A comprehensive review on nature inspired computing algorithms for the diagnosis of chronic disorders in human beings

Gautam, Ritu; Kaur, Prableen; Sharma, Manik

doi:10.1007/s13748-019-00191-1

Cited by 51 publications

(25 citation statements)

References 105 publications

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“…Meanwhile, previous literature [50] shows that the using of meta-heuristic optimization technique can significantly improve prediction performance by search the optimal combination of prediction models. In fact, many meta-heuristic optimization techniques are inspired by natural phenomena and have widely applied in mining [51], healthcare [52][53][54][55], finance [56] and query optimization problems [57] with good optimization performance. Hence, a nature-inspired algorithm (the Harris hawks optimization (HHO) algorithm) was tested in this paper because of its superior performance compared with the other 11 methods through 29 unconstrained benchmark problems and 6 constrained engineering design tasks carried out by Heidari et al [58].…”

Section: Harris Hawks Optimization (Hho) Algorithmmentioning

confidence: 99%

Effective Assessment of Blast-Induced Ground Vibration Using an Optimized Random Forest Model Based on a Harris Hawks Optimization Algorithm

Shi

Zhou

et al. 2020

Applied Sciences

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Most mines choose the drilling and blasting method which has the characteristics of being a cheap and efficient method to fragment rock mass, but blast-induced ground vibration damages the surrounding rock mass and structure and is a drawback. To predict, analyze and control the blast-induced ground vibration, the random forest (RF) model, Harris hawks optimization (HHO) algorithm and Monte Carlo simulation approach were utilized. A database consisting of 137 datasets was collected at different locations around the Tonglvshan open-cast mine, China. Seven variables were selected and collected as the input variables, and peak particle velocity was chosen as the output variable. At first, an RF model and a hybrid model, namely a HHO-RF model, were developed, and the prediction results checked by 3 performance indices to show that the proposed HHO-RF model can provide higher prediction performance. Then blast-induced ground vibration was simulated by using the Monte Carlo simulation approach and the developed HHO-RF model. After analyzing, the mean peak particle velocity value was 0.98 cm/s, and the peak particle velocity value did not exceed 1.95 cm/s with a probability of 90%. The research results of this study provided a simple, accurate method and basis for predicting, evaluating blast-induced ground vibration and optimizing the blast design before blast operation.

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Section: Harris Hawks Optimization (Hho) Algorithmmentioning

confidence: 99%

Effective Assessment of Blast-Induced Ground Vibration Using an Optimized Random Forest Model Based on a Harris Hawks Optimization Algorithm

Shi

Zhou

et al. 2020

Applied Sciences

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“…This paper gives the idea of various supervised learning approaches. Gautam, R [21] reviewed the nature inspired computing algorithms which gives the idea of various techniques to improve the results of the machine learning approaches. Sharma, M [22] et al presented various supervised learning techniques in Stock Data Mining which gives the idea of efficiency of prediction results in different scenerios.…”

Section: Related Workmentioning

confidence: 99%

Comparative Analysis of Wind Speed Forecasting Using LSTM and SVM

Gangwar¹,

Bali²,

Kumar³

2018

ICST Transactions on Scalable Information Systems

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The objective of this work is to present a comprehensive exploration of deep learning based wind forecasting model. The forecasting of speed of wind is called as the wind speed forecasting/prediction. It is basically done to achieve the better sustainability for power generation and production. The availability of wind energy in ample amount makes it quite comfortable to be utilized for various functionalities. In this research work the main aim is to forecast speed using LSTM including certain parameters and then comparative analysis is done using SVM. Both are machine learning approaches but have different functionalities in comparison to each other. This comparison is done to obtain the better technique which can be further applied on larger datasets to design a better, accurate, efficient forecasting model for speed of wind. The survey and implementation of both the techniques gave a clear idea about the utilisation of long short term memory for the better and enhanced wind speed forecasting. The forecasting is based on various atmospheric variables, and the data set is taken from the kaggle datsets which have numerous attributes but we have considered few of them only for the prediction purpose.

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“…Kim et al [2] examined whether the severity of posttraumatic stress disorder (PTSD) symptoms and perceived functional impairment in firefighters with current possible PTSD are correlated with the use of mental health services. Gautam et al [3] also studied the treatment of medical conditions in humans, and found the rate of predictive accuracy obtained by ant colonyoptimization (ACO) and neural network hybridization to be more promising than other individual or hybrid approaches. Based on their predicted human health implications, Van der Fels-Klerx et al [4] thoroughly reviewed criteria for rating risks related to food safety and dietary hazards.…”

Section: Introductionmentioning

confidence: 99%

Influenza-like illness prediction using a long short-term memory deep learning model with multiple open data sources

et al. 2020

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The influenza problem has always been an important global issue. It not only affects people's health problems but is also an essential topic of governments and health care facilities. Early prediction and response is the most effective control method for flu epidemics. It can effectively predict the influenza-like illness morbidity, and provide reliable information to the relevant facilities. For social facilities, it is possible to strengthen epidemic prevention and care for highly sick groups. It can also be used as a reminder for the public. This study collects information on the influenzalike illness emergency department visits to the Taiwan Centers for Disease Control, and the PM 2.5 open-source data from the Taiwan Environmental Protection Administration's air quality monitoring network. By using deep learning techniques, the relevance of short-term estimates and the outbreak calculation method can be determined. The techniques are published by the WHO to determine whether the influenza-like illness situation is still in a stage of reasonable control. Finally, historical data and future forecasted data are integrated on the web page for visual presentation, to show the actual regional air quality situation and influenza-like illness data and to predict whether there is an outbreak of influenza in the region.

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A comprehensive review on nature inspired computing algorithms for the diagnosis of chronic disorders in human beings

Cited by 51 publications

References 105 publications

Effective Assessment of Blast-Induced Ground Vibration Using an Optimized Random Forest Model Based on a Harris Hawks Optimization Algorithm

Effective Assessment of Blast-Induced Ground Vibration Using an Optimized Random Forest Model Based on a Harris Hawks Optimization Algorithm

Comparative Analysis of Wind Speed Forecasting Using LSTM and SVM

Influenza-like illness prediction using a long short-term memory deep learning model with multiple open data sources

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