[Retracted] Taxonomy of Adaptive Neuro‐Fuzzy Inference System in Modern Engineering Sciences

Chopra, Shivali; Dhiman, Gaurav; Sharma, Ashutosh; Shabaz, Mohammad; Shukla, Pratyush; Arora, Mohit

doi:10.1155/2021/6455592

Cited by 90 publications

(41 citation statements)

References 131 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In major depressive disorder, abnormalities in motor activity are linked to other characteristics such as a lack of interest in daily chores (MDD). As a consequence, patients with current MDD have lower motor activity [ 3 , 4 ], and treatment response has been linked to increased motor activity [ 5 , 6 ].…”

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

View full text Add to dashboard Cite

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.

show abstract

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

View full text Add to dashboard Cite

show abstract

“…It has a high rate of learning and flexible explanation skills, allowing it to represent complex features and understand nonlinear relationships. [ 10 ]. It is also possible to use MRI texture feature extraction technology [ 9 ], which is both faster and more accurate at categorization.…”

Section: Introductionmentioning

confidence: 99%

Supervised Computer-Aided Diagnosis (CAD) Methods for Classifying Alzheimer’s Disease-Based Neurodegenerative Disorders

Gupta

Saravanan²,

Choudhury³

et al. 2022

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

Alzheimer’s disease is incurable at the moment. If it can be appropriately diagnosed, the correct treatment can postpone the patient’s illness. To aid in the diagnosis of Alzheimer’s disease and to minimize the time and expense associated with manual diagnosis, a machine learning technique is employed, and a transfer learning method based on 3D MRI data is proposed. Machine learning algorithms can dramatically reduce the time and effort required for human treatment of Alzheimer’s disease. This approach extracts bottleneck features from the M-Net migration network and then adds a top layer to supervised training to further decrease the dimensionality and delete portions. As a consequence, the transfer network presented in this study has several advantages in terms of computational efficiency and training time savings when used as a machine learning approach for AD-assisted diagnosis. Finally, the properties of all subject slices are combined and trained in the classification layer, completing the categorization of Alzheimer’s disease symptoms and standard control. The results show that this strategy has a 1.5 percentage point better classification accuracy than the one that relies exclusively on VGG16 to extract bottleneck features. This strategy could cut the time it takes for the network to learn and improve its ability to classify things. The experiment shows that the method works by using data from OASIS. A typical transfer learning network’s classification accuracy is about 8% better with this method than with a typical network, and it takes about 1/60 of the time with this method.

show abstract

“…Another problem with this method is that its low temporal resolution (0.5–2 Hz) and vessel-based contrast make it impossible to directly study high frequencies, which are thought to be the information exchange mechanism between brain regions, called neocortical activity [ 7 ]. As a result, the time scale of neural activity is usually much faster than the recording speed of fMRI [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

Classification of EEG Signal-Based Encephalon Magnetic Signs for Identification of Epilepsy-Based Neurological Disorder

Kaur

Gupta

Kathiravan

et al. 2022

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

Magnetoencephalography (MEG) is now widely used in clinical examinations and medical research in many fields. Resting-state magnetoencephalography-based brain network analysis can be used to study the physiological or pathological mechanisms of the brain. Furthermore, magnetoencephalography analysis has a significant reference value for the diagnosis of epilepsy. The scope of the proposed research is that this research demonstrates how to locate spikes in the phase locking functional brain connectivity network of the Desikan-Killiany brain region division using a neural network approach. It also improves detection accuracy and reduces missed and false detection rates. The automatic classification of epilepsy encephalomagnetic signals can make timely judgments on the patient’s condition, which is of tremendous clinical significance. The existing literature’s research on the automatic type of epilepsy EEG signals is relatively sufficient, but the research on epilepsy EEG signals is relatively weak. A full-band machine learning automatic discrimination method of epilepsy brain magnetic spikes based on the brain functional connection network is proposed. The four classifiers are comprehensively compared. The classifier with the best effect is selected, and the discrimination accuracy can reach 93.8%. Therefore, this method has a good application prospect in automatically identifying and labeling epileptic spikes in magnetoencephalography.

show abstract

[Retracted] Taxonomy of Adaptive Neuro‐Fuzzy Inference System in Modern Engineering Sciences

Cited by 90 publications

References 131 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

Supervised Computer-Aided Diagnosis (CAD) Methods for Classifying Alzheimer’s Disease-Based Neurodegenerative Disorders

Classification of EEG Signal-Based Encephalon Magnetic Signs for Identification of Epilepsy-Based Neurological Disorder

Contact Info

Product

Resources

About