ABSTRACT
This paper presents a low cost, robust, portable and automated cataract detection system which can detect the presence of cataract from the colored digital eye images and grade their severity. Ophthalmologists detect cataract through visual screening using ophthalmoscope and slit lamps. Conventionally a patient has to visit an ophthalmologist for eye screening and treatment follows the course. Developing countries lack the proper health infrastructure and face huge scarcity of trained medical professionals as well as technicians. The condition is not very satisfactory with the rural and remote areas of developed nations. To bridge this barrier between the patient and the availability of resources, current work focuses on the development of portable low-cost, robust cataract screening and grading system. Similar works use fundus and retinal images which use costly imaging modules and image based detection algorithms which use much complex neural network models. Current work derives its benefit from the advancements in digital image processing techniques. A set of preprocessing has been done on the colored eye image and later texture information in form of mean intensity, uniformity, standard deviation and randomness has been calculated and mapped with the diagnostic opinion of doctor for cataract screening of over 200 patients. For different grades of cataract severity edge pixel count was calculated as per doctor’s opinion and later these data are used for calculating the thresholds using hybrid k-means algorithm, for giving a decision on the presence of cataract and grade its severity. Low value of uniformity and high value of other texture parameters confirm the presence of cataract as clouding in eye lens causes the uniformity function to take lower value due to presence of coarse texture. Higher the edge pixel count value, this confirms the presence of starting of cataract as solidified regions in lens are nonuniform. Lower value corresponds to fully solidified region or matured cataract. Proposed algorithm was initially developed on MATLAB, and tested on over 300 patients in an eye camp. The system has shown more than 98% accuracy in detection and grading of cataract. Later a cloud based system was developed with 3D printed image acquisition module to manifest an automated, portable and efficient cataract detection system for Tele-Ophthalmology. The proposed system uses a very simple and efficient technique by mapping the diagnostic opinion of the doctor as well, giving very promising results which suggest its potential use in teleophthalmology applications to reduce the cost of delivering eye care services and increasing its reach effectively. Developed system is simple in design and easy to operate and suitable for mass screening of cataracts. Due to non-invasive and non-mydriatic and mountable nature of device, in person screening is not required. Hence, social distancing norms are easy to follow and device is very useful in COVID-19 like situation.
ABSTRACT
The growth of the Internet has increased multi-folds in recent years. The Internet has become the medium of communication since the Covid-19 pandemic had started. The usage of communication via online means that the audio and visual content has also increased rapidly. Though it has been a boon for the major section of the people, differently-abled like the hearing-impaired people have limited resources to make use of. This project is aimed to design an application that converts the speech and text input into a sequence of sign language visuals. Speech recognition is used to convert the input audio to text and it is further translated into sign language. Natural Language Processing algorithms are used for word segmentation and extraction of root words. The translation happens to the Indian Sign Language in an effort to make this project more regional to the people of India._
ABSTRACT
Video conferencing applications have become an integral part of today’s world for attending interviews, classes, meetings, and assorted gatherings as well in the COVID-19 era. Alongside the increased use of such applications to facilitate the process of conducting interviews, the quality interview has taken a hit overall. This is largely because prospective candidates resort to fraud by switching tabs and using their phones during the course of an interview, and so come through with flying colors despite a clear lack of skills. Consequently, deserving candidates with the requisite skill set lose out to impostors who manage to clear the interviews. In this paper, we propose an approach to make interviews straightforward and fair to all candidates. Our Online Interview Platform, a web application built using Node.js and Express.js, offers indispensable features that are prerequisites for an interview. These include a real-time collaborative code editor that uses an operational transformation algorithm which allows users to collaborate in real time, test and run code;a video/audio conferencing feature using Peer JS;a chat box for communication, and a real-time collaborative whiteboard that lets users design or draw diagrams. The features are included in the same tab, thus ensuring that the candidate does not switch tabs. Using this application, candidates will be screened based on their technical knowledge, appropriately assessed, and performance-based hiring decisions made. The proposed approach proved that the malpractices strictly restricted while comparing with existing approaches.
ABSTRACT
BACKGROUND: To ensure adequate intensive care unit (ICU) capacity for SARS-CoV-2 patients, elective neurosurgery and neurosurgical ICU capacity were reduced. Further, the Finnish government enforced strict restrictions to reduce the spread. Our objective was to assess changes in ICU admissions and prognosis of traumatic brain injury (TBI) and aneurysmal subarachnoid hemorrhage (SAH) during the Covid-19 pandemic. METHODS: Retrospective review of all consecutive patients with TBI and aneurysmal SAH admitted to the neurosurgical ICU in Helsinki from January to May of 2019 and the same months of 2020. The pre-pandemic time was defined as weeks 1-11, and the pandemic time was defined as weeks 12-22. The number of admissions and standardized mortality rates (SMRs) were compared to assess the effect of the Covid-19 pandemic on these. Standardized mortality rates were adjusted for case mix. RESULTS: Two hundred twenty-four patients were included (TBI n = 123, SAH n = 101). There were no notable differences in case mix between TBI and SAH patients admitted during the Covid-19 pandemic compared with before the pandemic. No notable difference in TBI or SAH ICU admissions during the pandemic was noted in comparison with early 2020 or 2019. SMRs were no higher during the pandemic than before. CONCLUSION: In the area of Helsinki, Finland, there were no changes in the number of ICU admissions or in prognosis of patients with TBI or SAH during the Covid-19 pandemic.