Optimization algorithm comparison. by Wang Linfeng (17102068)

RE for partial flow prediction results of single and dual water level models based on the GA-BP algorithm. by Xiying Wang (4859998)

Optimization results of MBB multi-material structure under different methods. (a) A level-set method for multi-material topology optimization [33].... by Zhao Li (300229)

“…(a) A level-set method for multi-material topology optimization [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0321100#pone.0321100.ref033" target="_blank">33</a>]. …”

QSAR model for predicting neuraminidase inhibitors of influenza A viruses (H1N1) based on adaptive grasshopper optimization algorithm by Z.Y. Algamal (5547620)

“…Obtaining a reliable QSAR model with few descriptors is an essential procedure in chemometrics. The binary grasshopper optimization algorithm (BGOA) is a new meta-heuristic optimization algorithm, which has been used successfully to perform feature selection. …”

Parameter settings. by Qingbo Tu (22641033)

“…<div><p>To improve the intelligent and refined management level of power distribution systems in equipment operation and maintenance as well as emergency support, this work proposes an integrated “prediction-optimization” model that combines genetic algorithm (GA) with machine learning methods. …”

Indicator settings. by Qingbo Tu (22641033)

“…<div><p>To improve the intelligent and refined management level of power distribution systems in equipment operation and maintenance as well as emergency support, this work proposes an integrated “prediction-optimization” model that combines genetic algorithm (GA) with machine learning methods. …”

Parameter sets of the chosen algorithms. by WanRu Zhao (18980374)

“…According to the encouraging research results in this paper, the IERWHO algorithm proposed has a place in the field of optimization.…”

The flow chart of IERWHO algorithm. by WanRu Zhao (18980374)

“…According to the encouraging research results in this paper, the IERWHO algorithm proposed has a place in the field of optimization.…”

The flow chart of WHO algorithm. by WanRu Zhao (18980374)

“…According to the encouraging research results in this paper, the IERWHO algorithm proposed has a place in the field of optimization.…”

Function graph and algorithm iterative graph. by WanRu Zhao (18980374)

“…According to the encouraging research results in this paper, the IERWHO algorithm proposed has a place in the field of optimization.…”

The convergence curves of all the 10 algorithms. by WanRu Zhao (18980374)

“…According to the encouraging research results in this paper, the IERWHO algorithm proposed has a place in the field of optimization.…”

Algorithm flow of the GA-BPNN model. by Xiying Wang (4859998)

“…<div><p>This work explores an intelligent field irrigation warning system based on the Enhanced Genetic Algorithm—Backpropagation Neural Network (EGA-BPNN) model in the context of smart agriculture. …”

Secure MANET routing with blockchain-enhanced latent encoder coupled GANs and BEPO optimization by Sandeep Jagonda Patil (22048337)

“…To tackle these challenges, this paper proposes the Blockchain Based Trusted Distributed Routing Scheme for MANET using Latent Encoder Coupled Generative Adversarial Network Optimized with Binary Emperor Penguin Optimizer (LEGAN-BEPO-BCMANET). …”

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. …”

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. …”

IRBMO vs. feature selection algorithm 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. …”

Unsupervised Machine Learning Neural Gas Algorithm for Accurate Evaluations of the Hessian Matrix in Molecular Dynamics by Michele Gandolfi (11613834)

“…The algorithm belongs to the family of unsupervised machine learning methods, and it is based on the neural gas idea, where neurons are molecular configurations whose Hessians are adopted for groups of molecular dynamics configurations with similar geometries. …”

AI Algorithm Workflow: Image pre-processing with highest pixel intensity identification (a), RGB channel breakdown, Fluorescence isolation and grayscale conversion (b), Computation... by Gareth Gallagher (20874910)

Table 2_Intelligent grading of sugarcane leaf disease severity by integrating physiological traits with the SSA-XGBoost algorithm.docx by Xinrui Wang (488330)

“…</p>Methods<p>In this study, we propose an intelligent method for identifying sugarcane foliar disease severity based on physiological traits. Field-collected data—including Soil and Plant Analyzer Development (SPAD) values, leaf surface temperature, and nitrogen content—were acquired using a plant nutrient analyzer (TYS-4N) from sugarcane leaves infected with brown stripe disease, ring spot disease, and mosaic disease at four severity levels (mild, moderate, moderately severe, and severe). …”

Table 1_Intelligent grading of sugarcane leaf disease severity by integrating physiological traits with the SSA-XGBoost algorithm.xlsx by Xinrui Wang (488330)

“…</p>Methods<p>In this study, we propose an intelligent method for identifying sugarcane foliar disease severity based on physiological traits. Field-collected data—including Soil and Plant Analyzer Development (SPAD) values, leaf surface temperature, and nitrogen content—were acquired using a plant nutrient analyzer (TYS-4N) from sugarcane leaves infected with brown stripe disease, ring spot disease, and mosaic disease at four severity levels (mild, moderate, moderately severe, and severe). …”