we have presented the innovative LightweightUNet hybrid deep learning (DL) classifier for the accurate classification of breast cancer.
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