Relevant bibliographies by topics / EfficientNet

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Academic literature on the topic 'EfficientNet'

Author: Grafiati
Published: 28 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "EfficientNet"

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Munien, Chanaleä, and Serestina Viriri. "Classification of Hematoxylin and Eosin-Stained Breast Cancer Histology Microscopy Images Using Transfer Learning with EfficientNets." Computational Intelligence and Neuroscience 2021 (April 9, 2021): 1–17. http://dx.doi.org/10.1155/2021/5580914.

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Breast cancer is a fatal disease and is a leading cause of death in women worldwide. The process of diagnosis based on biopsy tissue is nontrivial, time-consuming, and prone to human error, and there may be conflict about the final diagnosis due to interobserver variability. Computer-aided diagnosis systems have been designed and implemented to combat these issues. These systems contribute significantly to increasing the efficiency and accuracy and reducing the cost of diagnosis. Moreover, these systems must perform better so that their determined diagnosis can be more reliable. This research investigates the application of the EfficientNet architecture for the classification of hematoxylin and eosin-stained breast cancer histology images provided by the ICIAR2018 dataset. Specifically, seven EfficientNets were fine-tuned and evaluated on their ability to classify images into four classes: normal, benign, in situ carcinoma, and invasive carcinoma. Moreover, two standard stain normalization techniques, Reinhard and Macenko, were observed to measure the impact of stain normalization on performance. The outcome of this approach reveals that the EfficientNet-B2 model yielded an accuracy and sensitivity of 98.33% using Reinhard stain normalization method on the training images and an accuracy and sensitivity of 96.67% using the Macenko stain normalization method. These satisfactory results indicate that transferring generic features from natural images to medical images through fine-tuning on EfficientNets can achieve satisfactory results.
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Wang, Jun, Qianying Liu, Haotian Xie, Zhaogang Yang, and Hefeng Zhou. "Boosted EfficientNet: Detection of Lymph Node Metastases in Breast Cancer Using Convolutional Neural Networks." Cancers 13, no. 4 (February 7, 2021): 661. http://dx.doi.org/10.3390/cancers13040661.

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(1) Purpose: To improve the capability of EfficientNet, including developing a cropping method called Random Center Cropping (RCC) to retain the original image resolution and significant features on the images’ center area, reducing the downsampling scale of EfficientNet to facilitate the small resolution images of RPCam datasets, and integrating attention and Feature Fusion (FF) mechanisms with EfficientNet to obtain features containing rich semantic information. (2) Methods: We adopt the Convolutional Neural Network (CNN) to detect and classify lymph node metastasis in breast cancer. (3) Results: Experiments illustrate that our methods significantly boost performance of basic CNN architectures, where the best-performed method achieves an accuracy of 97.96% ± 0.03% and an Area Under the Curve (AUC) of 99.68% ± 0.01% on RPCam datasets, respectively. (4) Conclusions: (1) To our limited knowledge, we are the only study to explore the power of EfficientNet on Metastatic Breast Cancer (MBC) classification, and elaborate experiments are conducted to compare the performance of EfficientNet with other state-of-the-art CNN models. It might provide inspiration for researchers who are interested in image-based diagnosis using Deep Learning (DL). (2) We design a novel data augmentation method named RCC to promote the data enrichment of small resolution datasets. (3) All of our four technological improvements boost the performance of the original EfficientNet.
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RIZAL, SYAMSUL, NUR IBRAHIM, NOR KUMALASARI CAESAR PRATIWI, SOFIA SAIDAH, and RADEN YUNENDAH NUR FU’ADAH. "Deep Learning untuk Klasifikasi Diabetic Retinopathy menggunakan Model EfficientNet." ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika 8, no. 3 (August 27, 2020): 693. http://dx.doi.org/10.26760/elkomika.v8i3.693.

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ABSTRAKDiabetic Retinopathy merupakan penyakit yang dapat mengakibatkan kebutaan mata yang disebabkan oleh adanya komplikasi penyakit diabetes melitus. Oleh karena itu mendeteksi secara dini sangat diperlukan untuk mencegah bertambah parahnya penyakit tersebut. Penelitian ini merancang sebuah sistem yang dapat mendeteksi Diabetic Retinopathy berbasis Deep Learning dengan menggunakan Convolutional Neural Network (CNN). EfficientNet model digunakan untuk melatih dataset yang telah di pre-prosesing sebelumnya. Hasil dari penelitian tersebut didapatkan akurasi sebesar 79.8% yang dapat mengklasifikasi 5 level penyakit Diabetic Retinopathy.Kata kunci: Diabetic Retinopathy, Deep Learning, CNN, EfficientNet, Diabetic Classification ABSTRACTDiabetic Retinopathy is a diseases which can cause blindness in the eyes because of the complications of diabetes mellitus. Therefore, an early detection for this diseases is very important to prevent the diseases become severe. This research builds the system which can detect the Diabetic Retinopathy based on Deep Learning by using Convolutional Neural Network (CNN). EfficientNet model is used to trained the dataset which have been pre-prossed. The result shows that the system can clasiffy the 5 level of Diabetic Retinopathy with accuracy 79.8%. Keywords: Diabetic Retinopathy, Deep Learning, CNN, EfficientNet, Diabetic Classification
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Et. al., Ushasukhanya S,. "SMART ELECTRICITY CONSERVATION SYSTEM USING EFFICIENTNET." INFORMATION TECHNOLOGY IN INDUSTRY 9, no. 2 (April 12, 2021): 978–83. http://dx.doi.org/10.17762/itii.v9i2.440.

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Conservation of electric resource has been one of the important challenges over the decades. Worldwide, many nations are struggling to conserve and to bridge the gap between the demand and production of the resource. Though many measures like several Government acts, replacing existing products with energy conserving products and many solar based systems are being invented and used in practise, the demand and the need to preserve the resource still persists. Hence, this paper focuses on a novel technique to conserve the electric resource using a deep learning technique. The system uses Convolutional Neural Networks to identify and localize humans in the CCTV footages using EfficientNet, a deep transfer learning model. The classifier processes and yields its output to an embedded Arduino microcontroller, after detecting the presence/absence of human. The microcontroller enables/disables the electric power supply in the area of human’s presence/absence, based on the classifier’s output respectively. The system achieves an accuracy percentage of 84.2% in detecting humans in the footages with the subsequent enabling/disabling of electric power resource to conserve electricity.
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Afzaal, Hassan, Aitazaz A. Farooque, Arnold W. Schumann, Nazar Hussain, Andrew McKenzie-Gopsill, Travis Esau, Farhat Abbas, and Bishnu Acharya. "Detection of a Potato Disease (Early Blight) Using Artificial Intelligence." Remote Sensing 13, no. 3 (January 25, 2021): 411. http://dx.doi.org/10.3390/rs13030411.

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This study evaluated the potential of using machine vision in combination with deep learning (DL) to identify the early blight disease in real-time for potato production systems. Four fields were selected to collect images (n = 5199) of healthy and diseased potato plants under variable lights and shadow effects. A database was constructed using DL to identify the disease infestation at different stages throughout the growing season. Three convolutional neural networks (CNNs), namely GoogleNet, VGGNet, and EfficientNet, were trained using the PyTorch framework. The disease images were classified into three classes (2-class, 4-class, and 6-class) for accurate disease identification at different growth stages. Results of 2-class CNNs for disease identification revealed the significantly better performance of EfficientNet and VGGNet when compared with the GoogleNet (FScore range: 0.84–0.98). Results of 4-Class CNNs indicated better performance of EfficientNet when compared with other CNNs (FScore range: 0.79–0.94). Results of 6-class CNNs showed similar results as 4-class, with EfficientNet performing the best. GoogleNet, VGGNet, and EfficientNet inference time values ranged from 6.8–8.3, 2.1–2.5, 5.95–6.53 frames per second, respectively, on a Dell Latitude 5580 using graphical processing unit (GPU) mode. Overall, the CNNs and DL frameworks used in this study accurately classified the early blight disease at different stages. Site-specific application of fungicides by accurately identifying the early blight infected plants has a strong potential to reduce agrochemicals use, improve the profitability of potato growers, and lower environmental risks (runoff of fungicides to water bodies).
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Duong, Linh T., Phuong T. Nguyen, Claudio Di Sipio, and Davide Di Ruscio. "Automated fruit recognition using EfficientNet and MixNet." Computers and Electronics in Agriculture 171 (April 2020): 105326. http://dx.doi.org/10.1016/j.compag.2020.105326.

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Bazi, Yakoub, Mohamad M. Al Rahhal, Haikel Alhichri, and Naif Alajlan. "Simple Yet Effective Fine-Tuning of Deep CNNs Using an Auxiliary Classification Loss for Remote Sensing Scene Classification." Remote Sensing 11, no. 24 (December 5, 2019): 2908. http://dx.doi.org/10.3390/rs11242908.

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The current literature of remote sensing (RS) scene classification shows that state-of-the-art results are achieved using feature extraction methods, where convolutional neural networks (CNNs) (mostly VGG16 with 138.36 M parameters) are used as feature extractors and then simple to complex handcrafted modules are added for additional feature learning and classification, thus coming back to feature engineering. In this paper, we revisit the fine-tuning approach for deeper networks (GoogLeNet and Beyond) and show that it has not been well exploited due to the negative effect of the vanishing gradient problem encountered when transferring knowledge to small datasets. The aim of this work is two-fold. Firstly, we provide best practices for fine-tuning pre-trained CNNs using the root-mean-square propagation (RMSprop) method. Secondly, we propose a simple yet effective solution for tackling the vanishing gradient problem by injecting gradients at an earlier layer of the network using an auxiliary classification loss function. Then, we fine-tune the resulting regularized network by optimizing both the primary and auxiliary losses. As for pre-trained CNNs, we consider in this work inception-based networks and EfficientNets with small weights: GoogLeNet (7 M) and EfficientNet-B0 (5.3 M) and their deeper versions Inception-v3 (23.83 M) and EfficientNet-B3 (12 M), respectively. The former networks have been used previously in the context of RS and yielded low accuracies compared to VGG16, while the latter are new state-of-the-art models. Extensive experimental results on several benchmark datasets reveal clearly that if fine-tuning is done in an appropriate way, it can settle new state-of-the-art results with low computational cost.
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Carmo, Diedre, Israel Campiotti, Lívia Rodrigues, Irene Fantini, Gustavo Pinheiro, Daniel Moraes, Rodrigo Nogueira, Leticia Rittner, and Roberto Lotufo. "Rapidly deploying a COVID-19 decision support system in one of the largest Brazilian hospitals." Health Informatics Journal 27, no. 3 (July 2021): 146045822110330. http://dx.doi.org/10.1177/14604582211033017.

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The COVID-19 pandemic generated research interest in automated models to perform classification and segmentation from medical imaging of COVID-19 patients, However, applications in real-world scenarios are still needed. We describe the development and deployment of COVID-19 decision support and segmentation system. A partnership with a Brazilian radiologist consortium, gave us access to 1000s of labeled computed tomography (CT) and X-ray images from São Paulo Hospitals. The system used EfficientNet and EfficientDet networks, state-of-the-art convolutional neural networks for natural images classification and segmentation, in a real-time scalable scenario in communication with a Picture Archiving and Communication System (PACS). Additionally, the system could reject non-related images, using header analysis and classifiers. We achieved CT and X-ray classification accuracies of 0.94 and 0.98, respectively, and Dice coefficient for lung and covid findings segmentations of 0.98 and 0.73, respectively. The median response time was 7 s for X-ray and 4 min for CT.
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Wang, Jing, Liu Yang, Zhanqiang Huo, Weifeng He, and Junwei Luo. "Multi-Label Classification of Fundus Images With EfficientNet." IEEE Access 8 (2020): 212499–508. http://dx.doi.org/10.1109/access.2020.3040275.

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Wu, Tao, Hongjin Zhu, Honghui Fan, and Hongyan Zhou. "An improved target detection algorithm based on EfficientNet." Journal of Physics: Conference Series 1983, no. 1 (July 1, 2021): 012017. http://dx.doi.org/10.1088/1742-6596/1983/1/012017.

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Dissertations / Theses on the topic "EfficientNet"

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Havelka, Martin. "Detekce aktuálního podlaží při jízdě výtahem." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2021. http://www.nusl.cz/ntk/nusl-444988.

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This diploma thesis deals with the detection of the current floor during elevator ride. This functionality is necessary for robot to move in multi-floor building. For this task, a fusion of accelerometric data during the ride of the elevator and image data obtained from the information display inside the elevator cabin is used. The research describes the already implemented solutions, data fusion methods and image classification options. Based on this part, suitable approaches for solving the problem were proposed. First, datasets from different types of elevator cabins were obtained. An algorithm for working with data from the accelerometric sensor was developed. A convolutional neural network, which was used to classify image data from displays, was selected and trained. Subsequently, the data fusion method was implemented. The individual parts were tested and evaluated. Based on their evaluation, integration into one functional system was performed. System was successfully verified and tested. Result of detection during the ride in different elevators was 97%.
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Prax, Jan. "Efektivnost hlubokých konvolučních neuronových sítí na elementární klasifikační úloze." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2021. http://www.nusl.cz/ntk/nusl-442831.

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In this thesis deep convolutional neural networks models and feature descriptor models are compared. Feature descriptors are paired with suitable chosen classifier. These models are a part of machine learning therefore machine learning types are described in this thesis. Further these chosen models are described, and their basics and problems are explained. Hardware and software used for tests is listed and then test results and results summary is listed. Then comparison based on the validation accuracy and training time of these said models is done.
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Currà, Pier Nicola. "Alma.Domus: residenza eco-efficiente per Solar Decathlon Europe." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2012. http://amslaurea.unibo.it/3619/.

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Bovet, Gérôme. "Architecture évolutive et efficiente du Web des bâtiments." Thesis, Paris, ENST, 2015. http://www.theses.fr/2015ENST0033/document.

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Les bâtiments sont de plus en plus équipés avec des réseaux d’automatisation dédiés, visant à réduire la consommation d’énergie ainsi que d’optimiser le confort. D’un autre côté, nous observons l’arrivée de capteurs et actionneurs liés à l’Internet des objets, pouvant naturellement se connecter à des réseaux IP. Dû à des contraintes d’obsolescence ou imposées par les propriétés physiques des bâtiments, il n’est pas rare que différentes technologies doivent cohabiter. Celles-ci fonctionnant avec des modèles et protocoles différents rend le développement de systèmes d’automatisation globaux compliqué. Les modèles classiques de systèmes distribués ne sont pas adaptés aux problématiques des réseaux de capteurs. Le paradigme du Web des objets est basé sur des ressources et vise quand à lui d’uniformiser la couche applicative entre différents objets à l’aide des technologies du Web, essentiellement HTTP et REST. Dans cette thèse, nous nous basons sur le Web des objets afin de créer un framework dédié au bâtiments intelligents, permettant aux développeurs de concevoir des applications composites sans connaissances des différentes technologies sous-jacentes. Grâce aux technologies Web, nous pouvons offrir des services homogènes tout en profitant des ressources disponibles à l’intérieur du réseau (capteurs et actionneurs), formant un nuage auto-géré. Dans le but de doter les bâtiments d’une plus grande intelligence, l’apprentissage automatique, souvent réservé aux experts, est rendu accessible à travers des interfaces Web cachant la complexité des processus
Buildings are increasingly equipped with dedicated automation networks, aiming to reduce the energy consumption and to optimize the comfort. On the other hand, we see the arrival of sensors and actuators related to the Internet of Things, which can naturally connect to IP networks. Due to constraints imposed by the obsolescence or physical properties of buildings, it is not uncommon that different technologies have to coexist. These networks operate with different models and protocols, making the development of global automation systems difficult. Traditional models of distributed systems are not adapted to the context of sensor networks. The paradigm of the Web of Things is resource-based and strives to standardize the application layer of different objects using Web technologies, primarily HTTP and REST. In this thesis, we use the Web of Things to create a framework dedicated to smart buildings, allowing developers to develop composite applications without knowledge of the underlying technologies. By relying on Web technologies, we can provide seamless service while reusing the available resources within the network (sensors and actuators), forming a self-managed cloud. In order to equip the buildings with a higher-level intelligence, machine learning, often reserved for experts, is made accessible through Web interfaces hiding the complexity of the process
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Bonelli, Michael. "Gestione Efficiente di Eventi Complessi su Piattaforma IoT ThingWorx." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2016.

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Nella prima parte di questa tesi viene introdotto il concetto di Internet of Things. Vengono discussi gli elementi costituitivi fondamentali di tale tecnologia, le differenti architetture proposte nel corso degli anni e le sfide che devono ancora essere affrontate per vedere realizzato l’IoT. Questa prima parte si conclude inoltre con due esempi di applicazione dell’IoT. Questi due esempi, Smart City e Smart Healthcare, hanno l’obbiettivo di evidenziare quali sono i vantaggi ed i servizi che possono essere offerti all’utente finale una volta applicato l’IoT. Nel secondo capitolo invece, vengono presentate le funzionalità della piattaforma IoT ThingWorx, la quale mette a disposizione un ambiente di sviluppo per applicazioni IoT con l’obbiettivo di ridurre i tempi e quindi anche i costi di sviluppo delle stesse. Questa piattaforma cerca di ridurre al minimo la necessità di scrivere codice, utilizzando un sistema di sviluppo di tipo “Drag and Drop”. ThingWorx mette anche a disposizione degli SDK per facilitare la programmazione dei device, gestendo soprattutto la parte di comunicazione nodo – piattaforma. Questo argomento viene trattato ampiamente nella parte finale di questo capitolo dopo aver visto quali sono i concetti fondamentali di modellazione e rappresentazione dei dati sui quali si basa la piattaforma. Nel terzo e ultimo capitolo di questa tesi viene presentato innanzitutto il tutorial Android di ThingWorx. Svolgere e successivamente estendere il tutorial ha evidenziato alcune limitazioni del modello iniziale e questo ci ha portato a progettare e sviluppare il componente Aggregated & Complex Event Manager per la gestione di eventi complessi e che permette di sgravare parzialmente la piattaforma da tale compito. La tesi si conclude evidenziando, tramite dei test, alcune differenze fra la situazione iniziale nella quale il componente non viene utilizzato e la situazione finale, nella quale invece viene usato.
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Le, Magoarou Luc. "Matrices efficientes pour le traitement du signal et l'apprentissage automatique." Thesis, Rennes, INSA, 2016. http://www.theses.fr/2016ISAR0008/document.

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Les matrices, en tant que représentations des applications linéaires en dimension finie, jouent un rôle central en traitement du signal et des images et en apprentissage automatique. L'application d'une matrice de rang plein à un vecteur implique a priori un nombre d'opérations arithmétiques de l'ordre du nombre d'entrées non-nulles que contient la matrice. Cependant, il existe des matrices pouvant être appliquées bien plus rapidement, cette propriété étant d'ailleurs un des fondements du succès de certaines transformations linéaires, telles que la transformée de Fourier ou la transformée en ondelettes. Quelle est cette propriété? Est-elle vérifiable aisément? Peut-on approcher des matrices quelconques par des matrices ayant cette propriété? Peut-on estimer des matrices ayant cette propriété? La thèse s'attaque à ces questions en explorant des applications telles que l'apprentissage de dictionnaire à implémentation efficace, l'accélération des itérations d'algorithmes de résolution de de problèmes inverses pour la localisation de sources, ou l'analyse de Fourier rapide sur graphe
Matrices, as natural representation of linear mappings in finite dimension, play a crucial role in signal processing and machine learning. Multiplying a vector by a full rank matrix a priori costs of the order of the number of non-zero entries in the matrix, in terms of arithmetic operations. However, matrices exist that can be applied much faster, this property being crucial to the success of certain linear transformations, such as the Fourier transform or the wavelet transform. What is the property that allows these matrices to be applied rapidly ? Is it easy to verify ? Can weapproximate matrices with ones having this property ? Can we estimate matrices having this property ? This thesis investigates these questions, exploring applications such as learning dictionaries with efficient implementations, accelerating the resolution of inverse problems or Fast Fourier Transform on graphs
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Grigoli, Francesco. "Studio dei codici, trasmissione e correzione efficiente di un messaggio." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/20965/.

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L'elaborato si prefigge di descrivere come avviene la codifica, la decodifica e la correzione di errori in una trasmissione dati, sfruttando l'entropia di Shannon, la codifica di Huffmann e i codici di Hamming.
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Vetrano, Vittorio <1977&gt. "Biomasse e loro quantificazione economica per un efficiente uso dell'energia." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2009. http://amsdottorato.unibo.it/2187/.

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Biomasses and their possible use as energy resource are of great interest today, and the general problem of energy resources as well. In the present study the key questions of the convenience, from both energy and economy standpoints, have been addressed without any bias: the problem has been handled starting from “philosophical” bases disregarding any pre-settled ideology or political trend, but simply using mathematical approaches as logical tools for defining balances in a right way. In this context quantitative indexes such as LCA and EROEI have been widely used, together with multicriteria methods (such as ELECTRE) as decision supporting tools. This approach permits to remove mythologies, such as the unrealistic concept of clean energy, or the strange idea of biomasses as a magic to solve every thing in the field of the energy. As a consequence the present study aims to find any relevant aspect potentially useful for the society, looking at any possible source of energy without prejudices but without unrealistic expectations too. For what concerns biomasses, we studied in great details four very different cases of study, in order to have a scenario as various as much we can. A relevant result is the need to use biomasses together with other more efficient sources, especially recovering by-products from silviculture activities: but attention should be paid to the transportation and environmental costs. Another relevant result is the very difficult possibility of reliable evaluation of dedicated cultures as sources for “biomasses for energy”: the problem has to be carefully evaluated case-by-case, because what seems useful in a context, becomes totally disruptive in another one. In any case the concept itself of convenience is not well defined at a level of macrosystem: it seems more appropriate to limit this very concept at a level of microsystem, considering that what sounds fine in a limited well defined microsystem may cause great damage in another slightly different, or even very similar, microsystem. This approach seems the right way to solve the controversy about the concept of convenience.
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Mendonca, Fernando. "Politiques polyvalentes et efficientes d'allocation de ressources pour les systèmes parallèles." Thesis, Université Grenoble Alpes (ComUE), 2017. http://www.theses.fr/2017GREAM021/document.

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Les plateformes de calcul à grande échelle ont beaucoup évoluées dernières années. La réduction des coûts des composants simplifie la construction de machines possédant des multicœurs et des accélérateurs comme les GPU.Ceci a permis une propagation des plateformes à grande échelle,dans lesquelles les machines peuvent être éloignées les unes des autres, pouvant même être situées sur différents continents. Le problème essentiel devient alors d'utiliser ces ressources efficacement.Dans ce travail nous nous intéressons d'abord à l'allocation efficace de tâches sur plateformes hétérogènes composées CPU et de GPU. Pour ce faire, nous proposons un outil nommé SWDUAL qui implémente l'algorithme de Smith-Waterman simultanément sur CPU et GPU, en choisissant quelles tâches il est plus intéressant de placer sur chaque type de ressource. Nos expériences montrent que SWDUAL donne de meilleurs résultats que les approches similaires de l'état de l'art.Nous analysons ensuite une nouvelle méthode d'ordonnancement enligne de tâches indépendantes de différentes tailles. Nous proposons une nouvelle technique qui optimise la métrique du stretch. Elle consiste à déplacer les jobs qui retardent trop de petites tâches sur des machines dédiées. Nos résultats expérimentaux montrent que notre méthode obtient de meilleurs résultats que la politique standard et qu'elle s'approche dans de nombreux cas des résultats d'une politique préemptive, qui peut être considérée comme une borne inférieure.Nous nous intéressons ensuite à l'impact de différentes contraintes sur la politique FCFS avec backfilling. La contrainte de contiguïté essaye de compacter les jobs et de réduire la fragmentation dans l'ordonnancement. La contrainte de localité basique place les jobs de telle sorte qu'ils utilisent le plus petit nombre de groupes de processeurs appelés textit. Nos résultats montrent que les bénéfices de telles contraintes sont suffisants pour compenser la réduction du nombre de jobs backfillés due à la réduction de la fragmentation.Nous proposons enfin une nouvelle contrainte nommée localité totale, dans laquelle l'ordonnanceur modélise la plateforme par un fat tree et se sert de cette information pour placer les jobs là où leur coût de communication est minimal.Notre campagne d'expériences montre que cette contrainte obtient de très bons résultats par rapport à un backfilling basique, et de meilleurs résultats que les contraintes précédentes
The field of parallel supercomputing has been changing rapidly inrecent years. The reduction of costs of the parts necessary to buildmachines with multicore CPUs and accelerators such as GPUs are ofparticular interest to us. This scenario allowed for the expansion oflarge parallel systems, with machines far apart from each other,sometimes even located on different continents. Thus, the crucialproblem is how to use these resources efficiently.In this work, we first consider the efficient allocation of taskssuitable for CPUs and GPUs in heterogeneous platforms. To that end, weimplement a tool called SWDUAL, which executes the Smith-Watermanalgorithm simultaneously on CPUs and GPUs, choosing which tasks aremore suited to one or another. Experiments show that SWDUAL givesbetter results when compared to similar approaches available in theliterature.Second, we study a new online method for scheduling independent tasksof different sizes on processors. We propose a new technique thatoptimizes the stretch metric by detecting when a reasonable amount ofsmall jobs is waiting while a big job executes. Then, the big job isredirected to separate set of machines, dedicated to running big jobsthat have been redirected. We present experiment results that show thatour method outperforms the standard policy and in many cases approachesthe performance of the preemptive policy, which can be considered as alower bound.Next, we present our study on constraints applied to the Backfillingalgorithm in combination with the FCFS policy: Contiguity, which is aconstraint that tries to keep jobs close together and reducefragmentation during the schedule, and Basic Locality, that aims tokeep jobs as much as possible inside groups of processors calledclusters. Experiment results show that the benefits of using theseconstrains outweigh the possible decrease in the number of backfilledjobs due to reduced fragmentation.Finally, we present an additional constraint to the Backfillingalgorithm called Full Locality, where the scheduler models the topologyof the platform as a fat tree and uses this model to assign jobs toregions of the platform where communication costs between processors isreduced. The experiment campaign is executed and results show that FullLocality is superior to all the previously proposed constraints, andspecially Basic Backfilling
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Bonfiglioli, Luca. "Identificazione efficiente di reti neurali sparse basata sulla Lottery Ticket Hypothesis." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020.

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Frankle e Carbin 2018, data una rete densa inizializzata casualmente, mostrano che esistono sottoreti sparse di tale rete che possono ottenere accuratezze superiori alla rete densa e richiedono meno iterazioni di addestramento per raggiungere l’early stop. Tali sottoreti sono indicate con il nome di winning ticket. L’identificazione di questi ultimi richiede tuttavia almeno un addestramento completo del modello denso, il che ne limita l’impiego pratico, se non come tecnica di compressione. In questa tesi, si mira a trovare una variante più efficiente dei metodi di magnitude based pruning proposti in letteratura, valutando diversi metodi euristici e data driven per ottenere winning ticket senza completare l’addestramento della rete densa. Confrontandosi con i risultati di Zhou et al. 2019, si mostra come l’accuratezza all’inizializzazione di un winning ticket non sia predittiva dell’accuratezza finale raggiunta dopo l’addestramento e come, di conseguenza, ottimizzare l’accuratezza al momento di inizializzazione non garantisca altrettanto elevate accuratezze dopo il riaddestramento. Viene inoltre mostrata la presenza di good ticket, ovvero un intero spettro di reti sparse con performance confrontabili, almeno lungo una dimensione, con quelle dei winning ticket, e come sia possibile identificare sottoreti che rientrano in questa categoria anche dopo poche iterazioni di addestramento della rete densa iniziale. L’identificazione di queste reti sparse avviene in modo simile a quanto proposto da You et al. 2020, mediante una predizione del winning ticket effettuata prima del completamento dell’addestramento della rete densa. Viene mostrato che l’utilizzo di euristiche alternative al magnitude based pruning per effettuare queste predizioni consente, con costi computazionali marginalmente superiori, di ottenere predizioni significativamente migliori sulle architetture prese in esame.
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Books on the topic "EfficientNet"

1

Hol, A. M. Gewogen recht: Billijkheid en efficientie bij onrechtmatige daad. Deventer: Kluwer, 1993.

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Granatstein, J. L. For efficient and effective military forces =: Des forces militaires efficientes et efficaces. Ottawa, Ont: Dept. of National Defence = Ministère de la défense nationale, 1997.

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Madagascar. Une bonne gouvernance n'est efficiente sans une intégrité certaine: Le code d'éthique. Antananarivo]: Repoblikan'i Madagasikara, Autorité de régulation des marchés publics, 2008.

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Orfeo, Maria, ed. La riforma dell'amministrazione e il sistema universitario tra semplificazione e trasparenza. Florence: Firenze University Press, 2012. http://dx.doi.org/10.36253/978-88-6655-138-6.

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Nel processo di riforma che ormai da anni caratterizza le Pubbliche Amministrazioni, la semplificazione e la trasparenza rappresentano due aspetti innovativi ed importanti per il modello organizzativo delle Università. Una tematica complessa che coinvolge molteplici profili e sollecita la riflessione su questioni ancora aperte, di significativo spessore. I relatori nei loro interventi ne esplorano i diversi aspetti evidenziando le ragioni per proseguire verso un cambiamento complessivo dell'amministrazione finalizzato a rendere la sua azione più efficiente, rapida ed economica.
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La solidarietà efficiente: Storia e prospettive del credito cooperativo in Italia : 1883-2000. Roma [etc.]: Laterza, 2002.

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Drouin, Francine. Évaluer pour enseigner: À la découverte d'une pédagogie efficiente auprès de l'élève sourd. Toronto: Ministère de l'éducation et de la formation de l'Ontario, 1993.

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Ciappei, Cristiano, and Massimiliano Pellegrini, eds. Facility management for global care. Florence: Firenze University Press, 2010. http://dx.doi.org/10.36253/978-88-6453-088-8.

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The aim of this work is to bring the study of facility management, that is the management of the services connected with the maintenance and valorisation of real estate, to a higher and more complete level. We have sought to overcome the – albeit inevitable – engineering/efficientist approach, to arrive at an all-round promotion and analysis of the discipline, hinging on the concept of service. This means, first and foremost, rediscovering the relational aspect apropos the clientele and, starting from this, moving towards a restructuring of the service where the aim is to meet personal requirements rather than purely technical standards. The aspiration, underscored in the title, is in fact that of arriving at a "global care" of the person.
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Una gestione bancaria efficiente: La Cassa di risparmio di Udine dalle origini alla prima guerra mondiale. Udine: Forum, 2007.

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Associazione nazionale magistrati italiani. Congresso nazionale. Giustizia più efficiente e indipendenza dei magistrati a garanzia dei cittadini: Atti del XXVII Congresso nazionale Associazione nazionale magistrati, Venezia, 5-8 febbraio 2004. [Milano]: IPSOA, 2004.

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Masciandaro, Donato. La giustizia civile è efficiente?: Costi ed effetti per il mercato del credito, le famiglie e le imprese : i rapporto del Laboratorio ABI-Bocconi sull'economia delle regole. Roma]: Bancaria, 2000.

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Book chapters on the topic "EfficientNet"

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Koonce, Brett. "EfficientNet." In Convolutional Neural Networks with Swift for Tensorflow, 109–23. Berkeley, CA: Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-6168-2_10.

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Kadri, Rahma, Mohamed Tmar, and Bassem Bouaziz. "Alzheimer’s Disease Prediction Using EfficientNet and Fastai." In Knowledge Science, Engineering and Management, 452–63. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-82147-0_37.

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Aruleba, Idowu, and Serestina Viriri. "Deep Learning for Age Estimation Using EfficientNet." In Advances in Computational Intelligence, 407–19. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-85030-2_34.

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Ravi, Vinayakumar, Harini Narasimhan, and Tuan D. Pham. "EfficientNet-Based Convolutional Neural Networks for Tuberculosis Classification." In Computational Biology, 227–44. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-69951-2_9.

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Pham, Hung N., Ren Jie Tan, Yu Tian Cai, Shahril Mustafa, Ngan Chong Yeo, Hui Juin Lim, Trang T. T. Do, Binh P. Nguyen, and Matthew Chin Heng Chua. "Automated Grading in Diabetic Retinopathy Using Image Processing and Modified EfficientNet." In Computational Collective Intelligence, 505–15. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-63007-2_39.

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Alquzi, Sahar, Haikel Alhichri, and Yakoub Bazi. "Detection of COVID-19 Using EfficientNet-B3 CNN and Chest Computed Tomography Images." In Advances in Intelligent Systems and Computing, 365–73. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2594-7_30.

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Kamble, Ravi, Pranab Samanta, and Nitin Singhal. "Optic Disc, Cup and Fovea Detection from Retinal Images Using U-Net++ with EfficientNet Encoder." In Ophthalmic Medical Image Analysis, 93–103. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-63419-3_10.

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Miglani, Vandana, and MPS Bhatia. "Skin Lesion Classification: A Transfer Learning Approach Using EfficientNets." In Advances in Intelligent Systems and Computing, 315–24. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3383-9_29.

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Zhang, Jianpeng, Yutong Xie, Zhibin Liao, Johan Verjans, and Yong Xia. "EfficientSeg: A Simple But Efficient Solution to Myocardial Pathology Segmentation Challenge." In Myocardial Pathology Segmentation Combining Multi-Sequence Cardiac Magnetic Resonance Images, 17–25. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-65651-5_2.

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Sahu, Priyanka, Anuradha Chug, Amit Prakash Singh, Dinesh Singh, and Ravinder Pal Singh. "Challenges and Issues in Plant Disease Detection Using Deep Learning." In Handbook of Research on Machine Learning Techniques for Pattern Recognition and Information Security, 56–74. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-3299-7.ch004.

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Deep learning (DL) has rapidly become an essential tool for image classification tasks. This technique is now being deployed to the tasks of classifying and detecting plant diseases. The encouraging results achieved with this methodology hide many problems that are rarely addressed in related experiments. This study examines the main factors influencing the efficiency of deep neural networks for plant disease detection. The challenges discussed in the study are based on the literature as well as experiments conducted using an image database, which contains approximately 1,296 leaf images of the beans crop. A pre-trained convolutional neural network, EfficientNet B0, is used for training and testing purposes. This study gives and emphasizes on factors and challenges that may potentially affect the use of DL techniques to detect and classify plant diseases. Some solutions are also suggested that may overcome these problems.
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Conference papers on the topic "EfficientNet"

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Lu, Qidong, Yingying Li, Zhiliang Qin, Xiaowei Liu, and Yun Xie. "Speech Recognition using EfficientNet." In ICMSSP 2020: 2020 5th International Conference on Multimedia Systems and Signal Processing. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3404716.3404717.

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Chetoui, Mohamed, and Moulay A. Akhloufi. "Explainable Diabetic Retinopathy using EfficientNET*." In 2020 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) in conjunction with the 43rd Annual Conference of the Canadian Medical and Biological Engineering Society. IEEE, 2020. http://dx.doi.org/10.1109/embc44109.2020.9175664.

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Yousfi, Yassine, Jan Butora, Jessica Fridrich, and Clément Fuji Tsang. "Improving EfficientNet for JPEG Steganalysis." In IH&MMSec '21: ACM Workshop on Information Hiding and Multimedia Security. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3437880.3460397.

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Lazuardi, Rachmadio Noval, Nyoman Abiwinanda, Tafwida Hesaputra Suryawan, Muhammad Hanif, and Astri Handayani. "Automatic Diabetic Retinopathy Classification with EfficientNet." In TENCON 2020 - 2020 IEEE REGION 10 CONFERENCE (TENCON). IEEE, 2020. http://dx.doi.org/10.1109/tencon50793.2020.9293941.

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Mathews, Mili Rosline, S. M. Anzar, R. Kalesh Krishnan, and Alavikunhu Panthakkan. "EfficientNet for retinal blood vessel segmentation." In 2020 3rd International Conference on Signal Processing and Information Security (ICSPIS). IEEE, 2020. http://dx.doi.org/10.1109/icspis51252.2020.9340135.

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Li, Chaoyi, Zihan Qiao, Kehan Wang, and Jiang Hongxing. "Improved EfficientNet-B4 for Melanoma Detection." In 2021 IEEE 2nd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE). IEEE, 2021. http://dx.doi.org/10.1109/icbaie52039.2021.9389915.

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Hoang, Van-Thanh, and Kang-Hyun Jo. "Practical Analysis on Architecture of EfficientNet." In 2021 14th International Conference on Human System Interaction (HSI). IEEE, 2021. http://dx.doi.org/10.1109/hsi52170.2021.9538782.

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Jagadish Kumar, S., U. Maheswaran, G. Jaikishan, and B. Divagar. "Melanoma Classification using XGB Classifier and EfficientNet." In 2021 International Conference on Intelligent Technologies (CONIT). IEEE, 2021. http://dx.doi.org/10.1109/conit51480.2021.9498424.

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Nonaka, Naoki, and Jun Seita. "Electrocardiogram Classification by Modified EfficientNet with Data Augmentation." In 2020 Computing in Cardiology Conference. Computing in Cardiology, 2020. http://dx.doi.org/10.22489/cinc.2020.063.

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Zhang, Yulong, Jingtao Sun, Mingkang Chen, Qiang Wang, Yuan Yuan, and Rongzhe Ma. "Multi-Weather Classification using Evolutionary Algorithm on EfficientNet." In 2021 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops). IEEE, 2021. http://dx.doi.org/10.1109/percomworkshops51409.2021.9430939.

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