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    Analyzing the Application of Machine Learning in Anemia Prediction by Li Yuxi

    Published 2025-01-01
    “…Machine learning techniques provide innovative solutions by analyzing complex datasets that incorporate questionnaire, clinical features, demographic information, and laboratory results, thereby enhancing the accuracy of anemia predictions. …”
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    Topological Attention-Based Convolution Neural Networks in Analyzing and Predicting Particulate Matter Pollution Level by Zixin Lin, Nur Fariha Syaqina Zulkepli, Mohd Shareduwan Mohd Kasihmuddin, R. U. Gobithaasan

    Published 2025-06-01
    “…PM10 category labels were predicted 6, 12, and 24 hours ahead. Data from 15 stations in Malaysia (2019–2020) were used, with feature selection based on correlation analysis. …”
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    Software pipeline for predicting and analyzing the structure of the receptor–ligand by A.S. Kozlova, A.R. Mukhametgalieva, A.N. Fattakhova, N.I. Akberova

    Published 2022-03-01
    “…Such a pipeline involves a complex algorithm, from predicting the structure of the receptor to searching and analyzing the interaction of ligands with the receptor. …”
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    Prediction of suicide using web based voice recordings analyzed by artificial intelligence by Agnieszka Ewa Krautz, Julia Volkening, Janik Raue, Christian Otte, Simon B. Eickhoff, Eike Ahlers, Jörg Langner

    Published 2025-07-01
    “…In this study, we utilized ML in a case-control design, we predicted completed suicides using publicly available, web-based, real-world voice data, and treating speech as a biomarker. …”
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    Analyzing Financial Stability by Predicting Bankruptcy Situations with Machine Learning by Mohd Naved, Ravi Kumar, Shaiku Saheb

    Published 2024-06-01
    “…Machine learning (ML) may help in bankruptcy prediction by analyzing massive quantities of historical financial data, identifying trends and anomalies that indicate trouble, and developing predictive models to estimate the possibility of default. …”
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    A robust and statistical analyzed predictive model for drug toxicity using machine learning by Deepak Rawat, Rohit Bajaj, Rachit Manchanda, Ankush Mehta, Prabhu Paramasivam, Suraj Kumar Bhagat, Abinet Gosaye Ayanie

    Published 2025-05-01
    “…Abstract Over the years, toxicity prediction has been a challenging task. Artificial intelligence and machine learning provide a platform to study toxicity prediction more accurately with a reduced time span. …”
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    MTFAP: a comprehensive platform for predicting and analyzing master transcription factors by Jianyuan Zhou, Haojie Yu, Chunhui Lou, Min Yang, Yanshang Li, Qian Yang, Shuhan Li, Chunwang Ji, Song Li, Shuang Wang, Haotian Cao, Xuecang Li, Lian Liu

    Published 2024-12-01
    “…Therefore, we developed a master transcription factors prediction and analysis web resource (MTFAP). MTFAP is a comprehensive web tool designed to predict and analyze MTFs with different data types. …”
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    Prediction of bronchopulmonary dysplasia course in children by analyzing clinico-biochemical indicators by L. M. Chernenko

    Published 2019-04-01
    “…The aim of the work is to improve the prediction of bronchopulmonary dysplasia course in children by analyzing the relationships between clinical and biochemical indicators and determine the risk factors for an unfavorable course of the disease. …”
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    Integrating bibliometrics and deep learning to analyze emerging trends and predict future directions in photoelectrochemical water splitting for hydrogen production: A Scopus-database driven study by Md. Saiful Islam, Sadit Bihongo Malitha, Md. Abu Sayid Mia, Md. Zahangir Alam

    Published 2025-12-01
    “…The progress of photoelectrochemical water splitting for hydrogen production was systematically analyzed based on articles, countries, authors, institutions, journals, and cited references. …”
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    Predictive Modelling of Protest Event Signatures: Analyzing Temporal Dynamics and Digital Activism Discourse Across Global Movements by Amila Chethana Nanayakkara, B. T. G. Samantha Kumara, R. M. Kapila Tharanga Rathnayaka

    Published 2025-03-01
    “…This research introduced a predictive modeling framework to analyze the temporal dynamics of social media discourse during three global movements: the Mahsa Amini Protests (2022), South African Unrest (2021), and the Black Lives Matter Movement (2020). …”
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