Notice of Violation of IEEE Publication Principles: Cognitive State Classification using Optimized Feature Selection Approach

Notice of Violation of IEEE Publication Principles

"Cognitive State Classification using Optimized Feature Selection Approach,"
by J. S. Ramakrishna,
in the Proceedings of the IEEE 18th India Council International Conference (INDICON), December 2021

After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE’s Publication Principles.

This paper is a duplication of the original text from the paper cited below. The original text was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission.

Due to the nature of this violation, reasonable effort should be made to remove all past references to this paper, and future references should be made to the following article:

“FFTPSOGA: Fast Fourier Transform with Particle Swarm Optimization and Genetic Algorithm Approach for Pattern Identification of Brain Responses in Multi Subject fMRI Data”
by Mamoon Rashid (1), Harjeet Singh (2), Vishal Goyal (3)
(1) Department of Computer Science & Engineering, Punjabi University, Patiala, India (2) Department of Computer Science, Mata Gujri College, Fatehgarh Sahib, India. (3) Department of Computer Science, Punjabi University, Patiala, India.

Functional Magnetic Resonance Imaging (fMRI) is the most widely used technique where it is possible to capture neural activity in human brain regions when subjected to different stimuli. However, due to the fMRI dataset’s high dimensional and sparse nature, the selection of appropriate features plays a crucial role in achieving the best classification accuracy. In this work, the stable features are selected from the fMRI dataset by combining Fast Fourier Transform (FFT) with Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). Then, the machine learning classifiers such as Support Vector Machine (SVM), Gaussian NB, and XGboost have been trained using these features. StarPlus fMRI dataset is used to examine the performance of the proposed feature selection framework. The experimental results reveal that the proposed feature selection algorithm resulted in optimum features with better classification accuracy. Comparison of the proposed scheme with state of the art models show that it performs better, and as a result, can be used for the pattern recognition of brain responses in multisubject fMRI data.