A New Face Image Recognition Algorithm Based on Cerebellum-Basal Ganglia Mechanism

Tang, Shoujun; Shabaz, Mohammad

doi:10.1155/2021/3688881

Cited by 40 publications

(25 citation statements)

References 46 publications

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“…Recall questionnaires, on the other hand, were found to have only limited validity in metaanalyses [ 13 ]. Women are diagnosed with depression at a higher incidence than men, which may be due to women's proclivity to seek treatment as soon as possible [ 14 , 15 ]. In accordance with this, latest long-term telehealth strategies use objectively assessed cognitive movement as a replacement for depressed clinical signs.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Mental Workload by Visual Motor Activity among Control Group and Patient Suffering from Depressive Disorder

Murugesan

Ahmed

Shabaz

et al. 2022

Computational Intelligence and Neuroscience

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Major depressive disorder (MDD) is a mood state that is not usually associated with vision problems. Recent research has found that the inhibitory neurotransmitter GABA levels in the occipital brain have dropped. Aim. The aim of the research is to evaluate mental workload by single channel electroencephalogram (EEG) approach through visual-motor activity and comparison of parameter among depressive disorder patient and in control group. Method. Two tests of a visual-motor task similar to reflect drawings were performed in this study to compare the visual information processing of patients with depression to that of a placebo group. The current study looks into the accuracy of monitoring cognitive burden with single-channel portable EEG equipment. Results. The alteration of frontal brain movement in reaction to fluctuations in cognitive burden stages generated through various vasomotor function was examined. By applying a computerised oculomotor activity analogous to reflector image diagram, we found that the complexity of the path to be drawn was more important than the real time required accomplishing the job in determining perceived difficulty in depressive disorder patients. The overall perceived difficulty of the exercise is positively linked with EEG activity measured from the motor cortex region at the start of every experiment test. The average rating for task completion for depression patients and in control group observed and no statistical significance association reported between rating scale and time spent on each trial ( p = 1.43 ) for control group while the normalised perceived difficulty rating had 0.512, 0.623, and 0.821 correlations with the length of the pathway, the integer of inclination in the pathway, and the time spent to complete every experiment test, respectively ( p < 0.0001 ) among depression patients. The findings imply that alterations in comparative cognitive burden levels during an oculomotor activity considerably modify frontal EEG spectrum. Conclusion. Patients with depression perceived the optical illusion in the arrays as weaker, resulting in a little bigger disparity than individuals who were not diagnosed with depression. This discovery provided light on the prospect of adopting a user-friendly mobile EEG technology to assess mental workload in everyday life.

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Section: Introductionmentioning

confidence: 99%

Assessment of Mental Workload by Visual Motor Activity among Control Group and Patient Suffering from Depressive Disorder

Murugesan

Ahmed

Shabaz

et al. 2022

Computational Intelligence and Neuroscience

Self Cite

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“…Using the wavelet transform, this paper comes up with a way to get the features out of the distance matrix. The dual-tree complex wavelet transform was created to solve some problems with the traditional dimensional discrete real wavelet transform in image processing [ 13 , 14 ]. It has been used to get good results in image processing [ 13 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Recognition of Protein Network for Bioinformatics Knowledge Analysis Using Support Vector Machine

Kaur

Chitre

Wanjale

et al. 2022

BioMed Research International

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Protein is the material foundation of living things, and it directly takes part in and runs the process of living things itself. Predicting protein complexes helps us understand the structure and function of complexes, and it is an important foundation for studying how cells work. Genome-wide protein interaction (PPI) data is growing as high-throughput experiments become more common. The aim of this research is that it provides a dual-tree complex wavelet transform which is used to find out about the structure of proteins. It also identifies the secondary structure of protein network. Many computer-based methods for predicting protein complexes have also been developed in the field. Identifying the secondary structure of a protein is very important when you are studying protein characteristics and properties. This is how the protein sequence is added to the distance matrix. The scope of this research is that it can confidently predict certain protein complexes rapidly, which compensates for shortcomings in biological research. The three-dimensional coordinates of C atom are used to do this. According to the texture information in the distance matrix, the matrix is broken down into four levels by the double-tree complex wavelet transform because it has four levels. The subband energy and standard deviation in different directions are taken, and then, the two-dimensional feature vector is used to show the secondary structure features of the protein in a way that is easy to understand. Then, the KNN and SVM classifiers are used to classify the features that were found. Experiments show that a new feature called a dual-tree complex wavelet can improve the texture granularity and directionality of the traditional feature extraction method, which is called secondary structure.

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“…Such techniques are not immediately applicable to image processing, and there are no easy ways to extend 1D technique to higher dimensions, owing to the fact that data points in dimensions more than one do not have a unique ordering. Because higher-dimensional medical imaging data are common, we choose to focus this study on adaptive filtering techniques that can be applied to multiple signals (2D pictures, 3D volumes, and 4D time volumes) [ 14 , 15 ]. Adaptive filtering methods mainly include digital filters and adaptive algorithms.…”

Section: Methodsmentioning

confidence: 99%

Retrospection of Nonlinear Adaptive Algorithm-Based Intelligent Plane Image Interaction System

Guan¹,

Pamba²,

Balachander

et al. 2022

Computational Intelligence and Neuroscience

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This paper introduces the application and classification of an adaptive filtering algorithm in the image enhancement algorithm. And the filtering noise reduction impact is compared using MATLAB software for programming, image processing, LMS algorithm, RLS algorithm, histogram equalisation algorithm, and Wiener filtering method filtering noise reduction effect. To optimize the intelligent graphic image interaction system, the proposed nonlinear adaptive algorithm of intelligent graphic image interaction system research is based on the digital filter and adaptive filtering algorithm for simulation experiment. The experimental results of several noise index data filtering algorithms show that the fuzzy coefficient k of LMS index is 0.86, RLS index is 0.91, the histogram equalization index is 0.53, and the Wiener filtering index is 0.62. LMS index of quality index Q is 0.90, RLS index is 0.95, histogram equalization index is 0.58, Wiener filtering index is 0.65. According to the above results, comparing LMS with the RLS method and according to SNR, k, and Q values in the simulation results in the process of processing, it is found that the convergence speed of the RLS algorithm is obviously better than that of the LMS algorithm, and the stability is also good. Additionally, the differential imaging data can provide a strong reference for the clinical diagnosis and qualitative differentiation of TBP and CP, and MSCT is worthy of extensive application in the clinical diagnosis of peritonitis. The processing effect of the image with high similarity to the original image is greatly improved compared with the histogram equalization and Wiener filtering methods used in the simulation.

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A New Face Image Recognition Algorithm Based on Cerebellum-Basal Ganglia Mechanism

Cited by 40 publications

References 46 publications

Assessment of Mental Workload by Visual Motor Activity among Control Group and Patient Suffering from Depressive Disorder

Assessment of Mental Workload by Visual Motor Activity among Control Group and Patient Suffering from Depressive Disorder

[Retracted] Recognition of Protein Network for Bioinformatics Knowledge Analysis Using Support Vector Machine

Retrospection of Nonlinear Adaptive Algorithm-Based Intelligent Plane Image Interaction System

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