Results 181 to 190 of about 303,008 (292)

Artificial intelligence in enzyme catalysis: Emerging trends and applications in biocatalyst engineering

The Canadian Journal of Chemical Engineering, EarlyView.
Schematic representation of artificial intelligence approaches in enzyme catalysis, integrating bibliometric analysis, emerging research trends, and machine learning tools for enzyme design, prediction, and industrial biocatalytic applications. Abstract This study systematically explores the applications of artificial intelligence (AI) in enzyme ...
Misael Bessa Sales, Francisco Izaias da Silva Aires, Paulo Gonçalves de Sousa Junior, Calebe Da Rocha Silva, Emanuel Lino Rodrigues, Maria Cristiane Martins de Souza, José C. S. dos Santos   +6 more
wiley   +1 more source

Probability prediction of true‐triaxial compressive strength of intact rocks based on the improved PSO‐RVM model

Deep Underground Science and Engineering, EarlyView.
In this work, we propose an improved particle swarm optimization (PSO) algorithm and develop an improved PSO‐relevance vector machine (RVM) model as a substitute for traditional true‐triaxial testing. The model's high prediction accuracy was validated through comparisons with two other machine learning methods and five three‐dimensional Hoek–Brown type
Qi Zhang, Maohui Wang, Ning Wang, Yixin Shen, Xuzhen He   +4 more
wiley   +1 more source

Rockburst prediction based on data preprocessing and hyperband‐RNN‐DNN

Deep Underground Science and Engineering, EarlyView.
A data preprocessing workflow is proposed to address challenges in rockburst data analysis. Coupled algorithms preprocess the data set, and hyperband optimization is used to enhance RNN performance. Results show that preprocessing improves accuracy, while dense layers enhance model stability and prediction performance.
Yong Fan, Chenxi Yin, Guangdong Yang, Shengyong Ding, Bin Tian   +4 more
wiley   +1 more source

Importance of balanced datasets with feature selection and ensemble methods on heart disease classification using distinctive machine learning techniques: a comparative analysis. [PDF]

Sci Rep
Ara J, Bhuiyan H, Roza II, Nahin ASM.
europepmc   +1 more source

Over three‐quarters of earthworm species lack protection in China, a crisis exacerbated by climate change

Ecography, EarlyView.
Earthworms, as ‘ecosystem engineers', play a crucial role in regulating ecosystem functions and shaping community structures. Due to climate change, earthworms face severe survival pressures and extinction risks. However, whether conservation efforts targeting aboveground biodiversity can cover the long‐neglected earthworm diversity remains unknown. To
Yajie Zhou, Shuo Zhang, Wen Qin, Zhuoyi Wu, , Shaolin Peng, Ting Zhou   +6 more
wiley   +1 more source

Machine Learning‐Assisted Design of BaTiO3‐Based Superparaelectric High‐Entropy Ceramics with Superior Energy Storage

ENERGY &ENVIRONMENTAL MATERIALS, EarlyView.
This study employed an adaptive iterative strategy combining machine learning algorithms, domain knowledge, experimental design, and experimental feedback to aim to precisely and quickly discover high‐entropy ceramics with excellent energy storage performance.
Haowen Liu, Xiaoyan Zhang, Zhiyuan Ma, Kailong Ma, Xiwei Qi   +4 more
wiley   +1 more source

Assessment of Pain Intensity Using Deep Learning Models in Non-Communicative Intensive Care Patients. [PDF]

Nurs Crit Care
Guven S, Aslan FE, Canayaz M.
europepmc   +1 more source

Can epilepsy be predicted after the first febrile seizure? Insights from machine learning of postictal EEG

Epileptic Disorders, EarlyView.
Abstract Objective Febrile seizures (FS) are the most common seizures in childhood, yet identifying children at risk of developing epilepsy after the first FS remains challenging. We aimed to evaluate the prognostic potential of machine learning (ML) algorithms applied to post‐febrile seizure electroencephalography (EEG) recordings.
Boran Şekeroğlu, Hüseyin Öztoprak, Özlem Yayıcı Köken, Hatice Demir, Mehpare Sarı Yanartaş, Deniz Yılmaz, Burçin Şanlıdağ, Ayşegül Neşe Çıtak Kurt   +7 more
wiley   +1 more source

Decoding epilepsy's molecular blueprint: Machine learning unravels transcriptomic subtypes and regulatory networks

Epilepsia, EarlyView.
Abstract Objective Drug‐resistant epilepsy (DRE) affects approximately one‐third of patients with epilepsy. The molecular heterogeneity underlying DRE remains poorly defined, largely due to limited access to resected brain tissue and substantial genetic diversity.
Yanping Weng, Yu Ma, Wanwan Hou, Haibo Li, Yuanfeng Zhou, Rui Zhao, Hao Li, Lian Chen, Yangyang Ma, Li Jin, Yi Wang, Yu An   +11 more
wiley   +1 more source

Prediction model for frailty risk in ischemic stroke patients: Application and validation of support vector machines and nomograms. [PDF]

Medicine (Baltimore)
Kang YL, Kong JT, Tian H.
europepmc   +1 more source