Refined Assessment Method of Offshore Wind Resources Based on Interpolation Method by Wenchuan Meng, Zaimin Yang, Zhi Rao, Shuang Li, Xin Lin, Jingkang Peng, Yuwei Cao, Yingquan Chen

“…To enhance the prediction accuracy of offshore wind speed, this study employs an interpolation algorithm to improve spatial resolution based on the ERA5 reanalysis dataset. …”
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Multi-Objective Optimization of the Forming Process Parameters of Disc Forgings Based on Grey Correlation Analysis and the Response Surface Method by Shizhong Wei, Yuna Liang, Hao Li, Guizhong Xie, Feng Mao, Ji Zhang

“…Then, a quadratic polynomial prediction model based on the RSM was constructed, and its accuracy was ensured through model validation. …”
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Optimization of Injection-Molding Process Parameters for Weight Control: Converting Optimization Problem to Classification Problem by Peng Zhao, Zhengyang Dong, Jianfeng Zhang, Yi Zhang, Mingyi Cao, Zhou Zhu, Hongwei Zhou, Jianzhong Fu

“…A particle swarm optimization (PSO) algorithm contributes to the tuning of the hyperparameters of the SVC model in order to minimize the error between the SVC prediction results and the experimental results. …”
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Committee Machine Learning for Electrofacies-Guided Well Placement and Oil Recovery Optimization by Adewale Amosu, Dung Bui, Oluwapelumi Oke, Abdul-Muaizz Koray, Emmanuel Appiah Kubi, Najmudeen Sibaweihi, William Ampomah

“…In this study, we develop a workflow that mitigates the variability in results produced by different clustering algorithms using a committee machine. Using several unsupervised machine learning methods, including k-means, k-median, hierarchical clustering, spectral clustering, and the Gaussian mixture model, we predict electrofacies from wireline well log data and generate their 3D vertical and lateral distributions and inferred geological properties. …”
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Prognostic significance of calcium-related genes in lung adenocarcinoma and the role of TNNC1 in macrophage polarization and erlotinib resistance by Jian Feng, Zhe Chen, Gaoming Wang, Yu Yao, Xuewen Min, Jing Luo, Kai Xie

“…The prognostic signature, comprising nine CRPGs, accurately predicted 1-, 2-, and 3-year overall survival. TNNC1 was identified as a crucial tumor suppressor gene and potential drug target. …”
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AADNet: An End-to-End Deep Learning Model for Auditory Attention Decoding by Nhan Duc Thanh Nguyen, Huy Phan, Simon Geirnaert, Kaare Mikkelsen, Preben Kidmose

“…First, the algorithm predicts representations of the attended speech signal envelopes; second, it identifies the attended speech by finding the highest correlation between the predictions and the representations of the actual speech signals. …”
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Selection of a Complex of Informative Features for Assessing the Internal Characteristics of Eggs for Consumption by Toncho Kolev, Mariya Georgieva-Nikolova, Miglena Kazakova, Danail Bonchev, Hristo Lukanov, Zlatin Zlatev

“…These results highlight the feature stability and predictability variation across different types of eggs and their manufacturers.…”
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基于数字图像比色法的食用油酸价检测研究Determination of acid value of edible oil by digital image colorimetry-based method by 李跃凡1，马改琴1，王媛媛1，张帆2，于修烛1LI Yuefan1, MA Gaiqin1, WANG Yuanyuan1, ZHANG Fan2, YU Xiuzhu1

“…The model established by the robust linear regression algorithm had the best prediction effect with a root mean square error of 0.734 3. …”
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Rationale and study protocol of the MAMELI Cohort study (Mapping the Methylation of repetitive elements to track the Exposome effects on health: the city of Legnano as a Living lab... by Valentina Bollati, Federica Rota, Laura Dioni, Chiara Favero, Simona Iodice, Marta Gallazzi, Andrea Spinazzè, Giacomo Fanti, Davide Campagnolo, Tiago Nardi, Davide Biganzoli, Eva Dariol, Rachele Matsagani, Mirjam Hoxha, Paola Monti, Benedetta Albetti, Letizia Tarantini, Davide Barbuto, Ester Luconi, Giuseppe Marano, Giacomo Biganzoli, Roberta Biciuffi, Patrizia Boracchi, Domenico M Cavallo, Pilar M Guerrieri, Fabio Mosca, Stefano Gustincich, Giovanni Sanesi, Luca Ferrari, Michele Carugno, Silvia Fustinoni, Angela Cecilia Pesatori, Luca Pandolfini, Michele Miragoli, Andrea Cattaneo, Elia Biganzoli

“…The goal will be to develop a predictive algorithm (the MAMELI algorithm) that links the exposome to RE methylation status, creating a "RE methylation signature" reflecting the environmental impact on DNA methylation; iii) In this phase, the MAMELI algorithm will be applied to a separate cohort of 3,500 participants to compare the measured RE methylation signature with values predicted by the algorithm. …”
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Applying Machine Learning Techniques to Identify Undiagnosed Patients with Exocrine Pancreatic Insufficiency by Bruce Pyenson, Maggie Alston, Jeffrey Gomberg, Feng Han, Nikhil Khandelwal, Motoharu Dei, Monica Son, Jaime Vora

“… # Methods A machine learning algorithm was developed in Scikit-learn, a Python module. …”
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Explainable Artificial Intelligence (XAI) for Flood Susceptibility Assessment in Seoul: Leveraging Evolutionary and Bayesian AutoML Optimization by Kounghoon Nam, Youngkyu Lee, Sungsu Lee, Sungyoon Kim, Shuai Zhang

“…The proposed framework, which integrates TPOT, Optuna, and SHAP within an XAI pipeline, not only improves predictive capability but also offers transparent insights into feature behavior and model logic. …”
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Toward a conceptual model to improve the user experience of a sustainable and secure intelligent transport system by Abdullah Alsaleh

“…A novel search and management protocol, supported by a tailored algorithm, was developed to enhance resource allocation success rates for vehicles within a defined area of interest. …”
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The doctor and patient of tomorrow: exploring the intersection of artificial intelligence, preventive medicine, and ethical challenges in future healthcare by Paulo Santos, Paulo Santos, Isabel Nazaré, Isabel Nazaré

“…AI-driven technologies, including real-time health monitoring and predictive analytics, offer new personalized preventive care possibilities. …”
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Modelling the future of cleaner energy: Explainable artificial intelligence model for green hydrogen production rate estimation by Okorie Ekwe Agwu, Saad Alatefi, Ahmad Alkouh

“…However, the complex dynamics associated with the process requires predictive tools for large-scale deployment. Despite advancements in machine learning-based models, previous studies often lack explainability, diminishing user trust in their deployment. …”
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Magnetic resonance imaging (MRI) radiomics in paediatric neuro-oncology: A systematic review of clinical applications, feature interpretation, and biological insights in the ch... by Teesta Mukherjee, Omid Pournik, Theodoros N Arvanitis

“…SVM was the most used classification algorithm ( n  = 9), with an AUC range of 0.858–0.977. …”
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Quantitative structure-properties relationship, molecular dynamic simulations and designs of some novel lubricant additives by Usman Abdulfatai, Adamu Uzairu, Sani Uba, Gideon Adamu Shallangwa

“…Quantitative structure-properties relationship (QSPR) method was used to design some novel antioxidant lubricant additives, while molecular dynamics simulations were used to calculate their dynamic binding energies on steel and to hydrogen-containing DLC (a-C: H) crystal surfaces. 29 synthesized antioxidant lubricant additives were collected from literature and geometrically optimized by Spartan’14 version 1.1.2 software while Genetic Function Algorithm (GFA) method of the material studio version 8.0 software was used to build the predictive QSPR model. …”
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Identification of priority conservation areas and potential corridors for jaguars in the Caatinga biome, Brazil. by Ronaldo Gonçalves Morato, Katia Maria Paschoaletto Micchi de Barros Ferraz, Rogério Cunha de Paula, Cláudia Bueno de Campos

“…A total of 62 points records of jaguar occurrence and 10 potential predictors were analyzed in a GIS environment. A predictive distributional map was obtained using Species Distribution Modeling (SDM) as performed by the Maximum Entropy (Maxent) algorithm. …”
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Integrated Planning for Shared Electric Vehicle System Considering Carbon Emission Reduction by Xiaohui Sun, Yumei Mi, Askar Ahtam, Zhi Zuo

“…Secondly, a charging demand simulation predictive model framework was designed. Finally, an SEV scheduling model with maximal operating income and minimal scheduling costs as optimization objectives was constructed considering the benefits of carbon emission reduction. …”
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Revealing High‐Temporal‐Resolution Flood Evolution With Low Latency Using GRACE Follow‐On Ranging Data by Hao‐si Li, Shuang Yi, Zi‐ren Luo, Peng Xu

“…We find that the potent manipulation of water resources by human activities might impact the predictive capabilities of these models, thereby misrepresenting the hydrological evolution during the flood event. …”
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CT-based radiomics features for the differential diagnosis of nodular goiter and papillary thyroid carcinoma: an analysis employing propensity score matching by Haiming Zhang, Zhenyu Li, Fengtao Zhang, Hengguo Li

“…Feature selection was carried out utilizing the least absolute shrinkage and selection operator (LASSO) logistic regression algorithm to generate the radiomics score (Rad-score). …”
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