Raman Spectra Classification of Pharmaceutical Compounds: A Benchmark of Machine Learning Models with SHAP-Based Explainability by Dimitris Kalatzis, Alkmini Nega, Yiannis Kiouvrekis

“…In addition to strong predictive performance, SHAP (SHapley Additive exPlanations) analysis was employed to interpret model decisions. …”
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Analysis of space solar array arc images based on deep learning techniques by Afaf M. Abd El-Hameed, Ahmed S. Farahat, Khaled Y. Youssef, M. Elfarran, I. M. Selim

“…Furthermore, algorithms and image processing tools, such as Python and Maxim-DL, are employed to examine variations in intensities and spatial variation in the arced region. …”
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GCBRGCN: Integration of ceRNA and RGCN to Identify Gastric Cancer Biomarkers by Peng Zhi, Yue Liu, Chenghui Zhao, Kunlun He

“…It demonstrates exceptional performance, surpassing traditional machine learning and graph neural network algorithms with an area under the curve (AUC) of 0.8172 in the task of predicting GC biomarkers. …”
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Capturing Preferences of New Users in Generative Tasks with Minimal Interactions Collaborative Filtering Using Siamese Networks and Soft Clustering by Subharag Sarkar, Manfred Huber

“…Prediction of user preferences is a challenge, in particular when the objective is to learn them without requiring the user to provide a profile or a significant number of interactions. …”
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Optimization of microwave components using machine learning and rapid sensitivity analysis by Slawomir Koziel, Anna Pietrenko-Dabrowska

“…On the other hand, the most widely used nature-inspired algorithms require large numbers of system simulations to yield a satisfactory design. …”
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Miniaturized Near-Infrared Analyzer for Quantitative Detection of Trace Water in Ethylene Glycol by Qunling Luo, Zhiqiang Guo, Danping Lin, Boxue Chang, Yinlan Ruan

“…The modeling performances of three algorithms including Support Vector Machine Regression (SVMR), Principal Component Regression (PCR), and Partial Least Squares Regression (PLSR) were systematically evaluated, with PLSR identified as the optimal algorithm. …”
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Urban sentinel: advancing structural health monitoring for building damage measurement in districts through IoT integration and self-optimizing machine learning by Parsa Parsafar

“…These sensors transmit data using LoRaWAN wireless technology to a centralized management system, where a regression AI model harnesses the power of machine learning algorithms to analyze the data and predict the health status of the buildings. …”
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High concentration formulation developability approaches and considerations by Jonathan Zarzar, Tarik Khan, Maniraj Bhagawati, Benjamin Weiche, Jasmin Sydow-Andersen, Alavattam Sreedhara

“…In order to accelerate and derisk development, new experimental and in-silico methods have emerged that can predict high concentration liabilities. Here, we review the challenges in developing high concentration formulations, the advances that have been made in establishing low mass and high-throughput predictive analytics, and advances in in-silico tools and algorithms aimed at identifying risks and understanding high concentration protein behavior.…”
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Comprehensive Fault Diagnosis of Three-Phase Induction Motors Using Synchronized Multi-Sensor Data Collection by Kevin Thomas, Ahasanur Rahman, Wesam Rohouma, Md. Faysal Ahamed, Fariya Bintay Shafi, Md. Nahiduzzaman, Amith Khandakar

“…The dataset, organized into ten distinct CSV files covering various operational states, provides a rich resource for developing and testing fault detection algorithms. A Random Forest classifier trained on this dataset achieved an accuracy of 99.82%, demonstrating its suitability for real-time fault diagnosis and predictive maintenance applications. …”
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Planning and layout of tourism and leisure facilities based on POI big data and machine learning. by Shifeng Wu, Jiangyun Wang, Yinuo Jia, Jintian Yang, Jixiu Li

“…Key findings include: Firstly, compared with the existing tourism and leisure facilities, the fitting degree tested by the machine learning algorithm is 83.5%, suggests that the proposed method is highly feasible. …”
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Investigating the hydrogen renaissance in the global energy transition with AI integration by Abderrahim Lakhouit

“…AI plays a pivotal role in optimizing hydrogen production methods, such as electrolysis, by enhancing process efficiencies through machine learning models that predict and optimize operational parameters. In storage, AI-driven algorithms are improving the management of hydrogen in large-scale storage systems, helping to mitigate issues such as leakage and optimizing storage conditions. …”
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Fault Diagnosis in a Four-Arm Delta Robot Based on Wavelet Scattering Networks and Artificial Intelligence Techniques by Claudio Urrea, Carlos Domínguez

“…This study compares time-domain signal features and wavelet scattering networks, applied by classification algorithms including wide neural networks (WNNs), efficient linear support vector machine (ELSVM), efficient logistic regression (ELR), and kernel naive Bayes (KNB). …”
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Performance of artificial neural networks and traditional methods in determining selected growth parameters of Alburnus sellal Heckel, 1843 by Ozcan Ebru Ifakat

“…In this study, predictions were made on the growth performance of Alburnus sellal Heckel, 1843 from the Munzur River using back propagation artificial neural networks and ANN algorithms. …”
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Towards a greener future: The role of sustainable methodologies in metabolomics research by Chiara Spaggiari, Kgalaletso Othibeng, Fidele Tugizimana, Gabriele Rocchetti, Laura Righetti

“…Computational advancements, including AI-driven models, machine learning-based semi-quantification, and predictive algorithms for solvent selection, further enhance sustainability by reducing resource consumption. …”
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Revolutionizing Sperm Analysis with AI: A Review of Computer-Aided Sperm Analysis Systems by Francisco J. Baldán, Diego García-Gil, Carlos Fernandez-Basso

“…These advanced systems offer significant advantages, including enhanced objectivity, improved consistency over manual methods, and the ability to detect subtle predictive patterns not discernible by human observation. …”
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Statistical assessment of interrelationship of marriage and divorces to fertility by A. B. Sinelnikov, O. A. Zolotareva, M. D. Khabib

“…A number of indicators complement the traditional methodology of marriage and divorce tables calculation. The algorithms for obtaining the indicators proposed for implementation (in particular, the cumulative marriage rate of divorcees, the level of divorces compensation by legal remarriages, etc.) have been prescribed, and their estimations have been made, which substantiates the scientific significance of the study. …”
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Tracking Secondary Raw Material Operational Framework—DataOps Case Study by Gabriel Pestana, Marisa Almeida, Nelson Martins

“…It enhances supply chain transparency through digital traceability, promotes predictive material sourcing, and streamlines logistics via algorithmic optimization. …”
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Smart Thermostat Development and Validation on an Environmental Chamber Using Surrogate Modelling by Leonidas Zouloumis, Nikolaos Ploskas, Nikolaos Taousanidis, Giorgos Panaras

“…Although dynamic simulation tools and decision-making algorithms are core components of energy management methodologies, they are often accompanied by excessive computational cost. …”
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A comprehensive review of flood damage in mountainous regions: challenges, solutions, and advanced management technologies by Mohammad Roohi, Jila Dehghani, Maryam Irani, Paramiss Mina

“…Modern technologies, early warning systems, predictive models, and international collaboration are key to reducing flood losses and informing sustainable flood management.…”
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Method for Determining Igneous Rock Mineral Content Using Element Logging Data Based on Variational AutoEncoder by JIA Ruilong, PAN Baozhi, WANG Qinghui, LI Yan, GUAN Yao, WANG Xinru

“…It employs a VAE (Variational AutoEncoder) approach to predict mineral content and reconstruct the elemental weight content. …”
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