Deep learning: A beginners guide to start coding neural networks and apply in areas of computer vision and natural language processing

2018 ◽  
Vol 11 ◽  
Author(s):  
Tanya Dixit ◽  
Sriharsha Allenki
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing

scholarly journals A Study of The Convolutional Neural Networks Applications

2019 ◽  
Vol 3 (2) ◽  
pp. 31-40 ◽  
Author(s):  
Ahmed Shamsaldin ◽  
Polla Fattah ◽  
Tarik Rashid ◽  
Nawzad Al-Salihi
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Face Recognition ◽  
Convolutional Neural Networks ◽  
Language Processing ◽  
Text Classification ◽  
Scene Labeling ◽  
Real World Problems

At present, deep learning is widely used in a broad range of arenas. A convolutional neural networks (CNN) is becoming the star of deep learning as it gives the best and most precise results when cracking real-world problems. In this work, a brief description of the applications of CNNs in two areas will be presented: First, in computer vision, generally, that is, scene labeling, face recognition, action recognition, and image classification; Second, in natural language processing, that is, the fields of speech recognition and text classification.

scholarly journals Enabling Intelligence through Deep Learning using IoT in a Classroom Environment based on a multimodal approach

2021 ◽  
Vol 12 (2) ◽  
pp. 381-393
Author(s):  
Lakshaga Jyothi M, Et. al.
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Classroom Environment ◽  
Educational Institution ◽  
Daily Lives ◽  
Learning Principles ◽  
Intelligent Classroom

Smart Classrooms are becoming very popular nowadays. The boom of recent technologies such as the Internet of Things, thanks to those technologies that are tremendously equipping every corner of a diverse set of fields. Every educational institution has set some benchmark on adopting these technologies in their daily lives. But due to some constraints and setbacks, these IoT technological embodiments in the educational sector is still in the premature stage. The major success of any technological evolution is based on its full-fledged implementation to fit the society in the broader concern. The breakthrough in recent years by Deep Learning principles as it outperforms traditional machine learning models to solve any tasks especially, Computer Vision and Natural language processing problems.  A fusion of Computer Vision and Natural Language Processing as a new astonishing field that have shown its existence in the recent years. Using such mixtures with the IoT platforms is a challenging task and and has not reached the eyes of many researchers across the globe.  Many researchers of the past have shown interest in designing an intelligent classroom on a different context. Hence to fill this gap, we have proposed an approach or a conceptual model through which Deep Learning architectures fused in the IoT systems results in an Intelligent Classroom via such hybrid systems. Apart from this, we have also discussed the major challenges, limitations as well as opportunities that can arise with Deep Learning-based IoT Solutions. In this paper, we have summarized the available applications of these technologies to suit our solution.  Thus, this paper can be taken as a kickstart for our research to have a glimpse of the available papers for the success of our proposed approach.

scholarly journals Neural Language Modeling for Molecule Generation

2021 ◽  
Author(s):  
Sanjar Adilov
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Drug Design ◽  
Natural Language ◽  
Language Processing ◽  
De Novo ◽  
Language Modeling ◽  
Machine Learning Methods

Generative neural networks have shown promising results in <i>de novo</i> drug design. Recent studies suggest that one of the efficient ways to produce novel molecules matching target properties is to model SMILES sequences using deep learning in a way similar to language modeling in natural language processing. In this paper, we present a survey of various machine learning methods for SMILES-based language modeling and propose our benchmarking results on a standardized subset of ChEMBL database.

scholarly journals Neural Language Modeling for Molecule Generation

2021 ◽  
Author(s):  
Sanjar Adilov
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Drug Design ◽  
Natural Language ◽  
Language Processing ◽  
De Novo ◽  
Language Modeling ◽  
Machine Learning Methods

Generative neural networks have shown promising results in <i>de novo</i> drug design. Recent studies suggest that one of the efficient ways to produce novel molecules matching target properties is to model SMILES sequences using deep learning in a way similar to language modeling in natural language processing. In this paper, we present a survey of various machine learning methods for SMILES-based language modeling and propose our benchmarking results on a standardized subset of ChEMBL database.

scholarly journals Deep learning in clinical natural language processing: a methodical review

2019 ◽  
Vol 27 (3) ◽  
pp. 457-470 ◽  
Author(s):  
Stephen Wu ◽  
Kirk Roberts ◽  
Surabhi Datta ◽  
Jingcheng Du ◽  
Zongcheng Ji ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Computational Linguistics ◽  
Language Processing ◽  
Recurrent Neural Networks ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Named Entity

Abstract Objective This article methodically reviews the literature on deep learning (DL) for natural language processing (NLP) in the clinical domain, providing quantitative analysis to answer 3 research questions concerning methods, scope, and context of current research. Materials and Methods We searched MEDLINE, EMBASE, Scopus, the Association for Computing Machinery Digital Library, and the Association for Computational Linguistics Anthology for articles using DL-based approaches to NLP problems in electronic health records. After screening 1,737 articles, we collected data on 25 variables across 212 papers. Results DL in clinical NLP publications more than doubled each year, through 2018. Recurrent neural networks (60.8%) and word2vec embeddings (74.1%) were the most popular methods; the information extraction tasks of text classification, named entity recognition, and relation extraction were dominant (89.2%). However, there was a “long tail” of other methods and specific tasks. Most contributions were methodological variants or applications, but 20.8% were new methods of some kind. The earliest adopters were in the NLP community, but the medical informatics community was the most prolific. Discussion Our analysis shows growing acceptance of deep learning as a baseline for NLP research, and of DL-based NLP in the medical community. A number of common associations were substantiated (eg, the preference of recurrent neural networks for sequence-labeling named entity recognition), while others were surprisingly nuanced (eg, the scarcity of French language clinical NLP with deep learning). Conclusion Deep learning has not yet fully penetrated clinical NLP and is growing rapidly. This review highlighted both the popular and unique trends in this active field.

scholarly journals Medical Information Extraction in the Age of Deep Learning

2020 ◽  
Vol 29 (01) ◽  
pp. 208-220 ◽  
Author(s):  
Udo Hahn ◽  
Michel Oleynik
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Information Extraction ◽  
Medical Informatics ◽  
Language Processing ◽  
Paradigm Shift ◽  
Medical Information ◽  
The Past

Objectives: We survey recent developments in medical Information Extraction (IE) as reported in the literature from the past three years. Our focus is on the fundamental methodological paradigm shift from standard Machine Learning (ML) techniques to Deep Neural Networks (DNNs). We describe applications of this new paradigm concentrating on two basic IE tasks, named entity recognition and relation extraction, for two selected semantic classes—diseases and drugs (or medications)—and relations between them. Methods: For the time period from 2017 to early 2020, we searched for relevant publications from three major scientific communities: medicine and medical informatics, natural language processing, as well as neural networks and artificial intelligence. Results: In the past decade, the field of Natural Language Processing (NLP) has undergone a profound methodological shift from symbolic to distributed representations based on the paradigm of Deep Learning (DL). Meanwhile, this trend is, although with some delay, also reflected in the medical NLP community. In the reporting period, overwhelming experimental evidence has been gathered, as illustrated in this survey for medical IE, that DL-based approaches outperform non-DL ones by often large margins. Still, small-sized and access-limited corpora create intrinsic problems for data-greedy DL as do special linguistic phenomena of medical sublanguages that have to be overcome by adaptive learning strategies. Conclusions: The paradigm shift from (feature-engineered) ML to DNNs changes the fundamental methodological rules of the game for medical NLP. This change is by no means restricted to medical IE but should also deeply influence other areas of medical informatics, either NLP- or non-NLP-based.

scholarly journals Artificial Intelligence in Business

2020 ◽  
pp. 1-38
Author(s):  
Amandeep Kaur ◽  
◽  
Anjum Mohammad Aslam ◽  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Case Studies ◽  
Language Processing ◽  
Learning Abilities ◽  
The Core

In this chapter we discuss the core concept of Artificial Intelligence. We define the term of Artificial Intelligence and its interconnected terms such as Machine learning, deep learning, Neural Networks. We describe the concept with the perspective of its usage in the area of business. We further analyze various applications and case studies which can be achieved using Artificial Intelligence and its sub fields. In the area of business already numerous Artificial Intelligence applications are being utilized and will be expected to be utilized more in the future where machines will improve the Artificial Intelligence, Natural language processing, Machine learning abilities of humans in various zones.

Deep Learning Models for Detection and Diagnosis of Alzheimer's Disease

2021 ◽  
pp. 140-149
Author(s):  
Gowhar Mohiuddin Dar ◽  
Ashok Sharma ◽  
Parveen Singh
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Reinforcement Learning ◽  
Natural Language ◽  
Language Processing ◽  
Computational Techniques ◽  
Medical Sciences ◽  
Data Application ◽  
Detection And Diagnosis

The chapter explores the implications of deep learning in medical sciences, focusing on deep learning concerning natural language processing, computer vision, reinforcement learning, big data, and blockchain influence on some areas of medicine and construction of end-to-end systems with the help of these computational techniques. The deliberation of computer vision in the study is mainly concerned with medical imaging and further usage of natural language processing to spheres such as electronic wellbeing record data. Application of deep learning in genetic mapping and DNA sequencing termed as genomics and implications of reinforcement learning about surgeries assisted by robots are also overviewed.

Image Captioning Using Deep Learning

2021 ◽  
pp. 381-395
Author(s):  
Bhavana D. ◽  
K. Chaitanya Krishna ◽  
Tejaswini K. ◽  
N. Venkata Vikas ◽  
A. N. V. Sahithya
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Feature Extraction ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Language Processing ◽  
Recurrent Neural Networks ◽  
Input Image ◽  
The Other ◽  
Image Caption

The task of image caption generator is mainly about extracting the features and ongoings of an image and generating human-readable captions that translate the features of the objects in the image. The contents of an image can be described by having knowledge about natural language processing and computer vision. The features can be extracted using convolution neural networks which makes use of transfer learning to implement the exception model. It stands for extreme inception, which has a feature extraction base with 36 convolution layers. This shows accurate results when compared with the other CNNs. Recurrent neural networks are used for describing the image and to generate accurate sentences. The feature vector that is extracted by using the CNN is fed to the LSTM. The Flicker 8k dataset is used to train the network in which the data is labeled properly. The model will be able to generate accurate captions that nearly describe the activities carried in the image when an input image is given to it. Further, the authors use the BLEU scores to validate the model.

scholarly journals Artificial Intelligence Surgery: How Do We Get to Autonomous Actions in Surgery?

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5526
Author(s):  
Andrew A. Gumbs ◽  
Isabella Frigerio ◽  
Gaya Spolverato ◽  
Roland Croner ◽  
Alfredo Illanes ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Computer Vision ◽  
Interventional Radiology ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Paradigm Shift ◽  
Timely Manner

Most surgeons are skeptical as to the feasibility of autonomous actions in surgery. Interestingly, many examples of autonomous actions already exist and have been around for years. Since the beginning of this millennium, the field of artificial intelligence (AI) has grown exponentially with the development of machine learning (ML), deep learning (DL), computer vision (CV) and natural language processing (NLP). All of these facets of AI will be fundamental to the development of more autonomous actions in surgery, unfortunately, only a limited number of surgeons have or seek expertise in this rapidly evolving field. As opposed to AI in medicine, AI surgery (AIS) involves autonomous movements. Fortuitously, as the field of robotics in surgery has improved, more surgeons are becoming interested in technology and the potential of autonomous actions in procedures such as interventional radiology, endoscopy and surgery. The lack of haptics, or the sensation of touch, has hindered the wider adoption of robotics by many surgeons; however, now that the true potential of robotics can be comprehended, the embracing of AI by the surgical community is more important than ever before. Although current complete surgical systems are mainly only examples of tele-manipulation, for surgeons to get to more autonomously functioning robots, haptics is perhaps not the most important aspect. If the goal is for robots to ultimately become more and more independent, perhaps research should not focus on the concept of haptics as it is perceived by humans, and the focus should be on haptics as it is perceived by robots/computers. This article will discuss aspects of ML, DL, CV and NLP as they pertain to the modern practice of surgery, with a focus on current AI issues and advances that will enable us to get to more autonomous actions in surgery. Ultimately, there may be a paradigm shift that needs to occur in the surgical community as more surgeons with expertise in AI may be needed to fully unlock the potential of AIS in a safe, efficacious and timely manner.

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