scholarly journals Prediction of Caption and Emoji of an Image using Deep Learning

2019 ◽  
Vol 8 (2S11) ◽  
pp. 3721-3724
Keyword(s):  
Deep Learning ◽  
Social Networking ◽  
Web Application ◽  
Automatic Generation ◽  
Application Framework ◽  
Challenging Problem ◽  
Artificial Intelligence Research ◽  
Learning Techniques ◽  
User Friendly ◽  
Visually Impaired Persons

With the invention of deep learning, there is a good progress in image classification. But automatic generation of captions for images is still a challenging problem and is in the initial stages of artificial intelligence research. Automatic description of images has applications in social networking and will be useful to visually impaired persons. This paper concentrates on designing a user-friendly web application framework which can predict the caption of an image using deep learning techniques. The verbs and objects present in the caption are used for forming the emoji and for predicting the major color of the image

scholarly journals AlgoLabel: A Large Dataset for Multi-Label Classification of Algorithmic Challenges

Mathematics ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 1995
Author(s):  
Radu Cristian Alexandru Iacob ◽  
Vlad Cristian Monea ◽  
Dan Rădulescu ◽  
Andrei-Florin Ceapă ◽  
Traian Rebedea ◽  
...  
Keyword(s):  
Deep Learning ◽  
Language Processing ◽  
Code Generation ◽  
Neural Model ◽  
Automatic Generation ◽  
Code Model ◽  
Learning Techniques ◽  
Solution Type ◽  
Algorithmic Solution ◽  
Reliable Solution

While semantic parsing has been an important problem in natural language processing for decades, recent years have seen a wide interest in automatic generation of code from text. We propose an alternative problem to code generation: labelling the algorithmic solution for programming challenges. While this may seem an easier task, we highlight that current deep learning techniques are still far from offering a reliable solution. The contributions of the paper are twofold. First, we propose a large multi-modal dataset of text and code pairs consisting of algorithmic challenges and their solutions, called AlgoLabel. Second, we show that vanilla deep learning solutions need to be greatly improved to solve this task and we propose a dual text-code neural model for detecting the algorithmic solution type for a programming challenge. While the proposed text-code model increases the performance of using the text or code alone, the improvement is rather small highlighting that we require better methods to combine text and code features.

scholarly journals Development of mobile application for Malay translated hadith search engine

2020 ◽  
Vol 20 (2) ◽  
pp. 932
Author(s):  
Nurazzah Abd Rahman ◽  
Faiz Ikhwan Mohd Rafhan Syamil ◽  
Shaiful Bakhtiar bin Rodzman
Keyword(s):  
Search Engine ◽  
Mobile Application ◽  
Web Application ◽  
Web Applications ◽  
Application Framework ◽  
User Evaluation ◽  
Functional Requirements ◽  
Desktop Computer ◽  
Internet Connection ◽  
User Friendly

This paper presents the development of mobile application for Malay Translated Hadith search engine. Limitations of current Hadith web application are the design is to optimize its usage on desktop computer but not on mobile devices, which requires simple and user friendly interface. Besides that, web application also needs internet connection to use. Due to increase usage of mobile application among mobile phone users, many existing web applications have moved to mobile based applications to cater for increasing numbers of mobile users. In this study, a mobile application for Android and iOS mobile application has been developed using Flutter framework, a hybrid mobile application framework. A Malay Translated hadith search engine mobile application can easily assist those who are seeking knowledge to learn more about certain topics in hadith, a second source of Islamic knowledge. This mobile application has search and directory features for users to browse the 2028 Sahih Bukhari hadith collection. Users can enter their query using search features to find selected hadith in Malay language. Queries will be processed for searching relevant hadith and display the results to the user. Evaluation using Recall and Precision shows that on the average Recall is 73% and Precision is 33%. Functionality testing is also conducted to test against the functional requirements or specifications. Results shows all requirements are successfully tested.

scholarly journals COVID-19 Chest X-Ray Image Classification Using Deep Learning

2021 ◽  
Author(s):  
Soumava Dey ◽  
Gunther Correia Bacellar ◽  
Mallikarjuna Basappa Chandrappa ◽  
Raj Kulkarni
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Image Classification ◽  
Web Application ◽  
Human Error ◽  
Disease Diagnosis ◽  
Disease Classification ◽  
X Ray ◽  
Chest X Ray ◽  
User Friendly

The rise of the coronavirus disease 2019 (COVID-19) pandemic has made it necessary to improve existing medical screening and clinical management of this disease. While COVID-19 patients are known to exhibit a variety of symptoms, the major symptoms include fever, cough, and fatigue. Since these symptoms also appear in pneumonia patients, this creates complications in COVID-19 detection especially during the flu season. Early studies identified abnormalities in chest X-ray images of COVID-19 infected patients that could be beneficial for disease diagnosis. Therefore, chest X-ray image-based disease classification has emerged as an alternative to aid medical diagnosis. However, manual detection of COVID-19 from a set of chest X-ray images comprising both COVID-19 and pneumonia cases is cumbersome and prone to human error. Thus, artificial intelligence techniques powered by deep learning algorithms, which learn from radiography images and predict presence of COVID-19 have potential to enhance current diagnosis process. Towards this purpose, here we implemented a set of deep learning pre-trained models such as ResNet, VGG, Inception and EfficientNet in conjunction with developing a computer vision AI system based on our own convolutional neural network (CNN) model: Deep Learning in Healthcare (DLH)-COVID. All these CNN models cater to image classification exercise. We used publicly available resources of 6,432 images and further strengthened our model by tuning hyperparameters to provide better generalization during the model validation phase. Our final DLH-COVID model yielded the highest accuracy of 96% in detection of COVID-19 from chest X-ray images when compared to images of both pneumonia-affected and healthy individuals. Given the practicality of acquiring chest X-ray images by patients, we also developed a web application (link: https://toad.li/xray) based on our model to directly enable users to upload chest X-ray images and detect the presence of COVID-19 within a few seconds. Taken together, here we introduce a state-of-the-art artificial intelligence-based system for efficient COVID-19 detection and a user-friendly application that has the capacity to become a rapid COVID-19 diagnosis method in the near future.

scholarly journals Neural Networks for Entity Matching: A Survey

2021 ◽  
Vol 15 (3) ◽  
pp. 1-37
Author(s):  
Nils Barlaug ◽  
Jon Atle Gulla
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Language Processing ◽  
Deep Neural Networks ◽  
Challenging Problem ◽  
Entity Matching ◽  
New Methods ◽  
Matching Process ◽  
Learning Techniques

Entity matching is the problem of identifying which records refer to the same real-world entity. It has been actively researched for decades, and a variety of different approaches have been developed. Even today, it remains a challenging problem, and there is still generous room for improvement. In recent years, we have seen new methods based upon deep learning techniques for natural language processing emerge. In this survey, we present how neural networks have been used for entity matching. Specifically, we identify which steps of the entity matching process existing work have targeted using neural networks, and provide an overview of the different techniques used at each step. We also discuss contributions from deep learning in entity matching compared to traditional methods, and propose a taxonomy of deep neural networks for entity matching.

Smart Computerized Essay Scoring Using Deep Neural Networks for Universities and Institutions

2020 ◽  
pp. 125-152
Author(s):  
J. Joshua Thomas ◽  
Lim Ting Wei ◽  
Y. Bevish Jinila ◽  
R. Subhashini
Keyword(s):  
Deep Learning ◽  
Web Application ◽  
Weighted Kappa ◽  
High Stakes ◽  
Web Based ◽  
Sequential Dependencies ◽  
Learning Techniques ◽  
Lstm Network ◽  
High Stakes Exams ◽  
The Web

This chapter develops a web-based automated text scoring (ATS) system that can grade essays and check for spelling errors. The main reason behind this work is to alleviate the labour-intensive marking of essays and ensures equality in scoring for high-stakes exams like TOEFL. The researcher had performed a detailed investigation on deep learning techniques used in the field of ATS and developed a recurrent neural network model that can score essays in an end-to-end approach. Using the developed deep learning model, a web application was also developed to showcase the process of ATS by letting the web application to communicate with the trained model. The model was trained using Keras framework and TensorFlow library and the web application was done using the Flask framework. This work is the LSTM network that can capture sequential dependencies. The evaluation metrics chosen to evaluate the model are the quadratic weighted kappa (QWK) score, and the trained model can achieve 0.6 in QWK score.

scholarly journals SimText: A text mining framework for interactive analysis and visualization of similarities among biomedical entities

2020 ◽  
Author(s):  
Marie Gramm ◽  
Eduardo Pérez-Palma ◽  
Sarah Schumacher-Bass ◽  
Jarrod Dalton ◽  
Costin Leu ◽  
...  
Keyword(s):  
Text Mining ◽  
Web Application ◽  
Data Generation ◽  
R Software ◽  
Online Data ◽  
Interactive Analysis ◽  
Learning Techniques ◽  
User Friendly ◽  
Analysis Platform ◽  
Command Line Version

AbstractLiterature exploration in PubMed on a large number of biomedical entities (e.g., genes, diseases, experiments) can be time consuming and challenging comparing many entities to one other. Here, we describe SimText, a user-friendly toolset that provides customizable and systematic workflows for the analysis of similarities among a set of entities based on words from abstracts and/or other text. SimText can be used for (i) data generation: text collection from PubMed and extraction of words with different text mining approaches, and (ii) interactive analysis of data using unsupervised learning techniques and visualization in a Shiny web application.Availability and ImplementationWe developed SimText as an open-source R software and integrated it into Galaxy, an online data analysis platform. A command line version of the toolset is available for download from GitHub at https://github.com/mgramm1/simtext.

scholarly journals DeepScreening: a deep learning-based screening web server for accelerating drug discovery

Database ◽  
2019 ◽  
Vol 2019 ◽  
Author(s):  
Zhihong Liu ◽  
Jiewen Du ◽  
Jiansong Fang ◽  
Yulong Yin ◽  
Guohuan Xu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Drug Discovery ◽  
Virtual Screening ◽  
De Novo ◽  
Web Server ◽  
Machine Learning Algorithms ◽  
Data Availability ◽  
Superior Performance ◽  
Learning Techniques ◽  
User Friendly

Abstract Deep learning contributes significantly to researches in biological sciences and drug discovery. Previous studies suggested that deep learning techniques have shown superior performance to other machine learning algorithms in virtual screening, which is a critical step to accelerate the drug discovery. However, the application of deep learning techniques in drug discovery and chemical biology are hindered due to the data availability, data further processing and lacking of the user-friendly deep learning tools and interface. Therefore, we developed a user-friendly web server with integration of the state of art deep learning algorithm, which utilizes either the public or user-provided dataset to help biologists or chemists perform virtual screening either the chemical probes or drugs for a specific target of interest. With DeepScreening, user could conveniently construct a deep learning model and generate the target-focused de novo libraries. The constructed classification and regression models could be subsequently used for virtual screening against the generated de novo libraries, or diverse chemical libraries in stock. From deep models training to virtual screening, and target focused de novo library generation, all those tasks could be finished with DeepScreening. We believe this deep learning-based web server will benefit to both biologists and chemists for probes or drugs discovery.

Application of Google Maps API with Grails Services for Mapping Highway Construction Sites

2013 ◽  
Vol 671-674 ◽  
pp. 3185-3188 ◽  
Author(s):  
Ida Aryanie Bahrudin ◽  
Mohd Ezree Abdullah ◽  
Rafizah Mohd Hanifa ◽  
Miswan Surip ◽  
Ab Aziz Abdul Latiff
Keyword(s):  
Web Application ◽  
Satellite Image ◽  
Highway Construction ◽  
Ease Of Use ◽  
Application Framework ◽  
Construction Sites ◽  
Google Maps ◽  
Web Mapping ◽  
Interactive Map ◽  
User Friendly

Most people agreed that Google Maps set the standard for ease of use when it comes to a free online web mapping services. In addition, different map views are available which user can choose from the options of a terrain view, normal map view, or satellite image view based on their needs. Embedding a Google Maps on a website will not only make it more user-friendly but also enable people to access certain location with more ease. This paper intends to introduce the use of this geoinformatics services from Google along with Grails services in creating a simple interactive map for highway construction sites. A discussion will cover up the use of programming interface provided by Google and Grails (open source web application framework) and also the advantages of embedding the map to the website.

scholarly journals Predicting Inpatient Status for the Next 30/60/90 Days With Machine Learning

2021 ◽  
Vol 6 (2) ◽  
pp. 1-18
Author(s):  
Lakshmi Prayaga ◽  
Krishna Devulapalli ◽  
Chandra Prayaga ◽  
Joe Carloni
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Web Application ◽  
Medical Condition ◽  
Relevant Information ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Random Forest Models ◽  
User Friendly ◽  
Future Patient

In this paper, we report the development of machine learning techniques which can help hospital authorities assess a patients' medical condition and also calculate the probability of readmission of the patient as inpatient, and thus identify patients with higher risks for readmissions. Factor Analysis is performed on patient data to understand the severity of mental health, and Random Forest models are used to determine the probability of a patient becoming an inpatient for the next 30/60/90 days from their last visit to the physician’s office. The Random Forest model fits the data with an overall OOB Error rate of 3.69% and an accuracy of 97.65%. The accuracy on the test data was 96.11%. A web application is also developed to provide a user-friendly interface for physicians and administrators to interact with and obtain relevant information for a given patient and or a group of patients. The web application affords physicians additional inputs to assist in their diagnosis and administrators, a window into anticipating and preparing for future patient needs.

Sign in / Sign up

Export Citation Format

Share Document