Estimation of mass loss under wear test of nanoclay-epoxy nanocomposite using response surface methodology and artificial neural networks by Manjunath Shettar, Ashwini Bhat, Nagaraj N. Katagi

“…The research demonstrates the efficiency of combining statistical and machine-learning methods for optimizing wear-resistant polymer nanocomposites.…”
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A fatigue-reliability approach using ultrasonic non-destructive inspection by Chouikh, Iheb, Bouraoui, Chokri

“…To analyse the performance and efficacity of the model, the probability of detection is determined using the “signal response” technique.The Paris model is used to predict the fatigue life taking into consideration the initial crack distributions, the dispersion of the parameters underlined by the Least-squares method and Monte-Carlo simulations.Reliability evaluation is discussed later for two cases: Detection and No-detection case.If no indication is presented, an inspection detection threshold is determined and optimized. …”
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Error Analysis and Compensation of 3‒PTT Parallel Robot by CHEN Mingfang, LIANG Hongjian, WEI Songpo, HE Chaoyin

“…Thus, the influence of the coordinate error and the link length error on the pose accuracy of the mobile platform is analyzed.Results and DiscussionsThe results of error analysis show that the length error of the link and the <italic>z</italic>‒coordinate error of the hinge point of the static platform have significant effects on the pose accuracy of the mobile platform (the average position error of the mobile platform in the three degrees of freedom directions is more than 1 mm), and appropriate attention is paid to the machining and assembly of robot parts. In addition, in order to overcome the shortcomings of traditional methods mentioned above, i.e., excessive error parameters, error accumulation, and difficulty in obtaining the optimal solution of statically indeterminate equations, an inverse kinematics error compensation algorithm is proposed in this study. …”
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Learning From Imbalanced Data Using Triplet Adversarial Samples by Jaesub Yun, Jong-Seok Lee

“…In addition, we present a model training approach to further improve the generalization of the model to small classes by providing a diverse set of synthetic samples optimized using our proposed loss function. …”
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Identification of hateful amharic language memes on facebook using deep learning algorithms by Mequanent Degu Belete, Girma Kassa Alitasb

“…The model with the optimal parameters is chosen after numerous experiments. …”
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Research on GNNs with stable learning by Wenbin Li, Wenxuan Wei, Peiyang Wang, Li Pan, Bo Yang, Yanling Xu

“…Moreover, this distribution discrepancy may lead to unreliable predictions by GNNs models within unknown OOD domains. Therefore, this paper proposes a machine learning method to enhance the stability of GNN cross-site classification from the perspective of stable learning. …”
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Establishing an AI-based diagnostic framework for pulmonary nodules in computed tomography by Ruiting Jia, Baozhi Liu, Mohsin Ali

“…Nodule detection was done using the Retina-UNet model, while the features were classified using a Support Vector Machine (SVM). …”
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Deep learning-enabled exploration of global spectral features for photosynthetic capacity estimation by Xianzhi Deng, Xiaolong Hu, Liangsheng Shi, Chenye Su, Jinmin Li, Shuai Du, Shenji Li

“…We explored the ability of the model to uncover the optimal vegetation indices form and illustrated its advantage over traditional methods. …”
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A Study of Forced Vibrations with Nonlinear Springs and Dry Friction: Application to a Mechanical Oscillator with Very Large Vibrating Blades for Soil Cutting by Dario Friso

“…To cut a clod of soil containing the roots of trees in nurseries, a semi-circular vibrating blade digging machine with diameters up to 1.2 m is increasingly used. …”
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Application of SVM algorithm in fluid prediction of volcanic reservoirs in Nanpu Sag, Bohai Bay Basin by ZHANG Ying,QU Lili,ZHU Lu,ZHANG Yan,HAN Siyang,ZENG Cheng

“…The research shows that: ① Comprehensive application of core, well logging, mud logging and other data to optimize fluid sensitive characteristic parameters, single information sensitive parameters are acoustic time difference, compensation density, resistivity, multi-information fusion parameters are natural gamma relative value, total hydrocarbon Ratio, hydrocarbon gas density index, hydrocarbon gas humidity index, the above seven parameters participate in the model establishment; ②Using the SVM algorithm for volcanic fluid prediction, the reservoir fluid is divided into three types: oil layer, oil-water layer and water layer. …”
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Correlation Method of Assistance Driving Function and Road Environment Factors in Investigation of Intelligent Vehicle Traffic Accident by Yanbin Hu, Wenhui Zhou

“…This analysis focuses on road static facilities, traffic dynamic characteristics, and instantaneous weather conditions in automobile traffic accidents that occur under the human-machine co-driving paradigm with integrated assisted driving functions. …”
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Shield Tunneling under Railway Station Throats in Soft Soil Areas: A Case Study by Chengyu Liu, Binglong Wang, Shunhua Zhou

“…Furthermore, the earth-pressure-balanced shield machine, tunnel segments, and synchronized slurry were optimized to the site conditions. …”
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Prediction of Neoadjuvant Chemoradiotherapy Sensitivity in Patients With Esophageal Squamous Cell Carcinoma Using CT-Based Radiomics Combined With Clinical Features by Xindi Li, Jigang Dong, Baosheng Li, Ouyang Aimei, Yahong Sun, Xia Wu, Wenjuan Liu, Ruobing Li, Zhongyuan Li, Yu Yang

“…Results: Nine optimal radiomics features were selected using the LASSO algorithm. …”
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Intelligent deep learning for human activity recognition in individuals with disabilities using sensor based IoT and edge cloud continuum by Mohammed Maray

“…Initially, the IDLTHAR-PDST technique utilizes min-max normalization-based data pre-processing model to optimize sensor data consistency and enhance model performance. …”
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An effective method for anomaly detection in industrial Internet of Things using XGBoost and LSTM by Zhen Chen, ZhenWan Li, Jia Huang, ShengZheng Liu, HaiXia Long

“…In this paper, we propose an optimal deep learning model for anomaly detection in IIoT. …”
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Advanced Deep Learning Framework for Predicting the Remaining Useful Life of Nissan Leaf Generation 01 Lithium-Ion Battery Modules by Shamaltha M. Wickramaarachchi, S. A. Dewmini Suraweera, D. M. Pasindu Akalanka, V. Logeeshan, Chathura Wanigasekara

“…The integration of memory features significantly enhances the model’s accuracy, and an autoencoder is incorporated to optimize the feature representation. …”
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An Enhanced Neural Network Forecasting System for July Precipitation over the Middle-Lower Reaches of the Yangtze River by Wenyan Liu, Xiangjun Shi

“…To enhance the stability of predicted precipitation, the final output precipitation is an ensemble of multiple NN models with optimal initial seeds. The precipitation data from anomalous years are amplified to focus on anomalous events rather than normal events. …”
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Image captioning using bidirectional LSTM neural network by Farnaz Hoseini, Anaram Yaghoobi Notash

“…Implemented in Python with TensorFlow and Cross on Google Colab, the proposed model is compared to five other LSTM-based models. …”
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Automated Detection of Tailing Impoundments in Multi-Sensor High-Resolution Satellite Images Through Advanced Deep Learning Architectures by Lin Qin, Wenyue Song

“…These components enhance the model’s ability to capture spatial dependencies, optimize inference speed, and ensure stable training dynamics. …”
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Exploring Drivers of AIGC-Designer Collaborative Innovation by Shao-Feng Wang, Chun-Ching Chen

“…The trend in technological development is to utilize AI-based methods to optimize workflows and swiftly achieve desired objectives. …”
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