neural network toolbox (MathWorks Inc)
96
Structured Review
MathWorks Inc
neural network toolbox
Neural Network Toolbox, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 951 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/deep+learning+toolbox/Deep+Learning+Toolbox/pm41776222-69-1-0
Average 96 stars, based on 951 article reviews
Neural Network Toolbox, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 951 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/deep+learning+toolbox/Deep+Learning+Toolbox/pm41776222-69-1-0
Average 96 stars, based on 951 article reviews
neural network toolbox - by Bioz Stars,
2026-11
96/100 stars
Images
Related Articles
other:Article Title: SNNLCnet: A shallow neural network integrated model for electroencephalogram-based classification of learning comprehension Article Snippet: The Article Title: Functional upcycling of nanomodified cementitious waste into recycled aggregate with multiscale reinforcement and environmental performance assessment. Article Snippet: The growing accumulation of cementitious waste and continued extraction of natural sand have increased the need for high-value recycling of cement-based materials.. However, recycled cementitious particles are often limited by old hydration products, residual pores, and crushing-induced microcracks.. Nano-modified cementitious waste may offer an alternative pathway because residual nanomaterials, if retained after crushing, could still influence pore structure and interfacial bonding. Article Title: Longitudinal Multi-Batch Characterisation of Beer Wort Fermentation Foam Using Image-Based Semantic Segmentation. Article Snippet: Development and per-batch training of the segmentation models The convolutional encoder–decoder network described above was implemented in Article Title: An Artificial Intelligence Framework for Detection of Leukemia with Comparison Between Segmented and Non-Segmented Images. Article Snippet: C. Proposed CNN Architecture We designed a lightweight custom CNN from Article Title: A Lagrangian Time‐Series Machine Learning Framework for Predicting Concentrations and Exploring Drivers of Atmospheric Aerosols: Model Development and Application to Cloud Condensation Nuclei in Marine Boundary Layer Article Snippet: The model is built based on the Article Title: Decoding plant x environment interactions through spatial gradients in leaf physiology and metabolism Article Snippet: Classification followed the two-layer feedforward approach described before , implemented in Article Title: A Hybrid Raytracing and Deep Learning-Based Approach for Single-Site Localization of Radio Emitters in Urban Environment. Article Snippet: Deep Learning Sector Classifier To achieve spatial filtering and isolate the geometric region of the non-cooperative transmitter, a deep Convolutional Neural Network (CNN) was developed and implemented utilizing the Software:Article Title: SFOA-based optimized SeResNet-BiLSTM network with an attention mechanism for the detection of exons. Article Snippet: .. The software framework, |