summary of the confidence intervals. by Yohannis Derbew Molla (16471771)

“…In Ethiopia, the prevalence of TBI is high, driven by factors such as road traffic incidents (RTIs), assaults, and falls. …”

PRISMA flowchart of the study selection process. by Yohannis Derbew Molla (16471771)

“…In Ethiopia, the prevalence of TBI is high, driven by factors such as road traffic incidents (RTIs), assaults, and falls. …”

Interventions for Psychological Stress in Pregnant African American Women: A Scoping Review by Taleah A. Frazier (22423294)

Subjects:

Factors affecting pyrethroid-LLINs integrity. by Job Oyweri (22041452)

Factors associated with mosquito net integrity. by Job Oyweri (22041452)

Factors affecting PBO-LLINs integrity. by Job Oyweri (22041452)

Odds ratio of multivariable analysis of factors for Shiga-toxin producing <i>Escherichia coli</i> (<i>stx</i> by Amete Mihret Teshale (12072758)

Subjects:

Interaction hotspot maps of influencing factors on older adults’ health level in the Yangtze River Economic Belt. by Mengmeng Yang (475288)

Subjects:

Description of Bivariable and multivariable binary logistic regression analysis showing an association between pathway to psychiatric care and associated factors among study partic... by Misrak Negash Shonor (22226735)

Subjects:

lncCyt b cooperates with hnRNPA2B1 to modulate glycolysis and exhibits physiological significance. by Jia Li (160557)

Morphological changes in the testis and epididymis. by Cao Wang (1500691)

Testing set error. by Xiangjuan Liu (618000)

“…<div><p>This study constructs a multi-stage hybrid forecasting model using hog price time series data and its influencing factors to improve prediction accuracy. First, seven benchmark models including Prophet, ARIMA, and LSTM were applied to raw price series, where results demonstrated that deep learning models significantly outperformed traditional methods. …”

Internal structure of an LSTM cell. by Xiangjuan Liu (618000)

“…<div><p>This study constructs a multi-stage hybrid forecasting model using hog price time series data and its influencing factors to improve prediction accuracy. First, seven benchmark models including Prophet, ARIMA, and LSTM were applied to raw price series, where results demonstrated that deep learning models significantly outperformed traditional methods. …”

Prediction effect of each model after STL. by Xiangjuan Liu (618000)

“…<div><p>This study constructs a multi-stage hybrid forecasting model using hog price time series data and its influencing factors to improve prediction accuracy. First, seven benchmark models including Prophet, ARIMA, and LSTM were applied to raw price series, where results demonstrated that deep learning models significantly outperformed traditional methods. …”

The kernel density plot for data of each feature. by Xiangjuan Liu (618000)

“…<div><p>This study constructs a multi-stage hybrid forecasting model using hog price time series data and its influencing factors to improve prediction accuracy. First, seven benchmark models including Prophet, ARIMA, and LSTM were applied to raw price series, where results demonstrated that deep learning models significantly outperformed traditional methods. …”

Analysis of raw data prediction results. by Xiangjuan Liu (618000)

“…<div><p>This study constructs a multi-stage hybrid forecasting model using hog price time series data and its influencing factors to improve prediction accuracy. First, seven benchmark models including Prophet, ARIMA, and LSTM were applied to raw price series, where results demonstrated that deep learning models significantly outperformed traditional methods. …”

Flowchart of the STL. by Xiangjuan Liu (618000)

“…<div><p>This study constructs a multi-stage hybrid forecasting model using hog price time series data and its influencing factors to improve prediction accuracy. First, seven benchmark models including Prophet, ARIMA, and LSTM were applied to raw price series, where results demonstrated that deep learning models significantly outperformed traditional methods. …”

SARIMA predicts season components. by Xiangjuan Liu (618000)

“…<div><p>This study constructs a multi-stage hybrid forecasting model using hog price time series data and its influencing factors to improve prediction accuracy. First, seven benchmark models including Prophet, ARIMA, and LSTM were applied to raw price series, where results demonstrated that deep learning models significantly outperformed traditional methods. …”

BWO-BiLSTM model prediction results. by Xiangjuan Liu (618000)

“…<div><p>This study constructs a multi-stage hybrid forecasting model using hog price time series data and its influencing factors to improve prediction accuracy. First, seven benchmark models including Prophet, ARIMA, and LSTM were applied to raw price series, where results demonstrated that deep learning models significantly outperformed traditional methods. …”

Bi-LSTM architecture diagram. by Xiangjuan Liu (618000)

“…<div><p>This study constructs a multi-stage hybrid forecasting model using hog price time series data and its influencing factors to improve prediction accuracy. First, seven benchmark models including Prophet, ARIMA, and LSTM were applied to raw price series, where results demonstrated that deep learning models significantly outperformed traditional methods. …”