Machine learning-based brain magnetic resonance imaging radiomics for identifying rapid eye movement sleep behavior disorder in Parkinson’s disease patients by Yandong lian, Yibin Xu, Linlin Hu, Yuguo Wei, Zhaoge Wang

“…Additionally, multi-factor logistic regression analysis identified clinical predictors associated with PD-RBD, and these clinical features were integrated with the radiomics signatures to develop predictive models using various machine learning algorithms. The model exhibiting the best performance was selected, and receiver operating characteristic (ROC) curves were used to evaluate its performance in both the training and testing sets. …”
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SpeakerNet for Cross-lingual Text-Independent Speaker Verification by Hafsa HABIB, Huma TAUSEEF, Muhammad Abuzar FAHIEM, Saima FARHAN, Ghousia USMAN

“…The phases of speaker verification protocol are training, enrollment of speakers and evaluation of unknown voice. …”
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Applications of artificial intelligence in neurosurgical education: a scoping review by Hector Julio Piñera-Castro, Christian Borges-García

“…Machine learning algorithms assessed surgical performance, identifying metrics. …”
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Quadratic Regression Models for Profile Picture NFT Valuation by Geun-Cheol Lee, Hoon-Young Koo, Heejung Lee

“…We collected actual BAYC transaction data and split it into a training set (70%) and a validation set (30%). For benchmarking purposes, we compare the proposed models against four machine learning algorithms: Random Forest, Support Vector Regression (SVR), XGBoost, and LightGBM. …”
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Efficient and Stable Learning for Distribution Network Operation: A Model-Based Reinforcement Learning Approach by Dong Yan, Zhan Shi, Xinying Wang, Yiying Gao, Tianjiao Pu, Jiye Wang

“…Modern artificial intelligence (AI) methods, particularly DRL methods, promise faster decision-making but face challenges, including inefficient training and real-world application. This study introduces a reward evaluation system to assess the effectiveness of various strategies and proposes an enhanced algorithm based on the Model-based DRL approach. …”
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Benchmark dataset on feeding intensity of the pearl gentian grouper(Epinephelus fuscoguttatus♀×E. lanceolatus♂) by Haijing Qin, Yunchen Tian, Jianing Quan, Xueqi Cong, Qingfei Li, Jinzhu Sui

“…In order to solve these problems, this study constructs a benchmark dataset of the feeding intensity of pearl gentian groupers in a factory circulating water environment, which is divided into feeding fish groups and fish aggregation areas by training Unet semantic segmentation network and compares standard clustering algorithms through clustering evaluation indexes to maximally select the optimal clustering method and the number of clusters that are suitable for this paper's dataset. …”
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TARREAN: A Novel Transformer with a Gate Recurrent Unit for Stylized Music Generation by Yumei Zhang, Yulin Zhou, Xiaojiao Lv, Jinshan Li, Heng Lu, Yuping Su, Honghong Yang

“…Music generation by AI algorithms like Transformer is currently a research hotspot. …”
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Real-time prediction of the rate of penetration via computational intelligence: a comparative study on complex lithology in Southwest Iran by Mohammad Najafi, Yousef Shiri

“…Data from wells 1 to 6 were employed for training and testing the models, and unseen data from wells 7 to 9 were used for evaluation. …”
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Machine learning for defect condition rating of wall wooden columns in ancient buildings by Yufeng Li, Wu Ouyang, Zhenbo Xin, Houjiang Zhang, Shuqi Sun, Dian Zhang, Wenbo Zhang

“…After re-splitting the training and test datasets, the rating accuracy of GWO-RBF neural network exceeded 95 %, with a Kappa coefficient of 0.94, demonstrating that the algorithms can effectively, conveniently, accurately, and stably assess the defect conditions of wall wooden columns. …”
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Efficient Design Optimization Assisted by Sequential Surrogate Models by Emiliano Iuliano

“…The surrogates are exploited to screen the design space and provide lower-fidelity predictions across it; on the other hand, specific criteria are designed to suggest new points for high-fidelity evaluation so as to enrich the optimizer database. Both Kriging and radial basis function network are used as surrogates with different training strategies. …”
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Towards automatic farrowing monitoring-A Noisy Student approach for improving detection performance of newborn piglets. by Martin Wutke, Clara Lensches, Ulrich Hartmann, Imke Traulsen

“…Firstly, the results contribute directly to the improvement of piglet detection in the context of birth monitoring systems and the evaluation of the farrowing progress. Secondly, the approach presented can be transferred to other research questions and species, thereby reducing the problem of cost-intensive annotation processes and increase training efficiency. …”
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Sentiment Analysis on Google Reviews Using Naïve Bayes, K-Nearest Neighbors, and Logistic Regression to Improve Novotel Services by Yonathan Arya Dhamma, Simon Prananta Barus

“…The dataset used was 1200 reviews, only 1190 reviews were used in the analysis. From the training results, the model produced by the Logistic Regression algorithm was the most accurate, namely 94.54% with unigrams (n = 1). …”
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Modelling Enterprise’s Coordinated Development Strategy with a Soft Fuzzy Rough Set by Tongtong Wang, Jianing Cui

“…Furthermore, we use training methods and soft fuzzy rough sets’ learning algorithm to calculate, and the evaluating indicator rough set is constructed with a three-tier model structure. …”
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Soil moisture retrieval over agricultural region through machine learning and sentinel 1 observations by Deepanshu Lakra, Deepanshu Lakra, Shobhit Pipil, Prashant K. Srivastava, Suraj Kumar Singh, Manika Gupta, Rajendra Prasad

“…The present study provided a comprehensive evaluation of a wide range of advanced machine learning algorithms for Soil Moisture (SM) estimation from microwave Synthetic Aperture Radar (SAR) backscatter observations over the wheat fields. …”
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A Comparative Study of Azure Custom Vision Versus Google Vision API Integrated into AI Custom Models Using Object Classification for Residential Waste by Cosmina-Mihaela Rosca, Adrian Stancu, Marius Radu Tănase

“…The CWSM employs the LbfgsMaximumEntropyMulti algorithm and a dataset of 1000 records for training, divided equally across the categories. …”
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Construction and Simulation of a Strategic HR Decision Model Based on Recurrent Neural Network by Xiaorong Li, Lijun Zhang, Dongchen Li, Dan Guo

“…In this paper, RNN (Recurrent Neural Network) algorithm is used to conduct an in-depth analysis of HR strategic decision-making and an HR strategic decision model is constructed for simulation. …”
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The analysis of rural revitalization serviceplatform in smart city under back propagation neural network. by Gongyi Jiang, Weijun Gao, Meng Xu, Mingjia Tong

“…The tourism transportation and safety services are mainly evaluated at level D, and the model can indicate the level of rural revitalization tourism service; (4) Compared with other algorithms, the GRA-BPNN algorithm performs the best in rural revitalization evaluation, with an accuracy of 92.3%, precision of 91.8%, recall rate of 93.7%, and F1 score of 92.7%. …”
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A model for shale gas well production prediction based on improved artificial neural network by LIN Hun, SUN Xinyi, SONG Xixiang, MENG Chun, XIONG Wenxin, HUANG Junhe, LIU Hongbo, LIU Cheng

“…Moreover, the model exhibits superior prediction accuracy and stability compared to the traditional BP（error backpropagation algorithm） neural network model. With its high accuracy and reliability, the proposed model can provide valuable support for fracturing optimization design and productivity evaluation in shale gas reservoirs.…”
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Stroke Risk Classification Using the Ensemble Learning Method of XGBoost and Random Forest by Gullam Almuzadid, Egia Rosi Subhiyakto

“…The dataset was split into 80% training and 20% testing data (hold-out test) to ensure objective evaluation. …”
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Data acquisition of exercise and fitness pressure measurement based on artificial intelligence technology by Ru Liu, Wenxi Shen

“…This project aims to improve the accuracy of fitness and physical pressure ratings, focusing on basketball, by integrating artificial intelligence (AI) into data collection and training. Athletes and fitness fanatics can benefit greatly from the data collected using complex AI algorithms to determine stress levels. …”
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