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检索条件"任意字段=Proceedings of the 2019 2nd International Conference on Signal Processing and Machine Learning"

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Active learning for spam email classification 2

Active learning for spam email classification

引用

2nd international conference on Algorithms, Computing and Artificial Intelligence, ACAI 2019

作者： Chen, Zheng Tao, Ruiwen Wu, Xiaoyang Wei, Zhimin Luo, Xiao School of Information and Software Engineering University of Electronic Science and Technology of China China National Key Laboratory of Science and Technology on Blind Signal Processing Chengdu Sichuan China

ISBN: (纸本)9781450372619

Deep learning has yielded state-of-the-art performance on text classification tasks. In this paper, a new neural network based on Long-Short-Term-Memory model is applied to classify spam emails. Using deep learning method to classify spam emails requires large amounts of labeled data. To solve this problem, active learning method is used to reduce labeling cost and increase model adaptability. In this paper, it is found that the new model performs better than standard CNNs and RNNs on email classification task, and active learning methods can match state-of-the-art performance with just 10% of the labeled data. © 2019 ACM.

关键词： Long short-term memory

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DMIP 2019 - proceedings of 2019 2nd international conference on Digital Medicine and Image processing

DMIP 2019 - Proceedings of 2019 2nd International Conference...

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8th international Symposium on Highly-Efficient Accelerators and Reconfigurable Technologies, HEART 2017

ISBN: (纸本)9781450376983

The proceedings contain 10 papers. The topics discussed include: segmentation of vestibular schwannoma from multiparametric magnetic resonance images using convolutional neural network;low dose brain CT, comparative study with brain post processing algorithm;predicting severity of autism spectrum disorder based on multi-center multi-modality data;evaluation of liver phantom for testing of the detectability multimodal for hepatocellular carcinoma;an image segmentation method based on peak-valley principle and K-means algorithm;on the radon transform and linear transformations of images;solar cell defect recognition based on orthogonal learning strategy;ultrasound tongue image classification using transfer learning;a lightweight channel-spatial attention network for real-time image de-raining;and towards tomography with random orientation.

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An Acoustic signal Identification Method Based on Convolutional Neural Networks 2

An Acoustic Signal Identification Method Based on Convolutio...

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2nd international conference on Safety Produce Informatization, IICSPI 2019

作者： Chen, Guorong Yao, Liu He, Hongli Jie, Li Min, Gao Hong, Ren Chongqing University of Science and Technology School of Intelligent Technology and Engineering Chongqing China

ISBN: (纸本)9781728145662

Acoustic signal Identification has become an important subject in the field of machine perception in recent years. It has achieved good results in application scenarios such as voice recognition, and it still has low precision in other Acoustic signal recognition applications. Therefore, this paper proposes an acoustic signal recognition model based on convolutional neural network to improve the recognition accuracy. In this model, the first problem to be solved is the processing of acoustic source data. The model converts acoustic signals such as barking dogs, crying babies, waves and rain into one-dimensional spectral signals by using Fourier transform, and then inputs the data into one-dimensional CNN for training, and finally obtains the classification accuracy of ten categories of acoustic signals. The classification accuracy of this model CNN classifier is 69 %. In addition, this paper adds the pipeline micro-leakage data collected from actual engineering projects to the CNN model, and obtains better identification results. In general, this model outperform others. © 2019 IEEE.

关键词： Convolution

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The classification of facial expressions with multi-cultural backgrounds: an event-related fMRI study 2

The classification of facial expressions with multi-cultural...

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2nd international conference on Image and Video processing, and Artificial Intelligence (IPVAI)

作者： Song, Sutao Liu, Chunyu Huang, Lijie Cao, Zhiyuan Zhang, Jiacai Wei, Shuhua Dong, Jimei Univ Jinan Sch Educ & Psychol Jinan 250022 Peoples R China Beijing Normal Univ Coll Informat Sci & Technol Beijing 100875 Peoples R China Beijing Normal Univ State Key Lab Cognit Neurosci & Learning Beijing 100875 Peoples R China

ISBN: (数字)9781510634107

ISBN: (纸本)9781510634107

Expression recognition is important for our social interaction and communications, but the role of face-selective regions in discriminating various facial expressions remain unclear, especially when the expressions came from multi-cultural backgrounds. In this study, 800 facial expressions collected from 5 different facial expression databases with western or eastern cultural backgrounds were shown to the subjects in a slow event-related fMRI experiment. The subjects were instructed to indicate the category of facial expressions (happy, disgust, angry or neutral) by pressing different buttons. One multivariate pattern analysis method, support vector machine was trained to predict the categories of facial expressions. Results showed that: (1) the face selective regions differed in their ability for expression decoding, but a similar pattern was observed, with a predominance to classify facial expressions with opposite valence, i.e. happy vs. fear and happy vs. disgust. Besides,angry vs. disgust and happy vs. neutral achieved the lowest results. (2) the accuracies of facial expression classification cross-databases were as high as the accuracy of the generalization across runs within-database. These results provided evidence for the consistency of the representation of facial expressions in human brain with different culture backgrounds.

关键词： culture expression recognition emotion pattern classification event-related fMRI multi-voxel pattern analysis

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A Deep learning Approach for SAR Image Fusion 2

A Deep Learning Approach for SAR Image Fusion

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2nd international conference on Intelligent Computing, Instrumentation and Control Technologies, ICICICT 2019

作者： Rout, Mantnay Nahak, Siddheswar Priyadarshinee, Subhashree Mohapatra, Priyanka Sa, Kodanda Dhar Dash, DIllip Electronics and Telecommunication Engineering Indira Gandhi Institute of Technology Odisha India

ISBN: (纸本)9781728102832

Image Fusion is an application of digital image processing. Image Fusion is a phenomenon of amalgamating the substantial features from similar pair of images into a single image, where the fused image will be of superior quality than either of the source images. This research work proposes a Pixel level Deep learning method using a 3-Channel convolutional neural network to fuse two multi focus Synthetic Aperture Radar (SAR) images obtain a high definition or high quality fused image. As the environment is in static condition and the radar is in floating, the radar imagery sensor captures source image at different time stamps. Size reduction of the image is performed before the fusion procedure in order to highly reduce the computational time and make the method more immune to noise. In the proposed method, source images are decomposed into pixels using the deep learning framework. After feature extraction, appropriate weights are assigned to all pixels. Then averaging and max pooling of pixel values of both the source images are done to get the resultant features of the fused image. Then smoothening filter is used to minimize noise in the fused image. Experimental results show that the proposed fusion technique demonstrates better PSNR value and less computational time. © 2019 IEEE.

关键词： signal to noise ratio

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AR food changer using deep learning and cross-modal effects 2

AR food changer using deep learning and cross-modal effects

引用

2nd IEEE international conference on Artificial Intelligence and Virtual Reality (AIVR)

作者： Ueda, Junya Okajima, Katsunori Yokohama Natl Univ Yokohama Kanagawa Japan

ISBN: (纸本)9781728156040

We propose an AR application that enables us to change the appearance of food without AR markers by applying machine learning and image processing. Modifying the appearance of real food is a difficult task because the shape of the food is atypical and deforms while eating. Therefore, we developed a real-time object region extraction method that combines two approaches in a complementary manner to extract food regions with high accuracy and stability. These approaches are based on color and edge information processing with a deep learning module trained with a small amount of data. Besides, we implemented some novel methods to improve the accuracy and reliability of the system. Then, we experimented and the results show that the taste and oral texture were affected by visual textures. Our application can change not only the appearance in real-time but also the taste and texture of actual real food. Therefore, in conclusion, our application can be virtually termed as an "AR food changer".

关键词： Markerless AR Deep learning Object detection Color Food modification Disguised food Cross-modality

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A Roadmap to Deep learning: A State-of-the-Art Step Towards machine learning 2nd

A Roadmap to Deep Learning: A State-of-the-Art Step Towards ...

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2nd international conference on Advanced Informatics for Computing Research (ICAICR)

作者： Garg, Dweepna Goel, Parth Kandaswamy, Gokulnath Ganatra, Amit Kotecha, Ketan Charotar Univ Sci & Technol Devang Patel Inst Adv Technol & Res Anand Gujarat India Lovely Profess Univ Lovely Fac Technol & Sci Phagwara Punjab India Symbiosis Int Univ Fac Engn Pune Maharashtra India

ISBN: (纸本)9789811331404;9789811331398

Deep learning is a new era of machine learning and belonging to the area of artificial intelligence. It has tried to mimic the working of the way the human brain does. The models of deep learning have the capability to deal with high dimensional data and perform the complicated tasks in an accurate manner with the use of graphical processing unit (GPU). Significant performance is observed to analyze images, videos, text and speech. This paper deals with the detailed comparison of various deep learning models and the area in which these various deep learning models can be applied. We also present the comparison of various deep networks of classification. The paper also describes deep learning libraries along with the platform and interface in which they can be used. The accuracy is evaluated with respect to various machine learning and deep learning models on the MNIST dataset. The evaluation shows classification on deep learning model is far better than a machine learning model.

关键词： Deep learning Deep learning models Deep learning libraries MNIST dataset

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Application and optimization of intrusion detection system based on Artificial Intelligence

Application and optimization of intrusion detection system b...

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IEEE international conference on Civil Aviation Safety and Information Technology (ICCASIT)

作者： Kaidi Zhu Xiangwen Wu Sun Yu Da Huo Rui Li Xikai Pei The Second Research Institute CAAC Chengdu China The Hulun Buir Branch of Inner Mongolia Autonomous Region Civil Airports Group Co. Ltd Hulunbeir China North China Regionnal Air Traffic Management Bureau of CAAC Beijing China Air Traffic Management Bureau of China Civilaviation Administration Beijing China

ISBN: (数字)9781728199481

ISBN: (纸本)9781728199498

The traditional perimeter intrusion detection system will be faced more challenges in the new era, such as high-accuracy positioning, low energy dependence, high environmental robustness and so on. This paper introduces a new type of artificial intelligence intrusion detection technology. The RF sensor chip is embedded in the detection cable, and the radio frequency electromagnetic environment is formed in space through the sending and receiving of radio frequency signal, which is used to sense the surrounding environment to detect electromagnetic field. Using advanced space-time signal processing technology and machine learning technology, the influence of environmental interference on the system is effectively eliminated, and the intrusion detection system based on artificial intelligence algorithm has lower false alarm rate and higher environmental robustness.

关键词： Radio frequency Intrusion detection signal processing algorithms signal processing Robustness Safety Optimization

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HOG, LBP and SVM based Traffic Density Estimation at Intersection 2

HOG, LBP and SVM based Traffic Density Estimation at Interse...

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2nd IEEE-Pune-Section Annual international conference (IEEE PuneCon)

作者： Prasad, Devashish Kapadni, Kshitij Gadpal, Ayan Visave, Manish Sultanpure, Kavita Pune Inst Comp Technol Dept Informat Technol Pune Maharashtra India

ISBN: (纸本)9781728119243

Increased amount of vehicular traffic on roads is a significant issue. High amount of vehicular traffic creates traffic congestion, unwanted delays, pollution, money loss, health issues, accidents, emergency vehicle passage and traffic violations that ends up in the decline in productivity. In peak hours, the issues become even worse. Traditional traffic management and control systems fail to tackle this problem. Currently, the traffic lights at intersections aren't adaptive and have fixed time delays. There's a necessity of an optimized and sensible control system which would enhance the efficiency of traffic flow. Smart traffic systems perform estimation of traffic density and create the traffic lights modification consistent with the quantity of traffic. We tend to propose an efficient way to estimate the traffic density on intersection using image processing and machine learning techniques in real time. The proposed methodology takes pictures of traffic at junction to estimate the traffic density. We use Histogram of Oriented Gradients (HOG), Local Binary Patterns (LBP) and Support Vector machine (SVM) based approach for traffic density estimation. The strategy is computationally inexpensive and might run efficiently on raspberry pi board.

关键词： Image processing Computer vision Traffic estimation Traffic density HOG Histogram of Oriented Gradients LBP Local Binary Patterns Smart traffic junction Traffic intersection Intelligent traffic control system machine learning SVM Support Vector machine

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Overview of Side Channel Cipher Analysis Based on Deep learning 2

Overview of Side Channel Cipher Analysis Based on Deep Learn...

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2019 2nd international conference on Advanced Algorithms and Control Engineering, ICAACE 2019

作者： Song, Shijie Chen, Kaiyan Zhang, Yang Virtual Simulation Department HePing West Road ShiJiaZhuang City XinHua District Hebei Prov China

According to the widespread use of embedded cryptographic devices, one of the major security threats, side channel attack and protection research, has grown rapidly in the last decade. Along with the popularity of machine learning and deep learning, artificial intelligence technology combined with side channel technology has gradually emerged, which has achieved quite good processing results. Due to the popularity of deep learning in recent years, it is indeed to complete the work with making an overview of side channel analysis based on deep learning. So in order to perfect the theory, this paper begins with the basic theory of the side channel attack. Combined with common models, the methodological analysis of deep learning side channel analysis is summarized and the existing research results are introduced next. Finally, the paper looks forward to the future deep learning side channel technology. © 2019 IOP Publishing Ltd. All rights reserved.

关键词： Deep learning

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