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Artykuły w czasopismach na temat „DEEP framework”

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Autor: Grafiati

Data publikacji: 4 czerwca 2021

Data aktualizacji: 2 lutego 2022

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1

V, Anjanadevi, Hemalatha R, Venkateshwar R, Naren J i Vithya G. "A framework for the Diagnosis of Diabetic Retinopathy Using Deep Learning Techniques". International Journal of Psychosocial Rehabilitation 23, nr 1 (20.02.2019): 405–11. http://dx.doi.org/10.37200/ijpr/v23i1/pr190252.

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Fiorini, Rodolfo A. "New CICT Framework for Deep Learning and Deep Thinking Application". International Journal of Software Science and Computational Intelligence 8, nr 2 (kwiecień 2016): 1–20. http://dx.doi.org/10.4018/ijssci.2016040101.

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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.

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Richards, Blake A., Timothy P. Lillicrap, Philippe Beaudoin, Yoshua Bengio, Rafal Bogacz, Amelia Christensen, Claudia Clopath i in. "A deep learning framework for neuroscience". Nature Neuroscience 22, nr 11 (28.10.2019): 1761–70. http://dx.doi.org/10.1038/s41593-019-0520-2.

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Charalampous, Konstantinos, i Antonios Gasteratos. "A tensor-based deep learning framework". Image and Vision Computing 32, nr 11 (listopad 2014): 916–29. http://dx.doi.org/10.1016/j.imavis.2014.08.003.

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Crunkhorn, Sarah. "Deep learning framework for repurposing drugs". Nature Reviews Drug Discovery 20, nr 2 (11.01.2021): 100. http://dx.doi.org/10.1038/d41573-021-00006-w.

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Zhang, Hongjing, Tianyang Zhan, Sugato Basu i Ian Davidson. "A framework for deep constrained clustering". Data Mining and Knowledge Discovery 35, nr 2 (17.01.2021): 593–620. http://dx.doi.org/10.1007/s10618-020-00734-4.

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Sassu, Alessandro, Jose Francisco Saenz-Cogollo i Maurizio Agelli. "Deep-Framework: A Distributed, Scalable, and Edge-Oriented Framework for Real-Time Analysis of Video Streams". Sensors 21, nr 12 (11.06.2021): 4045. http://dx.doi.org/10.3390/s21124045.

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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.

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Ye, Jong Chul, Yoseob Han i Eunju Cha. "Deep Convolutional Framelets: A General Deep Learning Framework for Inverse Problems". SIAM Journal on Imaging Sciences 11, nr 2 (styczeń 2018): 991–1048. http://dx.doi.org/10.1137/17m1141771.

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Xu, Erci, i Shanshan Li. "Revisiting Resource Management for Deep Learning Framework". Electronics 8, nr 3 (16.03.2019): 327. http://dx.doi.org/10.3390/electronics8030327.

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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.

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Sallab, AhmadEL, Mohammed Abdou, Etienne Perot i Senthil Yogamani. "Deep Reinforcement Learning framework for Autonomous Driving". Electronic Imaging 2017, nr 19 (29.01.2017): 70–76. http://dx.doi.org/10.2352/issn.2470-1173.2017.19.avm-023.

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Kalash, Mahmoud, Mrigank Rochan, Noman Mohammed, Neil Bruce, Yang Wang i Farkhund Iqbal. "A Deep Learning Framework for Malware Classification". International Journal of Digital Crime and Forensics 12, nr 1 (styczeń 2020): 90–108. http://dx.doi.org/10.4018/ijdcf.2020010105.

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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.

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12

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

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13

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

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14

Galanti, Tomer, Lior Wolf i Tamir Hazan. "A theoretical framework for deep transfer learning". Information and Inference 5, nr 2 (28.04.2016): 159–209. http://dx.doi.org/10.1093/imaiai/iaw008.

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15

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

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16

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

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

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18

Sánchez-DelaCruz, Eddy, Juan P. Salazar López, David Lara Alabazares, Edgar Tello Leal i Mirta Fuentes-Ramos. "Deep learning framework for leaf damage identification". Concurrent Engineering 29, nr 1 (marzec 2021): 25–34. http://dx.doi.org/10.1177/1063293x21994953.

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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.

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Koroniotis, Nickolaos, Nour Moustafa i 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 (wrzesień 2020): 91–106. http://dx.doi.org/10.1016/j.future.2020.03.042.

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

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

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22

Li, Xingyi, Zhongang Qi, Xiaoli Fern i Fuxin Li. "ScaleNet - Improve CNNs through Recursively Rescaling Objects". Proceedings of the AAAI Conference on Artificial Intelligence 34, nr 07 (3.04.2020): 11426–33. http://dx.doi.org/10.1609/aaai.v34i07.6806.

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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.

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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, nr 2 (kwiecień 2020): 44–57. http://dx.doi.org/10.4018/ijaeis.2020040104.

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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.

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24

Sharma, Dilip Kumar, i A. K. Sharma. "Deep Web Information Retrieval Process". International Journal of Information Technology and Web Engineering 5, nr 1 (styczeń 2010): 1–22. http://dx.doi.org/10.4018/jitwe.2010010101.

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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.

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25

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

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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.

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26

Ravi, Nagarathna, Vimala Rani P, Rajesh Alias Harinarayan R, Mercy Shalinie S, Karthick Seshadri i Pariventhan P. "Deep Learning-based Framework for Smart Sustainable Cities". International Journal of Intelligent Information Technologies 15, nr 4 (październik 2019): 76–107. http://dx.doi.org/10.4018/ijiit.2019100105.

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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.

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MATSUNO, Toshifumi, Tatsuya SONOBE, Ryosuke TAKAHASHI, Kazutoshi HIGASHIYAMA i Kazuhiro HOSHI. "Close in on Domestic Deep Learning Framework "Chainer"". Journal of The Institute of Electrical Engineers of Japan 138, nr 5 (1.05.2018): 294–97. http://dx.doi.org/10.1541/ieejjournal.138.294.

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

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Singari, Ranganath, Karun Singla i Gangesh Chawla. "Deep Learning Framework for Steel Surface Defects Classification". INTERNATIONAL JOURNAL OF ADVANCED PRODUCTION AND INDUSTRIAL ENGINEERING 4, nr 1 (5.01.2019): 25–32. http://dx.doi.org/10.35121/ijapie201901135.

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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.

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Ilidrissi, Amine, i Joo Kooi Tan. "A deep unified framework for suspicious action recognition". Artificial Life and Robotics 24, nr 2 (19.12.2018): 219–24. http://dx.doi.org/10.1007/s10015-018-0518-y.

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31

Gu, Yuanli, Zhuangzhuang Shao, Lingqiao Qin, Wenqi Lu i 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.

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Hou, Weilong, i Xinbo Gao. "Saliency-Guided Deep Framework for Image Quality Assessment". IEEE MultiMedia 22, nr 2 (kwiecień 2015): 46–55. http://dx.doi.org/10.1109/mmul.2014.55.

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Zhang, Weishan, Pengcheng Duan, Zhongwei Li, Qinghua Lu, Wenjuan Gong i 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.

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Kleftogiannis, Dimitrios, Panos Kalnis i Vladimir B. Bajic. "DEEP: a general computational framework for predicting enhancers". Nucleic Acids Research 43, nr 1 (5.11.2014): e6-e6. http://dx.doi.org/10.1093/nar/gku1058.

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Pang, Sh Ch, Anan Du i Zh Zh Yu. "Robust multi-object tracking using deep learning framework". Journal of Optical Technology 82, nr 8 (1.08.2015): 516. http://dx.doi.org/10.1364/jot.82.000516.

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Kim, Dahun, Sanghyun Woo, Joon-Young Lee i In So Kweon. "Recurrent Temporal Aggregation Framework for Deep Video Inpainting". IEEE Transactions on Pattern Analysis and Machine Intelligence 42, nr 5 (1.05.2020): 1038–52. http://dx.doi.org/10.1109/tpami.2019.2958083.

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Shih, Frank Y., Yucong Shen i Xin Zhong. "Development of Deep Learning Framework for Mathematical Morphology". International Journal of Pattern Recognition and Artificial Intelligence 33, nr 06 (21.04.2019): 1954024. http://dx.doi.org/10.1142/s0218001419540247.

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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.

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38

Zhang, Weishan, Liang Xu, Zhongwei Li, Qinghua Lu i Yan Liu. "A Deep-Intelligence Framework for Online Video Processing". IEEE Software 33, nr 2 (marzec 2016): 44–51. http://dx.doi.org/10.1109/ms.2016.31.

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Yi, Ping, Yuxiang Guan, Futai Zou, Yao Yao, Wei Wang i Ting Zhu. "Web Phishing Detection Using a Deep Learning Framework". Wireless Communications and Mobile Computing 2018 (26.09.2018): 1–9. http://dx.doi.org/10.1155/2018/4678746.

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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.

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Liu, Peng, Guoyu Wang, Hao Qi, Chufeng Zhang, Haiyong Zheng i 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.

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Jin, Zhi, Muhammad Zafar Iqbal, Dmytro Bobkov, Wenbin Zou, Xia Li i Eckehard Steinbach. "A Flexible Deep CNN Framework for Image Restoration". IEEE Transactions on Multimedia 22, nr 4 (kwiecień 2020): 1055–68. http://dx.doi.org/10.1109/tmm.2019.2938340.

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Lin, Ching-Nung, Jr-Chang Chen i Shi-Jim Yen. "Deep Learning Competition Framework on Othello for Education". IEEE Transactions on Games 11, nr 3 (wrzesień 2019): 300–304. http://dx.doi.org/10.1109/tg.2019.2931153.

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Masood, Khalid, i 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.

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Wang, Qi, Zequn Qin, Feiping Nie i Xuelong Li. "C2DNDA: A Deep Framework for Nonlinear Dimensionality Reduction". IEEE Transactions on Industrial Electronics 68, nr 2 (luty 2021): 1684–94. http://dx.doi.org/10.1109/tie.2020.2969072.

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Onihunwa, John, Olufade Onifade, Isaac Ariyo, Stephen Omotugba i Deji Joshua. "Scalable Framework for Locating Deep Web Entry Points". IOSR Journal of Computer Engineering 19, nr 02 (kwiecień 2017): 45–55. http://dx.doi.org/10.9790/0661-1902034555.

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

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

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

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Yonekura, Kazuo, i Hitoshi Hattori. "Framework for design optimization using deep reinforcement learning". Structural and Multidisciplinary Optimization 60, nr 4 (2.05.2019): 1709–13. http://dx.doi.org/10.1007/s00158-019-02276-w.

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Blackburn, Christopher, Anthony Harding i Juan Moreno-Cruz. "Toward Deep-Decarbonization: an Energy-Service System Framework". Current Sustainable/Renewable Energy Reports 4, nr 4 (9.08.2017): 181–90. http://dx.doi.org/10.1007/s40518-017-0088-y.

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