Results 91 to 100 of about 150,441 (331)

Effectiveness of Machine Learning Models with Bayesian Optimization-Based Method to Identify Important Variables that Affect GPA

JTAM (Jurnal Teori dan Aplikasi Matematika)
To produce superior human resources, the SPs-IPB Master Program must consider the factors influencing the GPA in the student selection process. The method that can be used to identify these factors is a machine learning algorithm.
Arifuddin R, Utami Dyah Syafitri, Erfiani Erfiani   +2 more
doaj   +1 more source

Smart Exploration of Perovskite Photovoltaics: From AI Driven Discovery to Autonomous Laboratories

Advanced Energy Materials, EarlyView.
In this review, we summarize the fundamentals of AI in automated materials science, and review AI applications in perovskite solar cells. Then, we sum up recent progress in AI‐guided manufacturing optimization, and highlight AI‐driven high‐throughput and autonomous laboratories.
Wenning Chen, Zheyu Zhang, Kelvian T. Mularso, Bonghyun Jo, Hyun Suk Jung   +4 more
wiley   +1 more source

Investigating factors influencing quality of life in thyroid eye disease: insight from machine learning approaches

European Thyroid Journal
Aims: Thyroid eye disease (TED) is an autoimmune orbital disorder that diminishes the quality of life (QOL) in affected individuals. Graves’ ophthalmopathy (GO)-QOL questionnaire effectively assesses TED’s effect on patients.
Haiyang Zhang, Shuo Wu, Lehan Yang, Chengjing Fan, Huifang Chen, Hui Wang, Tianyi Zhu, Yinwei Li, Jing Sun, Xuefei Song, Huifang Zhou, Terry J Smith, Xianqun Fan   +12 more
doaj   +1 more source

Data-Driven Prediction of Transesterification Reactions: Analyzing Zirconium-Based Metal-Organic Framework Catalysts with Machine Learning Models [PDF]

Iranian Journal of Chemistry & Chemical Engineering
This research study focuses on the utilization of machine learning models to predict transesterification reactions using zirconium-based Metal-Organic Framework (MOF) as a catalyst.
A. Gnanapraksam, M. Thirumarimurugan, D. Gomathi   +2 more
doaj   +1 more source

Beyond Descriptor‐Based AI Design: Sp2‐Hybridized Branched Side Chains Enable Pre‐Aggregation–Driven Seeding Effects in Green‐Solvent‐Processed Organic Solar Cells

Advanced Energy Materials, EarlyView.
sp2‐hybridized branched side chains are introduced as a new molecular design for NFAs, YBOV, inducing strong solution‐state pre‐aggregation. This pre‐aggregation enables universal seeding motifs, highly ordered film growth, and overcoming the intrinsic current–voltage trade‐off, achieving 19.67% efficiency via green‐solvent processing beyond descriptor‐
Seokhwan Jeong, Donghoo Won, Zhe Sun, Chihyung Lee, Jaewook Kim, Seunglok Lee, Sangjin Yang, Jieun Kim, Keonho Yoon, Dong Young Kim, Yongjoon Cho, Seung‐Jae Shin, Hee‐Seung Lee, Doo‐Hyun Ko, Changduk Yang   +14 more
wiley   +1 more source

Optimization and Validation of Two Machine Learning Algorithms for Accurate Prediction of Irrigated Wheat (Triticum aestivum L.) Yield and Identification of its Influential Factors in Khorasan Razavi Province [PDF]

پژوهشهای زراعی ایران
IntroductionThis study undertook a detailed comparison of two supervised machine-learning algorithms—Random Forest (RF) and eXtreme Gradient Boosting (XGBoost)—to predict irrigated wheat (Triticum aestivum L.) yield across 20 counties in Razavi Khorasan ...
M Jahan
doaj   +1 more source

Hyperparameter Optimization with Genetic Algorithms and XGBoost: A Step Forward in Smart Grid Fraud Detection

Italian National Conference on Sensors
This study provides a comprehensive analysis of the combination of Genetic Algorithms (GA) and XGBoost, a well-known machine-learning model. The primary emphasis lies in hyperparameter optimization for fraud detection in smart grid applications.
Adil Mehdary, Abdellah Chehri, A. Jakimi, R. Saadane   +3 more
semanticscholar   +1 more source

Can Machine Learning Reduce Unnecessary Surgeries? A Retrospective Analysis Using Threshold Optimization to Prevent Negative Appendectomies in Adults

Annals of Gastroenterological Surgery, EarlyView.
Threshold‐optimized machine learning models using routine clinical and laboratory data in 623 adults undergoing appendectomy. Logistic regression (AUC = 0.765) and random forest (AUC = 0.785) were the best‐performing models for appendicitis detection and complicated appendicitis prediction, respectively.
Ivan Males, Marko Kumric, Zvonimir Boban, Josip Vrdoljak, Dario Pecenkovic, Matej Ivanda, Marko Grahovac, Zenon Pogorelic, Josko Bozic   +8 more
wiley   +1 more source

Macrophage Phenotype Detection Methodology on Textured Surfaces via Nuclear Morphology Using Machine Learning

Advanced Intelligent Discovery, EarlyView.
A novel machine learning approach classifies macrophage phenotypes with up to 98% accuracy using only nuclear morphology from DAPI‐stained images. Bypassing traditional surface markers, the method proves robust even on complex textured biomaterial surfaces. It offers a simpler, faster alternative for studying macrophage behavior in various experimental
Oleh Mezhenskyi, Kateryna Andriienkova, Tetyana Goldanova, Ezgi Antmen, Thibaut Soubrie, Nihal Engin Vrana   +5 more
wiley   +1 more source

Interpretable Machine Learning for Solvent‐Dependent Carrier Mobility in Solution‐Processed Organic Thin Films

Advanced Intelligent Discovery, EarlyView.
This work establishes a correlation between solvent properties and the charge transport performance of solution‐processed organic thin films through interpretable machine learning. Strong dispersion interactions (δD), moderate hydrogen bonding (δH), closely matching and compatible with the solute (quadruple thiophene), and a small molar volume (MolVol)
Tianhao Tan, Lian Duan, Dong Wang
wiley   +1 more source