RETRACTED ARTICLE: Decision-level fusion scheme for nasopharyngeal carcinoma identification using machine learning techniques

Ghani, Mohd Khanapi Abd; Mohammed, Mazin Abed; Arunkumar, N.; Mostafa, Salama A.; Ibrahim, Dheyaa Ahmed; Abdullah, Mohamad Khir; Jaber, Mustafa Musa; Abdulhay, Enas; Ramírez-González, Gustavo; Burhanuddin, M. A.

doi:10.1007/s00521-018-3882-6

Cited by 82 publications

(55 citation statements)

References 29 publications

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“…In some cases, in its original representation, SVM cannot clearly find a linear separation between the data. Thus, a training data transformation suggested by Vapnik [1] is carried out from the original space to another higher dimensional space to be able to find a linear separator between the groups. A kernel function such as the quadratic, Gaussian, or polynomial kernel functions may be used to make this transformation.…”

Section: A Machine Learning Modelsmentioning

confidence: 99%

“…Bioinformatics for the medical field is interdisciplinary sciences of computer science, information science and medicine. The Bioinformatics lays on various techniques, computer-aided tools, equipment that used to analyse and retrieve health data to process and produce meaningful information for the health care field [1]. Medical professionals facing a big challenge when making decisions of using the computer-decision support system [6], [11] to provide a reliable solution which helps them investigate a particular sophisticated disease such as COVID-19 [2].…”

Section: Introductionmentioning

confidence: 99%

“…Most review papers on comparison the state-of-art and traditional classifiers focus on the evaluation process itself regardless of the evaluation and benchmarking criteria used in the comparison. One of the reasons might be the difficulties when dealing with conflicting evaluation criteria [1], [59]. Besides the importance of dealing with the trade-off of the evaluation criteria, the importance of each criterion is another issue in the evaluation and benchmarking of classifiers.…”

Section: Introductionmentioning

confidence: 99%

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Benchmarking Methodology for Selection of Optimal COVID-19 Diagnostic Model Based on Entropy and TOPSIS Methods

et al. 2020

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Nowadays, coronavirus (COVID-19) is getting international attention due it considered as a life-threatened epidemic disease that hard to control the spread of infection around the world. Machine learning (ML) is one of intelligent technique that able to automatically predict the event with reasonable accuracy based on the experience and learning process. In the meantime, a rapid number of ML models have been proposed for predicate the cases of COVID-19. Thus, there is need for an evaluation and benchmarking of COVID-19 ML models which considered the main challenge of this study. Furthermore, there is no single study have addressed the problem of evaluation and benchmarking of COVID diagnosis models. However, this study proposed an intelligent methodology is to help the health organisations in the selection COVID-19 diagnosis system. The benchmarking and evaluation of diagnostic models for COVID-19 is not a trivial process. There are multiple criteria requires to evaluate and some of the criteria are conflicting with each other. Our study is formulated as a decision matrix (DM) that embedded mix of ten evaluation criteria and twelve diagnostic models for COVID-19. The multi-criteria decision-making (MCDM) method is employed to evaluate and benchmarking the different diagnostic models for COVID19 with respect to the evaluation criteria. An integrated MCDM method are proposed where TOPSIS applied for the benchmarking and ranking purpose while Entropy used to calculate the weights of criteria. The study results revealed that the benchmarking and selection problems associated with COVID19 diagnosis models can be effectively solved using the integration of Entropy and TOPSIS. The SVM (linear) classifier is selected as the best diagnosis model for COVID19 with the closeness coefficient value of 0.9899 for our case study data. Furthermore, the proposed methodology has solved the significant variance for each criterion in terms of ideal best and worst best value, beside issue when specific diagnosis models have same ideal best value. INDEX TERMS COVID19 diagnostic, machine learning, benchmarking methodology, chest X-rays images, entropy, TOPSIS, multi-criteria decision-making. The associate editor coordinating the review of this manuscript and approving it for publication was Zheng Xiao .

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Section: A Machine Learning Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Benchmarking Methodology for Selection of Optimal COVID-19 Diagnostic Model Based on Entropy and TOPSIS Methods

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“…The most popular kind of discrete PSO is a binary. In binary PSO, the position of each particle will be 0 or 1 [20]. The position will update during the following equations [21] [22]:…”

Section: Discrete Particle Swarm Optimizationmentioning

confidence: 99%

A Multi-Objective Particle Swarm Optimization for Pavement Maintenance with Chaos and Discrete

Ahmed¹,

Al‐Khateeb²,

Mahmood³

2019

Journal of Southwest Jiaotong University

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Particle Swarm Optimization (PSO) is a very common algorithm in swarm intelligence algorithms. PSO has been used to solve a lot of problems with one or more goals. Actuality, the multi-objectives improvement issues in all real life are combinatorial in nature. Therefore, PSO has been improved to be able to handle very large number of decision variables and reduce or decrease computational complexity. In this work, a chaos multi objective PSO algorithm is improved for solving discrete (binary) optimization issues with crossover operation. The developed Chaos Discrete Multi Objective PSO (CDMOPSO) algorithm is applied to pavement management problem for flexible pavement to get optimal maintenance and rehabilitation plan. The results shown that there is significant improvement in the solutions satisfying pavement conditions and maintenance cost goals. It is required to a very short time of execution by the improved algorithm to reach a very good solution. Also, comparing the convergence of solutions with the rest of the PSO algorithms, it has found that the suggested algorithm is better.

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“…In literature, various machine learning [6] methodologies have been proposed [7] for health care problems [8,9] to provide a solution. Various entropy measures with the combination of classifiers were introduced to provide the classification of non-focal class (NFC) and focal class (FC) EEG signals in the Bern-Barcelona (BB) dataset [10].…”

Section: Introductionmentioning

confidence: 99%

Detection of Focal and Non-Focal Electroencephalogram Signals Using Fast Walsh-Hadamard Transform and Artificial Neural Network

Prasanna

Subathra

Mohammed

et al. 2020

Sensors

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The discrimination of non-focal class (NFC) and focal class (FC), is vital in localizing the epileptogenic zone (EZ) during neurosurgery. In the conventional diagnosis method, the neurologist has to visually examine the long hour electroencephalogram (EEG) signals, which consumes time and is prone to error. Hence, in this present work, automated diagnosis of FC EEG signals from NFC EEG signals is developed using the Fast Walsh–Hadamard Transform (FWHT) method, entropies, and artificial neural network (ANN). The FWHT analyzes the EEG signals in the frequency domain and decomposes it into the Hadamard coefficients. Five different nonlinear features, namely approximate entropy (ApEn), log-energy entropy (LogEn), fuzzy entropy (FuzzyEn), sample entropy (SampEn), and permutation entropy (PermEn) are extracted from the decomposed Hadamard coefficients. The extracted features detail the nonlinearity in the NFC and the FC EEG signals. The judicious entropy features are supplied to the ANN classifier, with a 10-fold cross-validation method to classify the NFC and FC classes. Two publicly available datasets such as the University of Bonn and Bern-Barcelona dataset are used to evaluate the proposed approach. A maximum sensitivity of 99.70%, the accuracy of 99.50%, and specificity of 99.30% with the 3750 pairs of NFC and FC signal are achieved using the Bern-Barcelona dataset, while the accuracy of 92.80%, the sensitivity of 91%, and specificity of 94.60% is achieved using University of Bonn dataset. Compared to the existing technique, the proposed approach attained a maximum classification performance in both the dataset.

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RETRACTED ARTICLE: Decision-level fusion scheme for nasopharyngeal carcinoma identification using machine learning techniques

Cited by 82 publications

References 29 publications

Benchmarking Methodology for Selection of Optimal COVID-19 Diagnostic Model Based on Entropy and TOPSIS Methods

Benchmarking Methodology for Selection of Optimal COVID-19 Diagnostic Model Based on Entropy and TOPSIS Methods

A Multi-Objective Particle Swarm Optimization for Pavement Maintenance with Chaos and Discrete

Detection of Focal and Non-Focal Electroencephalogram Signals Using Fast Walsh-Hadamard Transform and Artificial Neural Network

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