scholarly journals Image Restoration by Learning Morphological Opening-Closing Network

2020 ◽  
Vol 4 (1) ◽  
pp. 87-107
Author(s):  
Ranjan Mondal ◽  
Moni Shankar Dey ◽  
Bhabatosh Chanda
Keyword(s):  
Neural Network ◽  
Image Restoration ◽  
State Of The Art ◽  
Source Code ◽  
Back Propagation ◽  
Image Features ◽  
Main Difficulty ◽  
The Right ◽  
Right Order ◽  
Morphological Opening

AbstractMathematical morphology is a powerful tool for image processing tasks. The main difficulty in designing mathematical morphological algorithm is deciding the order of operators/filters and the corresponding structuring elements (SEs). In this work, we develop morphological network composed of alternate sequences of dilation and erosion layers, which depending on learned SEs, may form opening or closing layers. These layers in the right order along with linear combination (of their outputs) are useful in extracting image features and processing them. Structuring elements in the network are learned by back-propagation method guided by minimization of the loss function. Efficacy of the proposed network is established by applying it to two interesting image restoration problems, namely de-raining and de-hazing. Results are comparable to that of many state-of-the-art algorithms for most of the images. It is also worth mentioning that the number of network parameters to handle is much less than that of popular convolutional neural network for similar tasks. The source code can be found here https://github.com/ranjanZ/Mophological-Opening-Closing-Net

scholarly journals Deep Transfer Learning for Source Code Modeling

2020 ◽  
Vol 30 (05) ◽  
pp. 649-668
Author(s):  
Yasir Hussain ◽  
Zhiqiu Huang ◽  
Yu Zhou ◽  
Senzhang Wang
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Transfer Learning ◽  
Related Problem ◽  
State Of The Art ◽  
Source Code ◽  
Traditional Learning ◽  
Learning Models ◽  
Modeling And Analysis ◽  
F Measure

In recent years, deep learning models have shown great potential in source code modeling and analysis. Generally, deep learning-based approaches are problem-specific and data-hungry. A challenging issue of these approaches is that they require training from scratch for a different related problem. In this work, we propose a transfer learning-based approach that significantly improves the performance of deep learning-based source code models. In contrast to traditional learning paradigms, transfer learning can transfer the knowledge learned in solving one problem into another related problem. First, we present two recurrent neural network-based models RNN and GRU for the purpose of transfer learning in the domain of source code modeling. Next, via transfer learning, these pre-trained (RNN and GRU) models are used as feature extractors. Then, these extracted features are combined into attention learner for different downstream tasks. The attention learner leverages from the learned knowledge of pre-trained models and fine-tunes them for a specific downstream task. We evaluate the performance of the proposed approach with extensive experiments with the source code suggestion task. The results indicate that the proposed approach outperforms the state-of-the-art models in terms of accuracy, precision, recall and F-measure without training the models from scratch.

scholarly journals Vertical handover algorithm based on multi-attribute and neural network in heterogeneous integrated network

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Xiaonan Tan ◽  
Geng Chen ◽  
Hongyu Sun
Keyword(s):  
Neural Network ◽  
Wireless Network ◽  
Bp Neural Network ◽  
Transmission Rate ◽  
Back Propagation ◽  
Vertical Handover ◽  
Integrated Network ◽  
Neural Network Input ◽  
The Right ◽  
Handover Algorithm

Abstract A novel vertical handover algorithm based on multi-attribute and neural network for heterogeneous integrated network is proposed in this paper. The whole frame of the algorithm is constructed by setting the network environment in which we use the network resources by switching between UMTS, GPRS, WLAN, 4G, and 5G. Each network build their own three-layer BP (Back Propagation, BP) neural network model and then the maximum transmission rate, minimum delay, SINR (signal to interference and noise ratio, SINR), bit error rate, user moving speed, and packet loss rate which can affect the overall performance of the wireless network are employed as reference objects to participate in the setting of BP neural network input layer neurons and the training and learning process of subsequent neural network data. Finally, the network download rate is adopted as prediction target to evaluate performance on the five wireless networks and then the vertical handover algorithm will select the right wireless network to perform vertical handover decision. The simulation results on MATLAB platform show that the vertical handover algorithm designed in this paper has a handover success rate up to 90% and realizes efficient handover and seamless connectivity between multi-heterogeneous networks.

scholarly journals Video Person Re-ID: Fantastic Techniques and Where to Find Them (Student Abstract)

2020 ◽  
Vol 34 (10) ◽  
pp. 13893-13894
Author(s):  
Priyank Pathak ◽  
Amir Erfan Eshratifar ◽  
Michael Gormish
Keyword(s):  
Neural Network ◽  
Loss Function ◽  
State Of The Art ◽  
Source Code ◽  
Status Quo ◽  
Current Status ◽  
Neural Models ◽  
Academic Interest ◽  
Multiple Camera ◽  
The Common

The ability to identify the same person from multiple camera views without the explicit use of facial recognition is receiving commercial and academic interest. The current status-quo solutions are based on attention neural models. In this paper, we propose Attention and CL loss, which is a hybrid of center and Online Soft Mining (OSM) loss added to the attention loss on top of a temporal attention-based neural network. The proposed loss function applied with bag-of-tricks for training surpasses the state of the art on the common person Re-ID datasets, MARS and PRID 2011. Our source code is publicly available on github1.

scholarly journals Back Propagation Neural Network-Based Magnetic Resonance Imaging Image Features in Treating Intestinal Obstruction in Digestive Tract Diseases with Chengqi Decoction

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Yongfeng Li ◽  
Kaina Wang ◽  
Li Gao ◽  
Xiaojun Lu
Keyword(s):  
Neural Network ◽  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Intestinal Obstruction ◽  
Back Propagation ◽  
Block Size ◽  
Back Propagation Neural Network ◽  
Image Features ◽  
Resonance Imaging ◽  
Mri Scans

This study was to explore the adoption effect of magnetic resonance imaging (MRI) image features based on back propagation neural network (BPNN) in evaluating the curative effect of Chengqi Decoction (CD) for intestinal obstruction (ileus), so as to evaluate the clinical adoption value of this algorithm. Ninety patients with ileus were recruited, and the patients were treated with CD and underwent MRI scans of the lower abdomen. A BPNN model was fabricated and applied to segment the MRI images of patients and identify the lesion. As a result, when the overlap step was 16 and the block size was 32 × 32, the running time of the BPNN algorithm was the shortest. The segmentation accuracy was the highest if there were two hidden layer (HL) nodes, reaching 97.3%. The recognition rates of small intestinal stromal tumor (SIST), colon cancer, adhesive ileus, and volvulus of MRI images segmented by the algorithm were 91.5%, 88.33%, 90.3%, and 88.9%, respectively, which were greatly superior to those of manual interpretation ( P < 0.05 ). After the intervention of CD, the percentages of patients with ileus that were cured, markedly effective, effective, and ineffective were 65.38%, 23.16%, 5.38%, and 6.08%, respectively. The cure rate after intervention of CD (65.38%) was much higher in contrast to that before intervention (13.25%) ( P < 0.05 ). In short, CD showed a good therapeutic effect on ileus and can effectively improve the prognosis of patients. In addition, MRI images based on BPNN showed a good diagnostic effect on ileus, and it was worth applying to clinical diagnosis.

scholarly journals Towards a high robust neural network via feature matching

2021 ◽  
Author(s):  
Jian Li ◽  
Yanming Guo ◽  
Songyang Lao ◽  
Yulun Wu ◽  
Liang Bai ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Neural Networks ◽  
Feature Matching ◽  
Feature Vector ◽  
State Of The Art ◽  
Model Performance ◽  
Image Features ◽  
Classification Systems ◽  
Adversarial Attack

AbstractImage classification systems have been found vulnerable to adversarial attack, which is imperceptible to human but can easily fool deep neural networks. Recent researches indicate that regularizing the network by introducing randomness could greatly improve the model’s robustness against adversarial attack, but the randomness module would normally involve complex calculations and numerous additional parameters and seriously affect the model performance on clean data. In this paper, we propose a feature matching module to regularize the network. Specifically, our model learns a feature vector for each category and imposes additional restrictions on image features. Then, the similarity between image features and category features is used as the basis for classification. Our method does not introduce any additional network parameters than undefended model and can be easily integrated into any neural network. Experiments on the CIFAR10 and SVHN datasets highlight that our proposed module can effectively improve both clean data and perturbed data accuracy in comparison with the state-of-the-art defense methods and outperform the L2P method by 6.3$$\%$$ % , 24$$\%$$ % on clean and perturbed data, respectively, using ResNet-V2(18) architecture.

A Neural Network for Image Anomaly Detection with Deep Pyramidal Representations and Dynamic Routing

2020 ◽  
Vol 30 (10) ◽  
pp. 2050060
Author(s):  
Pankaj Mishra ◽  
Claudio Piciarelli ◽  
Gian Luca Foresti
Keyword(s):  
Neural Network ◽  
Anomaly Detection ◽  
State Of The Art ◽  
Structural Similarity ◽  
Input Image ◽  
Image Features ◽  
Anomalous Data ◽  
Scale Levels ◽  
Anomaly Detector ◽  
Better Than

Image anomaly detection is an application-driven problem where the aim is to identify novel samples, which differ significantly from the normal ones. We here propose Pyramidal Image Anomaly DEtector (PIADE), a deep reconstruction-based pyramidal approach, in which image features are extracted at different scale levels to better catch the peculiarities that could help to discriminate between normal and anomalous data. The features are dynamically routed to a reconstruction layer and anomalies can be identified by comparing the input image with its reconstruction. Unlike similar approaches, the comparison is done by using structural similarity and perceptual loss rather than trivial pixel-by-pixel comparison. The proposed method performed at par or better than the state-of-the-art methods when tested on publicly available datasets such as CIFAR10, COIL-100 and MVTec.

scholarly journals A Neural Network Based Intelligent Support Model for Program Code Completion

2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Md. Mostafizer Rahman ◽  
Yutaka Watanobe ◽  
Keita Nakamura
Keyword(s):  
Neural Network ◽  
Software Engineering ◽  
Error Detection ◽  
State Of The Art ◽  
Source Code ◽  
Experimental Results ◽  
Intelligent Support ◽  
Source Codes ◽  
Proposed Model ◽  
Code Completion

In recent years, millions of source codes are generated in different languages on a daily basis all over the world. A deep neural network-based intelligent support model for source code completion would be a great advantage in software engineering and programming education fields. Vast numbers of syntax, logical, and other critical errors that cannot be detected by normal compilers continue to exist in source codes, and the development of an intelligent evaluation methodology that does not rely on manual compilation has become essential. Even experienced programmers often find it necessary to analyze an entire program in order to find a single error and are thus being forced to waste valuable time debugging their source codes. With this point in mind, we proposed an intelligent model that is based on long short-term memory (LSTM) and combined it with an attention mechanism for source code completion. Thus, the proposed model can detect source code errors with locations and then predict the correct words. In addition, the proposed model can classify the source codes as to whether they are erroneous or not. We trained our proposed model using the source code and then evaluated the performance. All of the data used in our experiments were extracted from Aizu Online Judge (AOJ) system. The experimental results obtained show that the accuracy in terms of error detection and prediction of our proposed model approximately is 62% and source code classification accuracy is approximately 96% which outperformed a standard LSTM and other state-of-the-art models. Moreover, in comparison to state-of-the-art models, our proposed model achieved an interesting level of success in terms of error detection, prediction, and classification when applied to long source code sequences. Overall, these experimental results indicate the usefulness of our proposed model in software engineering and programming education arena.

scholarly journals Survival prediction for oral tongue cancer patients via probabilistic genetic algorithm optimized neural network models

2020 ◽  
Vol 93 (1112) ◽  
pp. 20190825
Author(s):  
Xiaoying Pan ◽  
Ting Zhang ◽  
QingPing Yang ◽  
Di Yang ◽  
Jean-Claude Rwigema ◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Patient Survival ◽  
Back Propagation ◽  
Network Models ◽  
Image Features ◽  
Survival Prediction ◽  
Reduction Algorithm ◽  
Neural Network Models ◽  
Pre Treatment

Objectives: High throughput pre-treatment imaging features may predict radiation treatment outcome and guide individualized treatment in radiotherapy (RT). Given relatively small patient sample (as compared with high dimensional imaging features), identifying potential prognostic imaging biomarkers is typically challenging. We aimed to develop robust machine learning methods for patient survival prediction using pre-treatment quantitative CT image features for a subgroup of head-and-neck cancer patients. Methods: Three neural network models, including back propagation (BP), Genetic Algorithm-Back Propagation (GA-BP), and Probabilistic Genetic Algorithm-Back Propagation (PGA-BP) neural networks were trained to simulate association between patient survival and radiomics data in radiotherapy. To evaluate the models, a subgroup of 59 head-and-neck patients with primary cancers in oral tongue area were utilized. Quantitative image features were extracted from planning CT images, a novel t-Distributed Stochastic Neighbor Embedding (t-SNE) method was used to remove irrelevant and redundant image features before fed into the network models. 80% patients were used to train the models, and remaining 20% were used for evaluation. Results: Of the three supervised machine-learning methods studied, PGA-BP yielded the best predictive performance. The reported actual patient survival interval of 30.5 ± 21.3 months, the predicted survival times were 47.3 ± 38.8, 38.5 ± 13.5 and 29.9 ± 15.3 months using the traditional PCA. Combining with the novel t-SNE dimensionality reduction algorithm, the predicted survival intervals are 35.8 ± 15.2, 32.3 ± 13.1 and 31.6 ± 15.8 months for the BP, GA-BP and PGA-BP neural network models, respectively. Conclusion: The work demonstrated that the proposed probabilistic genetic algorithm optimized neural network models, integrating with the t-SNE dimensionality reduction algorithm, achieved accurate prediction of patient survival. Advances in knowledge: The proposed PGA-BP neural network, integrating with an advanced dimensionality reduction algorithm (t-SNE), improved patient survival prediction accuracy using pre-treatment quantitative CT image features of head-and-neck cancer patients.

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