scholarly journals Towards Automatic and Interpretable Assignments of Patients Presenting with Pain to the Emergency Department

2021 ◽  
Author(s):  
J.A. Hughes ◽  
N.J. Brown ◽  
Thanh Vu ◽  
Anthony Nguyen
Keyword(s):  
Emergency Department ◽  
Deep Learning ◽  
Quality Of Care ◽  
Machine Learning Techniques ◽  
Initial Assessment ◽  
Common Symptom ◽  
Healthcare Applications ◽  
Learning Techniques ◽  
Real Time Identification

Introduction: Pain is the most common symptom that patients present with to the emergency department. It is hard to identify patients who have presented in pain to the emergency department when compliance with structured pain assessment is low. An ability to identify patients presenting in pain allows further investigation of the quality of care provided. Background: Machine and deep learning techniques are commonly used for text analysis in healthcare. Applications such as the classification of diagnosis and unplanned readmissions from textual medical records have previously been described. In other work, conventional and deep-learning techniques have demonstrated high performance in identifying patients presenting to the emergency department in pain. However, these models have lacked interpretability. Methods: This paper proposes the use of machine learning techniques to identify patients who present in pain based upon their initial assessment using interpretable deep learning models. Results: The interpretable deep learning model of pain identification was shown to have more accuracy and precision than other machine and deep learning techniques. This technique has significant application to large datasets for the identification of the quality of care and real-time identification of patients presenting in pain to improve their care.

scholarly journals An Enhanced Deep Neural Network for Predicting Workplace Absenteeism

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-12 ◽  
Author(s):  
Syed Atif Ali Shah ◽  
Irfan Uddin ◽  
Furqan Aziz ◽  
Shafiq Ahmad ◽  
Mahmoud Ahmad Al-Khasawneh ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Deep Neural Network ◽  
Learning Algorithms ◽  
Single Layer ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Proposed Model ◽  
The Cost

Organizations can grow, succeed, and sustain if their employees are committed. The main assets of an organization are those employees who are giving it a required number of hours per month, in other words, those employees who are punctual towards their attendance. Absenteeism from work is a multibillion-dollar problem, and it costs money and decreases revenue. At the time of hiring an employee, organizations do not have an objective mechanism to predict whether an employee will be punctual towards attendance or will be habitually absent. For some organizations, it can be very difficult to deal with those employees who are not punctual, as firing may be either not possible or it may have a huge cost to the organization. In this paper, we propose Neural Networks and Deep Learning algorithms that can predict the behavior of employees towards punctuality at workplace. The efficacy of the proposed method is tested with traditional machine learning techniques, and the results indicate 90.6% performance in Deep Neural Network as compared to 73.3% performance in a single-layer Neural Network and 82% performance in Decision Tree, SVM, and Random Forest. The proposed model will provide a useful mechanism to organizations that are interested to know the behavior of employees at the time of hiring and can reduce the cost of paying to inefficient or habitually absent employees. This paper is a first study of its kind to analyze the patterns of absenteeism in employees using deep learning algorithms and helps the organization to further improve the quality of life of employees and hence reduce absenteeism.

scholarly journals Diseño de un modelo de atención visual para campañas de comunicación. El caso de la Covid-19

2020 ◽  
Author(s):  
Juan-José Silva-Torres ◽  
Luz Martínez-Martínez ◽  
Ubaldo Cuesta-Cambra
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Eye Tracking ◽  
Influenza A ◽  
Social Consequences ◽  
Machine Learning Techniques ◽  
Mathematical Tool ◽  
Learning Techniques ◽  
Mathematical Techniques

Health is one of the main concerns of society. Empirical evidence underscores the growing importance of prevention and health education as a fundamental instrument to improve the quality of public health. Recent health crises, such as Ebola, influenza A, SARS, and Covid-19, have highlighted the importance of communication. When designing communication campaigns during a crisis, the speed of the creation of messages and their effectiveness have relevant social consequences. The objective of this work is to design and develop a mathematical tool, based on Machine Learning techniques, to enable predictions of areas of visual attention quickly and accurately without the use of eye-tracking technology. The methodology combines deep learning algorithms, to extract the characteristics of the images, and supervised modeling mathematical techniques, to predict the areas of attention. Validation is carried out by analyzing various institutional communications from the Covid-19 campaign, comparing the results with the areas of attention obtained using an eye-tracking solution with proven accuracy. The results obtained using the tool in the investigated Covid-19 communication pieces are analyzed, resulting in conclusions of interest for the development of new campaigns. Resumen La salud es una de las principales preocupaciones de la sociedad. La evidencia empírica subraya la importancia creciente de la prevención y la educación para la salud (EpS) como instrumento fundamental para mejorar la calidad de la salud pública. Las recientes crisis sanitarias, como la del Ébola, la gripe A, el SARS o la Covid-19, han puesto de relieve la importancia de la comunicación. En el diseño de campañas de comunicación en tiempos de crisis, la rapidez en la creación de mensajes y la validación de su eficacia tiene consecuencias sociales relevantes. En este trabajo se ha diseñado y desarrollado una herramienta matemática basada en machine learning que permite predecir las áreas de atención visual de forma rápida y precisa, sin necesidad de utilizar tecnología de seguimiento ocular (eye-tracking). La metodología combina algoritmos de deep learning, para extraer las características de las imágenes, con técnicas de modelización supervisada para predecir las áreas de atención. La validación se ha realizado analizando varias comunicaciones institucionales de la campaña de Covid-19, comparando los resultados con las áreas de atención obtenidas con una solución de seguimiento ocular de validez contrastada. Se analizan los resultados obtenidos mediante la herramienta en las piezas de comunicación Covid-19 investigadas, planteando conclusiones de interés para el desarrollo de nuevas campañas.

A Literature Review Study of Software Defect Prediction using Machine Learning Techniques

2018 ◽  
Vol 6 (6) ◽  
pp. 300 ◽  
Author(s):  
Feidu Akmel ◽  
Ermiyas Birihanu ◽  
Bahir Siraj
Keyword(s):  
Machine Learning ◽  
Software Metrics ◽  
Quality Standard ◽  
Machine Learning Techniques ◽  
Software Systems ◽  
Health Care Insurance ◽  
Software Defect ◽  
Learning Techniques ◽  
Software Product

Software systems are any software product or applications that support business domains such as Manufacturing,Aviation, Health care, insurance and so on.Software quality is a means of measuring how software is designed and how well the software conforms to that design. Some of the variables that we are looking for software quality are Correctness, Product quality, Scalability, Completeness and Absence of bugs, However the quality standard that was used from one organization is different from other for this reason it is better to apply the software metrics to measure the quality of software. Attributes that we gathered from source code through software metrics can be an input for software defect predictor. Software defect are an error that are introduced by software developer and stakeholders. Finally, in this study we discovered the application of machine learning on software defect that we gathered from the previous research works.

scholarly journals Auto-Colorization of Historical Images Using Deep Convolutional Neural Networks

Mathematics ◽  
2020 ◽  
Vol 8 (12) ◽  
pp. 2258
Author(s):  
Madhab Raj Joshi ◽  
Lewis Nkenyereye ◽  
Gyanendra Prasad Joshi ◽  
S. M. Riazul Islam ◽  
Mohammad Abdullah-Al-Wadud ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
User Study ◽  
Mean Squared Error ◽  
Color Image ◽  
Machine Learning Techniques ◽  
Global Features ◽  
Black And White ◽  
Historical Images ◽  
Learning Techniques

Enhancement of Cultural Heritage such as historical images is very crucial to safeguard the diversity of cultures. Automated colorization of black and white images has been subject to extensive research through computer vision and machine learning techniques. Our research addresses the problem of generating a plausible colored photograph of ancient, historically black, and white images of Nepal using deep learning techniques without direct human intervention. Motivated by the recent success of deep learning techniques in image processing, a feed-forward, deep Convolutional Neural Network (CNN) in combination with Inception- ResnetV2 is being trained by sets of sample images using back-propagation to recognize the pattern in RGB and grayscale values. The trained neural network is then used to predict two a* and b* chroma channels given grayscale, L channel of test images. CNN vividly colorizes images with the help of the fusion layer accounting for local features as well as global features. Two objective functions, namely, Mean Squared Error (MSE) and Peak Signal-to-Noise Ratio (PSNR), are employed for objective quality assessment between the estimated color image and its ground truth. The model is trained on the dataset created by ourselves with 1.2 K historical images comprised of old and ancient photographs of Nepal, each having 256 × 256 resolution. The loss i.e., MSE, PSNR, and accuracy of the model are found to be 6.08%, 34.65 dB, and 75.23%, respectively. Other than presenting the training results, the public acceptance or subjective validation of the generated images is assessed by means of a user study where the model shows 41.71% of naturalness while evaluating colorization results.

scholarly journals Detection and Severity Evaluation of Combined Rail Defects Using Deep Learning

Vibration ◽  
2021 ◽  
Vol 4 (2) ◽  
pp. 341-356
Author(s):  
Jessada Sresakoolchai ◽  
Sakdirat Kaewunruen
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Machine Learning Techniques ◽  
Rolling Stock ◽  
Raw Data ◽  
Learning Techniques ◽  
Combined Defects

Various techniques have been developed to detect railway defects. One of the popular techniques is machine learning. This unprecedented study applies deep learning, which is a branch of machine learning techniques, to detect and evaluate the severity of rail combined defects. The combined defects in the study are settlement and dipped joint. Features used to detect and evaluate the severity of combined defects are axle box accelerations simulated using a verified rolling stock dynamic behavior simulation called D-Track. A total of 1650 simulations are run to generate numerical data. Deep learning techniques used in the study are deep neural network (DNN), convolutional neural network (CNN), and recurrent neural network (RNN). Simulated data are used in two ways: simplified data and raw data. Simplified data are used to develop the DNN model, while raw data are used to develop the CNN and RNN model. For simplified data, features are extracted from raw data, which are the weight of rolling stock, the speed of rolling stock, and three peak and bottom accelerations from two wheels of rolling stock. In total, there are 14 features used as simplified data for developing the DNN model. For raw data, time-domain accelerations are used directly to develop the CNN and RNN models without processing and data extraction. Hyperparameter tuning is performed to ensure that the performance of each model is optimized. Grid search is used for performing hyperparameter tuning. To detect the combined defects, the study proposes two approaches. The first approach uses one model to detect settlement and dipped joint, and the second approach uses two models to detect settlement and dipped joint separately. The results show that the CNN models of both approaches provide the same accuracy of 99%, so one model is good enough to detect settlement and dipped joint. To evaluate the severity of the combined defects, the study applies classification and regression concepts. Classification is used to evaluate the severity by categorizing defects into light, medium, and severe classes, and regression is used to estimate the size of defects. From the study, the CNN model is suitable for evaluating dipped joint severity with an accuracy of 84% and mean absolute error (MAE) of 1.25 mm, and the RNN model is suitable for evaluating settlement severity with an accuracy of 99% and mean absolute error (MAE) of 1.58 mm.

Machine learning techniques based on security management in smart cities using robots

Work ◽  
2021 ◽  
pp. 1-12
Author(s):  
Zhang Mengqi ◽  
Wang Xi ◽  
V.E. Sathishkumar ◽  
V. Sivakumar
Keyword(s):  
Machine Learning ◽  
Information And Communication Technologies ◽  
Smart City ◽  
Smart Cities ◽  
Security Management ◽  
Machine Learning Techniques ◽  
Quality Of Services ◽  
Learning Techniques ◽  
Learning Concept

BACKGROUND: Nowadays, the growth of smart cities is enhanced gradually, which collects a lot of information and communication technologies that are used to maximize the quality of services. Even though the intelligent city concept provides a lot of valuable services, security management is still one of the major issues due to shared threats and activities. For overcoming the above problems, smart cities’ security factors should be analyzed continuously to eliminate the unwanted activities that used to enhance the quality of the services. OBJECTIVES: To address the discussed problem, active machine learning techniques are used to predict the quality of services in the smart city manages security-related issues. In this work, a deep reinforcement learning concept is used to learn the features of smart cities; the learning concept understands the entire activities of the smart city. During this energetic city, information is gathered with the help of security robots called cobalt robots. The smart cities related to new incoming features are examined through the use of a modular neural network. RESULTS: The system successfully predicts the unwanted activity in intelligent cities by dividing the collected data into a smaller subset, which reduces the complexity and improves the overall security management process. The efficiency of the system is evaluated using experimental analysis. CONCLUSION: This exploratory study is conducted on the 200 obstacles are placed in the smart city, and the introduced DRL with MDNN approach attains maximum results on security maintains.

scholarly journals A Data Science Analysis of Academic Staff Workload Profiles in Spanish Universities: Gender Gap Laid Bare

2021 ◽  
Vol 11 (7) ◽  
pp. 317
Author(s):  
Ismael Cabero ◽  
Irene Epifanio
Keyword(s):  
Gender Gap ◽  
Data Science ◽  
Academic Staff ◽  
Machine Learning Techniques ◽  
Academic Disciplines ◽  
University Environment ◽  
Learning Techniques ◽  
Spanish Universities ◽  
New Perspective

This paper presents a snapshot of the distribution of time that Spanish academic staff spend on different tasks. We carry out a statistical exploratory study by analyzing the responses provided in a survey of 703 Spanish academic staff in order to draw a clear picture of the current situation. This analysis considers many factors, including primarily gender, academic ranks, age, and academic disciplines. The tasks considered are divided into smaller activities, which allows us to discover hidden patterns. Tasks are not only restricted to the academic world, but also relate to domestic chores. We address this problem from a totally new perspective by using machine learning techniques, such as cluster analysis. In order to make important decisions, policymakers must know how academic staff spend their time, especially now that legal modifications are planned for the Spanish university environment. In terms of the time spent on quality of teaching and caring tasks, we expose huge gender gaps. Non-recognized overtime is very frequent.

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