Detection of Road Damages Using Machine Learning Methods with Data Collected from Various Geographies: A Study on Türkiye by Ahmet Cihangir Kavcı, Ömer Faruk Cansız

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Deep learning and radiomics for gastric cancer serosal invasion: automated segmentation and multi-machine learning from two centers by Hui Shang, Tao Feng, Dong Han, Fengying Liang, Bin Zhao, Lihang Xu, Zhendong Cao

“…The clinical features, radiomic features, and deep learning features were organized and integrated, and five machine learning methods were employed to develop 15 predictive models. …”
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A review of quantum communication and information networks with advanced cryptographic applications using machine learning, deep learning techniques by R. Ramya, P. Kumar, D. Dhanasekaran, R. Satheesh Kumar, S. Amithesh Sharavan

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Impact of machine learning on dietary and exercise behaviors in type 2 diabetes self-management: a systematic literature review by Rizwan Riaz Mir, Nazeef Ul Haq, Kashif Ishaq, Nurhizam Safie, Abdul Basit Dogar

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Machine learning-based identification and validation of amino acid metabolism related genes as novel biomarkers in chronic kidney disease by Guoqing Zhang, Hongyan Luo, Xiaohua Lu, Yonghua Liu, Mei Wang, Bo Li, Haixia Lu, Yali Zheng

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Construction and Validation of a Model for Predicting Fear of Childbirth: A Cross-Sectional Population Study via Machine Learning by Zhang ZL, Chen KJ, Chen H, Zhu MM, Gu JJ, Jiang LS, Zheng L, Zhou SG

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Identification of dequalinium as a potent inhibitor of human organic cation transporter 2 by machine learning based QSAR model by Fumihiro Yamane, Kenji Ikemura, Masayoshi Kondo, Manami Ueno, Masahiro Okuda

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Detection of Body Packs in Abdominal CT scans Through Artificial Intelligence; Developing a Machine Learning-based Model by Sayed Masoud Hosseini, Seyed Ali Mohtarami, Shahin Shadnia, Mitra Rahimi, Peyman Erfan Talab Evini, Babak Mostafazadeh, Azadeh Memarian, Elmira Heidarli

“…Methods: In this cross-sectional study, abdominal CT scan images were employed to create a machine learning-based model for detecting body packs. …”
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Radiomics based Machine Learning Models for Classification of Prostate Cancer Grade Groups from Multi Parametric MRI Images by Fatemeh Zandie, Mohammad Salehi, Asghar Maziar, Mohammad Reza Bayatiani, Reza Paydar

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Screening of Aβ and phosphorylated tau status in the cerebrospinal fluid through machine learning analysis of portable electroencephalography data by Masahiro Hata, Yuki Miyazaki, Kohji Mori, Kenji Yoshiyama, Shoshin Akamine, Hideki Kanemoto, Shiho Gotoh, Hisaki Omori, Atsuya Hirashima, Yuto Satake, Takashi Suehiro, Shun Takahashi, Manabu Ikeda

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SPACIER: on-demand polymer design with fully automated all-atom classical molecular dynamics integrated into machine learning pipelines by Shun Nanjo, Arifin, Hayato Maeda, Yoshihiro Hayashi, Kan Hatakeyama-Sato, Ryoji Himeno, Teruaki Hayakawa, Ryo Yoshida

“…Abstract Machine learning has rapidly advanced the design and discovery of new materials with targeted applications in various systems. …”
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Development and Validation of a Cost-Effective Machine Learning Model for Screening Potential Rheumatoid Arthritis in Primary Healthcare Clinics by Wu W, Hu X, Yan L, Li Z, Li B, Chen X, Lin Z, Zeng H, Li C, Mo Y, Wu Y, Wang Q

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Remote sensing-based winter wheat yield estimation integrating machine learning and crop growth multi-scenario simulations by Xin Du, Jiong Zhu, Jingyuan Xu, Qiangzi Li, Zui Tao, Yuan Zhang, Hongyan Wang, Haoxuan Hu

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Evaluation of Short-Term Freeway Speed Prediction Based on Periodic Analysis Using Statistical Models and Machine Learning Models by Xiaoxue Yang, Yajie Zou, Jinjun Tang, Jian Liang, Muhammad Ijaz

“…Overall, the findings in this paper suggest that the proposed hybrid prediction approach is effective for both statistical and machine learning models in short-term speed prediction.…”
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Machine Learning Approach and Model for Predicting Proton Stopping Power Ratio and Other Parameters Using Computed Tomography Images by Charles Ekene Chika

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Enhancing Cybersecurity and Privacy Protection for Cloud Computing-Assisted Vehicular Network of Autonomous Electric Vehicles: Applications of Machine Learning by Tiansheng Yang, Ruikai Sun, Rajkumar Singh Rathore, Imran Baig

Subjects: “…machine learning…”
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Metabolic Syndrome Prediction Using Machine Learning Models with Genetic and Clinical Information from a Nonobese Healthy Population by Eun Kyung Choe, Hwanseok Rhee, Seungjae Lee, Eunsoon Shin, Seung-Won Oh, Jong-Eun Lee, Seung Ho Choi

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Modeling Soil Behavior with Machine Learning: Static and Cyclic Properties of High Plasticity Clays Treated with Lime and Fly Ash by Gebrail Bekdaş, Yaren Aydın, Sinan Melih Nigdeli, İnci Süt Ünver, Wook-Won Kim, Zong Woo Geem

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1 SMART GRAFT: Surgeon’s expertise and Machine learning Automated biopsy Reading Towards improving GRaft Assessment For Transplant by A.J. Figueiredo, E.M.C.T. Silva, L.M.A. Rodrigues, C. Nunes P. T., L.S.O. Marconi, E.A.C. Retroz, M.N.C. Eliseu, M.J.G. Jarimba R.L.S.,Marques, A.R.G.C. Leal, L.S. Santos, C.P.R.M. Romaozinho, V.M.L. Sousa, B.A.C. Parada

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Exploration of Key Factors in the Preparation of Highly Hydrophobic Silica Aerogel from Rice Husk Ash Assisted by Machine Learning by Yun Deng, Ziyan Sha, Xingxing Wang, Ke Duan, Weijie Xue, Ian Beadham, Xiaolan Xiao, Changbo Zhang

“…To expand the applications of hydrophobic silica aerogels derived from rice husk ash (HSA) through simple traditional methods (without adding special materials or processes), this paper employs machine learning to establish mathematical models to identify optimal conditions for extracting water glass and investigates how preparation conditions and heat treatment temperatures affect properties such as the porosity and hydrophobicity of HSA. …”
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