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    The dual-edged potential of AI autonomously defining loss functions by Abbas Ghori

    Published 2025-07-01
    “…They predominantly act as guiding principles for any optimization algorithm, thereby influencing both the convergence characteristics as well as the generalization of the model. …”
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    H∞ positive filter-based control for positive linear systems by Montassar Ezzine, Mohamed Darouach, Harouna Souley Ali, Hassani Messaoud

    Published 2025-07-01
    “…We propose a new approach to numerically compute the controller, which is obtained via a function of the state to be estimated with the same order as the controller. …”
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    Model for selective vehicle problem considering mixed fleet with capacitated electric vehicles by Jiacheng Li, Masato Noto, Yang Zhang, Jia Guo

    Published 2025-07-01
    “…The performance and computational efficiency of this algorithm are validated through comparative analysis with other classic optimization algorithms on test instances. …”
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    Classification of Fritillaria thunbergii appearance quality based on machine vision and machine learning technology by DONG Chengye, LI Dongfang, FENG Huaiqu, LONG Sifang, XI Te, ZHOU Qin’an, WANG Jun

    Published 2023-12-01
    “…Several statistical learning and object detection algorithms were selected to train and test the F. thunbergii dataset. …”
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  12. 2712

    Deep Learning Methods and UAV Technologies for Crop Disease Detection by S. G. Mudarisov, I. R. Miftakhov

    Published 2024-12-01
    “…The paper proposes solutions to these issues, including algorithm optimization and improved data preprocessing techniques. …”
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    Enhanced water saturation estimation in hydrocarbon reservoirs using machine learning by Ali Akbari, Ali Ranjbar, Yousef Kazemzadeh, Dmitriy A. Martyushev

    Published 2025-08-01
    “…Nine well log parameters—Depth (DEPT), High-Temperature Neutron Porosity, True Resistivity, Computed Gamma Ray, Spectral Gamma Ray, Hole Caliper, Compressional Sonic Travel Time, Bulk Density, and Temperature—were used as input features to train and test five ML algorithms: Linear Regression, Support Vector Machine (SVM), Random Forest, Least Squares Boosting, and Bayesian methods. …”
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  15. 2715

    A Hybrid Approach of DenseNet121 with Attention and Bi-LSTM for Yoga Pose Estimation by Aarthy K., Alice Nithya

    Published 2025-01-01
    “…This model enhances accuracy by incorporating self-attention mechanisms, allowing the system to focus on significant features within the data. Performance optimization is achieved through the Enhanced Chicken Swarm Optimization (ECSO) method, which fine-tunes the parameters of the system to ensure optimal results. …”
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  16. 2716

    Toward Adaptive and Immune-Inspired Viable Supply Chains: A PRISMA Systematic Review of Mathematical Modeling Trends by Andrés Polo, Daniel Morillo-Torres, John Willmer Escobar

    Published 2025-07-01
    “…At the methodological level, a high degree of diversity in modeling techniques was observed, with an emphasis on mixed-integer linear programming (MILP), robust optimization, multi-objective modeling, and the increasing use of bio-inspired algorithms, artificial intelligence, and simulation frameworks. …”
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    AI-driven innovation in antibody-drug conjugate design by Heather A. Noriega, Xiang Simon Wang

    Published 2025-06-01
    “…This review is organized into six sections: (1) the progression from traditional modeling approaches to AI-driven design of individual ADC components; (2) the application of deep learning (DL) to antibody structure prediction and identification of optimal conjugation sites; (3) the use of AI/ML models for forecasting pharmacokinetic properties and toxicity profiles; (4) emerging generative algorithms for antibody sequence diversification and affinity optimization; (5) case studies demonstrating the integration of computational tools with experimental pipelines, including systems that link in silico predictions to high-throughput validation; and (6) persistent challenges, including data sparsity, model interpretability, validation complexity, and regulatory considerations. …”
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  18. 2718

    Prediction of true critical temperature and pressure of binary hydrocarbon mixtures: A Comparison between the artificial neural networks and the support vector machine by M. Etebarian, k. movagharnejad

    Published 2019-06-01
    “…Two main objectives have been considered in this paper: providing a good model to predict the critical temperature and pressure of binary hydrocarbon mixtures, and comparing the efficiency of the artificial neural network algorithms and the support vector regression as two commonly used soft computing methods. …”
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    Illumination-adaptative granularity progressive multimodal image fusion method by Chuanyun WANG, Dongdong SUN, Mingqi ZHOU, Tian WANG, Qian GAO, Zhaokui LI

    Published 2025-06-01
    “…This module enables global feature perception with linear computational complexity, minimizing the loss of critical information during transmission. …”
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