Application of Machine Learning for Predictive Analysis and Management of Mediterranean-Farmed Fish Mortalities: A Risk Management Case Study Using Apache Spark by Marios C. Gkikas, Dimitris C. Gkikas, Gerasimos Vonitsanos, John A. Theodorou, Spyros Sioutas

“…Future work could incorporate more predictive statistics models focusing on improving the models for extreme values by assessing non-linear models, feature engineering methods, and expanding research into less influential variables. …”
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Assessment of geotechnical behavior of gypseous soil under leaching effect using machine learning by Saif M. Hassan Al-Riahi, Nur Irfah Mohd Pauzi, Mohammed Y. Fattah, Hasan Ali Abbas

“…Model performance was evaluated using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Correlation Coefficient (R). …”
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Unveiling the Effects of Crop Rotation on Cropland Soil pH Mapping: A Remote Sensing-Based Soil Sample Grouping Strategy by Yuan Liu, Songchao Chen, Ge Shen, Cheng Chen, Zejiang Cai, Ji Zhu, Xia Zhang, Guofei Shang, Qingbo Zhou, Sonoko Dorothea Bellingrath-Kimura, Qiangyi Yu, Wenbin Wu

“…The results showed that simply incorporating crop rotation in one joint model was useful but could not obtain the expected accuracy, with a root mean squared error (RMSE) of 0.66 and an R² of 0.36. …”
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Enhancing analogy-based software cost estimation using Grey Wolf Optimization algorithm by Taghi Javdani Gandomani, Maedeh Dashti, Sadegh Ansaripour, Hazura Zulzalil

“…Experimental evaluations show that GWO-ABE achieves notable improvements in key performance metrics, leading to reduced mean magnitude of relative error (MMRE), median magnitude of relative error (MdMRE), and higher percentage of prediction (PRED) compared to other ABE-customized methods. …”
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Quantitative scale validation of the Dimensional Anhedonia Rating Scale in the treatment of Chinese patients with major depressive disorder by XiaoJing Gu, Xiaowei Chen, Yun-Ai Su, Tianmei Si, Jingyu Lin, Donald M Bushnell, Dongjing Fu, Carol Jamieson, Heather Rozjabek

“…Confirmatory factor analysis showed adequate fit statistics (comparative fit index 0.976, goodness-of-fit index 0.935 and root mean square error of approximation 0.055). …”
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CNN Based Fault Classification and Predition of 33kw Solar PV System with IoT Based Smart Data Collection Setup by K. Punitha, G. Sivapriya, T. Jayachitra

“…These faults can arise from a variety of factors, including environmental conditions, manufacturing defects, installation errors, and wear and tear of the components. Fault diagnosis in solar PV systems involves the detection, identification, and rectification of faults or abnormalities that can occur due to various reasons. …”
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Velocity-Constraint Kalman Filtering for Enhanced Bubble Tracking in Motion-Compensated Ultrasound Localization Microscopy by Yifei Zhu, Lingyin Jiang, Qi Zhang, Jun Yin, Bingze Du, Guofeng Zhang, Haijun Zhang, Bo Ding, Han Lin, Honghui Xue, Xiasheng Guo, Xiao-Yang Zhang, Jing-Ning Zhu, Dong Zhang, Juan Tu, Ning Gu

“…Meanwhile, microbubble brightness in adjacent frames was incorporated as multidimensional feature to further improve the matching efficacy. …”
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A Quantitative Evaluation of UAV Flight Parameters for SfM-Based 3D Reconstruction of Buildings by Inho Jo, Yunku Lee, Namhyuk Ham, Juhyung Kim, Jae-Jun Kim

“…Quantitative evaluation results using various analytical methodologies (multiple regression analysis, Kruskal–Wallis test, random forest feature importance, principal component analysis including K-means clustering, response surface methodology (RSM), preference ranking technique based on similarity to the ideal solution (TOPSIS), and Pareto optimization) revealed that the basic shooting pattern ‘type’ has a significant and statistically significant influence on all major SfM performance metrics (reprojection error, final point count, computation time, reconstruction completeness; Kruskal–Wallis <i>p</i> < 0.001). …”
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MORPHOGENESIS OF BRANCHIOGENIC GLANDS OF THE NECK IN THE FETAL PERIOD OF ONTOGENESIS by О. Білоокий, Т. Хмара, Д. Проняєв, В. Безрук

“…Digital parameters of organs and their correlation with neighboring organs and structures were grouped into appropriate age groups and processed by non-parametric methods of variation statistics with calculation of their arithmetic mean, its error and determination of probability of differences by Student's t-test using MS Excel and Biostatistics programs. …”
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3D behavior phenotyping and multi-object tracking system of mice based on deep learning by Lei He, Xuezhen Jia, Chen Li, Yan Liu, Jicheng Yu, Zhen-Xia Chen, Wanneng Yang, Xiuying Liang

“…Stratified Transformer was used to segment individual mouse from the environment and segment individual mouse into 3 parts, and 8 feature points were extracted. In individual mouse experiment, the whole individual mouse was segmented to 4 parts and 11 feature points were extracted. …”
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A hybrid approach to predicting and classifying dental impaction: integrating regularized regression and XG boost methods by Asok Mathew, Pradeep K. Yadalam, Ahmed Radeideh, Shrouk Hady, Rona Swed, Reyyan Cheema, Majd Mousa AL-Mohammad, Mohammed Alsaegh, SR Shetty

“…Enhancing data quality, refining feature selection, and using advanced modeling techniques are crucial for improving predictive capabilities. …”
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Advanced QSPR modeling of profens using machine learning and molecular descriptors for NSAID analysis by W. Eltayeb Ahmed, Muhammad Farhan Hanif, Muhammad Kamran Siddiqui, Brima Gegbe

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POSSIBILITIES OF CARIES PROGNOSIS IN CHILDREN OF SCHOOL-AGE ACCORDING TO DATA GAINED FROM THEM AND THEIR PARENTS QUESTIONNAIRE by L.F. Kaskova, T.B. Mandziuk, L.P. Ulasevych, L.D. Korovina, M.A. Sadovski

“…The correlation coefficient was considered significant in the case of of error probability “p <0,05”. Discriminant analysis was conducted in order to determine the factors of classification. …”
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Creativity, Invention and Linguistic Analysis. by Silvia Lilli

“…By identifying and formalizing these patterns through regular expressions, inventive languages can be treated analytically as natural ones, allowing their features to be statistically examined. While the approach involves subjective interpretation and risk errors in manual labelling, it demonstrates how computational tools can quantify and represent an author’s style even in extreme cases. …”
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A Novel Sea Surface Temperature Prediction Model Using DBN-SVR and Spatiotemporal Secondary Calibration by Yibo Liu, Zichen Zhao, Zhe Zhang, Yi Yang

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Observations of Uranus at High Phase Angle as Seen by New Horizons by Samantha N. Hasler, L. C. Mayorga, William M. Grundy, Amy A. Simon, Susan D. Benecchi, Carly J. A. Howett, Silvia Protopapa, Heidi B. Hammel, Daniel D. Wenkert, S. Alan Stern, Kelsi N. Singer, Simon B. Porter, Pontus C. Brandt, Joel W. Parker, Anne J. Verbiscer, John R. Spencer, The New Horizons Planetary Science Theme Team

“…The New Horizons reflectance in the blue (492 nm) and red (624 nm) filters does not exhibit statistically significant variation and is consistent with the expected error bars. …”
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Spectroscopic Ages for 4 Million Main-sequence Dwarf Stars from LAMOST DR10 Estimated with a Data-driven Approach by Jia-Hui Wang, Maosheng Xiang, Meng Zhang, Ji-Wei Xie, Jian Ge, Jinghua Zhang, Lanya Mou, Ji-Feng Liu

“…Given a spectral signal-to-noise ratio greater than 50, the age estimation is precise to 10%–25% for K-type stars, as younger stars have larger relative errors. Validations suggest that the underlying information used for our age estimation is largely attributed to the LAMOST spectral features of chemical abundances. …”
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New Findings From Explainable SYM‐H Forecasting Using Gradient Boosting Machines by Daniel Iong, Yang Chen, Gabor Toth, Shasha Zou, Tuija Pulkkinen, Jiaen Ren, Enrico Camporeale, Tamas Gombosi

“…We find that the GBMs yield a statistically significant improvement in root mean squared error over the best published black‐box neural network schemes and the Burton equation.…”
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Phase Retrieval Utilizing Particle Swarm Optimization by Li-Jing Li, Teng-Fei Liu, Ming-Jie Sun

“…However, due to the nonconvex feature of the phase optimization problem, it remains a challenge to globally obtain the optimal phase information. …”
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Automatic titration detection method of organic matter content based on machine vision by Bingjie Zhang, Meng Li, Qing Song, Lujian Xu

“…The test results showed that the device had a titration error of less than 0.2 ml and was more efficient than manual titration. …”
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