Relevant bibliographies by topics / Deep learning based

Academic literature on the topic 'Deep learning based'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Deep learning based"

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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.

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Jiang, 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.

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Daehyeon 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.

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<p>To ensure the confidentiality of the message, the AES (Advanced Encryption Standard) block cipher algorithm can be widely used. Furthermore, an implementation of masked AES is often used to resist side-channel attacks. To recover secret keys embedded in cryptographic devices with masked AES, we present some side-channel attacks based on deep learning models in profiling and non-profiling scenarios. The proposed method which applies the mask value profiling technique represents new approaches for extracting the secret key. To defeat the masked AES implementation, deep learning models such as multi-layer perceptron and convolutional neural networks are developed. In a non-profiling scenario, we adopt the DDLA (Differential Deep Learning Analysis) to extract sensitive information such as the secret key. The main idea of our method is that it is possible to adopt a new binary labeling method to conduct the DDLA based on the HW (Hamming Weight) model. We show several experiments using real power traces measured from the ChipWhisperer platform in profiling attacks and the ASCAD dataset in non-profiling attacks respectively. Whether we target na&iuml;ve or masked AES implementation, the experimental results show the predominant key recovery accuracy.</p> <p>&nbsp;</p>
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AL-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.

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Dilation of biliary tree can be an indicator of several diseases such as stones, tumors, benign strictures, and some cases cancer. This dilation can be due to many reasons such as gallstones, inflammation of the bile ducts, trauma, injury, severe liver damage. Automatic measurement of the biliary tree in magnetic resonance images (MRI) is helpful to assist hepatobiliary surgeons for minimally invasive surgery. In this paper, we proposed a model to segment biliary tree MRI images using a Fully Convolutional Neural (FCN) network. Based on the extracted area, seven features that include Entropy, standard deviation, RMS, kurtosis, skewness, Energy and maximum are computed. A database of images from King Hussein Medical Center (KHMC) is used in this work, containing 800 MRI images; 400 cases with normal biliary tree; and 400 images with dilated biliary tree labeled by surgeons. Once the features are extracted, four classifiers (Multi-Layer perceptron neural network, support vector machine, k-NN and decision tree) are applied to predict the status of patient in terms of biliary tree (normal or dilated). All classifiers show high accuracy in terms of Area Under Curve except support vector machine. The contributions of this work include introducing a fully convolutional network for biliary tree segmentation, additionally scientifically correlate the extracted features with the status of biliary tree (normal or dilated) that have not been previously investigated in the literature from MRI images for biliary tree status determinations.
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Wang, 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.

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Jimmington, 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.

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Debnath, 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.

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Gadri, 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.

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Liu, 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.

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Pan, 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.

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Traditional portfolio theory divides stocks into different categories using indicators such as industry, market value, and liquidity, and then selects representative stocks according to them. In this paper, we propose a novel portfolio learning approach based on deep learning and apply it to China’s stock market. Specifically, this method is based on the similarity of deep features extracted from candlestick charts. First, we obtained whole stock information from Tushare, a professional financial data interface. These raw time series data are then plotted into candlestick charts to make an image dataset for studying the stock market. Next, the method extracts high-dimensional features from candlestick charts through an autoencoder. After that, K-means is used to cluster these high-dimensional features. Finally, we choose one stock from each category according to the Sharpe ratio and a low-risk, high-return portfolio is obtained. Extensive experiments are conducted on stocks in the Chinese stock market for evaluation. The results demonstrate that the proposed portfolio outperforms the market’s leading funds and the Shanghai Stock Exchange Composite Index (SSE Index) in a number of metrics.
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Dissertations / Theses on the topic "Deep learning based"

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Hussein, Ahmed. "Deep learning based approaches for imitation learning." Thesis, Robert Gordon University, 2018. http://hdl.handle.net/10059/3117.

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Imitation learning refers to an agent's ability to mimic a desired behaviour by learning from observations. The field is rapidly gaining attention due to recent advances in computational and communication capabilities as well as rising demand for intelligent applications. The goal of imitation learning is to describe the desired behaviour by providing demonstrations rather than instructions. This enables agents to learn complex behaviours with general learning methods that require minimal task specific information. However, imitation learning faces many challenges. The objective of this thesis is to advance the state of the art in imitation learning by adopting deep learning methods to address two major challenges of learning from demonstrations. Firstly, representing the demonstrations in a manner that is adequate for learning. We propose novel Convolutional Neural Networks (CNN) based methods to automatically extract feature representations from raw visual demonstrations and learn to replicate the demonstrated behaviour. This alleviates the need for task specific feature extraction and provides a general learning process that is adequate for multiple problems. The second challenge is generalizing a policy over unseen situations in the training demonstrations. This is a common problem because demonstrations typically show the best way to perform a task and don't offer any information about recovering from suboptimal actions. Several methods are investigated to improve the agent's generalization ability based on its initial performance. Our contributions in this area are three fold. Firstly, we propose an active data aggregation method that queries the demonstrator in situations of low confidence. Secondly, we investigate combining learning from demonstrations and reinforcement learning. A deep reward shaping method is proposed that learns a potential reward function from demonstrations. Finally, memory architectures in deep neural networks are investigated to provide context to the agent when taking actions. Using recurrent neural networks addresses the dependency between the state-action sequences taken by the agent. The experiments are conducted in simulated environments on 2D and 3D navigation tasks that are learned from raw visual data, as well as a 2D soccer simulator. The proposed methods are compared to state of the art deep reinforcement learning methods. The results show that deep learning architectures can learn suitable representations from raw visual data and effectively map them to atomic actions. The proposed methods for addressing generalization show improvements over using supervised learning and reinforcement learning alone. The results are thoroughly analysed to identify the benefits of each approach and situations in which it is most suitable.
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Abrishami, Hedayat. "Deep Learning Based Electrocardiogram Delineation." University of Cincinnati / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1563525992210273.

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Al-Bander, B. Q. "Retinal image analysis based on deep learning." Thesis, University of Liverpool, 2018. http://livrepository.liverpool.ac.uk/3022573/.

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Widegren, 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.

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Deep learning and neural networks has recently become a powerful tool to solve complex problem due to improvements in training algorithms. Examples of successful application can be found in speech recognition and machine translation. There exist relative few finance articles were deep learning have been applied, but existing articles indicate that deep learning can be successfully applied to problems in finance.  This thesis studies forecasting of financial price movements using two types of neural networks, namely; feedforward and recurrent networks. For the feedforward neural networks we considered non-deep networks with more neurons and deep networks with fewer neurons. In addition to the comparison between feedforward and recurrent networks, a comparison between deep and non-deep networks will be made. The recurrent architecture consists of a recurrent layer mapping into a feedforward layer followed by an output layer. The networks are trained with two different feature setups, one less complex and one more complex. The findings for non-deep vs. deep feedforward neural networks imply that there does not exist any general pattern whether deep or non-deep networks are preferable. The findings for recurrent neural networks vs. feedforward neural networks imply that recurrent neural networks do not necessarily outperform feedforward neural networks even though financial data in general are time-dependent. In some cases, adding batch normalization can improve the accuracy for the feedforward neural networks. This can be preferable instead of using more complex models, such as a recurrent neural networks. Moreover, there are significant differences in accuracies between using the two different feature setups. The highest accuracy for all networks are 52.82%, which is significantly better than the simple benchmark.
Djupa 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.
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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.

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Text classification has always been a concern in area of natural language processing, especially nowadays the data are getting massive due to the development of internet. Recurrent neural network (RNN) is one of the most popular method for natural language processing due to its recurrent architecture which give it ability to process serialized information. In the meanwhile, Convolutional neural network (CNN) has shown its ability to extract features from visual imagery. This paper combine the advantages of RNN and CNN and proposed a model called BLSTM-C for Chinese text classification. BLSTM-C begins with a Bidirectional long short-term memory (BLSTM) layer which is an special kind of RNN to get a sequence output based on the past context and the future context. Then it feed this sequence to CNN layer which is utilized to extract features from the previous sequence. We evaluate BLSTM-C model on several tasks such as sentiment classification and category classification and the result shows our model’s remarkable performance on these text tasks.
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Zhou, 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.

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Measuring face similarity is a task in computer vision that is different from face recognition. It aims to find an embedding in which similar faces have a smaller distance than dissimilar ones. This project investigates two different Siamese networks to explore whether these specific networks outperform face recognition methods on face similarity. The best accuracy is from a Siamese convolution neural network, which is 65.11%. Moreover, the best results in a similarity ranking task are obtained from Siamese geometry-aware metric learning. Besides, this project creates a novel dataset with facial image pairs for face similarity.
Mätning 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.
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Thiele, Johannes C. "Deep learning in event-based neuromorphic systems." Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLS403/document.

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Inférence et apprentissage dans les réseaux de neurones profonds nécessitent une grande quantité de calculs qui, dans beaucoup de cas, limite leur intégration dans les environnements limités en ressources. Les réseaux de neurones évènementiels de type « spike » présentent une alternative aux réseaux de neurones artificiels classiques, et promettent une meilleure efficacité énergétique. Cependant, entraîner les réseaux spike demeure un défi important, particulièrement dans le cas où l’apprentissage doit être exécuté sur du matériel de calcul bio-inspiré, dit matériel neuromorphique. Cette thèse constitue une étude sur les algorithmes d’apprentissage et le codage de l’information dans les réseaux de neurones spike.A partir d’une règle d’apprentissage bio-inspirée, nous analysons quelles propriétés sont nécessaires dans les réseaux spike pour rendre possible un apprentissage embarqué dans un scénario d’apprentissage continu. Nous montrons qu’une règle basée sur le temps de déclenchement des neurones (type « spike-timing dependent plasticity ») est capable d’extraire des caractéristiques pertinentes pour permettre une classification d’objets simples comme ceux des bases de données MNIST et N-MNIST.Pour dépasser certaines limites de cette approche, nous élaborons un nouvel outil pour l’apprentissage dans les réseaux spike, SpikeGrad, qui représente une implémentation entièrement évènementielle de la rétro-propagation du gradient. Nous montrons comment cette approche peut être utilisée pour l’entrainement d’un réseau spike qui est capable d’inférer des relations entre valeurs numériques et des images MNIST. Nous démontrons que cet outil est capable d’entrainer un réseau convolutif profond, qui donne des taux de reconnaissance d’image compétitifs avec l’état de l’art sur les bases de données MNIST et CIFAR10. De plus, SpikeGrad permet de formaliser la réponse d’un réseau spike comme celle d’un réseau de neurones artificiels classique, permettant un entraînement plus rapide.Nos travaux introduisent ainsi plusieurs mécanismes d’apprentissage puissants pour les réseaux évènementiels, contribuant à rendre l’apprentissage des réseaux spike plus adaptés à des problèmes réels
Inference 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
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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/.

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Hand gesture recognition based on surface electromyographic (sEMG) signals is a promising approach for the development of Human-Machine Interfaces (HMIs) with a natural control, such as intuitive robot interfaces or poly-articulated prostheses. However, real-world applications are limited by reliability problems due to motion artifacts, postural and temporal variability, and sensor re-positioning. This master thesis is the first application of deep learning on the Unibo-INAIL dataset, the first public sEMG dataset exploring the variability between subjects, sessions and arm postures, by collecting data over 8 sessions of each of 7 able-bodied subjects executing 6 hand gestures in 4 arm postures. In the most recent studies, the variability is addressed with training strategies based on training set composition, which improve inter-posture and inter-day generalization of classical (i.e. non-deep) machine learning classifiers, among which the RBF-kernel SVM yields the highest accuracy. The deep architecture realized in this work is a 1d-CNN implemented in Pytorch, inspired by a 2d-CNN reported to perform well on other public benchmark databases. On this 1d-CNN, various training strategies based on training set composition were implemented and tested. Multi-session training proves to yield higher inter-session validation accuracies than single-session training. Two-posture training proves to be the best postural training (proving the benefit of training on more than one posture), and yields 81.2% inter-posture test accuracy. Five-day training proves to be the best multi-day training, and yields 75.9% inter-day test accuracy. All results are close to the baseline. Moreover, the results of multi-day trainings highlight the phenomenon of user adaptation, indicating that training should also prioritize recent data. Though not better than the baseline, the achieved classification accuracies rightfully place the 1d-CNN among the candidates for further research.
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Elkaref, Mohab. "Deep learning applications for transition-based dependency parsing." Thesis, University of Birmingham, 2018. http://etheses.bham.ac.uk//id/eprint/8620/.

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Dependency Parsing is a method that builds dependency trees consisting of binary relations that describe the syntactic role of words in sentences. Recently, dependency parsing has seen large improvements due to deep learning, which enabled richer feature representations and flexible architectures. In this thesis we focus on the application of these methods to Transition-based parsing, which is a faster variant. We explore current architectures and examine ways to improve their representation capabilities and final accuracies. Our first contribution is an improvement on the basic architecture at the heart of many current parsers. We show that using Recurrent Neural Network hidden layers, initialised with pretrained weights from a feed forward network, provides significant accuracy improvements. Second, we examine the best parser architecture. We show that separate classifiers for dependency parsing and labelling, with a shared input layer provides the best accuracy. We also show that a parser and labeller can be successfully trained separately. Finally, we propose Recursive LSTM Trees, which can represent an entire tree as a single dense vector, and achieve competitive accuracy with minimal features. The parsers that we develop in this thesis cover many aspects of this task, and are easy to integrate with current methods.
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Dsouza, Rodney Gracian. "Deep Learning Based Motion Forecasting for Autonomous Driving." The Ohio State University, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=osu1619139403696822.

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Books on the topic "Deep learning based"

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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.

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Mittag, Gabriel. Deep Learning Based Speech Quality Prediction. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-91479-0.

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Tsihrintzis, 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.

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Agarwal, 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.

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Alla, 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.

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Satapathy, 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.

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Chaudhuri, 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.

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Li, 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.

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Chellappa, Rama, Nalini K. Ratha, and Vishal M. Patel. Deep Learning-Based Face Analytics. Springer International Publishing AG, 2021.

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Chellappa, Rama, Nalini K. Ratha, and Vishal M. Patel. Deep Learning-Based Face Analytics. Springer International Publishing AG, 2022.

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Book chapters on the topic "Deep learning based"

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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.

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Paluszek, 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.

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Paluszek, 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.

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Kim, 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.

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Kim, 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.

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Pavitra, 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.

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Alla, 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.

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Sanghi, 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.

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Kim, 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.

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Manjón, 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.

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Conference papers on the topic "Deep learning based"

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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.

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Kaskavalci, 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.

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Lee, 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.

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Deep Learning Analytics uses predictive models that provide actionable information. It is a multidisciplinary approach based on data processing, AI technology-learning enhancement, educational data mining, and visualization. The problem is that embracing DLA(Deep Learning Analytics) in evaluating data in higher education diverts educators’ attention from clearly identifying methods, benefits, and challenges of using DLA in higher education. Predictive models including random forest (RF), support vector machines (SVM), logistic regression (logistic), and Deep Learning were trained and their performances compared. The predicted value of “source of sustainability” and selected input variables were utilized to predict the drop out of learner. Expected significant outcomes and impact is that using DLA we can find the optimal learning management model for supporting services for instructors significantly impact the quality of statistics education and for learners is necessary to support announcements from instructors, for providing appropriate learning environments.
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Viviani, 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.

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Huertas-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.

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Alvares, 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.

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Khan, 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.

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Lu, 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.

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Bougteb, 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.

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Hoe 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.

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Reports on the topic "Deep learning based"

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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.

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Dugan, 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.

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Dugan, 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.

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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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Kong, 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.

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Idakwo, 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.

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Deep learning (DL) has attracted the attention of computational toxicologists as it offers a potentially greater power for in silico predictive toxicology than existing shallow learning algorithms. However, contradicting reports have been documented. To further explore the advantages of DL over shallow learning, we conducted this case study using two cell-based androgen receptor (AR) activity datasets with 10K chemicals generated from the Tox21 program. A nested double-loop cross-validation approach was adopted along with a stratified sampling strategy for partitioning chemicals of multiple AR activity classes (i.e., agonist, antagonist, inactive, and inconclusive) at the same distribution rates amongst the training, validation and test subsets. Deep neural networks (DNN) and random forest (RF), representing deep and shallow learning algorithms, respectively, were chosen to carry out structure-activity relationship-based chemical toxicity prediction. Results suggest that DNN significantly outperformed RF (p < 0.001, ANOVA) by 22–27% for four metrics (precision, recall, F-measure, and AUPRC) and by 11% for another (AUROC). Further in-depth analyses of chemical scaffolding shed insights on structural alerts for AR agonists/antagonists and inactive/inconclusive compounds, which may aid in future drug discovery and improvement of toxicity prediction modeling.
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Cookson, 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.

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The purpose of this report is to make what is “invisible” visible and to suggest three evidence-based strategies that have the capacity to enable educators, in collaboration with the families and the communities they serve, to create learning environments where students living in deep poverty are supported and successful. The report begins by documenting the human cost of deep poverty and how past policy decisions have contributed to the persistence of deep poverty. Based on this background, the report focuses on three promising strategies for meeting the learning and social-emotional needs of all children, including those living in deep poverty: (1) begin with funding adequacy and equity, (2) develop community schools and partnerships, and (3) develop a whole child teaching and learning culture.
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Huang, 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.

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Riprap rock and aggregates are extensively used in structural, transportation, geotechnical, and hydraulic engineering applications. Field determination of morphological properties of aggregates such as size and shape can greatly facilitate the quality assurance/quality control (QA/QC) process for proper aggregate material selection and engineering use. Many aggregate imaging approaches have been developed to characterize the size and morphology of individual aggregates by computer vision. However, 3D field characterization of aggregate particle morphology is challenging both during the quarry production process and at construction sites, particularly for aggregates in stockpile form. This research study presents a 3D reconstruction-segmentation-completion approach based on deep learning techniques by combining three developed research components: field 3D reconstruction procedures, 3D stockpile instance segmentation, and 3D shape completion. The approach was designed to reconstruct aggregate stockpiles from multi-view images, segment the stockpile into individual instances, and predict the unseen side of each instance (particle) based on the partial visible shapes. Based on the dataset constructed from individual aggregate models, a state-of-the-art 3D instance segmentation network and a 3D shape completion network were implemented and trained, respectively. The application of the integrated approach was demonstrated on re-engineered stockpiles and field stockpiles. The validation of results using ground-truth measurements showed satisfactory algorithm performance in capturing and predicting the unseen sides of aggregates. The algorithms are integrated into a software application with a user-friendly graphical user interface. Based on the findings of this study, this stockpile aggregate analysis approach is envisioned to provide efficient field evaluation of aggregate stockpiles by offering convenient and reliable solutions for on-site QA/QC tasks of riprap rock and aggregate stockpiles.
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Fullan, 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.

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Disruptive innovators take advantage of unique opportunities. Prior to COVID-19 progress in Latin America and the Caribbean for integrating technology, learning, and system change has been exceedingly slow. In this paper we first offer a general framework for transforming education. The framework focuses on the provision of technology, innovative ideas in learning and well-being, and what we call systemness which are favorable change factors at the local, middle/regional, and policy levels. We then take up the matter of system reform in Latin America and the Caribbean noting problems and potential. Then, we turn to a specific model in system change that we have developed called New Pedagogies for Deep Learning, a model developed in partnerships with groups of schools in ten countries since 2014. The model consists of three main components: 6 Global Competences (character, citizenship, collaboration, communication, creativity, and critical thinking), 4 learning elements (pedagogy, learning partnerships, learning environments, leveraging digital), and three system conditions (school culture, district/regional culture, and system policy). We offer a case study of relative success based on Uruguay with whom we have been working since 2014. Finally, we identify steps and recommendations for next steps in Latin America for taking action on system reform in the next perioda time that we consider critical for taking advantage of the current pandemic disruption. The next few years will be crucial for either attaining positive breakthroughs or slipping backwards into a reinforced status quo.
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Kulhandjian, 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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In 2019, the United States experienced more than 6,500 pedestrian fatalities involving motor vehicles which resulted in a 67% rise in nighttime pedestrian fatalities and only a 10% rise in daytime pedestrian fatalities. In an effort to reduce fatalities, this research developed a pedestrian detection and alert system through the application of a visual camera, infrared camera, and radar sensors combined with machine learning. The research team designed the system concept to achieve a high level of accuracy in pedestrian detection and avoidance during both the day and at night to avoid potentially fatal accidents involving pedestrians crossing a street. The working prototype of pedestrian detection and collision avoidance can be installed in present-day vehicles, with the visible camera used to detect pedestrians during the day and the infrared camera to detect pedestrians primarily during the night as well as at high glare from the sun during the day. The radar sensor is also used to detect the presence of a pedestrian and calculate their range and direction of motion relative to the vehicle. Through data fusion and deep learning, the ability to quickly analyze and classify a pedestrian’s presence at all times in a real-time monitoring system is achieved. The system can also be extended to cyclist and animal detection and avoidance, and could be deployed in an autonomous vehicle to assist in automatic braking systems (ABS).
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