A Novel Crowdsourcing-Assisted 5G Wireless Signal Ranging Technique in MEC Architecture by Rui Lu, Lei Shi, Yinlong Liu, Zhongkai Dang

“…Experimental results demonstrate a mean positioning error of 5 m, with 95% of devices achieving errors within 10 m, as well as building and floor prediction error rates of 0.5% and 1%, respectively. …”
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Machine Learning-Based Classification and Statistical Analysis of Liver Cancer: A Comprehensive Study of Model Performance and Clinical Significance by Pratyush Kumar MAHARANA, Tapan Kumar BEHERA, Pradeep Kumar NAIK

“…Conclusion: After performing the complete process, we conclude that the extra tree classifier out of 17 models is the most suitable machine learning algorithm for liver cancer prediction. …”
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Unboxing Tree ensembles for interpretability: A hierarchical visualization tool and a multivariate optimal re-built tree by Giulia Di Teodoro, Marta Monaci, Laura Palagi

“…The interpretability of models has become a crucial issue in Machine Learning because of algorithmic decisions' growing impact on real-world applications. …”
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Pathway analysis of GWAS provides new insights into genetic susceptibility to 3 inflammatory diseases. by Hariklia Eleftherohorinou, Victoria Wright, Clive Hoggart, Anna-Liisa Hartikainen, Marjo-Riitta Jarvelin, David Balding, Lachlan Coin, Michael Levin

“…Using a variable selection algorithm, we identified variants responsible for the pathway association and evaluated their use for disease prediction using a 10 fold cross-validation framework in order to calculate out-of-sample area under the Receiver Operating Curve (AUC). …”
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Mechanistic role of miR-375 in regulating PDPK1 to promote progression of small bowel neuroendocrine tumors: a silico analysis by Tao Ren, Lu Zhou, Zhenlong Li, Mingmei Pan, Xueqiong Han

“…Drug targeting prediction and immune environment evaluation were identified. …”
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Computational hybrid analysis of drug diffusion in three-dimensional domain with the aid of mass transfer and machine learning techniques by Mohammed Alqarni, Ali Alqarni

“…Additionally, $$\:\nu\:$$ -SVR exhibits the lowest RMSE and MAE, showing excellent predictive accuracy compared to KRR and MLR. Overall, our analysis demonstrates the effectiveness of employing tree-based ensemble models coupled with BFO for accurately predicting chemical concentrations in three-dimensional space, with $$\:\nu\:$$ -SVR emerging as the most promising model for this task. …”
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Improvement of physics-based and data-driven model simulations based on multi-source soil moisture datasets by Xiao Liang, Haiting Gu, Yue-Ping Xu, Lu Wang, Yuxue Guo, Li Liu

“…The physics-based Distributed-Hydrological-Soil-Vegetable Model (DHSVM), coupled with the multi-objective genetic algorithm ε-NSGA-II, and data-driven Informer model, are chosen and applied to the study area. …”
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Analysis of approaches to identification of trend in the structure of the time series by U. S. Mokhnatkina, D. V. Parfenov, D. A. Petrusevich

“…A combination of polynomial model for trend description and ARIMA for seasonally description and combination of ACD algorithm for trend and ETS for seasonal model obtained good forecasts in case of seasonal time series, while Fourier time series as a trend model also achieved close quality of prediction.Conclusions. …”
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Research on the Molding Design and Optimization of the Molding Process Parameters of the Automobile Trunk Trim Panel by Youmin Wang, Zhaozhe Zhu, Lingfeng Tang, Qinshuai Jiang

“…In order to put forward the theoretical calculation formula for the compression force of the compression mold of the trunk trim panel, obtain the influence trend of the process parameters on the molding quality of the trunk trim panel, and obtain the optimal process parameters combination for the compression molding of the trunk trim panel, four process parameters, the heating temperature, time, compression pressure, and holding time, which affected the compression molding, were selected as the level factors; the maximum thinning rate, maximum thickening rate, and shrinkage rate of the trunk trim panel were selected as evaluation indicators and orthogonal experiments were designed and completed; the comprehensive weighted scoring method was used to obtain the comprehensive score results and obtain the comprehensive evaluation indicators of the best combination of process parameters of trunk trim panel; BP neural network and genetic algorithm were used to study the change trend of the evaluation indicators of trunk trim panel with the changes of process parameters; based on the optimal process parameter combination and the established neural network’s prediction function, the maximum thinning rate, maximum thickening rate, and shrinkage rate under a single process parameter change could be predicted, and the influence of a single process parameter on the maximum thinning rate, maximum thickening rate, and shrinkage rate could be obtained; the process parameters were optimized, and a maximum thinning rate of 28%, a maximum thickening rate of 4.3%, and a shrinkage rate of 0.8% were obtained; the optimal molding process parameters of the trunk trim panel were heating temperature of 209°C, heating time of 62 s, molding pressure of 14 kPa, and holding pressure time of 49 s; after optimization, the maximum shrinkage rate was 28.0880%, the maximum thickening rate was 44.3264%, and the shrinkage rate was 0.8901%; according to the optimal process parameters, the quality of the trunk trim panel was very good, which met the production quality requirements.…”
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Flood Detection and Susceptibility Mapping Using Sentinel-1 Time Series, Alternating Decision Trees, and Bag-ADTree Models by Ayub Mohammadi, Khalil Valizadeh Kamran, Sadra Karimzadeh, Himan Shahabi, Nadhir Al-Ansari

“…Based on AUC, success and prediction rates were 0.736 and 0.786 for bag-ADTree algorithm, in order, while these proportions were 0.714 and 0.784 for ADTree. …”
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Rock blasting evaluation - image recognition method based on deep learning by Haibao YI, Aixiang Wu, Xiliang Zhang

“…In order to efficiently evaluate the quality of rock blasting in mines, this paper developed a blasting effect image analysis and calculation model and recognition algorithm based on the established machine learning database, and carried out recognition and analysis work on the half-hole rate and rock blasting fragmentation of pre-splitting blasting. …”
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Comparative analysis of lumped and semi-distributed hydrological models for an upland watershed in Ethiopia by Gebiaw T. Ayele, Bofu Yu

“…The study aimed at evaluating model performance and sensitivity of parameters in predicting streamflow for tropical watersheds. Calibration and uncertainty analysis (UA) for SWAT was performed using four UA techniques available in the SWAT (SWAT-CUP) and the genetic algorithm was used for parameter estimation for conceptual models. …”
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Inversion Method for Permitting Loadings of Pollutant from Lateral Effluents Based on Adjoint Equations by SHI Xiaoyan, ZHANG Hong, TAO Chunhua, LU Lingjiang, WAN Xin, LIU Zhaowei

“…However, optimization objectives that rely on discrepancies between predicted and observed concentrations cannot be directly applied to determine the permissible loadings, limiting the application of the adjoint equation method to this issue. …”
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HIDS-RPL: A Hybrid Deep Learning-Based Intrusion Detection System for RPL in Internet of Medical Things Network by Abdelwahed Berguiga, Ahlem Harchay, Ayman Massaoudi

“…The suggested model, designated HIDS-RPL, results from the hybridization of the Convolutional Neural Network (CNN) for feature extraction and the Long Short Term Memory neural network (LSTM), typically employed for sequence data prediction. We evaluate the proposed algorithm to detect intrusions using the benchmark CIC-DDoS2019 dataset. …”
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Mechanical Behavior of Filled Rubber Compounds: Hyper-Elastic Models Based on Strain Amplification by Mir Hamid Reza Ghoreishy, Foroud Abbassi-Sourki

“…The previous theories for the prediction of the mechanical behavior of these materials arebased on phenomenological relationships. …”
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Object Detection in High-Resolution UAV Aerial Remote Sensing Images of Blueberry Canopy Fruits by Yun Zhao, Yang Li, Xing Xu

“…Blueberries, as one of the more economically rewarding fruits in the fruit industry, play a significant role in fruit detection during their growing season, which is crucial for orchard farmers’ later harvesting and yield prediction. Due to the small size and dense growth of blueberry fruits, manual detection is both time-consuming and labor-intensive. …”
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An object-based and Lagrangian view on an intense hailstorm day in Switzerland as represented in COSMO-1E ensemble hindcast simulations by K. P. Brennan, M. Sprenger, A. Walser, M. Arpagaus, H. Wernli

“…A tracking algorithm that facilitates object-based analysis of the simulated hailstorms is introduced. …”
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Synergistic Matching and Influencing Factors of Grain Production and Cropland Net Primary Productivity in the Black Soil Region of Northeast China by Quanxi Wang, Jun Ren, Maomao Zhang, Hongjun Sui, Xiaodan Li

“…This study analyzed the spatial–temporal mismatch characteristics of grain production and cropland net primary productivity (CNPP) using the gravity center model, spatial autocorrelation analysis, and spatial mismatch index (SMI), and identified the spatial heterogeneity and prediction–response relationships of influencing factors based on a geographically and temporally weighted regression (GTWR) model and boosted regression tree (BRT) machine learning algorithm. …”
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Numerical analysis method of stress wave transmission attenuation of coal and rock structural plane by Wenlong SHEN, Renren ZHU, Ziqiang CHEN, Guocang SHI

“…This study demonstrates that the machine learning prediction model based on BP artificial neural network technology has well-applicability, which can quickly determine the model parameters under the current inclination angle and axial static load of the coal rock structural plane, provide an efficient data-driven correction method for the parameters of the Barton-Bandis intrinsic model of the coal rock structural plane and also predict the parameters of numerical simulation of the coal rock structural plane under the larger inclination angle and axial static load ranges other than the given training samples.…”
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Optimal model-based insulin dosing strategy with offline and online optimization by Martin Dodek, Eva Miklovičová, Miroslav Halás

“…This paper presents the design of a model-based bolus calculator algorithm aimed at optimizing insulin therapy for patients with type 1 diabetes. …”
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