Signal-domain speed-of-sound correction for ring-array-based photoacoustic tomography by Daohuai Jiang, Hengrong Lan, Shangqing Tong, Xianzeng Zhang, Fei Gao

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Integrated approach to land degradation risk assessment in arid and semi-arid Ecosystems: Applying SVM and eDPSIR/ANP methods by Ehsan Moradi, Hassan Khosravi, Pouyan Dehghan Rahimabadi, Bahram Choubin, Zlatica Muchová

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Systematic bias between blinded independent central review and local assessment: literature review and analyses of 76 phase III randomised controlled trials in 45 688 patients with... by Jianrong Zhang, Yu Huang, Hengrui Liang, Wenhua Liang, Jianxing He, Yiyin Zhang, Jiaxi He, Jieyu Wu, Long Jiang, Shiyan Tang, Qihua He, Lindsey Tristine Hamblin, Zhiheng Xu, Yaoqi Chen, Difei Chen, Xinyu Wang, Kexin Deng, Shuhan Jiang, Jiaqing Zhou, Jiaxuan Xu, Xuanzuo Chen

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New Realities of the Enterprise Management System Information Support: Economic and Mathematical Models and Cloud Technologies by Anatolii Asaul Asaul, Mykhailo Voynarenko, Liudmyla Yemchuk, Larysa Dzhulii

“…The paper focuses on the urgency of the implementation of cloud technologies, which are a necessary condition for the development of enterprise management systems, give rise to a complex of insufficiently studied phenomena and processes and determine the need to find new tools in making and implementing reasonable management decisions. In the process of research, the sequence of construction and the overall structure of the enterprise management system, based on the use of cloud technologies, are determined, which allowed to build a mathematical model for calculating the probability of making an error-free decision, evaluating the efficiency of decision-making, a model of making a management decision for a certain time with the parallel method of operation of elements of the enterprise management system.…”
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Can the number of confirmed COVID-19 cases be predicted more accurately by including lifestyle data? An exploratory study for data-driven prediction of COVID-19 cases in metropolit... by Sungwook Jung

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Estimation of Oxidation Kinetics and Oxide Scale Void Position of Ferritic-Martensitic Steels in Supercritical Water by Li Sun, Weiping Yan

“…The research results show that the results of simulation at 600°C are approximately close to experimental results. …”
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Machine Learning for Precision Agriculture: Predicting Persimmon Peak Harvest Dates and Yield Using Meteorological Data by Atsushi Okayama, Atsushi Yamamoto, Yutaka Matsuno, Masaomi Kimura

“…The model was trained and validated using data from the JA Nara Prefecture Nishiyoshino Sorting Facility and Nara Prefecture Agriculture Research and Development Center. Its reliability was confirmed based on mean absolute error, demonstrating the ability to make predictions with an accuracy of approximately three days. …”
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Analyzing the efficacy of trimethylolpropane trioleate oil for predicting cutting power and surface roughness in high-speed drilling of Al-6061 through machine learning. by Pramod S Kathmore, Bhanudas D Bachchhav, Duran Kaya, Sachin Salunkhe, Lenka Cepova, Ondřej Mizera, Emad Abouel Nasr

“…Three different measures of accuracy were used to evaluate the performance of the projected values: coefficient of determination (R2), mean absolute percentage error, and mean square error. The decision tree performed better than other models in accurately predicting power and surface roughness. …”
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Design and Modeling of a High-Peak-Power Distributed Electric Propulsion System for a Super-STOL UAV by Jia Zong, Zhou Zhou, Jinhong Zhu, Zhuang Shao, Sanya Sun

“…The experimental and simulation results indicate that the maximum error of the high-peak-power propulsion unit model without considering temperature is 19.52%, and the maximum error when considering temperature is 1.2%. …”
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An Application of Hybrid Models for Weekly Stock Market Index Prediction: Empirical Evidence from SAARC Countries by Zhang Peng, Farman Ullah Khan, Faridoon Khan, Parvez Ahmed Shaikh, Dai Yonghong, Ihsan Ullah, Farid Ullah

“…The foremost aim of this research was to forecast the performance of three stock market indices using the multilayer perceptron (MLP), recurrent neural network (RNN), and autoregressive integrated moving average (ARIMA) on historical data. …”
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Machine Learning Impact on Modern Business Intelligence by Raziyeh Moghaddas, Farinaz Tanhaei, Maryam Al Moqbali, Solmaz Safari

“…Finally, we assessed the performance of each model using standard evaluation metrics, i.e., Mean Absolute Error (MAE), Mean Squared Error (MSE), and the R-squared (R²) score. …”
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Comparison of Machine Learning Methods for Predicting Electrical Energy Consumption by Retno Wahyusari, Sunardi Sunardi, Abdul Fadlil

“…This research investigates how to accurately predict electrical energy consumption to address growing global energy demands. …”
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FINANCIAL AND ECONOMIC SAFETY OF ECONOMIC AGENTS by V. I. Avdiysky, V. M. Bezdenezhnyh, V. E. Liechtenstein, G. V. Ross, K. I. Solodovnikova

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An Algorithm for Simplifying 3D Building Models with Consideration for Detailed Features and Topological Structure by Zhenglin Li, Zhanjie Zhao, Wujun Gao, Li Jiao

“…Compared to the QEM algorithm and the other two comparison algorithms selected in this paper, the simplified model resulting from this algorithm exhibit a reduction in Hausdorff distance, mean error, and mean square error to varying degrees. …”
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Drying kinetics of crushed mass of ‘jambu’: Effective diffusivity and activation energy by Francileni P. Gomes, Resende Osvaldo, Elisabete P. Sousa, Daneil E. C. de Oliveira, Francisco R. de Araújo Neto

“…Coefficient of determination (R2), mean relative error (P), mean estimated error (SE), Chi-square test (χ2), AIC and BIC were the selection criteria for the models. …”
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Inverse Kinematics of a 7-Degree-of-Freedom Robotic Arm Based on Deep Reinforcement Learning and Damped Least Squares by Shusheng Yu, Gongquan Tan

“…As we advance towards the future of the smart manufacturing industry, our research focuses on enhancing manipulator technology. …”
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Prototype of Automatic Waste Filter with Load Sensor Cell on Drainage Waters by Siti Nurlisa, Mulkan Iskandar Nasution, Nazaruddin Nasution

“…From the input and output test results states that the load cell sensor has a low error rate, namely 0.01%, with a waste weight reading on the load cell sensor, namely 316 g, and the highest error rate, namely 0.06%, with a waste weight reading on the load cell sensor, namely 109 g.…”
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Evaluation of Contemporary Computational Techniques to Optimize Adsorption Process for Simultaneous Removal of COD and TOC in Wastewater by Areej Alhothali, Hifsa Khurshid, Muhammad Raza Ul Mustafa, Kawthar Mostafa Moria, Umer Rashid, Omaimah Omar Bamasag

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Machine Learning Applications for Predicting High-Cost Claims Using Insurance Data by Esmeralda Brati, Alma Braimllari, Ardit Gjeçi

“…In order to evaluate and compare the performance of the models, we employed evaluation criteria, including classification accuracy (CA), area under the curve (AUC), confusion matrix, and error rates. We found that Random Forest performs better, achieving the highest classification accuracy (CA = 0.8867, AUC = 0.9437) with the lowest error rates, followed by the XGBoost model. …”
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Synergistic Framework for Fuel Cell Mass Transport Optimization: Coupling Reduced-Order Models with Machine Learning Surrogates by Shixin Li, Qingshan Liu, Yisong Chen

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