Zobrazeno 1 - 10
of 146
pro vyhledávání: '"Sheela Ramanna"'
Publikováno v:
Algorithms, Vol 17, Iss 6, p 238 (2024)
Facial emotion recognition (FER) is crucial across psychology, neuroscience, computer vision, and machine learning due to the diversified and subjective nature of emotions, varying considerably across individuals, cultures, and contexts. This study e
Externí odkaz:
https://doaj.org/article/8674c87a9e2346e9b72226b0542d9435
Publikováno v:
IEEE Access, Vol 11, Pp 97664-97681 (2023)
Numerical weather prediction (NWP) is a challenging task which involves working with micro and macro-scale spatio-temporal parameters susceptible to biases and accuracy problems. In recent years, machine learning has grown in popularity with the incr
Externí odkaz:
https://doaj.org/article/9c667ea3bdba4cbfa951cb5e2b511e76
Publikováno v:
IEEE Access, Vol 11, Pp 6973-7020 (2023)
Deep Neural Networks (DNNs) trained on one dataset (source domain) do not perform well on another set of data (target domain), which is different but has similar properties as the source domain. Domain Adaptation (DA) strives to alleviate this proble
Externí odkaz:
https://doaj.org/article/976c6744cfdb4418871c296bb683e17d
Publikováno v:
Frontiers in Artificial Intelligence, Vol 6 (2023)
Recently, research is emerging highlighting the potential of cannabinoids' beneficial effects related to anxiety, mood, and sleep disorders as well as pointing to an increased use of cannabinoid-based medicines since COVID-19 was declared a pandemic.
Externí odkaz:
https://doaj.org/article/d2281502bb704cc399aeaf8f38059af3
Publikováno v:
Applied Artificial Intelligence, Vol 35, Iss 11, Pp 803-833 (2021)
Road Weather Information Systems (RWIS) provide real-time weather information at point locations and are often used to produce road weather forecasts and provide input for pavement forecast models. Compared to the prevalant street cameras, however, R
Externí odkaz:
https://doaj.org/article/43d8d10a2b7c4e5cb84cd5ee4cabd0af
Autor:
Blair Jamieson, Matt Stubbs, Sheela Ramanna, John Walker, Nick Prouse, Ryosuke Akutsu, Patrick de Perio, Wojciech Fedorko
Publikováno v:
Frontiers in Big Data, Vol 5 (2022)
Water Cherenkov detectors like Super-Kamiokande, and the next generation Hyper-Kamiokande are adding gadolinium to their water to improve the detection of neutrons. By detecting neutrons in addition to the leptons in neutrino interactions, an improve
Externí odkaz:
https://doaj.org/article/eb9e502a042f4ce884be8b8560353d96
Publikováno v:
Results in Applied Mathematics, Vol 12, Iss , Pp 100185- (2021)
In this paper, we develop a direct formula for determining the coefficients in the canonical basis of the best polynomial of degree M that approximates a polynomial of degree N>Mon a symmetric interval for the L2-norm. We also formally prove that usi
Externí odkaz:
https://doaj.org/article/c72dae880a4445fea77472f7372ac40e
Publikováno v:
Algorithms, Vol 15, Iss 8, p 267 (2022)
Text classification aims to assign labels to textual units such as documents, sentences and paragraphs. Some applications of text classification include sentiment classification and news categorization. In this paper, we present a soft computing tech
Externí odkaz:
https://doaj.org/article/e4f8591474854475afbf4034f26bfa16
Autor:
Seungho Choe, Sheela Ramanna
Publikováno v:
Axioms, Vol 11, Iss 3, p 112 (2022)
Persistent homology is a powerful tool in topological data analysis (TDA) to compute, study, and encode efficiently multi-scale topological features and is being increasingly used in digital image classification. The topological features represent a
Externí odkaz:
https://doaj.org/article/f2743c9ebbbb40f795d7a40432edda32
Autor:
Hoora Rezaei Moghaddam, Sheela Ramanna
Publikováno v:
Patterns, Vol 1, Iss 4, Pp 100053- (2020)
Summary: Construction of knowledge repositories from web corpora by harvesting linguistic patterns is of benefit for many natural language-processing applications that rely on question-answering schemes. These methods require minimal or no human inte
Externí odkaz:
https://doaj.org/article/20bacf21a89e4777a3478f1ef0235afa