Bioinspired Computational Approach to Missing Value Estimation

Agbehadji, Israel Edem; Millham, Richard; Fong, Simon; Yang, Hongji

doi:10.1155/2018/9457821

Cited by 19 publications

(15 citation statements)

References 23 publications

(36 reference statements)

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“…An NIC model seeks to achieve an objective within a set of constraints to be satisfied and the optimal solution is measured by a performance index known as objective function [70]. The nature-inspired algorithm can explore and exploit the search space using the naturally endowed random hunting skills of animals to find a global optimal solution [71]. NIC algorithms are mainly categorized into evolutionary algorithms and swarm intelligence-based algorithms.…”

Section: Nature-inspired Computing Modelsmentioning

confidence: 99%

Review of Big Data Analytics, Artificial Intelligence and Nature-Inspired Computing Models towards Accurate Detection of COVID-19 Pandemic Cases and Contact Tracing

Agbehadji

Awuzie

Ngowi

et al. 2020

IJERPH

Self Cite

192

112

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The emergence of the 2019 novel coronavirus (COVID-19) which was declared a pandemic has spread to 210 countries worldwide. It has had a significant impact on health systems and economic, educational and social facets of contemporary society. As the rate of transmission increases, various collaborative approaches among stakeholders to develop innovative means of screening, detecting and diagnosing COVID-19’s cases among human beings at a commensurate rate have evolved. Further, the utility of computing models associated with the fourth industrial revolution technologies in achieving the desired feat has been highlighted. However, there is a gap in terms of the accuracy of detection and prediction of COVID-19 cases and tracing contacts of infected persons. This paper presents a review of computing models that can be adopted to enhance the performance of detecting and predicting the COVID-19 pandemic cases. We focus on big data, artificial intelligence (AI) and nature-inspired computing (NIC) models that can be adopted in the current pandemic. The review suggested that artificial intelligence models have been used for the case detection of COVID-19. Similarly, big data platforms have also been applied for tracing contacts. However, the nature-inspired computing (NIC) models that have demonstrated good performance in feature selection of medical issues are yet to be explored for case detection and tracing of contacts in the current COVID-19 pandemic. This study holds salient implications for practitioners and researchers alike as it elucidates the potentials of NIC in the accurate detection of pandemic cases and optimized contact tracing.

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Section: Nature-inspired Computing Modelsmentioning

confidence: 99%

Review of Big Data Analytics, Artificial Intelligence and Nature-Inspired Computing Models towards Accurate Detection of COVID-19 Pandemic Cases and Contact Tracing

Agbehadji

Awuzie

Ngowi

et al. 2020

IJERPH

Self Cite

192

112

View full text Add to dashboard Cite

show abstract

“…Missing data is a general weakness that can influence the consequences of the prediction system to be ineffective [1][2][3]. Ignoring the missing data has an impact on the results of the analysis [4][5][6][7][8][9], learning outcomes, predictive results [10] and potentially weakens the validity of the results and conclusions [8,9] and leads to estimation of biased parameters [7,[11][12][13][14]. Prediction and classification are the principle obligations required in many areas and spaces that expect admittance to finish and accurate data [15].…”

Section: Introductionmentioning

confidence: 99%

“…The behavior of fireflies during which the brighter fireflies attract fireflies with weaker flashes may be applied within the imputation of missing data. It is done by finding the estimated value that is closest to the value known and then replacing the missing data [9]. This research proposed class center-based imputation method of missing data by utilizing Firefly Algorithm in the imputation process.…”

Section: Introductionmentioning

confidence: 99%

Class Center-Based Firefly Algorithm for Handling Missing Data

Nugroho

Utama

Surendro

2020

Preprint

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Estimating missing data in a dataset is a significant advance during the data cleaning stage. Improper data handling can make inaccurate results when conducting data analysis. Most of the research about missing data estimation is irrespective of the correlation between attributes. However, an adaptive search procedure helps find the estimates of the missing data when correlations between attributes are considered in the process. Firefly Algorithm (FA) implements an adaptive search procedure in the imputation of the missing data by finding the estimated value that is closest to the value in other data known. Therefore, this study proposes a class center-based adaptive approach model for missing data by considering the attribute correlation in the imputation process (C3-FA). Based on the experiment, the general result find that the class center-based firefly algorithm is an efficient technique for getting the actual value in handling the missing data. This can be seen on the value of Pearson correlation coefficient (r) that close to 1 and the root mean squared error (RMSE) value is generally closer to 0. In addition, the proposed method can maintain the true distribution of data values. This is indicated by the Kolmogorov–Smirnov test that value of DKS for most of the attributes in the dataset is generally closer to 0. Also, the results of the accuracy evaluation using three classifiers, showed that the proposed method produces good accuracy.

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“…In a distributed environment, bio-inspired search strategies can apply randomization and efficient local and global search to achieve a new optimal solution. 44 The bio-inspired approach can help form basic rules that aim at some level of intelligent by providing an optimal way to adjust energy needs.…”

Section: Proposed Modelmentioning

confidence: 99%

“…x j represents a Kestrel with a better position; and f k i is the frequency of bobbing as expressed by Agbehadji et al 44 The random encircling formulation is expressed byx…”

Section: Proposed Clustering Algorithm Based On Ksamentioning

confidence: 99%

Intelligent energy optimization for advanced IoT analytics edge computing on wireless sensor networks

Agbehadji

Frimpong

Millham

et al. 2020

International Journal of Distributed Sensor Networks

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The current dispensation of big data analytics requires innovative ways of data capturing and transmission. One of the innovative approaches is the use of a sensor device. However, the challenge with a sensor network is how to balance the energy load of wireless sensor networks, which can be achieved by selecting sensor nodes with an adequate amount of energy from a cluster. The clustering technique is one of the approaches to solve this challenge because it optimizes energy in order to increase the lifetime of the sensor network. In this article, a novel bio-inspired clustering algorithm was proposed for a heterogeneous energy environment. The proposed algorithm (referred to as DEEC-KSA) was integrated with a distributed energy-efficient clustering algorithm to ensure efficient energy optimization and was evaluated through simulation and compared with benchmarked clustering algorithms. During the simulation, the dynamic nature of the proposed DEEC-KSA was observed using different parameters, which were expressed in percentages as 0.1%, 4.5%, 11.3%, and 34% while the percentage of the parameter for comparative algorithms was 10%. The simulation result showed that the performance of DEEC-KSA is efficient among the comparative clustering algorithms for energy optimization in terms of stability period, network lifetime, and network throughput. In addition, the proposed DEEC-KSA has the optimal time (in seconds) to send a higher number of packets to the base station successfully. The advantage of the proposed bio-inspired technique is that it utilizes random encircling and half-life period to quickly adapt to different rounds of iteration and jumps out of any local optimum that might not lead to an ideal cluster formation and better network performance.

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Bioinspired Computational Approach to Missing Value Estimation

Cited by 19 publications

References 23 publications

Review of Big Data Analytics, Artificial Intelligence and Nature-Inspired Computing Models towards Accurate Detection of COVID-19 Pandemic Cases and Contact Tracing

Review of Big Data Analytics, Artificial Intelligence and Nature-Inspired Computing Models towards Accurate Detection of COVID-19 Pandemic Cases and Contact Tracing

Class Center-Based Firefly Algorithm for Handling Missing Data

Intelligent energy optimization for advanced IoT analytics edge computing on wireless sensor networks

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