Machine learning algorithms predict breast cancer incidence risk: a data-driven retrospective study based on biochemical biomarkers by Qianqian Guo, Peng Wu, Junhao He, Ge Zhang, Wu Zhou, Qianjun Chen

“…This study aimed to identify novel breast cancer risk factors using machine learning algorithms. By integrating both personal clinical factors and peripheral blood biochemical biomarkers, it sought to enhance the understanding of breast cancer risk. …”
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Pneumatic Compliant End-effector Using Multi-compensation Integrated Nonlinear Active Disturbance Rejection Control by ZHANG Shuzhong, WU Qianxin, DAI Fuquan, WANG Yabing, ZHANG Gong

“…LuGre model parameters are fitted, and a compensation strategy is developed to offset frictional disturbances during piston movement.Data filtering: A first-order low-pass filter is integrated into the controller to reduce high-frequency noise in the force sensor readings and enhance control accuracy.The control algorithm is implemented on an STM32F103 microcontroller operating at 50 Hz. …”
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Additive value of computed tomography severity scores to predict lengths of stay in hospital and ICU for COVID-19 patients: a machine learning study by Seyed Salman Zakariaee, Mikaeil Molazadeh, Hossein Salmanipour, Negar Naderi

“…Four well-known ML classification models including kNN, MLP, SVM, and C4.5 decision tree algorithms were used to predict hospital and ICU LOSs of COVID-19 patients. …”
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Depth-Resolved and Depth-Integrated Primary Productivity Estimates From In-Situ and Satellite Data in the Global Ocean by Harish Kumar Kashtan Sundararaman, Palanisamy Shanmugam

“…To estimate the chlorophyll-specific maximum rate of carbon fixation at a depth equal to z (<inline-formula> <tex-math notation="LaTeX">$P_{z}^{b}$ </tex-math></inline-formula>), two different <inline-formula> <tex-math notation="LaTeX">$P_{rel}^{b}$ </tex-math></inline-formula> algorithms were developed based on the relative values of i) the subsurface photosynthetically available radiation <inline-formula> <tex-math notation="LaTeX">$(PAR_{rel}$ </tex-math></inline-formula>) and ii) the optical depth at depth z <inline-formula> <tex-math notation="LaTeX">$\left ({\zeta _{z} }\right)$ </tex-math></inline-formula>. …”
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Integrated impact of urban mixed land use on TOD ridership: A multi-radius comparative analysis by Xinyue Gu, Siyan Lin, Chengfang Wang

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The diagnostic value of two antigenic domains derived from the Trypanosoma cruzi Tc323 protein in chronic Chagas disease: a study in Brazil by Micaela S. Ossowski, Ângelo Antônio Oliveira Silva, Ângelo Antônio Oliveira Silva, Emily Ferreira Santos, Emily Ferreira Santos, Leonardo Maia Leony, Leonardo Maia Leony, Fred Luciano Neves Santos, Fred Luciano Neves Santos, Fred Luciano Neves Santos, Karina A. Gómez

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The Importance of Dunnian Runoff in Atlantic Forest Remnants: An Integrated Analysis Between Machine Learning and Spectral Indices by Alarcon Matos de Oliveira, Mara Rojane Barros de Matos, Marcos Batista Figueiredo, Lusanira Nogueira Aragão de Oliveira

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Integrating AI-generated content tools in higher education: a comparative analysis of interdisciplinary learning outcomes by Zhang Yan, Tang Qianjun

“…While these technologies demonstrated substantial value in breaking down disciplinary silos and accommodating diverse learning preferences, challenges emerged regarding algorithmic bias, digital equity, and maintenance of discipline-specific skills. …”
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Integration of physical information and reaction mechanism data for surrogate prediction model and multi-objective optimization of glycolic acid production by Zhibo Zhang, Yaowei Wang, Dongrui Zhang, Deming Zhao, Huibin Shi, Hao Yan, Xin Zhou, Xiang Feng, Chaohe Yang

“…After validating with 40% of experimental data and 16% of reaction mechanism data, the model's prediction error was within ±5%, and the linear correlation coefficient R2 between the predicted values and actual values was 0.998. Furthermore, this study integrated a multi-objective optimization algorithm based on the model, enabling surrogate optimization of reaction parameters during production. …”
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Improving stroke risk prediction by integrating XGBoost, optimized principal component analysis, and explainable artificial intelligence by Lesia Mochurad, Viktoriia Babii, Yuliia Boliubash, Yulianna Mochurad

“…To improve stroke risk prediction models in terms of efficiency and interpretability, we propose to integrate modern machine learning algorithms and data dimensionality reduction methods, in particular XGBoost and optimized principal component analysis (PCA), which provide data structuring and increase processing speed, especially for large datasets. …”
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A Tuning Method for Speed Tracking Controller Parameters of Autonomous Vehicles by Jianqiao Chen, Guofu Tian

“…These findings highlight the potential of genetic algorithms in supporting the future development of autonomous driving technology, ensuring its successful integration into intelligent transportation systems.…”
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Unveiling the ageing-related genes in diagnosing osteoarthritis with metabolic syndrome by integrated bioinformatics analysis and machine learning by Jian Huang, Lu Wang, Jiangfei Zhou, Tianming Dai, Weicong Zhu, Tianrui Wang, Hongde Wang, Yingze Zhang

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Integration of single‐cell and bulk RNA‐sequencing data reveals the prognostic potential of epithelial gene markers for prostate cancer by Zhuofan Mou, Lorna W. Harries

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Data-driven prediction of critical diameter for deterministic lateral displacement devices: an integrated DPD-ML approach by Shuai Liu, Peng Zhang, Anbin Wang, Keke Tang, Shuo Chen, Chensen Lin

“…This leads to significant economic and time costs, while the complexity of instruments and algorithms further raises the barrier for DLD design and optimization. …”
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Integrating transcriptomic and metabolomic analyses to characterize the potential function of SLC1A5 in thyroid cancer by Fengling Shan, Lan Wang, Xinyu Lu, Meihong Zhou, Zi Wang, Jingjing Lou, Xingdang Liu

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Molecular Clouds in the Outer Milky Way Disk: Sample, Integrated Properties, and Radial Trends with Galactocentric Radius by Yan Sun, Ji Yang, Qing-Zeng Yan, Shaobo Zhang, Yang Su, Xuepeng Chen, Xin Zhou, Yuehui Ma, Lixia Yuan

“…The catalog was produced using a DBSCAN algorithm applied to the Milky Way Imaging Scroll Painting project data. …”
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CONCRETE CREEP THEORY MODELS AND THEIR FINITE-ELEMENT IMPLEMENTATION by Petr Pavlovich Gaydzhurov, Elvira Rashidovna Iskhakova

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Voltage Quality Enhancement of Low-Voltage Smart Distribution System Using Robust and Optimized DVR Controllers: Application of the Harris Hawks Algorithm by Mohamed Metwally Mahmoud, Yahia M. Esmail, Basiony Shehata Atia, Omar Makram Kamel, Kareem M. AboRas, Mohit Bajaj, Syed Sabir Hussain Bukhari, Daniel Eutyche Mbadjoun Wapet

“…To enhance the DVR’s control system, for surpassing nonstandard voltage with a quick response and harmonics reduction at LVL under harsh operating events, an optimal controller design using the Harris Hawks algorithm (HHA) is proposed. To verify the value of the suggested solution, the hard operating events (voltage sag, voltage swell, fluctuating voltage, and imbalanced voltage) are examined and assessed. …”
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Using machine learning to discover DNA metabolism biomarkers that direct prostate cancer treatment by Ali Shakeri Abroudi, Melika Djamali, Hossein Azizi

“…Differentially expressed genes (DEGs) were identified using the edgeR algorithm with an FDR < 0.01 and a minimum fold change of 1.5. …”
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An NN Framework for Target Number Detection in FMCW Radar Systems via Hankelization: A Low-Dimensional Data Representation Perspective by Jung-Hwan Kim, Gyung-Eun Kim, Woong-Hee Lee

“…By integrating the mathematical processes of Hankelization and singular value extraction, we can perform input data manipulation for effective target number detection, resulting in constructing an efficient NN framework. …”
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