Mean and standard deviation of RBF classifier performance across different training algorithms with cross-validation. by Mohammad Hossein Hadizadeh Isfahani (22634016)

Mean and standard deviation of MLP classifier performance across different training algorithms with cross-validation. by Mohammad Hossein Hadizadeh Isfahani (22634016)

Data Sheet 1_Hybrid machine learning algorithms accurately predict marine ecological communities.pdf by Luciana Erika Yaginuma (10477013)

“…In the supervised stage, these associations were modeled as a function of the environmental features by five supervised algorithms (Support Vector Machine, Random Forest, k-Nearest Neighbors, Naive Bayes, and Stochastic Gradient Boosting), using 80% of the samples for training, leaving the remaining for testing. …”

Infrared Spectroscopy Coupled with Machine Learning Algorithms to Investigate Vascular Dysfunction in Ovariectomy: An Animal Model Study by Tháfanys S. Travezani (20609398)

Morpheus: A fragment-based algorithm to predict metamorphic behaviour in proteins across proteomes by Vijay Subramanian (20718933)

“…<p dir="ltr">Supplementary material for Morpheus: A fragment-based algorithm to predict metamorphic behaviour in proteins across proteomes.…”

IEEE CEC 2017 benchmark function specifications. by Kankan Tang (21474968)

IEEE CEC 2022 benchmark function specifications. by Kankan Tang (21474968)

Multimodal reference functions. by Ruiyu Zhan (21602031)

“…We performed comparative analyses against other methodologies across various functions and public datasets to assess their effectiveness. …”

Water consumption due to average value and fuzzy logic algorithm. by Gourab Saha (8987405)

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Crop yield prediction accuracy using different ML algorithms. by Gourab Saha (8987405)

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IRBMO vs. meta-heuristic algorithms boxplot. by Chenyi Zhu (9383370)

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IRBMO vs. feature selection algorithm boxplot. by Chenyi Zhu (9383370)

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Accuracy of the support vector machines for different proportion of test samples. by Abeer Aljohani (18497914)

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PhyCysID: Plant Cystatin Protein Prediction by an Artificial Intelligence Approach by Sadaf Aqil (22183571)

“…PhyCysID uses a set of 21 features derived from amino acid composition, in combination with 15 distinct machine learning algorithms, to classify phytocystatin sequences into one of the four subtypes. …”

PhyCysID: Plant Cystatin Protein Prediction by an Artificial Intelligence Approach by Sadaf Aqil (22183571)

“…PhyCysID uses a set of 21 features derived from amino acid composition, in combination with 15 distinct machine learning algorithms, to classify phytocystatin sequences into one of the four subtypes. …”

PhyCysID: Plant Cystatin Protein Prediction by an Artificial Intelligence Approach by Sadaf Aqil (22183571)

“…PhyCysID uses a set of 21 features derived from amino acid composition, in combination with 15 distinct machine learning algorithms, to classify phytocystatin sequences into one of the four subtypes. …”

PhyCysID: Plant Cystatin Protein Prediction by an Artificial Intelligence Approach by Sadaf Aqil (22183571)

“…PhyCysID uses a set of 21 features derived from amino acid composition, in combination with 15 distinct machine learning algorithms, to classify phytocystatin sequences into one of the four subtypes. …”

Multidimensional test results of BWEMFO and MFO on IEEE CEC 2017 test functions. by Jingjing Ma (419752)

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Order parameters versus temperature for amylase tasks: by Carlos A. Gomez-Uribe (21492788)

“…These were obtained from cooling then heating runs of our algorithm for the amylase task: on the left using the ESM2 mutant marginal score, and on the right using the machine learning model that predicts fitness for stain removal and dp3 function. …”

Automated Analysis of Astrocyte Cell Connectivity After Laser Ablation Using Machine Learning and Path-Finding Algorithms by Connor Lee (22327243)