Laser-Induced Breakdown Spectroscopy vs. Fluorescence Spectroscopy for Olive Oil Authentication by Marios Bekogianni, Theodoros Stamatoukos, Eleni Nanou, Stelios Couris

“…The acquired spectroscopic data were analyzed by several machine learning algorithms, and the constructed predictive models are evaluated thoroughly for their reliability and robustness. …”
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Modeling flood zones on the basis of time series forecasting and GIS-technologies by the example of the Republic of Bashkortostan by E.V. Palchevsky, V.V. Antonov, L.E. Rodionova, L.A. Kromina, A.R. Fakhrullina

“…At the same time, the possibility of early modeling and visualization of river floods is realized only on the basis of the obtained predictive values of the water level. This will enable specialized organizations and services, as well as management bodies to make decisions related to flood control measures in advance and as soon as possible.…”
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The Persistent Threat of Chronic Inflammation on the Mortality Among Cervical Cancer Survivors: A Mendelian Randomization and Machine Learning Analysis Using UK Biobank and Chinese... by Wang J, Chen Z, Guan M, Ma Z, Peng L, Chen J, Fiori PL, Carru C, Capobianco G, Coradduzza D, Zhou L

“…After feature selection with 3 algorithms (LASSO regression, Boruta and Support vector machines), the gradient boosting machine (GBM) model outperformed other models by achieving an area under the curve (AUC) of 0.930 and a Brier score of 0.027 in 1-year overall survival (OS) prediction. …”
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Index or illusion: The case of frailty indices in the Health and Retirement Study. by Yi-Sheng Chao, Hsing-Chien Wu, Chao-Jung Wu, Wei-Chih Chen

“…The input variables better predicted mortality than the FIs. The continuous FIs predicted mortality better than the frailty statuses. …”
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Low-Illumination Parking Scenario Detection Based on Image Adaptive Enhancement by Xixi Xu, Meiqi Zhang, Hao Tang, Weiye Xu, Bowen Sun, Zhu’an Zheng

“…Aiming at the problem of easily missed and misdetected parking spaces and obstacles in the automatic parking perception task under low-illumination conditions, this paper proposes a low-illumination parking space and obstacle detection algorithm based on image adaptive enhancement. The algorithm comprises an image adaptive enhancement module, which predicts adaptive parameters using CNN and integrates the low-light image enhancement via illumination map estimation and contrast-limited adaptive histogram equalization algorithms for image processing. …”
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Machine learning model for age related macular degeneration based on pesticides: the National Health and Nutrition Examination Survey 2007–2008 by Jiankang Liu, Bingli Wang, Qiuming Li

“…The model is then built using nine machine learning algorithms and verified on a validation set. Finally, it is found that the random forest model has high predictive value, and its Receiver Operating Characteristic (ROC) value is 0.75. …”
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Uncertainty quantification with graph neural networks for efficient molecular design by Lung-Yi Chen, Yi-Pei Li

“…Abstract Optimizing molecular design across expansive chemical spaces presents unique challenges, especially in maintaining predictive accuracy under domain shifts. This study integrates uncertainty quantification (UQ), directed message passing neural networks (D-MPNNs), and genetic algorithms (GAs) to address these challenges. …”
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Review on the Innovation of Investment Banks’ Credit Risk Assessment System in a Highly Volatile Market by Wei Yakun

“…., macroeconomic indicators, market sentiment, transactional behavior) and nonlinear algorithms to enhance predictive accuracy. Case analysis, including JPMorgan Chase’s post-2008 reforms, validates the system’s effectiveness in mitigating risks during extreme market fluctuations. …”
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Transfer Learning for Induction Motor Health Monitoring: A Brief Review by Prashant Kumar

“…Recent advancements in this domain have significantly improved fault diagnosis, condition monitoring, and the predictive maintenance of induction motors. This study reviews state-of-the-art transfer learning techniques, including domain adaptation, fine-tuning, and feature-based transfer for induction motor health monitoring. …”
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Determinants of depressive symptoms in multinational middle-aged and older adults by Can Lu, Shenwei Wan, Zhiyong Liu

“…Abstract This study harnesses machine learning to dissect the complex socioeconomic determinants of depression risk among older adults across five international cohorts (HRS, ELSA, SHARE, CHARLS, MHAS). Evaluating six predictive algorithms, XGBoost demonstrated superior performance in four cohorts (AUC 0.7677–0.8771), while LightGBM excelled in ELSA (AUC 0.9011). …”
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NMR-based metabolomic approach to estimate chemical and sensorial profiles of olive oil by Gaia Meoni, Leonardo Tenori, Francesca Di Cesare, Stefano Brizzolara, Pietro Tonutti, Chiara Cherubini, Laura Mazzanti, Claudio Luchinat

“…By integrating NMR data with traditional chemical analyses and sensory evaluation, we developed multivariate models to evaluate the predictive power of NMR spectra coupled with machine learning algorithms for 50 distinct olive oil quality parameters, including physicochemical properties, fatty acid composition, total polyphenols, tocopherols, and sensory attributes. …”
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An Approach Based on the Use of Commercial Codes and Engineering Judgement for the Battle of Water Demand Forecasting by Alfredo Iglesias-Rey, Carlos Alfonso López Hojas, F. Javier Martínez-Solano, Pedro L. Iglesias-Rey

“…Initial data filtering, input variable determination, and simultaneous application of RNN, LSTM, and GRU forecasting algorithms are conducted. Results are evaluated based on Battle of Water Demand Forecasting criteria, refining parameters iteratively for enhanced prediction accuracy. …”
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Deep Mining on the Formation Cycle Features for Concurrent SOH Estimation and RUL Prognostication in Lithium-Ion Batteries by Dongchen Yang, Weilin He, Xin He

“…By integrating diverse datasets with advanced algorithms and models, we perform correlation analyses of parameters such as capacity, voltage, temperature, pressure, and strain, enabling precise SOH estimation and RUL prediction. …”
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High throughput computational screening and interpretable machine learning for iodine capture of metal-organic frameworks by Haoyi Tan, Yukun Teng, Guangcun Shan

“…Subsequently, two machine learning regression algorithms—Random Forest and CatBoost, were employed to predict the iodine adsorption capabilities of MOF materials. …”
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Innovative Solutions for Oil Well Monitoring: Data-Driven Multiphase Virtual Flow Metering Using Ensemble Machine Learning and Historical Field Data by Wael A. Farag, Hussein H. Ismail, Muhammad Nadeem

“…By combining data-driven ensemble machine-learning algorithms and historical oil field portable test reports, this paper proposes a Data-Drive Multiphase Virtual Flow Meter (DD-MVFM) that estimates oil, gas, and water flow rates and provides real-time monitoring, and predicts future production with appropriate accuracy. …”
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Learning the Value of Place: Machine Learning Models for Real Estate Appraisal in Istanbul’s Diverse Urban Landscape by Ahmet Hilmi Erciyes, Toygun Atasoy, Abdurrahman Tursun, Sibel Canaz Sevgen

“…The prediction of real estate values is vital for taxation, transactions, mortgages, and urban policy development. …”
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Remaining Useful Life Estimation through Deep Learning Partial Differential Equation Models: A Framework for Degradation Dynamics Interpretation Using Latent Variables by Sergio Cofre-Martel, Enrique Lopez Droguett, Mohammad Modarres

“…For the past decade, researchers have explored the application of deep learning (DL) regression algorithms to predict the system’s health state behavior based on sensor readings from the monitoring system. …”
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Presenting a model for solving lot-streaming hybrid flow shop scheduling problem by considering independent setup time and transportation time by Roja Ruhbakhsh, Esmaeil Mehdizadeh, Mohammad Amin Adibi

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New Approach for Unambiguous High-Resolution Wide-Swath SAR Imaging by Yueguan Lin, Yida Fan, Hai Jiang, Wei Wang

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Addressing imbalanced data classification with Cluster-Based Reduced Noise SMOTE. by Javad Hemmatian, Rassoul Hajizadeh, Fakhroddin Nazari

“…CRN-SMOTE combines SMOTE for oversampling minority classes with a novel cluster-based noise reduction technique. In this cluster-based noise reduction approach, it is crucial that samples from each category form one or two clusters, a feature that conventional noise reduction methods do not achieve. …”
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