Large language models for chart review: how machine learning can accelerate hematology research by Barbara D. Lam, Peiqi Wang, Shengling Ma, Omid Jafari, Iuliia Kovalenko, Ang Li

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On-site quantitative detection of fentanyl in heroin by machine learning-enabled SERS on super absorbing metasurfaces by Yingkun Zhu, Haomin Song, Ruiying Liu, Yunyun Mu, Murali Gedda, Abdullah N. Alodhay, Lei Ying, Qiaoqiang Gan

“…Our study introduces a novel approach for on-site quantitative detection of fentanyl in heroin, employing machine learning-enabled surface-enhanced Raman spectroscopy (SERS) on superabsorbing metasurfaces. …”
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Integrating radiological and clinical data for clinically significant prostate cancer detection with machine learning techniques by Luis Mariano Esteban, Ángel Borque-Fernando, Maria Etelvina Escorihuela, Javier Esteban-Escaño, Jose María Abascal, Pol Servian, Juan Morote

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Machine learning-based anomaly detection and prediction in commercial aircraft using autonomous surveillance data. by Tian Xia, Lanju Zhou, Khalil Ahmad

“…The creation of a machine learning model employing data from autonomous-reliant surveillance transmissions is essential for the detection and prediction of commercial aircraft accidents. …”
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Analysis of Gas Pipeline Failure Factors Based on the Novel Bayesian Network by Machine Learning Optimization by Shuangqing Chen, Shun Zhou, Zhe Xu, Yongbin Liu, Bing Guan, Xiaoyu Jiang, Wencheng Li

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Geographical origin discrimination of Chenpi using machine learning and enhanced mid-level data fusion by Xin Kang Li, Li Jun Tang, Ze Ying Li, Dian Qiu, Zhuo Ling Yang, Xiao Yi Zhang, Xiang-Zhi Zhang, Jing Jing Guo, Bao Qiong Li

“…Thirty-nine samples from eight regions in Xinhui district (Guangdong, China) are analyzed by gas chromatography (GC) and mid-infrared (MIR) technique. Four machine learning methods are employed to establish discrimination models based on GC and MIR data, with two mid-level data fusion strategies to combine the data. …”
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Predictive Modeling of Pesticides Reproductive Toxicity in Earthworms Using Interpretable Machine-Learning Techniques on Imbalanced Data by Mihkel Kotli, Geven Piir, Uko Maran

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Prediction of buckling damage of steel equal angle structural members using hybrid machine learning techniques by Nang Xuan Ho, Tien-Thinh Le, The-Hung Dinh, Van-Hai Nguyen

“…Abstract This article deals with prediction of buckling damage of steel equal angle structural members using a surrogate model combining machine learning and metaheuristic optimization technique. …”
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Prediction of Multidimensional Poverty Status With Machine Learning Classification at Household Level: Empirical Evidence From Tanzania by Ngong'Ho Bujiku Sende, Snehanshu Saha, Leon Ruganzu, Saibal Kar

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Progress in prediction of photocatalytic CO2 reduction using machine learning approach: A mini review by Md Mohshin Ali, Md. Arif Hossen, Azrina Abd Aziz

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Cetacean feeding modelling using machine learning: A case study of the Central-Eastern Mediterranean Sea by Carla Cherubini, Giulia Cipriano, Leonardo Saccotelli, Giovanni Dimauro, Giovanni Coppini, Roberto Carlucci, Carmelo Fanizza, Rosalia Maglietta

Subjects: “…Machine learning…”
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Machine learning assisted radiomics in predicting postoperative occurrence of deep venous thrombosis in patients with gastric cancer by Yuan Zeng, Yuhao Chen, Dandan Zhu, Jun Xu, Xiangting Zhang, Huiya Ying, Xian Song, Ruoru Zhou, Yixiao Wang, Fujun Yu

Subjects: “…Machine learning…”
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Development of a respiratory virus risk model with environmental data based on interpretable machine learning methods by Shuting Shi, Haowen Lin, Leiming Jiang, Zhiqi Zeng, ChuiXu Lin, Pei Li, Yinghua Li, Zifeng Yang

“…This study aimed to develop a nationwide respiratory virus infection risk prediction model through machine learning approach. We utilized the CRFC algorithm, a random forest-based method for multi-label classification, to predict the presence of various respiratory viruses. …”
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Perbandingan Metode Supervised Machine Learning untuk Prediksi Prevalensi Stunting di Provinsi Jawa Timur by M Syauqi Haris, Ahsanun Naseh Khudori, Wahyu Teja Kusuma

“…In addition, several methods in supervised machine learning are also compared, namely, linear regression, support vector regression, and random forest regression. …”
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Unlocking the link: predicting cardiovascular disease risk with a focus on airflow obstruction using machine learning by Xiyu Cao, Jianli Ma, Xiaoyi He, Yufei Liu, Yang Yang, Yaqi Wang, Chuantao Zhang

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Machine learning optimized design of THz piezoelectric perovskite-based biosensor for the detection of formalin in aqueous environments by Jacob Wekalao, Shobhit K. Patel, Om Prakash Kumar, Fahad Ahmed Al-zahrani

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Komparasi Machine Learning Berbasis Pso Untuk Prediksi Tingkat Keberhasilan Belajar Berbasis E-Learning by Elin Panca Saputra, Siti Nurajizah, Mawadatul Maulidah, Nadiyah Hidayati, Taufik Rahman

“…Permasalahan pada penelitian ini yaitu jumlah atribut pada data dapat mengurangi akurasi, maka dari percobaan dengan beberapa algoritma pada machine learning kami mencoba menerapkan Particle Swarm Optimizatio(PSO) untuk meningkatkan akurasi yang lebih tinggi. …”
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Integrating fault detection and classification in microgrids using supervised machine learning considering fault resistance uncertainty by Morteza Barkhi, Javad Pourhossein, Seyed Ali Hosseini

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Identification of Plasma Proteins Associated with Alzheimer's Disease Using Feature Selection Techniques and Machine Learning Algorithms by Zakaria Mokadem, Mohamed Djerioui, Bilal Attallah, Youcef Brik

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Prediction of the axial compression capacity of ECC-CES columns using adaptive sampling and machine learning techniques by Khaled Megahed

Subjects: “…Machine learning…”
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