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1.
J Acoust Soc Am ; 148(3): EL260, 2020 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-33003883

RESUMO

A transfer learning approach is proposed to classify grouper species by their courtship-associated sounds produced during spawning aggregations. Vessel sounds are also included in order to potentially identify human interaction with spawning fish. Grouper sounds recorded during spawning aggregations were first converted to time-frequency representations. Two types of time frequency representations were used in this study: spectrograms and scalograms. These were converted to images, and then fed to pretrained deep neural network models: VGG16, VGG19, Google Net, and MobileNet. The experimental results revealed that transfer learning significantly outperformed the manually identified features approach for grouper sound classification. In addition, both time-frequency representations produced almost identical results in terms of classification accuracy.

2.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 1404-1407, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018252

RESUMO

Diabetic retinopathy (DR) is a progressive eye disease that affects a large portion of working-age adults. DR, which may progress to an irreversible state that causes blindness, can be diagnosed with a comprehensive dilated eye exam. With the eye dilated, the Doctor takes pictures of the inside of the eye via a medical procedure called Fluorescein Angiography, in which a dye is injected into the bloodstream. The dye highlights the blood vessels in the back of the eye so they can be photographed. In addition, the Doctor may request an Optical Coherence Tomography (OCT) exam, by which cross-sectional photos of the retina are produced to measure the thickness of the retina. Early prognostication is vital in treating the disease and preventing it from progressing into advanced irreversible stages. Skilled medical personnel and necessary medical facilities are required to detect DR in its five major stages. In this paper, we propose a diagnostic tool to detect Diabetic retinopathy from fundus images by using an ensemble of multi-inception CNN networks. Our inception block consists of three Convolutional layers with kernel sizes of 3x3, 5x5, and 1x1 that are concatenated deeply and forwarded to the max-pooling layer. We experimentally compare our proposed method with two pre-trained models: VGG16 and GoogleNets. The experiment results show that the proposed method can achieve an accuracy of 93.2% by an ensemble of 10 random networks, compared to 81% obtained with transfer learning based on VGG19.


Assuntos
Diabetes Mellitus , Retinopatia Diabética , Estudos Transversais , Retinopatia Diabética/diagnóstico , Fundo de Olho , Humanos , Redes Neurais de Computação , Tomografia de Coerência Óptica
3.
J Acoust Soc Am ; 146(4): 2155, 2019 10.
Artigo em Inglês | MEDLINE | ID: mdl-31671953

RESUMO

In this paper, a method is introduced for the classification of call types of red hind grouper, an important fishery resource in the Caribbean that produces sounds associated with reproductive behaviors during yearly spawning aggregations. For the undertaken task, two distinct call types of red hind are analyzed. An ensemble of stacked autoencoders (SAEs) is then designed by randomly selecting the hyperparameters of SAEs in the network. These hyperparameters include a number of hidden layers in each SAE and a number of nodes in each hidden layer. Spectrograms of red hind calls are used to train this randomly generated ensemble of SAEs one at a time. Once all individual SAEs are trained, this ensemble is used as a whole to classify call types of red hind. More specifically, the outputs of individual SAEs are combined with a fusion mechanism to produce a final decision on the call type of the input red hind sound. Experimental results show that the innovative approach produces superior results in comparison with those obtained by non-ensemble methods. The algorithm reliably classified red hind call types with over 90% accuracy and successfully detected some calls missed by human observers.

4.
Cancers (Basel) ; 11(3)2019 Mar 26.
Artigo em Inglês | MEDLINE | ID: mdl-30917548

RESUMO

Certain small noncoding microRNAs (miRNAs) are differentially expressed in normal tissues and cancers, which makes them great candidates for biomarkers for cancer. Previously, a selected subset of miRNAs has been experimentally verified to be linked to breast cancer. In this paper, we validated the importance of these miRNAs using a machine learning approach on miRNA expression data. We performed feature selection, using Information Gain (IG), Chi-Squared (CHI2) and Least Absolute Shrinkage and Selection Operation (LASSO), on the set of these relevant miRNAs to rank them by importance. We then performed cancer classification using these miRNAs as features using Random Forest (RF) and Support Vector Machine (SVM) classifiers. Our results demonstrated that the miRNAs ranked higher by our analysis had higher classifier performance. Performance becomes lower as the rank of the miRNA decreases, confirming that these miRNAs had different degrees of importance as biomarkers. Furthermore, we discovered that using a minimum of three miRNAs as biomarkers for breast cancers can be as effective as using the entire set of 1800 miRNAs. This work suggests that machine learning is a useful tool for functional studies of miRNAs for cancer detection and diagnosis.

5.
J Acoust Soc Am ; 144(3): EL196, 2018 09.
Artigo em Inglês | MEDLINE | ID: mdl-30424627

RESUMO

In this paper, the effectiveness of deep learning for automatic classification of grouper species by their vocalizations has been investigated. In the proposed approach, wavelet denoising is used to reduce ambient ocean noise, and a deep neural network is then used to classify sounds generated by different species of groupers. Experimental results for four species of groupers show that the proposed approach achieves a classification accuracy of around 90% or above in all of the tested cases, a result that is significantly better than the one obtained by a previously reported method for automatic classification of grouper calls.


Assuntos
Aprendizado Profundo/classificação , Redes Neurais de Computação , Som , Vocalização Animal/fisiologia , Animais , Peixes
6.
J Acoust Soc Am ; 143(2): 666, 2018 02.
Artigo em Inglês | MEDLINE | ID: mdl-29495690

RESUMO

Grouper, a family of marine fishes, produce distinct vocalizations associated with their reproductive behavior during spawning aggregation. These low frequencies sounds (50-350 Hz) consist of a series of pulses repeated at a variable rate. In this paper, an approach is presented for automatic classification of grouper vocalizations from ambient sounds recorded in situ with fixed hydrophones based on weighted features and sparse classifier. Group sounds were labeled initially by humans for training and testing various feature extraction and classification methods. In the feature extraction phase, four types of features were used to extract features of sounds produced by groupers. Once the sound features were extracted, three types of representative classifiers were applied to categorize the species that produced these sounds. Experimental results showed that the overall percentage of identification using the best combination of the selected feature extractor weighted mel frequency cepstral coefficients and sparse classifier achieved 82.7% accuracy. The proposed algorithm has been implemented in an autonomous platform (wave glider) for real-time detection and classification of group vocalizations.

8.
Int J Bioinform Res Appl ; 7(3): 220-38, 2011.
Artigo em Inglês | MEDLINE | ID: mdl-21816712

RESUMO

Specific entities of naturally-occurring DNA hydrolytic/cytotoxic antibodies (abzymes) are linked to autoimmune and lymphoproliferative disorders. Suggested sequence of underlying activities conform to such entities penetrating the living cells, trans-locating to nucleus and recognising specific binding sites within single- or double-stranded DNA. Their origin is unknown since corresponding immunogens are unidentified. These anti-DNA antibodies could be the organism's immune response to microbial attack. Their structure, function and pathogenicity were investigated in wet-lab and via bioinformatics in context of Rational Vaccine Designs. This paper offers a comprehensive critical review on the subject in the light of known and newly proposed concepts.


Assuntos
Anticorpos Antinucleares , Autoanticorpos , Anticorpos Catalíticos , Sequência de Bases , DNA , Vacinas
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