The Prince William Sound Plankton Camera: a profiling in situ observatory of plankton and particulates

Abstract A novel plankton imager was developed and deployed aboard a profiling mooring in Prince William Sound in 2016–2018. The imager consisted of a 12-MP camera and a 0.137× telecentric lens, along with darkfield illumination produced by an in-line ring/condenser lens system. Just under 2.5 × 106 images were collected during 3 years of deployments. A subset of almost 2 × 104 images was manually identified into 43 unique classes, and a hybrid convolutional neural network classifier was developed and trained to identify the images. Classification accuracy varied among the different classes, and applying thresholds to the output of the neural network (interpretable as probabilities or classifier confidence), improved classification accuracy in non-ambiguous groups to between 80% and 100%.

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Deep learning application for box-office evaluation of images

Computer Optics ◽

10.18287/2412-6179-co-515 ◽

2020 ◽

Vol 44 (1) ◽

pp. 127-132

Author(s):

V.G. Efremtsev ◽

N.G. Efremtsev ◽

E.P. Teterin ◽

P.E. Teterin ◽

V.V. Gantsovsky

Keyword(s):

Neural Network ◽

Deep Learning ◽

Sample Preparation ◽

Convolutional Neural Network ◽

Preliminary Data ◽

Classification Accuracy ◽

Training Sample ◽

Data Preparation ◽

Box Office ◽

The Neural Network

The possibility of application a convolutional neural network to assess the box-office effect of digital images is reviewed. We studied various conditions for sample preparation, optimizer algorithms, the number of pixels in the samples, the size of the training sample, color schemes, compression quality, and other photometric parameters in view of effect on training the neural network. Due to the proposed preliminary data preparation, the optimum of the architecture and hyperparameters of the neural network we achieved a classification accuracy of at least 98%.

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Text classification using convolutional neural network

Information Technology and Nanotechnology ◽

10.18287/1613-0073-2019-2416-219-226 ◽

2019 ◽

pp. 219-226

Author(s):

L E Sapozhnikova ◽

O A Gordeeva

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Text Classification ◽

Classification Accuracy ◽

Convolutional Network ◽

The Neural Network ◽

Internet Information ◽

And Control ◽

Classification Quality ◽

Control Samples

In this article, the method of text classification using a convolutional neural network is presented. The problem of text classification is formulated, the architecture and the parameters of a convolutional neural network for solving the problem are described, the steps of the solution and the results of classification are given. The convolutional network which was used was trained to classify the texts of the news messages of Internet information portals. The semantic preprocessing of the text and the translation of words into attribute vectors are generated using the open word2vec model. The analysis of the dependence of the classification quality on the parameters of the neural network is presented. The using of the network allowed obtaining a classification accuracy of about 84%. In the estimation of the accuracy of the classification, the texts were checked to belong to the group of semantically similar classes. This approach allowed analyzing news messages in cases where the text themes and the number of classification classes in the training and control samples do not equal.

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Neural network classifier for hyperspectral images of skin pathologies

Journal of Physics Conference Series ◽

10.1088/1742-6596/2127/1/012026 ◽

2021 ◽

Vol 2127 (1) ◽

pp. 012026

Author(s):

V Vinokurov ◽

Yu Khristoforova ◽

O Myakinin ◽

I Bratchenko ◽

A Moryatov ◽

...

Keyword(s):

Neural Network ◽

Classification Accuracy ◽

Hyperspectral Data ◽

Hyperspectral Images ◽

Malignant Neoplasms ◽

Neural Network Classifier ◽

Training Set ◽

The Neural Network ◽

2D Images ◽

Entire Spectral Range

Abstract This paper describes the use and results of a neural network classifier trained on a set of hyperspectral images of benign and malignant neoplasms. The analysis is carried out on 2D images extruded from hyperspectral data. The ranges of wavelengths at which the research is carried out is represented by the intervals 530–570 nm and 600–606 nm, which is caused by the assumption that the analysis of the entire spectral range is redundant and the prospect of saving resources. Melanoma, basal cell carcinoma (BCC), nevus and papilloma are accepted as primary classes, as the most dangerous, most common and non-malignant types of neoplasms, respectively. The neural network classifier is based on a three-block VGG network. With a training set included 1944 images, the classification accuracy for 4 types of samples was 92%.

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WaterNet: A Convolutional Neural Network for Chlorophyll-a Concentration Retrieval

Remote Sensing ◽

10.3390/rs12121966 ◽

2020 ◽

Vol 12 (12) ◽

pp. 1966 ◽

Cited By ~ 2

Author(s):

Muhammad Aldila Syariz ◽

Chao-Hung Lin ◽

Manh Van Nguyen ◽

Lalu Muhamad Jaelani ◽

Ariel C. Blanco

Keyword(s):

Neural Network ◽

Remote Sensing ◽

Neural Networks ◽

Convolutional Neural Network ◽

Chlorophyll A ◽

Remote Sensing Images ◽

Two Stage ◽

Chl A ◽

The Neural Network

The retrieval of chlorophyll-a (Chl-a) concentrations relies on empirical or analytical analyses, which generally experience difficulties from the diversity of inland waters in statistical analyses and the complexity of radiative transfer equations in analytical analyses, respectively. Previous studies proposed the utilization of artificial neural networks (ANNs) to alleviate these problems. However, ANNs do not consider the problem of insufficient in situ samples during model training, and they do not fully utilize the spatial and spectral information of remote sensing images in neural networks. In this study, a two-stage training is introduced to address the problem regarding sample insufficiency. The neural network is pretrained using the samples derived from an existing Chl-a concentration model in the first stage, and the pretrained model is refined with in situ samples in the second stage. A novel convolutional neural network for Chl-a concentration retrieval called WaterNet is proposed which utilizes both spectral and spatial information of remote sensing images. In addition, an end-to-end structure that integrates feature extraction, band expansion, and Chl-a estimation into the neural network leads to an efficient and effective Chl-a concentration retrieval. In experiments, Sentinel-3 images with the same acquisition days of in situ measurements over Laguna Lake in the Philippines were used to train and evaluate WaterNet. The quantitative analyses show that the two-stage training is more likely than the one-stage training to reach the global optimum in the optimization, and WaterNet with two-stage training outperforms, in terms of estimation accuracy, related ANN-based and band-combination-based Chl-a concentration models.

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Decision Support for Psychiatric Diagnosis Based on a Simple Questionnaire

Methods of Information in Medicine ◽

10.1055/s-0038-1636858 ◽

1997 ◽

Vol 36 (04/05) ◽

pp. 349-351

Author(s):

H. Mizuta ◽

K. Kawachi ◽

H. Yoshida ◽

K. Iida ◽

Y. Okubo ◽

...

Keyword(s):

Neural Network ◽

Decision Support ◽

Psychiatric Diagnosis ◽

Psychiatric Patients ◽

Bayesian Classifier ◽

Neural Network Classifier ◽

Correct Decision ◽

Neurotic Disorders ◽

The Neural Network

Abstract:This paper compares two classifiers: Pseudo Bayesian and Neural Network for assisting in making diagnoses of psychiatric patients based on a simple yes/no questionnaire which is provided at the outpatient’s first visit to the hospital. The classifiers categorize patients into three most commonly seen ICD classes, i.e. schizophrenic, emotional and neurotic disorders. One hundred completed questionnaires were utilized for constructing and evaluating the classifiers. Average correct decision rates were 73.3% for the Pseudo Bayesian Classifier and 77.3% for the Neural Network classifier. These rates were higher than the rate which an experienced psychiatrist achieved based on the same restricted data as the classifiers utilized. These classifiers may be effectively utilized for assisting psychiatrists in making their final diagnoses.

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Low-Latency In Situ Image Analytics With FPGA-Based Quantized Convolutional Neural Network

IEEE Transactions on Neural Networks and Learning Systems ◽

10.1109/tnnls.2020.3046452 ◽

2021 ◽

pp. 1-14

Author(s):

Maolin Wang ◽

Kelvin C. M. Lee ◽

Bob M. F. Chung ◽

Sharatchandra Varma Bogaraju ◽

Ho-Cheung Ng ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Low Latency

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Face Recognition Based on Shallow Convolutional Neural Network Classifier

Proceedings of the 2020 2nd International Conference on Image, Video and Signal Processing ◽

10.1145/3388818.3388825 ◽

2020 ◽

Author(s):

Roshan Shrestha ◽

Sanjeeb Prasad Panday

Keyword(s):

Neural Network ◽

Face Recognition ◽

Convolutional Neural Network ◽

Neural Network Classifier

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A visual terrain classification method for mobile robots’ navigation based on convolutional neural network and support vector machine

Transactions of the Institute of Measurement and Control ◽

10.1177/0142331220987917 ◽

2021 ◽

pp. 014233122098791

Author(s):

Wanli Wang ◽

Botao Zhang ◽

Kaiqi Wu ◽

Sergey A Chepinskiy ◽

Anton A Zhilenkov ◽

...

Keyword(s):

Neural Network ◽

Support Vector Machine ◽

Mobile Robots ◽

Convolutional Neural Network ◽

Hybrid Method ◽

Classification Accuracy ◽

Support Vector ◽

High Classification Accuracy ◽

Enhance Efficiency ◽

Multi Class Classification

In this paper, a hybrid method based on deep learning is proposed to visually classify terrains encountered by mobile robots. Considering the limited computing resource on mobile robots and the requirement for high classification accuracy, the proposed hybrid method combines a convolutional neural network with a support vector machine to keep a high classification accuracy while improve work efficiency. The key idea is that the convolutional neural network is used to finish a multi-class classification and simultaneously the support vector machine is used to make a two-class classification. The two-class classification performed by the support vector machine is aimed at one kind of terrain that users are mostly concerned with. Results of the two classifications will be consolidated to get the final classification result. The convolutional neural network used in this method is modified for the on-board usage of mobile robots. In order to enhance efficiency, the convolutional neural network has a simple architecture. The convolutional neural network and the support vector machine are trained and tested by using RGB images of six kinds of common terrains. Experimental results demonstrate that this method can help robots classify terrains accurately and efficiently. Therefore, the proposed method has a significant potential for being applied to the on-board usage of mobile robots.

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