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    Brain age prediction from MRI images based on a convolutional neural network with MRMR feature selection layer by Mustafa Hatem Al Ghariri, Seyed Omid Shahdi

    Published 2025-05-01
    “… An sophisticated medical technique used to diagnose illnesses and brain disorders including multiple sclerosis, Alzheimer's, and other neurological ailments is the ability to predict the biological age of the brain using MRI pictures. …”
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  6. 1526

    Unsupervised Machine Learning Approaches for Test Suite Reduction by Anila Sebastian, Hira Naseem, Cagatay Catal

    Published 2024-12-01
    “…Therefore, the Test Suite Reduction (TSR) process is of great importance, contributing to the reduction of time and costs in executing test suites for complex software by minimizing the number of test cases to be executed. …”
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  7. 1527

    Improving Moth-Flame Optimization Algorithm by using Slime-Mould Algorithm by Sami N. Hussein, Nazar K. Hussein

    Published 2022-12-01
    “…The two predicted new algorithms were tested with standard test functions and the results were encouraging compared to the standard algorithms. …”
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  8. 1528

    Strength prediction and failure mode classification for SRC shear beams using GA-BP ANN method by Gangfeng Yao, Bingyi Li

    Published 2025-07-01
    “…Considering the advantages of machine-learning (ML) approaches, the back-propagation (BP) artificial neural network (ANN) method combined with genetic algorithm (GA) was employed to the prediction of strength and failure mode of SRC shear beams. …”
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  9. 1529

    Enhancing the prediction of vitamin D deficiency levels using an integrated approach of deep learning and evolutionary computing by Ahmed Alzahrani, Muhammad Zubair Asghar

    Published 2025-02-01
    “…Specifically, we employ a hybrid deep learning model that includes convolutional neural networks (CNN) and bidirectional long short-term memory (BiLSTM) networks to predict VDD data effectively. To improve the models effectiveness and guarantee the optimal choice of the features and hyper-parameters, we incorporate evolutionary computing methods, particularly genetic algorithms (GA). …”
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  10. 1530

    RSA modulus length regression prediction based on the Run Test and machine learning in the ciphertext-only scenarios by Ke Yuan, Chenmeng Zhao, Longwei Yang, Hanlin Sun, Sufang Zhou, Chunfu Jia

    Published 2025-07-01
    “…Abstract RSA is a classical public key cryptographic algorithm, over 40 years of widespread use has proven that its security is reliable when the key parameters are properly configured. …”
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    Prediction of maximum forming depth in single point incremental forming of 6061 aluminum alloy based on Adaboost regression by LIANG Zhikai, ZHANG Zhichao, HU Lan, PANG Qiu

    Published 2025-04-01
    “…Based on the experimental results, a regression model using the Adaboost algorithm is developed to predict the forming depth of 6061 aluminum alloy thin sheets at the forming diameter of 100 mm. …”
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  13. 1533

    Re-Imagining a 'We' Beyond the Gathering of Reductions by Lila Athanasiadou, Goda Klumbyte, Antoinette Rouvroy

    Published 2022-07-01
    “…Lastly, within the mental ecology, Rouvroy reconceptualizes the legal subject as a performance that operates within the proliferation of asignifying data signs, reimagining a “we” beyond the gathering of reductions.    …”
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    Peak-to-average power ratio reduction of orthogonal frequency division multiplexing signals using improved salp swarm optimization-based partial transmit sequence model by Vandana Tripathi, Prabhat Patel, Prashant Kumar Jain, Shailja Shukla

    Published 2025-04-01
    “…Several peak-to-average power ratio (PAPR) reduction methods have been used in orthogonal frequency division multiplexing (OFDM) applications. …”
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    A Machine Learning Approach to Predict Site Selection from the Perspective of Vitality Improvement by Bin Zhao, Hao Zheng, Xuesong Cheng

    Published 2024-12-01
    “…To enhance site selection and planning efficiency, we developed a predictive model integrating Artificial Neural Networks (ANNs) and Genetic Algorithms (GAs). …”
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