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Deep transfer learning-based bird species classification using mel spectrogram images.
PLoS One
August 2024
Department of Information and Communication Engineering, Yeungnam University, Gyeongsan-si, Gyeongsangbuk-do, South Korea.
The classification of bird species is of significant importance in the field of ornithology, as it plays an important role in assessing and monitoring environmental dynamics, including habitat modifications, migratory behaviors, levels of pollution, and disease occurrences. Traditional methods of bird classification, such as visual identification, were time-intensive and required a high level of expertise. However, audio-based bird species classification is a promising approach that can be used to automate bird species identification.
View Article and Find Full Text PDFBird song comparison using deep learning trained from avian perceptual judgments.
PLoS Comput Biol
August 2024
Department of Psychology, Royal Holloway University of London, Egham, United Kingdom.
Our understanding of bird song, a model system for animal communication and the neurobiology of learning, depends critically on making reliable, validated comparisons between the complex multidimensional syllables that are used in songs. However, most assessments of song similarity are based on human inspection of spectrograms, or computational methods developed from human intuitions. Using a novel automated operant conditioning system, we collected a large corpus of zebra finches' (Taeniopygia guttata) decisions about song syllable similarity.
View Article and Find Full Text PDFPreparation and Characterization of Guaiacol-Furfuramine Benzoxazine and Its Modification of Bisphenol A-Aniline Oxazine Resin.
Polymers (Basel)
March 2024
Beijing Key Laboratory of Electrochemical Process and Technology for Materials, Beijing University of Chemical Technology, Beijing 100029, China.
A new type of benzoxazine resin has been synthesized using a natural phenol source, guaiacol, and a biomass amines, furfuramine. The synthesis conditions were optimized; when the reaction molar ratio of guaiacol, furfuramine, and polyformaldehyde was 1:1:4, the highest synthetic yield was reached. The product was characterized via testing using transform infrared spectroscopy (FT-IR), gel permeation chromatography (GPC), mass spectrogram (MS), and nuclear magnetic resonance (H-NMR) to confirm its molecular structure.
View Article and Find Full Text PDFDataset for boiling acoustic emissions: A tool for data driven boiling regime prediction.
Data Brief
February 2024
Thermal and Fluid Transport Laboratory, Department of Mechanical Engineering, Indian Institute of Technology Patna, Bihta 801103, Bihar, India.
Boiling is used for the thermal management of high-energy-density devices and systems. However, sudden thermal runaway at boiling crisis often results in catastrophic failures. Machine learning is a promising tool for in-situ monitoring of boiling-based systems for preemptive control of boiling crisis.
View Article and Find Full Text PDFAn Improved AlexNet Model and Cepstral Coefficient-Based Classification of Autism Using EEG.
Clin EEG Neurosci
January 2024
School of Electronics Engineering, Vellore Institute of Technology, Chennai, India.
Autism is a neurodevelopmental disorder that cannot be completely cured, but early intervention during childhood can improve outcomes. Identifying autism spectrum disorder (ASD) has relied on subjective detection methods that involve questionnaires, medical professionals, and therapists and are subject to observer variability. The need for early diagnosis and the limitations of subjective detection methods has led researchers to explore machine learning-based approaches, such as Random Forests, K-Nearest Neighbors, Naive Bayes, and Support Vector Machines, to predict ASD meltdowns.
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