Postal Automation System in Gurmukhi Script using Deep Learning

2021 ◽  
Author(s):  
Sandhya Sharma ◽  
Sheifali Gupta ◽  
Neeraj Kumar ◽  
Tanvi Arora
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Holistic Approach ◽  
North India ◽  
Automation System ◽  
Official Language ◽  
Major Research ◽  
Research Areas ◽  
Postal Automation ◽  
Gurmukhi Script

Nowadays in the era of automation, the postal automation system is one of the major research areas. Developing a postal automation system for a nation like India is much troublesome than other nations because of India’s multi-script and multi-lingual behavior. This proposed work will be helpful in the postal automation of district names of Punjab (state) written in Gurmukhi script, which is the official language of the state in North India. For this, a holistic approach i.e. a segmentation-free technique has been used with the help of Convolutional Neural Network (CNN) and Deep learning (DL). For the purpose of recognition, a database of 22[Formula: see text]000 images (samples) which are handwritten in Gurmukhi script for all the 22 districts of Punjab is prepared. Each sample is written two times by 500 different writers generating 1000 samples for each district name. Two CNN models are proposed which are named as ConvNetGuru and ConvNetGuruMod for the purpose of recognition. Maximum validation accuracy achieved by ConvNetGuru is 90% and ConvNetGuruMod is 98%.

scholarly journals Trials, Skills, and Future Standpoints of AI Based Research in Bioinformatics

2020 ◽  
Vol 9 (1) ◽  
pp. 968-972
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Future Research ◽  
Multidisciplinary Research ◽  
Major Research ◽  
Research Areas ◽  
Genomics And Proteomics ◽  
Drug Designing ◽  
Level Analysis

In recent times, computer field has entered in all types of business and industries. Recent advancements in the information technology field, has open up many possibilities in multidisciplinary research. Machine learning, deep learning, convolution neural network, etc. are recent development in computer fields which has change the way of development of algorithms. Such algorithms can learn over a period of time while in execution and improves its performance and continue learning. Bioinformatics is the recent example of the science which strives to use such recent technologies of computer science for betterment in its own field. This article reviews Artificial Intelligence subset such as Machine learning and Deep learning in the genomics and proteomics domain. This article provides profound insights of various AI techniques which can be incorporated in the field of bioinformatics. The paper also highlighted the future research potential of this field. Computational biology, genomics, proteomics, Drug designing, gene expression level analysis are the major research areas in bioinformatics. These areas are also discussed in the paper.

scholarly journals Devanagari Handwritten Character Recognition Using Deep Learning

2022 ◽  
Vol 10 (1) ◽  
pp. 102-105
Author(s):  
Kannuru Padmaja
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Character Recognition ◽  
Automation System ◽  
Handwritten Character Recognition ◽  
Image Processing Techniques ◽  
Handwritten Character ◽  
Processing Techniques ◽  
Deep Learning Model

Abstract: In this paper, we present the implementation of Devanagari handwritten character recognition using deep learning. Hand written character recognition gaining more importance due to its major contribution in automation system. Devanagari script is one of various languages script in India. It consists of 12 vowels and 36 consonants. Here we implemented the deep learning model to recognize the characters. The character recognition mainly five steps: pre-processing, segmentation, feature extraction, prediction, post-processing. The model will use convolutional neural network to train the model and image processing techniques to use the character recognition and predict the accuracy of rcognition. Keywords: convolutional neural network, character recognition, Devanagari script, deep learning.

scholarly journals A deep learning-based iterative digital pathology annotation tool

2021 ◽  
Author(s):  
Mustafa I. Jaber ◽  
Bing Song ◽  
Liudmila Beziaeva ◽  
Christopher W. Szeto ◽  
Patricia Spilman ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Gold Standard ◽  
Image Annotation ◽  
Digital Pathology ◽  
Tumor Stroma ◽  
Automation System ◽  
Annotation Tool ◽  
Comparable Performance ◽  
Pathology Image

ABSTRACTWell-annotated exemplars are an important prerequisite for supervised deep learning schemes. Unfortunately, generating these annotations is a cumbersome and laborious process, due to the large amount of time and effort needed. Here we present a deep-learning-based iterative digital pathology annotation tool that is both easy to use by pathologists and easy to integrate into machine vision systems. Our pathology image annotation tool greatly reduces annotation time from hours to a few minutes, while maintaining high fidelity with human-expert manual annotations. Here we demonstrate that our active learning tool can be used for a variety of pathology annotation tasks including masking tumor, stroma, and lymphocyte-rich regions, among others. This annotation automation system was validated on 90 unseen digital pathology images with tumor content from the CAMELYON16 database and it was found that pathologists’ gold standard masks were re-produced successfully using our tool. That is, an average of 2.7 positive selections (mouse clicks) and 8.0 negative selections (mouse clicks) were sufficient to generate tumor masks similar to pathologists’ gold standard in CAMELYON16 test WSIs. Furthermore, the developed image annotation tool has been used to build gold standard masks for hundreds of TCGA digital pathology images. This set was used to train a convolutional neural network for identification of tumor epithelium. The developed pan-cancer deep neural network was then tested on TCGA and internal data with comparable performance. The validated pathology image annotation tool described herein has the potential to be of great value in facilitating accurate, rapid pathological analysis of tumor biopsies.

Developing an Artificial Intelligence Project in your Radiology Department

2020 ◽  
Vol 2 ◽  
pp. 58-61 ◽  
Author(s):  
Syed Junaid ◽  
Asad Saeed ◽  
Zeili Yang ◽  
Thomas Micic ◽  
Rajesh Botchu
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Health Care ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Radiology Department ◽  
Short Article ◽  
Computing Power ◽  
Road Map ◽  
Key Concepts

The advances in deep learning algorithms, exponential computing power, and availability of digital patient data like never before have led to the wave of interest and investment in artificial intelligence in health care. No radiology conference is complete without a substantial dedication to AI. Many radiology departments are keen to get involved but are unsure of where and how to begin. This short article provides a simple road map to aid departments to get involved with the technology, demystify key concepts, and pique an interest in the field. We have broken down the journey into seven steps; problem, team, data, kit, neural network, validation, and governance.

Convolutional Neural Network of Atomic Surface Structures to Predict Binding Energies for High-Throughput Screening of Catalysts

2019 ◽  
Author(s):  
Seoin Back ◽  
Junwoong Yoon ◽  
Nianhan Tian ◽  
Wen Zhong ◽  
Kevin Tran ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
High Throughput ◽  
High Throughput Screening ◽  
Binding Energies ◽  
Surface Structures ◽  
Voronoi Polyhedra ◽  
Atomic Surface

We present an application of deep-learning convolutional neural network of atomic surface structures using atomic and Voronoi polyhedra-based neighbor information to predict adsorbate binding energies for the application in catalysis.

Design Of A Voice Controlled Home Automation System Using Deep Learning Convolutional Neural Network (DL-CNN)

2020 ◽  
Vol 8 (1) ◽  
pp. 57-73
Author(s):  
Lery Sakti Ramba
Keyword(s):  
Deep Learning ◽  
Speech Recognition ◽  
Background Noise ◽  
Electronic Devices ◽  
Recognition System ◽  
Background Intensity ◽  
Automation System ◽  
Home Automation ◽  
Speech Recognition System ◽  
Home Automation System

The purpose of this research is to design home automation system that can be controlled using voice commands. This research was conducted by studying other research related to the topics in this research, discussing with competent parties, designing systems, testing systems, and conducting analyzes based on tests that have been done. In this research voice recognition system was designed using Deep Learning Convolutional Neural Networks (DL-CNN). The CNN model that has been designed will then be trained to recognize several kinds of voice commands. The result of this research is a speech recognition system that can be used to control several electronic devices connected to the system. The speech recognition system in this research has a 100% success rate in room conditions with background intensity of 24dB (silent), 67.67% in room conditions with 42dB background noise intensity, and only 51.67% in room conditions with background intensity noise 52dB (noisy). The percentage of the success of the speech recognition system in this research is strongly influenced by the intensity of background noise in a room. Therefore, to obtain optimal results, the speech recognition system in this research is more suitable for use in rooms with low intensity background noise.

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