Quality Assessment of Cocoa Bean Using Regression Convolutional Neural Network

Authors: Sangsawad, Seksan, Jintawatsakoon, Supanat

Source: 2023 27th International Computer Science and Engineering Conference (ICSEC) Computer Science and Engineering Conference (ICSEC), 2023 27th International. :1-6 Sep, 2023

Relation: 2023 27th International Computer Science and Engineering Conference (ICSEC)

SSRNet: In-Field Counting Wheat Ears Using Multi-Stage Convolutional Neural Network

Authors: Wang, D., Zhang, D., Yang, G., Xu, B., Luo, Y., Yang, X.

Source: IEEE Transactions on Geoscience and Remote Sensing IEEE Trans. Geosci. Remote Sensing Geoscience and Remote Sensing, IEEE Transactions on. 60:1-11 2022

RegCNN: A Deep Multi-output Regression Method for Wastewater Treatment

Authors: Zhang, Luoyi, Ma, Xiaowen, Shi, Peng, Bi, Sheng, Wang, Chongjun

Source: 2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI) Tools with Artificial Intelligence (ICTAI), 2019 IEEE 31st International Conference on. :816-823 Nov, 2019

Relation: 2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI)

Regression Convolutional Neural Network for Automated Pediatric Bone Age Assessment From Hand Radiograph

Authors: Ren, X., Li, T., Yang, X., Wang, S., Ahmad, S., Xiang, L., Stone, S.R., Li, L., Zhan, Y., Shen, D., Wang, Q.

Source: IEEE Journal of Biomedical and Health Informatics IEEE J. Biomed. Health Inform. Biomedical and Health Informatics, IEEE Journal of. 23(5):2030-2038 Sep, 2019

Developing the regression network generated from the classification network with deep learning

Source: Pidvodni tehnologii; Vol. 1 No. 14 (2024): SMART TECHNOLOGIES 1(14); 13-17
"Подводные технологии: промышленная и гражданская инженерия"; Том 1 № 14 (2024): SMART TECHNOLOGIES 1(14); 13-17
Pidvodni tehnologii; Том 1 № 14 (2024): SMART TECHNOLOGIES 1(14); 13-17

Subject Terms: MATLAB, регресійна згорткова нейронна мережа, convolutional neural network (CNN), deep learning, глибоке навчання, згорткова нейронна мережа (CNN), regression convolutional neural network

File Description: application/pdf

Access URL: http://uwtech.knuba.edu.ua/article/view/307214

Developing the regression network generated from the classification network with deep learning ; Розробка регресійної мережі створеної з класифікаційної мережі з використанням глибокого навчання

Authors: Абдулрахман, Асма Абдулела, Тахір, Фуад Шакер

Source: Pidvodni tehnologii; Vol. 1 No. 14 (2024): SMART TECHNOLOGIES 1(14); 13-17 ; "Подводные технологии: промышленная и гражданская инженерия"; Том 1 № 14 (2024): SMART TECHNOLOGIES 1(14); 13-17 ; Pidvodni tehnologii; Том 1 № 14 (2024): SMART TECHNOLOGIES 1(14); 13-17 ; 2415-8569 ; 2415-8550 ; 10.32347/uwt.2024.14

Subject Terms: згорткова нейронна мережа (CNN), MATLAB, глибоке навчання, регресійна згорткова нейронна мережа, convolutional neural network (CNN), deep learning, regression convolutional neural network

File Description: application/pdf

Relation: http://uwtech.knuba.edu.ua/article/view/307214/298531; http://uwtech.knuba.edu.ua/article/view/307214

Availability: http://uwtech.knuba.edu.ua/article/view/307214
https://doi.org/10.32347/uwt.2024.14.1102

Skeletal bone age assessments for young children based on regression convolutional neural networks

Authors: Cong Bai, Peng Yi Hao, Jian Wu, Fu Li Wu, Xu Hang Xie, Sharon Chokuwa

Source: Mathematical Biosciences and Engineering, Vol 16, Iss 6, Pp 6454-6466 (2019)

Subject Terms: Male, China, Adolescent, 02 engineering and technology, Pattern Recognition, Automated, Age Determination by Skeleton, QA1-939, Image Processing, Computer-Assisted, 0202 electrical engineering, electronic engineering, information engineering, Humans, Child, Carpal Bones, carpal bones extraction, Observer Variation, bone age assessment, X-Rays, Infant, Newborn, Infant, Reproducibility of Results, regression convolutional neural network, Child, Preschool, Data Interpretation, Statistical, Regression Analysis, Female, Neural Networks, Computer, TP248.13-248.65, Mathematics, Algorithms, Biotechnology

Access URL: https://pubmed.ncbi.nlm.nih.gov/31698572
https://doaj.org/article/41b54085022148069a5404f14922efd3
https://www.aimspress.com/article/10.3934/mbe.2019323
https://www.ncbi.nlm.nih.gov/pubmed/31698572
https://pubmed.ncbi.nlm.nih.gov/31698572/

Predicting the Porosity in Selective Laser Melting Parts Using Hybrid Regression Convolutional Neural Network

Authors: Nawaf Mohammad H. Alamri, Michael Packianather, Samuel Bigot

Source: Applied Sciences, Vol 12, Iss 12571, p 12571 (2022)

Subject Terms: deep learning, regression convolutional neural network, bees algorithm, selective laser melting, artificial porosity images, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999

Relation: https://www.mdpi.com/2076-3417/12/24/12571; https://doaj.org/toc/2076-3417; https://doaj.org/article/9694cf7beadf41f788c94abdc6bcff80

Availability: https://doi.org/10.3390/app122412571
https://doaj.org/article/9694cf7beadf41f788c94abdc6bcff80

Skeletal bone age assessments for young children based on regression convolutional neural networks

Authors: Pengyi Hao, Sharon Chokuwa, Xuhang Xie, Fuli Wu, Jian Wu, Cong Bai

Source: Mathematical Biosciences and Engineering, Vol 16, Iss 6, Pp 6454-6466 (2019)

Subject Terms: bone age assessment, carpal bones extraction, regression convolutional neural network, Biotechnology, TP248.13-248.65, Mathematics, QA1-939

File Description: electronic resource

Relation: https://www.aimspress.com/article/10.3934/mbe.2019323?viewType=HTML; https://doaj.org/toc/1551-0018

Access URL: https://doaj.org/article/41b54085022148069a5404f14922efd3

Predicting Solar Radiation using a Deep Neural Network

Authors: Alpire, Adam

Index Terms: Deep Learning; Climate Science Prediction; Regression; Convolutional Neural Network; Solar Radiation; Tensorflow., Djupinlärning; klimatprediktion; regression; neurala faltningsnätverk; solstrålning; Tensorflow., Computer Sciences, Datavetenskap (datalogi), Student thesis, info:eu-repo/semantics/bachelorThesis, text

URL: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-215715
TRITA-ICT-EX ; 2017:105

Modified dragonfly algorithm based multilevel thresholding method for color images segmentation.

Authors: Peng XX; College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, China., Jia HM; College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, China., Lang CB; College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, China.

Source: Mathematical biosciences and engineering : MBE [Math Biosci Eng] 2019 Jul 15; Vol. 16 (6), pp. 6467-6511.

Publication Type: Journal Article; Research Support, Non-U.S. Gov't

Journal Info: Publisher: AIMS Press Country of Publication: United States NLM ID: 101197794 Publication Model: Print Cited Medium: Internet ISSN: 1551-0018 (Electronic) Linking ISSN: 15471063 NLM ISO Abbreviation: Math Biosci Eng Subsets: PubMed not MEDLINE; MEDLINE