scholarly journals Arcobacter Identification and Species Determination Using Raman Spectroscopy Combined with Neural Networks

2020 ◽  
Vol 86 (20) ◽  
Author(s):  
Kaidi Wang ◽  
Lei Chen ◽  
Xiangyun Ma ◽  
Lina Ma ◽  
Keng C. Chou ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Raman Spectroscopy ◽  
Bacterial Species ◽  
Rapid Identification ◽  
Species Level ◽  
Food Sources ◽  
Species Determination ◽  
Data Set ◽  
Fully Connected

ABSTRACT Rapid and accurate identification of Arcobacter is of great importance because it is considered an emerging food- and waterborne pathogen and potential zoonotic agent. Raman spectroscopy can differentiate bacteria based on Raman scattering spectral patterns of whole cells in a fast, reagentless, and easy-to-use manner. We aimed to detect and discriminate Arcobacter bacteria at the species level using confocal micro-Raman spectroscopy (785 nm) coupled with neural networks. A total of 82 reference and field isolates of 18 Arcobacter species from clinical, environmental, and agri-food sources were included. We determined that the bacterial cultivation time and growth temperature did not significantly influence the Raman spectral reproducibility and discrimination capability. The genus Arcobacter could be successfully differentiated from the closely related genera Campylobacter and Helicobacter using principal-component analysis. For the identification of Arcobacter to the species level, an accuracy of 97.2% was achieved for all 18 Arcobacter species using Raman spectroscopy combined with a convolutional neural network (CNN). The predictive capability of Raman-CNN was further validated using an independent data set of 12 Arcobacter strains. Furthermore, a Raman spectroscopy-based fully connected artificial neural network (ANN) was constructed to determine the actual ratio of a specific Arcobacter species in a bacterial mixture ranging from 5% to 100% by biomass (regression coefficient >0.99). The application of both CNN and fully connected ANN improved the accuracy of Raman spectroscopy for bacterial species determination compared to the conventional chemometrics. This newly developed approach enables rapid identification and species determination of Arcobacter within an hour following cultivation. IMPORTANCE Rapid identification of bacterial pathogens is critical for developing an early warning system and performing epidemiological investigation. Arcobacter is an emerging foodborne pathogen and has become more important in recent decades. The incidence of Arcobacter species in the agro-ecosystem is probably underestimated mainly due to the limitation in the available detection and characterization techniques. Raman spectroscopy combined with machine learning can accurately identify Arcobacter at the species level in a rapid and reliable manner, providing a promising tool for epidemiological surveillance of this microbe in the agri-food chain. The knowledge elicited from this study has the potential to be used for routine bacterial screening and diagnostics by the government, food industry, and clinics.

scholarly journals Optimizing Convolutional Neural Network Parameters for Better Image Classification

2020 ◽  
Author(s):  
Manik Dhingra ◽  
Sarthak Rawat ◽  
Jinan Fiaidhi
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Image Classification ◽  
Web Service ◽  
Recognition Task ◽  
Extreme Learning Machines ◽  
Data Set ◽  
Learning Machines ◽  
Fully Connected ◽  
Fully Connected Networks

The work presented here works on getting higher performances for image recognition task using convolutional neural networks on the MNIST handwritten digits data-set. A range of techniques are compared for improvements with respect to time and accuracy, such as using one-shot Extreme Learning Machines (ELM) in place of the iteratively tuned fully-connected networks for classification, using transfer learning for faster convergence of image classification, and improving the size of data-set and making robust models by image augmentation. The final implementation is hosted on cloud as a web-service for better visualization of the prediction results.

On Computer Memory Saving Methods in Performing Data Classification Using Fully Connected Neural Networks

Vestnik MEI ◽  
2021 ◽  
Vol 3 (3) ◽  
pp. 103-109
Author(s):  
Andrey I. Mamontov ◽  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Computing System ◽  
Classification Problem ◽  
Double Precision ◽  
Data Set ◽  
The Neural Network ◽  
Study Results ◽  
System Memory ◽  
Fully Connected

In solving the classification problem, a fully connected trainable neural network (with adjusting the parameters represented by double-precision real numbers) is used as a mathematical model. After the training is completed, the neural network parameters are rounded and represented as fixed-point numbers (integers). The aim of the study is to reduce the required amount of the computing system memory for storing the obtained integer parameters. To reduce the amount of memory, the following methods for storing integer parameters are developed, which are based on representing the linear polynomials included in a fully connected neural network using compositions of simpler functions: - a method based on representing the considered polynomial as a sum of simpler polynomials; - a method based on separately storing the information about additions and multiplications. In the experiment with the MNIST data set, it took 1.41 MB to store real parameters of a fully connected neural network, 0.7 MB to store integer parameters without using the proposed methods, 0.47 MB in the RAM and 0.3 MB in compressed form on the disk when using the first method, and 0.25 MB on the disk when using the second method. In the experiment with the USPS data set, it took 0.25 MB to store real parameters of a fully connected neural network, 0.1 MB to store integer parameters without using the proposed methods, 0.05 MB in the RAM and approximately the same amount in compressed form on the disk when using the first method, and 0.03 MB on the disk when using the second method. The study results can be applied in using fully connected neural networks to solve various recognition problems under the conditions of limited hardware capacities.

scholarly journals Optimizing Convolutional Neural Network Parameters for Better Image Classification

2020 ◽  
Author(s):  
Manik Dhingra ◽  
Sarthak Rawat ◽  
Jinan Fiaidhi
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Image Classification ◽  
Web Service ◽  
Recognition Task ◽  
Extreme Learning Machines ◽  
Data Set ◽  
Learning Machines ◽  
Fully Connected ◽  
Fully Connected Networks

The work presented here works on getting higher performances for image recognition task using convolutional neural networks on the MNIST handwritten digits data-set. A range of techniques are compared for improvements with respect to time and accuracy, such as using one-shot Extreme Learning Machines (ELM) in place of the iteratively tuned fully-connected networks for classification, using transfer learning for faster convergence of image classification, and improving the size of data-set and making robust models by image augmentation. The final implementation is hosted on cloud as a web-service for better visualization of the prediction results.

Estimation of Site Amplification from Geotechnical Array Data Using Neural Networks

2021 ◽  
Author(s):  
Daniel Roten ◽  
Kim B. Olsen
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Site Response ◽  
Site Amplification ◽  
Data Driven ◽  
Vertical Array ◽  
The Neural Network ◽  
Simplifying Assumptions ◽  
The Mean ◽  
Fully Connected

ABSTRACT We use deep learning to predict surface-to-borehole Fourier amplification functions (AFs) from discretized shear-wave velocity profiles. Specifically, we train a fully connected neural network and a convolutional neural network using mean AFs observed at ∼600 KiK-net vertical array sites. Compared with predictions based on theoretical SH 1D amplifications, the neural network (NN) results in up to 50% reduction of the mean squared log error between predictions and observations at sites not used for training. In the future, NNs may lead to a purely data-driven prediction of site response that is independent of proxies or simplifying assumptions.

scholarly journals KLASIFIKASI BATIK RIAU DENGAN MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORKS (CNN)

2020 ◽  
Vol 9 (1) ◽  
pp. 7-10
Author(s):  
Hendry Fonda
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
18Th Century ◽  
The Public ◽  
Artificial Neural ◽  
The Difference ◽  
Fully Connected

ABSTRACT Riau batik is known since the 18th century and is used by royal kings. Riau Batik is made by using a stamp that is mixed with coloring and then printed on fabric. The fabric used is usually silk. As its development, comparing Javanese  batik with riau batik Riau is very slowly accepted by the public. Convolutional Neural Networks (CNN) is a combination of artificial neural networks and deeplearning methods. CNN consists of one or more convolutional layers, often with a subsampling layer followed by one or more fully connected layers as a standard neural network. In the process, CNN will conduct training and testing of Riau batik so that a collection of batik models that have been classified based on the characteristics that exist in Riau batik can be determined so that images are Riau batik and non-Riau batik. Classification using CNN produces Riau batik and not Riau batik with an accuracy of 65%. Accuracy of 65% is due to basically many of the same motifs between batik and other batik with the difference lies in the color of the absorption in the batik riau. Kata kunci: Batik; Batik Riau; CNN; Image; Deep Learning   ABSTRAK   Batik Riau dikenal sejak abad ke 18 dan digunakan oleh bangsawan raja. Batik Riau dibuat dengan menggunakan cap yang dicampur dengan pewarna kemudian dicetak di kain. Kain yang digunakan biasanya sutra. Seiring perkembangannya, dibandingkan batik Jawa maka batik Riau sangat lambat diterima oleh masyarakat. Convolutional Neural Networks (CNN) merupakan kombinasi dari jaringan syaraf tiruan dan metode deeplearning. CNN terdiri dari satu atau lebih lapisan konvolutional, seringnya dengan suatu lapisan subsampling yang diikuti oleh satu atau lebih lapisan yang terhubung penuh sebagai standar jaringan syaraf. Dalam prosesnya CNN akan melakukan training dan testing terhadap batik Riau sehingga didapat kumpulan model batik yang telah terklasi    fikasi berdasarkan ciri khas yang ada pada batik Riau sehingga dapat ditentukan gambar (image) yang merupakan batik Riau dan yang bukan merupakan batik Riau. Klasifikasi menggunakan CNN menghasilkan batik riau dan bukan batik riau dengan akurasi 65%. Akurasi 65% disebabkan pada dasarnya banyak motif yang sama antara batik riau dengan batik lainnya dengan perbedaan terletak pada warna cerap pada batik riau. Kata kunci: Batik; Batik Riau; CNN; Image; Deep Learning

scholarly journals Binary and Multiclass Text Classification by Means of Separable Convolutional Neural Network

Inventions ◽  
2021 ◽  
Vol 6 (4) ◽  
pp. 70
Author(s):  
Elena Solovyeva ◽  
Ali Abdullah
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Recurrent Neural Networks ◽  
Low Cost ◽  
Computational Cost ◽  
High Accuracy ◽  
Activation Functions ◽  
Fully Connected ◽  
Fully Connected Networks

In this paper, the structure of a separable convolutional neural network that consists of an embedding layer, separable convolutional layers, convolutional layer and global average pooling is represented for binary and multiclass text classifications. The advantage of the proposed structure is the absence of multiple fully connected layers, which is used to increase the classification accuracy but raises the computational cost. The combination of low-cost separable convolutional layers and a convolutional layer is proposed to gain high accuracy and, simultaneously, to reduce the complexity of neural classifiers. Advantages are demonstrated at binary and multiclass classifications of written texts by means of the proposed networks under the sigmoid and Softmax activation functions in convolutional layer. At binary and multiclass classifications, the accuracy obtained by separable convolutional neural networks is higher in comparison with some investigated types of recurrent neural networks and fully connected networks.

scholarly journals Adaptation of the Diagnostic Artificial Neural Network Structure When New Training Examples Appear

2020 ◽  
Vol 6 (4) ◽  
pp. 120-126
Author(s):  
A. Malikov
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Information Security ◽  
Classification Problem ◽  
Data Set ◽  
Neural Network Structure ◽  
Artificial Neural ◽  
Training Examples ◽  
Information Image

In this paper we can see that identified computer incidents are subject for diagnostics, during which the characteristics of information security violations are clarified (purpose, causes, consequences, etc.). To diagnose computer incidents, we can use methods of automation while collection and processing the events that occur as a result of the implementation of scenarios for information security violations. Artificial neural networks can be used to solve the classification problem of assigning diagnostic data set (information image of a computer incident) to one of the possible values of the violation characteristic. The purpose of this work is to adapt the structure of an artificial neural network that allows the accuracy diagnostics of computer incidents when new training examples appear.

scholarly journals NEURAL NETWORK ANALYSIS FOR TUMOR INVESTIGATION AND CANCER PREDICTION

2019 ◽  
Vol 2019 (02) ◽  
pp. 89-98
Author(s):  
Vijayakumar T
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Early Stage ◽  
Original Data ◽  
Data Set ◽  
Cancer Prediction ◽  
The Neural Network ◽  
Feed Forward Neural Networks ◽  
The Neural Networks ◽  
Human Nervous System

Predicting the category of tumors and the types of the cancer in its early stage remains as a very essential process to identify depth of the disease and treatment available for it. The neural network that functions similar to the human nervous system is widely utilized in the tumor investigation and the cancer prediction. The paper presents the analysis of the performance of the neural networks such as the, FNN (Feed Forward Neural Networks), RNN (Recurrent Neural Networks) and the CNN (Convolutional Neural Network) investigating the tumors and predicting the cancer. The results obtained by evaluating the neural networks on the breast cancer Wisconsin original data set shows that the CNN provides 43 % better prediction than the FNN and 25% better prediction than the RNN.

scholarly journals Multiple Ensemble Neural Network Models with Fuzzy Response Aggregation for Predicting COVID-19 Time Series: The Case of Mexico

Healthcare ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 181 ◽  
Author(s):  
Patricia Melin ◽  
Julio Cesar Monica ◽  
Daniela Sanchez ◽  
Oscar Castillo
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Fuzzy Logic ◽  
Network Models ◽  
Neural Network Models ◽  
Validation Data ◽  
Data Set ◽  
Ensemble Neural Networks ◽  
Ensemble Neural Network

In this paper, a multiple ensemble neural network model with fuzzy response aggregation for the COVID-19 time series is presented. Ensemble neural networks are composed of a set of modules, which are used to produce several predictions under different conditions. The modules are simple neural networks. Fuzzy logic is then used to aggregate the responses of several predictor modules, in this way, improving the final prediction by combining the outputs of the modules in an intelligent way. Fuzzy logic handles the uncertainty in the process of making a final decision about the prediction. The complete model was tested for the case of predicting the COVID-19 time series in Mexico, at the level of the states and the whole country. The simulation results of the multiple ensemble neural network models with fuzzy response integration show very good predicted values in the validation data set. In fact, the prediction errors of the multiple ensemble neural networks are significantly lower than using traditional monolithic neural networks, in this way showing the advantages of the proposed approach.

scholarly journals The Goldilocks Zone: Towards Better Understanding of Neural Network Loss Landscapes

2019 ◽  
Vol 33 ◽  
pp. 3574-3581
Author(s):  
Stanislav Fort ◽  
Adam Scherlis
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Configuration Space ◽  
Loss Function ◽  
Positive Curvature ◽  
Local Convexity ◽  
Convolutional Networks ◽  
Hollow Spherical Shell ◽  
Low Dimensional ◽  
Fully Connected

We explore the loss landscape of fully-connected and convolutional neural networks using random, low-dimensional hyperplanes and hyperspheres. Evaluating the Hessian, H, of the loss function on these hypersurfaces, we observe 1) an unusual excess of the number of positive eigenvalues of H, and 2) a large value of Tr(H)/||H|| at a well defined range of configuration space radii, corresponding to a thick, hollow, spherical shell we refer to as the Goldilocks zone. We observe this effect for fully-connected neural networks over a range of network widths and depths on MNIST and CIFAR-10 datasets with the ReLU and tanh non-linearities, and a similar effect for convolutional networks. Using our observations, we demonstrate a close connection between the Goldilocks zone, measures of local convexity/prevalence of positive curvature, and the suitability of a network initialization. We show that the high and stable accuracy reached when optimizing on random, low-dimensional hypersurfaces is directly related to the overlap between the hypersurface and the Goldilocks zone, and as a corollary demonstrate that the notion of intrinsic dimension is initialization-dependent. We note that common initialization techniques initialize neural networks in this particular region of unusually high convexity/prevalence of positive curvature, and offer a geometric intuition for their success. Furthermore, we demonstrate that initializing a neural network at a number of points and selecting for high measures of local convexity such as Tr(H)/||H||, number of positive eigenvalues of H, or low initial loss, leads to statistically significantly faster training on MNIST. Based on our observations, we hypothesize that the Goldilocks zone contains an unusually high density of suitable initialization configurations.

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