An Animal Classification based on Light Convolutional Network Neural Network

Due to the rapid development of deep learning and artificial intelligence techniques, denoising via neural networks has drawn great attention due to their flexibility and excellent performances. However, for most convolutional network denoising methods, the convolution kernel is only one layer deep, and features of distinct scales are neglected. Moreover, in the convolution operation, all channels are treated equally; the relationships of channels are not considered. In this paper, we propose a multi-scale feature extraction-based normalized attention neural network (MFENANN) for image denoising. In MFENANN, we define a multi-scale feature extraction block to extract and combine features at distinct scales of the noisy image. In addition, we propose a normalized attention network (NAN) to learn the relationships between channels, which smooths the optimization landscape and speeds up the convergence process for training an attention model. Moreover, we introduce the NAN to convolutional network denoising, in which each channel gets gain; channels can play different roles in the subsequent convolution. To testify the effectiveness of the proposed MFENANN, we used both grayscale and color image sets whose noise levels ranged from 0 to 75 to do the experiments. The experimental results show that compared with some state-of-the-art denoising methods, the restored images of MFENANN have larger peak signal-to-noise ratios (PSNR) and structural similarity index measure (SSIM) values and get better overall appearance.

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Convolutional Neural Network-Based Artificial Intelligence for Classification of Protein Localization Patterns

Biomolecules ◽

10.3390/biom11020264 ◽

2021 ◽

Vol 11 (2) ◽

pp. 264

Author(s):

Kaisa Liimatainen ◽

Riku Huttunen ◽

Leena Latonen ◽

Pekka Ruusuvuori

Keyword(s):

Neural Network ◽

Artificial Intelligence ◽

Convolutional Neural Network ◽

High Throughput ◽

Protein Function ◽

Protein Localization ◽

Convolutional Network ◽

High Throughput Analysis ◽

Fully Convolutional Network

Identifying localization of proteins and their specific subpopulations associated with certain cellular compartments is crucial for understanding protein function and interactions with other macromolecules. Fluorescence microscopy is a powerful method to assess protein localizations, with increasing demand of automated high throughput analysis methods to supplement the technical advancements in high throughput imaging. Here, we study the applicability of deep neural network-based artificial intelligence in classification of protein localization in 13 cellular subcompartments. We use deep learning-based on convolutional neural network and fully convolutional network with similar architectures for the classification task, aiming at achieving accurate classification, but importantly, also comparison of the networks. Our results show that both types of convolutional neural networks perform well in protein localization classification tasks for major cellular organelles. Yet, in this study, the fully convolutional network outperforms the convolutional neural network in classification of images with multiple simultaneous protein localizations. We find that the fully convolutional network, using output visualizing the identified localizations, is a very useful tool for systematic protein localization assessment.

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FetNet: a recurrent convolutional network for occlusion identification in fetoscopic videos

International Journal of Computer Assisted Radiology and Surgery ◽

10.1007/s11548-020-02169-0 ◽

2020 ◽

Vol 15 (5) ◽

pp. 791-801 ◽

Cited By ~ 1

Author(s):

Sophia Bano ◽

Francisco Vasconcelos ◽

Emmanuel Vander Poorten ◽

Tom Vercauteren ◽

Sebastien Ourselin ◽

...

Keyword(s):

Neural Network ◽

Network Architecture ◽

Laser Photocoagulation ◽

Short Term Memory ◽

Total Duration ◽

Frame Rate ◽

Superior Performance ◽

Computer Assisted ◽

Convolutional Network ◽

Spatio Temporal

Abstract Purpose Fetoscopic laser photocoagulation is a minimally invasive surgery for the treatment of twin-to-twin transfusion syndrome (TTTS). By using a lens/fibre-optic scope, inserted into the amniotic cavity, the abnormal placental vascular anastomoses are identified and ablated to regulate blood flow to both fetuses. Limited field-of-view, occlusions due to fetus presence and low visibility make it difficult to identify all vascular anastomoses. Automatic computer-assisted techniques may provide better understanding of the anatomical structure during surgery for risk-free laser photocoagulation and may facilitate in improving mosaics from fetoscopic videos. Methods We propose FetNet, a combined convolutional neural network (CNN) and long short-term memory (LSTM) recurrent neural network architecture for the spatio-temporal identification of fetoscopic events. We adapt an existing CNN architecture for spatial feature extraction and integrated it with the LSTM network for end-to-end spatio-temporal inference. We introduce differential learning rates during the model training to effectively utilising the pre-trained CNN weights. This may support computer-assisted interventions (CAI) during fetoscopic laser photocoagulation. Results We perform quantitative evaluation of our method using 7 in vivo fetoscopic videos captured from different human TTTS cases. The total duration of these videos was 5551 s (138,780 frames). To test the robustness of the proposed approach, we perform 7-fold cross-validation where each video is treated as a hold-out or test set and training is performed using the remaining videos. Conclusion FetNet achieved superior performance compared to the existing CNN-based methods and provided improved inference because of the spatio-temporal information modelling. Online testing of FetNet, using a Tesla V100-DGXS-32GB GPU, achieved a frame rate of 114 fps. These results show that our method could potentially provide a real-time solution for CAI and automating occlusion and photocoagulation identification during fetoscopic procedures.

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Evaluation of Power Insulator Detection Efficiency with the Use of Limited Training Dataset

Applied Sciences ◽

10.3390/app10062104 ◽

2020 ◽

Vol 10 (6) ◽

pp. 2104

Author(s):

Michał Tomaszewski ◽

Paweł Michalski ◽

Jakub Osuchowski

Keyword(s):

Neural Network ◽

Neural Networks ◽

Object Detection ◽

Convolutional Neural Network ◽

Deep Neural Networks ◽

Detection Efficiency ◽

Training Data ◽

Training Dataset ◽

Training Set ◽

Convolutional Network

This article presents an analysis of the effectiveness of object detection in digital images with the application of a limited quantity of input. The possibility of using a limited set of learning data was achieved by developing a detailed scenario of the task, which strictly defined the conditions of detector operation in the considered case of a convolutional neural network. The described solution utilizes known architectures of deep neural networks in the process of learning and object detection. The article presents comparisons of results from detecting the most popular deep neural networks while maintaining a limited training set composed of a specific number of selected images from diagnostic video. The analyzed input material was recorded during an inspection flight conducted along high-voltage lines. The object detector was built for a power insulator. The main contribution of the presented papier is the evidence that a limited training set (in our case, just 60 training frames) could be used for object detection, assuming an outdoor scenario with low variability of environmental conditions. The decision of which network will generate the best result for such a limited training set is not a trivial task. Conducted research suggests that the deep neural networks will achieve different levels of effectiveness depending on the amount of training data. The most beneficial results were obtained for two convolutional neural networks: the faster region-convolutional neural network (faster R-CNN) and the region-based fully convolutional network (R-FCN). Faster R-CNN reached the highest AP (average precision) at a level of 0.8 for 60 frames. The R-FCN model gained a worse AP result; however, it can be noted that the relationship between the number of input samples and the obtained results has a significantly lower influence than in the case of other CNN models, which, in the authors’ assessment, is a desired feature in the case of a limited training set.

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Graph Convolutional Network and Convolutional Neural Network Based Method for Predicting lncRNA-Disease Associations

Cells ◽

10.3390/cells8091012 ◽

2019 ◽

Vol 8 (9) ◽

pp. 1012 ◽

Cited By ~ 12

Author(s):

Xuan ◽

Pan ◽

Zhang ◽

Liu ◽

Sun

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Heterogeneous Network ◽

Heterogeneous Data ◽

Superior Performance ◽

Convolutional Network ◽

Topological Information ◽

Disease Pair ◽

Disease Associations ◽

The Right

Aberrant expressions of long non-coding RNAs (lncRNAs) are often associated with diseases and identification of disease-related lncRNAs is helpful for elucidating complex pathogenesis. Recent methods for predicting associations between lncRNAs and diseases integrate their pertinent heterogeneous data. However, they failed to deeply integrate topological information of heterogeneous network comprising lncRNAs, diseases, and miRNAs. We proposed a novel method based on the graph convolutional network and convolutional neural network, referred to as GCNLDA, to infer disease-related lncRNA candidates. The heterogeneous network containing the lncRNA, disease, and miRNA nodes, is constructed firstly. The embedding matrix of a lncRNA-disease node pair was constructed according to various biological premises about lncRNAs, diseases, and miRNAs. A new framework based on a graph convolutional network and a convolutional neural network was developed to learn network and local representations of the lncRNA-disease pair. On the left side of the framework, the autoencoder based on graph convolution deeply integrated topological information within the heterogeneous lncRNA-disease-miRNA network. Moreover, as different node features have discriminative contributions to the association prediction, an attention mechanism at node feature level is constructed. The left side learnt the network representation of the lncRNA-disease pair. The convolutional neural networks on the right side of the framework learnt the local representation of the lncRNA-disease pair by focusing on the similarities, associations, and interactions that are only related to the pair. Compared to several state-of-the-art prediction methods, GCNLDA had superior performance. Case studies on stomach cancer, osteosarcoma, and lung cancer confirmed that GCNLDA effectively discovers the potential lncRNA-disease associations.

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Improving the Learning of Self-driving Vehicles Based on Real Driving Behavior using Deep Neural Network Techniques

10.20944/preprints202001.0283.v3 ◽

2020 ◽

Author(s):

Nayere Zaghari ◽

Mahmood Fathy ◽

Seyed Mahdi Jameii ◽

Mohammad Sabokrou ◽

Mohammad Shahverdy

Keyword(s):

Neural Network ◽

Deep Neural Network ◽

Autonomous Vehicle ◽

Driving Behavior ◽

Obstacle Detection ◽

Convolutional Network ◽

The Real ◽

Cloning Method ◽

Vehicle Acceleration ◽

Performance Results

Considering the significant advancements in autonomous vehicle technology, research in this field is of interest to researchers. To drive vehicles autonomously, controlling steer angle, gas hatch, and brakes need to be learned. The behavioral cloning method is used to imitate humans’ driving behavior. We created a dataset of driving in different routes and conditions and using the designed model, the output used for controlling the vehicle is obtained. In this paper, the Learning of Self-driving Vehicles Based on Real Driving Behavior Using Deep Neural Network Techniques (LSV-DNN) is proposed. We designed a convolutional network which uses the real driving data obtained through the vehicle’s camera and computer. The response of the driver is during driving is recorded in different situations and by converting the real driver’s driving video to images and transferring the data to an excel file, obstacle detection is carried out with the best accuracy and speed using the Yolo algorithm version 3. This way, the network learns the response of the driver to obstacles in different locations and the network is trained with the Yolo algorithm version 3 and the output of obstacle detection. Then, it outputs the steer angle and amount of brake, gas, and vehicle acceleration. The LSV-DNN is evaluated here via extensive simulations carried out in Python and TensorFlow environment. We evaluated the network error using the loss function. By comparing other methods which were conducted on the simulator’s data, we obtained good performance results for the designed network on the data from KITTI benchmark, the data collected using a private vehicle, and the data we collected.

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COMPUTER VISION BASED TRAFFIC SIGN SENSING FOR SMART TRANSPORT

Journal of Innovative Image Processing - December 2019 ◽

10.36548/jiip.2019.1.002 ◽

2019 ◽

Vol 1 (01) ◽

pp. 11-19 ◽

Cited By ~ 35

Author(s):

James Deva Koresh H

Keyword(s):

Neural Network ◽

High Reliability ◽

Traffic Data ◽

Traffic Sign ◽

Spatial Variance ◽

Convolutional Network ◽

Data Set ◽

Real Time Traffic ◽

Frame Work ◽

Detection And Recognition

The paper puts forward a real time traffic sign sensing (detection and recognition) frame work for enhancing the vehicles capability in order to have a save driving, path planning. The proposed method utilizes the capsules neural network that outperforms the convolutional neural network by eluding the necessities for the manual effort. The capsules network provides a better resistance for the spatial variance and the high reliability in the sensing of the traffic sign compared to the convolutional network. The evaluation of the capsule network with the Indian traffic data set shows a 15% higher accuracy when compared with the CNN and the RNN.

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Aspect-level sentiment analysis merged with knowledge graph and graph convolutional neural network

Journal of Physics Conference Series ◽

10.1088/1742-6596/2083/4/042044 ◽

2021 ◽

Vol 2083 (4) ◽

pp. 042044

Author(s):

Zuhua Dai ◽

Yuanyuan Liu ◽

Shilong Di ◽

Qi Fan

Keyword(s):

Neural Network ◽

Sentiment Analysis ◽

Structural Information ◽

Knowledge Graph ◽

Convolutional Network ◽

Text Data ◽

Short Text ◽

Fine Grained ◽

Syntactic Information ◽

Text Information

Abstract Aspect level sentiment analysis belongs to fine-grained sentiment analysis, w hich has caused extensive research in academic circles in recent years. For this task, th e recurrent neural network (RNN) model is usually used for feature extraction, but the model cannot effectively obtain the structural information of the text. Recent studies h ave begun to use the graph convolutional network (GCN) to model the syntactic depen dency tree of the text to solve this problem. For short text data, the text information is not enough to accurately determine the emotional polarity of the aspect words, and the knowledge graph is not effectively used as external knowledge that can enrich the sem antic information. In order to solve the above problems, this paper proposes a graph co nvolutional neural network (GCN) model that can process syntactic information, know ledge graphs and text semantic information. The model works on the “syntax-knowled ge” graph to extract syntactic information and common sense information at the same t ime. Compared with the latest model, the model in this paper can effectively improve t he accuracy of aspect-level sentiment classification on two datasets.

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An Improved Fully Convolutional Network Based on Post-Processing with Global Variance Equalization and Noise-Aware Training for Speech Enhancement

Journal of Advanced Computational Intelligence and Intelligent Informatics ◽

10.20965/jaciii.2021.p0130 ◽

2021 ◽

Vol 25 (1) ◽

pp. 130-137

Author(s):

Wenlong Li ◽

◽

Kaoru Hirota ◽

Yaping Dai ◽

Zhiyang Jia

Keyword(s):

Neural Network ◽

Speech Enhancement ◽

Deep Neural Network ◽

Voice Conversion ◽

Post Processing ◽

Generalization Capability ◽

Convolutional Network ◽

Fully Convolutional Network ◽

Subjective Score ◽

Model Training

An improved fully convolutional network based on post-processing with global variance (GV) equalization and noise-aware training (PN-FCN) for speech enhancement model is proposed. It aims at reducing the complexity of the speech improvement system, and it solves overly smooth speech signal spectrogram problem and poor generalization capability. The PN-FCN is fed with the noisy speech samples augmented with an estimate of the noise. In this way, the PN-FCN uses additional online noise information to better predict the clean speech. Besides, PN-FCN uses the global variance information, which improve the subjective score in a voice conversion task. Finally, the proposed framework adopts FCN, and the number of parameters is one-seventh of deep neural network (DNN). Results of experiments on the Valentini-Botinhaos dataset demonstrate that the proposed framework achieves improvements in both denoising effect and model training speed.

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MNCN: A Multilingual Ngram-Based Convolutional Network for Aspect Category Detection in Online Reviews

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v33i01.33016441 ◽

2019 ◽

Vol 33 ◽

pp. 6441-6448 ◽

Cited By ~ 1

Author(s):

Erfan Ghadery ◽

Sajad Movahedi ◽

Heshaam Faili ◽

Azadeh Shakery

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Sentiment Analysis ◽

English Language ◽

Online Reviews ◽

The Internet ◽

Word Embeddings ◽

Convolutional Network ◽

Significant Growth ◽

Multiple Languages

The advent of the Internet has caused a significant growth in the number of opinions expressed about products or services on e-commerce websites. Aspect category detection, which is one of the challenging subtasks of aspect-based sentiment analysis, deals with categorizing a given review sentence into a set of predefined categories. Most of the research efforts in this field are devoted to English language reviews, while there are a large number of reviews in other languages that are left unexplored. In this paper, we propose a multilingual method to perform aspect category detection on reviews in different languages, which makes use of a deep convolutional neural network with multilingual word embeddings. To the best of our knowledge, our method is the first attempt at performing aspect category detection on multiple languages simultaneously. Empirical results on the multilingual dataset provided by SemEval workshop demonstrate the effectiveness of the proposed method1.

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