This is an exploratory study to model the determinants of actual use of a digital library system. To do so, a research model was developed using Delone and McLean’s information system success model and explained as an empirical study. Data were collected from 978 respondents using a structured questionnaire from four different universities of Malaysia. The findings showed that the quality factors of digital library systems have a strong influence on satisfaction, behavioral intention, and variance in actual use. Information quality is the strongest predictor to measure user satisfaction, and satisfaction has a strong effect on students’ behavioral intention to use the system. In addition, user satisfaction and behavioral intention to use the system also have a strong positive relationship with the actual use of a digital library system. In brief, behavioral intentions are greatly influenced by system quality, information quality and service quality.

Among the most important systems in the body is the eyes. Although their small stature, humans are unable to imagine existence without it. The human optic is safe against dust particles by a narrow layer called the conjunctiva. It prevents friction during the opening and shutting of the eye by acting as a lubricant. A cataract is an opacification of the eye's lens. There are various forms of eye problems. Because the visual system is the most important of the four sensory organs, external eye abnormalities must be detected early. The classification technique can be used in a variety of situations. A few of these uses are in the healthcare profession. We use visual geometry group (VGG-16), ResNet-50, and Inception-v3 architectures of convolutional neural networks (CNNs) to distinguish between normal eyes, conjunctivitis eyes, and cataract eyes throughout this paper. With a detection time of 485 seconds, Inception-v3 is the most accurate at detecting eye disease, with a 97.08% accuracy, ResNet-50 performs the second-highest accuracy with 95.68% with 1090 seconds and lastly, VGG-16 performs 95.48% accuracy taking the highest time of 2510 seconds to detect eye diseases.