Results 191 to 200 of about 4,847 (258)

Explainable Machine Learning With Hybrid Feature Selection for Thyroid Disease Classification: A Case Study in Bangladesh

Engineering Reports, Volume 8, Issue 8, August 2026.
Our research establishes a hybrid feature selection and ensemble machine learning pipeline for the classification of euthyroid, hyperthyroid, hypothyroid, and subclinical hypo‐ and hyperthyroid disorders. Variance Threshold with Backward Feature Elimination attained nearly 100% accuracy, while SHAP and LIME clarified the significance of features and ...
Md. Minhajul Abedin, Arka Chakraborty, Sawrav Das, Manoshi Das Turjo, Aditta Chowdhury, Mahdy Rahman Chowdhury   +5 more
wiley   +1 more source

A hybrid recommendation framework utilizing domain-adaptive RoBERTa embeddings for enhanced personalization in e-commerce. [PDF]

Sci Rep
Rajpoot CS, Tiwari V, Vishwakarma SK.
europepmc   +1 more source

A Framework for Context‐Aware and Adaptive Large Language Models for Autism Spectrum Disorder Therapy

Engineering Reports, Volume 8, Issue 8, August 2026.
The proposed framework demonstrates the potential of multilingual LLMs to transform ASD therapy by providing scalable, inclusive, and adaptive support across clinical, educational, and home environments. ABSTRACT Gene mutations and exposure to toxins are two examples of environmental and genetic variables that may contribute to ASD. In addition, people
Achhiya Sultana, Mohammad Shidujaman, Md Tarek Habib   +2 more
wiley   +1 more source

Embeddings from language models are good learners for single-cell data analysis. [PDF]

Patterns (N Y)
Liu T, Chen T, Zheng W, Luo X, Chen Y, Zhao H.   +5 more
europepmc   +1 more source

Unraveling the Impact of Serum Zinc Levels on Chronic Kidney Disease: Machine Learning and SHAP Value Interpretation

Food Science &Nutrition, Volume 14, Issue 8, August 2026.
Using nationally representative NHANES 2011–2016 data, this cross‐sectional study included 4192 U.S. adults, of whom 685 had prevalent chronic kidney disease (CKD). Survey‐weighted logistic regression showed that higher serum zinc levels were associated with lower odds of prevalent CKD.
Xiaoxin Liu, Kexin Zhao, Ningxu Li, Yanyan Liu   +3 more
wiley   +1 more source

scMagnifier: Resolving fine-grained cell subtypes via GRN-informed perturbations and consensus clustering. [PDF]

PLoS Comput Biol
He Z, Dong K.
europepmc   +1 more source

Machine Learning Approaches to Identify Influential Factors of the Comorbid Psychiatric Symptoms and Hypertension Among Rural Adults in Bangladesh: A Cross‐Sectional Study

Health Science Reports, Volume 9, Issue 8, August 2026.
ABSTRACT Background and Aim The burden of both psychiatric symptoms (anxiety and/or depression) and hypertension poses significant public health challenges in Bangladesh, especially in rural areas with limited healthcare access. Thus, this study aimed to identify the influential factors associated with comorbid psychiatric symptoms and hypertension in ...
Mohammad Rocky Khan Chowdhury, Md. Mobarak Hossain Khan, Ali Ahmed, M Tasdik Hasan, Bodrun Naher Siddiquea, Md Nuruzzaman Khan   +5 more
wiley   +1 more source

Machine Learning-Powered Vision for Robotic Inspection in Manufacturing: A Review. [PDF]

Sensors (Basel)
Patrashko DY, Gurau V.
europepmc   +1 more source

Enhancing Rock Strength Prediction and Features Selection by Coupling Well Log Data and Deep Learning Approaches in Reservoir Geomechanics

International Journal for Numerical and Analytical Methods in Geomechanics, Volume 50, Issue 11, Page 4724-4738, 10 August 2026.
ABSTRACT Accurate rock strength parameters play a vital role in petroleum and mining operations for sustainable drilling activities, and wellbore stability analysis. This study investigates the applicability of deep learning techniques for assessing data‐driven models, and to conduct parametric sensitivity examination for feature attributes ranking to ...
Mohammad Islam Miah, Md. Shakil Rahaman, Travis Wiens   +2 more
wiley   +1 more source

RNA-seq-derived sequence variations are excellent features for cell line identification. [PDF]

Comput Struct Biotechnol J
Müller L, Müller S, Mir KUI, Lange J, Hagemann S, Wedler A, Hause F, Misiak C, Misiak D, Gutschner T, Hüttelmaier S, Glaß M.   +11 more
europepmc   +1 more source