Deep Learning for Natural Language Processing and Language Modelling

Kłosowski, Piotr

doi:10.23919/spa.2018.8563389

Cited by 54 publications

(21 citation statements)

References 22 publications

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“…The application of deep learning in the field of machine learning and pattern recognition has achieved tremendous results, especially in tasks such as object detection [ 25 ], image analysis [ 26 ], and natural language processing [ 27 ], promoting their development in hyperspectral remote sensing tasks. In hyperspectral remote sensing, deep learning approaches are introduced into the HSIC problem to learn hierarchical representations [ 28 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Hyperspectral Image Classification Using Deep Genome Graph-Based Approach

Tinega

Chen

et al. 2021

Sensors

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Recently developed hybrid models that stack 3D with 2D CNN in their structure have enjoyed high popularity due to their appealing performance in hyperspectral image classification tasks. On the other hand, biological genome graphs have demonstrated their effectiveness in enhancing the scalability and accuracy of genomic analysis. We propose an innovative deep genome graph-based network (GGBN) for hyperspectral image classification to tap the potential of hybrid models and genome graphs. The GGBN model utilizes 3D-CNN at the bottom layers and 2D-CNNs at the top layers to process spectral–spatial features vital to enhancing the scalability and accuracy of hyperspectral image classification. To verify the effectiveness of the GGBN model, we conducted classification experiments on Indian Pines (IP), University of Pavia (UP), and Salinas Scene (SA) datasets. Using only 5% of the labeled data for training over the SA, IP, and UP datasets, the classification accuracy of GGBN is 99.97%, 96.85%, and 99.74%, respectively, which is better than the compared state-of-the-art methods.

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Section: Literature Reviewmentioning

confidence: 99%

Hyperspectral Image Classification Using Deep Genome Graph-Based Approach

Tinega

Chen

et al. 2021

Sensors

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“…Deep Learning models and applications have been used in tasks such as image classification, [21][22][23] document analysis and text recognition, [24][25][26] natural language processing, [27][28][29] and video analysis [30][31][32] in industries ranging from automated driving to medical devices as shown in Figure 3. In References 35-37, the authors investigated the use of visual information to detect and interpret road signs using hierarchical classifier structures that combine Support Vector Machines (SVM) for image verification and Convolutional Neural Networks (CNN) for final recognition.…”

Section: Applications Of Deep Learningmentioning

confidence: 99%

Deep learning support for intelligent transportation systems

Ibáñez

Contreras-Castillo

Zeadally

2020

Trans Emerging Tel Tech

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Intelligent Transportation Systems (ITS) help improve the ever-increasing vehicular flow and traffic efficiency in urban traffic to reduce the number of accidents. The generation of massive amounts of data generated by all the digital devices connected to the transportation network enables the creation of datasets to perform an in-depth analysis of the data using deep learning methods. Such methods can help predict traffic performance, automated traffic light management, lane detection, and identifying objects near vehicles to increase the safety and efficiency of ITS. We discuss some of the challenges that need to be solved to achieve seamless integration between ITS and deep learning methods to address issues such as (1) improving traffic flow/transportation logistics, (2) predicting best routes for the transportation of goods, (3) optimal fuel consumption, (4) intelligent environmental conditions perception, (5) traffic speed management, and accident prevention.

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“…RNN is called recurrent because it performs the same task for every element of a sequence, with the output are depended on the previous computations. In other words, RNN has a memory to capture the information about what has been computed so far [15,30].…”

Section: Related Workmentioning

confidence: 99%

Deep Learning-Based Complaint Classification for Indonesia Telecommunication Company's Call Center

Lukitasari¹,

Hidayat²

2020

Proceedings of the Proceedings of the 7th Mathematics, Science, and Computer Science Education International Seminar, MSCEIS 20

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The preliminary research was held to utilize the call center conversations records from a broadband telecommunications company in Indonesia. There is a need from the company to classify customer's complaints automatically by a system to minimize human errors and at once streamline the business processes and resources. Natural Language Processing (NLP), as an integral part of artificial intelligence (AI), empower machines to understand human languages for performing beneficial tasks. The growth of deep learning is the main driver behind NLP for performing various practical applications and business, therefore deep learning is expected to overcome the problems encountered. This paper explains the methods used in designing the classification systems based on deep learning. A literature review is conducted to find the proper algorithm used in classifying problems. Furthermore, it also explains the stages performed in preparing the data and building the system model. From experiments conducted, it can be stated that the RNN algorithm can be used in the classification of customer complaints with the results shown by the accuracy value of the model.

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Deep Learning for Natural Language Processing and Language Modelling

Cited by 54 publications

References 22 publications

Hyperspectral Image Classification Using Deep Genome Graph-Based Approach

Hyperspectral Image Classification Using Deep Genome Graph-Based Approach

Deep learning support for intelligent transportation systems

Deep Learning-Based Complaint Classification for Indonesia Telecommunication Company's Call Center

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