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

“…The training data should ideally be collected from a range of diverse species and for each be based on different experimental techniques that support a training protein’s localisation (e.g. reporters, mass spectrometry, coexpression, interactions). …”

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

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Accuracy of different electrical and optical neural network on Caltech and ETH-80 data sets. by Reyhane Ahmadi (14857363)

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

The errors of the predictions based on different algorithm methods. by Zhang Wei (317854)

“…<p>The errors of the predictions based on different algorithm methods.</p>…”

Comparison of feature-based and Uniform Manifold Approximation and Projection (UMAP)-based spike sorting for neuron classification. by Daniel Suárez-Barrera (22676654)

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Comparison of Uniform Manifold Approximation and Projection (UMAP)-based and feature-based spike sorting on synthetic data. by Daniel Suárez-Barrera (22676654)

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Performance comparison of different algorithms based on five indicators. by Yunyun Dong (6936965)

“…<p>Performance comparison of different algorithms based on five indicators.</p>…”

Fitness versus iterations based on different clustering algorithms. by Yiwei Zhao (2729758)

“…<p>Fitness versus iterations based on different clustering algorithms.</p>…”

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

Comparison of different algorithms. by Jiandong Qiu (20389944)

Comparison of different algorithms. by Dawei Wang (471687)

Precision values based on the number of different clusters for the FCM algorithm. by Ali Asghar Salehi Solaiman Abadi (20846222)

“…<p>Precision values based on the number of different clusters for the FCM algorithm.…”

Comparison of different optimization algorithms. by Hang Zhao (143592)

Performance of different algorithms. by Shiwei Yu (6060308)

Iteration curves of different algorithms. by Tengfei Ma (597633)

“…<div><p>To address the limitations of the Zebra Optimization Algorithm (ZOA), including insufficient late-stage optimization search capability, susceptibility to local optima, slow convergence, and inadequate exploration, this paper proposes an enhanced Zebra Optimization Algorithm integrating opposition-based learning and a dynamic elite-pooling strategy (OP-ZOA: Opposition-Based Learning Dynamic Elite-Pooling Zebra Optimization Algorithm). he proposed search algorithm employs a good point set-elite opposition-based learning mechanism to initialize the population, enhancing diversity and facilitating escape from local optima. …”

The AD-PSO-Guided WOA LSTM algorithm RMSE is based on the objective function compared to different algorithms. by Ahmed M. Elshewey (21463867)

“…<p>The AD-PSO-Guided WOA LSTM algorithm RMSE is based on the objective function compared to different algorithms.…”

Uniform Manifold Approximation and Projection (UMAP)-based sorting enhances detection of low-firing rate neurons and boosts mutual information. by Daniel Suárez-Barrera (22676654)

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ROC curves of different algorithms. by Mengyao Zhou (9266132)

Comparative analysis of spike sorting using Uniform Manifold Approximation and Projection (UMAP) vs. feature-based methods in in vivo extracellular recordings. by Daniel Suárez-Barrera (22676654)

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