AI KRegression by CHIN-CHENG CHU (20718575)

A novel RNN architecture to improve the precision of ship trajectory predictions by Martha Dais Ferreira (18704596)

“…To solve these challenges, Recurrent Neural Network (RNN) models have been applied to STP to allow scalability for large data sets and to capture larger regions or anomalous vessels behavior. This research proposes a new RNN architecture that decreases the prediction error up to 50% for cargo vessels when compared to the OU model. …”

Clinical diagnostic efficacy comparison of the proposed hybrid model and benchmark machine learning models. Metrics are reported as point estimates with 95% CIs.... by Medhat A. Tawfeek (22522087)

“…<p>Clinical diagnostic efficacy comparison of the proposed hybrid model and benchmark machine learning models. Metrics are reported as point estimates with 95% CIs. …”

Importance of random forest model. by Yu Zhang (12946)

“…The results showed that: 1) the degree of coastal openness had the greatest influence on human perception while the coastal landscape with a high green visual index decreases the distinctiveness perception; 2) the random forest model can effectively predict human perception on urban coastal roads with an accuracy rate of 87% and 77%; 3) The proportion of low perception road sections with potential for improvement is 60.6%, among which the proportion of low aesthetic perception and low distinctiveness perception road sections is 10.5%. …”

Schematic of the Baidu SVI collection. by Yu Zhang (12946)

“…The results showed that: 1) the degree of coastal openness had the greatest influence on human perception while the coastal landscape with a high green visual index decreases the distinctiveness perception; 2) the random forest model can effectively predict human perception on urban coastal roads with an accuracy rate of 87% and 77%; 3) The proportion of low perception road sections with potential for improvement is 60.6%, among which the proportion of low aesthetic perception and low distinctiveness perception road sections is 10.5%. …”

Map of the study area. by Yu Zhang (12946)

“…The results showed that: 1) the degree of coastal openness had the greatest influence on human perception while the coastal landscape with a high green visual index decreases the distinctiveness perception; 2) the random forest model can effectively predict human perception on urban coastal roads with an accuracy rate of 87% and 77%; 3) The proportion of low perception road sections with potential for improvement is 60.6%, among which the proportion of low aesthetic perception and low distinctiveness perception road sections is 10.5%. …”

Research framework. by Yu Zhang (12946)

“…The results showed that: 1) the degree of coastal openness had the greatest influence on human perception while the coastal landscape with a high green visual index decreases the distinctiveness perception; 2) the random forest model can effectively predict human perception on urban coastal roads with an accuracy rate of 87% and 77%; 3) The proportion of low perception road sections with potential for improvement is 60.6%, among which the proportion of low aesthetic perception and low distinctiveness perception road sections is 10.5%. …”

Perception type distribution and typical images. by Yu Zhang (12946)

“…The results showed that: 1) the degree of coastal openness had the greatest influence on human perception while the coastal landscape with a high green visual index decreases the distinctiveness perception; 2) the random forest model can effectively predict human perception on urban coastal roads with an accuracy rate of 87% and 77%; 3) The proportion of low perception road sections with potential for improvement is 60.6%, among which the proportion of low aesthetic perception and low distinctiveness perception road sections is 10.5%. …”

Presents the bibliometric search results. by Yan She (8639796)

S1 Data. Bibliometric analysis. S2 Data. Estrogen level analysis. S3 Data. MicroCT quantification. S4 Data. Markers of bone metabolism. by Yan She (8639796)

Modeling results of 8-week-old female C57BL/6J mice. by Yan She (8639796)

Sexual maturity status of female C57BL/6J Mice at different ages. by Yan She (8639796)

Flow chart of data screening and analysis. by Yan She (8639796)

Comparison of Mean Absolute Error (MAE) in Millimeters as a Function of Kernel Size. by Liu Liu (512237)

Graphical representation of the optimization process in machine learning, focusing on the adjustment of weights during gradient descent. by Liu Liu (512237)

Exploring the relationship between graft dysfunction with serum metabolites and inflammatory proteins: integrating Mendelian randomization, single-cell analysis, machine learning,... by Jiyuan Li (4784196)

“…Second, we further intergrated the single-cell analysis, machine learning, and Shapley Additive exPlanations (SHAP) methods to validate the role of the inflammatory protein in rejected transplanted kidneys.…”

Table 3_High-throughput sequencing metabarcoding and network analysis elucidate the effects of soil fumigation and biostimulant on potato yield, rhizoctonia canker, and fungal comm... by Honoré Tekeu (16972074)

Table 1_High-throughput sequencing metabarcoding and network analysis elucidate the effects of soil fumigation and biostimulant on potato yield, rhizoctonia canker, and fungal comm... by Honoré Tekeu (16972074)

Table 2_High-throughput sequencing metabarcoding and network analysis elucidate the effects of soil fumigation and biostimulant on potato yield, rhizoctonia canker, and fungal comm... by Honoré Tekeu (16972074)

Characteristics of women at admission. by Guiyou Yang (10259597)

“…., the PIERS Machine Learning [PIERS-ML] model, and the logistic regression-based fullPIERS model) accurately identify individuals at greatest or least risk of adverse maternal outcomes within 48 h following admission. …”