Digital device use, dry eye and quality of life in youth

Purpose: Excessive screen use is a pervasive global phenomenon, recently aggravated by COVID-19-related mobility restrictions. Wide-ranging implications for health and quality of life are linked to extended screen time, the early onset of which may place young people at risk. This study evaluated screen use habits, dry eye disease markers and the associated impacts on quality of life and vision in a young cohort of extended screen users. Method(s): A total of 456 attendees of a gaming convention in Auckland, New Zealand completed a self-directed iPad-based survey on personal screen use habits, ocular symptoms and quality of life. Habitual blinking was assessed covertly using the front-facing iPad camera and proxy tear film stability measurements were conducted. Result(s): Participants (aged 24+/-10, 38% female, 11% contact lens wearers) reported a weekly average screen time of 44+/-24 hours. When compared to non-lens wearers, contact lens wearers reported a higher impact severity on daily quality of life (38% vs 29%), on vision-related quality of life (40% vs 31%) and more severe and frequent dryness symptoms (42% vs 32%;all p<0.009). Overall, 27% of respondents qualified as symptomatic for dry eye disease based on a Dry Eye Questionnaire-5 (DEQ-5) score >= 6 and proxy tear film stability values of <10 seconds. Extended screen use was associated with ocular symptomology, blink frequency and proxy tear film stability (all p<0.05). Conclusion(s): Young participants commonly report extended habitual screen use that are associated with typical symptoms and signs of dry eye disease, as well as significant impacts on quality of life. This may place youth at risk of deteriorating ocular health and comfort, underlining a pressing need for evidence to guide policy development on safe screen use, and for screening and educational interventions around screen use in routine clinical practice. Copyright © 2022

Intrusion detection in computer networks using machine learning algorithms

With the dawn of the COVID-19 age, the communication industry has witnessed a large spike in users as home networks, workplaces and even conferences have gone online. This has led to a rise in the number of victims of cyber network attacks due to lack of ample security measures being taken in most network environments. Hence the introduction of Intrusion Detection Systems (IDSs) is proven to provide an increased security level. Machine Learning (ML) algorithms have been put into extensive use in tasks of intrusion detection. An ML technique that adds to the performance of standard IDS is the Support Vector Machine (SVM) algorithm, owing to their decent generalization nature and the capability to surpass the barriers of dimensionality. The objective of the project is to determine and compare the performance and accuracy of several ML algorithms like k-means clustering, SVM and KNN. The data set used to derive these results is “kddcup99”, which contains 41 features. Data preprocessing is the first step towards achieving this goal, by performing feature extraction, followed by calculating the variance of features. This facilitates the filtering of relevant features from the non-linear dataset. Final objective is to separate the dataset into dissimilar classes based on the attack type faced by the network. © 2021 IEEE.

Forecasting the potential influence of Covid-19 using Data Science and Analytics

The major purpose of the study topic is to use data science to anticipate the future effect of COVID-19 using existing data. The goal of this research is to use data science and analytics to generate precise forecasts of the number of substantiations and deaths. LSTM, GRUs, and Prophet are the major models created and tested for the solution. An LSTM model is a type of Recurrent Neural Network that is used to forecast datasets with increasingly changing patterns. Gated recurrent units only has two gateways: reboot and update. The prophet is best suited for forecasting assignments involving observation swith at least a year of history. The various models discussed above were used to the covid-19 data set to forecast the number of positive cases, active cases, and deaths associated with covid-19. We trained the model using data from April and May 2021 to demonstrate a comparison between the observed and expected number of positive events. To assume the future happing of COVID-19 by applying models which are in use, so that we will be able to calculate the impact of the disease's potential spread throughout the human being, preparing our selves to make proper planning and idea to prevent further transmission and equip health systems to manage the disease properly and battle the worldwide pandemic. © 2021 IEEE.