Modeling the relationship between acoustic stimulus and EEG with a dilated convolutional neural network

2021 ◽  
Author(s):  
Bernd Accou ◽  
Mohammad Jalilpour Monesi ◽  
Jair Montoya ◽  
Hugo Van hamme ◽  
Tom Francart
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Acoustic Stimulus ◽  
The Relationship

scholarly journals Review of Road Detection and Segmentation Technique Using CNN

2020 ◽  
pp. 161-165
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Aerial Images ◽  
Road Detection ◽  
Insurance Plan ◽  
Toll Road ◽  
White Pixel ◽  
Road Segmentation ◽  
Matlab Programming ◽  
The Relationship

This research proposes form shape mounted on “the deep convolutional neural network (CNN) for the detection of roads and the segmentation of aerial pix. Those images are received by using a UAV. The photograph segmentation set of rules has two levels: the studying segment and the working phase. The aerial images of the data deteriorated into their coloration additives, had been pre-processed in matlab on hue, after which divided into small 33 × 33 pixel packing containers the usage of a sliding container set of rules. CNN was once designed with matconvnet and had the accompanying structure: 4 convolutional levels, 4 grouping stages, a relu layer, a totally linked layer, and a softmax layer. The entire community has been organized for the use of 2,000 boxes. CNN was implemented the use of matlab programming on the gpu and the outcomes are promising. The CNN output offers pixel-by means of-pixel records, which class it has a location with (road / non-road). White pixel and choppy terrain are known as "0" (dark). Monitoring roads is a troublesome venture in aerial picture segmentation due to quite more than a few sizes and surfaces. One of the vastest steps in CNN training is the pre-processing phase. Due to toll road segmentation, dismissal structures and complexity enhancement have been applied.” this is an audited article on the relationship between representative upkeep techniques with work pleasure and responsibility in insurance plan businesses.

scholarly journals Foreign exchange prediction based on indices and commodities price using convolutional neural network

2020 ◽  
Vol 18 (1) ◽  
pp. 494
Author(s):  
Rian Rassetiadi ◽  
Suharjito Suharjito
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Test Data ◽  
Learning Algorithm ◽  
External Factors ◽  
Deep Learning Algorithm ◽  
Network Method ◽  
Combined Test ◽  
Trading Activities ◽  
The Relationship

The level of accuracy in predicting is the key in conducting forex trading activities in gaining profits. Some predictions are made only by using historical currency data to be predicted, this makes predictions less accurate because they do not consider external influences. This study examines external factors that can influence the results of predictions, by looking for the relationship between the value of indices such as NTFSE and S & P 500 and the value of commodities such as gold and silver to the prediction process of EUR / USD. Prediction carried out using a deep learning algorithm with the Convolutional Neural Network method uses 2 1-dimensional convolution layers with ReL activation. The data used is the value of Open, High, Low and Close prices on forex, indices and commodities which are combined into one with the close forex value target for the next 5 days. Testing of EUR / USD test data only gets MSE results of 0.00081894. While the results of testing of the combined test data between EUR / USD, indices and commodities producing MSE vary between 0.00068717 to 0.0109606 where the best combination is a combination of FTSE 100 and Natural Gas values. So it can be concluded that other factors included in predicting have an influence on the results obtained.

scholarly journals Filling the binary images of draped fabric with pix2pix convolutional neural network

2020 ◽  
Vol 15 ◽  
pp. 155892502092154
Author(s):  
Zhicai Yu ◽  
Yueqi Zhong ◽  
R Hugh Gong ◽  
Haoyang Xie
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Point Cloud ◽  
Three Dimensional ◽  
Ground Truth ◽  
Binary Image ◽  
Grayscale Image ◽  
Binary Images ◽  
The Relationship ◽  
Grayscale Images

To fill the binary image of draped fabric into a comparable grayscale image with detailed shade information, the three-dimensional point cloud of draped fabric was obtained with a self-built three-dimensional scanning device. The three-dimensional point cloud of drape fabric is encapsulated into a triangular mesh, and the binary and grayscale images of draped fabric were rendered in virtual environments separately. A pix2pix convolutional neural network with the binary image of draped fabric as input and the grayscale image of draped fabric as output was constructed and trained. The relationship between the binary image and the grayscale image was established. The results show that the trained pix2pix neural network can fill unknown binary top view images of draped fabric to grayscale images. The average pixel cosine similarity between filling results and ground truth could reach 0.97.

scholarly journals Study on Aberration Correction of Adaptive Optics Based on Convolutional Neural Network

Photonics ◽  
2021 ◽  
Vol 8 (9) ◽  
pp. 377
Author(s):  
Jin Li ◽  
Luwei Wang ◽  
Yong Guo ◽  
Yangrui Huang ◽  
Zhigang Yang ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Adaptive Optics ◽  
Correction Method ◽  
Limiting Factor ◽  
Aberration Correction ◽  
Microscopy Imaging ◽  
Imaging Resolution ◽  
Short Time ◽  
The Relationship

The existence of aberrations has always been an important limiting factor in the imaging field. Especially in optical microscopy imaging, the accumulated aberration of the optical system and the biological samples distorts the wavefront on the focal plane, thereby reducing the imaging resolution. Here, we propose an adaptive optical aberration correction method based on convolutional neural network. By establishing the relationship between the Zernike polynomial and the distorted wavefront, with the help of the fast calculation advantage of an artificial intelligence neural network, the distorted wavefront information can be output in a short time for the reconstruction of the wavefront to achieve the purpose of improving imaging resolution. Experimental results show that this method can effectively compensate the aberrations introduced by the system, agarose and HeLa cells. After correcting, the point spread function restored the doughnut-shape, and the resolution of the HeLa cell image increased about 20%.

scholarly journals Effective Melanoma Recognition Using Deep Convolutional Neural Network with Covariance Discriminant Loss

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5786
Author(s):  
Lei Guo ◽  
Gang Xie ◽  
Xinying Xu ◽  
Jinchang Ren
Keyword(s):  
Neural Network ◽  
Skin Lesion ◽  
Convolutional Neural Network ◽  
Deep Convolutional Neural Network ◽  
Cross Entropy ◽  
Recognition Method ◽  
Minority Class ◽  
Lesion Analysis ◽  
The Relationship ◽  
Hard Samples

Melanoma recognition is challenging due to data imbalance and high intra-class variations and large inter-class similarity. Aiming at the issues, we propose a melanoma recognition method using deep convolutional neural network with covariance discriminant loss in dermoscopy images. Deep convolutional neural network is trained under the joint supervision of cross entropy loss and covariance discriminant loss, rectifying the model outputs and the extracted features simultaneously. Specifically, we design an embedding loss, namely covariance discriminant loss, which takes the first and second distance into account simultaneously for providing more constraints. By constraining the distance between hard samples and minority class center, the deep features of melanoma and non-melanoma can be separated effectively. To mine the hard samples, we also design the corresponding algorithm. Further, we analyze the relationship between the proposed loss and other losses. On the International Symposium on Biomedical Imaging (ISBI) 2018 Skin Lesion Analysis dataset, the two schemes in the proposed method can yield a sensitivity of 0.942 and 0.917, respectively. The comprehensive results have demonstrated the efficacy of the designed embedding loss and the proposed methodology.

scholarly journals Analisis Sentimen pada Data Saran Mahasiswa Terhadap Kinerja Departemen di Perguruan Tinggi Menggunakan Convolutional Neural Network

2021 ◽  
Vol 8 (5) ◽  
pp. 1067
Author(s):  
Yuliska Yuliska ◽  
Dini Hidayatul Qudsi ◽  
Juanda Hakim Lubis ◽  
Khairul Umum Syaliman ◽  
Nina Fadilah Najwa
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Sentiment Analysis ◽  
Text Classification ◽  
Word Embedding ◽  
Great Performance ◽  
Word Representation ◽  
The Relationship

<p class="Abstrak"><em>Review</em> atau saran dari <em>customer</em> dapat menjadi sangat penting bagi penyedia layanan, begitu pula saran dari mahasiswa mengenai layanan sebuah unit kerja di perguruan tinggi. <em>Review</em> menjadi penting karena dapat menjadi indikator kinerja penyedia layanan. Pengolahan review juga sangat penting karena dapat menjadi referensi untuk pengambilan keputusan dan peningkatan layanan yang lebih baik ke depannya. Penelitian ini menerapkan analisis sentimen pada data saran atau <em>review</em> mahasiswa terhadap kinerja unit kerja atau departemen di perguruan tinggi, yaitu Politeknik Caltex Riau. Analisis sentimen dilakukan dengan menggunakan <em>Convolutional Neural Network (CNN)</em> dan <em>word embedding</em> <em>Word2vec</em> sebagai representasi kata. <em>CNN</em> merupakan metode yang memiliki performa yang baik dalam mengklasifikasi teks, yaitu dengan teknik <em>convolutional</em> yang menggabungkan beberapa <em>window</em> kata pada kalimat dan mengambil <em>window</em> yang paling <em>representative</em>. <em>Word2Vec</em> digunakan sebagai representasi data saran dan inputan awal pada <em>CNN</em>, dimana <em>Word2Vec</em> merupakan <em>dense vectors</em> yang dapat merepresentasikan hubungan antar kata pada data saran dengan baik. Saran mahasiswa dapat mengandung kalimat yang sangat panjang, karena itu perpaduan <em>Word2Vec</em> sebagai representasi kata dan <em>CNN</em> dengan teknik <em>convolutional</em>, dapat menghasilkan representasi yang <em>representative</em> dari kalimat panjang tersebut. Penelitian ini menggunakan dua arsitektur <em>CNN</em>, yaitu <em>Simple</em> <em>CNN</em> dan <em>DoubleMax CNN</em> untuk mengidentifikasi pengaruh kompleksitas arsitektur terhadap hasil klasifikasi sentimen.  Berdasarkan hasil pengujian, <em>DoubleMax CNN</em> dapat mengklasifikasi sentimen pada saran mahasiswa dengan sangat baik, yaitu mencapai Akurasi tertinggi sebesar 98%, <em>Recall</em> 97%, <em>Precision</em> 98% dan <em>F1-Score</em> 98%.</p><p class="Abstrak"> </p><p class="Abstrak"><em><strong>Abstract</strong></em></p><p class="Abstract"><em>Student’s reviews about department performance can be essential for a college for it can be used to evaluate the department performance and to take an immediate action to improve its performance. This research applies sentiment analysis in the student’s reviews of college department in Politeknik Caltex Riau. Convolutional Neural Network and Word2Vec are employed to analyze the sentiment. CNN is known for its good performance in text classification by applying a convolutional technique to the input sentences. Word2Vec is used as word representation and as an input to the CNN. Word2Vec are dense vectors which can represent the relationship between words excellently. Student’s reviews can be a long sentence; hence the combination of Word2Vec as word representation and CNN with convolutional technique can produce a representative fiture from that long sentence. This research utilizes two CNN architectures, which are Simple CNN dan DoubleMax CNN to identify the effect of the complexity of CNN architecture to final result. Our experiments show that DoubleMax CNN has a great performance in classifying sentiment in the student’s reviews with the best Accuracy value of 98%, Recall 97%, Precision 98% and F1-Score value of 98%.<strong> </strong></em></p><p class="Abstrak"><em><strong><br /></strong></em></p>

CONVOLUTIONAL NEURAL NETWORK BASED ALGORITHM FOR CECUM ACHIEVEMENT CONFIRMATION

2020 ◽  
Author(s):  
S Kashin ◽  
D Zavyalov ◽  
A Rusakov ◽  
V Khryashchev ◽  
A Lebedev
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network

Non-Blind Image Deconvolution Based on “Ringing” Removal Using Convolutional Neural Network

2020 ◽  
Vol 2020 (10) ◽  
pp. 181-1-181-7
Author(s):  
Takahiro Kudo ◽  
Takanori Fujisawa ◽  
Takuro Yamaguchi ◽  
Masaaki Ikehara
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Large Scale ◽  
Blind Deconvolution ◽  
Training Dataset ◽  
Image Deconvolution ◽  
Classic Problem ◽  
Key Points ◽  
Blind Image

Image deconvolution has been an important issue recently. It has two kinds of approaches: non-blind and blind. Non-blind deconvolution is a classic problem of image deblurring, which assumes that the PSF is known and does not change universally in space. Recently, Convolutional Neural Network (CNN) has been used for non-blind deconvolution. Though CNNs can deal with complex changes for unknown images, some CNN-based conventional methods can only handle small PSFs and does not consider the use of large PSFs in the real world. In this paper we propose a non-blind deconvolution framework based on a CNN that can remove large scale ringing in a deblurred image. Our method has three key points. The first is that our network architecture is able to preserve both large and small features in the image. The second is that the training dataset is created to preserve the details. The third is that we extend the images to minimize the effects of large ringing on the image borders. In our experiments, we used three kinds of large PSFs and were able to observe high-precision results from our method both quantitatively and qualitatively.

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