Table_1_A scheduling route planning algorithm based on the dynamic genetic algorithm with ant colony binary iterative optimization for unmanned aerial vehicle spraying in multiple tea fields.XLSX by Yangyang Liu (807797)

Illustration of the BRIDES algorithm and the selection process. by Cindy Bouchard (6844979)

Subjects:

Optimal ALARM BN discovered by CausNet. by Nand Sharma (21519845)

Subjects:

Genetic Algorithm (GA) and CAGE-based personalization block diagrams. by Dmitrii Smirnov (8822324)

The feature selection process of the IG-GPSO. by Fangyuan Yang (2567629)

Optimal ovarian cancer BNs learned by CausNet and CausNet-partial. by Nand Sharma (21519845)

Subjects:

The feature ranking grouping process of the IG-GPSO. by Fangyuan Yang (2567629)

Expression values and standard errors of genes within each patient group under the optimal feature filtering method (ALOGA < 128) used for (blue bars) distinguishing each individua... by Song Cui (553627)

Inferring Gene Regulatory Networks Using the Improved Markov Blanket Discovery Algorithm by Interdis Sci Comp Life Sci (7335308)

“…</p><p dir="ltr"> (2) Isolated genes were properly processed by the IDS to optimize the network structure.…”

Inferring Gene Regulatory Networks Using the Improved Markov Blanket Discovery Algorithm by Interdis Sci Comp Life Sci (7335308)

“…This work mainly focuses on the following aspects: (1) On the basis of the IPC-MB and DPI, we presented a novel feature selection method called the improved MB discovery algorithm (IMBDA), which can accurately identify direct and indirect regulatory genes when inferring networks. (2) Isolated genes were properly processed by the IDS to optimize the network structure. (3) The performance of IMBDANET was assessed with extensive experiments. …”

Optimal image settings for object detection of Iba1 immunolabelled cells in DAB. by Danish M. Anwer (15383441)

The pseudocode related to the process of initial harmony generation, with considering SNP locus data. by Fariba Esmaeili (532865)

The Pseudo-Code of the IRBMO Algorithm. by Chenyi Zhu (9383370)

“…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. The experiments are based on 12 medical datasets, and the results show that IRBMO achieves optimal overall performance in key metrics such as fitness value, classification accuracy and specificity. …”

Parameter settings of the comparison algorithms. by Ying Li (38224)

“…<div><p>Feature selection is an important solution for dealing with high-dimensional data in the fields of machine learning and data mining. In this paper, we present an improved mountain gazelle optimizer (IMGO) based on the newly proposed mountain gazelle optimizer (MGO) and design a binary version of IMGO (BIMGO) to solve the feature selection problem for medical data. …”

DataSheet1_Optimal Sparsity Selection Based on an Information Criterion for Accurate Gene Regulatory Network Inference.PDF by Deniz Seçilmiş (13028616)

DataSheet1_Optimal Sparsity Selection Based on an Information Criterion for Accurate Gene Regulatory Network Inference.PDF by Deniz Seçilmiş (13028616)

A* Path-Finding Algorithm to Determine Cell Connections by Max Weng (22327159)

Datasets and their properties. by Olaide N. Oyelade (14047002)

“…<div><p>Feature selection problem represents the field of study that requires approximate algorithms to identify discriminative and optimally combined features. …”

Parameter settings. by Olaide N. Oyelade (14047002)

“…<div><p>Feature selection problem represents the field of study that requires approximate algorithms to identify discriminative and optimally combined features. …”

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

“…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. The experiments are based on 12 medical datasets, and the results show that IRBMO achieves optimal overall performance in key metrics such as fitness value, classification accuracy and specificity. …”