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. …”

Convergence curve of the DBO algorithm. by Ma Haohao (22177538)

“…The improved Dung Beetle Optimization algorithm, Back Propagation Neural Network, Finite Element Analysis, and Response Surface Methodology provide a strong guarantee for the selection of robot polishing process parameters. …”

The run time for each algorithm in seconds. by Edward Antonian (21453161)

“…<div><p>In this paper, we study a class of non-parametric regression models for predicting graph signals as a function of explanatory variables . …”

Performance of algorithms outside the training species. by Sven B. Gould (12237287)

“…<p>Comparison of predicted versus experimentally localised plastid <b>(a)</b> and mitochondrial <b>(b)</b> proteome numbers. …”

Dataset for Fine-Tuning Code Generation Models: Kannada-English Algorithmic Statements and Python Code by Goutami Sooda (20714729)

The genome coding scheme. by Wenbing Shi (5806160)

Subjects:

Computational Micromechanics and Machine Learning-Informed Design of Composite Carbon Fiber-Based Structural Battery for Multifunctional Performance Prediction by Mohamad A. Raja (19640297)

Subjects:

(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.…”

Data and code resources. by Sam Hall-McMaster (10343795)

Process followed to develop and validate the MAMELI algorithm. by Valentina Bollati (106778)

Source Code and Dataset by Swathi Ganesan (14394924)

Pseudocode for the missForestPredict algorithm. by Elena Albu (15181070)

“…Missing data in input variables often occur at model development and at prediction time. The missForestPredict R package proposes an adaptation of the missForest imputation algorithm that is fast, user-friendly and tailored for prediction settings. …”

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.…”

The parameter setting of the evolutionary neural network. by Wenbing Shi (5806160)

Subjects:

Code used for data analysis in this study. by Nourin Sultana (22339463)

“…This study applies and evaluates machine learning (ML) algorithms to predict LBW and identify its key risk factors in Bangladesh. …”

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

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

Stress Nephogram of the Splitting Specimen Process. by Nan Ru (9594384)

Flowchart of Random Aggregate Placement Process. by Nan Ru (9594384)