Address of the President, Sir Douglas Galton, Before the British Association for the Advancement of Science

Galton, Douglas

doi:10.1126/science.2.40.421

Cited by 3 publications

(3 citation statements)

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“…The following network layers build on prior layers' representations, allowing the network to learn increasingly complicated and abstract properties. The network's final layers are generally fully connected layers that conduct classification or regression based on characteristics retrieved from previous levels [22]. CNNs have been used effectively in various computer vision applications such as object detection, picture segmentation, and image production.…”

Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

Enhancing child safety with accurate fingerprint identification using deep learning technology

Sahy¹,

Niu²,

Khalaf³

et al. 2023

PEN

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Utilizing deep learning algorithms to differentiate the fingerprints of children can greatly enhance their safety. This advanced technology enables precise identification of individual children, facilitating improved monitoring and tracking of their activities and movements. This can effectively prevent abductions and other forms of harm, while also providing a valuable resource for law enforcement and other organizations responsible for safeguarding children. Furthermore, the use of deep learning algorithms minimizes the potential for errors and enhances the overall accuracy of fingerprint recognition. Overall, implementing this technology has immense potential to significantly improve the safety of children in various settings. Our experiments have demonstrated that deep learning significantly enhances the accuracy of fingerprint recognition for children. The model accurately classified fingerprints with an overall accuracy rate of 93%, surpassing traditional fingerprint recognition techniques by a significant margin. Additionally, it correctly identified individual children's fingerprints with an accuracy rate of 89%, showcasing its ability to distinguish between different sets of fingerprints belonging to different children.

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Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

Enhancing child safety with accurate fingerprint identification using deep learning technology

Sahy¹,

Niu²,

Khalaf³

et al. 2023

PEN

View full text Add to dashboard Cite

show abstract

“…Their inclusion renders it impossible to predict the peculiarities of individual children, though it does not prevent the prediction of average results. (Galton, 1885(Galton, , p. 1213 Of course, this is at best a simple taxonomy of the possible kinds of influences that could impact segregation. They could either be directed at maintaining the fitness of the organism, and within that category either local (involving only immediate neighbor-elements) or global (involving a more general structuring cause), or they could be random.…”

Section: Galton's 'Stirps'mentioning

confidence: 99%

Of stirps and chromosomes: Generality through detail

Pence

2022

Studies in History and Philosophy of Science

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“…Height in adulthood has been associated with overall and cause-specific morbidity and mortality risk for over a century (1,2). Taller height is generally associated with protective effects on overall health status and mortality, as well as lower incidence of most forms of cardiorespiratory disease (3).…”

Section: Introductionmentioning

confidence: 99%

Effects of childhood and adult height on later life cardiovascular disease risk estimated through Mendelian randomization

Richardson,

Urquijo,

Howe

et al. 2023

Preprint

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Background: Taller individuals are at elevated and protected risk of various cardiovascular disease endpoints. Whether this is due to a direct consequence of their height during childhood, a long-term effect of remaining tall throughout the lifecourse, or confounding by other factors, is unknown. Methods: We sought to address this by harnessing human genetic data to separate the independent effects of childhood and adulthood height using an approach known as lifecourse Mendelian randomization (MR). We analysed 5 cardiovascular disease endpoints (coronary artery disease (CAD), stroke, peripheral arterial disease (PAD), atrial fibrillation (AF) and thoracic aortic aneurysm (TAA)) using findings from large-scale genome-wide consortia (n=184,305 to 1,030,836). Results: Protective effects of taller childhood height on risk of later life CAD (OR=0.78 per change in height category, 95% CI=0.70 to 0.86, P=4x10-10) and stroke (OR=0.93, 95% CI=0.86 to 1.00, P=0.03) were found using a univariable model, although evidence of these effects attenuated in a multivariable setting upon accounting for adulthood height. In contrast, direct effects of taller childhood height on increased risk of later life AF (OR=1.61, 95% CI=1.42 to 1.79, P=5x10-7) and TAA (OR=1.55, 95% CI=1.16 to 1.95, P=0.03) were found even after accounting for adulthood height in the multivariable model. Conclusions: The protective effect of childhood height on risk of CAD and stroke is largely attributed to the causal pathway involving adulthood height, which may therefore be explained by taller children typically becoming taller individuals in later life. Conversely, the independent effect of childhood height on increased risk of AF and TAA may point towards developmental mechanisms in early life which confer a lifelong risk on these disease outcomes.

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Address of the President, Sir Douglas Galton, Before the British Association for the Advancement of Science

Cited by 3 publications

References 0 publications

Enhancing child safety with accurate fingerprint identification using deep learning technology

Enhancing child safety with accurate fingerprint identification using deep learning technology

Of stirps and chromosomes: Generality through detail

Effects of childhood and adult height on later life cardiovascular disease risk estimated through Mendelian randomization

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