Dr. Bharati Bidikar

https://doi.org/10.64823/ijter.2503013

Keratoconus is a progressive, non-inflammatory corneal disorder that can significantly impair vision if not detected and treated early. Accurate diagnosis of keratoconus, especially in its early stages, is crucial to prevent severe visual deterioration and reduce the need for invasive treatments such as corneal transplantation. This study proposes a machine learning-based approach for the diagnosis of keratoconus using topographic and tomographic features of the cornea. A large dataset containing 423 features was analyzed, and univariate feature selection was applied to identify the most discriminative attributes. Several supervised learning algorithms—including Random Forest, Support Vector Machines, k-Nearest Neighbors, and Logistic Regression—were trained and evaluated. The Random Forest classifier achieved the highest diagnostic accuracy of 95.8%, showcasing the potential of machine learning in aiding clinicians with accurate and early detection of keratoconus.

https://doi.org/10.64823/ijter.2503011

The interaction with the computers by utilizing a revolutionary gesture-based mouse control system has many applications in a variety of sectors, including virtual reality, media management, productivity tools, and gaming. This technology makes use of hand motions tracked by a regular webcam or depth camera to navigate and manage the computer interface, rather than relying on physical mouse devices. The system uses computer vision algorithms to recognize, track, and understand hand gestures in real time in order to execute mouse actions like cursor movement, clicking, and scrolling. The system's key components include hand detection, gesture recognition, and cursor mapping, all of which collaborate to create a seamless and user-friendly interface. The system tackles issues like gesture accuracy and user adaptability via ongoing improvement and feedback integration. Because it provides a more accessible and inclusive mode of interaction, it is particularly helpful for those with motor impairments. This solution, at its heart, illustrates how cutting-edge computer vision and pattern recognition can facilitate smooth and intuitive human-computer interaction, paving the way for intelligent, touchless interfaces.