Log in

Relevant bibliographies by topics / DEEP framework / Journal articles

Journal articles on the topic 'DEEP framework'

To see the other types of publications on this topic, follow the link: DEEP framework.

Author: Grafiati

Published: 4 June 2021

Last updated: 2 February 2022

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic 'DEEP framework.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

1

V, Anjanadevi, Hemalatha R, Venkateshwar R, Naren J, and Vithya G. "A framework for the Diagnosis of Diabetic Retinopathy Using Deep Learning Techniques." International Journal of Psychosocial Rehabilitation 23, no. 1 (February 20, 2019): 405–11. http://dx.doi.org/10.37200/ijpr/v23i1/pr190252.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Fiorini, Rodolfo A. "New CICT Framework for Deep Learning and Deep Thinking Application." International Journal of Software Science and Computational Intelligence 8, no. 2 (April 2016): 1–20. http://dx.doi.org/10.4018/ijssci.2016040101.

Full text

Abstract:

To achieve reliable system intelligence outstanding results, current computational system modeling and simulation community has to face and to solve two orders of modeling limitations at least. As a solution, the author proposes an exponential, pre-spatial arithmetic scheme (“all-powerful scheme”) by computational information conservation theory (CICT) to overcome the Information Double-Bind (IDB) problem and to thrive on both deterministic noise (DN) and random noise (RN) to develop powerful cognitive computational framework for deep learning, towards deep thinking applications. In a previous paper the author showed and discussed how this new CICT framework can help us to develop even competitive advanced quantum cognitive computational systems. An operative example is presented. This paper is a relevant contribution towards an effective and convenient “Science 2.0” universal computational framework to develop deeper learning and deep thinking system and application at your fingertips and beyond.

APA, Harvard, Vancouver, ISO, and other styles

3

Richards, Blake A., Timothy P. Lillicrap, Philippe Beaudoin, Yoshua Bengio, Rafal Bogacz, Amelia Christensen, Claudia Clopath, et al. "A deep learning framework for neuroscience." Nature Neuroscience 22, no. 11 (October 28, 2019): 1761–70. http://dx.doi.org/10.1038/s41593-019-0520-2.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

Charalampous, Konstantinos, and Antonios Gasteratos. "A tensor-based deep learning framework." Image and Vision Computing 32, no. 11 (November 2014): 916–29. http://dx.doi.org/10.1016/j.imavis.2014.08.003.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

Crunkhorn, Sarah. "Deep learning framework for repurposing drugs." Nature Reviews Drug Discovery 20, no. 2 (January 11, 2021): 100. http://dx.doi.org/10.1038/d41573-021-00006-w.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

Zhang, Hongjing, Tianyang Zhan, Sugato Basu, and Ian Davidson. "A framework for deep constrained clustering." Data Mining and Knowledge Discovery 35, no. 2 (January 17, 2021): 593–620. http://dx.doi.org/10.1007/s10618-020-00734-4.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

Sassu, Alessandro, Jose Francisco Saenz-Cogollo, and Maurizio Agelli. "Deep-Framework: A Distributed, Scalable, and Edge-Oriented Framework for Real-Time Analysis of Video Streams." Sensors 21, no. 12 (June 11, 2021): 4045. http://dx.doi.org/10.3390/s21124045.

Full text

Abstract:

Edge computing is the best approach for meeting the exponential demand and the real-time requirements of many video analytics applications. Since most of the recent advances regarding the extraction of information from images and video rely on computation heavy deep learning algorithms, there is a growing need for solutions that allow the deployment and use of new models on scalable and flexible edge architectures. In this work, we present Deep-Framework, a novel open source framework for developing edge-oriented real-time video analytics applications based on deep learning. Deep-Framework has a scalable multi-stream architecture based on Docker and abstracts away from the user the complexity of cluster configuration, orchestration of services, and GPU resources allocation. It provides Python interfaces for integrating deep learning models developed with the most popular frameworks and also provides high-level APIs based on standard HTTP and WebRTC interfaces for consuming the extracted video data on clients running on browsers or any other web-based platform.

APA, Harvard, Vancouver, ISO, and other styles

8

Ye, Jong Chul, Yoseob Han, and Eunju Cha. "Deep Convolutional Framelets: A General Deep Learning Framework for Inverse Problems." SIAM Journal on Imaging Sciences 11, no. 2 (January 2018): 991–1048. http://dx.doi.org/10.1137/17m1141771.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

Xu, Erci, and Shanshan Li. "Revisiting Resource Management for Deep Learning Framework." Electronics 8, no. 3 (March 16, 2019): 327. http://dx.doi.org/10.3390/electronics8030327.

Full text

Abstract:

The recent adoption of deep learning for diverse applications has required infrastructures to be scaled horizontally and hybrid configured vertically. As a result, efficient resource management for distributed deep learning (DDL) frameworks is becoming increasingly important. However, existing techniques for scaling DDL applications rely on general-purpose resource managers originally designed for data intensive applications. In contrast, DDL applications present unique challenges for resource management as compared to traditional big data frameworks, such as a different master–slave communication paradigm, deeper ML models that are more computationally and network bounded than I/O, the use of heterogeneous resources (e.g., GPUs, TPUs) and the variable memory requirement. In addition, most DDL frameworks require data scientists to manually configure the task placement and resource assignment to execute DDL models. In this paper, we present Dike, an automatic resource management framework that transparently makes scheduling decisions for placement and resource assignment to DDL workers and parameter servers, based on the unique characteristics of the DDL model (number and type of parameters and neural network layers), node heterogeneity (CPU/GPU ratios), and input dataset. We implemented Dike as a resource manager for DDL jobs in Tensorflow on top of Apache Mesos. We show that Dike significantly outperformed both manual and static assignment of resource offers to Tensorflow tasks, and achieved at least 95% of the optimal throughput for different DDL models such as ResNet and Inception.

APA, Harvard, Vancouver, ISO, and other styles

10

Sallab, AhmadEL, Mohammed Abdou, Etienne Perot, and Senthil Yogamani. "Deep Reinforcement Learning framework for Autonomous Driving." Electronic Imaging 2017, no. 19 (January 29, 2017): 70–76. http://dx.doi.org/10.2352/issn.2470-1173.2017.19.avm-023.

Full text

APA, Harvard, Vancouver, ISO, and other styles

11

Kalash, Mahmoud, Mrigank Rochan, Noman Mohammed, Neil Bruce, Yang Wang, and Farkhund Iqbal. "A Deep Learning Framework for Malware Classification." International Journal of Digital Crime and Forensics 12, no. 1 (January 2020): 90–108. http://dx.doi.org/10.4018/ijdcf.2020010105.

Full text

Abstract:

In this article, the authors propose a deep learning framework for malware classification. There has been a huge increase in the volume of malware in recent years which poses serious security threats to financial institutions, businesses, and individuals. In order to combat the proliferation of malware, new strategies are essential to quickly identify and classify malware samples. Nowadays, machine learning approaches are becoming popular for malware classification. However, most of these approaches are based on shallow learning algorithms (e.g. SVM). Recently, convolutional neural networks (CNNs), a deep learning approach, have shown superior performance compared to traditional learning algorithms, especially in tasks such as image classification. Inspired by this, the authors propose a CNN-based architecture to classify malware samples. They convert malware binaries to grayscale images and subsequently train a CNN for classification. Experiments on two challenging malware classification datasets, namely Malimg and Microsoft, demonstrate that their method outperforms competing state-of-the-art algorithms.

APA, Harvard, Vancouver, ISO, and other styles

12

Nguyen, Thanh Thi, Ngoc Duy Nguyen, Peter Vamplew, Saeid Nahavandi, Richard Dazeley, and Chee Peng Lim. "A multi-objective deep reinforcement learning framework." Engineering Applications of Artificial Intelligence 96 (November 2020): 103915. http://dx.doi.org/10.1016/j.engappai.2020.103915.

Full text

APA, Harvard, Vancouver, ISO, and other styles

13

Dai, Yinglong, and Guojun Wang. "A deep inference learning framework for healthcare." Pattern Recognition Letters 139 (November 2020): 17–25. http://dx.doi.org/10.1016/j.patrec.2018.02.009.

Full text

APA, Harvard, Vancouver, ISO, and other styles

14

Galanti, Tomer, Lior Wolf, and Tamir Hazan. "A theoretical framework for deep transfer learning." Information and Inference 5, no. 2 (April 28, 2016): 159–209. http://dx.doi.org/10.1093/imaiai/iaw008.

Full text

APA, Harvard, Vancouver, ISO, and other styles

15

Li, Ruichang, Honglei Zhu, Liao Fan, and Xuekun Song. "Hybrid Deep Framework for Group Event Recommendation." IEEE Access 8 (2020): 4775–84. http://dx.doi.org/10.1109/access.2019.2962780.

Full text

APA, Harvard, Vancouver, ISO, and other styles

16

Bedi, Jatin, and Durga Toshniwal. "Deep learning framework to forecast electricity demand." Applied Energy 238 (March 2019): 1312–26. http://dx.doi.org/10.1016/j.apenergy.2019.01.113.

Full text

APA, Harvard, Vancouver, ISO, and other styles

17

Tzelepi, Maria, and Anastasios Tefas. "Deep convolutional image retrieval: A general framework." Signal Processing: Image Communication 63 (April 2018): 30–43. http://dx.doi.org/10.1016/j.image.2018.01.007.

Full text

APA, Harvard, Vancouver, ISO, and other styles

18

Sánchez-DelaCruz, Eddy, Juan P. Salazar López, David Lara Alabazares, Edgar Tello Leal, and Mirta Fuentes-Ramos. "Deep learning framework for leaf damage identification." Concurrent Engineering 29, no. 1 (March 2021): 25–34. http://dx.doi.org/10.1177/1063293x21994953.

Full text

Abstract:

Foliar disease is common problem in plants; it appears as an abnormal change in the plant’s characteristics, such as the presence of lesions and discolorations, among others. These problems may be related to plant growth, which causes a decrease in crop production, impacting the agricultural economy. The causes of leaf damage can be variable, such as bacteria, viruses, nutritional deficiencies, or even consequences of climate change. Motivated to find a solution for this problem, we aim that using image processing and machine learning algorithms (MLA), these symptomatic characteristics of the leaf can be used to classify diseases. Then, contributions of this research are (i) the use of image processing methods in the feature extraction (characteristics), and (ii) the combination of assembled algorithms with deep learning to classify foliar features of Valencia orange (Citrus Sinensis) tree leaves. Combining these two classification approaches, we get optimal rates in binary datasets and highly competitive percentages in multiclass sets. This, using a database of images of three types of foliar damage of local plants. Result of combination of these two classification strategies is an exceptional reliable alternative for leaf damage identification of orange and other citrus plants.

APA, Harvard, Vancouver, ISO, and other styles

19

Koroniotis, Nickolaos, Nour Moustafa, and Elena Sitnikova. "A new network forensic framework based on deep learning for Internet of Things networks: A particle deep framework." Future Generation Computer Systems 110 (September 2020): 91–106. http://dx.doi.org/10.1016/j.future.2020.03.042.

Full text

APA, Harvard, Vancouver, ISO, and other styles

20

Murillo, Raul, Alberto A. Del Barrio, and Guillermo Botella. "Deep PeNSieve: A deep learning framework based on the posit number system." Digital Signal Processing 102 (July 2020): 102762. http://dx.doi.org/10.1016/j.dsp.2020.102762.

Full text

APA, Harvard, Vancouver, ISO, and other styles

21

Yang, Lei, Huaixin Wang, Qingshan Zeng, Yanhong Liu, and Guibin Bian. "A hybrid deep segmentation network for fundus vessels via deep-learning framework." Neurocomputing 448 (August 2021): 168–78. http://dx.doi.org/10.1016/j.neucom.2021.03.085.

Full text

APA, Harvard, Vancouver, ISO, and other styles

22

Li, Xingyi, Zhongang Qi, Xiaoli Fern, and Fuxin Li. "ScaleNet - Improve CNNs through Recursively Rescaling Objects." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (April 3, 2020): 11426–33. http://dx.doi.org/10.1609/aaai.v34i07.6806.

Full text

Abstract:

Deep networks are often not scale-invariant hence their performance can vary wildly if recognizable objects are at an unseen scale occurring only at testing time. In this paper, we propose ScaleNet, which recursively predicts object scale in a deep learning framework. With an explicit objective to predict the scale of objects in images, ScaleNet enables pretrained deep learning models to identify objects in the scales that are not present in their training sets. By recursively calling ScaleNet, one can generalize to very large scale changes unseen in the training set. To demonstrate the robustness of our proposed framework, we conduct experiments with pretrained as well as fine-tuned classification and detection frameworks on MNIST, CIFAR-10, and MS COCO datasets and results reveal that our proposed framework significantly boosts the performances of deep networks.

APA, Harvard, Vancouver, ISO, and other styles

23

Tiwari, Shamik. "A Comparative Study of Deep Learning Models With Handcraft Features and Non-Handcraft Features for Automatic Plant Species Identification." International Journal of Agricultural and Environmental Information Systems 11, no. 2 (April 2020): 44–57. http://dx.doi.org/10.4018/ijaeis.2020040104.

Full text

Abstract:

The classification of plants is one of the most important aims for botanists since plants have a significant part in the natural life cycle. In this work, a leaf-based automatic plant classification framework is investigated. The aim is to compare two different deep learning approaches named Deep Neural Network (DNN) and deep Convolutional Neural Network (CNN). In the case of deep neural network, hybrid shapes and texture features are utilized as hand-crafted features while in the case of the convolution non-handcraft, features are applied for classification. The offered frameworks are evaluated with a public leaf database. From the simulation results, it is confirmed that the deep CNN-based deep learning framework demonstrates superior classification performance than the handcraft feature based approach.

APA, Harvard, Vancouver, ISO, and other styles

24

Sharma, Dilip Kumar, and A. K. Sharma. "Deep Web Information Retrieval Process." International Journal of Information Technology and Web Engineering 5, no. 1 (January 2010): 1–22. http://dx.doi.org/10.4018/jitwe.2010010101.

Full text

Abstract:

Web crawlers specialize in downloading web content and analyzing and indexing from surface web, consisting of interlinked HTML pages. Web crawlers have limitations if the data is behind the query interface. Response depends on the querying party’s context in order to engage in dialogue and negotiate for the information. In this article, the authors discuss deep web searching techniques. A survey of technical literature on deep web searching contributes to the development of a general framework. Existing frameworks and mechanisms of present web crawlers are taxonomically classified into four steps and analyzed to find limitations in searching the deep web.

APA, Harvard, Vancouver, ISO, and other styles

25

Pacheco, Maria Leonor, and Dan Goldwasser. "Modeling Content and Context with Deep Relational Learning." Transactions of the Association for Computational Linguistics 9 (February 2021): 100–119. http://dx.doi.org/10.1162/tacl_a_00357.

Full text

Abstract:

Building models for realistic natural language tasks requires dealing with long texts and accounting for complicated structural dependencies. Neural-symbolic representations have emerged as a way to combine the reasoning capabilities of symbolic methods, with the expressiveness of neural networks. However, most of the existing frameworks for combining neural and symbolic representations have been designed for classic relational learning tasks that work over a universe of symbolic entities and relations. In this paper, we present DRaiL, an open-source declarative framework for specifying deep relational models, designed to support a variety of NLP scenarios. Our framework supports easy integration with expressive language encoders, and provides an interface to study the interactions between representation, inference and learning.

APA, Harvard, Vancouver, ISO, and other styles

26

Ravi, Nagarathna, Vimala Rani P, Rajesh Alias Harinarayan R, Mercy Shalinie S, Karthick Seshadri, and Pariventhan P. "Deep Learning-based Framework for Smart Sustainable Cities." International Journal of Intelligent Information Technologies 15, no. 4 (October 2019): 76–107. http://dx.doi.org/10.4018/ijiit.2019100105.

Full text

Abstract:

Pure air is vital for sustaining human life. Air pollution causes long-term effects on people. There is an urgent need for protecting people from its profound effects. In general, people are unaware of the levels to which they are exposed to air pollutants. Vehicles, burning various kinds of waste, and industrial gases are the top three onset agents of air pollution. Of these three top agents, human beings are exposed frequently to the pollutants due to motor vehicles. To aid in protecting people from vehicular air pollutants, this article proposes a framework that utilizes deep learning models. The framework utilizes a deep belief network to predict the levels of air pollutants along the paths people travel and also a comparison with the predictions made by a feed forward neural network and an extreme learning machine. When evaluating the deep belief neural network for the case study undertaken, a deep belief network was able to achieve a higher index of agreement and lower RMSE values.

APA, Harvard, Vancouver, ISO, and other styles

27

MATSUNO, Toshifumi, Tatsuya SONOBE, Ryosuke TAKAHASHI, Kazutoshi HIGASHIYAMA, and Kazuhiro HOSHI. "Close in on Domestic Deep Learning Framework "Chainer"." Journal of The Institute of Electrical Engineers of Japan 138, no. 5 (May 1, 2018): 294–97. http://dx.doi.org/10.1541/ieejjournal.138.294.

Full text

APA, Harvard, Vancouver, ISO, and other styles

28

Lore, Kin Gwn, Daniel Stoecklein, Michael Davies, Baskar Ganapathysubramanian, and Soumik Sarkar. "A deep learning framework for causal shape transformation." Neural Networks 98 (February 2018): 305–17. http://dx.doi.org/10.1016/j.neunet.2017.12.003.

Full text

APA, Harvard, Vancouver, ISO, and other styles

29

Singari, Ranganath, Karun Singla, and Gangesh Chawla. "Deep Learning Framework for Steel Surface Defects Classification." INTERNATIONAL JOURNAL OF ADVANCED PRODUCTION AND INDUSTRIAL ENGINEERING 4, no. 1 (January 5, 2019): 25–32. http://dx.doi.org/10.35121/ijapie201901135.

Full text

Abstract:

Deep learning has offered new avenues in the field of industrial management. Traditional methods of quality inspection such as Acceptance Sampling relies on a probabilistic measure derived from inspecting a sample of finished products. Evaluating a fixed number of products to derive the quality level for the complete batch is not a robust approach. Visual inspection solutions based on deep learning can be employed in the large manufacturing units to improve the quality inspection units for steel surface defect detection. This leads to optimization of the human capital due to reduction in manual intervention and turnaround time in the overall supply chain of the industry. Consequently, the sample size in the Acceptance sampling can be increased with minimal effort vis-à-vis an increase in the overall accuracy of the inspection. The learning curve of this work is supported by Convolutional Neural Network which has been used to extract feature representations from grayscale images to classify theinputs into six types of surface defects. The neural network architecture is compiled in Keras framework using Tensorflow backend with state of the art Adam RMS Prop with Nesterov Momentum (NADAM) optimizer. The proposed classification algorithm holds the potential to identify the dominant flaws in the manufacturing system responsible for leaking costs.

APA, Harvard, Vancouver, ISO, and other styles

30

Ilidrissi, Amine, and Joo Kooi Tan. "A deep unified framework for suspicious action recognition." Artificial Life and Robotics 24, no. 2 (December 19, 2018): 219–24. http://dx.doi.org/10.1007/s10015-018-0518-y.

Full text

APA, Harvard, Vancouver, ISO, and other styles

31

Gu, Yuanli, Zhuangzhuang Shao, Lingqiao Qin, Wenqi Lu, and Meng Li. "A Deep Learning Framework for Cycling Maneuvers Classification." IEEE Access 7 (2019): 28799–809. http://dx.doi.org/10.1109/access.2019.2898852.

Full text

APA, Harvard, Vancouver, ISO, and other styles

32

Hou, Weilong, and Xinbo Gao. "Saliency-Guided Deep Framework for Image Quality Assessment." IEEE MultiMedia 22, no. 2 (April 2015): 46–55. http://dx.doi.org/10.1109/mmul.2014.55.

Full text

APA, Harvard, Vancouver, ISO, and other styles

33

Zhang, Weishan, Pengcheng Duan, Zhongwei Li, Qinghua Lu, Wenjuan Gong, and Su Yang. "A Deep Awareness Framework for Pervasive Video Cloud." IEEE Access 3 (2015): 2227–37. http://dx.doi.org/10.1109/access.2015.2497278.

Full text

APA, Harvard, Vancouver, ISO, and other styles

34

Kleftogiannis, Dimitrios, Panos Kalnis, and Vladimir B. Bajic. "DEEP: a general computational framework for predicting enhancers." Nucleic Acids Research 43, no. 1 (November 5, 2014): e6-e6. http://dx.doi.org/10.1093/nar/gku1058.

Full text

APA, Harvard, Vancouver, ISO, and other styles

35

Pang, Sh Ch, Anan Du, and Zh Zh Yu. "Robust multi-object tracking using deep learning framework." Journal of Optical Technology 82, no. 8 (August 1, 2015): 516. http://dx.doi.org/10.1364/jot.82.000516.

Full text

APA, Harvard, Vancouver, ISO, and other styles

36

Kim, Dahun, Sanghyun Woo, Joon-Young Lee, and In So Kweon. "Recurrent Temporal Aggregation Framework for Deep Video Inpainting." IEEE Transactions on Pattern Analysis and Machine Intelligence 42, no. 5 (May 1, 2020): 1038–52. http://dx.doi.org/10.1109/tpami.2019.2958083.

Full text

APA, Harvard, Vancouver, ISO, and other styles

37

Shih, Frank Y., Yucong Shen, and Xin Zhong. "Development of Deep Learning Framework for Mathematical Morphology." International Journal of Pattern Recognition and Artificial Intelligence 33, no. 06 (April 21, 2019): 1954024. http://dx.doi.org/10.1142/s0218001419540247.

Full text

Abstract:

Mathematical morphology has been applied as a collection of nonlinear operations related to object features in images. In this paper, we present morphological layers in deep learning framework, namely MorphNet, to perform atomic morphological operations, such as dilation and erosion. For propagation of losses through the proposed deep learning framework, we approximate the dilation and erosion operations by differential and smooth multivariable functions of the softmax function, and therefore enable the neural network to be optimized. The proposed operations are analyzed by the derivative of approximation functions in the deep learning framework. Experimental results show that the output structuring element of a morphological neuron and the target structuring element are matched to confirm the efficiency and correctness of the proposed framework.

APA, Harvard, Vancouver, ISO, and other styles

38

Zhang, Weishan, Liang Xu, Zhongwei Li, Qinghua Lu, and Yan Liu. "A Deep-Intelligence Framework for Online Video Processing." IEEE Software 33, no. 2 (March 2016): 44–51. http://dx.doi.org/10.1109/ms.2016.31.

Full text

APA, Harvard, Vancouver, ISO, and other styles

39

Yi, Ping, Yuxiang Guan, Futai Zou, Yao Yao, Wei Wang, and Ting Zhu. "Web Phishing Detection Using a Deep Learning Framework." Wireless Communications and Mobile Computing 2018 (September 26, 2018): 1–9. http://dx.doi.org/10.1155/2018/4678746.

Full text

Abstract:

Web service is one of the key communications software services for the Internet. Web phishing is one of many security threats to web services on the Internet. Web phishing aims to steal private information, such as usernames, passwords, and credit card details, by way of impersonating a legitimate entity. It will lead to information disclosure and property damage. This paper mainly focuses on applying a deep learning framework to detect phishing websites. This paper first designs two types of features for web phishing: original features and interaction features. A detection model based on Deep Belief Networks (DBN) is then presented. The test using real IP flows from ISP (Internet Service Provider) shows that the detecting model based on DBN can achieve an approximately 90% true positive rate and 0.6% false positive rate.

APA, Harvard, Vancouver, ISO, and other styles

40

Liu, Peng, Guoyu Wang, Hao Qi, Chufeng Zhang, Haiyong Zheng, and Zhibin Yu. "Underwater Image Enhancement With a Deep Residual Framework." IEEE Access 7 (2019): 94614–29. http://dx.doi.org/10.1109/access.2019.2928976.

Full text

APA, Harvard, Vancouver, ISO, and other styles

41

Jin, Zhi, Muhammad Zafar Iqbal, Dmytro Bobkov, Wenbin Zou, Xia Li, and Eckehard Steinbach. "A Flexible Deep CNN Framework for Image Restoration." IEEE Transactions on Multimedia 22, no. 4 (April 2020): 1055–68. http://dx.doi.org/10.1109/tmm.2019.2938340.

Full text

APA, Harvard, Vancouver, ISO, and other styles

42

Lin, Ching-Nung, Jr-Chang Chen, and Shi-Jim Yen. "Deep Learning Competition Framework on Othello for Education." IEEE Transactions on Games 11, no. 3 (September 2019): 300–304. http://dx.doi.org/10.1109/tg.2019.2931153.

Full text

APA, Harvard, Vancouver, ISO, and other styles

43

Masood, Khalid, and Mohammed A. Alghamdi. "Modeling Mental Stress Using a Deep Learning Framework." IEEE Access 7 (2019): 68446–54. http://dx.doi.org/10.1109/access.2019.2917718.

Full text

APA, Harvard, Vancouver, ISO, and other styles

44

Wang, Qi, Zequn Qin, Feiping Nie, and Xuelong Li. "C2DNDA: A Deep Framework for Nonlinear Dimensionality Reduction." IEEE Transactions on Industrial Electronics 68, no. 2 (February 2021): 1684–94. http://dx.doi.org/10.1109/tie.2020.2969072.

Full text

APA, Harvard, Vancouver, ISO, and other styles

45

Onihunwa, John, Olufade Onifade, Isaac Ariyo, Stephen Omotugba, and Deji Joshua. "Scalable Framework for Locating Deep Web Entry Points." IOSR Journal of Computer Engineering 19, no. 02 (April 2017): 45–55. http://dx.doi.org/10.9790/0661-1902034555.

Full text

APA, Harvard, Vancouver, ISO, and other styles

46

Bu, Shuhui, Pengcheng Han, Zhenbao Liu, Junwei Han, and Hongwei Lin. "Local deep feature learning framework for 3D shape." Computers & Graphics 46 (February 2015): 117–29. http://dx.doi.org/10.1016/j.cag.2014.09.007.

Full text

APA, Harvard, Vancouver, ISO, and other styles

47

Lyu, Yuting, Junghui Chen, and Zhihuan Song. "Image-based process monitoring using deep learning framework." Chemometrics and Intelligent Laboratory Systems 189 (June 2019): 8–17. http://dx.doi.org/10.1016/j.chemolab.2019.03.008.

Full text

APA, Harvard, Vancouver, ISO, and other styles

48

Harrou, Fouzi, Abdelkader Dairi, Farid Kadri, and Ying Sun. "Forecasting emergency department overcrowding: A deep learning framework." Chaos, Solitons & Fractals 139 (October 2020): 110247. http://dx.doi.org/10.1016/j.chaos.2020.110247.

Full text

APA, Harvard, Vancouver, ISO, and other styles

49

Yonekura, Kazuo, and Hitoshi Hattori. "Framework for design optimization using deep reinforcement learning." Structural and Multidisciplinary Optimization 60, no. 4 (May 2, 2019): 1709–13. http://dx.doi.org/10.1007/s00158-019-02276-w.

Full text

APA, Harvard, Vancouver, ISO, and other styles

50

Blackburn, Christopher, Anthony Harding, and Juan Moreno-Cruz. "Toward Deep-Decarbonization: an Energy-Service System Framework." Current Sustainable/Renewable Energy Reports 4, no. 4 (August 9, 2017): 181–90. http://dx.doi.org/10.1007/s40518-017-0088-y.

Full text

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!