(a) Prediction using traditional algorithm. (b) Prediction using optimization algorithm. by Dandan Wang (286632)

“…<p>(a) Prediction using traditional algorithm. (b) Prediction using optimization algorithm.…”

A framework for improving localisation prediction algorithms. by Sven B. Gould (12237287)

“…Proteins with non-canonical internal motifs, or those dually targeted need to be taken into account (as they help to better distinguish between pNTS and mNTS features) and validated data could be sorted according to whether it is part of a core- or pan-proteome. Classifiers on which the algorithms are trained could include parameters such as the evolutionary distance of a species, non-coding regions, or a protein’s abundance as a currently neglected factor. …”

One-step trajectory prediction results for the X-coordinate: (a) one-step prediction error for the basic single algorithm; (b) one-step prediction error for each single algorithm in the algorithm; (c) one-step prediction error comparison for the ensemble prediction algorithm. by Lin Li (28817)

“…<p>One-step trajectory prediction results for the X-coordinate: (a) one-step prediction error for the basic single algorithm; (b) one-step prediction error for each single algorithm in the algorithm; (c) one-step prediction error comparison for the ensemble prediction algorithm.…”

One-step trajectory prediction results for the Z-coordinate: (a) one-step prediction error for the basic single algorithm; (b) one-step prediction error for each single algorithm in the algorithm; (c) one-step prediction error comparison for the ensemble prediction algorithm. by Lin Li (28817)

“…<p>One-step trajectory prediction results for the Z-coordinate: (a) one-step prediction error for the basic single algorithm; (b) one-step prediction error for each single algorithm in the algorithm; (c) one-step prediction error comparison for the ensemble prediction algorithm.…”

One-step trajectory prediction results for the Y-coordinate: (a) one-step prediction error for the basic single algorithm; (b) one-step prediction error for each single algorithm in the algorithm; (c) one-step prediction error comparison for the ensemble prediction algorithm. by Lin Li (28817)

“…<p>One-step trajectory prediction results for the Y-coordinate: (a) one-step prediction error for the basic single algorithm; (b) one-step prediction error for each single algorithm in the algorithm; (c) one-step prediction error comparison for the ensemble prediction algorithm.…”

A comparison of with other published cancer prediction algorithms. by Ruiyu Zhan (21602031)

“…<p>A comparison of with other published cancer prediction algorithms.…”

Statistical analysis of the prediction results from the ML algorithms. by M. A. Habib (16839161)

Flowchart of algorithm principles. by Wenjuan Zhou (115285)

“…In order to address the limitations of traditional neural network algorithms, the Informer model is employed for wind power prediction, delivering higher accuracy. …”

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)

“…However, only one gene Zm00001eb038720 encoding RNA-binding protein AU-1/Ribonuclease E/G, predicted by the PLSDA algorithm, was found commonly expressed under both biotic and abiotic stress. …”

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)

“…However, only one gene Zm00001eb038720 encoding RNA-binding protein AU-1/Ribonuclease E/G, predicted by the PLSDA algorithm, was found commonly expressed under both biotic and abiotic stress. …”

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)

“…However, only one gene Zm00001eb038720 encoding RNA-binding protein AU-1/Ribonuclease E/G, predicted by the PLSDA algorithm, was found commonly expressed under both biotic and abiotic stress. …”

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)

“…However, only one gene Zm00001eb038720 encoding RNA-binding protein AU-1/Ribonuclease E/G, predicted by the PLSDA algorithm, was found commonly expressed under both biotic and abiotic stress. …”

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)

“…However, only one gene Zm00001eb038720 encoding RNA-binding protein AU-1/Ribonuclease E/G, predicted by the PLSDA algorithm, was found commonly expressed under both biotic and abiotic stress. …”

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)

“…However, only one gene Zm00001eb038720 encoding RNA-binding protein AU-1/Ribonuclease E/G, predicted by the PLSDA algorithm, was found commonly expressed under both biotic and abiotic stress. …”

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)

“…However, only one gene Zm00001eb038720 encoding RNA-binding protein AU-1/Ribonuclease E/G, predicted by the PLSDA algorithm, was found commonly expressed under both biotic and abiotic stress. …”

Algorithm parameter settings. by Peiwen Zhang (1863202)

“…This study proposes a CO₂ emission prediction method based on an improved back propagation (BP) neural network, where the Improved Sparrow Search Algorithm (ISSA) is employed to optimize the hyperparameters of the BP neural network, thereby enhancing the prediction capability for CO₂ emissions in civil aviation. …”

Predictions from a Universal Value Function Approximator (UVFA) algorithm. by Sam Hall-McMaster (10343795)

“…Each panel includes three plots: the theoretical predictions of a UVFA algorithm, the theoretical predictions of an SF&GPI algorithm, and empirical choices from human participants. …”

Data prediction results of SVM algorithm. by Yanqing Men (20283876)

Data prediction results of SARIMA algorithm. by Yanqing Men (20283876)

Types of training algorithms. by Caixia Yan (6647072)