Cognitive Vigilance Enhancement using Audio Stimulation of Pure Tone at 250 Hz by Yahya, Fares

“…We then quantified vigilance levels using statistical analysis and support vector machines (SVM) classifier. We found that the proposed VE method has significantly reduced the reaction time (RT) by 44% and improved the accuracy of target detection by 25%, (p < 0.001) compared to VD state. …”
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Spatial, temporal, and demographic patterns in prevalence of smoking tobacco use and attributable disease burden in 204 countries and territories, 1990–2019: a systematic analysis... by Marissa B Reitsma (8674341)

“…Although prevalence of smoking had decreased significantly since 1990 among both males (27·5% [26·5–28·5] reduction) and females (37·7% [35·4–39·9] reduction) aged 15 years and older, population growth has led to a significant increase in the total number of smokers from 0·99 billion (0·98–1·00) in 1990. …”

Dual-stage segmentation and classification framework for skin lesion analysis using deep neural network by Khadija Manzoor (22565369)

“…For SLICE-3D, we evaluated both tabular-only and image + metadata fusion approaches using XGBoost classifier and ResNet-based classifier, respectively.…”

Waste Classification for Sustainable Development Using Image Recognition with Deep Learning Neural Network Models by Meena Malik (17542089)

“…These are compound-scaling based models proposed by Google that are pretrained on ImageNet and have an accuracy of 74% to 84% in top-1 over ImageNet. …”

School water, sanitation, and hygiene inequalities: a bane of sustainable development goal six in Nigeria by Ojima Zechariah Wada (14159031)

“…</p> <h2>Results</h2> <p>Classifying the available WASH facilities based on the WHO/UNICEF Joint Monitoring Programme, none of the public schools provided any sanitation and hygiene service, while all the private schools provided both services. …”

How global spine sagittal alignment and spinal degeneration affect locomotive syndrome risk in the elderly by Ramada R. Khasawneh (6837734)

“…LS evaluation used the locomotive syndrome risk test based on LS risk criteria, classifying participants into no risk, stage 1 LS, and stage 2 LS groups. …”

Artificial Intelligence Frameworks for Sentiment Variations’ Reasoning and Emerging Topic Detection by ALATTAR, FUAD ABDELWAHAB ABDELQADER

“…Subsequently, the selected classifier is applied on a real-life large Twitter dataset, which includes around two million tweets, to extract positive/negative/neutral sentiments. …”
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