Fully-Gated Denoising Auto-Encoder for Artifact Reduction in ECG Signals by Ahmed Shaheen, Liang Ye, Chrishni Karunaratne, Tapio Seppänen

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The future of critical care: AI-powered mortality prediction for acute variceal gastrointestinal bleeding and acute non-variceal gastrointestinal bleeding patients by Zhou Liu, Guijun Jiang, Liang Zhang, Palpasa Shrestha, Yugang Hu, Yi Zhu, Guang Li, Yuanguo Xiong, Liying Zhan

“…In case of ANGIB patients, gradient boosting model proven to be the optimal machine learning models, with the AUC of 0.985 ± 0.002, accuracy of 0.948 ± 0.009, precision of 0.949 ± 0.009, recall of 0.968 ± 0.009, and F1 score of 0.959 ± 0.007. …”
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Differentiating idiopathic Parkinson’s disease from multiple system atrophy-P using brain MRI-based radiomics: a multicenter study by Yin-Hui Huang, Mei-Li Yang, Yuan-Zhe Li, Ya-Fang Chen, Chi Cai, Jing Huang, Yi Wang, Tie-Qiang Li, Qin-Yong Ye

“…Radiomic features were extracted from T1-weighted imaging and T2-weighted imaging sequences, and various machine learning classifiers were applied, including logistic regression, support vector machine (SVM), ExtraTrees, extreme gradient boosting, and Light Gradient Boosting Machine. …”
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Peritumoral features for assessing invasiveness of lung adenocarcinoma manifesting as ground-glass nodules by Xiao Wang, Hui Xue, Wei Ding, Fei Huang, Yu Zhang, Xin Pang

“…All machine learning models demonstrated good predictive performance for both MIA and IAC. …”
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Accuracy Prediction of Compressive Strength of Concrete Incorporating Recycled Aggregate Using Ensemble Learning Algorithms: Multinational Dataset by Menghay Phoeuk, Minho Kwon

“…Although the random forest regression algorithm performed the least well among the four models, it still outperformed conventional machine learning algorithms such as support vector machines and artificial neural networks. …”
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Prediction of Rut Depth in Soil Caused by Wheels Using Artificial Neural Networks by N. Farhadi, A. Mardani, A. Hosainpour, B. Golanbari

“…In contrast, the optimal RBF model, with an expansion rate of 0.23456, yielded an RMSE of 0.12. …”
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Integrating artificial intelligence into thermodynamics: A new paradigm for sustainable future by Marwan Al-Raeei

“…By integrating machine learning algorithms and statistical techniques into predictive modeling, we demonstrate that it is possible to develop highly accurate models that forecast performance based on historical data. …”
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Artificial Empathy and Imprecise Communication in a Multi-Agent System by Joanna Siwek, Konrad Pierzyński, Przemysław Siwek, Adrian Wójcik, Patryk Żywica

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Dynamic Workload Management System in the Public Sector: A Comparative Analysis by Konstantinos C. Giotopoulos, Dimitrios Michalopoulos, Gerasimos Vonitsanos, Dimitris Papadopoulos, Ioanna Giannoukou, Spyros Sioutas

“…Using a dataset encompassing public/private sector experience, educational history, and age, we evaluate the effectiveness of seven machine learning algorithms: Linear Regression, Artificial Neural Networks (ANNs), Adaptive Neuro-Fuzzy Inference System (ANFIS), Support Vector Machine (SVM), Gradient Boosting Machine (GBM), Bagged Decision Trees, and XGBoost in predicting employee capability and optimizing task allocation. …”
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Algorithms Facilitating the Observation of Urban Residential Vacancy Rates: Technologies, Challenges and Breakthroughs by Binglin Liu, Weijia Zeng, Weijiang Liu, Yi Peng, Nini Yao

“…With their powerful nonlinear modeling ability, machine learning algorithms have significant advantages in capturing the nonlinear relationships of data. …”
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An Ensemble Hybrid Framework: A Comparative Analysis of Metaheuristic Algorithms for Ensemble Hybrid CNN Features for Plants Disease Classification by Khaoula Taji, Ali Sohail, Tariq Shahzad, Bilal Shoaib Khan, Muhammad Adnan Khan, Khmaies Ouahada

“…The optimized feature vector is classified using state-of-the-art machine learning algorithms. …”
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Radiomic study on preoperative multi‐modal magnetic resonance images identifies IDH‐mutant TERT promoter‐mutant gliomas by Haoyu Wang, Shuxin Zhang, Xiang Xing, Qiang Yue, Wentao Feng, Siliang Chen, Jun Zhang, Dan Xie, Ni Chen, Yanhui Liu

“…The discovery cohort was split into training and test sets by a 4:1 ratio. A diagnostic model (multilayer perceptron classifier) for detecting the IDHmut pTERTmut gliomas was trained using an automatic machine‐learning algorithm named tree‐based pipeline optimization tool (TPOT). …”
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Research on False Alarm Rate Reducing Strategies for Subway Train Fire Alarm System by ZHANG Qian, XU Shaohong, XUE Hongquan

“…The false alarm rate can be reduced by hardware optimization methods such as adding dust filter devices, adopting dual light source detectors, multi-parameter gas detectors and pyrolysis particle detectors; the rate can also be reduced by optimizing software algorithms (optimization modeling method, automatic adjustment of alarm thresholds, machine self-learning algorithm, etc.). …”
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Laptop power tuning based on system state prediction by Qihua Xiao, Zhihui Shan, Xueli Ban, Zhengcao Jin

“…The traditional method is to manually determine various power parameters, which is time-consuming and labor-intensive, and may not be able to achieve optimal machine performance. In this paper, we propose using a deep learning model to automatically learn the setting of the power system parameters to improve the performance of the machine and the user experience. …”
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Bitcoin Return Dynamics Volatility and Time Series Forecasting by Punit Anand, Anand Mohan Sharan

“…We show that conventional time series modeling using ARMA and ARMA GARCH run on a rolling basis produces better or comparable forecasting errors than those that machine learning techniques produce. …”
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Automatic Identification of Critical Damage Value with In-situ Shearing Test and Notched Plate Tensile Test with Image Analysis by Yoshida Yoshinori, Kutsukake Asuka

“…To validate fracture behavior, we perform in-situ shearing and tensile tests on notched steel plates, employing image analysis. In ADFEA, a machine-learning-based optimization algorithm searches for critical damage values by minimizing the error between simulated and experimental fracture test results. …”
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Design Study of Hole Types for Improved Cooling of Experimental Heatsinks Manufactured by SLM Technology Using an AlSi10Mg Alloy by Rudolf Madaj, Robert Kohar, Frantisek Brumercik, Matus Veres

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M2M Access With Dynamic Cognitive Virtual Operators: A Data Aggregator’s Perspective by Dapeng Li, Haitao Zhao, Lin Gao, Hongbo Zhu

“…In this paper, an aggregator-assisted model for machine-to-machine (M2M) communications is proposed. …”
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Porosity Characterization of Thermal Barrier Coatings by Ultrasound with Genetic Algorithm Backpropagation Neural Network by Shuxiao Zhang, Gaolong Lv, Shifeng Guo, Yanhui Zhang, Wei Feng

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About managing the number of simultaneously functioning software robots of different types by A. S. Zuev, D. A. Leonov

“…A proposed solution is based on models and methods of scenario management, linear programming, inventory management, queuing theory, and machine learning. …”
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