Glaucoma is one of the leading causes of irreversible vision loss, it progresses gradually without easily noticeable symptoms. The detection of glaucoma in the early stage is crucial as it may help to decelerate the progress. The traditional instrument methods are manual, time-consuming and less accurate. Hence, the automated diagnosis of glaucoma is needed for detection of glaucoma in the early stage with high accuracy. The flexible analytic wavelet transform (FAWT) based novel method has been proposed for the classification of glaucoma stages. In the proposed method, FAWT has been used to decompose the preprocessed images into various sub-band images. Then, ReliefF and sequential box-counting (SBC) algorithms are applied to extract the various entropies and fractal dimension (FD) based features, respectively. Further, the extracted feature values are ranked using Fisher’s linear discriminant analysis (LDA) dimensionally reduction. Finally, the higher rank features have been used for the classification of glaucoma stages using least squares-support vector machine (LS-SVM) classifier. The proposed method has been evaluated on publicly available large and diverse glaucoma database. The classification accuracy of the proposed method is 93.40% using tenfold cross-validation. The proposed method has demonstrated better performance for glaucoma classification as compared to the existing methods. The proposed method is ready to help the ophthalmologist in their daily screening for glaucoma detection.

Glaucoma is one of the leading causes of vision loss worldwide. It leads to reduced quality of life for individuals and substantial economic loss for society. This problem can be reduced by the early and reliable diagnosis of glaucoma. The traditional instrument-based methods are nonautomated and laborious. Recently, many computer-based approaches have been proposed for glaucoma detection. However, none of the existing approaches can be efficiently used for the classification of glaucoma stages. In this study, we proposed a novel method to classify the glaucoma stages (healthy, early-stage, and advanced-stage) using a two-dimensional compact variational mode decomposition (2D-C-VMD) algorithm. In this work, the preprocessed input images are first decomposed into several variational modes employing 2D-C-VMD. Next, various features namely, Kapur entropy (KE), Renyi entropy (RE), Shannon entropy (SE), Yager entropy (YE), Energy (En), and Fractal dimension (FD) features, which are extracted from the first variational mode. Then, linear discriminant analysis (LDA) has been used for dimensionality reduction. Finally, a trained multi-class least squares-support vector machine (MC-LS-SVM) classifier has been utilized for classification purpose. The proposed approach has been tested on two different public glaucoma database. Our method achieved the highest classification accuracy of 98.11% with tenfold cross-validation. The experimental results show that the proposed approach performed far better as compared to state-of-the-art approaches.

Glaucoma is a chronic eye disease, causing damage to the optic nerve; it may cause permanent vision loss. The conventional instrument methods for glaucoma detection are manual and time-consuming. Many approaches have recently been proposed for automatic glaucoma classification using retinal fundus images. However, none of the existing methods can efficiently use for early-stage glaucoma detection. In this letter, we proposed a novel method for glaucoma classification based on the newly introduced two-dimensional tensor empirical wavelet transform (2D-T-EWT). In this study, the pre-processed images are decomposed into sub-band images (SBIs) using 2D-T-EWT. Then, texture-based grey level co-occurrence matrix (GLCM), chip histogram, and moment invariant features have been extracted from decomposed SBIs. Afore, robust features have been selects and ranked using the student's t -test algorithm. Finally, trained multi-class least squares-support vector machine (MC-LS-SVM) classifier has been used for the classification. The experimental results show that our method outperformed state-of-the-art approaches for glaucoma classification. The proposed method achieved the highest classification accuracy of 93.65% using tenfold cross-validation.

Maintaining an optimal stress level is vital in our lives, yet many individuals struggle to identify the sources of their stress. As emotional stability and mental awareness become increasingly important, wearable medical technology has gained popularity in recent years. This technology enables real-time monitoring, providing medical professionals with crucial physiological data to enhance patient care. Current stress-detection methods, such as ECG, BVP, and body movement analysis, are limited by their rigidity and susceptibility to noise interference. To overcome these limitations, we introduce STRESS-CARE, a versatile stress detection sensor employing a hybrid approach. This innovative system utilizes a sweat sensor, cutting-edge context identification methods, and machine learning algorithms. STRESS-CARE processes sensor data and models environmental fluctuations using an XG Boost classifier. By combining these advanced techniques, we aim to revolutionize stress detection, offering a more adaptive and robust solution for improved stress management and overall well-being. • In the proposed method, we introduce a state-of-the-art stress detection device with Galvanic Skin Response (GSR) sweat sensors, outperforming traditional Electrocardiogram (ECG) methods while remaining non-invasive • Integrating machine learning, particularly XG-Boost algorithms, enhances detection accuracy and reliability. • This study sheds light on noise context comprehension for various wearable devices, offering crucial guidance for optimizing stress detection in multiple contexts and applications.

The significant number of road traffic accidents caused by fatigued drivers presents substantial risks to the public’s overall safety. In recent years, there has been a notable convergence of intelligent cameras and artificial intelligence (AI), leading to significant advancements in identifying driver drowsiness. Advances in computer vision technology allow for the identification of driver drowsiness by monitoring facial expressions such as yawning, eye movements, and head movements. These physical indications, together with assessments of the driver’s physiological condition and behavior, aid in assessing fatigue and lowering the likelihood of drowsy driving-related incidents. This study presents an extensive variety of meticulously designed algorithms that were thoroughly analyzed to assess their effectiveness in detecting drowsiness. At the core of this attempt lay the essential concept of feature extraction, an efficient technique for isolating facial and ocular regions from a particular set of input images. Following this, various deep learning models, such as a traditional CNN, VGG16, and MobileNet, facilitated detecting drowsiness. Among these approaches, the MobileNet model was a valuable choice for drowsiness detection in drivers due to its real-time processing capability and suitability for deployment in resource-constrained environments, with the highest achieved accuracy of 92.75%.