Seaweed in the Diet as a Source of Bioactive Metabolites and a Potential Natural Immunity Booster: A Comprehensive Review by Amiya Kumar Mandal, Sudhamayee Parida, Akshaya Kumar Behera, Siba Prasad Adhikary, Andrey A. Lukatkin, Alexander S. Lukatkin, Mrutyunjay Jena

“…Thus, to incorporate seaweed into the global diet and to make it familiar as a functional food, issues such as large-scale cultivation, processing, consumer acceptance and the development of seaweed-based food products need to be addressed. …”
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Enhancing Healthcare With WBAN and Digital Twins: A Machine Learning Approach for Predictive Health Monitoring by Rishit Mahapatra, Deepak Sethi, Kaushik Mishra

“…The collected data undergoes processing and is then sent to a remote medical server over the Internet. …”
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Early detection of vascular catheter-associated infections employing supervised machine learning - a case study in Lleida region by Radu Spaimoc, Jordi Mateo, Francesc Solsona, Alfredo Jover-Sáenz, Fernando Barcenilla, María Ramírez-Hidalgo, Marcos Serrano, Miquel Mesas, Dídac Florensa

“…Among the evaluated classifiers, the Gradient Boosting (GB) model outperformed others, attaining the highest balanced accuracy (82.5%) and sensitivity (67%), underscoring its potential utility for early VCAI detection. …”
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Analysis of Tropospheric NO₂ Observation Using Pandora and MAX-DOAS Instrument in Xianghe, North China by Chunjiao Wang, Ting Wang, Zhaonan Cai, Xiaoyi Zhao, Wannan Wang, Yi Liu, Pucai Wang

“…The measurement process began with establishing a spectral calibration system for processing the Pandora’s raw observations, followed by enhancing the differential optical absorption spectroscopy (DOAS) algorithm to retrieve both the slant column densities (SCDs) and tropospheric vertical column densities (VCDs) of NO₂. …”
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Enhancement of in vitro digestive properties of silver carp (Hypophthalmichthys olivaceus) surimi gels by oxidized dihydromyricetin by Qianhui Yu, Jinlin Li, Qinye Yu, Yongjie Zhou, Yuqing Tan, Yongkang Luo, Hui Hong

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Prediction of recurrence-free survival and risk factors of sinonasal inverted papilloma after surgery by machine learning models by Siyu Miao, Yang Cheng, Yaqi Li, Xiaodong Chen, Fuquan Chen, Dingjun Zha, Tao Xue

“…Six machine learning algorithms including ExtraSurvivalTrees (EST), GradientBoostingSurvivalAnalysis (GBSA), RandomSurvivalForest (RSF), SurvivalSVM, Coxnet and Coxph, were used to construct the prediction models. …”
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A comparative study and simple baseline for travel time prediction by Chuang-Chieh Lin, Ming-Chu Ho, Chih-Chieh Hung, Hui-Huang Hsu

“…The intricacy of these processes makes it difficult to pinpoint which steps or factors most significantly influence prediction accuracy. …”
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Solid Oxide Fuel Cell Voltage Prediction by a Data-Driven Approach by Hristo Ivanov Beloev, Stanislav Radikovich Saitov, Antonina Andreevna Filimonova, Natalia Dmitrievna Chichirova, Egor Sergeevich Mayorov, Oleg Evgenievich Babikov, Iliya Krastev Iliev

“…However, these models require substantial computational resources, leading to high processing times. White box and gray box models are unable to achieve real-time optimization of control parameters. …”
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Towards Self-Conscious AI Using Deep ImageNet Models: Application for Blood Cell Classification by Mohamad Abou Ali, Fadi Dornaika, Ignacio Arganda-Carreras

“…TL has been hailed for boosting performance, shortening learning time and reducing computational effort. …”
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Artificial intelligence for screening and early diagnosis of pancreatic neoplasms in the context of centralization of the laboratory service in the region by S. I. Panin, V. A. Suvorov, A. V. Zubkov, S. A. Bezborodov, A. A. Panina, N. V. Kovalenko, A. R. Donsckaia, I. G. Shushkova, A. V. Bykov, Ya. A. Marenkov

“…In 28 (20.1 %) cases, at the time of tumor detection, patients did not have clinical symptoms. Statistical processing of the data was carried out using the Python programming language. …”
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A Two-Stage Feature Selection Approach for Fruit Recognition Using Camera Images With Various Machine Learning Classifiers by Tri Tran Minh Huynh, Tuan Minh Le, Long Ton That, Ly Van Tran, Son Vu Truong Dao

“…Therefore, it is necessary to build an effective automated tool to meet the needs of the market by boosting the outcome, in order to improve economic efficiency. …”
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Flood risk mapping and performance efficiency evaluation of machine learning algorithms: Best practice in northern Iran by M. Shirmohammadi, M. Shirmohammadi, S. Pirasteh, W. Li, D. Mafi-Gholami

“…In this study, we applied several ML algorithms, including Random Forest (RF), XGBoost (Extreme Gradient Boosting), LightGBM, CatBoost, and Support Vector Machine (SVM), to develop flood risk maps for a region in northern Iran. …”
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Comparative analysis of single-view and multiple-view data collection strategies for detecting partially-occluded grape bunches: Field trials by Mar Ariza-Sentís, Hilmy Baja, Sergio Vélez, Rick van Essen, João Valente

“…This is important for optimising resources, enhancing marketing strategies, and boosting overall agricultural productivity. While most research concentrates on data processing algorithms, this study focused on the preceding step: collecting reliable data. …”
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Machine Learning-Driven Optimization of Machining Parameters Optimization for Cutting Forces and Surface Roughness in Micro-Milling of AlSi10Mg Produced by Powder Bed Fusion Additi... by Zihni Alp Cevik, Koray Ozsoy, Ali Ercetin, Gencay Sariisik

“…This study focuses on optimizing machining parameters in the micro-milling of AlSi10Mg aluminum alloy produced via the powder bed fusion additive manufacturing process. Although additive manufacturing enables complex geometries and minimizes material waste, challenges remain in reducing surface roughness and cutting forces during post-processing. …”
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TSB-Forecast: A Short-Term Load Forecasting Model in Smart Cities for Integrating Time Series Embeddings and Large Language Models by Mohamed Mahmoud Hasan, Neamat El-Tazi, Ramadan Moawad, Amany H. B. Eissa

“…Subsequently, Extreme Gradient Boosting (XGBoost) and Extra Trees Regressor (ETR) collectively enhance the robustness of a stacked ensemble. …”
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Valorization of Cork Residues for Biomass Pellet Production: Meeting ENplus^® Standards Through Strategic Blending by Amadeu D. S. Borges, Paulo Matos, Miguel Oliveira

“…Cork processing generates significant by-products that pose environmental challenges and waste management concerns. …”
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Automatic construction of global cloud sample database based on Landsat imagery by Tao He, Guihua Huang, Lei Zhang, Daiqiang Wu, Yichuan Ma

“…In addition to RF, light gradient boosting machine (LightGBM), multilayer perceptron (MLP), and support vector machine (SVM) were also trained based on UAC-CSD sample database and verified using L8_Biome. …”
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Bioactive components and clinical potential of Astragalus species by Shuo Li, Shuo Li, Xunpeng Hu, Xunpeng Hu, Xunpeng Hu, Fen Liu, Weiming Hu, Weiming Hu

“…It also examines Astragalus species in medicine and food homology (MFH) and how processing methods affect its efficacy. Furthermore, the mechanisms behind its anti-inflammatory, antioxidant, immune-boosting, antitumor, neuroprotective, and hypoglycemic effects are discussed. …”
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Enhanced leukemia prediction using hybrid ant colony and ant lion optimization for gene selection and classification by Santhakumar D, Gnanajeyaraman Rajaram, Elankavi R, Viswanath J, Govindharaj I, Raja J

“…Gene selection plays a crucial role in the pre-processing of microarray data, aiming to identify a small set of genes that enhances classification accuracy and reduces costs. …”
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Software Defect Prediction For Quality Evaluation Using Learning Techniques Ensemble Stacking by Muhammad Romadhona Kusuma, Windu Gata, Sigit Kurniawan, Dedi Dwi Saputra, Supriadi Panggabean

“…We performed hyperparameter tuning with grid search CV on Machine Learning algorithms such as Ada Boost and Gradient Boosting. The results showed that the ensemble stacking approach with a combination of Gradient Boosting, Ada Boost, Decision Tree, Bayesian Ridge, and LightGBM meta learner algorithms provided high accuracy with R2 score reaching 0.97, MAE of 0.037, and MSE of 0.006. …”
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