Detection, Treatment Planning, and Genetic Predisposition of Bruxism: A Systematic Mapping Process and Network Visualization Technique

Heyat, Md Belal Bin; Akhtar, Faijan; Khan, Masood Hasan; Ullah, Najeeb; Gul, Ijaz; Khan, Haroon; Lai, Dakun

doi:10.2174/1871527319666201110124954

Cited by 30 publications

(18 citation statements)

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“…Recent CNN-based works have allowed for DNA sequence training rather than preliminary feature extraction. RNN connections can generate a directory graph in a sequence, allowing RNNs to extract features from DNA sequences in a novel and efficient way [52][53][54][55][56][57][58][59][60].…”

Section: Dl-ac4cmentioning

confidence: 99%

“…The PRC has the best choice to 'test the model's efficiency due to the imbalance of the benchmark data sets. Furthermore, in several recently published studies, accuracy (ACC), specificity (Sp) and sensitivity (Sn) have helped to evaluate the consistency of bioinformatics classification systems [52,[54][55][56][57]61]. We are also using them to determine the model's efficiency.…”

Section: Performance Metricsmentioning

confidence: 99%

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Recognition of mRNA N4 Acetylcytidine (ac4C) by Using Non-Deep vs. Deep Learning

et al. 2022

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Deep learning models have been successfully applied in a wide range of fields. The creation of a deep learning framework for analyzing high-performance sequence data have piqued the research community’s interest. N4 acetylcytidine (ac4C) is a post-transcriptional modification in mRNA, is an mRNA component that plays an important role in mRNA stability control and translation. The ac4C method of mRNA changes is still not simple, time consuming, or cost effective for conventional laboratory experiments. As a result, we developed DL-ac4C, a CNN-based deep learning model for ac4C recognition. In the alternative scenario, the model families are well-suited to working in large datasets with a large number of available samples, especially in biological domains. In this study, the DL-ac4C method (deep learning) is compared to non-deep learning (machine learning) methods, regression, and support vector machine. The results show that DL-ac4C is more advanced than previously used approaches. The proposed model improves the accuracy recall area by 9.6 percent and 9.8 percent, respectively, for cross-validation and independent tests. More nuanced methods of incorporating prior bio-logical knowledge into the estimation procedure of deep learning models are required to achieve better results in terms of predictive efficiency and cost-effectiveness. Based on an experiment’s acetylated dataset, the DL-ac4C sequence-based predictor for acetylation sites in mRNA can predict whether query sequences have potential acetylation motifs.

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Section: Dl-ac4cmentioning

confidence: 99%

Section: Performance Metricsmentioning

confidence: 99%

Recognition of mRNA N4 Acetylcytidine (ac4C) by Using Non-Deep vs. Deep Learning

et al. 2022

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“…[31]). Recently, using psychological signals to investigate Alzheimer, mental and sleep disorders becomes very common [32][33][34][35][36][37][38][39]. Moreover, the EEG has become a crucial non-invasive measure of brain activities, and it has a vital potentiality to diagnose mental disorders, abnormalities, and the state of the brain [40][41][42].…”

Section: Introductionmentioning

confidence: 99%

Frontal lobe real-time EEG analysis using machine learning techniques for mental stress detection

AlShorman¹,

Masadeh²,

Heyat³

et al. 2022

J. Integr. Neurosci.

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Stress has become a dangerous health problem in our life, especially in student education journey. Accordingly, previous methods have been conducted to detect mental stress based on biological and biochemical e fects. Moreover, hormones, physiological e fects, and skin temperature have been extensively used for stress detection. However, based on the recent literature, biological, biochemical, and physiological-based methods have shown inconsistent findings, which are initiated due to hormones' instability. Therefore, it is crucial to study stress using di ferent mechanisms such as Electroencephalogram (EEG) signals. In this research study, the frontal lobes EEG spectrum analysis is applied to detect mental stress. Initially, we apply a Fast Fourier Transform (FFT) as a feature extraction stage to measure all bands' power density for the frontal lobe. A ter that, we used two type of classifications such as subject wise and mix (mental stress vs. control) using Support Vector Machine (SVM) and Naive Bayes (NB) machine learning classifiers. Our obtained results of the average subject wise classification showed that the proposed technique has better accuracy (98.21%). Moreover, this technique has low complexity, high accuracy, simple and easy to use, no over fitting, and it could be used as a real-time and continuous monitoring technique for medical applications.

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“…The symptoms progress slowly over several years in most cases, because cognitive loss observed in patients with AD and pathological changes caused by brain degeneration commence at least 10–20 years prior to dementia onset [ 14 ]. Neuropsychiatric symptoms, including psychosis, agitation, apathy, depression, bruxism and sleep disturbances could be hallmarks of AD [ 15 , 16 ].…”

Section: Introductionmentioning

confidence: 99%

A Night-Time Monitoring System (eNightLog) to Prevent Elderly Wandering in Hostels: A Three-Month Field Study

Cheung

Tam

Mak

et al. 2022

IJERPH

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Older people are increasingly dependent on others to support their daily activities due to geriatric symptoms such as dementia. Some of them stay in long-term care facilities. Elderly people with night wandering behaviour may lose their way, leading to a significant risk of injuries. The eNightLog system was developed to monitor the night-time bedside activities of older people in order to help them cope with this issue. It comprises a 3D time-of-flight near-infrared sensor and an ultra-wideband sensor for detecting human presence and to determine postures without a video camera. A threshold-based algorithm was developed to classify different activities, such as leaving the bed. The system is able to send alarm messages to caregivers if an elderly user performs undesirable activities. In this study, 17 sets of eNightLog systems were installed in an elderly hostel with 17 beds in 9 bedrooms. During the three-month field test, 26 older people with different periods of stay were included in the study. The accuracy, sensitivity and specificity of detecting non-assisted bed-leaving events was 99.8%, 100%, and 99.6%, respectively. There were only three false alarms out of 2762 bed-exiting events. Our results demonstrated that the eNightLog system is sufficiently accurate to be applied in the hostel environment. Machine learning with instance segmentation and online learning will enable the system to be used for widely different environments and people, with improvements to be made in future studies.

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Detection, Treatment Planning, and Genetic Predisposition of Bruxism: A Systematic Mapping Process and Network Visualization Technique

Cited by 30 publications

References 0 publications

Recognition of mRNA N4 Acetylcytidine (ac4C) by Using Non-Deep vs. Deep Learning

Recognition of mRNA N4 Acetylcytidine (ac4C) by Using Non-Deep vs. Deep Learning

Frontal lobe real-time EEG analysis using machine learning techniques for mental stress detection

A Night-Time Monitoring System (eNightLog) to Prevent Elderly Wandering in Hostels: A Three-Month Field Study

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