Design a FPGA, fuzzy based, insolent method for prediction of multi-diseases in rural area

Satpathy, Sambit; Mohan, Prakash; Debbarma, Swapan; Sengupta, Aditya Sankar; Bhattacaryya, Bidyut K.

doi:10.3233/jifs-181577

Cited by 31 publications

(20 citation statements)

References 15 publications

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“…This experiment also focused on reducing the noise data by eradicating inappropriate dependencies that arise due to the absence of analysis through time dimension. Rather than Principal Component Analysis (PCA) [18][19][20], the Boruta algorithm [21,22] is used for the feature selection process for dimensionality reduction problem. This developed research can derive substantial patterns from factual customers transmit and transactional data [23,24] authenticated by the financial institutions.…”

Section: Knn [6]mentioning

confidence: 99%

Adaptive XGBOOST Hyper Tuned Meta Classifier for Prediction of Churn Customers

Srikanth¹,

Papineni²,

Sridevi³

et al. 2022

Intelligent Automation &Amp; Soft Computing

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In India, the banks have a formidable edge in maintaining their customer retention ratio for past few decades. Downfall makes the private banks to reduce their operations and the nationalised banks merge with other banks. The researchers have used the traditional and ensemble algorithms with relevant feature engineering techniques to better classify the customers. The proposed algorithm uses a Meta classifier instead of an ensemble algorithm with an adaptive genetic algorithm for feature selection. Churn prediction is the number of customers who wants to terminate their services in the banking sector. The model considers twelve attributes like credit score, geography, gender, age, etc, to predict customer churn. The project consists of five modules as follows. First is the pre-processing module that identifies the missing data and fills the value with mean and mode. Second is the data transformation module where, the categorical data is converted into numerical data using label encoding to fasten the computations. The converted numerical data is normalized using the standard scalar technique. The feature selection module identifies the essential attributes using DragonFly and Firefly (Hybrid Fly) algorithms. The classification module designs an intelligent Meta learner, which combines the Ensemble Algorithm Extreme Gradient Boosting (XGBOOST) with base classifiers as "Extra Tree Classifier" and "Logistic Regression" to predict the churn customers.

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Section: Knn [6]mentioning

confidence: 99%

Adaptive XGBOOST Hyper Tuned Meta Classifier for Prediction of Churn Customers

Srikanth¹,

Papineni²,

Sridevi³

et al. 2022

Intelligent Automation &Amp; Soft Computing

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“…On an Internet of Things (IoT)-based analysis system, Satpathy, et al [9] developed a fuzzy classifier which results in a lower execution time in comparison with that of previous models, such as K-nearest neighbor, decision tree, support vector machine, and naive Bayes. In the field-programmable gate array (FPGA) analysis system with the help of fuzzy system, Satpathy, et al [10] designed a novel method for prediction multiple diseases in a rural area. In cooking, Fuzzy logic control enables to obtain a cooking time for different type of rice and for different quantity of water [11].…”

Section: Fuzzy Time Series Forecastingmentioning

confidence: 99%

Application of Weighted Fuzzy Time Series Model to Forecast Epidemic Injuries

Moneim

2020

CJAST

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Aims: It is important to predict the amount of COVID-19 injuries. Since the first suspected case of novel coronavirus (2019-nCoV) on December 1st, 2019, in Wuhan, Hubei Province, China, a total of 40,235 confirmed cases and 909 deaths have been reported in China up to February 10, 2020, evoking fear locally and internationally. Here, based on the large amounts of daily publicly available epidemiological data and the need to make an accurate prediction of future behavior requires the definition of powerful and effective techniques capable of inferring random dependency between the past and the future from observations. In this paper, we apply a rewarding model to predict injuries in areas where COVID-19 is, especially in the Arab region. This forecast uses epidemic injuries data from March 2nd, 2020 to July 20th, 2020 in Saudi Arabia. Methodology: We propose the use of weighted fuzzy time series techniques (WFTS) and weighted non-stationary fuzzy time series techniques (WNSFTS) to be compared with the classical Auto-Regressive Integrated Moving Average (ARIMA) statistical method. The available data is not a stationary and should therefore be converted first to stationary to forecast it with (ARIMA) and (WFTS) techniques. We do a log transform and differencing on our injuries dataset. Results: When we examine the original data by Dickey-Fuller Test (DFT) to get p-value, we find it is equal to 0.646, it is more than 0.05 which implies the non-stationarity. The mean square error (MSE), the root mean square error (RMSE) and normalization root mean square error (NRMSE), are applied to compare the accuracy of the methods. The results show that WFTS methods give good services for predicting epidemic injuries in the territory by COVID-19. Conclusion: The use of Weighted Non Stationary Fuzzy Time Series (WNSFTS) in forecasting epidemic injuries problem can provide significantly better results because it is able to predict the infected cases at the next time and achieve great predictive accuracy.

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“…Still, the morbidity rate is very high due to the unpredicted and unexpected reasons. Currently, there are no proven and concrete interventions that reduce the risk of cardiovascular disease [5,6].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Cardiovascular Disease Using Machine Learning Technique—A Modern Approach

Kim¹,

Jeong²

2022

Computers, Materials &Amp; Continua

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Cardio Vascular disease (CVD), involving the heart and blood vessels is one of the most leading causes of death throughout the world. There are several risk factors for causing heart diseases like sedentary lifestyle, unhealthy diet, obesity, diabetes, hypertension, smoking and consumption of alcohol, stress, hereditary factory etc. Predicting cardiovascular disease and improving and treating the risk factors at an early stage are of paramount importance to save the precious life of a human being. At present, the highly stressful life with bad lifestyle activities causes heart disease at a very young age. The main aim of this research is to predict the premature heart disease based on machine learning algorithms. This paper deals with a novel approach using the machine learning algorithm for predicting the cardiovascular disease at the premature stage itself. Support Vector Machine (SVM) is used for segregating the CVD patients based on their symptoms and medical observation. The experimentation results by using the proposed method will facilitate the medical practitioners to provide suitable treatment for the patients on time. A sophisticated model has been developed with the current approach to examine the various stages of CVD and the performance metrics used have given effective and fruitful results as compared to other machine learning techniques.

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Design a FPGA, fuzzy based, insolent method for prediction of multi-diseases in rural area

Cited by 31 publications

References 15 publications

Adaptive XGBOOST Hyper Tuned Meta Classifier for Prediction of Churn Customers

Adaptive XGBOOST Hyper Tuned Meta Classifier for Prediction of Churn Customers

Application of Weighted Fuzzy Time Series Model to Forecast Epidemic Injuries

Prediction of Cardiovascular Disease Using Machine Learning Technique—A Modern Approach

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