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检索条件"任意字段=3rd International Conference on Signal Processing and Machine Learning, SPML 2020"

共 729 条记录，以下是531-540 订阅

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Stock Forecasting Using Natural Language and Recurrent Network

Stock Forecasting Using Natural Language and Recurrent Netwo...

引用

r Engineering: machine learning and Internet of Things (ICETCE), 2020 3rd international conference on

作者： Dileep Kumar Department of Aerospace Engineering Indian Institute of Science Bengaluru India

ISBN: (数字)9781728116839

ISBN: (纸本)9781728116846

The factors affecting the stock price of a company include financial aspects, government policies, international policies, emerging news, which affects the investors and hence the market. Critical technical indicators are taken into account while proposing multi-level machine learning in this paper. Firstly, the sentiments of news articles analyzed using lexicon-based Natural Language processing (NLP). The lexicon used is exclusively based on financial, social media. Each news article analyzed for aspect extraction and aspect-based sentiment analysis. Along with market news sentiments and the company's historical financial data, feature vector also includes critical technical analyses based on trend, momentum, and volatility. The future stock price movements forecast utilizes Long Short-Term Memory-Recursive Neural Network (LSTM-RNN) model. The results indicate that the discussed model performs well without requiring any data preprocessing, cycle analyses, or seasonality testing.

关键词： Forecasting Autoregressive processes Market research Analytical models Stock markets machine learning Data models

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Skin Cancer Classification Model Based on VGG 19 and Transfer learning

Skin Cancer Classification Model Based on VGG 19 and Transfe...

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international conference on signal processing and Information Security (ICSPIS)

作者： Nour Abuared Alavikunhu Panthakkan Mina Al-Saad Saad Ali Amin Wathiq Mansoor College of Engineering and IT University of Dubai Dubai UAE

ISBN: (数字)9781728189987

ISBN: (纸本)9781728189994

Skin cancer is a concerning health issue with yearly increasing numbers. Detecting and classifying cancer type is problematic, especially since patients have to undergo several diagnosis over lengthy periods of time, which hinders early treatment and survival chances. With the aid of digital image processing, features can be extracted to identify skin cancer and its different types. Convolutional Neural Networks (CNNs) recently emerged as powerful autonomous feature extractors, and they have high potential to achieve high accuracy with skin cancer diagnosis. In this paper, two cancer types in addition to one non-cancer type taken from Human Against machine (HAM10000) dataset are classified using CNN model based on VGG 19 and Transfer learning technique. The training strategy is explained, tested, and evaluated by calculating the network's overall accuracy and loss.

关键词： Training Transfer learning Information security signal processing Feature extraction Skin Skin cancer

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Exploration of a Balanced Reference Corpus with a Wide Variety of Text Mining Tools 20

Exploration of a Balanced Reference Corpus with a Wide Varie...

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Proceedings of the 2020 3rd international conference on Algorithms, Computing and Artificial Intelligence

作者： Nicolas Turenne Bokai Xu Xinyue Li Xindi Xu Hongyu Liu Xiaolin Zhu BNU-HKBU United International College China

ISBN: (纸本)9781450388115

To compare various techniques, the same platform is generally used into which the user will import a text dataset. Another approach uses an evaluation based on a gold standard for a specific task, but a balanced common language corpus is not often used. We choose the Corpus of Contemporary American English Corpus (COCA) as a balanced reference corpus, and split this corpus into categories, such as topics and genres, to apply families of feature extraction and machine learning algorithms. We found that the Stanford CoreNLP method was faster and more accurate than the NLTK method, and was more reliable and easier to understand. The results of clustering show that a higher modularity influences interpretation. For genre and topic classification, all techniques achieved a relatively high score, though these were below the state-of-the-art scores from challenge text datasets. Naïve Bayes outperformed the other alternatives. We hope that balanced corpora from a variety of different vernacular (or low-resource) languages can be used as references to determine the efficiency of the wide diversity of state-of-the-art text mining tools.

关键词： natural language processing

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Practical Issues in Contingency Planning for UAVs with Engine Failures 3

Practical Issues in Contingency Planning for UAVs with Engin...

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3rd international conference on Vision, Image and signal processing (ICVISP)

作者： Ayhan, Bulent Kwan, Chiman Signal Proc Inc 9605 Med Ctr Dr Rockville MD 20850 USA

ISBN: (纸本)9781450376259

Unmanned Air Vehicles (UAV), also known as Unmanned Air Systems (UAS), are gaining more attention in recent years. Some potential commercial applications with UAVs may include small cargo transport, search and rescue operations, drought and pest monitoring, etc. It is well-known that UAVs are less reliable as compared to manned aircraft. This is probably one of the consequential reasons that Federal Aviation Administration (FAA) is hesitant to open up the national airspace (NAS) and imposes tight restrictions to UAVs. Reliability of UAVs can be improved using engines and equipment with high quality and fault diagnostic algorithms using machine learning and artificial intelligence techniques, and robust and fault tolerant controllers. Despite the above measures, engine and equipment malfunctions may still appear in various applications. In this paper, we summarize some recent research results by us with respect to engine failures encountered in UAVs. Due to engine failure, there is limited hanging time and the mishap UAV needs to land preferably in an unpopulated area. In particular, we explicitly address some practical issues related to engine failures.

关键词： Uavs Contingency Planning Engine Failures

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Image Dust Storm Synthetic Method Based on Optical Model 3rd

Image Dust Storm Synthetic Method Based on Optical Model

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3rd international conference on machine learning for Cyber Security, ML4CS 2020

作者： Huang, Jiayan Li, Zuoyong Wang, Chuansheng College of Mathematics and Computer Science Fuzhou University Fuzhou350108 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou350121 China School of Computer Science and Technology Harbin University of Science and Technology Harbin150080 China

ISBN: (纸本)9783030624620

To address the problem that the dusty image dataset is small and difficult to collect, this paper presents a synthetic method for generating dusty image based on a classical optical model. The proposed method first learns the physical process of generating dusty image according to the classical optical model. Then, the transmission map is estimated and combined with the presupposed dust storm color map as inputs for obtaining a synthetic dusty image. Finally, considering the impact of image scene depth on the synthesis of dusty image, the proposed method selects an appropriate value of input parameter to obtain final synthetic dusty image. Experimental results on an image dataset captured in clear weather show that the synthetic dusty images obtained by the proposed method can be used as a good substitute for real dusty images. © 2020, Springer Nature Switzerland AG.

关键词： Storms

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Liver Tumor Segmentation of CT Image by Using Deep Fully Convolutional Network 1

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3rd international conference on machine learning for Cyber Security, ML4CS 2020

作者： Jin, Lingmin Ma, Rui Zhao, Meng Teng, Shenghua Li, Zuoyong College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao266590 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou350121 China

ISBN: (数字)9783030622237

ISBN: (纸本)9783030622220

Accurate segmentation of liver tumors is an important guarantee for the success of liver cancer surgery, where convolutional network has been a type of popular method. However, the performance of the traditional convolutional network is limited by the network depth. To improve the accuracy of liver tumor segmentation, we propose a cascaded deep fully convolutional network (DFCN) which uses ResNet as the basis network followed by side output layer in the upsampling stage to fuse multi-scale image features. For better localizing the liver tumors, the segmentation result is further refined by a fully connected conditional random field. Experimental results show that the proposed method achieves higher segmentation accuracy than several state-of-the-art methods. © 2020, Springer Nature Switzerland AG.

关键词： Convolution

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Information processing in Extended Hodgkin-Huxley Neuron Model

Information Processing in Extended Hodgkin-Huxley Neuron Mod...

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r Engineering: machine learning and Internet of Things (ICETCE), 2020 3rd international conference on

作者： M. Devi Durga Choudhary Akhil Ranjan Garg J.N.V.U. Jodhpur India

ISBN: (数字)9781728116839

ISBN: (纸本)9781728116846

Models of a variety of neurons share the same form as the Hodgkin-Huxley (HH) neuron and evidence suggests that single-compartment models can capture the key properties of in vivo and in vitro neurons. In this paper we are going to discuss about how the conductance of the excitatory and inhibitory synapse vary when the pre synaptic action potential arrived at the synapse and how this synaptic conductance affects the information transfer in the single neuron. In our model the injected current is replaced with the synaptic current in the HH model. In order to validate the impact of excitatory and inhibitory synapse in the generation of action potential, the extended HH model is examined with various synaptic inputs.

关键词： Firing Entropy Neurons Synapses Biological system modeling Mathematical model Computational modeling

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Far Point Algorithm Active Semi-supervised Clustering for Rare Category Detection 3

Far Point Algorithm Active Semi-supervised Clustering for Ra...

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3rd international conference on Vision, Image and signal processing (ICVISP)

作者： Loveland, Rohan Amdahl, Jonathan Whitman Coll Dept Comp Sci Walla WA 99362 USA New Mexico State Univ Klipsch Sch Elect & Comp Engn Las Cruces NM 88003 USA

ISBN: (纸本)9781450376259

In some data sets the number of categories (i.e. classes) that are represented is not known in advance. The process of discovering these categories can be difficult, particularly when a data set is skewed, such that the number of data points of some classes may greatly exceed those of other classes. Rare category detection algorithms address this problem by trying to present a user with at least one data point from each category, while minimizing the overall number of data points presented. We present an algorithm based on active and semi-supervised learning that finds category clusters using a query selection strategy that maximizes the distance from a set of already labeled data points to a query data point. We evaluate the algorithm's performance on artificially skewed versions of the MNIST data set as a rare category detection algorithm, investigating differences in performance due to both the effects of relative frequency and inherent class structure differences in feature space.

关键词： detection algorithms machine learning algorithms partitioning algorithms

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Phase Retrieval Method for Phase-shifting Interferometry with machine learning 2019

Phase Retrieval Method for Phase-shifting Interferometry wit...

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Proceedings of the 3rd international conference on Vision, Image and signal processing

作者： Ryosuke Ueda Hiroyuki Kudo University of Tsukuba Tsukuba Ibaraki Japan

ISBN: (纸本)9781450376259

Phase contrast imaging attracts attention in the biomedical field thanks to higher contrast than absorption contrast. However, the phase retrieval for phase-shifting interferometry (PSI) often involves the problems, e.g., the noise, stepping error and phase wrapping. In the conventional way, each issue had to be addressed individually. In this paper, we propose the machine learning based method, which uses the neural network and learns features in an end-to-end manner. The proposed method can resolve the noise, stepping error artifacts and phase wrapping, simultaneously. The results are shown by numerical simulation.

关键词： Phase Retrieval Neural Network machine learning Phase Shifting Interferometry

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A Malicious Web Page Detection Model based on SVM Algorithm: Research on the Enhancement of SVM Efficiency by Multiple machine learning Algorithms 20

A Malicious Web Page Detection Model based on SVM Algorithm:...

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Proceedings of the 2020 3rd international conference on Algorithms, Computing and Artificial Intelligence

作者： Jingbing Chen Jie Yuan Yuewei Li Yiqi Zhang Yufan Yang Ruiqi Feng Beijing University of Posts and Telecommunications China

ISBN: (纸本)9781450388115

In recent years, due to the high availability and convenience of the Internet, more and more information provides corresponding services through the Internet. As people are increasingly relying on the Internet, network security issues have become increasingly prominent, and a large number of malicious web pages have also emerged. How to achieve proactive and efficient detection of malicious web pages has become a research focus in the field of network security worldwide. This paper uses the Support Vector machine algorithm to realize autonomous learning and build the classifier; chooses the TF-IDF method to process the data, and obtains the feature matrix of the collected URL data, which is stored in the sparse matrix after normalization and standardization. To avoid the existence of relatively strong features from affecting the classification results of the classifier, the K-Means method and TruncatedSVD method are used to reduce the dimension of the data features. The linear kernel function is used for large samples, and the Gaussian kernel function is used for small samples, so that the performance of the classifier is optimal. In the training process, the grid search method is used to obtain the optimal parameters forming a complete and mature detection system. And a ten-fold cross-validation method is used to test the correct rate, recall rate, accuracy rate and F1 value of the classifier. Finally the experimental result shows the malicious web page detection model has a good reference for big data processing.

关键词： K means clustering

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