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Dissecting the Predictors of Cyber-Aggression Through an Explainable Machine Learning Model.
Aggress Behav
January 2025
Key Research Base of Humanities and Social Sciences of the Ministry of Education, Academy of Psychology and Behavior, Tianjin Normal University, Tianjin, China.
The general aggression model (GAM) suggests that cyber-aggression stems from individual characteristics and situational contexts. Previous studies have focused on limited factors using linear models, leading to oversimplified predictions. This study used the light gradient boosting machine (LightGBM) to identify and rank the importance of various risk and protective factors in cyber-aggression.
View Article and Find Full Text PDFIs home environment predictive of early grammar development?
J Child Lang
December 2024
Stellenbosch University, Department of General Linguistics, Stellenbosch, South Africa.
Research shows that children's home environment (e.g., the composition of their household and the resources available in it) has an impact on children's language development.
View Article and Find Full Text PDFA Comparative Study on Imputation Techniques: Introducing a Transformer Model for Robust and Efficient Handling of Missing EEG Amplitude Data.
Bioengineering (Basel)
July 2024
Department of Computer Engineering, Jeju National University, Jeju 63243, Jeju-do, Republic of Korea.
In clinical datasets, missing data often occur due to various reasons including non-response, data corruption, and errors in data collection or processing. Such missing values can lead to biased statistical analyses, reduced statistical power, and potentially misleading findings, making effective imputation critical. Traditional imputation methods, such as Zero Imputation, Mean Imputation, and k-Nearest Neighbors (KNN) Imputation, attempt to address these gaps.
View Article and Find Full Text PDFEffects of Quality of Integrity on Clinical and Structural Outcomes of Rotator Cuff Repair: A Retrospective Cohort Study of 504 Cases.
Am J Sports Med
July 2024
Department of Biostatistics, SMG-SNU Boramae Medical Center, Dongjak-gu, Seoul, Republic of Korea.
Background: The clinical implications of structural integrity have been a subject of long debate. The oversimplified binary categorization of structural integrity into either healing or retear, along with faulty preoperative baselines for comparison, may contribute to the controversy.
Purpose: To determine how the quality of structural integrity in a repaired cuff tendon affects both clinical and structural outcomes by dividing the patients into groups based on integrity and using the immediate postoperative baseline (time zero).
A machine learning predictive model for recurrence of resected distal cholangiocarcinoma: Development and validation of predictive model using artificial intelligence.
Eur J Surg Oncol
July 2024
Hepato Pancreato Biliary Division, Hospital Del Mar, Universitat Pompeu Fabra, Barcelona, Spain. Electronic address:
Introduction: Distal Cholangiocarcinoma (dCCA) represents a challenge in hepatobiliary oncology, that requires nuanced post-resection prognostic modeling. Conventional staging criteria may oversimplify dCCA complexities, prompting the exploration of novel prognostic factors and methodologies, including machine learning algorithms. This study aims to develop a machine learning predictive model for recurrence after resected dCCA.
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