Fast Learning Through Deep Multi-Net CNN Model For Violence Recognition In Video Surveillance

2020 ◽  
Author(s):  
Aqib Mumtaz ◽  
Allah Bux Sargano ◽  
Zulfiqar Habib
Keyword(s):  
Deep Learning ◽  
Activity Recognition ◽  
Research Work ◽  
Superior Performance ◽  
Data Sets ◽  
Camera Motion ◽  
Learning Capability ◽  
Effective Learning ◽  
Fast Learning ◽  
Violence Detection

Abstract The violence detection is mostly achieved through handcrafted feature descriptors, while some researchers have also employed deep learning-based representation models for violent activity recognition. Deep learning-based models have achieved encouraging results for fight activity recognition on benchmark data sets such as hockey and movies. However, these models have limitations in learning discriminating features for violence activity classification with abrupt camera motion. This research work investigated deep representation models using transfer learning for handling the issue of abrupt camera motion. Consequently, a novel deep multi-net (DMN) architecture based on AlexNet and GoogleNet is proposed for violence detection in videos. AlexNet and GoogleNet are top-ranked pre-trained models for image classification with distinct pre-learnt potential features. The fusion of these models can yield superior performance. The proposed DMN unleashed the integrated potential by concurrently coalescing both networks. The results confirmed that DMN outperformed state-of-the-art methods by learning finest discriminating features and achieved 99.82% and 100% accuracy on hockey and movies data sets, respectively. Moreover, DMN has faster learning capability i.e. 1.33 and 2.28 times faster than AlexNet and GoogleNet, which makes it an effective learning architecture on images and videos.

Helicopter Motion Control Using a General Regression Neural Network

2011 ◽  
pp. 41-68 ◽  
Author(s):  
T. G.B. Amaral ◽  
M. M. Crisostomo ◽  
V. Fernao Pires
Keyword(s):  
Neural Network ◽  
Clustering Algorithm ◽  
Training Data ◽  
General Regression Neural Network ◽  
Parallel Structure ◽  
Data Sets ◽  
Learning Capability ◽  
Fast Learning ◽  
Control Flight ◽  
General Regression

This chapter describes the application of a general regression neural network (GRNN) to control the flight of a helicopter. This GRNN is an adaptive network that provides estimates of continuous variables and is a one-pass learning algorithm with a highly parallel structure. Even with sparse data in a multidimensional measurement space, the algorithm provides smooth transitions from one observed value to another. An important reason for using the GRNN as a controller is the fast learning capability and its non-iterative process. The disadvantage of this neural network is the amount of computation required to produce an estimate, which can become large if many training instances are gathered. To overcome this problem, it is described as a clustering algorithm to produce representative exemplars from a group of training instances that are close to one another reducing the computation amount to obtain an estimate. The reduction of training data used by the GRNN can make it possible to separate the obtained representative exemplars, for example, in two data sets for the coarse and fine control. Experiments are performed to determine the degradation of the performance of the clustering algorithm with less training data. In the control flight system, data training is also reduced to obtain faster controllers, maintaining the desired performance.

scholarly journals Automatic Reclaimed Wafer Classification Using Deep Learning Neural Networks

Symmetry ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 705
Author(s):  
Po-Chou Shih ◽  
Chun-Chin Hsu ◽  
Fang-Chih Tien
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Large Scale ◽  
Manufacturing Industry ◽  
Superior Performance ◽  
Asymmetric Distribution ◽  
Learning Capability ◽  
Competitive Edge ◽  
Proper Design ◽  
The Cost

Silicon wafer is the most crucial material in the semiconductor manufacturing industry. Owing to limited resources, the reclamation of monitor and dummy wafers for reuse can dramatically lower the cost, and become a competitive edge in this industry. However, defects such as void, scratches, particles, and contamination are found on the surfaces of the reclaimed wafers. Most of the reclaimed wafers with the asymmetric distribution of the defects, known as the “good (G)” reclaimed wafers, can be re-polished if their defects are not irreversible and if their thicknesses are sufficient for re-polishing. Currently, the “no good (NG)” reclaimed wafers must be first screened by experienced human inspectors to determine their re-usability through defect mapping. This screening task is tedious, time-consuming, and unreliable. This study presents a deep-learning-based reclaimed wafers defect classification approach. Three neural networks, multilayer perceptron (MLP), convolutional neural network (CNN) and Residual Network (ResNet), are adopted and compared for classification. These networks analyze the pattern of defect mapping and determine not only the reclaimed wafers are suitable for re-polishing but also where the defect categories belong. The open source TensorFlow library was used to train the MLP, CNN, and ResNet networks using collected wafer images as input data. Based on the experimental results, we found that the system applying CNN networks with a proper design of kernels and structures gave fast and superior performance in identifying defective wafers owing to its deep learning capability, and the ResNet averagely exhibited excellent accuracy, while the large-scale MLP networks also acquired good results with proper network structures.

scholarly journals A Survey of Deep Learning-Based Human Activity Recognition in Radar

2019 ◽  
Vol 11 (9) ◽  
pp. 1068 ◽  
Author(s):  
Xinyu Li ◽  
Yuan He ◽  
Xiaojun Jing
Keyword(s):  
Deep Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Health Assessment ◽  
Human Activity Recognition ◽  
Superior Performance ◽  
Learning Approaches ◽  
The Difference ◽  
High Level ◽  
2D And 3D

Radar, as one of the sensors for human activity recognition (HAR), has unique characteristics such as privacy protection and contactless sensing. Radar-based HAR has been applied in many fields such as human–computer interaction, smart surveillance and health assessment. Conventional machine learning approaches rely on heuristic hand-crafted feature extraction, and their generalization capability is limited. Additionally, extracting features manually is time–consuming and inefficient. Deep learning acts as a hierarchical approach to learn high-level features automatically and has achieved superior performance for HAR. This paper surveys deep learning based HAR in radar from three aspects: deep learning techniques, radar systems, and deep learning for radar-based HAR. Especially, we elaborate deep learning approaches designed for activity recognition in radar according to the dimension of radar returns (i.e., 1D, 2D and 3D echoes). Due to the difference of echo forms, corresponding deep learning approaches are different to fully exploit motion information. Experimental results have demonstrated the feasibility of applying deep learning for radar-based HAR in 1D, 2D and 3D echoes. Finally, we address some current research considerations and future opportunities.

Incorporating heterogeneous information in deep learning with informative meta-paths for community recommendations

2021 ◽  
pp. 016555152110474
Author(s):  
Weiwei Deng ◽  
Wei Du ◽  
Cong Han
Keyword(s):  
Deep Learning ◽  
Domain Knowledge ◽  
Superior Performance ◽  
Data Sets ◽  
Heterogeneous Information ◽  
Heterogeneous Information Networks ◽  
Communities Of Interest ◽  
Meta Path ◽  
Heterogeneous Interactions ◽  
Deep Learning Model

Communities of interest promote knowledge sharing and discovery in social network platforms. However, platform users face difficulties of finding suitable communities, given their increasing number. Although recommendations have been proposed to help users find communities of interest, these methods ignore or exclude heterogeneous interactions between users and communities. In addition, widely used meta-paths help capture the complex semantic relation among entities but heavily rely on domain knowledge. In this study, we propose a novel recommendation model based on informative meta-path discovery in heterogeneous information networks and deep learning. Users, communities, relevant items and their relations are considered as entities in a heterogeneous information network, from where informative meta-paths are extracted on the basis of information theory to measure user-community similarities. Finally, similarities are incorporated in a deep learning model to predict whether target users join candidate communities. The proposed recommendation model is evaluated and compared against baseline methods using two data sets. Results demonstrate the superior performance of the present model in terms of precision, recall and F score.

scholarly journals Adam Deep Learning With SOM for Human Sentiment Classification

2019 ◽  
Vol 10 (3) ◽  
pp. 92-116 ◽  
Author(s):  
Md. Nawab Yousuf Ali ◽  
Md. Golam Sarowar ◽  
Md. Lizur Rahman ◽  
Jyotismita Chaki ◽  
Nilanjan Dey ◽  
...  
Keyword(s):  
Deep Learning ◽  
Social Network ◽  
Language Processing ◽  
Research Work ◽  
Principal Component ◽  
Data Sets ◽  
Self Organizing Map ◽  
Significant Information ◽  
Social Network Data ◽  
Traditional Natural

Nowadays, with the improvement in communication through social network services, a massive amount of data is being generated from user's perceptions, emotions, posts, comments, reactions, etc., and extracting significant information from those massive data, like sentiment, has become one of the complex and convoluted tasks. On other hand, traditional Natural Language Processing (NLP) approaches are less feasible to be applied and therefore, this research work proposes an approach by integrating unsupervised machine learning (Self-Organizing Map), dimensionality reduction (Principal Component Analysis) and computational classification (Adam Deep Learning) to overcome the problem. Moreover, for further clarification, a comparative study between various well known approaches and the proposed approach was conducted. The proposed approach was also used in different sizes of social network data sets to verify its superior efficient and feasibility, mainly in the case of Big Data. Overall, the experiments and their analysis suggest that the proposed approach is very promissing.

scholarly journals Evaluation of automated cephalometric analysis based on the latest deep learning method

2021 ◽  
Author(s):  
Hye-Won Hwang ◽  
Jun-Ho Moon ◽  
Min-Gyu Kim ◽  
Richard E. Donatelli ◽  
Shin-Jae Lee
Keyword(s):  
Deep Learning ◽  
Test Data ◽  
Training Data ◽  
Superior Performance ◽  
Cephalometric Analysis ◽  
Data Sets ◽  
Learning Method ◽  
Test Results ◽  
Classification Rate ◽  
Data Set

ABSTRACT Objectives To compare an automated cephalometric analysis based on the latest deep learning method of automatically identifying cephalometric landmarks (AI) with previously published AI according to the test style of the worldwide AI challenges at the International Symposium on Biomedical Imaging conferences held by the Institute of Electrical and Electronics Engineers (IEEE ISBI). Materials and Methods This latest AI was developed by using a total of 1983 cephalograms as training data. In the training procedures, a modification of a contemporary deep learning method, YOLO version 3 algorithm, was applied. Test data consisted of 200 cephalograms. To follow the same test style of the AI challenges at IEEE ISBI, a human examiner manually identified the IEEE ISBI-designated 19 cephalometric landmarks, both in training and test data sets, which were used as references for comparison. Then, the latest AI and another human examiner independently detected the same landmarks in the test data set. The test results were compared by the measures that appeared at IEEE ISBI: the success detection rate (SDR) and the success classification rates (SCR). Results SDR of the latest AI in the 2-mm range was 75.5% and SCR was 81.5%. These were greater than any other previous AIs. Compared to the human examiners, AI showed a superior success classification rate in some cephalometric analysis measures. Conclusions This latest AI seems to have superior performance compared to previous AI methods. It also seems to demonstrate cephalometric analysis comparable to human examiners.

A Survey of Network Embedding for Drug Analysis and Prediction

2020 ◽  
Vol 21 ◽  
Author(s):  
Zhixian Liu ◽  
Qingfeng Chen ◽  
Wei Lan ◽  
Jiahai Liang ◽  
Yiping Pheobe Chen ◽  
...  
Keyword(s):  
Deep Learning ◽  
Protein Function ◽  
Dimensional Space ◽  
Auxiliary Information ◽  
Matrix Decomposition ◽  
Drug Analysis ◽  
Machine Learning Algorithms ◽  
Superior Performance ◽  
Network Embedding ◽  
Similarity Estimation

: Traditional network-based computational methods have shown good results in drug analysis and prediction. However, these methods are time consuming and lack universality, and it is difficult to exploit the auxiliary information of nodes and edges. Network embedding provides a promising way for alleviating the above problems by transforming network into a low-dimensional space while preserving network structure and auxiliary information. This thus facilitates the application of machine learning algorithms for subsequent processing. Network embedding has been introduced into drug analysis and prediction in the last few years, and has shown superior performance over traditional methods. However, there is no systematic review of this issue. This article offers a comprehensive survey of the primary network embedding methods and their applications in drug analysis and prediction. The network embedding technologies applied in homogeneous network and heterogeneous network are investigated and compared, including matrix decomposition, random walk, and deep learning. Especially, the Graph neural network (GNN) methods in deep learning are highlighted. Further, the applications of network embedding in drug similarity estimation, drug-target interaction prediction, adverse drug reactions prediction, protein function and therapeutic peptides prediction are discussed. Several future potential research directions are also discussed.

Deep Learning Trends for Video Based Activity Recognition: A Survey

2018 ◽  
Vol 8 (3) ◽  
pp. 165-171 ◽  
Author(s):  
Chandni ◽  
Alok Kumar Singh Kushwaha ◽  
Jagwinder Kaur Dhillon
Keyword(s):  
Deep Learning ◽  
Activity Recognition

Human Activity Recognition using Fourier Transform Inspired Deep Learning Combination Model

2019 ◽  
Vol 9 (1) ◽  
pp. 16-31
Author(s):  
Kyungkoo Jun
Keyword(s):  
Fourier Transform ◽  
Deep Learning ◽  
Short Term Memory ◽  
Window Size ◽  
Sensor Data ◽  
Data Sets ◽  
Data Set ◽  
Proposed Model ◽  
Testing Data ◽  
Labeling Scheme

Background & Objective: This paper proposes a Fourier transform inspired method to classify human activities from time series sensor data. Methods: Our method begins by decomposing 1D input signal into 2D patterns, which is motivated by the Fourier conversion. The decomposition is helped by Long Short-Term Memory (LSTM) which captures the temporal dependency from the signal and then produces encoded sequences. The sequences, once arranged into the 2D array, can represent the fingerprints of the signals. The benefit of such transformation is that we can exploit the recent advances of the deep learning models for the image classification such as Convolutional Neural Network (CNN). Results: The proposed model, as a result, is the combination of LSTM and CNN. We evaluate the model over two data sets. For the first data set, which is more standardized than the other, our model outperforms previous works or at least equal. In the case of the second data set, we devise the schemes to generate training and testing data by changing the parameters of the window size, the sliding size, and the labeling scheme. Conclusion: The evaluation results show that the accuracy is over 95% for some cases. We also analyze the effect of the parameters on the performance.

scholarly journals Deep convolutional neural networks for cardiovascular vulnerable plaque detection

2019 ◽  
Vol 277 ◽  
pp. 02024 ◽  
Author(s):  
Lincan Li ◽  
Tong Jia ◽  
Tianqi Meng ◽  
Yizhe Liu
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Vulnerable Plaque ◽  
Recall Rate ◽  
Superior Performance ◽  
Learning Approaches ◽  
Deep Convolutional Neural Networks ◽  
Vulnerable Plaques ◽  
Plaque Detection

In this paper, an accurate two-stage deep learning method is proposed to detect vulnerable plaques in ultrasonic images of cardiovascular. Firstly, a Fully Convonutional Neural Network (FCN) named U-Net is used to segment the original Intravascular Optical Coherence Tomography (IVOCT) cardiovascular images. We experiment on different threshold values to find the best threshold for removing noise and background in the original images. Secondly, a modified Faster RCNN is adopted to do precise detection. The modified Faster R-CNN utilize six-scale anchors (122,162,322,642,1282,2562) instead of the conventional one scale or three scale approaches. First, we present three problems in cardiovascular vulnerable plaque diagnosis, then we demonstrate how our method solve these problems. The proposed method in this paper apply deep convolutional neural networks to the whole diagnostic procedure. Test results show the Recall rate, Precision rate, IoU (Intersection-over-Union) rate and Total score are 0.94, 0.885, 0.913 and 0.913 respectively, higher than the 1st team of CCCV2017 Cardiovascular OCT Vulnerable Plaque Detection Challenge. AP of the designed Faster RCNN is 83.4%, higher than conventional approaches which use one-scale or three-scale anchors. These results demonstrate the superior performance of our proposed method and the power of deep learning approaches in diagnose cardiovascular vulnerable plaques.

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