Vote for your favorite mineral in #MinCup25! - Kosmochlor vs. Azurite
It's a battle of green vs blue as rare but vibrant chromium-bearing kosmochlor up against the deep blue copper alteration mineral azurite.
Log InRegister
Quick Links : The Mindat ManualThe Rock H. Currier Digital LibraryMindat Newsletter [Free Download]
Home PageAbout MindatThe Mindat ManualHistory of MindatCopyright StatusWho We AreContact UsAdvertise on Mindat
Donate to MindatCorporate SponsorshipSponsor a PageSponsored PagesMindat AdvertisersAdvertise on Mindat
Learning CenterWhat is a mineral?The most common minerals on earthInformation for EducatorsMindat ArticlesThe ElementsThe Rock H. Currier Digital LibraryGeologic Time
Minerals by PropertiesMinerals by ChemistryAdvanced Locality SearchRandom MineralRandom LocalitySearch by minIDLocalities Near MeSearch ArticlesSearch GlossaryMore Search Options
Search For:
Mineral Name:
Locality Name:
Keyword(s):
 
The Mindat ManualAdd a New PhotoRate PhotosLocality Edit ReportCoordinate Completion ReportAdd Glossary Item
Mining CompaniesStatisticsUsersMineral MuseumsClubs & OrganizationsMineral Shows & EventsThe Mindat DirectoryDevice SettingsThe Mineral Quiz
Photo SearchPhoto GalleriesSearch by ColorNew Photos TodayNew Photos YesterdayMembers' Photo GalleriesPast Photo of the Day GalleryPhotography

Jaiswal, Bhavesh, Gajjar, Nagendra (2021) Performance Analysis of Deep Neural Network based on Transfer Learning for Pet Classification. International Journal of Advanced Computer Science and Applications, 12 (3) doi:10.14569/ijacsa.2021.0120309

Advanced
   -   Only viewable:
Reference TypeJournal (article/letter/editorial)
TitlePerformance Analysis of Deep Neural Network based on Transfer Learning for Pet Classification
JournalInternational Journal of Advanced Computer Science and Applications
AuthorsJaiswal, BhaveshAuthor
Gajjar, NagendraAuthor
Year2021Volume12
Issue3
PublisherThe Science and Information Organization
DOIdoi:10.14569/ijacsa.2021.0120309Search in ResearchGate
Generate Citation Formats
Mindat Ref. ID13257835Long-form Identifiermindat:1:5:13257835:8
GUID0
Full ReferenceJaiswal, Bhavesh, Gajjar, Nagendra (2021) Performance Analysis of Deep Neural Network based on Transfer Learning for Pet Classification. International Journal of Advanced Computer Science and Applications, 12 (3) doi:10.14569/ijacsa.2021.0120309
Plain TextJaiswal, Bhavesh, Gajjar, Nagendra (2021) Performance Analysis of Deep Neural Network based on Transfer Learning for Pet Classification. International Journal of Advanced Computer Science and Applications, 12 (3) doi:10.14569/ijacsa.2021.0120309
In(2021) International Journal of Advanced Computer Science and Applications Vol. 12 (3) The Science and Information Organization

See Also

These are possibly similar items as determined by title/reference text matching only.

Jitendra, Mukkamala S.N.V., Radhika, Y. (2021) Singer Gender Classification using Feature-based and Spectrograms with Deep Convolutional Neural Network. International Journal of Advanced Computer Science and Applications, 12 (2) doi:10.14569/ijacsa.2021.0120218
Hassan, Noor Hazirah, Fakharudin, Abdul Sahli (2023) Web Phishing Classification Model using Artificial Neural Network and Deep Learning Neural Network. International Journal of Advanced Computer Science and Applications, 14 (7) doi:10.14569/ijacsa.2023.0140759
Wadud, Md. Anwar Hussen, Rashadul, Md. (2021) Text Coherence Analysis based on Misspelling Oblivious Word Embeddings and Deep Neural Network. International Journal of Advanced Computer Science and Applications, 12 (1) doi:10.14569/ijacsa.2021.0120124
Gharghory, Sawsan Morkos (2020) Performance Analysis of Efficient Pre-trained Networks based on Transfer Learning for Tomato Leaf Diseases Classification. International Journal of Advanced Computer Science and Applications, 11 (8) doi:10.14569/ijacsa.2020.0110830
Tripathi, Neha, Singh, Pushpinder (2021) Deep Wavelet Neural Network based Robust Text Recognition for Overlapping Characters. International Journal of Advanced Computer Science and Applications, 12 (2) doi:10.14569/ijacsa.2021.0120258
Alotaibi, Aziz (2020) A Hybird Framework based on Autoencoder and Deep Neural Networks for Fashion Image Classification. International Journal of Advanced Computer Science and Applications, 11 (12) doi:10.14569/ijacsa.2020.0111237
Claypo, Niphat, Jaiyen, Saichon, Hanskunatai, Anantaporn (2023) Inspection System for Glass Bottle Defect Classification based on Deep Neural Network. International Journal of Advanced Computer Science and Applications, 14 (7) doi:10.14569/ijacsa.2023.0140738
Alshaya, Shaya A. (2021) Smart Internet of Vehicles Architecture based on Deep Learning for Occlusion Detection. International Journal of Advanced Computer Science and Applications, 12 (3) doi:10.14569/ijacsa.2021.0120328
Xuan, Cho Do, Duong, Lai Van (2021) A New Approach for Network Steganography Detection based on Deep Learning Techniques. International Journal of Advanced Computer Science and Applications, 12 (7) doi:10.14569/ijacsa.2021.0120705
Nawaz, Majid, A., Adel, Hassan, Taysir (2018) Multi-Class Breast Cancer Classification using Deep Learning Convolutional Neural Network. International Journal of Advanced Computer Science and Applications, 9. doi:10.14569/ijacsa.2018.090645
Swapnika, K., Vasumathi, D. (2022) Event Detection and Classification Using Deep Compressed Convolutional Neural Network. International Journal of Advanced Computer Science and Applications, 13 (12) doi:10.14569/ijacsa.2022.0131238
 
and/or  
Mindat.org is an outreach project of the Hudson Institute of Mineralogy, a 501(c)(3) not-for-profit organization.
Copyright © mindat.org and the Hudson Institute of Mineralogy 1993-2025, except where stated. Most political location boundaries are Β© OpenStreetMap contributors. Mindat.org relies on the contributions of thousands of members and supporters. Founded in 2000 by Jolyon Ralph.
To cite: Ralph, J., Von Bargen, D., Martynov, P., Zhang, J., Que, X., Prabhu, A., Morrison, S. M., Li, W., Chen, W., & Ma, X. (2025). Mindat.org: The open access mineralogy database to accelerate data-intensive geoscience research. American Mineralogist, 110(6), 833–844. doi:10.2138/am-2024-9486.
Privacy Policy - Terms & Conditions - Contact Us / DMCA issues - Report a bug/vulnerability Current server date and time: September 9, 2025 01:50:44
Go to top of page