WITHDRAWN: Enhanced age prediction and gender classification (EAP-GC) framework using regression and SVM techniques

Reddy, Pratap; Sarma, K.S.R.K.; Sharma, Avinash; Rao, P. Varaprasada; Rao, S. Govinda; Sakthidharan, G. R.; Kavitha, K.

doi:10.1016/j.matpr.2020.10.857

Cited by 3 publications

(2 citation statements)

References 16 publications

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“…Therefore, computer-based photos are required to aid birdwatchers in identifying species. In this study, convolutional neural networks [1][2][3][4][5][6][7] are used to extract information from photos in order to investigate the utility of deep learning for bird identification.…”

Section: Introductionmentioning

confidence: 99%

Automated System for Bird Species Identification Using CNN

Siri,

Desu,

Alladi

et al. 2023

E3S Web Conf.

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There are around 11000 different bird species in the globe. Rarely are certain bird species encountered. Bird identification is a challenging task that usually leads to unclear labelling. When presented a picture of a bird, even professional bird watchers differ on the species. Despite having the same basic components across all bird species, form and appearance can vary greatly. Intraclass variance is substantial due to variations in lighting and backdrop, as well as a wide range of instances. Additionally, visual recognition of birds by humans is more comprehensible than audible recognition of birds. Consequently, the convolutional neural networks(CNN) is utilized for an automated bird species identification system. CNNs are a powerful Deep Learning ensemble that have shown to be effective in image processing. The dataset is used for both training and testing of a CNN system that classifies bird species. This will lead to quick identification of the bird species using an automated process.

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Section: Introductionmentioning

confidence: 99%

Automated System for Bird Species Identification Using CNN

Siri,

Desu,

Alladi

et al. 2023

E3S Web Conf.

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“…Several biometric signs have been used for gender recognition until today. Face ( (Rwigema et al, 2021), (Chandra Sekhar Reddy et al, 2020), (Swaminathan et al, 2020), (Chen et al, 2019), (Duan et al, 2018)), pupil size ( (Cascone et al, 2020)), handwriting ( (Gattal et al, 2018)), speech ((Qawaqneh et al, 2017), (Barkana & Zhou, 2015), (Kaya et al, 2017)), nose shape ( (Lv et al, 2019)), and social media recordings ( (Reynaldo et al, 2019)) are the mediums used in gender recognition.…”

Section: Introductionmentioning

confidence: 99%

Gait Data for Efficient Gender Recognition

Şentürk¹

2022

European Journal of Science and Technology

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Biometric recognition applications have been frequently used nowadays mostly because of reliability and ease of use for automated detection. There are many applications based on eyes, face, fingerprint, and voice for authentication and gender classification. In this paper, we focused on gender detection using the features of the steps of people. A different biometric sign has been investigated. Gait analyses were examined to determine the gender information of the people. Basic parameters like speed, variability, and symmetry of a gait, its several temporary, spatial, and height parameters, which were obtained via Physilog 5 sensor, were used in the analysis. A 321-D feature vector was comprised based on these features and an Artificial Neural Networks (ANN) model was trained with them. 95.83% accuracy was obtained. The experimental results show the success of the proposed ANN-based gait analysis system against the stateof-the-art for gender classification.

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