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Статті в журналах з теми "CNN MODEL"
Prasad, G. Shyam Chandra, and K. Adi Narayana Reddy. "Sentiment Analysis Using Multi-Channel CNN-LSTM Model." Journal of Advanced Research in Dynamical and Control Systems 11, no. 12-SPECIAL ISSUE (December 31, 2019): 489–94. http://dx.doi.org/10.5373/jardcs/v11sp12/20193243.
Повний текст джерелаHasan, Moh Arie, Yan Riyanto, and Dwiza Riana. "Grape leaf image disease classification using CNN-VGG16 model." Jurnal Teknologi dan Sistem Komputer 9, no. 4 (July 5, 2021): 218–23. http://dx.doi.org/10.14710/jtsiskom.2021.14013.
Повний текст джерелаChoi, Jiwoo, Sangil Choi, and Taewon Kang. "Personal Identification CNN Model using Gait Cycle." Journal of Korean Institute of Information Technology 20, no. 11 (November 30, 2022): 127–36. http://dx.doi.org/10.14801/jkiit.2022.20.11.127.
Повний текст джерелаSen, Amit Prakash, Nirmal Kumar Rout, Tuhinansu Pradhan, and Amrit Mukherjee. "Hybrid Deep CNN Model for the Detection of COVID-19." Indian Journal Of Science And Technology 15, no. 41 (November 5, 2022): 2121–28. http://dx.doi.org/10.17485/ijst/v15i41.1421.
Повний текст джерелаVyshnavi, Ramineni, and Goo-Rak Kwon. "A Comparative Study of the CNN Model for AD Diagnosis." Korean Institute of Smart Media 12, no. 7 (August 31, 2023): 52–58. http://dx.doi.org/10.30693/smj.2023.12.7.52.
Повний текст джерелаTajalsir, Mohammed, Susana Mu˜noz Hern´andez, and Fatima Abdalbagi Mohammed. "ASERS-CNN: Arabic Speech Emotion Recognition System based on CNN Model." Signal & Image Processing : An International Journal 13, no. 1 (February 28, 2022): 45–53. http://dx.doi.org/10.5121/sipij.2022.13104.
Повний текст джерелаEt. al., Ms K. N. Rode,. "Unsupervised CNN model for Sclerosis Detection." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 2 (April 10, 2021): 2577–83. http://dx.doi.org/10.17762/turcomat.v12i2.2223.
Повний текст джерелаKamundala, Espoir K., and Chang Hoon Kim. "CNN Model to Classify Malware Using Image Feature." KIISE Transactions on Computing Practices 24, no. 5 (May 31, 2018): 256–61. http://dx.doi.org/10.5626/ktcp.2018.24.5.256.
Повний текст джерелаLee, Seonggu, and Jitae Shin. "Hybrid Model of Convolutional LSTM and CNN to Predict Particulate Matter." International Journal of Information and Electronics Engineering 9, no. 1 (March 2019): 34–38. http://dx.doi.org/10.18178/ijiee.2019.9.1.701.
Повний текст джерелаSrinivas, Dr Kalyanapu, and Reddy Dr.B.R.S. "Deep Learning based CNN Optimization Model for MR Braing Image Segmentation." Journal of Advanced Research in Dynamical and Control Systems 11, no. 11 (November 20, 2019): 213–20. http://dx.doi.org/10.5373/jardcs/v11i11/20193190.
Повний текст джерелаДисертації з теми "CNN MODEL"
Meng, Zhaoxin. "A deep learning model for scene recognition." Thesis, Mittuniversitetet, Institutionen för informationssystem och –teknologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-36491.
Повний текст джерелаHubková, Helena. "Named-entity recognition in Czech historical texts : Using a CNN-BiLSTM neural network model." Thesis, Uppsala universitet, Institutionen för lingvistik och filologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-385682.
Повний текст джерелаAl-Kadhimi, Staffan, and Paul Löwenström. "Identification of machine-generated reviews : 1D CNN applied on the GPT-2 neural language model." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-280335.
Повний текст джерелаI och med de senaste framstegen inom maskininlärning kan datorer skapa mer och mer övertygande text, vilket skapar en oro för ökad falsk information på internet. Samtidigt vägs detta upp genom att forskare skapar verktyg för att identifiera datorgenererad text. Forskare har kunnat utnyttja svagheter i neurala språkmodeller och använda dessa mot dem. Till exempel tillhandahåller GLTR användare en visuell representation av texter, som hjälp för att klassificera dessa som människo- skrivna eller maskingenererade. Genom att träna ett faltningsnätverk (convolutional neural network, eller CNN) på utdata från GLTR-analys av maskingenererade och människoskrivna filmrecensioner, tar vi GLTR ett steg längre och använder det för att genomföra klassifikationen automatiskt. Emellertid tycks det ej vara tillräckligt att använda en CNN med GLTR som huvuddatakälla för att klassificera på en nivå som är jämförbar med de bästa existerande metoderna.
Huss, Anders. "Hybrid Model Approach to Appliance Load Disaggregation : Expressive appliance modelling by combining convolutional neural networks and hidden semi Markov models." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-179200.
Повний текст джерелаDen ökande energikonsumtionen är en stor utmaning för en hållbar utveckling. Bostäder står för en stor del av vår totala elförbrukning och är en sektor där det påvisats stor potential för besparingar. Non Intrusive Load Monitoring (NILM), dvs. härledning av hushållsapparaters individuella elförbrukning utifrån ett hushålls totala elförbrukning, är en tilltalande metod för att fortlöpande ge detaljerad information om elförbrukningen till hushåll. Detta utgör ett underlag för medvetna beslut och kan bidraga med incitament för hushåll att minska sin miljöpåverakan och sina elkostnader. För att åstadkomma detta måste precisa och tillförlitliga algoritmer för el-disaggregering utvecklas. Denna masteruppsats föreslår ett nytt angreppssätt till el-disaggregeringsproblemet, inspirerat av ledande metoder inom taligenkänning. Tidigare angreppsätt inom NILM (i frekvensområdet
Laine, Emmi. "Desirability, Values and Ideology in CNN Travel -- Discourse Analysis on Travel Stories." Thesis, Stockholms universitet, Institutionen för mediestudier, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-102742.
Повний текст джерелаAppelstål, Michael. "Multimodal Model for Construction Site Aversion Classification." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-421011.
Повний текст джерелаAnam, Md Tahseen. "Evaluate Machine Learning Model to Better Understand Cutting in Wood." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-448713.
Повний текст джерелаGhibellini, Alessandro. "Trend prediction in financial time series: a model and a software framework." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/24708/.
Повний текст джерелаRydén, Anna, and Amanda Martinsson. "Evaluation of 3D motion capture data from a deep neural network combined with a biomechanical model." Thesis, Linköpings universitet, Institutionen för medicinsk teknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-176543.
Повний текст джерелаGerima, Kassaye. "Night Setback Identification of District Heating Substations." Thesis, Högskolan Dalarna, Mikrodataanalys, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:du-36071.
Повний текст джерелаКниги з теми "CNN MODEL"
Greene, Carol. I can be a model. Chicago: Childrens Press, 1985.
Знайти повний текст джерелаGreene, Carol. I can be a model. Chicago: Childrens Press, 1985.
Знайти повний текст джерелаTrackside scenes you can model. Waukesha, WI: Kalmbach Books, 2003.
Знайти повний текст джерелаGreene, Carol. I can be a model. Chicago: Childrens Press, 1985.
Знайти повний текст джерелаEngel, Charles. Can the Markov switching model forecast exchange rates? Cambridge, MA: National Bureau of Economic Research, 1992.
Знайти повний текст джерелаDanna, Theresa M. Rollover, Mona Lisa!: How anyone can model for artists. Beverly Hills, CA: Big Guy Pub., 1992.
Знайти повний текст джерелаGestures Can Create Models that Help Thinking. [New York, N.Y.?]: [publisher not identified], 2019.
Знайти повний текст джерелаThe can do workplace: A strength-based model for nonprofits. Melbourne, Florida: Motivational Press, 2015.
Знайти повний текст джерелаSutherland, H. Constructing a tax-benefit model: What advice can one give? London: Taxation, Incentives and the Distribution of Income Programme, Suntory-Toyota International Centre for Economics and Related Disciplines, London School of Economics, 1989.
Знайти повний текст джерелаPenalver, Adrian. How can the IMF catalyse private capital flows? A model. London: Bank of England, 2004.
Знайти повний текст джерелаЧастини книг з теми "CNN MODEL"
Beniwal, Rohit, Divyakshi Bhardwaj, Bhanu Pratap Raghav, and Dhananjay Negi. "Text Similarity Identification Based on CNN and CNN-LSTM Model." In Second International Conference on Sustainable Technologies for Computational Intelligence, 47–58. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-4641-6_5.
Повний текст джерелаZhang, Shizhou, Yihong Gong, Jinjun Wang, and Nanning Zheng. "A Biologically Inspired Deep CNN Model." In Lecture Notes in Computer Science, 540–49. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-48890-5_53.
Повний текст джерелаSaadat, Sumaya, and V. Joseph Raymond. "Malware Classification Using CNN-XGBoost Model." In Artificial Intelligence Techniques for Advanced Computing Applications, 191–202. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5329-5_19.
Повний текст джерелаMoin, Kashif, Mayank Shrivastava, Amlan Mishra, Lambodar Jena, and Soumen Nayak. "Diabetic Retinopathy Detection Using CNN Model." In Smart Innovation, Systems and Technologies, 133–43. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-6068-0_13.
Повний текст джерелаChen, Xutong. "CNN Model Optimization Cheme and Applications." In Lecture Notes in Electrical Engineering, 1771–77. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5959-4_216.
Повний текст джерелаGoswami, Tilottama, and Shashidhar Reddy Javaji. "CNN Model for American Sign Language Recognition." In Lecture Notes in Electrical Engineering, 55–61. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-7961-5_6.
Повний текст джерелаZhang, Ru, Hao Dong, Zhen Yang, Wenbo Ying, and Jianyi Liu. "A CNN Based Visual Audio Steganography Model." In Lecture Notes in Computer Science, 431–42. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-06794-5_35.
Повний текст джерелаSakshi, Chetan Sharma, and Vinay Kukreja. "CNN-Based Handwritten Mathematical Symbol Recognition Model." In Cyber Intelligence and Information Retrieval, 407–16. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-4284-5_35.
Повний текст джерелаDas, Parimita, Dipak Kumar Sahoo, and Biswa Mohan Acharya. "Environmental Pollution Detection Mechanism Using CNN Model." In Lecture Notes in Networks and Systems, 476–82. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-4807-6_45.
Повний текст джерелаKolla, Morarjee, and T. Venugopal. "Diabetic Retinopathy Classification Using Lightweight CNN Model." In Lecture Notes in Electrical Engineering, 1263–69. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-7985-8_131.
Повний текст джерелаТези доповідей конференцій з теми "CNN MODEL"
Ben Alaya, Karim, and Laszlo Czuni. "CNN-based Tree Model Extraction." In 2021 11th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS). IEEE, 2021. http://dx.doi.org/10.1109/idaacs53288.2021.9660841.
Повний текст джерелаTambi, Ritiz, Paul Li, and Jun Yang. "An efficient CNN model for transportation mode sensing." In SenSys '18: The 16th ACM Conference on Embedded Networked Sensor Systems. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3274783.3275160.
Повний текст джерелаNagy, Zoltan, Laszlo Kek, Zoltan Kincses, and Peter Szolgay. "CNN model on cell multiprocessor array." In 2007 European Conference on Circuit Theory and Design (ECCTD 2007). IEEE, 2007. http://dx.doi.org/10.1109/ecctd.2007.4529590.
Повний текст джерелаFuredi, Laszlo, and Peter Szolgay. "CNN model on stream processing platform." In 2009 European Conference on Circuit Theory and Design (ECCTD 2009). IEEE, 2009. http://dx.doi.org/10.1109/ecctd.2009.5275115.
Повний текст джерелаSun, Yuxuan, Jining Xie, Pujie Li, and Bowei Sun. "BLSTM-CNN Relationship Classification Network Model." In 2021 IEEE 11th International Conference on Electronics Information and Emergency Communication (ICEIEC). IEEE, 2021. http://dx.doi.org/10.1109/iceiec51955.2021.9463812.
Повний текст джерелаDiana, Mery, Juntaro Chikama, Motoki Amagasaki, Masahiro Iida, and Morihiro Kuga. "Characteristic Similarity Using Classical CNN Model." In 2019 34th International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC). IEEE, 2019. http://dx.doi.org/10.1109/itc-cscc.2019.8793442.
Повний текст джерелаSzolgay, Peter, and Zoltan Nagy. "A CNN motivated array computing model." In 2010 12th International Workshop on Cellular Nanoscale Networks and their Applications (CNNA 2010). IEEE, 2010. http://dx.doi.org/10.1109/cnna.2010.5430341.
Повний текст джерелаSlavova, Angela. "Memristor CNN Model for Image Denoising." In 2019 26th IEEE International Conference on Electronics, Circuits and Systems (ICECS). IEEE, 2019. http://dx.doi.org/10.1109/icecs46596.2019.8964780.
Повний текст джерелаTan, Jiaxing, Yongfeng Gao, Weiguo Cao, Marc Pomeroy, Shu Zhang, Yumei Huo, Lihong Li, and Zhengrong Liang. "GLCM-CNN: Gray Level Co-occurrence Matrix based CNN Model for Polyp Diagnosis." In 2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI). IEEE, 2019. http://dx.doi.org/10.1109/bhi.2019.8834585.
Повний текст джерелаZhang, Chenkai, Yuki Okafuji, and Takahiro Wada. "Evaluation of visualization performance of CNN models using driver model." In 2021 IEEE/SICE International Symposium on System Integration (SII). IEEE, 2021. http://dx.doi.org/10.1109/ieeeconf49454.2021.9382776.
Повний текст джерелаЗвіти організацій з теми "CNN MODEL"
Slavova, Angela, and Nikolay Kyurkchiev. On CNN Model of Black–Scholes Equation with Leland Correction. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, January 2018. http://dx.doi.org/10.7546/crabs.2018.02.03.
Повний текст джерелаSlavova, Angela, and Nikolay Kyurkchiev. On CNN Model of Black–Scholes Equation with Leland Correction. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, February 2018. http://dx.doi.org/10.7546/grabs2018.2.03.
Повний текст джерелаMbani, Benson, Valentin Buck, and Jens Greinert. Megabenthic Fauna Detection with Faster R-CNN (FaunD-Fast) Short description of the research software. GEOMAR, 2023. http://dx.doi.org/10.3289/sw_1_2023.
Повний текст джерелаZhang, Yongping, Wen Cheng, and Xudong Jia. Enhancement of Multimodal Traffic Safety in High-Quality Transit Areas. Mineta Transportation Institute, February 2021. http://dx.doi.org/10.31979/mti.2021.1920.
Повний текст джерелаBarhak, Jacob. Supplemental Information: The Reference Model is a Multi-Scale Ensemble Model of COVID-19. Outbreak, May 2021. http://dx.doi.org/10.34235/b7eaa32b-1a6b-444f-9848-76f83f5a733c.
Повний текст джерелаNovy-Marx, Robert. How Can a Q-Theoretic Model Price Momentum? Cambridge, MA: National Bureau of Economic Research, February 2015. http://dx.doi.org/10.3386/w20985.
Повний текст джерелаEngel, Charles. Can the Markov Switching Model Forecast Exchange Rates? Cambridge, MA: National Bureau of Economic Research, November 1992. http://dx.doi.org/10.3386/w4210.
Повний текст джерелаCochrane, John. Can Learnability Save New-Keynesian Models? Cambridge, MA: National Bureau of Economic Research, October 2009. http://dx.doi.org/10.3386/w15459.
Повний текст джерелаde Miguel Beriain, Iñigo, Aliuska Duardo Sánchez, and José Antonio Castillo Parrilla. What Can We Do with the Data of Deceased People? A Normative Proposal. Universitätsbibliothek J. C. Senckenberg, Frankfurt am Main, 2021. http://dx.doi.org/10.21248/gups.64580.
Повний текст джерелаBlundell, S. Micro-terrain and canopy feature extraction by breakline and differencing analysis of gridded elevation models : identifying terrain model discontinuities with application to off-road mobility modeling. Engineer Research and Development Center (U.S.), April 2021. http://dx.doi.org/10.21079/11681/40185.
Повний текст джерела