Academic literature on the topic 'Deep learning based'
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Journal articles on the topic "Deep learning based"
Jaiswal, Tarun, and Sushma Jaiswal. "Deep Learning Based Pain Treatment." International Journal of Trend in Scientific Research and Development Volume-3, Issue-4 (June 30, 2019): 193–211. http://dx.doi.org/10.31142/ijtsrd23639.
Full textJiang, Zhengfen, Boyi Li, Tho N. H. T. Tran, Jiehui Jiang, Xin Liu, and Dean Ta. "Fluo-Fluo translation based on deep learning." Chinese Optics Letters 20, no. 3 (2022): 031701. http://dx.doi.org/10.3788/col202220.031701.
Full textDaehyeon Bae, Daehyeon Bae, Jongbae Hwang Daehyeon Bae, and Jaecheol Ha Jongbae Hwang. "Deep Learning-based Attacks on Masked AES Implementation." 網際網路技術學刊 23, no. 4 (July 2022): 897–902. http://dx.doi.org/10.53106/160792642022072304024.
Full textAL-Oudat, Mohammad, Mohammad Azzeh, Hazem Qattous, Ahmad Altamimi, and Saleh Alomari. "Image Segmentation based Deep Learning for Biliary Tree Diagnosis." Webology 19, no. 1 (January 20, 2022): 1834–49. http://dx.doi.org/10.14704/web/v19i1/web19123.
Full textWang, Weihao, Xing Zhao, Zhixiang Jiang, and Ya Wen. "Deep learning-based scattering removal of light field imaging." Chinese Optics Letters 20, no. 4 (2022): 041101. http://dx.doi.org/10.3788/col202220.041101.
Full textJimmington, Anjana. "A Baseline Based Deep Learning Approach of Live Tweets." International Journal of Trend in Scientific Research and Development Volume-3, Issue-4 (June 30, 2019): 829–33. http://dx.doi.org/10.31142/ijtsrd23918.
Full textDebnath, Tanmoy, and Suvvari Sai Dileep. "A Deep-Learning based Approach for Automatic Lyric Generation." International Journal of Science and Research (IJSR) 11, no. 11 (November 5, 2022): 382–86. http://dx.doi.org/10.21275/sr221104005352.
Full textGadri, Said. "Efficient Arabic Handwritten Character Recognition based on Machine Learning and Deep Learning Approaches." Journal of Advanced Research in Dynamical and Control Systems 12, SP7 (July 25, 2020): 9–17. http://dx.doi.org/10.5373/jardcs/v12sp7/20202076.
Full textLiu, Qingzhong, Zhaoxian Zhou, Sarbagya Ratna Shakya, Prathyusha Uduthalapally, Mengyu Qiao, and Andrew H. Sung. "Smartphone Sensor-Based Activity Recognition by Using Machine Learning and Deep Learning Algorithms." International Journal of Machine Learning and Computing 8, no. 2 (April 2018): 121–26. http://dx.doi.org/10.18178/ijmlc.2018.8.2.674.
Full textPan, Wei, Jide Li, and Xiaoqiang Li. "Portfolio Learning Based on Deep Learning." Future Internet 12, no. 11 (November 18, 2020): 202. http://dx.doi.org/10.3390/fi12110202.
Full textDissertations / Theses on the topic "Deep learning based"
Hussein, Ahmed. "Deep learning based approaches for imitation learning." Thesis, Robert Gordon University, 2018. http://hdl.handle.net/10059/3117.
Full textAbrishami, Hedayat. "Deep Learning Based Electrocardiogram Delineation." University of Cincinnati / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1563525992210273.
Full textAl-Bander, B. Q. "Retinal image analysis based on deep learning." Thesis, University of Liverpool, 2018. http://livrepository.liverpool.ac.uk/3022573/.
Full textWidegren, Philip. "Deep learning-based forecasting of financial assets." Thesis, KTH, Matematisk statistik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-208308.
Full textDjupa neuronnät har under det senaste årtiondet blivit ett väldigt användarbart verktyg för att lösa komplexa problem, tack vare förbättringar i träningsalgoritmer. Två områden där djupinlärning visat sig väldigt användbart är inom taligenkänning och maskinöversättning. Det finns relativt få artiklar där djupinlärning används inom finans men i de få som existerar finns det tydliga tecken på att djupinlärning skulle kunna appliceras framgångsrikt på finansiella problem. Denna uppsats studerar prediktering av finansiella prisrörelser med framåtkopplade nätverk och rekurrenta nätverk. För de framåtkopplade nätverken kommer vi använda oss av djupa nätverk med färre neuroner per lager och mindre djupa nätverk med fler neuroner per lager. Förutom en jämförelse mellan framåtkopplade nätverk och rekurrenta nätverk kommer även en jämförelse mellan de djupa och mindre djupa framåtkopplade nätverken att göras. De rekurrenta nätverket består av ett rekurrent lager som sedan projicerar på ett framåtkopplande lager följt av ett outputlager. Nätverken är tränade med två olika uppsättningar av insignaler, ett mindre komplext och ett mer komplext. Resultaten för jämförelsen mellan de olika framåtkopplade nätverken indikerar att det inte med säkerhet går att säga om man vill använda sig av ett djupare nätverk eller inte, då det beror på många olika faktorer som tex. variabeluppsättning. Resultaten för jämförelsen mellan de rekurrent nätverken och framåtkopplade nätverken indikerar att rekurrenta nätverk nödvändigtvis inte presterar bättre än framåtkopplade nätverk trots att finansiell data vanligtvis är tidsberoende. Det finns signifikanta resultat där den mer komplexa variabeluppsättningen presterar bättre än den mindre komplexa. Den högsta träffsäkerheten för att prediktera rätt tecken på nästkommande prisrörelse är 52.82% vilket är signifikant bättre än ett enkelt benchmark.
Wang, Xutao. "Chinese Text Classification Based On Deep Learning." Thesis, Mittuniversitetet, Avdelningen för informationssystem och -teknologi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-35322.
Full textZhou, Chenyang. "Measure face similarity based on deep learning." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-262675.
Full textMätning av ansiktslikhet är en uppgift i datorseende som skiljer sig från ansiktsigenkänning. Det syftar till att hitta en inbäddning där liknande ansikten har ett mindre avstånd än olika ansikten. Detta projekt undersöker två olika siamesiska nätverk för att utforska om dessa specifika nätverk överträffar ansiktsigenkänningsmetoder på ansiktslikhet. Den bästa noggrannheten är från ett Siamesiskt faltningsnätverk, vilket är 65,11%. Dessutom erhålls de bästa resultaten i en likhetsrankningsuppgift från Siamesisk geometrimedveten metrisk inlärning. Projektet skapar också ett nytt dataset med ansiktsbildpar för ansiktslikhet.
Thiele, Johannes C. "Deep learning in event-based neuromorphic systems." Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLS403/document.
Full textInference and training in deep neural networks require large amounts of computation, which in many cases prevents the integration of deep networks in resource constrained environments. Event-based spiking neural networks represent an alternative to standard artificial neural networks that holds the promise of being capable of more energy efficient processing. However, training spiking neural networks to achieve high inference performance is still challenging, in particular when learning is also required to be compatible with neuromorphic constraints. This thesis studies training algorithms and information encoding in such deep networks of spiking neurons. Starting from a biologically inspired learning rule, we analyze which properties of learning rules are necessary in deep spiking neural networks to enable embedded learning in a continuous learning scenario. We show that a time scale invariant learning rule based on spike-timing dependent plasticity is able to perform hierarchical feature extraction and classification of simple objects of the MNIST and N-MNIST dataset. To overcome certain limitations of this approach we design a novel framework for spike-based learning, SpikeGrad, which represents a fully event-based implementation of the gradient backpropagation algorithm. We show how this algorithm can be used to train a spiking network that performs inference of relations between numbers and MNIST images. Additionally, we demonstrate that the framework is able to train large-scale convolutional spiking networks to competitive recognition rates on the MNIST and CIFAR10 datasets. In addition to being an effective and precise learning mechanism, SpikeGrad allows the description of the response of the spiking neural network in terms of a standard artificial neural network, which allows a faster simulation of spiking neural network training. Our work therefore introduces several powerful training concepts for on-chip learning in neuromorphic devices, that could help to scale spiking neural networks to real-world problems
Zanghieri, Marcello. "sEMG-based hand gesture recognition with deep learning." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/18112/.
Full textElkaref, Mohab. "Deep learning applications for transition-based dependency parsing." Thesis, University of Birmingham, 2018. http://etheses.bham.ac.uk//id/eprint/8620/.
Full textDsouza, Rodney Gracian. "Deep Learning Based Motion Forecasting for Autonomous Driving." The Ohio State University, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=osu1619139403696822.
Full textBooks on the topic "Deep learning based"
Ratha, Nalini K., Vishal M. Patel, and Rama Chellappa, eds. Deep Learning-Based Face Analytics. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-74697-1.
Full textMittag, Gabriel. Deep Learning Based Speech Quality Prediction. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-91479-0.
Full textTsihrintzis, George A., Maria Virvou, and Lakhmi C. Jain, eds. Advances in Machine Learning/Deep Learning-based Technologies. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-76794-5.
Full textAgarwal, Basant, Richi Nayak, Namita Mittal, and Srikanta Patnaik, eds. Deep Learning-Based Approaches for Sentiment Analysis. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-1216-2.
Full textAlla, Sridhar, and Suman Kalyan Adari. Beginning Anomaly Detection Using Python-Based Deep Learning. Berkeley, CA: Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-5177-5.
Full textSatapathy, Suresh Chandra, Ajay Kumar Jena, Jagannath Singh, and Saurabh Bilgaiyan. Automated Software Engineering: A Deep Learning-Based Approach. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-38006-9.
Full textChaudhuri, Arindam, and Soumya K. Ghosh. Bankruptcy Prediction through Soft Computing based Deep Learning Technique. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-6683-2.
Full textLi, Yuecheng, and Hongwen He. Deep Reinforcement Learning-Based Energy Management for Hybrid Electric Vehicles. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-79206-9.
Full textChellappa, Rama, Nalini K. Ratha, and Vishal M. Patel. Deep Learning-Based Face Analytics. Springer International Publishing AG, 2021.
Find full textChellappa, Rama, Nalini K. Ratha, and Vishal M. Patel. Deep Learning-Based Face Analytics. Springer International Publishing AG, 2022.
Find full textBook chapters on the topic "Deep learning based"
Sewak, Mohit. "Policy-Based Reinforcement Learning Approaches." In Deep Reinforcement Learning, 127–40. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-8285-7_10.
Full textPaluszek, Michael, and Stephanie Thomas. "Terrain-Based Navigation." In Practical MATLAB Deep Learning, 169–201. Berkeley, CA: Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-5124-9_9.
Full textPaluszek, Michael, Stephanie Thomas, and Eric Ham. "Terrain-Based Navigation." In Practical MATLAB Deep Learning, 173–207. Berkeley, CA: Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-7912-0_9.
Full textKim, Kwangjo, and Harry Chandra Tanuwidjaja. "X-Based PPDL." In Privacy-Preserving Deep Learning, 23–44. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-3764-3_3.
Full textKim, Kwangjo, Muhamad Erza Aminanto, and Harry Chandra Tanuwidjaja. "Deep Learning-Based IDSs." In SpringerBriefs on Cyber Security Systems and Networks, 35–45. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1444-5_5.
Full textPavitra, Gandhi, and Chauhan Anamika. "Deep Learning-Based Yoga Learning Application." In Computer Vision and Robotics, 365–80. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8225-4_29.
Full textAlla, Sridhar, and Suman Kalyan Adari. "Introduction to Deep Learning." In Beginning Anomaly Detection Using Python-Based Deep Learning, 73–122. Berkeley, CA: Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-5177-5_3.
Full textSanghi, Nimish. "Model-Based Algorithms." In Deep Reinforcement Learning with Python, 49–76. Berkeley, CA: Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-6809-4_3.
Full textKim, Kwangjo, and Harry Chandra Tanuwidjaja. "Pros and Cons of X-Based PPDL." In Privacy-Preserving Deep Learning, 45–53. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-3764-3_4.
Full textManjón, José V., and Pierrick Coupe. "MRI Denoising Using Deep Learning." In Patch-Based Techniques in Medical Imaging, 12–19. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00500-9_2.
Full textConference papers on the topic "Deep learning based"
Narong, Tina, Denis Sharoukhov, Tonislav Ivanov, Vadim Pinskiy, and Matthew Putman. "Deep photometric learning (DPL)." In Oxide-based Materials and Devices XI, edited by Ferechteh H. Teherani, David C. Look, and David J. Rogers. SPIE, 2020. http://dx.doi.org/10.1117/12.2555925.
Full textKaskavalci, Halil Can, and Sezer Goren. "A Deep Learning Based Distributed Smart Surveillance Architecture using Edge and Cloud Computing." In 2019 International Conference on Deep Learning and Machine Learning in Emerging Applications (Deep-ML). IEEE, 2019. http://dx.doi.org/10.1109/deep-ml.2019.00009.
Full textLee, Taerim. "A deep learning analytics to facilitate sustainability of statistics education." In Decision Making Based on Data. International Association for Statistical Education, 2019. http://dx.doi.org/10.52041/srap.19306.
Full textViviani, Paolo, Maurizio Drocco, Daniele Baccega, Iacopo Colonnelli, and Marco Aldinucci. "Deep Learning at Scale." In 2019 27th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP). IEEE, 2019. http://dx.doi.org/10.1109/empdp.2019.8671552.
Full textHuertas-Company, Marc. "Galaxy Morphology in the deep learning era." In 2021 International Conference on Content-Based Multimedia Indexing (CBMI). IEEE, 2021. http://dx.doi.org/10.1109/cbmi50038.2021.9461889.
Full textAlvares, Joao D., Jose A. Font, Felipe F. Freitas, Osvaldo G. Freitas, Antonio P. Morais, Solange Nunes, Antonio Onofre, and Alejandro Torres-Forne. "Gravitational-wave parameter inference using Deep Learning." In 2021 International Conference on Content-Based Multimedia Indexing (CBMI). IEEE, 2021. http://dx.doi.org/10.1109/cbmi50038.2021.9461893.
Full textKhan, U. A., N. Ejaz, M. A. Martinez-del-Amor, and H. Sparenberg. "Movies tags extraction using deep learning." In 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS). IEEE, 2017. http://dx.doi.org/10.1109/avss.2017.8078459.
Full textLu, Jia, Wei Qi Yan, and Minh Nguyen. "Human Behaviour Recognition Using Deep Learning." In 2018 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS). IEEE, 2018. http://dx.doi.org/10.1109/avss.2018.8639413.
Full textBougteb, Yahya, Brahim Ouhbi, Bouchra Frikh, and El moukhtar Zemmouri. "Deep Learning Based Topics Detection." In 2019 Third International Conference on Intelligent Computing in Data Sciences (ICDS). IEEE, 2019. http://dx.doi.org/10.1109/icds47004.2019.8942245.
Full textHoe Chiang, Jason Wei, and Li Zhang. "Deep learning-based fall detection." In 14th International FLINS Conference (FLINS 2020). WORLD SCIENTIFIC, 2020. http://dx.doi.org/10.1142/9789811223334_0107.
Full textReports on the topic "Deep learning based"
Yoon, Hongkyu, Teeratorn Kadeethum, Robert Ringer, and Trevor Harris. Deep learning-based spatio-temporal estimate of greenhouse gas emissions using satellite data. Office of Scientific and Technical Information (OSTI), September 2022. http://dx.doi.org/10.2172/1888359.
Full textDugan, Peter J., Christopher W. Clark, Yann A. LeCun, and Sofie M. Van Parijs. DCL System Using Deep Learning Approaches for Land-based or Ship-based Real-Time Recognition and Localization of Marine Mammals. Fort Belvoir, VA: Defense Technical Information Center, September 2012. http://dx.doi.org/10.21236/ada573473.
Full textDugan, Peter J., Christopher W. Clark, Yann A. LeCun, and Sofie M. Van Parijs. DCL System Using Deep Learning Approaches for Land-based or Ship-based Real-Time Recognition and Localization of Marine Mammals. Fort Belvoir, VA: Defense Technical Information Center, September 2014. http://dx.doi.org/10.21236/ada617980.
Full textAlhasson, Haifa F., and Shuaa S. Alharbi. New Trends in image-based Diabetic Foot Ucler Diagnosis Using Machine Learning Approaches: A Systematic Review. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, November 2022. http://dx.doi.org/10.37766/inplasy2022.11.0128.
Full textKong, Q. Deep Learning Based Approach to Integrate MyShake's Trigger Data with ShakeAlert for Faster and Robust EEW Alerts. Office of Scientific and Technical Information (OSTI), December 2021. http://dx.doi.org/10.2172/1836932.
Full textIdakwo, Gabriel, Sundar Thangapandian, Joseph Luttrell, Zhaoxian Zhou, Chaoyang Zhang, and Ping Gong. Deep learning-based structure-activity relationship modeling for multi-category toxicity classification : a case study of 10K Tox21 chemicals with high-throughput cell-based androgen receptor bioassay data. Engineer Research and Development Center (U.S.), July 2021. http://dx.doi.org/10.21079/11681/41302.
Full textCookson, Jr., Peter W., and Linda Darling-Hammond. Building school communities for students living in deep poverty. Learning Policy Institute, May 2022. http://dx.doi.org/10.54300/121.698.
Full textHuang, Haohang, Erol Tutumluer, Jiayi Luo, Kelin Ding, Issam Qamhia, and John Hart. 3D Image Analysis Using Deep Learning for Size and Shape Characterization of Stockpile Riprap Aggregates—Phase 2. Illinois Center for Transportation, September 2022. http://dx.doi.org/10.36501/0197-9191/22-017.
Full textFullan, Michael, and Joanne Quinn. How Do Disruptive Innovators Prepare Today's Students to Be Tomorrow's Workforce?: Deep Learning: Transforming Systems to Prepare Tomorrow’s Citizens. Inter-American Development Bank, December 2020. http://dx.doi.org/10.18235/0002959.
Full textKulhandjian, Hovannes. AI-based Pedestrian Detection and Avoidance at Night using an IR Camera, Radar, and a Video Camera. Mineta Transportation Institute, November 2022. http://dx.doi.org/10.31979/mti.2022.2127.
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