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 effects. Moreover, hormones, physiological effects, 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 different 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. After 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.

https://www.imrpress.com/journal/JIN/21/1/10.31083/j.jin2101020/pdf

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

Recent advances in computer vision have allowed broad applications in every area of life, and agriculture is not left out. For the agri-food industry, the use of advanced technology is essential. Owing to deep learning’s capability to learn robust features from images, it has witnessed enormous application in several fields. Fruit detection and classification remains challenging due to the form, color, and texture of different fruit species. While studying the impact of computer vision on fruit detection and classification, we pointed out that till 2018 many conventional machine learning methods were utilized while a few methods exploited the application of deep learning methods for fruit detection and classification. This has prompted us to pursue an extensive study on surveying and implementing deep learning models for fruit detection and classification. In this article, we intensively discussed the datasets used by many scholars, the practical descriptors, the model’s implementation, and the challenges of using deep learning to detect and categorize fruits. Lastly, we summarized the results of different deep learning methods applied in previous studies for the purpose of fruit detection and classification. This review covers the study of recently published articles that utilized deep learning models for fruit identification and classification. Additionally, we also implemented from scratch a deep learning model for fruit classification using the popular dataset “Fruit 360” to make it easier for beginner researchers in the field of agriculture to understand the role of deep learning in the agriculture domain.

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Recent Advancements in Fruit Detection and Classification Using Deep Learning Techniques

/pdf/wearable-flexible-electronics-based-cardiac-electrode-for-21dklsfz.pdf

Wearable Flexible Electronics Based Cardiac Electrode for Researcher Mental Stress Detection System Using Machine Learning Models on Single Lead Electrocardiogram Signal

Recently, cardiac arrhythmia recognition from electrocardiography (ECG) with deep learning approaches is becoming popular in clinical diagnosis systems due to its good prognosis findings, where expert data preprocessing and feature engineering are not usually required. But a lightweight and effective deep model is highly demanded to face the challenges of deploying the model in real-life applications and diagnosis accurately. In this work, two effective and lightweight deep learning models named Deep-SR and Deep-NSR are proposed to recognize ECG beats, which are based on two-dimensional convolution neural networks (2D CNNs) while using different structural regularizations. First, 97720 ECG beats extracted from all records of a benchmark MIT-BIH arrhythmia dataset have been transformed into 2D RGB (red, green, and blue) images that act as the inputs to the proposed 2D CNN models. Then, the optimization of the proposed models is performed through the proper initialization of model layers, on-the-fly augmentation, regularization techniques, Adam optimizer, and weighted random sampler. Finally, the performance of the proposed models is evaluated by a stratified 5-fold cross-validation strategy along with callback features. The obtained overall accuracy of recognizing normal beat and three arrhythmias (V-ventricular ectopic, S-supraventricular ectopic, and F-fusion) based on the Association for the Advancement of Medical Instrumentation (AAMI) is 99.93%, and 99.96% for the proposed Deep-SR model and Deep-NSR model, which demonstrate that the effectiveness of the proposed models has surpassed the state-of-the-art models and also expresses the higher model generalization. The received results with model size suggest that the proposed CNN models especially Deep-NSR could be more useful in wearable devices such as medical vests, bracelets for long-term monitoring of cardiac conditions, and in telemedicine to accurate diagnose the arrhythmia from ECG automatically. As a result, medical costs of patients and work pressure on physicians in medicals and clinics would be reduced effectively.

/pdf/an-effective-and-lightweight-deep-electrocardiography-2m07ffyu.pdf

An Effective and Lightweight Deep Electrocardiography Arrhythmia Recognition Model Using Novel Special and Native Structural Regularization Techniques on Cardiac Signal

Insomnia is a common sleep disorder in which patients cannot sleep properly. Accurate detection of insomnia disorder is a crucial step for mental disease analysis in the early stages. The disruption in getting quality sleep is one of the big sources of cardiovascular syndromes such as blood pressure and stroke. The traditional insomnia detection methods are time-consuming, cumbersome, and more expensive because they demand a long time from a trained neurophysiologist, and they are prone to human error, hence, the accuracy of diagnosis gets compromised. Therefore, the automatic insomnia diagnosis from the electrocardiogram (ECG) records is vital for timely detection and cure. In this paper, a novel hybrid artificial intelligence (AI) approach is proposed based on the power spectral density (PSD) of the heart rate variability (HRV) to detect insomnia in three classification scenarios: (1) subject-based classification scenario (normal Vs. insomnia), (2) sleep stage-based classification (REM Vs. W. stage), and (3) the combined classification scenario using both subject-based and sleep stage-based deep features. The ensemble learning of random forest (RF) and decision tree (DT) classifiers are used to perform the first and second classification scenarios, while the linear discriminant analysis (LDA) classifier is used to perform the third combined scenario. The proposed framework includes data collection, investigation of the ECG signals, extraction of the signal HRV, estimation of the PSD, and AI-based classification via hybrid machine learning classifiers. The proposed framework is fine-tuned and evaluated using the free public PhysioNet dataset over fivefold trails cross-validation. For the subject-based classification scenario, the detection performance in terms of sensitivity, specificity, and accuracy is recorded to be 96.0%, 94.0%, and 96.0%, respectively. For the sleep stage-based classification scenario, the detection evaluation results are recorded equally with 96.0% for ceiling level accuracy, sensitivity, and specificity. For the combined classification scenario, the LDA classifier has achieved the best insomnia detection accuracy with 99.0%. In the future, the proposed hybrid AI approach could be applicable for mobile observation schemes to automatically detect insomnia disorders.

https://ieeexplore.ieee.org/ielx7/6287639/6514899/09917483.pdf

Ensemble Computational Intelligent for Insomnia Sleep Stage Detection via the Sleep ECG Signal

Maha Mrityunjaya HYMN is Hindu religious belief to control the mind to dedicate Lord Shiva Many seers have been practicing it since ancient time The main aim of this paper is to find the changes in brain signal using C4-A1 channel of the Electroencephalogram (EEG) signal during recitation of this hymn This study was completed in some steps such as real time EEG recording, extraction of the C4-A1 channel, preprocessing, calculation of the power spectral density, and classification using Decision Tree (DT) and K-Nearest Neighbor (KNN) on five yoga practitioners The KNN classifier having 5-fold cross validation model achieved the highest recall (974%) and accuracy (974%) of the system We obtained that the average frequency of the EEG wave increased prominently This study will be helpful for the people to understand in the scientific way for the recitation of the religious hymn

Effect of Maha Mrityunjaya HYMN Recitation on Human Brain for the Analysis of Single EEG Channel C4-A1 Using Machine Learning Classifiers on Yoga Practitioner

Balaram Pardhan

Papers

Effect of Maha Mrityunjaya HYMN Recitation on Human Brain for the Analysis of Single EEG Channel C4-A1 Using Machine Learning Classifiers on Yoga Practitioner