Prognostic Analysis of the Screened Mitophagy-Related Genes. by Jie Chu (293795)

GSVA analysis of differentially expressed genes in high and low risk groups. by Jie Chu (293795)

Comparison of different crossover operators. by Jalal- ud-Din (21474307)

Identification of the role of dusp1 in gastric cancer. by Jie Chu (293795)

Prognostic Correlation Analysis of Different Risk Factors in GC. by Jie Chu (293795)

Nomogram and DCA Analysis of Prognostic Assessment. by Jie Chu (293795)

Analysis Results of Mitophagy Genes Based on Multi-Omics. by Jie Chu (293795)

Effect of Mitophagy Score on Immunotherapy for Different Datasets. by Jie Chu (293795)

Differences in Immune Signatures Between High and Low-Risk Groups. by Jie Chu (293795)

Developing an Integrated Framework to Guide the Ecological Design of Electrical and Electronic Equipment by Guo-Guo Liu (21625651)

“…However, there is no effective tool to generate ecological design schemes while considering the requirements of regulation and customers, the technical feasibility of the scheme, and the trade-off between environmental impacts and costs. This study developed a framework that integrates knowledge graphs, quality function deployment, and a Pareto-optimal algorithm to guide the ecological design of EEE, achieving market demand evaluation, technical feasibility analysis, and optimal design scheme generation. …”

The structural mutation of neuroevolution. by Wenbing Shi (5806160)

“…In constructing smart mines, predicting CGO risks efficiently and accurately is necessary. This paper proposes a CGO risk prediction method based on data augmentation and a neuroevolution algorithm, denoted as ANEAT. …”

The genome coding scheme. by Wenbing Shi (5806160)

“…In constructing smart mines, predicting CGO risks efficiently and accurately is necessary. This paper proposes a CGO risk prediction method based on data augmentation and a neuroevolution algorithm, denoted as ANEAT. …”

The speciation of ANEAT model evolution. by Wenbing Shi (5806160)

“…In constructing smart mines, predicting CGO risks efficiently and accurately is necessary. This paper proposes a CGO risk prediction method based on data augmentation and a neuroevolution algorithm, denoted as ANEAT. …”

The analysis of feature importance. by Wenbing Shi (5806160)

“…In constructing smart mines, predicting CGO risks efficiently and accurately is necessary. This paper proposes a CGO risk prediction method based on data augmentation and a neuroevolution algorithm, denoted as ANEAT. …”

S1 Data - by Wenbing Shi (5806160)

“…In constructing smart mines, predicting CGO risks efficiently and accurately is necessary. This paper proposes a CGO risk prediction method based on data augmentation and a neuroevolution algorithm, denoted as ANEAT. …”

The fitness of ANEAT model evolution. by Wenbing Shi (5806160)

“…In constructing smart mines, predicting CGO risks efficiently and accurately is necessary. This paper proposes a CGO risk prediction method based on data augmentation and a neuroevolution algorithm, denoted as ANEAT. …”

The structure of the data sample. by Wenbing Shi (5806160)

“…In constructing smart mines, predicting CGO risks efficiently and accurately is necessary. This paper proposes a CGO risk prediction method based on data augmentation and a neuroevolution algorithm, denoted as ANEAT. …”

The genome recombination of neuroevolution. by Wenbing Shi (5806160)

“…In constructing smart mines, predicting CGO risks efficiently and accurately is necessary. This paper proposes a CGO risk prediction method based on data augmentation and a neuroevolution algorithm, denoted as ANEAT. …”

The principle of sample data augmentation. by Wenbing Shi (5806160)

“…In constructing smart mines, predicting CGO risks efficiently and accurately is necessary. This paper proposes a CGO risk prediction method based on data augmentation and a neuroevolution algorithm, denoted as ANEAT. …”

The fitness of NANEAT model evolution. by Wenbing Shi (5806160)

“…In constructing smart mines, predicting CGO risks efficiently and accurately is necessary. This paper proposes a CGO risk prediction method based on data augmentation and a neuroevolution algorithm, denoted as ANEAT. …”