Advanced deep learning algorithms for infectious disease modeling using clinical data- A Case Study on CoVID-19

2021 ◽  
Vol 17 ◽  
Author(s):  
Ajay Kumar ◽  
Smita Nivrutti Kolnure ◽  
Kumar Abhishek ◽  
Fadi-Al-Turjman ◽  
Pranav Nerurkar ◽  
...  
Keyword(s):  
Infectious Disease ◽  
Deep Learning ◽  
Time Series Data ◽  
Disease Modeling ◽  
Learning Algorithms ◽  
Image Data ◽  
Series Data ◽  
Text Data ◽  
Infectious Disease Modeling

Background: Infectious disease happens when an individual is defiled by a micro-organism/virus from another person or an animal. It is troublesome that causes hurt at both individual and huge scope scales. Case Presentation : The ongoing episode of COVID-19 ailment brought about by the new coronavirus first distinguished in Wuhan China, and its quick spread far and wide, revived the consideration of the world towards the impacts of such plagues on individual’s regular daily existence. We attempt to exploit this effectiveness of Advanced deep learning algorithms to predict the Growth of Infectious disease based on time series data and classification based on (symptoms) text data and X-ray image data. Conclusion: Goal is identifying the nature of the phenomenon represented by the sequence of observations and forecasting.

scholarly journals A deep learning approach for anomaly detection with industrial time series data: a refrigerators manufacturing case study

2019 ◽  
Vol 38 ◽  
pp. 233-240 ◽  
Author(s):  
Mattia Carletti ◽  
Chiara Masiero ◽  
Alessandro Beghi ◽  
Gian Antonio Susto
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Anomaly Detection ◽  
Time Series Data ◽  
Series Data ◽  
Learning Approach

scholarly journals A Case Study on Hospital Readmission Prediction Using Deep Learning Algorithms on EHRs

2021 ◽  
Vol 12 (3) ◽  
pp. 4267-4273
Author(s):  
B. Sushrith Et.al
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Financial Risk ◽  
Time Series Data ◽  
Learning Algorithms ◽  
Series Data ◽  
Critical Areas ◽  
Avoidable Readmission ◽  
Low Dimensional ◽  
Medical Concepts

In this paper, focus is made on predicting the patients who are going to be re-admitted back in the hospital before discharge using latest deep-learning algorithms is applied on the electronic health records of patients which is a time-series data. To begin with the study of the data and its analysis this project deployed the conventional supervised ML algorithms like the Logistic Regression, Naïve Bayes, Random Forest and SVM and compared their performances on different portion sizes of dataset. The final model built uses deep-learning architectures such as RNN and LSTM to improve the prediction results taking advantage of the time series data. Another feature added has been of low dimensional descriptions of medical concepts as the input to the model. Ultimately, this work tests, validates, and explains the developed system using the MIMIC-III dataset, which contains around 38000 patient’s information and about 61,155 patient’s data who admitted in ICU, duration of 10 years. The support from this exhaustive dataset is used to train the models that provide healthcare workers with proper information regarding their discharge and readmission in hospitals. These ML and deep learning models are used to know about the patient who is getting to be readmitted in the ICU before his discharge will help the hospital to allocate resources properly and also reduce the financial risk of patients. In order to reduce ICU readmission that can be avoided, hospitals have to be able to recognize patients who have a higher risk of ICU readmission. Those patients can then continue to stay in the ICU so that they will not have the risk of getting admit back to the hospital. Also, the resources of hospitals that were required for avoidable readmission can be re-allocated to more critical areas in the hospital that need them. A more effective model of predicting readmission system can play an important role in helping hospitals and ICU doctors to find the patients who are going to be readmitted before discharge. To build this system here we use different ML and deep-learning algorithms. Predictive models based on huge amounts of data are made to predict the patients who are going to be admitted back in the hospital after discharge.

scholarly journals A Deep Learning Approach for Peak Load Forecasting: A Case Study on Panama

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3039
Author(s):  
Bibi Ibrahim ◽  
Luis Rabelo
Keyword(s):  
Deep Learning ◽  
Time Series Data ◽  
Short Term Memory ◽  
Research Question ◽  
Peak Load ◽  
Series Data ◽  
Peak Demand ◽  
Demand Growth ◽  
Residential Consumption

Predicting the future peak demand growth becomes increasingly important as more consumer loads and electric vehicles (EVs) start connecting to the grid. Accurate forecasts will enable energy suppliers to meet demand more reliably. However, this is a challenging problem since the peak demand is very nonlinear. This study addresses the research question of how deep learning methods, such as convolutional neural networks (CNNs) and long-short term memory (LSTM) can provide better support to these areas. The goal is to build a suitable forecasting model that can accurately predict the peak demand. Several data from 2004 to 2019 was collected from Panama’s power system to validate this study. Input features such as residential consumption and monthly economic index were considered for predicting peak demand. First, we introduced three different CNN architectures which were multivariate CNN, multivariate CNN-LSTM and multihead CNN. These were then benchmarked against LSTM. We found that the CNNs outperformed LSTM, with the multivariate CNN being the best performing model. To validate our initial findings, we then evaluated the robustness of the models against Gaussian noise. We demonstrated that CNNs were far more superior than LSTM and can support spatial-temporal time series data.

Producers' Response to Changes in Prices and Marketing Policies: A Case Study of Sugarcane and Paddy in Eastern U.P. by S. C. Gupta and A. Majid. New York: Asia Publishing House, 1965.

1968 ◽  
Vol 8 (2) ◽  
pp. 308-309
Author(s):  
Mohammad Irshad Khan
Keyword(s):  
New York ◽  
Time Series ◽  
Time Series Data ◽  
Publishing House ◽  
Economic Incentives ◽  
The Other ◽  
Series Data ◽  
Poor Countries ◽  
Northern India

It is alleged that the agricultural output in poor countries responds very little to movements in prices and costs because of subsistence-oriented produc¬tion and self-produced inputs. The work of Gupta and Majid is concerned with the empirical verification of the responsiveness of farmers to prices and marketing policies in a backward region. The authors' analysis of the respon¬siveness of farmers to economic incentives is based on two sets of data (concern¬ing sugarcane, cash crop, and paddy, subsistence crop) collected from the district of Deoria in Eastern U.P. (Utter Pradesh) a chronically foodgrain deficit region in northern India. In one set, they have aggregate time-series data at district level and, in the other, they have obtained data from a survey of five villages selected from 170 villages around Padrauna town in Deoria.

scholarly journals Load forecasting of refrigerated display cabinet based on CEEMD–IPSO–LSTM combined model

Open Physics ◽  
2021 ◽  
Vol 19 (1) ◽  
pp. 360-374
Author(s):  
Yuan Pei ◽  
Lei Zhenglin ◽  
Zeng Qinghui ◽  
Wu Yixiao ◽  
Lu Yanli ◽  
...  
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Time Series Data ◽  
Load Forecasting ◽  
Series Data ◽  
Forecasting Model ◽  
Combined Model ◽  
Forecasting Accuracy ◽  
Forecasting Method ◽  
Consumption Reduction

Abstract The load of the showcase is a nonlinear and unstable time series data, and the traditional forecasting method is not applicable. Deep learning algorithms are introduced to predict the load of the showcase. Based on the CEEMD–IPSO–LSTM combination algorithm, this paper builds a refrigerated display cabinet load forecasting model. Compared with the forecast results of other models, it finally proves that the CEEMD–IPSO–LSTM model has the highest load forecasting accuracy, and the model’s determination coefficient is 0.9105, which is obviously excellent. Compared with other models, the model constructed in this paper can predict the load of showcases, which can provide a reference for energy saving and consumption reduction of display cabinet.

scholarly journals Deep Learning for Anomaly Detection in Time-Series Data: Review, Analysis, and Guidelines

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 120043-120065
Author(s):  
Kukjin Choi ◽  
Jihun Yi ◽  
Changhwa Park ◽  
Sungroh Yoon
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Anomaly Detection ◽  
Time Series Data ◽  
Series Data ◽  
Review Analysis
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