Literatura académica sobre el tema "VGG16 MODEL"
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Artículos de revistas sobre el tema "VGG16 MODEL"
Sukegawa, Shintaro, Kazumasa Yoshii, Takeshi Hara, Katsusuke Yamashita, Keisuke Nakano, Norio Yamamoto, Hitoshi Nagatsuka y Yoshihiko Furuki. "Deep Neural Networks for Dental Implant System Classification". Biomolecules 10, n.º 7 (1 de julio de 2020): 984. http://dx.doi.org/10.3390/biom10070984.
Texto completoKumar, Vijay, Anis Zarrad, Rahul Gupta y Omar Cheikhrouhou. "COV-DLS: Prediction of COVID-19 from X-Rays Using Enhanced Deep Transfer Learning Techniques". Journal of Healthcare Engineering 2022 (11 de abril de 2022): 1–13. http://dx.doi.org/10.1155/2022/6216273.
Texto completoLai, Ren Yu, Kim Gaik Tay, Audrey Huong, Chang Choon Chew y Shuhaida Ismail. "Dorsal hand Vein Authentication System Using Convolution Neural Network". International Journal of Emerging Technology and Advanced Engineering 12, n.º 8 (2 de agosto de 2022): 83–90. http://dx.doi.org/10.46338/ijetae0822_11.
Texto completoBodavarapu, Pavan Nageswar Reddy y P. V. V. S. Srinivas. "Facial expression recognition for low resolution images using convolutional neural networks and denoising techniques". Indian Journal of Science and Technology 14, n.º 12 (27 de marzo de 2021): 971–83. http://dx.doi.org/10.17485/ijst/v14i12.14.
Texto completoShinde, Krishna K. y C. N. Kayte. "Fingerprint Recognition Based on Deep Learning Pre-Train with Our Best CNN Model for Person Identification". ECS Transactions 107, n.º 1 (24 de abril de 2022): 2209–20. http://dx.doi.org/10.1149/10701.2209ecst.
Texto completoAthavale, Vijay Anant, Suresh Chand Gupta, Deepak Kumar y Savita. "Human Action Recognition Using CNN-SVM Model". Advances in Science and Technology 105 (abril de 2021): 282–90. http://dx.doi.org/10.4028/www.scientific.net/ast.105.282.
Texto completoKo, Kyung-Kyu y Eun-Sung Jung. "Improving Air Pollution Prediction System through Multimodal Deep Learning Model Optimization". Applied Sciences 12, n.º 20 (15 de octubre de 2022): 10405. http://dx.doi.org/10.3390/app122010405.
Texto completoHasan, Moh Arie, Yan Riyanto y Dwiza Riana. "Grape leaf image disease classification using CNN-VGG16 model". Jurnal Teknologi dan Sistem Komputer 9, n.º 4 (5 de julio de 2021): 218–23. http://dx.doi.org/10.14710/jtsiskom.2021.14013.
Texto completoSingh, Tajinder Pal, Sheifali Gupta, Meenu Garg, Amit Verma, V. V. Hung, H. H. Thien y Md Khairul Islam. "Transfer and Deep Learning-Based Gurmukhi Handwritten Word Classification Model". Mathematical Problems in Engineering 2023 (3 de mayo de 2023): 1–20. http://dx.doi.org/10.1155/2023/4768630.
Texto completoShakhovska, Nataliya y Pavlo Pukach. "Comparative Analysis of Backbone Networks for Deep Knee MRI Classification Models". Big Data and Cognitive Computing 6, n.º 3 (21 de junio de 2022): 69. http://dx.doi.org/10.3390/bdcc6030069.
Texto completoTesis sobre el tema "VGG16 MODEL"
Albert, Florea George y Filip Weilid. "Deep Learning Models for Human Activity Recognition". Thesis, Malmö universitet, Fakulteten för teknik och samhälle (TS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-20201.
Texto completoThe Augmented Multi-party Interaction(AMI) Meeting Corpus database is used to investigate group activity recognition in an office environment. The AMI Meeting Corpus database provides researchers with remote controlled meetings and natural meetings in an office environment; meeting scenario in a four person sized office room. To achieve the group activity recognition video frames and 2-dimensional audio spectrograms were extracted from the AMI database. The video frames were RGB colored images and audio spectrograms had one color channel. The video frames were produced in batches so that temporal features could be evaluated together with the audio spectrogrames. It has been shown that including temporal features both during model training and then predicting the behavior of an activity increases the validation accuracy compared to models that only use spatial features [1]. Deep learning architectures have been implemented to recognize different human activities in the AMI office environment using the extracted data from the AMI database.The Neural Network models were built using the Keras API together with TensorFlow library. There are different types of Neural Network architectures. The architecture types that were investigated in this project were Residual Neural Network, Visual Geometry Group 16, Inception V3 and RCNN(Recurrent Neural Network). ImageNet weights have been used to initialize the weights for the Neural Network base models. ImageNet weights were provided by Keras API and was optimized for each base model[2]. The base models uses ImageNet weights when extracting features from the input data.The feature extraction using ImageNet weights or random weights together with the base models showed promising results. Both the Deep Learning using dense layers and the LSTM spatio-temporal sequence prediction were implemented successfully.
GUPTA, RASHI. "IMAGE FORGERY DETECTION USING CNN MODEL". Thesis, 2022. http://dspace.dtu.ac.in:8080/jspui/handle/repository/19175.
Texto completoCapítulos de libros sobre el tema "VGG16 MODEL"
Gupta, Pranjal Raaj, Disha Sharma y Nidhi Goel. "Image Forgery Detection by CNN and Pretrained VGG16 Model". En Advances in Intelligent Systems and Computing, 141–52. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-6887-6_13.
Texto completoAnju, T. E. y S. Vimala. "Finetuned-VGG16 CNN Model for Tissue Classification of Colorectal Cancer". En Intelligent Sustainable Systems, 73–84. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1726-6_7.
Texto completoLincy, R. Babitha y R. Gayathri. "Off-Line Tamil Handwritten Character Recognition Based on Convolutional Neural Network with VGG16 and VGG19 Model". En Advances in Automation, Signal Processing, Instrumentation, and Control, 1935–45. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8221-9_180.
Texto completoRamya Manaswi, V. y B. Sankarababu. "A Flexible Accession on Brain Tumour Detection and Classification Using VGG16 Model". En Smart Intelligent Computing and Applications, Volume 1, 225–38. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9669-5_21.
Texto completoVidya, D., Shivanand Rumma y Mallikarjun Hangargi. "Apple Classification Based on MRI Images Using VGG16 Convolutional Deep Learning Model". En Proceedings of the First International Conference on Advances in Computer Vision and Artificial Intelligence Technologies (ACVAIT 2022), 114–21. Dordrecht: Atlantis Press International BV, 2023. http://dx.doi.org/10.2991/978-94-6463-196-8_10.
Texto completoKumar, Ashish, Raied Razi, Anshul Singh y Himansu Das. "Res-VGG: A Novel Model for Plant Disease Detection by Fusing VGG16 and ResNet Models". En Communications in Computer and Information Science, 383–400. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-6318-8_32.
Texto completoRanjan, Amit, Chandrashekhar Kumar, Rohit Kumar Gupta y Rajiv Misra. "Transfer Learning Based Approach for Pneumonia Detection Using Customized VGG16 Deep Learning Model". En Internet of Things and Connected Technologies, 17–28. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-94507-7_2.
Texto completoAhmed, Mohammed Junaid, Ashutosh Satapathy, Ch Raga Madhuri, K. Yashwanth Chowdary y A. Naveen Sai. "A Hybrid Model Built on VGG16 and Random Forest Algorithm for Land Classification". En Inventive Systems and Control, 267–80. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1624-5_20.
Texto completoChhabra, Mohit y Rajneesh Kumar. "An Advanced VGG16 Architecture-Based Deep Learning Model to Detect Pneumonia from Medical Images". En Lecture Notes in Electrical Engineering, 457–71. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8774-7_37.
Texto completoHason Rudd, David, Huan Huo y Guandong Xu. "An Extended Variational Mode Decomposition Algorithm Developed Speech Emotion Recognition Performance". En Advances in Knowledge Discovery and Data Mining, 219–31. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-33380-4_17.
Texto completoActas de conferencias sobre el tema "VGG16 MODEL"
Zhang, Jing-Wei, Kuang-Chyi Lee y Gadi Ashok Kumar Reddy. "Rubber Gasket Defect Classification by VGG16 model". En 2022 IEEE 4th Eurasia Conference on IOT, Communication and Engineering (ECICE). IEEE, 2022. http://dx.doi.org/10.1109/ecice55674.2022.10042837.
Texto completoSurekha, G., Patlolla Sai Keerthana, Nallantla Jaswanth Varma y Tummala Sai Gopi. "Hybrid Image Classification Model using ResNet101 and VGG16". En 2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC). IEEE, 2023. http://dx.doi.org/10.1109/icaaic56838.2023.10140790.
Texto completoAntonio, Elbren, Cyrus Rael y Elmer Buenavides. "Changing Input Shape Dimension Using VGG16 Network Model". En 2021 IEEE International Conference on Automatic Control & Intelligent Systems (I2CACIS). IEEE, 2021. http://dx.doi.org/10.1109/i2cacis52118.2021.9495858.
Texto completoLi, Ziheng, Yuelong Zhang, Jiankai Zuo, Yupeng Zou y Mingxuan Song. "Improved image-based lung opacity detection of VGG16 model". En 2nd International Conference on Computer Vision, Image and Deep Learning, editado por Fengjie Cen y Badrul Hisham bin Ahmad. SPIE, 2021. http://dx.doi.org/10.1117/12.2604522.
Texto completoAlbashish, Dheeb, Rizik Al-Sayyed, Azizi Abdullah, Mohammad Hashem Ryalat y Nedaa Ahmad Almansour. "Deep CNN Model based on VGG16 for Breast Cancer Classification". En 2021 International Conference on Information Technology (ICIT). IEEE, 2021. http://dx.doi.org/10.1109/icit52682.2021.9491631.
Texto completoPanthakkan, Alavikunhu, S. M. Anzar, Saeed Al Mansoori y Hussain Al Ahmad. "Accurate Prediction of COVID-19 (+) Using AI Deep VGG16 Model". En 2020 3rd International Conference on Signal Processing and Information Security (ICSPIS). IEEE, 2020. http://dx.doi.org/10.1109/icspis51252.2020.9340145.
Texto completoZiyue, Chen y Gao Yuanyuan. "Primate Recognition System Design Based on Deep Learning Model VGG16". En 2022 7th International Conference on Image, Vision and Computing (ICIVC). IEEE, 2022. http://dx.doi.org/10.1109/icivc55077.2022.9886310.
Texto completoN, Valarmathi, Bavya S, Deepika p, Dharani L y Hemalatha P. "Deep Learning Model for Automated Kidney Stone Detection using VGG16". En 2023 Second International Conference on Electronics and Renewable Systems (ICEARS). IEEE, 2023. http://dx.doi.org/10.1109/icears56392.2023.10085509.
Texto completoQassim, Hussam, Abhishek Verma y David Feinzimer. "Compressed residual-VGG16 CNN model for big data places image recognition". En 2018 IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC). IEEE, 2018. http://dx.doi.org/10.1109/ccwc.2018.8301729.
Texto completoJin, Xuesong, Xin Du y Huiyuan Sun. "VGG-S: Improved Small Sample Image Recognition Model Based on VGG16". En 2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture (AIAM). IEEE, 2021. http://dx.doi.org/10.1109/aiam54119.2021.00054.
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