scholarly journals Automatic Image Captioning Using Neural Networks

2020 ◽  
Vol 3 (1) ◽  
pp. 138-146
Author(s):  
Subash Pandey ◽  
Rabin Kumar Dhamala ◽  
Bikram Karki ◽  
Saroj Dahal ◽  
Rama Bastola
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Computer Vision ◽  
Natural Language ◽  
Language Processing ◽  
Model Performance ◽  
Image Description ◽  
Image Captioning ◽  
Top Down

 Automatically generating a natural language description of an image is a major challenging task in the field of artificial intelligence. Generating description of an image bring together the fields: Natural Language Processing and Computer Vision. There are two types of approaches i.e. top-down and bottom-up. For this paper, we approached top-down that starts from the image and converts it into the word. Image is passed to Convolutional Neural Network (CNN) encoder and the output from it is fed further to Recurrent Neural Network (RNN) decoder that generates meaningful captions. We generated the image description by passing the real time images from the camera of a smartphone as well as tested with the test images from the dataset. To evaluate the model performance, we used BLEU (Bilingual Evaluation Understudy) score and match predicted words to the original caption.

scholarly journals Compression of Convolutional Neural Network for Natural Language Processing

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Krzysztof Wróbel ◽  
Michał Karwatowski ◽  
Maciej Wielgosz ◽  
Marcin Pietroń ◽  
Kazimierz Wiatr
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Original Model ◽  
Floating Point ◽  
Memory Footprint ◽  
Classification Tasks

Convolutional Neural Networks (CNNs) were created for image classification tasks. Quickly, they were applied to other domains, including Natural Language Processing (NLP). Nowadays, the solutions based on artificial intelligence appear on mobile devices and in embedded systems, which places constraints on, among others, the memory and power consumption. Due to CNNs memory and computing requirements, to map them to hardware they need to be compressed.This paper presents the results of compression of the efficient CNNs for sentiment analysis. The main steps involve pruning and quantization. The process of mapping the compressed network to FPGA and the results of this implementation are described. The conducted simulations showed that 5-bit width is enough to ensure no drop in accuracy when compared to the floating point version of the network. Additionally, the memory footprint was significantly reduced (between 85% and 93% comparing to the original model).

scholarly journals Physics Inspired Deep Neural Networks for Top Quark Reconstruction

2020 ◽  
Vol 245 ◽  
pp. 06029
Author(s):  
Kevin Greif ◽  
Kevin Lannon
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Computer Vision ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Top Quark ◽  
Deep Neural Networks ◽  
Great Success ◽  
Fully Connected

Deep neural networks (DNNs) have been applied to the fields of computer vision and natural language processing with great success in recent years. The success of these applications has hinged on the development of specialized DNN architectures that take advantage of specific characteristics of the problem to be solved, namely convolutional neural networks for computer vision and recurrent neural networks for natural language processing. This research explores whether a neural network architecture specific to the task of identifying t → Wb decays in particle collision data yields better performance than a generic, fully-connected DNN. Although applied here to resolved top quark decays, this approach is inspired by an DNN technique for tagging boosted top quarks, which consists of defining custom neural network layers known as the combination and Lorentz layers. These layers encode knowledge of relativistic kinematics applied to combinations of particles, and the output of these specialized layers can then be fed into a fully connected neural network to learn tasks such as classification. This research compares the performance of these physics inspired networks to that of a generic, fully-connected DNN, to see if there is any advantage in terms of classification performance, size of the network, or ease of training.

A Hindi Image Caption Generation Framework Using Deep Learning

2021 ◽  
Vol 20 (2) ◽  
pp. 1-19
Author(s):  
Santosh Kumar Mishra ◽  
Rijul Dhir ◽  
Sriparna Saha ◽  
Pushpak Bhattacharyya
Keyword(s):  
Computer Vision ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
English Language ◽  
Image Captioning ◽  
Textual Description ◽  
Proposed Model ◽  
Hindi Language ◽  
The Given

Image captioning is the process of generating a textual description of an image that aims to describe the salient parts of the given image. It is an important problem, as it involves computer vision and natural language processing, where computer vision is used for understanding images, and natural language processing is used for language modeling. A lot of works have been done for image captioning for the English language. In this article, we have developed a model for image captioning in the Hindi language. Hindi is the official language of India, and it is the fourth most spoken language in the world, spoken in India and South Asia. To the best of our knowledge, this is the first attempt to generate image captions in the Hindi language. A dataset is manually created by translating well known MSCOCO dataset from English to Hindi. Finally, different types of attention-based architectures are developed for image captioning in the Hindi language. These attention mechanisms are new for the Hindi language, as those have never been used for the Hindi language. The obtained results of the proposed model are compared with several baselines in terms of BLEU scores, and the results show that our model performs better than others. Manual evaluation of the obtained captions in terms of adequacy and fluency also reveals the effectiveness of our proposed approach. Availability of resources : The codes of the article are available at https://github.com/santosh1821cs03/Image_Captioning_Hindi_Language ; The dataset will be made available: http://www.iitp.ac.in/∼ai-nlp-ml/resources.html .

Prediction of Emergency Department Hospital Admission Based on Natural Language Processing and Neural Networks

2017 ◽  
Vol 56 (05) ◽  
pp. 377-389 ◽  
Author(s):  
Xingyu Zhang ◽  
Joyce Kim ◽  
Rachel E. Patzer ◽  
Stephen R. Pitts ◽  
Aaron Patzer ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Emergency Department ◽  
Logistic Regression ◽  
Natural Language Processing ◽  
Natural Language ◽  
Hospital Admission ◽  
Language Processing ◽  
Predictive Accuracy ◽  
Free Text

SummaryObjective: To describe and compare logistic regression and neural network modeling strategies to predict hospital admission or transfer following initial presentation to Emergency Department (ED) triage with and without the addition of natural language processing elements.Methods: Using data from the National Hospital Ambulatory Medical Care Survey (NHAMCS), a cross-sectional probability sample of United States EDs from 2012 and 2013 survey years, we developed several predictive models with the outcome being admission to the hospital or transfer vs. discharge home. We included patient characteristics immediately available after the patient has presented to the ED and undergone a triage process. We used this information to construct logistic regression (LR) and multilayer neural network models (MLNN) which included natural language processing (NLP) and principal component analysis from the patient’s reason for visit. Ten-fold cross validation was used to test the predictive capacity of each model and receiver operating curves (AUC) were then calculated for each model.Results: Of the 47,200 ED visits from 642 hospitals, 6,335 (13.42%) resulted in hospital admission (or transfer). A total of 48 principal components were extracted by NLP from the reason for visit fields, which explained 75% of the overall variance for hospitalization. In the model including only structured variables, the AUC was 0.824 (95% CI 0.818-0.830) for logistic regression and 0.823 (95% CI 0.817-0.829) for MLNN. Models including only free-text information generated AUC of 0.742 (95% CI 0.7310.753) for logistic regression and 0.753 (95% CI 0.742-0.764) for MLNN. When both structured variables and free text variables were included, the AUC reached 0.846 (95% CI 0.839-0.853) for logistic regression and 0.844 (95% CI 0.836-0.852) for MLNN.Conclusions: The predictive accuracy of hospital admission or transfer for patients who presented to ED triage overall was good, and was improved with the inclusion of free text data from a patient’s reason for visit regardless of modeling approach. Natural language processing and neural networks that incorporate patient-reported outcome free text may increase predictive accuracy for hospital admission.

A Matter of Perspective

2020 ◽  
pp. 182-202
Author(s):  
Katie Miller
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Human Rights ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Equal Opportunity ◽  
Facial Recognition ◽  
Phone Service

The challenge presented is an age when some decisions are made by humans, some are made by AI, and some are made by a combination of AI and humans. For the person refused housing, a phone service, or employment, the experience is the same, but the ability to understand what has happened and obtain a remedy may be very different if the discrimination is attributable to or contributed by an AI system. If we are to preserve the policy intentions of our discrimination, equal opportunity, and human rights laws, we need to understand how discrimination arises in AI systems; how design in AI systems can mitigate such discrimination; and whether our existing laws are adequate to address discrimination in AI. This chapter endeavours to provide this understanding. In doing so, it focuses on narrow but advanced forms of artificial intelligence, such as natural language processing, facial recognition, and cognitive neural networks.

scholarly journals Middle-Level Attribute-Based Language Retouching for Image Caption Generation

2018 ◽  
Vol 8 (10) ◽  
pp. 1850 ◽  
Author(s):  
Zhibin Guan ◽  
Kang Liu ◽  
Yan Ma ◽  
Xu Qian ◽  
Tongkai Ji
Keyword(s):  
Natural Language ◽  
Language Processing ◽  
Middle Level ◽  
Generation Model ◽  
Image Description ◽  
Image Captioning ◽  
Benchmark Datasets ◽  
Intermediate Image ◽  
Image Caption Generation ◽  
Image Caption

Image caption generation is attractive research which focuses on generating natural language sentences to describe the visual content of a given image. It is an interdisciplinary subject combining computer vision (CV) and natural language processing (NLP). The existing image captioning methods are mainly focused on generating the final image caption directly, which may lose significant identification information of objects contained in the raw image. Therefore, we propose a new middle-level attribute-based language retouching (MLALR) method to solve this problem. Our proposed MLALR method uses the middle-level attributes predicted from the object regions to retouch the intermediate image description, which is generated by our language generation model. The advantage of our MLALR method is that it can correct descriptive errors in the intermediate image description and make the final image caption more accurate. Moreover, evaluation using benchmark datasets—MSCOCO, Flickr8K, and Flickr30K—validated the impressive performance of our MLALR method with evaluation metrics—BLEU, METEOR, ROUGE-L, CIDEr, and SPICE.

The Concept of Integrating Artificial Intelligence Technologies Into Human Resources in a Digital Paradigm

2020 ◽  
Vol 9 (2) ◽  
pp. 5-9
Author(s):  
Oksana Chulanova
Keyword(s):  
Artificial Intelligence ◽  
Computer Vision ◽  
Natural Language Processing ◽  
Decision Support ◽  
Speech Recognition ◽  
Human Resources ◽  
Natural Language ◽  
Language Processing

The article discusses the capabilities of artificial intelligence technologies - technologies based on the use of artificial intelligence, including natural language processing, intellectual decision support, computer vision, speech recognition and synthesis, and promising methods of artificial intelligence. The results of the author's study and the analysis of artificial intelligence technologies and their capabilities for optimizing work with staff are presented. A study conducted by the author allowed us to develop an author's concept of integrating artificial intelligence technologies into work with personnel in the digital paradigm.

scholarly journals An Overview of Image Caption Generation Methods

2020 ◽  
Vol 2020 ◽  
pp. 1-13 ◽  
Author(s):  
Haoran Wang ◽  
Yue Zhang ◽  
Xiaosheng Yu
Keyword(s):  
Artificial Intelligence ◽  
Computer Vision ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Rapid Development ◽  
Evaluation Criteria ◽  
Arduous Task ◽  
Image Caption Generation ◽  
Image Caption

In recent years, with the rapid development of artificial intelligence, image caption has gradually attracted the attention of many researchers in the field of artificial intelligence and has become an interesting and arduous task. Image caption, automatically generating natural language descriptions according to the content observed in an image, is an important part of scene understanding, which combines the knowledge of computer vision and natural language processing. The application of image caption is extensive and significant, for example, the realization of human-computer interaction. This paper summarizes the related methods and focuses on the attention mechanism, which plays an important role in computer vision and is recently widely used in image caption generation tasks. Furthermore, the advantages and the shortcomings of these methods are discussed, providing the commonly used datasets and evaluation criteria in this field. Finally, this paper highlights some open challenges in the image caption task.

scholarly journals Machine translation using natural language processing

2019 ◽  
Vol 277 ◽  
pp. 02004
Author(s):  
Middi Venkata Sai Rishita ◽  
Middi Appala Raju ◽  
Tanvir Ahmed Harris
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Natural Language Processing ◽  
Natural Language ◽  
Machine Translation ◽  
Language Processing ◽  
Deep Neural Network ◽  
English Text ◽  
French Translation ◽  
Rule Based

Machine Translation is the translation of text or speech by a computer with no human involvement. It is a popular topic in research with different methods being created, like rule-based, statistical and examplebased machine translation. Neural networks have made a leap forward to machine translation. This paper discusses the building of a deep neural network that functions as a part of end-to-end translation pipeline. The completed pipeline would accept English text as input and return the French Translation. The project has three main parts which are preprocessing, creation of models and Running the model on English Text.

scholarly journals Pat-in-the-Loop: Declarative Knowledge for Controlling Neural Networks

2020 ◽  
Vol 12 (12) ◽  
pp. 218
Author(s):  
Dario Onorati ◽  
Pierfrancesco Tommasino ◽  
Leonardo Ranaldi ◽  
Francesca Fallucchi ◽  
Fabio Massimo Zanzotto
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Cost Effective ◽  
Specific Class ◽  
Human Control ◽  
The Neural Network ◽  
Parse Trees

The dazzling success of neural networks over natural language processing systems is imposing an urgent need to control their behavior with simpler, more direct declarative rules. In this paper, we propose Pat-in-the-Loop as a model to control a specific class of syntax-oriented neural networks by adding declarative rules. In Pat-in-the-Loop, distributed tree encoders allow to exploit parse trees in neural networks, heat parse trees visualize activation of parse trees, and parse subtrees are used as declarative rules in the neural network. Hence, Pat-in-the-Loop is a model to include human control in specific natural language processing (NLP)-neural network (NN) systems that exploit syntactic information, which we will generically call Pat. A pilot study on question classification showed that declarative rules representing human knowledge, injected by Pat, can be effectively used in these neural networks to ensure correctness, relevance, and cost-effective.

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