Letteratura scientifica selezionata sul tema "Deep Learning, Database"
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Articoli di riviste sul tema "Deep Learning, Database"
Karthick Chaganty, Siva. "Database Failure Prediction Based on Deep Learning Model". International Journal of Science and Research (IJSR) 10, n. 4 (27 aprile 2021): 83–86. https://doi.org/10.21275/sr21329110526.
Testo completoWang, Wei, Meihui Zhang, Gang Chen, H. V. Jagadish, Beng Chin Ooi e Kian-Lee Tan. "Database Meets Deep Learning". ACM SIGMOD Record 45, n. 2 (28 settembre 2016): 17–22. http://dx.doi.org/10.1145/3003665.3003669.
Testo completoLukic, Vesna, e Marcus Brüggen. "Galaxy Classifications with Deep Learning". Proceedings of the International Astronomical Union 12, S325 (ottobre 2016): 217–20. http://dx.doi.org/10.1017/s1743921316012771.
Testo completoLiu, Rukun, Teng Wang, Yuxue Yang e Bingjie Yu. "Database Development Based on Deep Learning and Cloud Computing". Mobile Information Systems 2022 (29 aprile 2022): 1–10. http://dx.doi.org/10.1155/2022/6208678.
Testo completoZhou, Lixi, Jiaqing Chen, Amitabh Das, Hong Min, Lei Yu, Ming Zhao e Jia Zou. "Serving deep learning models with deduplication from relational databases". Proceedings of the VLDB Endowment 15, n. 10 (giugno 2022): 2230–43. http://dx.doi.org/10.14778/3547305.3547325.
Testo completoBaimakhanova, A. S., K. M. Berkimbayev, A. K. Zhumadillayeva e E. T. Abdrashova. "Technology of using deep learning algorithms". Bulletin of the National Engineering Academy of the Republic of Kazakhstan 89, n. 3 (15 settembre 2023): 35–45. http://dx.doi.org/10.47533/2023.1606-146x.30.
Testo completoOh, Jaeho, Mincheol Kim e Sang-Woo Ban. "Deep Learning Model with Transfer Learning to Infer Personal Preferences in Images". Applied Sciences 10, n. 21 (29 ottobre 2020): 7641. http://dx.doi.org/10.3390/app10217641.
Testo completoMaji, Subhadip, e Smarajit Bose. "CBIR Using Features Derived by Deep Learning". ACM/IMS Transactions on Data Science 2, n. 3 (31 agosto 2021): 1–24. http://dx.doi.org/10.1145/3470568.
Testo completoZhou, Xiaoshu, Qide Xiao e Han Wang. "Metamaterials Design Method based on Deep learning Database". Journal of Physics: Conference Series 2185, n. 1 (1 gennaio 2022): 012023. http://dx.doi.org/10.1088/1742-6596/2185/1/012023.
Testo completoLiu, Yue, Rashmi Sharan Sinha, Shu-Zhi Liu e Seung-Hoon Hwang. "Side-Information-Aided Preprocessing Scheme for Deep-Learning Classifier in Fingerprint-Based Indoor Positioning". Electronics 9, n. 6 (12 giugno 2020): 982. http://dx.doi.org/10.3390/electronics9060982.
Testo completoTesi sul tema "Deep Learning, Database"
Khaghani, Farnaz. "A Deep Learning Approach to Predict Accident Occurrence Based on Traffic Dynamics". Thesis, Virginia Tech, 2020. http://hdl.handle.net/10919/98801.
Testo completoM.S.
Rapid traffic accident detection/prediction is essential for scaling down non-recurrent conges- tion caused by traffic accidents, avoiding secondary accidents, and accelerating emergency system responses. In this study, we propose a framework that uses large-scale historical traffic speed and traffic flow data along with the relevant weather information to obtain robust traffic patterns. The predicted traffic patterns can be coupled with the real traffic data to detect anomalous behavior that often results in traffic incidents in the roadways. Our framework consists of two major steps. First, we estimate the speed values of traffic at each point based on the historical speed and flow values of locations before and after each point on the roadway. Second, we compare the estimated values with the actual ones and introduce the ones that are significantly different as an anomaly. The anomaly points are the potential points and times that an accident occurs and causes a change in the normal behavior of the roadways. Our study shows the potential of the approach in detecting the accidents while exhibiting promising performance in detecting the accident occurrence at a time close to the actual time of occurrence.
Jiang, Haotian. "WEARABLE COMPUTING TECHNOLOGIES FOR DISTRIBUTED LEARNING". Case Western Reserve University School of Graduate Studies / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=case1571072941323463.
Testo completoChillet, Alice. "Sensitive devices Identification through learning of radio-frequency fingerprint". Electronic Thesis or Diss., Université de Rennes (2023-....), 2024. http://www.theses.fr/2024URENS051.
Testo completoIdentifying so-called sensitive devices is subject to various security or energy consumption constraints, making conventional identification methods unsuitable. To meet these constraints, it is possible to use intrinsic faults in the device’s transmission chain to identify them. These faults alter the transmitted signal, creating an inherently unique and non-reproducible signature known as the Radio Frequency (RF) fingerprint. To identify a device using its RF fingerprint, it is possible to use imperfection estimation methods to extract a signature that can be used by a classifier, or to use learning methods such as neural networks. However, the ability of a neural network to recognize devices in a particular context is highly dependent on the training database. This thesis proposes a virtual database generator based on RF transmission and imperfection models. These virtual databases allow us to better understand the ins and outs of RF identification and to propose solutions to make identification more robust. Secondly, we are looking at the complexity of the identification solution in two ways. The first involves the use of intricate programmable graphs, which are reinforcement learning models based on genetic evolution techniques that are less complex than neural networks. The second is to use pruning on neural networks found in the literature to reduce their complexity
Tamascelli, Nicola. "A Machine Learning Approach to Predict Chattering Alarms". Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020.
Cerca il testo completoMcCullen, Jeffrey Reynolds. "Predicting the Effects of Sedative Infusion on Acute Traumatic Brain Injury Patients". Thesis, Virginia Tech, 2020. http://hdl.handle.net/10919/105140.
Testo completoMaster of Science
Patients with Traumatic Brain Injury (TBI) often require sedative agents to facilitate intubation and prevent further brain injury by reducing anxiety and decreasing level of consciousness. It is important for clinicians to choose the sedative that is most conducive to optimizing patient outcomes. Hence, the purpose of our research is to provide guidance to aid this decision. Additionally, we compare different modeling approaches to provide insights into their relative strengths and weaknesses. To achieve this goal, we investigated whether the exposure of particular sedatives (fentanyl, propofol, versed, ativan, and precedex) was associated with different hospital discharge locations for patients with TBI. From best to worst, these discharge locations are home, rehabilitation, nursing home, remains hospitalized, and death. Our results show that versed was associated with better discharge locations and ativan was associated with worse discharge locations. The fact that versed is often used for alternative purposes may account for its association with better discharge locations. Further research is necessary to further investigate this and the possible negative effects of using ativan to facilitate intubation. We also found that other variables that influence discharge disposition are age, the Northeast region, and other variables pertaining to the clinical state of the patient (severity of illness metrics, etc.). By comparing the different modeling approaches, we found that the new deep learning methods were difficult to interpret but provided a slight improvement in performance after optimization. Traditional methods such as linear ii i regression allowed us to interpret the model output and make the aforementioned clinical insights. However, generalized additive models (GAMs) are often more practical because they can better accommodate other class distributions and domains.
Mondani, Lorenzo. "Analisi dati inquinamento atmosferico mediante machine learning". Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amslaurea.unibo.it/16168/.
Testo completoBarbieri, Edoardo. "Analisi dell'efficienza di System on Chip su applicazioni parallele". Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amslaurea.unibo.it/16759/.
Testo completoTallman, Jake T. "SOARNET, Deep Learning Thermal Detection For Free Flight". DigitalCommons@CalPoly, 2021. https://digitalcommons.calpoly.edu/theses/2339.
Testo completoFalade, Joannes Chiderlos. "Identification rapide d'empreintes digitales, robuste à la dissimulation d'identité". Thesis, Normandie, 2020. http://www.theses.fr/2020NORMC231.
Testo completoBiometrics are increasingly used for identification purposes due to the close relationship between the person and their identifier (such as fingerprint). We focus this thesis on the issue of identifying individuals from their fingerprints. The fingerprint is a biometric data widely used for its efficiency, simplicity and low cost of acquisition. The fingerprint comparison algorithms are mature and it is possible to obtain in less than 500 ms a similarity score between a reference template (enrolled on an electronic passport or database) and an acquired template. However, it becomes very important to check the identity of an individual against an entire population in a very short time (a few seconds). This is an important issue due to the size of the biometric database (containing a set of individuals of the order of a country). Thus, the first part of the subject of this thesis concerns the identification of individuals using fingerprints. Our topic focuses on the identification with N being at the scale of a million and representing the population of a country for example. Then, we use classification and indexing methods to structure the biometric database and speed up the identification process. We have implemented four identification methods selected from the state of the art. A comparative study and improvements were proposed on these methods. We also proposed a new fingerprint indexing solution to perform the identification task which improves existing results. A second aspect of this thesis concerns security. A person may want to conceal their identity and therefore do everything possible to defeat the identification. With this in mind, an individual may provide a poor quality fingerprint (fingerprint portion, low contrast by lightly pressing the sensor...) or provide an altered fingerprint (impression intentionally damaged, removal of the impression with acid, scarification...). It is therefore in the second part of this thesis to detect dead fingers and spoof fingers (silicone, 3D fingerprint, latent fingerprint) used by malicious people to attack the system. In general, these methods use machine learning techniques and deep learning. Secondly, we proposed a new presentation attack detection solution based on the use of statistical descriptors on the fingerprint. Thirdly, we have also build three presentation attacks detection workflow for fake fingerprint using deep learning. Among these three deep solutions implemented, two come from the state of the art; then the third an improvement that we propose. Our solutions are tested on the LivDet competition databases for presentation attack detection
Frizzi, Sebastien. "Apprentissage profond en traitement d'images : application pour la détection de fumée et feu". Electronic Thesis or Diss., Toulon, 2021. http://www.theses.fr/2021TOUL0007.
Testo completoResearchers have found a strong correlation between hot summers and the frequency and intensity of forestfires. Global warming due to greenhouse gases such as carbon dioxide is increasing the temperature in someparts of the world. Fires release large amounts of greenhouse gases, causing an increase in the earth'saverage temperature, which in turn causes an increase in forest fires... Fires destroy millions of hectares offorest areas, ecosystems sheltering numerous species and have a significant cost for our societies. Theprevention and control of fires must be a priority to stop this infernal spiral.In this context, smoke detection is very important because it is the first clue of an incipient fire. Fire andespecially smoke are difficult objects to detect in visible images due to their complexity in terms of shape, colorand texture. However, deep learning coupled with video surveillance can achieve this goal. Convolutionalneural network (CNN) architecture is able to detect smoke and fire in RGB images with very good accuracy.Moreover, these structures can segment smoke as well as fire in real time. The richness of the deep networklearning database is a very important element allowing a good generalization.This manuscript presents different deep architectures based on convolutional networks to detect and localizesmoke and fire in video images in the visible domain
Libri sul tema "Deep Learning, Database"
Vasudevan, Shriram K., Subashri Vasudevan e Sini Raj Pulari. Deep Learning. Taylor & Francis Group, 2021.
Cerca il testo completoSejnowski, Terrence J. Deep Learning Revolution. MIT Press, 2018.
Cerca il testo completoSejnowski, Terrence J. Deep Learning Revolution. MIT Press, 2018.
Cerca il testo completoLin, Jerry Chun-Wei, e Thi Thi Zin. Big Data Analysis and Deep Learning Applications: Proceedings of the First International Conference on Big Data Analysis and Deep Learning. Springer, 2018.
Cerca il testo completoThe deep learning revolution. The MIT Press, 2018.
Cerca il testo completoSejnowski, Terrence J. The Deep Learning Revolution. Tantor Audio, 2019.
Cerca il testo completoDeep Learning: A Comprehensive Guide. Taylor & Francis Group, 2021.
Cerca il testo completoVasudevan, Shriram K., Siniraj Pulari e Subashri Vasudevan. Deep Learning: A Comprehensive Guide. Taylor & Francis Group, 2021.
Cerca il testo completoVasudevan, Shriram K., Subashri Vasudevan e Sini Raj Pulari. Deep Learning: A Comprehensive Guide. CRC Press LLC, 2021.
Cerca il testo completoVasudevan, Shriram K., Siniraj Pulari e Subashri Vasudevan. Deep Learning: A Comprehensive Guide. Taylor & Francis Group, 2021.
Cerca il testo completoCapitoli di libri sul tema "Deep Learning, Database"
Ren, Qiang, Yinpeng Wang, Yongzhong Li e Shutong Qi. "Building Database". In Sophisticated Electromagnetic Forward Scattering Solver via Deep Learning, 43–71. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-6261-4_3.
Testo completoSun, Bo, Di Wu, Mingsheng Shang e Yi He. "Toward Auto-Learning Hyperparameters for Deep Learning-Based Recommender Systems". In Database Systems for Advanced Applications, 323–31. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-00126-0_25.
Testo completoSun, Bo, Di Wu, Mingsheng Shang e Yi He. "Toward Auto-Learning Hyperparameters for Deep Learning-Based Recommender Systems". In Database Systems for Advanced Applications, 323–31. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-00126-0_25.
Testo completoLin, Hongjie, Hao Wang, Dongfang Du, Han Wu, Biao Chang e Enhong Chen. "Patent Quality Valuation with Deep Learning Models". In Database Systems for Advanced Applications, 474–90. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-91458-9_29.
Testo completoSumon, Shakil Ahmed, MD Tanzil Shahria, MD Raihan Goni, Nazmul Hasan, A. M. Almarufuzzaman e Rashedur M. Rahman. "Violent Crowd Flow Detection Using Deep Learning". In Intelligent Information and Database Systems, 613–25. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-14799-0_53.
Testo completoLi, Xiaocui, Hongzhi Yin, Ke Zhou, Hongxu Chen, Shazia Sadiq e Xiaofang Zhou. "Semi-supervised Clustering with Deep Metric Learning". In Database Systems for Advanced Applications, 383–86. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-18590-9_50.
Testo completoXu, Hengpeng, Yao Zhang, Jinmao Wei, Zhenglu Yang e Jun Wang. "Spatiotemporal-Aware Region Recommendation with Deep Metric Learning". In Database Systems for Advanced Applications, 491–94. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-18590-9_73.
Testo completoKluska, Piotr, e Maciej Zięba. "Post-training Quantization Methods for Deep Learning Models". In Intelligent Information and Database Systems, 467–79. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-41964-6_40.
Testo completoWang, Yifan, Yongkang Li, Shuai Li, Weiping Song, Jiangke Fan, Shan Gao, Ling Ma et al. "Deep Graph Mutual Learning for Cross-domain Recommendation". In Database Systems for Advanced Applications, 298–305. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-00126-0_22.
Testo completoKuo, Che-Wei, e Josh Jia-Ching Ying. "An Unsupervised Deep Learning Framework for Anomaly Detection". In Intelligent Information and Database Systems, 284–95. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-5834-4_23.
Testo completoAtti di convegni sul tema "Deep Learning, Database"
Jayachandiran, U., Sujaritha P, Sahana A e Surendhar J. "Deep Learning Enabled Graph Database for Complex Queries". In 2024 International Conference on Power, Energy, Control and Transmission Systems (ICPECTS), 1–6. IEEE, 2024. https://doi.org/10.1109/icpects62210.2024.10780400.
Testo completoYu, Yongle, Yixuan Zhan, Lin Zhu e Xu Liu. "Establishment and Research of Liver Medical Image Online Database Based on Deep Learning". In 2024 9th International Conference on Signal and Image Processing (ICSIP), 770–73. IEEE, 2024. http://dx.doi.org/10.1109/icsip61881.2024.10671404.
Testo completoDong, Wenlong, Wei Liu, Rui Xi, Mengshu Hou e Shuhuan Fan. "MLETune: Streamlining Database Knob Tuning via Multi-LLMs Experts Guided Deep Reinforcement Learning". In 2024 IEEE 30th International Conference on Parallel and Distributed Systems (ICPADS), 226–35. IEEE, 2024. https://doi.org/10.1109/icpads63350.2024.00038.
Testo completoGomez, Sharon, R. Jegan e Nimi W. S. "Smart Health Solutions: Harnessing Deep Learning Models For Accurate Myocardial Infarction Detection Via PPG Database". In 2024 10th International Conference on Advanced Computing and Communication Systems (ICACCS), 1715–21. IEEE, 2024. http://dx.doi.org/10.1109/icaccs60874.2024.10717133.
Testo completoZhong, Rui, e Taro Tezuka. "Parametric Learning of Deep Convolutional Neural Network". In the 19th International Database Engineering & Applications Symposium. New York, New York, USA: ACM Press, 2014. http://dx.doi.org/10.1145/2790755.2790791.
Testo completoChoudhary, Chinmay, e Colm O’Riordan. "Cross-lingual Semantic Role Labelling with the ValPaL Database Knowledge". In Proceedings of Deep Learning Inside Out (DeeLIO 2022): The 3rd Workshop on Knowledge Extraction and Integration for Deep Learning Architectures. Stroudsburg, PA, USA: Association for Computational Linguistics, 2022. http://dx.doi.org/10.18653/v1/2022.deelio-1.1.
Testo completoRoj, Lea, Štefan Kohek, Aleksander Pur e Niko Lukač. "Integration of Named Entity Extraction Based on Deep Learning for Neo4j Graph Database". In 10th Student Computing Research Symposium, 11–14. University of Maribor Press, 2024. https://doi.org/10.18690/um.feri.6.2024.3.
Testo completoMontresor, Silvio, Ketao Yan, Marie Tahon, Kemao Qian, Yingjie Yu e Pascal Picart. "Benchmark of deep learning approaches for phase denoising in digital holography". In Digital Holography and Three-Dimensional Imaging. Washington, D.C.: Optica Publishing Group, 2023. http://dx.doi.org/10.1364/dh.2023.hw3c.4.
Testo completoKang, Dylan Myungchul, Charles Cheolgi Lee, Suan Lee e Wookey Lee. "Patent prior art search using deep learning language model". In IDEAS 2020: 24th International Database Engineering & Applications Symposium. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3410566.3410597.
Testo completoZhao, Dongdong, Pingchuan Zhang, Jianwen Xiang e Jing Tian. "NegDL: Privacy-preserving Deep Learning Based on Negative Database". In 2022 4th International Conference on Data Intelligence and Security (ICDIS). IEEE, 2022. http://dx.doi.org/10.1109/icdis55630.2022.00026.
Testo completoRapporti di organizzazioni sul tema "Deep Learning, Database"
Zhou, Yifu. Self-configured Elastic Database with Deep Q-Learning Approach. Ames (Iowa): Iowa State University, gennaio 2019. http://dx.doi.org/10.31274/cc-20240624-1271.
Testo completoChang, Ke-Vin. Deep Learning Algorithm for Automatic Localization and Segmentation of the Median Nerve: a Protocol for Systematic Review and Meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, maggio 2022. http://dx.doi.org/10.37766/inplasy2022.5.0074.
Testo completoAlhasson, Haifa F., e 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, novembre 2022. http://dx.doi.org/10.37766/inplasy2022.11.0128.
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