Relevant bibliographies by topics / Deep Learning techniques

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

Academic literature on the topic 'Deep Learning techniques'

Author: Grafiati
Published: 4 June 2021
Last updated: 11 September 2023

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Journal articles on the topic "Deep Learning techniques"

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Firdaus, Naina, and Madhuvan Dixit. "Deep Learning Techniques, Applications and Challenges: An Assessment." International Journal of Trend in Scientific Research and Development Volume-2, Issue-4 (June 30, 2018): 1710–14. http://dx.doi.org/10.31142/ijtsrd14437.

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M, Leo Francis, Darshan K. S, Ankith M. C, and Divakara V. "CombiningNLP and Deep Learning Techniques to Generate Captions." International Journal of Research Publication and Reviews 4, no. 5 (June 2023): 4682–91. http://dx.doi.org/10.55248/gengpi.4.523.42704.

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Agarwal, Sohit, and Mukesh Kumar Gupta. "Context Aware Image Sentiment Classification using Deep Learning Techniques." Indian Journal Of Science And Technology 15, no. 47 (December 20, 2022): 2619–27. http://dx.doi.org/10.17485/ijst/v15i47.1907.

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Sravani, L., N. Rama Venkat Sai, K. Noomika, M. Upendra Kumar, and K. V. Adarsh. "Image Enhancement of Underwater Images using Deep Learning Techniques." International Journal of Research Publication and Reviews 4, no. 4 (April 3, 2023): 81–86. http://dx.doi.org/10.55248/gengpi.2023.4.4.34620.

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Ibrahim, Dr Abdul-Wahab Sami, and Dr Baidaa Abdul khaliq Atya. "Detection of Diseases in Rice Leaf Using Deep Learning and Machine Learning Techniques." Webology 19, no. 1 (January 20, 2022): 1493–503. http://dx.doi.org/10.14704/web/v19i1/web19100.

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Plant diseases have a negative impact on the agricultural sector. The diseases lower the productivity of the production yield and give huge losses to the farmers. For the betterment of agriculture, it is very essential to detect the diseases in the plants to protect the agricultural crop yield while it is also important to reduce the use of pesticides to improve the quality of the agricultural yield. Image processing and data mining algorithms together help analyze and detection of diseases. Using these techniques diseases detection can be done in rice leaves. In this research, the image processing technique is used to extract the feature from the leaf images. Further for the classification of diseases various machine learning algorithm like the random forest, J48 and support vector machine is used and the result is compared among different machine learning algorithm. After model evaluation, classification accuracy is verified using the n-fold cross-validation technique.
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S., Gayathri, Santhiya S., Nowneesh T., Sanjana Shuruthy K., and Sakthi S. "Deep fake detection using deep learning techniques." Applied and Computational Engineering 2, no. 1 (March 22, 2023): 1010–19. http://dx.doi.org/10.54254/2755-2721/2/20220655.

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Deep fake is the artificial manipulation and creation of data, primarily through photo-graphs or videos into the likeness of another person. This technology has a variety of ap-plications. Despite its uses, it can also influence society in a controversial way like de-faming a person, Political distress, etc. Many models had been proposed by different re-searchers which give an average accuracy of 90%. To improve the detection efficiency, this proposed paper uses 3 different deep learning techniques: Inception ResNetV2, Effi-cientNet, and VGG16. These proposed models are trained by the combination of Facfo-rensic++ and DeepFake Detection Challenge Dataset. This proposed system gives the highest accuracy of 97%.
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T., Senthil Kumar. "Systematic Study on Deep Learning Techniques for Prediction of Movies." Journal of Advanced Research in Dynamical and Control Systems 12, SP4 (March 31, 2020): 31–38. http://dx.doi.org/10.5373/jardcs/v12sp4/20201463.

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Nandini, L. Surya, L. Haritha Priya, N. Sruthi, K. S. N. Murthy, M. Ashish Kumar, and N. Lakshmi Devi. "Survey on Aspect Based Sentimental Analysis using Deep Learning Techniques." International Journal of Research Publication and Reviews 4, no. 3 (March 2023): 634–46. http://dx.doi.org/10.55248/gengpi.2023.31886.

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Harsha, Sanda Sri. "Prediction of Silica Impurity Using Deep Learning Techniques for Mining Environment." Revista Gestão Inovação e Tecnologias 11, no. 3 (June 30, 2021): 506–17. http://dx.doi.org/10.47059/revistageintec.v11i3.1953.

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Kibria, Md Golam, and Mehmet Sevkli. "Application of Deep Learning for Credit Card Approval: A Comparison with Two Machine Learning Techniques." International Journal of Machine Learning and Computing 11, no. 4 (August 2021): 286–90. http://dx.doi.org/10.18178/ijmlc.2021.11.4.1049.

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The increased credit card defaulters have forced the companies to think carefully before the approval of credit applications. Credit card companies usually use their judgment to determine whether a credit card should be issued to the customer satisfying certain criteria. Some machine learning algorithms have also been used to support the decision. The main objective of this paper is to build a deep learning model based on the UCI (University of California, Irvine) data sets, which can support the credit card approval decision. Secondly, the performance of the built model is compared with the other two traditional machine learning algorithms: logistic regression (LR) and support vector machine (SVM). Our results show that the overall performance of our deep learning model is slightly better than that of the other two models.
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Dissertations / Theses on the topic "Deep Learning techniques"

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Hossain, Md Zakir. "Deep learning techniques for image captioning." Thesis, Hossain, Md. Zakir (2020) Deep learning techniques for image captioning. PhD thesis, Murdoch University, 2020. https://researchrepository.murdoch.edu.au/id/eprint/60782/.

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Generating a description of an image is called image captioning. Image captioning is a challenging task because it involves the understanding of the main objects, their attributes, and their relationships in an image. It also involves the generation of syntactically and semantically meaningful descriptions of the images in natural language. A typical image captioning pipeline comprises an image encoder and a language decoder. Convolutional Neural Networks (CNNs) are widely used as the encoder while Long short-term memory (LSTM) networks are used as the decoder. A variety of LSTMs and CNNs including attention mechanisms are used to generate meaningful and accurate captions. Traditional image captioning techniques have limitations in generating semantically meaningful and superior captions. In this research, we focus on advanced image captioning techniques, which are able to generate semantically more meaningful and superior captions. As such we have made four contributions in this thesis. First, we investigate an attention based LSTM on image features extracted by DenseNet, which is a newer type of CNN. We integrate DenseNet features with attention mechanism and we show that this combination can generate more relevant image captions than other CNNs. Second, we use bi-directional self-attention as a language decoder. Bi-directional decoder can capture the context in both forward and backward directions, i.e., past context as well as any future context, in caption generation. Consequently, the generated captions are more meaningful and superior to those generated by typical LSTMs and CNNs. Third, we further extend the work by using an additional CNN layer to incorporate the structured local context together with the past and the future contexts attained by Bi-directional LSTM. A pooling scheme namely Attention Pooling is also used to enhance the information extraction capability of the pooling layer. Consequently, it is able to generate contextually superior captions. Fourth, existing image captioning techniques use human-annotated real images for training and testing, which involve an expensive and time-consuming process. Moreover, nowadays bulk of the images are synthetic or generated by machines. There is also a need for generating captions for such images. We investigate the use of synthetic images for training and testing image captioning. We show that such images can help improving the captions of real images and they can effectively be used in caption generation of synthetic images.
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Domeniconi, Federico. "Deep Learning Techniques applied to Photometric Stereo." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/20031/.

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La tesi si focalizza sullo studio dello stato dell’arte della fotometria stereo con deep learning: Self-calibrating Deep Photometric Stereo Networks. Il modello è composto è composto di due reti, la prima predice la direzione e l’intensità delle luci, la seconda predice le normali della superficie. L’obiettivo della tesi è individuare i limiti del modello e capire se possa essere modifcato per avere buone prestazioni anche in scenari reali. Il progetto di tesi è basato su fine-tuning, una tecnica supervisionata di transfer learning. Per questo scopo un nuovo dataset è stato creato acquisendo immagini in laboratorio. La ground-truth è ottenuta tramite una tecnica di distillazione. In particolare la direzione delle luci è ottenuta utilizzando due algoritmi di calibrazione delle luci e unendo i due risultati. Analogamente le normali delle superfici sono ottenute unendo i risultati di vari algoritmi di fotometria stereo. I risultati della tesi sono molto promettenti. L’errore nella predizione della direzione e dell’intensità delle luci è un terzo dell’errore del modello originale. Le predizioni delle normali delle superfici possono essere analizzate solo qualitativamente, ma i miglioramenti sono evidenti. Il lavoro di questa tesi ha mostrato che è possibile applicare transfer-learning alla fotometria stereo con deep learning. Perciò non è necessario allenare un nuovo modello da zero ma è possibile approfittare di modelli già esistenti per migliorare le prestazioni e ridurre il tempo di allenamento.
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Cruz, Edmanuel. "Robotics semantic localization using deep learning techniques." Doctoral thesis, Universidad de Alicante, 2020. http://hdl.handle.net/10045/109462.

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The tremendous technological advance experienced in recent years has allowed the development and implementation of algorithms capable of performing different tasks that help humans in their daily lives. Scene recognition is one of the fields most benefited by these advances. Scene recognition gives different systems the ability to define a context for the identification or recognition of objects or places. In this same line of research, semantic localization allows a robot to identify a place semantically. Semantic classification is currently an exciting topic and it is the main goal of a large number of works. Within this context, it is a challenge for a system or for a mobile robot to identify semantically an environment either because the environment is visually different or has been gradually modified. Changing environments are challenging scenarios because, in real-world applications, the system must be able to adapt to these environments. This research focuses on recent techniques for categorizing places that take advantage of DL to produce a semantic definition for a zone. As a contribution to the solution of this problem, in this work, a method capable of updating a previously trained model is designed. This method was used as a module of an agenda system to help people with cognitive problems in their daily tasks. An augmented reality mobile phone application was designed which uses DL techniques to locate a customer’s location and provide useful information, thus improving their shopping experience. These solutions will be described and explained in detail throughout the following document.
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Nguyen, Tien Dung. "Multimodal emotion recognition using deep learning techniques." Thesis, Queensland University of Technology, 2020. https://eprints.qut.edu.au/180753/1/Tien%20Dung_Nguyen_Thesis.pdf.

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This thesis investigates the use of deep learning techniques to address the problem of machine understanding of human affective behaviour and improve the accuracy of both unimodal and multimodal human emotion recognition. The objective was to explore how best to configure deep learning networks to capture individually and jointly, the key features contributing to human emotions from three modalities (speech, face, and bodily movements) to accurately classify the expressed human emotion. The outcome of the research should be useful for several applications including the design of social robots.
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Singh, Praveer. "Processing high-resolution images through deep learning techniques." Thesis, Paris Est, 2018. http://www.theses.fr/2018PESC1172.

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Dans cette thèse, nous discutons de quatre scénarios d’application différents qui peuvent être largement regroupés dans le cadre plus large de l’analyse et du traitement d’images à haute résolution à l’aide de techniques d’apprentissage approfondi. Les trois premiers chapitres portent sur le traitement des images de télédétection (RS) captées soit par avion, soit par satellite à des centaines de kilomètres de la Terre. Nous commençons par aborder un problème difficile lié à l’amélioration de la classification des scènes aériennes complexes par le biais d’un paradigme d’apprentissage profondément faiblement supervisé. Nous montrons comment en n’utilisant que les étiquettes de niveau d’image, nous pouvons localiser efficacement les régions les plus distinctives dans les scènes complexes et éliminer ainsi les ambiguïtés qui mènent à une meilleure performance de classification dans les scènes aériennes très complexes. Dans le deuxième chapitre, nous traiterons de l’affinement des étiquettes de segmentation des empreintes de pas des bâtiments dans les images aériennes. Pour ce faire, nous détectons d’abord les erreurs dans les masques de segmentation initiaux et corrigeons uniquement les pixels de segmentation où nous trouvons une forte probabilité d’erreurs. Les deux prochains chapitres de la thèse portent sur l’application des Réseaux Adversariatifs Génératifs. Dans le premier, nous construisons un modèle GAN nuageux efficace pour éliminer les couches minces de nuages dans l’imagerie Sentinel-2 en adoptant une perte de consistance cyclique. Ceci utilise une fonction de perte antagoniste pour mapper des images nuageuses avec des images non nuageuses d’une manière totalement non supervisée, où la perte cyclique aide à contraindre le réseau à produire une image sans nuage correspondant a` l’image nuageuse d’entrée et non à aucune image aléatoire dans le domaine cible. Enfin, le dernier chapitre traite d’un ensemble différent d’images `à haute résolution, ne provenant pas du domaine RS mais plutôt de l’application d’imagerie à gamme dynamique élevée (HDRI). Ce sont des images 32 bits qui capturent toute l’étendue de la luminance présente dans la scène. Notre objectif est de les quantifier en images LDR (Low Dynamic Range) de 8 bits afin qu’elles puissent être projetées efficacement sur nos écrans d’affichage normaux tout en conservant un contraste global et une qualité de perception similaires à ceux des images HDR. Nous adoptons un modèle GAN multi-échelle qui met l’accent à la fois sur les informations plus grossières et plus fines nécessaires aux images à haute résolution. Les sorties finales cartographiées par ton ont une haute qualité subjective sans artefacts perçus
In this thesis, we discuss four different application scenarios that can be broadly grouped under the larger umbrella of Analyzing and Processing high-resolution images using deep learning techniques. The first three chapters encompass processing remote-sensing (RS) images which are captured either from airplanes or satellites from hundreds of kilometers away from the Earth. We start by addressing a challenging problem related to improving the classification of complex aerial scenes through a deep weakly supervised learning paradigm. We showcase as to how by only using the image level labels we can effectively localize the most distinctive regions in complex scenes and thus remove ambiguities leading to enhanced classification performance in highly complex aerial scenes. In the second chapter, we deal with refining segmentation labels of Building footprints in aerial images. This we effectively perform by first detecting errors in the initial segmentation masks and correcting only those segmentation pixels where we find a high probability of errors. The next two chapters of the thesis are related to the application of Generative Adversarial Networks. In the first one, we build an effective Cloud-GAN model to remove thin films of clouds in Sentinel-2 imagery by adopting a cyclic consistency loss. This utilizes an adversarial lossfunction to map cloudy-images to non-cloudy images in a fully unsupervised fashion, where the cyclic-loss helps in constraining the network to output a cloud-free image corresponding to the input cloudy image and not any random image in the target domain. Finally, the last chapter addresses a different set of high-resolution images, not coming from the RS domain but instead from High Dynamic Range Imaging (HDRI) application. These are 32-bit imageswhich capture the full extent of luminance present in the scene. Our goal is to quantize them to 8-bit Low Dynamic Range (LDR) images so that they can be projected effectively on our normal display screens while keeping the overall contrast and perception quality similar to that found in HDR images. We adopt a Multi-scale GAN model that focuses on both coarser as well as finer-level information necessary for high-resolution images. The final tone-mapped outputs have a high subjective quality without any perceived artifacts
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FANTAZZINI, ALICE. "Deep Learning Techniques to Support Endovascular Surgical Procedures." Doctoral thesis, Università degli studi di Genova, 2022. http://hdl.handle.net/11567/1076603.

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Clinical Problem. Medical image analysis plays a crucial role in all the stages included in endovascular surgery, from screening to follow-up monitoring. Given the growing availability of clinical images, automatic tools that can process data in a quick and effective way are essential for clinical support. Methods. In this thesis, deep learning (DL) methodologies are designed to support clinicians in three different phases of endovascular surgery: the preoperative phase, the intraoperative phase, and the postoperative phase. In the preoperative phase, deep learning is exploited to perform automatic segmentation of aortic lumen and thrombus dealing with spatial coherence. Then, geometric measurements are extracted from the segmentation, allowing geometric evaluation and aneurysm screening. For the intraoperative phase, a deep learning model is used as a surrogate of finite-element analysis to predict the intraoperative aortic deformations induced by tools-tissue interaction. Finally, for the postoperative phase, deep learning is exploited to perform aortic lumen segmentation and geometric analysis is performed on multiple follow-up patient acquisitions. Results. For the preoperative stage, the developed segmentation pipelines provided better results compared to state-of-the art approaches. Automated geometric measurements showed comparable results to manual ones, and aneurysm screening provided promising results. For the intraoperative stage, the deep learning model showed good accuracy in predicting intraoperative aortic deformations. For the postoperative stage, the preliminary longitudinal analysis of aortic geometry showed that landing zone diameters tend to change over the follow-up acquisitions. Conclusions. This work presents a platform for the automatic analysis of CTA scans of patients affected by aortic diseases. The developed methodologies allow to rapidly process large image databases; the results of such analysis (e.g., thrombus and lumen segmentation, geometric measurements) can be useful in the research field as well as in clinical practice.
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Calvanese, Giordano. "Volumetric deep learning techniques in oil & gas exploration." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/20556/.

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This work consisted in the study and application of volumetric Deep Learning (DL) approach to seismic data provided by Eni S.p.A., with an industrial utility perspective. After a series of fruitful meetings with the Upstream & Technical Services team, we clearly defined the final objective of this approach: the automatic search for geological structures such as turbidite channel-bases, as potential regions of interest for the Oil & Gas industry. Therefore, we defined a workflow based on the training of volumetric DL models over seismic horizons containing channel bases providing “windrose” input patches, i.e. a planar approximation of a three-dimensional volume. All components and sources of criticality were systematically analyzed. For this purpose we studied: the effect of preprocessing, the contribution of the dataset augmentation, the sensitivity for the channel-base manual segmentation, the effect of the spatial expansion of the input patches. Evaluating both qualitatively and quantitatively through K-fold cross-validation. This work showed: how an appropriate preprocessing of the original data substantially helps DL models, how the dataset augmentation is fundamental for good model generalization given the poor representativity of the accessible examples compared to all possible configurations, how this DL approach is susceptible to the channel-base segmentation imposing to invest sufficient effort in the generation of reliable labels, how the size of input patches must be large enough to allow models to perceive around each voxel the structure concavity and the texture of any sediment infill. We conclude that the volumetric DL approach developed in this work has proved to be very promising.
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De, la Torre Gallart Jordi. "Diabetic Retinopathy Classification and Interpretation using Deep Learning Techniques." Doctoral thesis, Universitat Rovira i Virgili, 2019. http://hdl.handle.net/10803/667077.

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La retinopatia diabètica és una malaltia crònica i una de les principals causes de ceguesa i discapacitat visual en els pacients diabètics. L'examen ocular a través d'imatges de la retina és utilitzat pels metges per detectar les lesions relacionades amb aquesta malaltia. En aquesta tesi, explorem diferents mètodes innovadors per a la classificació automàtica del grau de malaltia utilitzant imatges del fons d'ull. Per a aquest propòsit, explorem mètodes basats en l'extracció i classificació automàtica, basades en xarxes neuronals profundes. A més, dissenyem un nou mètode per a la interpretació dels resultats. El model està concebut de manera modular per a que pugui ser utilitzat en d'altres xarxes i dominis de classificació. Demostrem experimentalment que el nostre model d'interpretació és capaç de detectar lesions de retina a la imatge únicament a partir de la informació de classificació. A més, proposem un mètode per comprimir la representació interna de la informació de la xarxa. El mètode es basa en una anàlisi de components independents sobre la informació del vector d'atributs intern de la xarxa generat pel model per a cada imatge. Usant el nostre mètode d'interpretació esmentat anteriorment també és possible visualitzar aquests components en la imatge. Finalment, presentem una aplicació experimental del nostre millor model per classificar imatges de retina d'una població diferent, concretament de l'Hospital de Reus. Els mètodes proposats arriben al nivell de rendiment de l'oftalmòleg i són capaços d'identificar amb gran detall les lesions presents en les imatges, que es dedueixen només de la informació de classificació de la imatge.
La retinopatía diabética es una enfermedad crónica y una de las principales causas de ceguera y discapacidad visual en los pacientes diabéticos. El examen ocular a través de imágenes de la retina es utilizado por los médicos para detectar las lesiones relacionadas con esta enfermedad. En esta tesis, exploramos diferentes métodos novedosos para la clasificación automática del grado de enfermedad utilizando imágenes del fondo de la retina. Para este propósito, exploramos métodos basados en la extracción y clasificación automática, basadas en redes neuronales profundas. Además, diseñamos un nuevo método para la interpretación de los resultados. El modelo está concebido de manera modular para que pueda ser utilizado utilizando otras redes y dominios de clasificación. Demostramos experimentalmente que nuestro modelo de interpretación es capaz de detectar lesiones de retina en la imagen únicamente a partir de la información de clasificación. Además, proponemos un método para comprimir la representación interna de la información de la red. El método se basa en un análisis de componentes independientes sobre la información del vector de atributos interno de la red generado por el modelo para cada imagen. Usando nuestro método de interpretación mencionado anteriormente también es posible visualizar dichos componentes en la imagen. Finalmente, presentamos una aplicación experimental de nuestro mejor modelo para clasificar imágenes de retina de una población diferente, concretamente del Hospital de Reus. Los métodos propuestos alcanzan el nivel de rendimiento del oftalmólogo y son capaces de identificar con gran detalle las lesiones presentes en las imágenes, que se deducen solo de la información de clasificación de la imagen.
Diabetic Retinopathy is a chronic disease and one of the main causes of blindness and visual impairment for diabetic patients. Eye screening through retinal images is used by physicians to detect the lesions related with this disease. In this thesis, we explore different novel methods for the automatic diabetic retinopathy disease grade classification using retina fundus images. For this purpose, we explore methods based in automatic feature extraction and classification, based on deep neural networks. Furthermore, as results reported by these models are difficult to interpret, we design a new method for results interpretation. The model is designed in a modular manner in order to generalize its possible application to other networks and classification domains. We experimentally demonstrate that our interpretation model is able to detect retina lesions in the image solely from the classification information. Additionally, we propose a method for compressing model feature-space information. The method is based on a independent component analysis over the disentangled feature space information generated by the model for each image and serves also for identifying the mathematically independent elements causing the disease. Using our previously mentioned interpretation method is also possible to visualize such components on the image. Finally, we present an experimental application of our best model for classifying retina images of a different population, concretely from the Hospital de Reus. The methods proposed, achieve ophthalmologist performance level and are able to identify with great detail lesions present on images, inferred only from image classification information.
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Rangel, José Carlos. "Scene Understanding for Mobile Robots exploiting Deep Learning Techniques." Doctoral thesis, Universidad de Alicante, 2017. http://hdl.handle.net/10045/72503.

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Every day robots are becoming more common in the society. Consequently, they must have certain basic skills in order to interact with humans and the environment. One of these skills is the capacity to understand the places where they are able to move. Computer vision is one of the ways commonly used for achieving this purpose. Current technologies in this field offer outstanding solutions applied to improve data quality every day, therefore producing more accurate results in the analysis of an environment. With this in mind, the main goal of this research is to develop and validate an efficient object-based scene understanding method that will be able to help solve problems related to scene identification for mobile robotics. We seek to analyze state-of-the-art methods for finding the most suitable one for our goals, as well as to select the kind of data most convenient for dealing with this issue. Another primary goal of the research is to determine the most suitable data input for analyzing scenes in order to find an accurate representation for the scenes by meaning of semantic labels or point cloud features descriptors. As a secondary goal we will show the benefits of using semantic descriptors generated with pre-trained models for mapping and scene classification problems, as well as the use of deep learning models in conjunction with 3D features description procedures to build a 3D object classification model that is directly related with the representation goal of this work. The research described in this thesis was motivated by the need for a robust system capable of understanding the locations where a robot usually interacts. In the same way, the advent of better computational resources has allowed to implement some already defined techniques that demand high computational capacity and that offer a possible solution for dealing with scene understanding issues. One of these techniques are Convolutional Neural Networks (CNNs). These networks have the capacity of classifying an image based on their visual appearance. Then, they generate a list of lexical labels and the probability for each label, representing the likelihood of the present of an object in the scene. Labels are derived from the training sets that the networks learned to recognize. Therefore, we could use this list of labels and probabilities as an efficient representation of the environment and then assign a semantic category to the regions where a mobile robot is able to navigate, and at the same time construct a semantic or topological map based on this semantic representation of the place. After analyzing the state-of-the-art in Scene Understanding, we identified a set of approaches in order to develop a robust scene understanding procedure. Among these approaches we identified an almost unexplored gap in the topic of understanding scenes based on objects present in them. Consequently, we propose to perform an experimental study in this approach aimed at finding a way of fully describing a scene considering the objects lying in place. As the Scene Understanding task involves object detection and annotation, one of the first steps is to determine the kind of data to use as input data in our proposal. With this in mind, our proposal considers to evaluate the use of 3D data. This kind of data suffers from the presence of noise, therefore, we propose to use the Growing Neural Gas (GNG) algorithm to reduce noise effect in the object recognition procedure. GNGs have the capacity to grow and adapt their topology to represent 2D information, producing a smaller representation with a slight noise influence from the input data. Applied to 3D data, the GNG presents a good approach able to tackle with noise. However, using 3D data poses a set of problems such as the lack of a 3D object dataset with enough models to generalize methods and adapt them to real situations, as well as the fact that processing three-dimensional data is computationally expensive and requires a huge storage space. These problems led us to explore new approaches for developing object recognition tasks. Therefore, considering the outstanding results obtained by the CNNs in the latest ImageNet challenge, we propose to carry out an evaluation of the former as an object detection system. These networks were initially proposed in the 90s and are nowadays easily implementable due to hardware improvements in the recent years. CNNs have shown satisfying results when they tested in problems such as: detection of objects, pedestrians, traffic signals, sound waves classification, and for medical image processing, among others. Moreover, an aggregate value of CNNs is the semantic description capabilities produced by the categories/labels that the network is able to identify and that could be translated as a semantic explanation of the input image. Consequently, we propose using the evaluation of these semantic labels as a scene descriptor for building a supervised scene classification model. Having said that, we also propose using semantic descriptors to generate topological maps and test the description capabilities of lexical labels. In addition, semantic descriptors could be suitable for unsupervised places or environment labeling, so we propose using them to deal with this kind of problem in order to achieve a robust scene labeling method. Finally, for tackling the object recognition problem we propose to develop an experimental study for unsupervised object labeling. This will be applied to the objects present in a point cloud and labeled using a lexical labeling tool. Then, objects will be used as the training instances of a classifier mixing their 3D features with label assigned by the external tool.
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Fan, Gao. "Clustering and Deep Learning Techniques for Structural Health Monitoring." Thesis, Curtin University, 2020. http://hdl.handle.net/20.500.11937/80611.

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This thesis proposes the development and application of clustering and deep learning techniques for improved automated modal identification, lost vibration data recovery, vibration signal denoising, and dynamic response reconstruction under operational and extreme loading conditions in the area of structural health monitoring. The effectiveness and performances of the proposed approaches are validated by numerical and experimental studies. The outstanding results demonstrate that these proposed approaches are reliable and very promising for practical applications.
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Books on the topic "Deep Learning techniques"

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Huang, Lei. Normalization Techniques in Deep Learning. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-14595-7.

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Briot, Jean-Pierre, Gaëtan Hadjeres, and François-David Pachet. Deep Learning Techniques for Music Generation. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-319-70163-9.

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Devi, K. Gayathri, Kishore Balasubramanian, and Le Anh Ngoc. Machine Learning and Deep Learning Techniques for Medical Science. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003217497.

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Phuong, Nguyen Hoang, and Vladik Kreinovich, eds. Deep Learning and Other Soft Computing Techniques. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-29447-1.

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Abdel-Basset, Mohamed, Nour Moustafa, Hossam Hawash, and Weiping Ding. Deep Learning Techniques for IoT Security and Privacy. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-89025-4.

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Dash, Sujata, Biswa Ranjan Acharya, Mamta Mittal, Ajith Abraham, and Arpad Kelemen, eds. Deep Learning Techniques for Biomedical and Health Informatics. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-33966-1.

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Mangrulkar, Ramchandra S., Antonis Michalas, Narendra M. Shekokar, Meera Narvekar, and Pallavi V. Chavan. Design of Intelligent Applications Using Machine Learning and Deep Learning Techniques. Boca Raton: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003133681.

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Chaki, Jyotismita. Diagnosis of Neurological Disorders Based on Deep Learning Techniques. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/9781003315452.

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Suganthi, K., R. Karthik, G. Rajesh, and Peter Ho Chiung Ching. Machine Learning and Deep Learning Techniques in Wireless and Mobile Networking Systems. Boca Raton: CRC Press, 2021. http://dx.doi.org/10.1201/9781003107477.

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Huang, Lei. Normalization Techniques in Deep Learning. Springer International Publishing AG, 2022.

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Book chapters on the topic "Deep Learning techniques"

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Ketkar, Nikhil. "Regularization Techniques." In Deep Learning with Python, 209–14. Berkeley, CA: Apress, 2017. http://dx.doi.org/10.1007/978-1-4842-2766-4_13.

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Qamar, Usman, and Muhammad Summair Raza. "Deep Learning." In Data Science Concepts and Techniques with Applications, 217–70. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-17442-1_8.

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Moons, Bert, Daniel Bankman, and Marian Verhelst. "Circuit Techniques for Approximate Computing." In Embedded Deep Learning, 89–113. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99223-5_4.

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Dupuis, Etienne, Silviu Filip, Olivier Sentieys, David Novo, Ian O’Connor, and Alberto Bosio. "Approximations in Deep Learning." In Approximate Computing Techniques, 467–512. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-94705-7_15.

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Bharadwaj, Yellapragada Sai Srinivasa. "Advanced Deep Learning Techniques." In Advanced Deep Learning for Engineers and Scientists, 145–81. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-66519-7_6.

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Mathew, Amitha, P. Amudha, and S. Sivakumari. "Deep Learning Techniques: An Overview." In Advances in Intelligent Systems and Computing, 599–608. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3383-9_54.

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Choo, Sanghyun, and Chang S. Nam. "Deep Learning Techniques in Neuroergonomics." In Neuroergonomics, 115–38. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-34784-0_7.

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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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Poonam Chaudhari and Himanshu Agarwal. "Progressive Review Towards Deep Learning Techniques." In Proceedings of the International Conference on Data Engineering and Communication Technology, 151–58. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-1675-2_17.

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Ramasubbareddy, Somula, D. Saidulu, V. Devasekhar, V. Swathi, Sahaj Singh Maini, and K. Govinda. "Music Generation Using Deep Learning Techniques." In Innovations in Computer Science and Engineering, 327–35. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-7082-3_37.

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Conference papers on the topic "Deep Learning techniques"

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Goularas, Dionysis, and Sani Kamis. "Evaluation of Deep Learning Techniques in Sentiment Analysis from Twitter Data." 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.2019.00011.

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"Session: Deep Learning Techniques." In 2019 IEEE 15th International Conference on Intelligent Computer Communication and Processing (ICCP). IEEE, 2019. http://dx.doi.org/10.1109/iccp48234.2019.8959780.

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Glassner, Andrew. "Deep learning." In SIGGRAPH '18: Special Interest Group on Computer Graphics and Interactive Techniques Conference. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3214834.3214856.

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Glassner, Andrew. "Deep learning." In SIGGRAPH '19: Special Interest Group on Computer Graphics and Interactive Techniques Conference. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3305366.3328026.

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Yu, Wenhua. "Deep Learning Mesh Generation Techniques." In 2021 International Applied Computational Electromagnetics Society (ACES-China) Symposium. IEEE, 2021. http://dx.doi.org/10.23919/aces-china52398.2021.9582049.

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Fan, Tongtian, Roozbeh Sadeghian, and Siamak Aram. "Deer-Vehicle Collisions Prevention using Deep Learning Techniques." In 2020 IEEE Cloud Summit. IEEE, 2020. http://dx.doi.org/10.1109/ieeecloudsummit48914.2020.00021.

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Smith, Jason T., Nathan Un, Ruoyang Yao, Nattawut Sinsuebphon, Alena Rudkouskaya, Joseph Mazurkiewicz, Margarida Barroso, Pingkun Yan, and Xavier Intes. "Fluorescent Lifetime Imaging improved via Deep Learning." In Novel Techniques in Microscopy. Washington, D.C.: OSA, 2019. http://dx.doi.org/10.1364/ntm.2019.nm3c.4.

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Batool, Syeda Fareeha, Faisal Rehman, Hanan Sharif, Maheen Jaffer, Anza Gul, and Sameen Butt. "Intrusion Detection using Deep Learning Techniques." In 2022 3rd International Conference on Innovations in Computer Science & Software Engineering (ICONICS). IEEE, 2022. http://dx.doi.org/10.1109/iconics56716.2022.10100584.

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Dawra, Bhavya, Ananya Navneet Chauhan, Ritu Rani, Amita Dev, Poonam Bansal, and Arun Sharma. "Malware Classification using Deep Learning Techniques." In 2023 2nd Edition of IEEE Delhi Section Flagship Conference (DELCON). IEEE, 2023. http://dx.doi.org/10.1109/delcon57910.2023.10127303.

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Tripathi, Kshitij, and Pooja Pathak. "Deep Learning Techniques for Air Pollution." In 2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS). IEEE, 2021. http://dx.doi.org/10.1109/icccis51004.2021.9397130.

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Reports on the topic "Deep Learning techniques"

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Jiang, M., and B. Matei. Mesh Failure Prediction Using Deep Learning Techniques. Office of Scientific and Technical Information (OSTI), February 2020. http://dx.doi.org/10.2172/1601556.

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Holm, Jennifer, Trevor Keenan, Daniel Ricciuto, and Vincent Emanuele. Deep learning techniques to disentangle water use efficiency, climate change, and carbon sequestration across ecosystem scales1. Office of Scientific and Technical Information (OSTI), April 2021. http://dx.doi.org/10.2172/1769694.

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Moon, Jarrett. Using Deep Learning Techniques to Search for the MiniBooNE Low Energy Excess in MicroBooNE with > 3$\sigma$ Sensitivity. Office of Scientific and Technical Information (OSTI), October 2020. http://dx.doi.org/10.2172/1767032.

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Maher, Nicola, Pedro DiNezio, Antonietta Capotondi, and Jennifer Kay. Identifying precursors of daily to seasonal hydrological extremes over the USA using deep learning techniques and climate model ensembles. Office of Scientific and Technical Information (OSTI), April 2021. http://dx.doi.org/10.2172/1769719.

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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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Tayeb, Shahab. Taming the Data in the Internet of Vehicles. Mineta Transportation Institute, January 2022. http://dx.doi.org/10.31979/mti.2022.2014.

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As an emerging field, the Internet of Vehicles (IoV) has a myriad of security vulnerabilities that must be addressed to protect system integrity. To stay ahead of novel attacks, cybersecurity professionals are developing new software and systems using machine learning techniques. Neural network architectures improve such systems, including Intrusion Detection System (IDSs), by implementing anomaly detection, which differentiates benign data packets from malicious ones. For an IDS to best predict anomalies, the model is trained on data that is typically pre-processed through normalization and feature selection/reduction. These pre-processing techniques play an important role in training a neural network to optimize its performance. This research studies the impact of applying normalization techniques as a pre-processing step to learning, as used by the IDSs. The impacts of pre-processing techniques play an important role in training neural networks to optimize its performance. This report proposes a Deep Neural Network (DNN) model with two hidden layers for IDS architecture and compares two commonly used normalization pre-processing techniques. Our findings are evaluated using accuracy, Area Under Curve (AUC), Receiver Operator Characteristic (ROC), F-1 Score, and loss. The experimentations demonstrate that Z-Score outperforms no-normalization and the use of Min-Max normalization.
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Celik, Ozer. Detection of Impacted Teeth Using Deep Learning Technique. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, February 2021. http://dx.doi.org/10.7546/crabs.2021.02.14.

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Patwa, B., P. L. St-Charles, G. Bellefleur, and B. Rousseau. Predictive models for first arrivals on seismic reflection data, Manitoba, New Brunswick, and Ontario. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/329758.

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First arrivals are the primary waves picked and analyzed by seismologists to infer properties of the subsurface. Here we try to solve a problem in a small subsection of the seismic processing workflow: first break picking of seismic reflection data. We formulate this problem as an image segmentation task. Data is preprocessed, cleaned from outliers and extrapolated to make the training of deep learning models feasible. We use Fully Convolutional Networks (specifically UNets) to train initial models and explore their performance with losses, layer depths, and the number of classes. We propose to use residual connections to improve each UNet block and residual paths to solve the semantic gap between UNet encoder and decoder which improves the performance of the model. Adding spatial information as an extra channel helped increase the RMSE performance of the first break predictions. Other techniques like data augmentation, multitask loss, and normalization methods, were further explored to evaluate model improvement.
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Mohammadian, Abolfazl, Amir Bahador Parsa, Homa Taghipour, Amir Davatgari, and Motahare Mohammadi. Best Practice Operation of Reversible Express Lanes for the Kennedy Expressway. Illinois Center for Transportation, September 2021. http://dx.doi.org/10.36501/0197-9191/21-033.

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Reversible lanes in Chicago’s Kennedy Expressway are an available infrastructure that can significantly improve traffic performance; however, a special focus on congestion management is required to improve their operation. This research project aims to evaluate and improve the operation of reversible lanes in the Kennedy Expressway. The Kennedy Expressway is a nearly 18-mile-long freeway in Chicago, Illinois, that connects in the southeast to northwest direction between the West Loop and O’Hare International Airport. There are two approximately 8-mile reversible lanes in the Kennedy Expressway’s median, where I-94 merges into I-90, and there are three entrance gates in each direction of this corridor. The purpose of the reversible lanes is to help the congested direction of the Kennedy Expressway increase its traffic flow and decrease the delay in the whole corridor. Currently, experts in a control location switch the direction of the reversible lanes two to three times per day by observing real-time traffic conditions captured by a traffic surveillance camera. In general, inbound gates are opened and outbound gates are closed around midnight because morning traffic is usually heavier toward the central city neighborhoods. In contrast, evening peak-hour traffic is usually heavier toward the outbound direction, so the direction of the reversible lanes is switched from inbound to outbound around noon. This study evaluates the Kennedy Expressway’s current reversing operation. Different indices are generated for the corridor to measure the reversible lanes’ performance, and a data-driven approach is selected to find the best time to start the operation. Subsequently, real-time and offline instruction for the operation of the reversible lanes is provided through employing deep learning and statistical techniques. In addition, an offline timetable is also provided through an optimization technique. Eventually, integration of the data-driven and optimization techniques results in the best practice operation of the reversible lanes.
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Huang, Haohang, Jiayi Luo, Kelin Ding, Erol Tutumluer, John Hart, and Issam Qamhia. I-RIPRAP 3D Image Analysis Software: User Manual. Illinois Center for Transportation, June 2023. http://dx.doi.org/10.36501/0197-9191/23-008.

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Riprap rock and aggregates are commonly used in various engineering applications such as structural, transportation, geotechnical, and hydraulic engineering. To ensure the quality of the aggregate materials selected for these applications, it is important to determine their morphological properties such as size and shape. There have been many imaging approaches developed to characterize the size and shape of individual aggregates, but obtaining 3D characterization of aggregates in stockpiles at production or construction sites can be a challenging task. This research study introduces a new approach based on deep learning techniques that combines three developed research components: field 3D reconstruction procedures, 3D stockpiles instance segmentation, and 3D shape completion. The approach is designed to reconstruct aggregate stockpiles from multiple images, segment the stockpile into individual instances, and predict the unseen sides of each instance (particle) based on the partially visible shapes. The approach was validated using ground-truth measurements and demonstrated satisfactory algorithm performance in capturing and predicting the unseen sides of aggregates. For better user experience, the integrated approach has been implemented into a software application named “I-RIPRAP 3D,” with a user-friendly graphical user interface (GUI). This stockpile aggregate analysis approach is envisioned to provide efficient field evaluation of aggregate stockpiles by offering convenient and reliable solutions for on-site quality assurance and quality control tasks of riprap rock and aggregate stockpiles. This document provides information for users of the I-RIPRAP 3D software to make the best use of the software’s capabilities.
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