Vision-Based American Sign Language Classification Approach via Deep Learning

Hearing-impaired is the disability of partial or total hearing loss that causes a significant problem for communication with other people in society. American Sign Language (ASL) is one of the sign languages that most commonly used language used by Hearing impaired communities to communicate with each other. In this paper, we proposed a simple deep learning model that aims to classify the American Sign Language letters as a step in a path for removing communication barriers that are related to disabilities.Comment: 4 pages, Accepted in the The Florida AI Research Society (FLAIRS-35) 202

A General Study on Langevin Equations of Arbitrary Order

In this paper, the broad study depends on Langevin differential equations (LDE) of arbitrary order.The fractional order is in terms of ψ-Hilfer fractional operator. This work reveals the dynamicalbehaviour such as existence, uniqueness and stability solutions for LDE involving ψ-Hilfer fractionalerivative (HFD). Thus the fractional LDE with boundary condition, impulsive effect and nonlocalconditions are taken in account to prove the result

Editorial: Subclinical hypothyroidism in children with Down syndrome: To treat or not to treat???

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Efficient multiscale modeling of heterogeneous materials using deep neural networks

Material modeling using modern numerical methods accelerates the design process and reduces the costs of developing new products. However, for multiscale modeling of heterogeneous materials, the well-established homogenization techniques remain computationally expensive for high accuracy levels. In this contribution, a machine learning approach, convolutional neural networks (CNNs), is proposed as a computationally efficient solution method that is capable of providing a high level of accuracy. In this work, the data-set used for the training process, as well as the numerical tests, consists of artificial/real microstructural images (“input”). Whereas, the output is the homogenized stress of a given representative volume element RVE . The model performance is demonstrated by means of examples and compared with traditional homogenization methods. As the examples illustrate, high accuracy in predicting the homogenized stresses, along with a significant reduction in the computation time, were achieved using the developed CNN model

Efficiency of tree-based water status indicators at the onset of water deficit in citrus

5 páginas.-- 3 figuras.-- 5 tablas.-- 18 referenciasThis experiment evaluates the potential of using parameters based on tree trunk fluctuations for detecting water deficit in citrus trees under two different water saving-irrigation strategies: sustained deficit irrigation and partial root-zone drying. Three irrigation treatments were applied: 1) Control: trees were irrigated with 100% of their evapotranspirative needs (ETc); 2) 60 sustained deficit irrigation (SDI): 60% ETc; and 3) partial root-zone drying (PRD): 100% ETc needs, applied to only one-half of root zone. Maximum daily shrinkage (MDS), trunk growth rate (TGR), and MDS ratio (ratio between MDS of stressed trees and control trees) were determined. Day-to-day MDS values varied largely and could not be used to determine tree water deficit. TGR did not show significant differences among treatments at this level of stress. Nevertheless, the MDS ratio was a reliable indicator to measure tree water status, and it was more sensitive for detecting water deficit at the onset of a water deficit in trees under SDI than in trees under PRD.The authors thank the Consejo Superiorde Investigaciones Científicas (CSIC) for funding the stay of S. Elsayed-Farag at the Texas A&M University-Kingsville Citrus Center and her JAE-predoc fellowship and Ayako Kusakabe, research associate at the Citrus Center, for her technical support.Peer reviewe