scholarly journals Gender and Age Detection using Deep Learning

2021 ◽  
pp. 604-610
Author(s):  
Utkarsha Kumbhar ◽  
Prof. A. S. Shingare
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Human Computer Interaction ◽  
Comparative Study ◽  
Language Processing ◽  
Recognition System ◽  
Quality Research ◽  
The Past ◽  
Gender And Age

For the past few years, gender and age detection has been an active area of study and researchers have been putting a lot of effort to contribute quality research in this area. Starting from preprocessing of data to building a model which gives high precision results is tedious task for researchers. There is a immense dormant field of study as it can be used in monitoring, surveillance, human-computer interaction and security. However, there is still a lack of the performance of existing methods on real live images. Many difficult tasks such as computer vision, speech recognition, and natural language processing are easily solved with deep learning. Therefore, the approach of deep learning remarkably growing and this also takes place in image classification. Therefore, to analyses and focuses on comparative study of different algorithms for gender and age recognition system to give elevated degree of precision is required.

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 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 Performance Analysis and Study of Chatbots

2021 ◽  
pp. 345-348
Author(s):  
Prof. Ahlam Ansari ◽  
Fakhruddin Bootwala ◽  
Owais Madhia ◽  
Anas Lakdawala
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Performance Analysis ◽  
User Experience ◽  
Language Processing ◽  
Conversational Agents ◽  
Learning Machines ◽  
The Past

Artificial intelligence, machine learning and deep learning machines are being used as conversational agents. They are used to impersonate a human and provide the user a human-like experience. Conversational software agents that use natural language processing is called a chatbot and it is widely used for interacting with users. It provides appropriate and satisfactory answers to the user. In this paper we have analyzed and compared various chatbots and provided a score to each of them on different parameters. We have asked each chatbot the same questions, and we have evaluated each answer, whether it’s satisfactory or not. This analysis is based on user experience rather than analyzing the software of each chatbot. This paper proves that even though chatbot performance has highly increased compared to the past, there is still quite a lot of room for improvement.

scholarly journals Visual Dialog Agent Based On Deep Q Learning and Memory Module Networks

2021 ◽  
pp. 41-47
Author(s):  
Arundhati Raj ◽  
Shubhangi Srivastava ◽  
Aniruddh Suresh Pillai ◽  
Ajay Kumar
Keyword(s):  
Computer Vision ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Memory Module ◽  
Agent Based ◽  
Q Learning ◽  
The Past ◽  
Learning Policies ◽  
New Algorithms

In the past many years, it has been observed that there has been an increase in methods to solve problems and the solution involves a combination of Computer Vision and Natural Language Processing. New algorithms and systems are emerging and are being developed every day to solve the above-mentioned kind of problems. Visual Dialog Agent is one of them. This kind of system utilizes both Computer Vision and Natural Language Processing algorithms. With this technology many variants of Visual Dialog Agents have been designed till date and many exclusive algorithms are created for Visual Dialog Agent. In this paper we propose an idea to create a Visual Dialog Agent which utilizes the present state of art End to End Memory Module Networks along with Reinforcement Learning Policies to answer the questions prompted by the user and as well understand the inclination of the user in the conversation which it holds. The goal of the proposed Visual Dialog Agent is to have a more engaging conversation with the highest user inclination.

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.

scholarly journals Evolving Artificial Perception by Integrating Nlp and Cv

2019 ◽  
Vol 8 (4) ◽  
pp. 3656-3659
Keyword(s):  
Artificial Intelligence ◽  
Computer Vision ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Computer Systems ◽  
External Environment ◽  
Two Dimensions ◽  
The Past

This paper discusses the concept of integrating artificial perception of an artificial intelligence by integrating NLP and CV, this should be able to solve 50% of problems where the data is usually in a raw format and not understandable by the machine. This method helps in the automatic labelling and understanding the data so it is easier for the machine to understand and help in our day to day tasks. “Perception is the ability to become aware of something which is internal or in the external environment through the use of the 5 senses” this is a natural capability of humans but has never properly been achieved in a machine. In the past five years massive strides have taken place in both natural language processing and computer vision but none of these advancements have increased the intelligence and perception of computer systems in the dramatic way that was expected. This difference in what was expected and what has finally been delivered is due to the fact that both these fields have evolved separately whereas perception requires these two dimensions of hearing (Natural Language Processing) and vision (Computer Vision) to be integrated.

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.

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