Implementasi Algoritma Catboost Dan Shapley Additive Explanations (SHAP) Dalam Memprediksi Popularitas Game Indie Pada Platform Steam by Mohammad Teddy Syamkalla, Siti Khomsah, Yohani Setya Rafika Nur

“…The SHAP method reveals the influence of features on prediction results. The existence of steam trading cards category, RPG genre and compatibility on mac operating system will increase the popularity. …”
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Comprehensive symptom assessment using Integrated Palliative care Outcome Scale in hospitalized heart failure patients by Yasuhiro Hamatani, Moritake Iguchi, Yurika Ikeyama, Atsuko Kunugida, Megumi Ogawa, Natsushige Yasuda, Kana Fujimoto, Hidenori Ichihara, Misaki Sakai, Tae Kinoshita, Yasuyo Nakashima, Masaharu Akao

“…The total IPOS score on admission was not correlated with the HF severity, including LVEF (Spearman's ρ = −0.05, P = 0.43), NT‐proBNP levels (Spearman's ρ = 0.08, P = 0.20) or in‐hospital mortality prediction model (GWTG‐HF risk score) (Spearman's ρ = 0.01, P = 0.90). …”
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Assessment of prognosis and responsiveness to immunotherapy in colorectal cancer patients based on the level of immune cell infiltration by Kaili Liao, Minqi Zhu, Lei Guo, Zijun Gao, Jinting Cheng, Bing Sun, Yihui Qian, Bingying Lin, Jingyan Zhang, Tingyi Qian, Yixin Jiang, Yanmei Xu, Qionghui Zhong, Xiaozhong Wang

“…Furthermore, an accurate prognostic risk prediction model based on the co-expression of relevant genes by immune cells was developed, enabling precise prediction of survival of colon cancer patients. …”
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Monitoring the Concentrations of Na, Mg, Ca, Cu, Fe, and K in <i>Sargassum fusiforme</i> at Different Growth Stages by NIR Spectroscopy Coupled with Chemometrics by Sisi Wei, Jing Huang, Ying Niu, Haibin Tong, Laijin Su, Xu Zhang, Mingjiang Wu, Yue Yang

“…Superior CARS-PLS models were established for Na, Mg, Ca, Cu, Fe, and K with root mean square error of prediction (<i>RMSEP</i>) values of 0.8196 × 10³ mg kg⁻¹, 0.4370 × 10³ mg kg⁻¹, 1.544 × 10³ mg kg⁻¹, 0.9745 mg kg⁻¹, 49.88 mg kg⁻¹, and 7.762 × 10³ mg kg⁻¹, respectively, and coefficient of determination of prediction (<i>R_P</i>²) values of 0.9787, 0.9371, 0.9913, 0.9909, 0.9874, and 0.9265, respectively. …”
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Validity of a machine learning estimation of blood volumes during altitude training by Basile Moreillon, Bastien Krumm, Lena Mettraux, Julian Wackernell, James Spragg, Martin Faulhaber, Raphael Faiss

“…Conversely, predicted values for Hbmass underestimated the actual gains, indicating that the predictive model may not be sensitive enough to discriminate actual variations due to a prolonged hypoxic expopsure. …”
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Programmed cell death-related gene IL20RA facilitates tumor progression and remodels tumor microenvironment in thyroid cancer by Yuqi Wang, Yunlong Zhang, Min Liu, Yu Liu, Yu Zeng, Wei Zhang, Shuping Wu, Linfei Hu, Xianhui Ruan, Xiangqian Zheng, Ming Gao, Jingzhu Zhao

“…We constructed a prognostic prediction model and identified several PCD-related genes. …”
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Prospective Analysis of Confocal Laser Endomicroscopy for Assessment of the Resection Bed for Bladder Tumor by Ben-Max de Ruiter, Jan E. Freund, C. Dilara Savci-Heijink, Jons W. van Hattum, Marinka J. Remmelink, Theo M. de Reijke, Joyce Baard, Guido M. Kamphuis, D. Martijn de Bruin, Jorg R. Oddens

“…The sensitivity, specificity, positive predictive value, and negative predictive value were 0.5 (95% confidence interval [CI] 0.07–0.93), 0.83 (95% CI 0.59–0.96), 0.4 (95% CI 0.05–0.85), and 0.88 (95% CI 0.64–0.99) for CLE prediction of rT, and 0.69 (95% CI 0.39–0.91), 0.33 (95% CI 0.07–0.7), 0.6 (95% CI 0.32–0.84), and 0.43 (95% CI 0.1–0.82) for prediction of DM, respectively. …”
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Analisis Perilaku Entitas untuk Pendeteksian Serangan Internal Menggunakan Kombinasi Model Prediksi Memori dan Metode PCA by Rahmat - Budiarto, Yanif Dwi Kuntjoro

“…The memory-prediction model recognizes bottom-up inputs that matched in hierarchy and evokes a series of top-down expectations. …”
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Mitochondrial reactive oxygen species trigger metformin-dependent antitumor immunity via activation of Nrf2/mTORC1/p62 axis in tumor-infiltrating CD8T lymphocytes by Kazuhiro Kakimi, Hirokazu Matsushita, Masaaki Komatsu, Eiryo Kawakami, Mikako Nishida, Nahoko Yamashita, Taisaku Ogawa, Keita Koseki, Eiji Warabi, Tomoyuki Ohue, Katsuyuki Shiroguchi, Heiichiro Udono

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Penerapan Metode K-Means Clustering dan Simple Moving Average untuk Memprediksi Jenis Penyakit di Provinsi Jawa Timur by Shynta Ayu Dwi Darmawan, Karmilasari

“…The research objectives are to cluster cases into relevant and identifiable groups, predict trends in disease cases based on historical data in each region from year to year, build a website-based system as a medium for implementing predictions and clustering. …”
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Study on Applicability of Xin'anjiang Model and Tank Model in Flood Forecasting in Majiagou Reservoir by MA Jinghang, XIAN Yongcai, HE Xueping, LIU Ming, HAN Muyuan, DU Bailin, RUAN Bingnan, XU Liujia, WU Lei

“…Flood forecasting is one of the important non-engineering flood control measures and is the main basis for flood control command and decision-making.In order to avoid the uncertainty of the prediction results of a single model,the Majiagou Reservoir in Chenggu County was taken as the object to simulate the daily runoff and flood process from 2019 to 2021 by using the Xin'anjiang model and tank model respectively,and the simulation results and accuracy of the two models were compared by using the model parameters calibrated and optimized by the genetic algorithm.In the daily runoff simulation,the simulation effect of the tank model is better than that of the Xin'anjiang model,with a relative error of flood volume of less than 16%,a relative error of flood peak of less than 4%,a difference of peak time of less than 1 h,and a Nash-Sutcliffe efficiency coefficient of greater than 0.58,all of which meet the evaluation accuracy requirements of the Standard for Hydrological Information and Hydrological Forecasting,and the simulation effect of deluge in the reservoir is ideal.In the flood process simulation,the difference of peak time between the two models is similar;the simulation effect of the Xin'anjiang model is smoother,and the flood volume and flood peak simulated by the tank model are closer to the measured flow process.On the whole,the tank model is more suitable for flood forecasting in Majiagou Reservoir than the Xin'anjiang model.…”
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Determinants of individual preferences for unconventional water for irrigation use: empirical literature review by Ahmed Amghar, El Houssaine Erraoui, Fouad Elame

“…Furthermore, examination of the literature review revealed the richness of elicitation modes adopted in determining farmers' individual preferences (IP) for non-conventional water, such as payment card and referendum methods. Also, the prediction results obtained from the econometric analysis of previous studies made it possible to emphasize the determinants of farmers' (IP) for the use of non-conventional water in irrigation. …”
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The Partial Power Control Algorithm of Underwater Acoustic Sensor Networks Based on Outage Probability Minimization by Yun Li, Yishan Su, Zhigang Jin, Sumit Chakravarty

“…The proposed algorithm captures transmission loss (TL) using the Markov chain Monte Carlo (MCMC) method and estimates CSI in the next moment using AR prediction. The simulation results show that the proposed algorithm can effectively reduce the accumulative interference to the receiver and then reduce the outage probability by 19.3% at the maximum.…”
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Mixed-frequency VAR: a new approach to forecasting migration in Europe using macroeconomic data by Emily R. Barker, Jakub Bijak

“…For the longer term, the proposed methods, despite high prediction errors, can still be useful as tools for setting coherent migration scenarios and analysing responses to exogenous shocks.…”
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Assimilation of MWHS-2/FY-3C 183 GHz Channels Using a Dynamic Emissivity Retrieval and Its Impacts on Precipitation Forecasts: A Southwest Vortex Case by Keyi Chen, Jiao Fan, Zhipeng Xian

“…The dynamic emissivity retrieved from window channels of the microwave humidity sounder II (MWHS-2) onboard the China Meteorological Administration’s FengYun (FY)-3C polar orbiting satellite can provide more realistic emissivity over lands and potentially improve the numerical weather prediction (NWP) forecasts. However, whether the assimilation with the dynamic emissivity works for the precipitation forecasts over the complex geography is less investigated. …”
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Building occupancy type classification and uncertainty estimation using machine learning and open data by Tom Narock, J. Michael Johnson, Justin Singh-Mohudpur, Arash Modaresi Rad

“…We address strategies to handle significant class imbalance and introduce Bayesian neural networks to handle prediction uncertainty. The 100-year flood in North Carolina is provided as a practical application in disaster preparedness.…”
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The Sensitivity of Heavy Precipitation to Horizontal Resolution, Domain Size, and Rain Rate Assimilation: Case Studies with a Convection-Permitting Model by Xingbao Wang, Peter Steinle, Alan Seed, Yi Xiao

“…The result indicates that model resolution and domain size should be considered as part of probabilistic precipitation forecasts and ensemble prediction system design besides the model initial field uncertainty.…”
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Copula-Based Probabilistic Hazard Assessment Model for Debris Flow Considering the Uncertainties of Multiple Influencing Factors by Mi Tian, Yuan Shen, Long Fan, Xiao-Tao Sheng

“…The developed model is then used to make probabilistic prediction of debris-flow volume for a specific hazard level, and compared with the empirical approaches. …”
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Morphological Biomarker Differentiating MCI Converters from Nonconverters: Longitudinal Evidence Based on Hemispheric Asymmetry by Xiaojing Long, Chunxiang Jiang, Lijuan Zhang

“…Hemispheric asymmetry in specific brain regions as a neuroimaging biomarker can provide helpful information for prediction of MCI conversion.…”
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Bifurcation Scenarios of Neural Firing Patterns across Two Separated Chaotic Regions as Indicated by Theoretical and Biological Experimental Models by Huaguang Gu, Baobao Pan, Jian Xu

“…The deterministic dynamics of the chaotic firing patterns were identified using a nonlinear prediction method. These results provided details regarding the processes and dynamics of bifurcation containing the chaotic bursting between period-1 and period-2 burstings and other chaotic firing patterns within the comb-shaped chaotic region. …”
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