Ganoderma Disease in Oil Palm Trees Using Hyperspectral Imaging and Machine Learning by Chee Seng Kwang, Siti Fatimah Abdul Razak, Sumendra Yogarayan, M. Z. Adli Zahisham, Tze Huey Tam, M. K. Anuar Mohd Noor, Haryati Abidin

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Research Progress and Applications of Artificial Intelligence in Agricultural Equipment by Yong Zhu, Shida Zhang, Shengnan Tang, Qiang Gao

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METHOD AND ALGORITHMS OF CALCULATION OF PARAMETERS OF RELIABILITY IN ACCORDANCE WITH MULTIVARIATE DATA by E. M. Farhadzadeh, A. Z. Muradaliyev, T. K. Rafiyeva, S. A. Abdullayeva

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An explainable machine learning framework for railway predictive maintenance using data streams from the metro operator of Portugal by Silvia García-Méndez, Francisco de Arriba-Pérez, Fátima Leal, Bruno Veloso, Benedita Malheiro, Juan Carlos Burguillo-Rial

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A Novel Dataset for Early Cardiovascular Risk Detection in School Children Using Machine Learning by Rafael Alejandro Olivera Solís, Emilio Francisco González Rodríguez, Roberto Castañeda Sheissa, Juan Valentín Lorenzo-Ginori, José García

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Sediment flow connectivity index data for the Apulia region (Italy): An open-source geodatabase and the innovative CONNECTOSED WebGIS platformZenodo by Alok Kushabaha, Domenico Capolongo, Giovanni Scicchitano, Floriana Rizzo, Marina Zingaro

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Non-invasive PNET grading using CT radiomics and machine learning by Faeze Salahshour, Mahsa Taherzadeh, Ghasem Hajianfar, Gholamreza Bayat, Farid Azmoudeh Ardalan, Soroush Bagheri, Arman Esmailzadeh, Majid Kahe, Sajad P. Shayesteh

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Investigating the relationship between groundwater level variations and geologic drought factor using MODFLOW and wavelet theory (case study: Southern Iran) by Mehrdad Donyadideh, Alireza Nikbakht Shahbazi, Narges Zohrabi, Hossein Fathian, Ali Afroos

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Automated Cough Analysis with Convolutional Recurrent Neural Network by Yiping Wang, Mustafaa Wahab, Tianqi Hong, Kyle Molinari, Gail M. Gauvreau, Ruth P. Cusack, Zhen Gao, Imran Satia, Qiyin Fang

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AppAuth: Authorship Attribution for Android App Clones by Guoai Xu, Chengpeng Zhang, Bowen Sun, Xinyu Yang, Yanhui Guo, Chengze Li, Haoyu Wang

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Prediction of zero-dose children using supervised machine learning algorithm in Tanzania: evidence from the recent 2022 Tanzania Demographic and Health Survey by Beminate Lemma Seifu, Angwach Abrham Asnake, Alemayehu Kasu Gebrehana

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Enhancing Power Grid Reliability With Machine Learning and Auxiliary Classifier Generative Adversarial Networks: A Study on Fault Detection Using the Georgia Electric System Load D... by Hafeez Ur Rehman Siddiqui, Robert Brown, Adil Ali Saleem, Muhammad Amjad Raza, Sandra Dudley

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MDFT-GAN: A Multi-Domain Feature Transformer GAN for Bearing Fault Diagnosis Under Limited and Imbalanced Data Conditions by Chenxi Guo, Vyacheslav V. Potekhin, Peng Li, Elena A. Kovalchuk, Jing Lian

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Revolutionizing the treatment of intervertebral disc degeneration: an approach based on molecular typing by Shaofeng Chen, Wei Zhang, Yifan Liu, Runzhi Huang, Xiaoyi Zhou, Xianzhao Wei

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A simplified frailty index and nomogram to predict the postoperative complications and survival in older patients with upper urinary tract urothelial carcinoma by Jianyong Liu, Jianyong Liu, Haoran Wang, Haoran Wang, Pengjie Wu, Pengjie Wu, Jiawen Wang, Jiawen Wang, Jianye Wang, Jianye Wang, Huimin Hou, Huimin Hou, Jianlong Wang, Jianlong Wang, Yaoguang Zhang, Yaoguang Zhang

“…By using this predictive model, clinicians may increase their accuracy in predicting complications and prognosis and improve preoperative decision-making.…”
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Prediction of new-onset migraine using clinical-genotypic data from the HUNT Study: a machine learning analysis by Fahim Faisal, Antonios Danelakis, Marte-Helene Bjørk, Bendik Winsvold, Manjit Matharu, Parashkev Nachev, Knut Hagen, International Headache Genetics Consortium, Erling Tronvik, Anker Stubberud

“…The primary outcome was new-onset migraine defined as a change in disease status from headache-free in HUNT2 to migraine in HUNT3. The migraine risk variants identified in the largest GWAS meta-analysis of migraine were used to identify genetic input features for the models. …”
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Sustainable authentication of molasses' botanical origin using infrared spectroscopy: Accuracy and greenness evaluation of spectral techniques by Macarena Rojas-Rioseco, Mecit Öztop, Cristian A. Fuentes, Martin Bravo, Ivan Smajlovic, Margarita Smajlovic, Karol Kołodziejski, Danuta Kruk, Víctor Muñoz, Rosario del P. Castillo

“…In contrast, portable FT-NIR was the most sustainable technique (AGREE score = 0.86, scale from 0 to 1), albeit with a slightly higher classification error (8.3 %). These findings demonstrate the potential of infrared spectroscopy as a reliable and sustainable solution for molasses authentication and show that sustainability–accuracy trade-offs can be quantitatively assessed to support informed decision-making in the analytical process of sugar industry.…”
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Acute Approaches to the Development of Closed Cycle Economy in the Field of Hard Communal Waste Treatment by A. I. Sukhorukov, E. A. Zakharova

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The Extraction of <i>Torreya grandis</i> Growing Areas Using a Spatial–Spectral Fused Attention Network and Multitemporal Sentinel-2 Images: A Case Study of the Kuaiji Mountain Reg... by Yanyan Lyu, Yong Wang, Xiaoling Shen

“…Compared with traditional deep learning models such as 2D-CNN, 3D-CNN, and HybridSN, the SSFAN model achieved superior performance, with an overall accuracy of 99.1% and a Kappa coefficient of 0.961. …”
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Nomograms predicting cancer-specific survival and overall survival of advanced salivary gland malignancy patients: a study based on the SEER database by Congzhi Ma, Xiaolin Nong

“…Abstract Objective To establish a clinical prediction model for specific and overall survival in advanced salivary gland malignant tumors, providing a potential reference tool for personalized clinical care and adjunct treatment decision-making. …”
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