Relevant bibliographies by topics / Deep Learning, Database

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Deep Learning, Database'

Author: Grafiati
Published: 25 January 2025

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Journal articles on the topic "Deep Learning, Database"

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Karthick Chaganty, Siva. "Database Failure Prediction Based on Deep Learning Model." International Journal of Science and Research (IJSR) 10, no. 4 (April 27, 2021): 83–86. https://doi.org/10.21275/sr21329110526.

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Wang, Wei, Meihui Zhang, Gang Chen, H. V. Jagadish, Beng Chin Ooi, and Kian-Lee Tan. "Database Meets Deep Learning." ACM SIGMOD Record 45, no. 2 (September 28, 2016): 17–22. http://dx.doi.org/10.1145/3003665.3003669.

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Lukic, Vesna, and Marcus Brüggen. "Galaxy Classifications with Deep Learning." Proceedings of the International Astronomical Union 12, S325 (October 2016): 217–20. http://dx.doi.org/10.1017/s1743921316012771.

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AbstractMachine learning techniques have proven to be increasingly useful in astronomical applications over the last few years, for example in object classification, estimating redshifts and data mining. One example of object classification is classifying galaxy morphology. This is a tedious task to do manually, especially as the datasets become larger with surveys that have a broader and deeper search-space. The Kaggle Galaxy Zoo competition presented the challenge of writing an algorithm to find the probability that a galaxy belongs in a particular class, based on SDSS optical spectroscopy data. The use of convolutional neural networks (convnets), proved to be a popular solution to the problem, as they have also produced unprecedented classification accuracies in other image databases such as the database of handwritten digits (MNIST †) and large database of images (CIFAR ‡). We experiment with the convnets that comprised the winning solution, but using broad classifications. The effect of changing the number of layers is explored, as well as using a different activation function, to help in developing an intuition of how the networks function and to see how they can be applied to radio galaxy images.
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Liu, Rukun, Teng Wang, Yuxue Yang, and Bingjie Yu. "Database Development Based on Deep Learning and Cloud Computing." Mobile Information Systems 2022 (April 29, 2022): 1–10. http://dx.doi.org/10.1155/2022/6208678.

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In this research, the author develops databases based on deep learning and cloud computing technology. On the basis of designing the overall architecture of the database with the distributed C/S mode as the core, use J2EE (Java 2 Platform, Enterprise Edition) as the development tool, apply Oracle server database, extract data features with in-depth learning technology, allocate data processing tasks based with cloud computing technology, so as to finally complete data fusion and compression. Finally, the overall development of the database is completed by designing the database backup scheme and external encryption. The test results show that the database developed by the above method has low performance loss, can quickly complete the processing of subdatabase and subtable, and can effectively support the distributed storage of data.
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Zhou, Lixi, Jiaqing Chen, Amitabh Das, Hong Min, Lei Yu, Ming Zhao, and Jia Zou. "Serving deep learning models with deduplication from relational databases." Proceedings of the VLDB Endowment 15, no. 10 (June 2022): 2230–43. http://dx.doi.org/10.14778/3547305.3547325.

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Serving deep learning models from relational databases brings significant benefits. First, features extracted from databases do not need to be transferred to any decoupled deep learning systems for inferences, and thus the system management overhead can be significantly reduced. Second, in a relational database, data management along the storage hierarchy is fully integrated with query processing, and thus it can continue model serving even if the working set size exceeds the available memory. Applying model deduplication can greatly reduce the storage space, memory footprint, cache misses, and inference latency. However, existing data deduplication techniques are not applicable to the deep learning model serving applications in relational databases. They do not consider the impacts on model inference accuracy as well as the inconsistency between tensor blocks and database pages. This work proposed synergistic storage optimization techniques for duplication detection, page packing, and caching, to enhance database systems for model serving. Evaluation results show that our proposed techniques significantly improved the storage efficiency and the model inference latency, and outperformed existing deep learning frameworks in targeting scenarios.
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Baimakhanova, A. S., K. M. Berkimbayev, A. K. Zhumadillayeva, and E. T. Abdrashova. "Technology of using deep learning algorithms." Bulletin of the National Engineering Academy of the Republic of Kazakhstan 89, no. 3 (September 15, 2023): 35–45. http://dx.doi.org/10.47533/2023.1606-146x.30.

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Deep learning is a branch of machine learning (machine learning-ML). Deep learning methods utilize high-level model abstraction of nonlinear transformations in large databases. In other areas, the implementation of deep learning architectures has contributed significantly to the development of artificial intelligence. This paper presents recent research on newly applied deep learning algorithms. Convolutional Neural Networks are used in deep learning. Database Management System PostgreSQL object-relational database. The implementation resulted in achieving the set goals and objectives. The method of analyzing the input data is described, the differences between machine learning and deep learning are explained, and an example of classifying an image representing a sign language image using logistic regression, one of the deep learning algorithms, is presented. Deep neural networks can work with the full set of available data better than alternative approaches. During the learning process, the neural network itself determines which features in the data are important and which are not. Artificial neural networks can predict symptoms that humans cannot. Thus, with the help of deep neural networks, we can solve problems that traditional machine learning algorithms cannot perform.
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Oh, Jaeho, Mincheol Kim, and Sang-Woo Ban. "Deep Learning Model with Transfer Learning to Infer Personal Preferences in Images." Applied Sciences 10, no. 21 (October 29, 2020): 7641. http://dx.doi.org/10.3390/app10217641.

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In this paper, we propose a deep convolutional neural network model with transfer learning that reflects personal preferences from inter-domain databases of images having atypical visual characteristics. The proposed model utilized three public image databases (Fashion-MNIST, Labeled Faces in the Wild [LFW], and Indoor Scene Recognition) that include images with atypical visual characteristics in order to train and infer personal visual preferences. The effectiveness of transfer learning for incremental preference learning was verified by experiments using inter-domain visual datasets with different visual characteristics. Moreover, a gradient class activation mapping (Grad-CAM) approach was applied to the proposed model, providing explanations about personal visual preference possibilities. Experiments showed that the proposed preference-learning model using transfer learning outperformed a preference model not using transfer learning. In terms of the accuracy of preference recognition, the proposed model showed a maximum of about 7.6% improvement for the LFW database and a maximum of about 9.4% improvement for the Indoor Scene Recognition database, compared to the model that did not reflect transfer learning.
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Maji, Subhadip, and Smarajit Bose. "CBIR Using Features Derived by Deep Learning." ACM/IMS Transactions on Data Science 2, no. 3 (August 31, 2021): 1–24. http://dx.doi.org/10.1145/3470568.

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In a Content-based Image Retrieval (CBIR) System, the task is to retrieve similar images from a large database given a query image. The usual procedure is to extract some useful features from the query image and retrieve images that have a similar set of features. For this purpose, a suitable similarity measure is chosen, and images with high similarity scores are retrieved. Naturally, the choice of these features play a very important role in the success of this system, and high-level features are required to reduce the “semantic gap.” In this article, we propose to use features derived from pre-trained network models from a deep-learning convolution network trained for a large image classification problem. This approach appears to produce vastly superior results for a variety of databases, and it outperforms many contemporary CBIR systems. We analyse the retrieval time of the method and also propose a pre-clustering of the database based on the above-mentioned features, which yields comparable results in a much shorter time in most of the cases.
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Zhou, Xiaoshu, Qide Xiao, and Han Wang. "Metamaterials Design Method based on Deep learning Database." Journal of Physics: Conference Series 2185, no. 1 (January 1, 2022): 012023. http://dx.doi.org/10.1088/1742-6596/2185/1/012023.

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Abstract In recent years, deep learning has risen to the forefront of many fields, overcoming challenges previously considered difficult to solve by traditional methods. In the field of metamaterials, there are significant challenges in the design and optimization of metamaterials, including the need for a large number of labeled data sets and one-to-many mapping when solving inverse problems. Here, we will use deep learning methods to build a metamaterial database to achieve rapid design and analysis methods of metamaterials. These technologies have significantly improved the feasibility of more complex metamaterial designs and provided new metamaterial design and analysis ideas.
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Liu, Yue, Rashmi Sharan Sinha, Shu-Zhi Liu, and Seung-Hoon Hwang. "Side-Information-Aided Preprocessing Scheme for Deep-Learning Classifier in Fingerprint-Based Indoor Positioning." Electronics 9, no. 6 (June 12, 2020): 982. http://dx.doi.org/10.3390/electronics9060982.

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Deep-learning classifiers can effectively improve the accuracy of fingerprint-based indoor positioning. During fingerprint database construction, all received signal strength indicators from each access point are combined without any distinction. Therefore, the database is created and utilised for deep-learning models. Meanwhile, side information regarding specific conditions may help characterise the data features for the deep-learning classifier and improve the accuracy of indoor positioning. Herein, a side-information-aided preprocessing scheme for deep-learning classifiers is proposed in a dynamic environment, where several groups of different databases are constructed for training multiple classifiers. Therefore, appropriate databases can be employed to effectively improve positioning accuracies. Specifically, two kinds of side information, namely time (morning/afternoon) and direction (forward/backward), are considered when collecting the received signal strength indicator. Simulations and experiments are performed with the deep-learning classifier trained on four different databases. Moreover, these are compared with conventional results from the combined database. The results show that the side-information-aided preprocessing scheme allows better success probability than the conventional method. With two margins, the proposed scheme has 6.55% and 5.8% improved performances for simulations and experiments compared to the conventional scheme. Additionally, the proposed scheme, with time as the side information, obtains a higher success probability when the positioning accuracy requirement is loose with larger margin. With direction as the side information, the proposed scheme shows better performance for high positioning precision requirements. Thus, side information such as time or direction is advantageous for preprocessing data in deep-learning classifiers for fingerprint-based indoor positioning.
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Dissertations / Theses on the topic "Deep Learning, Database"

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Khaghani, Farnaz. "A Deep Learning Approach to Predict Accident Occurrence Based on Traffic Dynamics." Thesis, Virginia Tech, 2020. http://hdl.handle.net/10919/98801.

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Traffic accidents are of concern for traffic safety; 1.25 million deaths are reported each year. Hence, it is crucial to have access to real-time data and rapidly detect or predict accidents. Predicting the occurrence of a highway car accident accurately any significant length of time into the future is not feasible since the vast majority of crashes occur due to unpredictable human negligence and/or error. However, rapid traffic incident detection could reduce incident-related congestion and secondary crashes, alleviate the waste of vehicles’ fuel and passengers’ time, and provide appropriate information for emergency response and field operation. While the focus of most previously proposed techniques is predicting the number of accidents in a certain region, the problem of predicting the accident occurrence or fast detection of the accident has been little studied. To address this gap, we propose a deep learning approach and build a deep neural network model based on long short term memory (LSTM). We apply it to forecast the expected speed values on freeways’ links and identify the anomalies as potential accident occurrences. Several detailed features such as weather, traffic speed, and traffic flow of upstream and downstream points are extracted from big datasets. We assess the proposed approach on a traffic dataset from Sacramento, California. The experimental results demonstrate the potential of the proposed approach in identifying the anomalies in speed value and matching them with accidents in the same area. We show that this approach can handle a high rate of rapid accident detection and be implemented in real-time travelers’ information or emergency management systems.
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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.
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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.

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Chillet, Alice. "Sensitive devices Identification through learning of radio-frequency fingerprint." Electronic Thesis or Diss., Université de Rennes (2023-....), 2024. http://www.theses.fr/2024URENS051.

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L’identification de dispositifs dits sensibles est soumise à différentes contraintes de sécurité ou de consommation d’énergie, ce qui rend les méthodes d’identification classiques peu adaptées. Pour répondre à ces contraintes, il est possible d’utiliser les défauts intrinsèques de la chaîne de transmission des dispositifs pour les identifier. Ces défauts altèrent le signal transmis et créent alors une signature par nature unique et non reproductible appelée empreinte Radio Fréquence (RF). Pour identifier un dispositif grâce à son empreinte RF, il est possible d’utiliser des méthodes d’estimation d’imperfections pour extraire une signature qui peut être utilisée par un classifieur, ou bien d’utiliser des méthodes d’apprentissage telles que les réseaux de neurones. Toutefois, la capacité d’un réseau de neurones à reconnaître les dispositifs dans un contexte particulier dépend fortement de la base de données d’entraînement. Dans cette thèse, nous proposons un générateur de bases de données virtuelles basé sur des modèles de transmission et d’imperfections RF. Ces bases de données virtuelles permettent de mieux comprendre les tenants et aboutissants de l’identification RF et de proposer des solutions pour rendre l’identification plus robuste. Dans un second temps, nous nous intéressons aux problématiques de complexité de la solution d’identification via deux axes. Le premier consiste à utiliser des graphes programmables intriqués, qui sont des modèles d’apprentissage par renforcement, basés sur des techniques d’évolution génétique moins complexes que les réseaux de neurones. Le second axe propose l’utilisation de l’élagage sur des réseaux de neurones de la littérature pour réduire la complexité de ces derniers
Identifying 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
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Tamascelli, Nicola. "A Machine Learning Approach to Predict Chattering Alarms." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020.

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The alarm system plays a vital role to grant safety and reliability in the process industry. Ideally, an alarm should inform the operator about critical conditions only; during alarm floods, the operator may be overwhelmed by several alarms in a short time span. Crucial alarms are more likely to be missed during these situations. Poor alarm management is one of the main causes of unintended plant shut down, incidents and near misses in the chemical industry. Most of the alarms triggered during a flood episode are nuisance alarms –i.e. alarms that do not communicate new information to the operator, or alarms that do not require an operator action. Chattering alarms –i.e. that repeat three or more times in a minute, and redundant alarms –i.e. duplicated alarms, are common forms of nuisance. Identifying nuisance alarms is a key step to improve the performance of the alarm system. Advanced techniques for alarm rationalization have been developed, proposing methods to quantify chattering, redundancy and correlation between alarms. Although very effective, these techniques produce static results. Machine Learning appears to be an interesting opportunity to retrieve further knowledge and support these techniques. This knowledge can be used to produce more flexible and dynamic models, as well as to predict alarm behaviour during floods. The aim of this study is to develop a machine learning-based algorithm for real-time alarm classification and rationalization, whose results can be used to support the operator decision-making procedure. Specifically, efforts have been directed towards chattering prediction during alarm floods. Advanced techniques for chattering, redundancy and correlation assessment have been performed on a real industrial alarm database. A modified approach has been developed to dynamically assess chattering, and the results have been used to train three different machine learning models, whose performance has been evaluated and discussed.
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McCullen, Jeffrey Reynolds. "Predicting the Effects of Sedative Infusion on Acute Traumatic Brain Injury Patients." Thesis, Virginia Tech, 2020. http://hdl.handle.net/10919/105140.

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Healthcare analytics has traditionally relied upon linear and logistic regression models to address clinical research questions mostly because they produce highly interpretable results [1, 2]. These results contain valuable statistics such as p-values, coefficients, and odds ratios that provide healthcare professionals with knowledge about the significance of each covariate and exposure for predicting the outcome of interest [1]. Thus, they are often favored over new deep learning models that are generally more accurate but less interpretable and scalable. However, the statistical power of linear and logistic regression is contingent upon satisfying modeling assumptions, which usually requires altering or transforming the data, thereby hindering interpretability. Thus, generalized additive models are useful for overcoming this limitation while still preserving interpretability and accuracy. The major research question in this work involves investigating whether particular sedative agents (fentanyl, propofol, versed, ativan, and precedex) are associated with different discharge dispositions for patients with acute traumatic brain injury (TBI). To address this, we compare the effectiveness of various models (traditional linear regression (LR), generalized additive models (GAMs), and deep learning) in providing guidance for sedative choice. We evaluated the performance of each model using metrics for accuracy, interpretability, scalability, and generalizability. Our results show that the new deep learning models were the most accurate while the traditional LR and GAM models ii i maintained better interpretability and scalability. The GAMs provided enhanced interpretability through pairwise interaction heat maps and generalized well to other domains and class distributions since they do not require satisfying the modeling assumptions used in LR. By evaluating the model results, we found that versed was associated with better discharge dispositions while ativan was associated with worse discharge dispositions. We also identified other significant covariates including age, the Northeast region, the Acute Physiology and Chronic Health Evaluation (APACHE) score, Glasgow Coma Scale (GCS), and ethanol level. The versatility of versed may account for its association with better discharge dispositions while ativan may have negative effects when used to facilitate intubation. Additionally, most of the significant covariates pertain to the clinical state of the patient (APACHE, GCS, etc.) whereas most non-significant covariates were demographic (gender, ethnicity, etc.). Though we found that deep learning slightly improved over LR and generalized additive models after fine-tuning the hyperparameters, the deep learning results were less interpretable and therefore not ideal for making the aforementioned clinical insights. However deep learning may be preferable in cases with greater complexity and more data, particularly in situations where interpretability is not as critical. Further research is necessary to validate our findings, investigate alternative modeling approaches, and examine other outcomes and exposures of interest.
Master 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.
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Mondani, Lorenzo. "Analisi dati inquinamento atmosferico mediante machine learning." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amslaurea.unibo.it/16168/.

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Descrizione del processo di raccolta dati relativi all'inquinamento atmosferico ed alle condizioni meteorologiche in Emilia-Romagna. Introduzione alle principali tecniche di machine learning: le reti neurali artificiali. Utilizzo di alcuni framework specifici in tale ambito (TensorFlow, Keras) per la definizione di un modello capace di prevedere la concentrazione di un particolare inquinante (biossido di azoto), partendo dai dati raccolti nella prima fase. Descrizione e analisi dei risultati ottenuti.
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Barbieri, 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/.

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In questa tesi analizzeremo le prestazioni di un System on Chip (SoC) in alcuni contesti di calcolo parallelo: in questo caso il SoC in questione è il Raspberry Pi. L'ultima versione rilasciata (Raspberry Pi 3 B+) è dotata di 1 GB di RAM e di un processore quad-core con architettura ARM NEON, caratterizzata dai prezzi e consumi ridotti. Tali specifiche danno i presupposti per un inserimento di questo dispositivo in contesti di high performace computing (HPC) tramite l'utilizzo di una programmazione parallela specifica. Per valutare le prestazioni di questa scheda si è voluto implementare un'applicazione che sia di uso comune in ambienti HPC, evitando semplici benchmark sulle singole componenti hardware. L'applicazione deve inoltre essere implementata tenendo conto dell'architettura di cui si dispone, in modo da ottenere risultati quanto più possibili legati alle caratteristiche hardware. Il progetto prevede di sviluppare un sistema di machine learning: una rete neurale artificiale che si addestra nel riconoscere cifre scritte a mano libera. Verranno quindi descritte e implementate alcune tecniche per parallelizzare la fase di addestramento della rete neurale. Questa applicazione verrà sfruttata per effettuare dei benchmark sia sul Raspberry Pi, che su un calcolatore "standard": l'università di Bologna ha messo a disposizione un server di calcolo che dispone di due processori Intel Xeon. I dati raccolti su queste due architetture (Raspberry Pi e Xeon) saranno messi a confronto. L'obiettivo è quello di analizzare se effettivamente dispositivi come il Raspberry Pi possono avere un qualche tipo di vantaggio in contesti HPC: in particolare verrà svolta un'analisi sulla capacità di svolgere calcolo parallelo.
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Tallman, Jake T. "SOARNET, Deep Learning Thermal Detection For Free Flight." DigitalCommons@CalPoly, 2021. https://digitalcommons.calpoly.edu/theses/2339.

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Thermals are regions of rising hot air formed on the ground through the warming of the surface by the sun. Thermals are commonly used by birds and glider pilots to extend flight duration, increase cross-country distance, and conserve energy. This kind of powerless flight using natural sources of lift is called soaring. Once a thermal is encountered, the pilot flies in circles to keep within the thermal, so gaining altitude before flying off to the next thermal and towards the destination. A single thermal can net a pilot thousands of feet of elevation gain, however estimating thermal locations is not an easy task. Pilots look for different indicators: color variation on the ground because the difference in the amount of heat absorbed by the ground varies based on the color/composition, birds circling in an area gaining lift, and certain types of cloud formations (cumulus clouds). The above methods are not always reliable enough and pilots study the weather for thermals by estimating solar heating of the ground using cloud cover and time of year and the lapse rate and dew point of the troposphere. In this paper, we present a Machine Learning based solution for assisting in forecasting thermals. We created a custom dataset using flight data recorded and uploaded to public databases by soaring pilots. We determine where and when the pilot encountered thermals to pull weather and satellite images corresponding to the location and time of the flight. Using this dataset we train an algorithm to automatically predict the location of thermals given as input the current weather conditions and terrain information obtained from Google Earth Engine and thermal regions encountered as truth labels. We were able to converge very well on the training and validation set, proving our method with around a 0.98 F1 score. These results indicate success in creating a custom dataset and a powerful neural network with the necessity of bolstering our custom dataset.
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Falade, Joannes Chiderlos. "Identification rapide d'empreintes digitales, robuste à la dissimulation d'identité." Thesis, Normandie, 2020. http://www.theses.fr/2020NORMC231.

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La biométrie est de plus en plus utilisée à des fins d’identification compte tenu de la relation étroite entre la personne et son identifiant (comme une empreinte digitale). Nous positionnons cette thèse sur la problématique de l’identification d’individus à partir de ses empreintes digitales. L’empreinte digitale est une donnée biométrique largement utilisée pour son efficacité, sa simplicité et son coût d’acquisition modeste. Les algorithmes de comparaison d’empreintes digitales sont matures et permettent d’obtenir en moins de 500 ms un score de similarité entre un gabarit de référence (stocké sur un passeport électronique ou une base de données) et un gabarit acquis. Cependant, il devient très important de déterminer l'identité d'un individu contre une population entière en un temps très court (quelques secondes). Ceci représente un enjeu important compte tenu de la taille de la base de données biométriques (contenant un ensemble d’individus de l’ordre d’un pays). Par exemple, avant de délivrer un nouveau passeport à un individu qui en fait la demande, il faut faire une recherche d'identification sur la base des données biométriques du pays afin de s'assurer que ce dernier n'en possède pas déjà un autre mais avec les mêmes empreintes digitales (éviter les doublons). Ainsi, la première partie du sujet de cette thèse concerne l’identification des individus en utilisant les empreintes digitales. D’une façon générale, les systèmes biométriques ont pour rôle d’assurer les tâches de vérification (comparaison 1-1) et d’identification (1-N). Notre sujet se concentre sur l’identification avec N étant à l’échelle du million et représentant la population d’un pays par exemple. Dans le cadre de nos travaux, nous avons fait un état de l’art sur les méthodes d’indexation et de classification des bases de données d’empreintes digitales. Nous avons privilégié les représentations binaires des empreintes digitales pour indexation. Tout d’abord, nous avons réalisé une étude bibliographique et rédigé un support sur l’état de l’art des techniques d’indexation pour la classification des empreintes digitales. Ensuite, nous avons explorer les différentes représentations des empreintes digitales, puis réaliser une prise en main et l’évaluation des outils disponibles à l’imprimerie Nationale (IN Groupe) servant à l'extraction des descripteurs représentant une empreinte digitale. En partant de ces outils de l’IN, nous avons implémenté quatre méthodes d’identification sélectionnées dans l’état de l’art. Une étude comparative ainsi que des améliorations ont été proposées sur ces méthodes. Nous avons aussi proposé une nouvelle solution d'indexation d'empreinte digitale pour réaliser la tâche d’identification qui améliore les résultats existant. Les différents résultats sont validés sur des bases de données de tailles moyennes publiques et nous utilisons le logiciel Sfinge pour réaliser le passage à l’échelle et la validation complète des stratégies d’indexation. Un deuxième aspect de cette thèse concerne la sécurité. Une personne peut avoir en effet, la volonté de dissimuler son identité et donc de mettre tout en œuvre pour faire échouer l’identification. Dans cette optique, un individu peut fournir une empreinte de mauvaise qualité (portion de l’empreinte digitale, faible contraste en appuyant peu sur le capteur…) ou fournir une empreinte digitale altérée (empreinte volontairement abîmée, suppression de l’empreinte avec de l’acide, scarification…). Il s'agit donc dans la deuxième partie de cette thèse de détecter les doigts morts et les faux doigts (silicone, impression 3D, empreinte latente) utilisés par des personnes mal intentionnées pour attaquer le système. Nous avons proposé une nouvelle solution de détection d'attaque basée sur l'utilisation de descripteurs statistiques sur l'empreinte digitale. Aussi, nous avons aussi mis en place trois chaînes de détections des faux doigts utilisant les techniques d'apprentissages profonds
Biometrics 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
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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.

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Les chercheurs ont établi une forte corrélation entre les étés chauds et la fréquence ainsi que l'intensité desincendies de forêt. Le réchauffement climatique dû aux gaz à effet de serre tels que le dioxyde de carboneaugmente la température dans certaines parties du monde. Or, les incendies libèrent des quantitésimportantes de gaz à effet de serre, engendrant une augmentation de la température moyenne sur terreinduisant à son tour une augmentation des incendies de forêt... Les incendies détruisent des millionsd'hectares de zones forestières, des écosystèmes abritant de nombreuses espèces et ont un cout importantpour nos sociétés. La prévention et les moyens de maîtrise des incendies doivent être une priorité pour arrêtercette spirale infernale.Dans ce cadre, la détection de la fumée est très importante, car elle est le premier indice d'un début d'incendie.Le feu et surtout la fumée sont des objets difficiles à détecter dans les images visibles en raison de leurcomplexité en termes de forme, de couleur et de texture. Cependant, l'apprentissage profond couplé à lasurveillance vidéo peut atteindre cet objectif. L'architecture des réseaux de neurones convolutifs (CNN) estcapable de détecter avec une très bonne précision la fumée et le feu dans les images RVB. De plus, cesstructures peuvent segmenter la fumée ainsi que le feu en temps réel. La richesse de la base de donnéesd'apprentissage des réseaux profonds est un élément très important permettant une bonne généralisation.Ce manuscrit présente différentes architectures profondes basées sur des réseaux convolutifs permettant dedétecter et localiser la fumée et le feu dans les images vidéo dans le domaine du visible
Researchers 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
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Books on the topic "Deep Learning, Database"

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Vasudevan, Shriram K., Subashri Vasudevan, and Sini Raj Pulari. Deep Learning. Taylor & Francis Group, 2021.

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Sejnowski, Terrence J. Deep Learning Revolution. MIT Press, 2018.

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Sejnowski, Terrence J. Deep Learning Revolution. MIT Press, 2018.

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Lin, Jerry Chun-Wei, and 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.

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The deep learning revolution. The MIT Press, 2018.

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Sejnowski, Terrence J. The Deep Learning Revolution. Tantor Audio, 2019.

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Deep Learning: A Comprehensive Guide. Taylor & Francis Group, 2021.

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Vasudevan, Shriram K., Siniraj Pulari, and Subashri Vasudevan. Deep Learning: A Comprehensive Guide. Taylor & Francis Group, 2021.

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Vasudevan, Shriram K., Subashri Vasudevan, and Sini Raj Pulari. Deep Learning: A Comprehensive Guide. CRC Press LLC, 2021.

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Vasudevan, Shriram K., Siniraj Pulari, and Subashri Vasudevan. Deep Learning: A Comprehensive Guide. Taylor & Francis Group, 2021.

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Book chapters on the topic "Deep Learning, Database"

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Ren, Qiang, Yinpeng Wang, Yongzhong Li, and 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.

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Sun, Bo, Di Wu, Mingsheng Shang, and 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.

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Sun, Bo, Di Wu, Mingsheng Shang, and 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.

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Lin, Hongjie, Hao Wang, Dongfang Du, Han Wu, Biao Chang, and 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.

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Sumon, Shakil Ahmed, MD Tanzil Shahria, MD Raihan Goni, Nazmul Hasan, A. M. Almarufuzzaman, and 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.

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Li, Xiaocui, Hongzhi Yin, Ke Zhou, Hongxu Chen, Shazia Sadiq, and 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.

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Xu, Hengpeng, Yao Zhang, Jinmao Wei, Zhenglu Yang, and 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.

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Kluska, Piotr, and 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.

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Wang, 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.

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Kuo, Che-Wei, and 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.

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Conference papers on the topic "Deep Learning, Database"

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Jayachandiran, U., Sujaritha P, Sahana A, and 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.

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Yu, Yongle, Yixuan Zhan, Lin Zhu, and 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.

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Dong, Wenlong, Wei Liu, Rui Xi, Mengshu Hou, and 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.

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Gomez, Sharon, R. Jegan, and 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.

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Zhong, Rui, and 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.

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Choudhary, Chinmay, and 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.

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Roj, Lea, Štefan Kohek, Aleksander Pur, and 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.

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The increase in unstructured textual data has created a pressing demand for effective information extraction techniques. This paper explores the integration of Named Entity Extraction (NEE) using deep learning within the Neo4j graph database. Utilizing the Rebel Large Model, we converted raw text into structured knowledge graphs. The primary objective is to evaluate the efficacy of this integration by examining performance metrics, such as process-ing time, graph growth, and entity representation. The findings highlight how the structure and complexity of graphs vary with different text lengths, offering insights into the potential of combin-ing deep learning-based NEE with graph databases for improved data analysis and decision-making.
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Montresor, Silvio, Ketao Yan, Marie Tahon, Kemao Qian, Yingjie Yu, and 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.

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This paper presents a comparative study of deep learning based algorithms to de-noise wrapped phase maps in digital holography interferometry. In order to compare two deep neural networks trained on two different databases, we propose to train both networks on both databases. The four resulting networks are then benchmarked with one unique database. We present the assessment between two models developed in Python. A third model developed in matlab is iadded in evaluation presented in this paper but will be not subject to retraining in the second step of the benchmark.
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Kang, Dylan Myungchul, Charles Cheolgi Lee, Suan Lee, and 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.

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Zhao, Dongdong, Pingchuan Zhang, Jianwen Xiang, and 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.

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Reports on the topic "Deep Learning, Database"

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Zhou, Yifu. Self-configured Elastic Database with Deep Q-Learning Approach. Ames (Iowa): Iowa State University, January 2019. http://dx.doi.org/10.31274/cc-20240624-1271.

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Chang, 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, May 2022. http://dx.doi.org/10.37766/inplasy2022.5.0074.

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Review question / Objective: To explore/summarize the performance of deep learning in automatic localization and segmentation of the median nerve at the carpal tunnel level. Condition being studied: Participants with and without carpal tunnel syndrome. Information sources: The following electronic databases will be searched, encompassing PubMed, Medline, Embase and Web of Science. We target the studies investigating in the utility of deep neural network on the evaluation of the median nerve in the carpal tunnel.
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Alhasson, 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.

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Review question / Objective: A significant amount of research has been conducted to detect and recognize diabetic foot ulcers (DFUs) using computer vision methods, but there are still a number of challenges. DFUs detection frameworks based on machine learning/deep learning lack systematic reviews. With Machine Learning (ML) and Deep learning (DL), you can improve care for individuals at risk for DFUs, identify and synthesize evidence about its use in interventional care and management of DFUs, and suggest future research directions. Information sources: A thorough search of electronic databases such as Science Direct, PubMed (MIDLINE), arXiv.org, MDPI, Nature, Google Scholar, Scopus and Wiley Online Library was conducted to identify and select the literature for this study (January 2010-January 01, 2023). It was based on the most popular image-based diagnosis targets in DFu such as segmentation, detection and classification. Various keywords were used during the identification process, including artificial intelligence in DFu, deep learning, machine learning, ANNs, CNNs, DFu detection, DFu segmentation, DFu classification, and computer-aided diagnosis.
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