Robert Rosen’s Relational Biology Theory and His Emphasis on Non-Algorithmic Approaches to Living Systems by Patricia A. Lane

“…This paper examines the use of algorithms and non-algorithmic models in mathematics and science, especially in biology, during the past century by summarizing the gradual development of a conceptual rationale for non-algorithmic models in biology. …”
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A GA-BP neural network algorithm for fault detection of transmission tower bolts by Ziqiang Lu, Pengjie He, Huiwei Liu, Jie Li, Ziying Lu

“…If not detected and addressed in a timely manner, serious adverse events, such as tower collapse, may occur which will endanger the normal operation of the power system.MethodsBased on this, in order to ensure the normal operation of the transmission tower and improve the identification effect of bolt loosening, the GP-BP neural network algorithm was applied to the detection process. The feasibility of this algorithm was evaluated through the quantitative analysis of different damage degrees.ResultThe results are as follows: 1) except for the average accuracy rate of substructure 7, which is 89.74%, the identification accuracy of other substructures is more than 90%, indicating that the GA-BP neural network algorithm is effective in identifying the single-damage degree of the tower bolt loosening in the main material; 2) the identification accuracy of double-damage substructure is also more than 90%, indicating that the GA-BP algorithm is effective in identifying the double-damage degree of the tower bolt loosening in the main material.ConclusionIn summary, it can be concluded that both the single- and double-damage degree conditions exhibit a relatively considerable recognition accuracy. …”
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Seismic Vulnerability Assessment of Reinforced Concrete Educational Buildings Using Machine Learning Algorithm by Tapan Kumar, Mohammad Al Amin Siddique, Raquib Ahsan

“…The main objective of this paper is to assess the vulnerability of reinforced concrete (RC) educational buildings in Dhaka city to seismic activity by utilizing machine learning (ML) algorithms. …”
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Ship Formation and Route Optimization Design Based on Improved PSO and D-P Algorithm by Peilong Xu, Dan Lan, Haolin Yang, Shengtian Zhang, Hyeonseok Kim, Incheol Shin

“…The particle swarm algorithm introduces dynamic adaptive parameter adjustment and the cross mutation strategy of genetic algorithm, while the Douglas-Peucker algorithm integrates density-based noise application spatial clustering algorithm to improve model performance. …”
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Load optimization of cogeneration units based on intuitive multi-objective fish swarm algorithm by Xueqiang Shen, Jiaxin Wang

“…Evaluated via the Zitzler-Deb-Thiele (ZDT) benchmark functions, IFEMOAFSA achieves a 42.63% comprehensive performance improvement over four benchmark algorithms, verified by Mean Inverted Generational Distance (MIGD) and Mean Hypervolume Metric (MHV). …”
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OPTIMAL DESIGN OF SUSPENSION SYSTEM BASED ON IMPROVED PARTICLE SWARM-TABOO SEARCH ALGORITHM by WANG Jun, TIAN ZhongXu

“…At the same time,the suspension system is proposed by the improved particle swarm optimization tabu search optimization algorithm with the decoupling rate of the main direction of the system excitation force as the optimization goal. …”
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Feeder Reconfiguration of Distribution Systems for Loss Reduction and Emissions Reduction Using MVO Algorithm by A. V. Reddy, M. Damodar Reddy, Y. V. Reddy

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Development of an Algorithm and Module for Automatic Evaluation of Student Papers Based on Semantic Analysis of Text by A. A. Poguda, Jean Max Habib Tape

“…The following main results were obtained in the course of this research. …”
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Comparative Performance Analysis of Optimization Algorithms in Artificial Neural Networks for Stock Price Prediction by Ekaprana Wijaya, Moch. Arief Soeleman, Pulung Nurtantio Andono

“…Performance metrics such as Mean Squared Error (MSE), Mean Absolute Error (MAE), and R Square are utilized to evaluate the effectiveness of each method. …”
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The data analysis of sports training by ID3 decision tree algorithm and deep learning by Kaigong Wang, Lei Wang, Jiduo Sun

“…Based on this background, this paper comprehensively evaluates the performance of each model in different dimensions by comparing six key indicators: mean square error (MSE), mean absolute error (MAE), information gain, feature importance, sports performance improvement rate and training target achievement rate. …”
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Research on Tool Wear Monitoring Based on ET-GD and K-nearest Neighbor Algorithm by QIN Yiyuan, LIU Xianli, YUE Caixu, GUO Bin, DING Mingna

“…Finally the fitting degree between the tool wear monitoring model based on extreme random tree and Gaussian distribution and K-nearest neighbor is 99. 17%,and the mean square error and mean absolute error are 13. 0688 and 1. 8241 respectively. …”
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Predictive PID Control for Automated Guided Vehicles Using Genetic Algorithm and Machine Learning by Kinza Nazir, Yong-Woon Kim, Yung-Cheol Byun

“…Specifically, the GA-optimized true PID values achieved a mean error of 11.8402, while the SVR-predicted PID values had a mean error of 15.4438, with SVR attaining a recall of 86.55%. …”
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Application of machine learning algorithm for prediction of abortion among reproductive age women in Ethiopia by Angwach Abrham Asnake, Alemayehu Kasu Gebrehana, Hiwot Altaye Asebe, Beminate Lemma Seifu, Bezawit Melak Fente, Meklit Melaku Bezie, Mamaru Melkam, Sintayehu Simie Tsega, Yohannes Mekuria Negussie, Zufan Alamrie Asmare

“…Moreover, the XGBoost was the 2nd best-performing algorithm with 0.87 accuracy and 0.94 AUC. According to the SHAP beeswarm and bar plots, younger age was identified as the strongest predictor of abortion, with a mean SHAP value of + 0.060. …”
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Research on a PCA-transformer-based prediction algorithm for gas concentration in working face by YANG Jian, SHU Longyong, ZHANG Shulin, QIN Kai, CUI Cong

“…To address this issue, this study proposes a Principal Component Analysis (PCA)-Transformer-based prediction algorithm for gas concentration in working faces. Firstly, raw gas concentration-related data was cleaned and normalized using min-max scaling. …”
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Stacking modeling with genetic algorithm-based hyperparameter tuning for uniaxial compressive strength prediction by Tanveer Alam Munshi, Khanum Popi, Labiba Nusrat Jahan, M. Farhad Howladar, Mahamudul Hashan

“…Additionally, a hybrid stacking model that combines these algorithms was developed. Hyperparameter optimization was conducted using grid search and genetic algorithm. …”
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Management and prediction of river flood utilizing optimization approach of artificial intelligence evolutionary algorithms by Rana Muhammad Adnan Ikram, Mo Wang, Hossein Moayedi, Atefeh Ahmadi Dehrashid

“…Four specific algorithms—black hole algorithm (BHA), future search algorithm (FSA), heap-based optimization (HBO), and multiverse optimization (MVO)—were tested for predicting flood occurrences in the Fars region of Iran. …”
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Calibrating Microscopic Traffic Simulation Model Using Connected Vehicle Data and Genetic Algorithm by Abolfazl Afshari, Joyoung Lee, Dejan Besenski, Branislav Dimitrijevic, Lazar Spasovic

“…., desire speed), a Genetic Algorithm was adopted to minimize these differences. …”
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Apple leaf disease severity grading based on deep learning and the DRL-Watershed algorithm by Zhifang Bi, Fumin Ma, Jiaxiong Guan, Jie Wu, Juxia Li, Fuzhong Li, Yanwen Li, Zhanli Liu

“…Experimental results demonstrate that the modified HRNet model achieved a mean intersection over union (mIoU) of 88.91% and a mean pixel accuracy (mPA) of 94.13%, representing improvements of 8.77 and 7.25% points, respectively, over the original HRNet. …”
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Wind energy resource assessment based on joint wolf pack intelligent optimization algorithm. by Jiayuan Wang

“…The hybrid prediction model has a smaller relative error value compared to a single algorithm, with a maximum value of less than 0.2. It has better prediction performance than the combination model, with a coefficient of determination approaching 1.0, a fitting accuracy of 0.994, a mean square error of 0.1947, a root mean square error of 0.3847, and an average absolute percentage error of 15.23%. …”
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Modified rainflow algorithm for temperature-time-dependent counting in lifetime estimation of power devices by Renata O. de Sousa, Rodrigo C. de Barros, William C. S. Amorim, Allan F. Cupertino, Heverton Augusto Pereira, Lenin M. F. Morais

“…Specifically, the conventional rainflow algorithm cannot compute the effective heating time and the time-dependent equivalent mean temperature of the thermal cycles. …”
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