Glaucoma is the second leading cause of blindness worldwide. It is a disease in which fluid pressure in the eye increases continuously, damaging the optic nerve and causing vision loss. Computational decision support systems for the early detection of glaucoma can help prevent this complication. The retinal optic nerve fiber layer can be assessed using optical coherence tomography, scanning laser polarimetry, and Heidelberg retina tomography scanning methods. In this paper, we present a novel method for glaucoma detection using a combination of texture and higher order spectra (HOS) features from digital fundus images. Support vector machine, sequential minimal optimization, naive Bayesian, and random-forest classifiers are used to perform supervised classification. Our results demonstrate that the texture and HOS features after z-score normalization and feature selection, and when combined with a random-forest classifier, performs better than the other classifiers and correctly identifies the glaucoma images with an accuracy of more than 91%. The impact of feature ranking and normalization is also studied to improve results. Our proposed novel features are clinically significant and can be used to detect glaucoma accurately.

With the rapid advancement of information discovery techniques, machine learning and data mining continue to play a significant role in cybersecurity. Although several conferences, workshops, and journals focus on the fragmented research topics in this area, there has been no single interdisciplinary resource on past and current works and possible

Personalized healthcare (PHC) is a booming sector in the health science domain wherein researchers from diverse technical backgrounds are focusing on the need for remote human health monitoring. PHC employs wearable electronics, viz. group of sensors integrated on a flexible substrate, embedded in the clothes, or attached to the body via adhesive. PHC wearable flexible electronics (FE) offer numerous advantages including being versatile, comfortable, lightweight, flexible, and body conformable. However, finding the appropriate mass manufacturing technologies for these PHC devices is still a challenge. It needs an understanding of the physics, performance, and applications of printing technologies for PHC wearables, ink preparation, and bio-compatible device fabrication. Moreover, the detailed study of the operating principle, ink, and substrate materials of the printing technologies such as inkjet printing will help identify the opportunities and emerging challenges of applying them in manufacturing of PHC wearable devices. In this article, we attempt to bridge this gap by reviewing the printing technologies in the PHC domain, especially inkjet printing in depth. This article presents a brief review of the state-of-the-art wearable devices made by various printing methods and their applications in PHC. It focuses on the evaluation and application of these printing technologies for PHC wearable FE devices, along with advancements in ink preparation and bio-compatible device fabrication. The performance of inkjet, screen, gravure, and flexography printing, as well as the inks and substrates, are comparatively analyzed to aid PHC wearable sensor design, research, fabrication, and mass manufacturing. Moreover, it identifies the application of the emerging mass-customizable printing technologies, such as inkjet printing, in the manufacturing of PHC wearable devices, and reviews the printing principles, drop generation mechanisms, ink formulations, ink-substrate interactions, and matching strategies for printing wearable devices on stretchable substrates. Four surface matching strategies are extracted from literature for the guidance of inkjet printing of PHC stretchable electronics. The electro-mechanical performance of the PHC FE devices printed using four surface matching strategies is comparatively evaluated. Further, the article extends its review by describing the scalable integration of PHC devices and finally presents the future directions of research in printing technologies for PHC wearable devices.