Results 161 to 170 of about 13,817 (251)

Sea Clutter Suppression Method of HFSWR Based on RBF Neural Network Model Optimized by Improved GWO Algorithm. [PDF]

Comput Intell Neurosci, 2020
Shang S, He KN, Wang ZB, Yang T, Liu M, Li X.   +5 more
europepmc   +1 more source

Application of Artificial Intelligence in Food Science and Nutrition: Challenges and Future Perspectives

eFood, Volume 7, Issue 3, June 2026.
AI application can be very helpful in addressing different issues and shaping novel techniques in food production, food safety and quality, and food intake. AI application in food science, such as the food industry and processing, food safety and packaging, and nutrition.
Yaseen Galali, Pary Hadi, Arkan Mohammed Hassan, Holem Hashm Balaky, Tavga Sulaiman Rashid, Bashdar Abuzed Sadee, Tanya Salam Salih, Hamed Hassanzadeh   +7 more
wiley   +1 more source

Research on the characteristics of electro-hydraulic position servo system of RBF neural network under fuzzy rules. [PDF]

Sci Rep
Li J, Li W, Du X.
europepmc   +1 more source

QSPR model for Caco-2 cell permeability prediction using a combination of HQPSO and dual-RBF neural network. [PDF]

RSC Adv, 2020
Wang Y, Chen X.
europepmc   +1 more source

DualPath‐DRNet: A Self‐Annotating Dual‐Path Networks for End‐To‐End Diabetic Retinopathy Diagnosis

Engineering Reports, Volume 8, Issue 6, June 2026.
DualPath‐DRNet for automated diabetic retinopathy diagnosis and grading. ABSTRACT One of the challenges in detecting Diabetic Retinopathy (DR) is the detection of subtle early‐stage microaneurysms. DualPath‐DRNet is an end‐to‐end deep learning pipeline for 5‐class DR prediction. The novelty of this method lies in combining lesion detection using YOLOv8,
Ankur Chaudhary, Neetu Faujdar, Ghanshyam Singh   +2 more
wiley   +1 more source

Optimization and Simulation of Enterprise Management Resource Scheduling Based on the Radial Basis Function (RBF) Neural Network.

Comput Intell Neurosci, 2021
Wu Y, Sun X.
europepmc   +1 more source

The Construction Safety Risk Assessment Method for Hybrid Integrated Learning Based on SNS

Engineering Reports, Volume 8, Issue 6, June 2026.
This study proposes a hybrid ensemble framework for construction safety risk assessment, combining t‐SNE, XGBoost, BP neural network, random forest, and CatBoost with SNS optimization. Using questionnaire and on‐site data, the method improves accuracy and generalization over single models, providing an efficient tool for safety management.
Ruijiang Ran, Jun Hu, Xuehe Wu, Xiangpeng Xu, Shifa Dai, Zhe Zhang, Yong'an Wang   +6 more
wiley   +1 more source

Weighted Deformed Gram Matrices for Quantum‐Inspired Learning With Applications to Biomedical Signal Analysis

Engineering Reports, Volume 8, Issue 6, June 2026.
Graphical abstract of the (q,τ)$$ \left(q,\tau \right) $$‐deformed kernel framework for quantum‐inspired learning and biomedical signal analysis ABSTRACT This paper introduces a weighted (q,τ)$$ \left(q,\tau \right) $$‐deformed Gram matrix framework for quantum‐inspired learning systems, with particular emphasis on applications in biomedical signal ...
Rabha W. Ibrahim, Dumitru Baleanu, Soheil Salahshour   +2 more
wiley   +1 more source

LCDLNet: Attention Mechanism‐Based Deep Learning Framework for Lung Cancer Diagnosis from Computed Tomography (CT) Images

Engineering Reports, Volume 8, Issue 6, June 2026.
LCDLNet, a deep learning architecture, achieved 99.81% accuracy and an AUC of 0.998 for lung cancer diagnosis. This model effectively integrates multiscale features from a Custom CNN, DenseNet121, and a Transformer, leveraging Squeeze‐and‐Excitation (SE) blocks and Spatial Attention to capture complex local and global context, ensuring robust ...
Farjana Akter Chumki, Tahasin Ahmed Fahim, Sabrina Alam   +2 more
wiley   +1 more source

A Comparative Analysis of Asymmetric Z‐Number Fuzzy Logistic Regression and Conventional Machine Learning Models in COVID‐19 Disease Diagnosis

Engineering Reports, Volume 8, Issue 6, June 2026.
In this study, a fuzzy logistic regression framework based on asymmetric Z‐numbers is introduced. The proposed model is designed to explicitly incorporate data uncertainty and expert‐defined linguistic information into the diagnostic process. The results demonstrate improved diagnostic performance compared to conventional machine learning methods under
M. Habibi Ganjgah, P. Azhdari, K. Fathi Vajargah   +2 more
wiley   +1 more source