Free field Active Noise Control system development using a 3D Finite Element based approach by Budnik Maximilian, Mees Valentin

“…It further allows for the optimization of control algorithms, evaluation of the attenuation performance, and methodical iteration of the overall concept design. …”
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Multi-function supported privacy protection data aggregation scheme for V2G network by Baiji HU, Xiaojuan ZHANG, Yuancheng LI, Rongxin LAI

“…In view of the problem that the functions of the current privacy protection data aggregation scheme were insufficient to meet the increasingly rich application requirements, a multi-function supported privacy protection data aggregation (MFPDA) scheme for V2G network was proposed.By using cryptographic algorithms such as BGN, BLS, and Shamir’s secret sharing, as well as fog computing and consortium blockchain technology, multiple security functions like fault tolerance, resistance to internal attacks, batch signature verification, no need for trusted third parties, and multiple aggregation functions were integrated into one privacy protection data aggregation scheme.Security analysis shows that the proposed scheme can protect data aggregation’s security, privacy and reliability.The performance evaluation shows that the introduction of fog computing can significantly reduce the computing overhead of the control center, and the reduction rate can be as high as 66.6%; the improvement of the consortium blockchain can effectively reduce the communication and storage overhead of the system, and the reduction rate can reach 16.7% and 24.9% respectively.…”
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Cardiac computed tomography: Current practice, guidelines, applications, and prospects by Yu-Tai Shih, Jia-Hao Zhou, Jong-Kai Hsiao

“…Cardiac computed tomography (CT) has evolved significantly as a critical tool in diagnosing and managing cardiac diseases, greatly facilitated by technological advancements in multidetector systems, dose-reduction techniques, and sophisticated imaging algorithms. …”
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Machine learning analysis of molecular dynamics properties influencing drug solubility by Zeinab Sodaei, Saeid Ekrami, Seyed Majid Hashemianzadeh

“…The Gradient Boosting algorithm achieved the best performance with a predictive R2 of 0.87 and an RMSE of 0.537 in test set. …”
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Diagnostic value of leukocytosis in patients presenting to the emergency department with abdominal pain: A retrospective observational study by Erkan Boğa

“…Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of leukocytosis in predicting surgical needs were calculated. …”
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Predictors of Acute Myocardial Infarction: A Machine Learning Analysis After a 7-Year Follow-Up by Marco Casciaro, Pierpaolo Di Micco, Alessandro Tonacci, Marco Vatrano, Vincenzo Russo, Carmine Siniscalchi, Sebastiano Gangemi, Egidio Imbalzano

“…We found that the potential of machine learning to predict life-threatening events is significant. <b>Conclusions:</b> Machine learning algorithms can be used to create models to identify patients at risk for acute myocardial infarction. …”
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A joint three-plane physics-constrained deep learning based polynomial fitting approach for MR electrical properties tomography by Kyu-Jin Jung, Thierry G. Meerbothe, Chuanjiang Cui, Mina Park, Cornelis A.T. van den Berg, Stefano Mandija, Dong-Hyun Kim

“…Within this framework, deep learning is used to discern the optimal polynomial fitting weights for a physics based polynomial fitting reconstruction on the complex B1+ data. For the prediction of optimal fitting coefficients, three neural networks were separately trained on simulated heterogeneous brain models to predict optimal polynomial weighting parameters in three orthogonal planes. …”
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Leveraging AI for Enhanced Operational Risk Management in Sports Events by Jibraili Malak, Rharib Abderrahim, Jibraili Zineb

“…The results reveal that, although AI offers many opportunities to minimise operational failures, concerns remain about the complexity of algorithms and the management of real-time data. Despite these obstacles, the paper concludes that future prospects are promising, with potential innovations such as the automation of critical processes and the prediction of operational incidents.…”
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Making JavaScript Render Decisions to Optimize Security-Oriented Crawler Process by Onur Aktas, Ahmet Burak Can

“…This approach seeks to reduce computational requirements and quicken the security evaluation process through the use of machine learning algorithms. By utilizing a dataset containing the source code from the main pages of 17,160 websites, our experimental results demonstrate a 20% reduction in execution time compared to full JavaScript rendering, indicating an improvement in resource usage without any significant reduction in coverage. …”
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A Feasibility Study of Domain Adaptation for Exercise Intensity Recognition Based on Wearable Sensors by Lei Pang, Yi Li, Ming-Xia Liao, Jia-Gang Qiu, Hui Li, Zhen Wang, Gang Sun

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Novel meta-ensemble modelling approach and comparison of machine-learning models for rebar price estimation by Sahin Tolga Guvel, Abdulkadir Budak, Ibrahim Karataş

“…Making realistic cost calculations is an effective way of preventing cost overruns that may occur in later stages. Rebar price prediction by considering economic indicators significantly affects investment costs and decisions. …”
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Long short-term-memory-based depth of anesthesia index computation for offline and real-time clinical application in pigs by Benjamin Caillet, Gilbert Maître, Steve Devènes, Darren Hight, Alessandro Mirra, Olivier L. Levionnois, Alena Simalatsar

“…They are analyzed using classical signal processing algorithms combined with pharmacokinetic and pharmacodynamic (PK/PD) predictions of anesthetic concentrations and their effects on DoA and the prediction of DoA using a novel deep learning-based algorithm. …”
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Modeling and long-term forecasting of CO2 emissions in Asia: An optimized Artificial Neural Network approach with consideration of renewable energy scenarios by Erfan Abbasian Hamedani, S. Talebi

“…Also, in order to predict the CO2 emission from 2021 to 2035, the input variables are forecast using the nonlinear autoregressive exogenous (NARX) for implementation in the optimal ANN model. …”
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Event recognition technology and short-term rockburst early warning model based on microseismic monitoring and ensemble learning by Zibin Li, Dengpan Qiao, Tianyu Yang, Jun Wang, Hao Chen

“…The rockburst early warning model V-soft achieves peak accuracy and F1 scores of 0.9394 and 0.9173, respectively, demonstrating performance improvements of 6.59–15.68% in accuracy and 14.11–27.21% in F1 score compared to conventional machine learning algorithms and ensemble classifiers. This highlights its superior discriminative capability and robustness in predicting high-intensity rockburst events. …”
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Nondestructive Detection of Rice Milling Quality Using Hyperspectral Imaging with Machine and Deep Learning Regression by Zhongjie Tang, Shanlin Ma, Hengnian Qi, Xincheng Zhang, Chu Zhang

“…Among single-task models, BPNNs outperformed the others in predicting BRR and HRR, with correlation coefficients (<i>r</i>) up to 0.9. …”
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Impact of morphological traits and irrigation levels on fresh herbage yield of sorghum x sudangrass hybrid: Modelling data mining techniques. by Halit Tutar, Senol Celik, Hasan Er, Erdal Gönülal

“…For this purpose, Artificial Neural Networks (ANN), Automatic Linear Model (ALM), Random Forest (RF) Algorithm and Multivariate Adaptive Regression Spline (MARS) Algorithm were used, and the prediction performances of these methods were compared. …”
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Machine Learning Approach to Model Soil Resistivity Using Field Instrumentation Data by Md Jobair Bin Alam, Ashish Gunda, Asif Ahmed

“…This research leverages various machine learning algorithms to develop predictive models trained on a comprehensive dataset of sensor-based soil moisture, matric suction, and soil temperature obtained from prototype ET covers, with known resistivity values. …”
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Machine learning potential predictor of idiopathic pulmonary fibrosis by Chenchun Ding, Quan Liao, Renjie Zuo, Shichao Zhang, Zhenzhen Guo, Junjie He, Ziwei Ye, Weibin Chen, Sunkui Ke

“…To construct an IPF risk prediction model, we employed LASSO-Cox regression analysis and the SVM-RFE algorithm. …”
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Trends in indirect liver function marker testing in Wales from 2000 to 2017 and their association with age and sex: an observational study by Joe West, Joanne R Morling, Trevor Alexander Hill, Colin John Crooks

“…AST and ALT values varied by age and sex, particularly in males with the upper normal range of ALT values decreasing rapidly from 90 U/L at age 30 to 45 U/L by age 80.Conclusion The reduction in AST testing to only 1% of the adult population limits the use of many non-invasive liver marker algorithms. …”
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Surface water quality assessment for drinking and pollution source characterization: A water quality index, GIS approach, and performance evaluation utilizing machine learning anal... by Abhijeet Das

“…Basically, all approaches cumulatively predicted surface WQI with lower accuracy. Overall, the LOR, KNN, ANN, and SVM algorithms were all surpassed by the RF algorithm. …”
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