A New Approach for Missing Data Imputation in Big Data Interface

Wang, Chunzhi; Shakhovska, Nataliya; Sachenko, Anatoliy; Komar, Myroslav

doi:10.5755/j01.itc.49.4.27386

Cited by 10 publications

(8 citation statements)

References 25 publications

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“…In this manner, to bring down the issue of lost values in other attributes, the "Mean substitution-based imputation" approach has been utilized to substitute the lost entries with measurable approximations of the adjacent entries. The selected substitution approach i.e., "mean substitution-based imputation" [27] figure out the average estimate of the features and custom this average estimate to supply the lost entry.…”

Section: Filter For Substituting Lost Valuesmentioning

confidence: 99%

Profiling Casualty Severity Levels of Road Accident Using Weighted Majority Voting

Awan¹,

Mehmood²,

Chaudhry³

et al. 2022

Computers, Materials &Amp; Continua

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To determine the individual circumstances that account for a road traffic accident, it is crucial to consider the unplanned connections amongst various factors related to a crash that results in high casualty levels. Analysis of the road accident data concentrated mainly on categorizing accidents into different types using individually built classification methods which limit the prediction accuracy and fitness of the model. In this article, we proposed a multi-model hybrid framework of the weighted majority voting (WMV) scheme with parallel structure, which is designed by integrating individually implemented multinomial logistic regression (MLR) and multilayer perceptron (MLP) classifiers using three different accident datasets i.e., IRTAD, NCDB, and FARS. The proposed WMV hybrid scheme overtook individual classifiers in terms of modern evaluation measures like ROC, RMSE, Kappa rate, classification accuracy, and performs better than state-of-theart approaches for the prediction of casualty severity level. Moreover, the proposed WMV hybrid scheme adds up to accident severity analysis through knowledge representation by revealing the role of different accident-related factors which expand the risk of casualty in a road crash. Critical aspects related to casualty severity recognized by the proposed WMV hybrid approach can surely support the traffic enforcement agencies to develop better road safety plans and ultimately save lives.

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Section: Filter For Substituting Lost Valuesmentioning

confidence: 99%

Profiling Casualty Severity Levels of Road Accident Using Weighted Majority Voting

Awan¹,

Mehmood²,

Chaudhry³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…Therefore, they showed that missing data imputation helps improve the application's result. Wang et al [34] proposed a missing data imputation method based on functional dependencies and association rules. The authors first modeled a big data schema in which the record's attributes describe a specific object.…”

Section: Related Workmentioning

confidence: 99%

“…Regarding the time analysis, their approach has better results than SVM, EM, and association rules. The main difference between [34] and our work is that they did not address an online scenario, thus using all the data available in the dataset.…”

Section: Related Workmentioning

confidence: 99%

“…This concern is crucial in an IoT environment since the hardware usually has limited resources. Only a few of them [14,17,18,29,30,34] considered the execution time of the proposed methods. The works in [12,19,27,29] claimed that their methods might execute in an edge computing device, such as IoT gateways or IoT devices, but they did not provide experimental results.…”

Section: Related Workmentioning

confidence: 99%

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Missing Data Imputation in Internet of Things Gateways

2021

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In an Internet of Things (IoT) environment, sensors collect and send data to application servers through IoT gateways. However, these data may be missing values due to networking problems or sensor malfunction, which reduces applications’ reliability. This work proposes a mechanism to predict and impute missing data in IoT gateways to achieve greater autonomy at the network edge. These gateways typically have limited computing resources. Therefore, the missing data imputation methods must be simple and provide good results. Thus, this work presents two regression models based on neural networks to impute missing data in IoT gateways. In addition to the prediction quality, we analyzed both the execution time and the amount of memory used. We validated our models using six years of weather data from Rio de Janeiro, varying the missing data percentages. The results show that the neural network regression models perform better than the other imputation methods analyzed, based on the averages and repetition of previous values, for all missing data percentages. In addition, the neural network models present a short execution time and need less than 140 KiB of memory, which allows them to run on IoT gateways.

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“…This is due to many forms of incompleteness and uncertainty, which is typical of many application tasks. In terms of the missing data issue, there are many effective methods of regression modeling based on machine learning for data imputation [22]. As far as large amounts of data are concerned, the development of a modern neural network toolkit for data flow processing allows of fulfilling these tasks with high accuracy [5,6].…”

Section: Introductionmentioning

confidence: 99%

Neuro-Fuzzy Diagnostics Systems Based on SGTM Neural-Like Structure and T-Controller

Tkachenko

Izonin

Tkachenko

2021

Lecture Notes on Data Engineering and Communications Technologies

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Neuro-fuzzy models of management nowadays are becoming more widespread in various industries. Many papers deal with the synthesis of neuro-fuzzy models of diagnostics in economics, medicine, and industrial tasks. Most of them are based on iterative topologies of artificial neural networks and traditional fuzzy inference systems. The latter ones do not always ensure high accuracy, which affects the entire system that is being developed. This paper presents a new neuro-fuzzy diagnostic system based on non-iterative ANN and a new fuzzy model, a T-controller. The flowchart of the system proposed is given. All the operation stages of the system are described in detail, from the collection and preliminary processing of data to two different stages of diagnostics. The last stage can be performed manually or using a T-controller. Simulation of the system is conducted by means of a real set of data. The task was to predict the generator power based on a set of 13 independent variables. To improve the accuracy of the system performance, the Padé polynomial has been used, the coefficients of which are synthesized on the basis of a pre-trained SGTM neural-like structure using an optimization simulated annealing simulation. High accuracy of the system performance is shown using various performance indicators. The coefficients of the required polynomial are synthesized, which allows of fulfilling the task in a more understandable form.

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A New Approach for Missing Data Imputation in Big Data Interface

Cited by 10 publications

References 25 publications

Profiling Casualty Severity Levels of Road Accident Using Weighted Majority Voting

Profiling Casualty Severity Levels of Road Accident Using Weighted Majority Voting

Missing Data Imputation in Internet of Things Gateways

Neuro-Fuzzy Diagnostics Systems Based on SGTM Neural-Like Structure and T-Controller

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