Results 61 to 70 of about 7,680 (217)

Artificial intelligence–driven decoupling structure–activity relationship for lithium‐ion batteries

InfoScience, EarlyView.
Artificial intelligence can efferently accelerate the high‐throughput screening of battery materials, the analysis of multiphase mechanisms, and the precise prediction of capacity and cycle life. This review systematically summarizes the applications of machine learning (ML) in decoupling the complex structure‐activity relationships of lithium‐ion ...
Tao Wang, Yuhang Meng, Tianhao Chen, Xiangfen Jiang, Yoshio Bando, Jinjue Zeng, Xuebin Wang   +6 more
wiley   +1 more source

Machine Learning‐Based Estimation of Reference Evapotranspiration and Crop Coefficients for Wheat Under Diverse Climatic Conditions

Irrigation and Drainage, EarlyView.
ABSTRACT Accurate estimation of reference evapotranspiration (ET0) and crop coefficients (Kc) is critical for irrigation planning, particularly in data‐limited regions where agriculture dominates freshwater consumption. Although machine learning (ML) methods have been widely applied to ET0 and Kc estimation, most studies address these parameters ...
Ilker Angin, Hasan Er, Mete Yaganoglu, Muhammed Sungur Demir, Abdullah Muratoglu   +4 more
wiley   +1 more source

Predicting the Strength of Eccentrically Compressed Short Circular Concrete Filled Steel Tube Columns

Structural Mechanics of Engineering Constructions and Buildings
The process of predicting the load-bearing capacity of eccentrically compressed circular concrete filled steel tube (CFST) columns using machine learning algorithms is investigated.
Tatiana N. Kondratieva, Anton S. Chepurnenko, Batyr M. Yazyev   +2 more
doaj   +1 more source

A Comprehensive Study to Compare Different Compound Representations for Predicting Carcinogenicity In Vivo

Journal of Applied Toxicology, EarlyView.
ABSTRACT Carcinogenicity evaluation is a critical component of chemical risk assessment, yet traditional in vivo testing remains time consuming, costly, and ethically challenging. Computational approaches based on machine learning offer promising alternatives, but the relative contributions of different molecular representation strategies for ...
Iuri Barbosa Pereira, Rogerio Salvini, Eloisa Dutra Caldas   +2 more
wiley   +1 more source

Protective Effects of Riociguat Against Contrast‐Induced Nephropathy: An Experimental and Machine Learning‐Based Study in Rats

The Kaohsiung Journal of Medical Sciences, EarlyView.
ABSTRACT Contrast‐induced nephropathy (CIN) is an important cause of acute kidney injury following exposure to iodinated contrast media, and effective preventive strategies remain limited. This study investigated the renoprotective effects of riociguat, a soluble guanylate cyclase stimulator, in an experimental rat model of CIN and explored machine ...
Mustafa Begenc Tascanov, Kenan Toprak, Sibel Turedi, İsmail Koyuncu, Zulkif Tanrıverdi, Necip Fazıl Dedeoglu, Halil Fedai, Asuman Bicer, İbrahim Halil Altıparmak, Recep Demirbag, Gencay Sarıısık   +10 more
wiley   +1 more source

Detection and Comparative Evaluation of Noise Perturbations in Simulated Dynamical Systems and ECG Signals Using Complexity-Based Features

Machine Learning and Knowledge Extraction
Noise contamination is a common challenge in the analysis of time series data, where stochastic perturbations can obscure deterministic dynamics and complicate the interpretation of signals from chaotic and physiological systems.
Kevin Mallinger, Sebastian Raubitzek, Sebastian Schrittwieser, Edgar Weippl   +3 more
doaj   +1 more source

Designing γ‐TiAl Alloys With Refined Twin‐Lamellar Microstructures via Machine Learning‐Driven Phase‐Field Methods

Materials Genome Engineering Advances, EarlyView.
Dual‐phase γ‐TiAl alloys with refined twin‐lamellar (RTL) structures exhibit optimal strength‐ductility synergy. Here, phase‐field simulations reveal composition‐stress‐dependent refined twin‐lamellar (RTL) microstructure evolution. Machine learning (CatBoost + NSGA‐III) trained on phase‐field images predicts optimal RTL‐stress‐composition design space.
Jianwei Li, Zhenqiang Xiong, Chuanxin Liang, Tianjiao Dong, Heran Wang, Mengyuan Hao, Liqiang He, Tianlong Zhang, Dong Wang   +8 more
wiley   +1 more source

Building Energy Consumption Prediction Using CatBoost With Hybrid Random Search and Bayesian Optimization

IEEE Access
Residential buildings are major contributors to global energy consumption, with cooling and heating loads representing a substantial portion of this demand.
Kadir Ileri
doaj   +1 more source

From Prediction to Prevention: An Explainable GeoAI Framework for Flood Susceptibility and Urban Exposure Assessment Using Machine and Deep Learning Models

Sustainable Development, EarlyView.
ABSTRACT Rapid urbanisation and intensifying rainfall have increased cities' vulnerability to flooding, posing major challenges to sustainable development. Although machine learning models have improved flood prediction accuracy, most remain limited by their black‐box nature and lack of actionable insights.
Abdulwaheed Tella, Izni Zahidi, Chow Ming Fai, Chukwuma Okolie, Quoc Bao Pham   +4 more
wiley   +1 more source

Machine learning ensemble models for predicting the antibacterial efficacy of gold nanoparticles

Materials Research Express
Antimicrobial resistance (AMR) has been increasing rapidly, emerging as a major global health challenge. Gold nanoparticles (AuNPs) are promising antibacterial agents due to their biocompatibility, low toxicity, and ease of functionalization.
Priya Mary, A Mujeeb
doaj   +1 more source