Stander deviation values of different benchmark functions. by Jalal- ud-Din (21474307)

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

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DataSheet1_Design of a temperature-feedback controlled automated magnetic hyperthermia therapy device.pdf by Anirudh Sharma (1295760)

“…A proportional-integral-derivative (PID) control algorithm was designed and implemented in NI LabVIEW®. …”

Machine Learning Models for Efficient Property Prediction of ABX₃ Materials: A High-Throughput Approach by Soundous Touati (20282599)

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Machine Learning Models for Efficient Property Prediction of ABX₃ Materials: A High-Throughput Approach by Soundous Touati (20282599)

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Machine Learning Models for Efficient Property Prediction of ABX₃ Materials: A High-Throughput Approach by Soundous Touati (20282599)

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Mitochondrial toxic prediction of marine alga toxins using a predictive model based on feature coupling and ensemble learning algorithms by Guangyin Jia (21075537)

“…By comparing 8 machine learning algorithms and using a weighted soft voting method to integrate the two optimal algorithms, we established 108 prediction models and identified the best ensemble learning model MACCS_LK for screening and defining its application domain. …”

Determination of cyclic behavior of various cross-section composite column-beam connections reinforced with FRP composite using FEM analysis and ML models by Yasemin Simsek Turker (22340676)

Table 6_Predictive prioritization of genes significantly associated with biotic and abiotic stresses in maize using machine learning algorithms.xlsx by Anjan Kumar Pradhan (9386369)

“…These genes had an abundance of antioxidant responsive elements and abscisic acid responsive elements in their promoter region, suggesting their role in stress response. …”

Table 7_Predictive prioritization of genes significantly associated with biotic and abiotic stresses in maize using machine learning algorithms.xlsx by Anjan Kumar Pradhan (9386369)

“…These genes had an abundance of antioxidant responsive elements and abscisic acid responsive elements in their promoter region, suggesting their role in stress response. …”

Table 3_Predictive prioritization of genes significantly associated with biotic and abiotic stresses in maize using machine learning algorithms.xlsx by Anjan Kumar Pradhan (9386369)

“…These genes had an abundance of antioxidant responsive elements and abscisic acid responsive elements in their promoter region, suggesting their role in stress response. …”

Table 2_Predictive prioritization of genes significantly associated with biotic and abiotic stresses in maize using machine learning algorithms.xlsx by Anjan Kumar Pradhan (9386369)

“…These genes had an abundance of antioxidant responsive elements and abscisic acid responsive elements in their promoter region, suggesting their role in stress response. …”

Table 1_Predictive prioritization of genes significantly associated with biotic and abiotic stresses in maize using machine learning algorithms.xlsx by Anjan Kumar Pradhan (9386369)

“…These genes had an abundance of antioxidant responsive elements and abscisic acid responsive elements in their promoter region, suggesting their role in stress response. …”

Table 4_Predictive prioritization of genes significantly associated with biotic and abiotic stresses in maize using machine learning algorithms.xlsx by Anjan Kumar Pradhan (9386369)

“…These genes had an abundance of antioxidant responsive elements and abscisic acid responsive elements in their promoter region, suggesting their role in stress response. …”

Table 5_Predictive prioritization of genes significantly associated with biotic and abiotic stresses in maize using machine learning algorithms.xlsx by Anjan Kumar Pradhan (9386369)

“…These genes had an abundance of antioxidant responsive elements and abscisic acid responsive elements in their promoter region, suggesting their role in stress response. …”

Confusion matrices depict the performance of various ML algorithms. by Caroline L. Alves (14271413)

<b>Codes for "Fatigue analysis of wind turbine and load reduction through wind-farm-level yaw control"</b> by Yize Wang (19535173)

“…<p dir="ltr">The farm-level yaw control optimization algorithm Codes for "Fatigue analysis of wind turbine and load reduction through wind-farm-level yaw control"</p>…”

Accuracy of different electrical and optical neural networks for various input noise levels. by Reyhane Ahmadi (14857363)

Code by Baoqiang Chen (21099509)

Panel (a): Schematic diagram of model predictive control for optimising epidemic interventions. by Sandor Beregi (22177739)