A Novel Classifier based on Firefly Algorithm

Mashhour, Emad Mohamed; Houby, Enas M. F. El; Wassif, Khaled; Salah, Akram

doi:10.1016/j.jksuci.2018.11.009

Cited by 8 publications

(5 citation statements)

References 12 publications

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“…Some authors put forward the Kullback-Leibler FCM algorithm to process Gauss-multinomial-distributed data sets (KL-FCM-GM) [17]. In reference [18], the authors have applied a firefly algorithm in the objective function for cluster center selection. By combining the first step of cluster center selection with the second step of calculating the objective function, this algorithm effectively overcomes the limitation of local cluster optimization, and thus was useful for clustering a large medical data set [19].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Structured Data in Biomedicine Using Soft Computing Techniques and Computational Analysis

Rahman

2022

Computational Intelligence and Neuroscience

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In the field of biomedicine, enormous data are generated in a structured and unstructured form every day. Soft computing techniques play a major role in the interpretation and classification of the data to make appropriate decisions for making policies. The field of medical science and biomedicine needs efficient soft computing-based methods which can process all kind of data such as structured data, categorical data, and unstructured data to generate meaningful outcome for decision-making. The soft-computing methods allow clustering of similar data, classification of data, predictions from big-data analysis, and decision-making on the basis of analysis of data. A novel method is proposed in the paper using soft-computing methods where clustering mechanisms and classification mechanisms are used to process the biomedicine data for productive outcomes. Fuzzy logic and C-means clustering are devised as a collaborative approach to analyze the biomedicine data by reducing the time and space complexity of the clustering solutions. This research work is considering categorical data, numeric data, and structured data for the interpretation of data to make further decisions. Timely decisions are very important especially in the field of biomedicine because human health and human lives are involved in this field and delays in decision-making may cause threats to human lives. The COVID-19 situation was a recent example where timely diagnosis and interpretations played significant roles in saving the lives of people. Therefore, this research work has attempted to use soft computing techniques for the successful clustering of similar medical data and for quicker interpretation of data to support the decision-making processes related to medical fields.

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Section: Introductionmentioning

confidence: 99%

Analysis of Structured Data in Biomedicine Using Soft Computing Techniques and Computational Analysis

Rahman

2022

Computational Intelligence and Neuroscience

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“…Thus, according to Goodarzian et al [7], the FA is inspired by the natural species behaviour to optimize nonlinear functions that simultaneously use low-cost algorithms. Each member of the fireflies' group in the FA moves to a point where their most efficient outcome has occurred [19].…”

Section: Aco and Famentioning

confidence: 99%

Supervised Learning by Evolutionary Computation Tuning: An Application to Blockchain-Based Pharmaceutical Supply Chain Cost Model

2023

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A pharmaceutical supply chain (PSC) is a system of processes, operations, and organisations for drug delivery. This paper provides a new PSC mathematical cost model, which includes Blockchain technology (BT), that can improve the safety, performance, and transparency of medical information sharing in a healthcare system. We aim to estimate the costs of the BT-based PSC model, select algorithms with minimum prediction errors, and determine the cost components of the model. After the data generation, we applied four Supervised Learning algorithms (k-nearest neighbour, decision tree, support vector machine, and naive Bayes) combined with two Evolutionary Computation algorithms (ant colony optimization and the firefly algorithm). We also used the Feature Weighting approach to assign appropriate weights to all cost model components, revealing their importance. Four performance metrics were used to evaluate the cost model, and the total ranking score (TRS) was used to determine the most reliable predictive algorithms. Our findings show that the ACO-NB and FA-NB algorithms perform better than the other six algorithms in estimating the costs of the model with lower errors, whereas ACO-DT and FA-DT show the worst performance. The findings also indicate that the shortage cost, holding cost, and expired medication cost more strongly influence the cost model than other cost components.

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“…Mashhour et al [ 12 ] proposed a new classifier method depending on a firefly algorithm made as a managed learning algorithm. Hence, the analysis depended on the firefly algorithm created by simulating a firefly's character to bring different mates, dependent on intensity and distance.…”

Section: Related Workmentioning

confidence: 99%

Investigations on Brain Tumor Classification Using Hybrid Machine Learning Algorithms

Rinesh

Maheswari²,

Arthi

et al. 2022

Journal of Healthcare Engineering

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The imaging modalities are used to view other organs and analyze different tissues in the body. In such imaging modalities, a new and developing imaging technique is hyperspectral imaging. This multicolour representation of tissues helps us to better understand the issues compared to the previous image models. This research aims to analyze the tumor localization in the brain by performing different operations on hyperspectral images. The tumor is located using the combination of k-based clustering processes like k-nearest neighbour and k-means clustering. The value of k in both methods is determined using the optimization process called the firefly algorithm. The optimization processes reduce the manual calculation for finding K’s optimal value to segment the brain regions. The labelling of the areas of the brain is done using the multilayer feedforward neural network. The proposed technique produced better results than the existing methods like hybrid k-means clustering and parallel k-means clustering by having a higher peak signal-to-noise ratio and a lesser mean absolute error value. The proposed model achieved 96.47% accuracy, 96.32% sensitivity, and 98.24% specificity, which are improved compared to other techniques.

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A Novel Classifier based on Firefly Algorithm

Cited by 8 publications

References 12 publications

Analysis of Structured Data in Biomedicine Using Soft Computing Techniques and Computational Analysis

Analysis of Structured Data in Biomedicine Using Soft Computing Techniques and Computational Analysis

Supervised Learning by Evolutionary Computation Tuning: An Application to Blockchain-Based Pharmaceutical Supply Chain Cost Model

Investigations on Brain Tumor Classification Using Hybrid Machine Learning Algorithms

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