Exploring the effectiveness of adaptive randomized sine cosine algorithm in wind integrated scenario based power system optimization with FACTS devices by Sunilkumar P. Agrawal, Pradeep Jangir, Arpita, Sundaram B. Pandya, Anil Parmar, Ahmad O. Hourani, Bhargavi Indrajit Trivedi, Mohammad Khishe

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Volt/VAr Regulation of the West Mediterranean Regional Electrical Grids Using SVC/STATCOM Devices With Neural Network Algorithms by H. Feza Carlak, Ergin Kayar

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Evaluation of GLOCK 9 mm Firing Pin Aperture Shear Mark Individuality Based on 3,156 Different Pistols (Manufactured Over a 30 Year Period in Two Countries) Using Additional Patter... by James E. Hamby, Eric Warren, Stephen Norris, Nicholas D.K. Petraco

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In-Memory Versus Disk-Based Computing with Random Forest for Stock Analysis: A Comparative Study by Chitra Joshi, Chitrakant Banchorr, Omkaresh Kulkarni, Kirti Wanjale

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Determination of the Optimal Algorithm for the Operation of a Hybrid Power System with a Regulating Generator by Kuznetsov N., Lysenko O.

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Integrated transportation system planning with gravitational search algorithm approach based on fuzzy mutant controller by Alireza Hosseinzadeh Kashani, Seyed Ahmad Shayannia, Mohammad Mehdi Movahedi, Soheila Sardar

“…In these relations, <strong>3r </strong>is a uniform random variable in the interval [1,0], which is used to create the random property of the speed of the particle population optimization algorithm and the acceleration of the gravitational search algorithm in the gravitational particle population algorithm, and <strong>3C </strong>and <strong>4C</strong> are two constants to determine the degree of the speed of the particle population optimizer algorithm and the acceleration of the gravitational search algorithm in the gravitational particle population algorithm the values of which are considered 3C and 4C. …”
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Optimization of Adaptive Sliding Mode Controllers Using Customized Metaheuristics in DC-DC Buck-Boost Converters by Daniel F. Zambrano-Gutierrez, Jorge M. Cruz-Duarte, Herman Castañeda, Juan Gabriel Avina-Cervantes

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GATCGGenerator: New Software for Generation of Quasirandom Nucleotide Sequences by O. Yu. Kiryanova, R. R. Garafutdinov, I. M. Gubaydullin, A. V. Chemeris

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Forest canopy closure estimation in mountainous southwest China using multi-source remote sensing data by Wenwu Zhou, Wenwu Zhou, Qingtai Shu, Cuifen Xia, Li Xu, Qin Xiang, Lianjin Fu, Zhengdao Yang, Shuwei Wang

“…Then, the multi-source remote sensing image Sentinel-1/2 and terrain factors were combined to perform regional-scale FCC remote sensing estimation based on the geographically weighted regression (GWR) model. The research results showed that (1) among the 50 extracted ATLAS LiDAR feature indices, the best footprint-scale modeling factors are Landsat_perc, h_dif_canopy, asr, h_min_canopy, toc_roughness, and n_touc_photons after random forest (RF) feature variable optimization; (2) among the BO-RFR, BO-KNN, and BO-GBRT models developed at the footprint scale, the FCC results estimated by the BO-GBRT model were the best (R2 = 0.65, RMSE = 0.10, RS = 0.079, and P = 79.2%), which was used as the FCC estimation model for 74,808 footprints in the study area; (3) taking the FCC value of ATLAS footprint scale in forest land as the training sample data of the regional-scale GWR model, the model accuracy was R2 = 0.70, RMSE = 0.06, and P = 88.27%; and (4) the R² between the FCC estimates from regional-scale remote sensing and the measured values is 0.70, with a correlation coefficient of 0.784, indicating strong agreement. …”
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Finding high posterior density phylogenies by systematically extending a directed acyclic graph by Chris Jennings-Shaffer, David H. Rich, Matthew Macaulay, Michael D. Karcher, Tanvi Ganapathy, Shosuke Kiami, Anna Kooperberg, Cheng Zhang, Marc A. Suchard, Frederick A. Matsen

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An artificial intelligence approach to palaeogeographic studies: a case study of the Late Ordovician brachiopods of Laurentia by Akbar Sohrabi

“…Based on the training algorithm and after 146 periods, the training error decreased, but the validation error increased (Fig. 7). …”
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Prediction of Anemia from Multi-Data Attribute Co-Existence by Talal Qadah, Asmaa Munshi

“…Therefore, this study has reevaluated the claims within the domain of detecting and predicting anemia with the best machine learning algorithm. Another research problem, lies with the fact that previous studies on anemia prediction utilized limited machine learning algorithms across a narrow range of datasets, whereas this current study employed numerous machine learning algorithms across a wide range of anemia datasets and tested three hypotheses. …”
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Improving fluoroprobe sensor performance through machine learning by D. Lafer, A. Sukenik, T. Zohary, O. Tal

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Data Mining Classification Techniques for Diabetes Prediction by Hindreen Rashid Abdulqadir, Adnan Mohsin Abdulazeez, Dilovan Assad Zebari

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Acoustic-based models to assess herd-level calves' emotional state: A machine learning approach by Maíra Martins da Silva, Robson Mateus Freitas Silveira, Gean Gobo da Cruz, Karen Airosa Machado de Azevedo, Carla Maris Machado Bittar, Iran José Oliveira da Silva

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Neoantigen prioritization based on antigen processing and presentation by Serina Tokita, Serina Tokita, Takayuki Kanaseki, Takayuki Kanaseki, Toshihiko Torigoe

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UAV-Multispectral Based Maize Lodging Stress Assessment with Machine and Deep Learning Methods by Minghu Zhao, Dashuai Wang, Qing Yan, Zhuolin Li, Xiaoguang Liu

“…The results indicate that the Random Forest (RF) model outperforms the other four ML algorithms, achieving an overall accuracy (OA) of 89.29% and a Kappa coefficient of 0.8852. …”
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Influences of Sampling Design and Model Selection on Predictions of Chemical Compounds in Petroferric Formations in the Brazilian Amazon by Niriele Bruno Rodrigues, Theresa Rocco Barbosa, Helena Saraiva Koenow Pinheiro, Marcelo Mancini, Quentin D. Read, Joshua Blackstock, Edwin H. Winzeler, David Miller, Phillip R. Owens, Zamir Libohova

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Certainty of Linear Interrelation of Technical and Economic Parameters of Power Units of 300 МWt Thermal Power Stations by E. M. Farhadzadeh, A. Z. Muradaliyev, T. K. Rafiyeva, A. A. Rustamova

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The Efficiency of Complex Differential Treatment of the Patients with Systemic Lupus Erythematous Considering the Features of Bone Tissue Structure Remodeling by Abrahamovych U., Tsyhanyk L., Abrahamovych O., Farmaha M., Romaniuk O.

“…The evaluation of the application forms of LQ proves this fact. A general score of the patients of research group has increased up to 37.93 % versus 3.19 % for the women of research group, the same has a complex objective evaluation of the results of treatment by the doctors and researchers: the patients out of research group experienced a positive result of treatment more often while the ones out of comparison group -satisfactory and negative. …”
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