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Research on short term stock selection strategy based on machine learning

Research on short term stock selection strategy based on mac...

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international conference on Machine Learning, Big Data and Business Intelligence (MLBDBI)

作者： Yanzheng Ren School of Mechatronics Beijing Institute of Technology Beijing China

ISBN: (纸本)9781665417914

Based on theoretical analysis, this paper constructs a short-term stock selection strategy based on machine learning. The sample set is constructed based on the closing price trend of individual stocks in the last 20 trading days, and the machine learning algorithms GBDT and GBRank are used for training, and machine learning is used to automatically perform pattern recognition on the processing capabilities of high-dimensional nonlinear data. When forecasting, the former will rank stocks with higher rising probability in the next 3 trading days, and the latter will rank stocks with greater gains in the next 3 trading days. The stock selection strategy swaps positions every 3 trading days, and each time the top 10 stocks given by the equal-weight buying algorithm are used to construct an investment portfolio. The experimental results show that the short-term quantitative stock selection strategy based on the GBDT algorithm can outperform the market combination, namely the Shanghai and Shenzhen 300 Index, and has certain practicability.

关键词： Analytical models Machine learning algorithms Computational modeling Machine learning Mathematical models Data models pattern recognition

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Face Morphing Attack Detection in the Presence of Post-processed Image Sources Using Neighborhood Component Analysis and Decision Tree Classifier 3rd

Face Morphing Attack Detection in the Presence of Post-proce...

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3rd international conference on Information and Communication Technology and Applications, ICTA 2020

作者： Kenneth, Ogbuka Mary Bashir, Sulaimon Adebayo Abisoye, Opeyemi Aderiike Mohammed, Abdulmalik Danlami Department of Computer Science Federal University of Technology Minna Nigeria

ISBN: (纸本)9783030691424

Recently, Face Morphing Attack Detection (MAD) has gained a great deal of attention as criminals have started to use freely and easily available digital manipulation techniques to combine two or more subject facial images to create a new facial image that can be viewed as an accurate image of any of the individual images that constitute it. Some of these morphing tools create morphed images of high quality which pose a serious threat to existing Face recognition Systems (FRS). In the literatures, it has been identified that FRS is vulnerable to multiform morphing attacks. Based on this vulnerability, several types of research on the detection of this morph attack was conducted using several techniques. Despite the remarkable levels of MAD reported in various literature, so far no suitable solution has been found to handle post-processed images such as images modified after morphing with sharpening operation that can dramatically reduce visible artifacts of morphed photos. In this work, an approach is proposed for MAD before image post-processing and after image post-processing built on a combination of Local Binary pattern (LBP) for extraction of feature, Neighborhood Component Analysis (NCA) for selection of features and classification using K-Nearest Neighbor (KNN), Decision Tree Classifier (DTC) and Naïve Bayes (NB) classifier. The outcome gotten by training the different classifiers with feature vectors selected using the NCA algorithm improved the classification accuracy from 90% to 94%, consequently improving the general performance of the MAD. © 2020, Springer Nature Switzerland AG.

关键词： Local binary pattern

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Digital Advancement in AgriTech by Using Evolutionary Techniques 3rd

Digital Advancement in AgriTech by Using Evolutionary Techni...

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3rd international conference on Smart Computing and Informatics (SCI)

作者： Padhy, Neelmadhab Satapathy, Suresh Chandra GIET Univ Sch Engn & Technol CSE Gunupur Odishsa India KIIT Deemed Univ Bhubaneswar India

ISBN: (纸本)9789813296909;9789813296893

The Strategic decision to take machine learning and AI as a tool to enhance agriculture production is to enrich a farmer's valuable time. It has an immense opportunity to make secured crops and healthy environment in due time. In extreme weather condition and drastic change in a situation many predictive and recognition techniques will improve not only the quality of crops but also their ability to producible. In this context, we have dedicated a proposed model which will predict for a real-time environment which crop will be benefitted on producing and for any particular area what should be the environmental condition for healthy productivity. This is done by using a machine learning algorithm in seasonal trend scenario with predictive analysis and applying a pattern recognition technique for discovering cropping pattern and their evaluation.

关键词： Machine learning Predictive analysis pattern recognition

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A No-reference Image Quality Assessment Method for Real Foggy Images 20

A No-reference Image Quality Assessment Method for Real Fogg...

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3rd international conference on Artificial Intelligence and pattern recognition (AIPR)

作者： Wang, Dianwei Zhai, Jing Han, Pengfei Jiang, Jing Ren, Xincheng Qin, Yongrui Xu, Zhijie Xian Univ Posts & Telecommun Sch Commun & Informat Engn Xian 710121 Peoples R China Yanan Univ Sch Phys & Elect Informat Yanan 716000 Peoples R China Univ Huddersfield Sch Comp & Engn Huddersfield HD1 3DH W Yorkshire England

ISBN: (纸本)9781450375511

The image quality assessment results for foggy images are of great significance in the objective measurement of image quality and the design and optimization of dehazing algorithm. Initially, to address the issue that there are few no-reference evaluation algorithms for foggy image quality in real scenes, this paper proposes a no-reference quality assessment method for foggy image quality in real scenes. Firstly, we establish a real scene foggy image database and evaluate it subjectively to obtain the mean opinion score (MOS). Then, we propose a feature selection method combining correlation coefficients and union ideas, which can pick out features positively correlated with haze image quality, to simplify the features without affecting the prediction accuracy of the model. Finally, we use the support vector regression method to learn the regression mapping between features and subjective scores of the foggy images, by which we can obtain the image quality assessment results. The experimental results on the database show that the algorithm in this paper is better than other algorithms. The objective image quality evaluation results of the proposed algorithm are in good agreement with the human eye's subjective perception results. Besides, the experimental results prove that the model in this paper has better performance in predicting the quality of the image after defogging.

关键词： reference image quality assessment foggy images in real scene feature selection support vector regression

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THE EPRI GAS TURBINE DIGITAL TWIN - A PLATFORM FOR OPERATOR FOCUSED INTEGRATED DIAGNOSTICS AND PERFORMANCE FORECASTING

THE EPRI GAS TURBINE DIGITAL TWIN - A PLATFORM FOR OPERATOR ...

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ASME Turbomachinery Technical conference and Exposition (Turbo Expo) - Sustainable Energy - Accelerating the Transition by Advancing Turbine Technology

作者： Lim, Jamie Perullo, Christopher A. Milton, Joe Whitacre, Rachel Jackson, Chris Griffin, Chris Noble, David Boche, Lea Seachman, Steve Angello, Leonard Maley, Susan Lieuwen, Timothy C. Turbine Log Atlanta GA 30332 USA Chevron Pipeline & Power Houston TX USA Luminant Dallas TX USA Duke Energy Charlotte NC USA Elect Power Res Inst Charlotte NC USA

ISBN: (纸本)9780791884966

EPRI has been developing a digital twin of simple and combined cycle gas turbines over the last 5+ years to provide owners and operators with improved capabilities that typically reside in the expert domain of OEMs and 3rd party service providers. The digital twin is a digital model, a physics-based representation of the actual asset. The model is thermodynamic and is created with the intent to support 5 M&D areas: Integrate with existing M&D tools such as advanced pattern recognition (APR) Power plant performance prediction and trending such as day, week, and month ahead performance prediction for capacity and generation planning Health Monitoring and Fault Diagnostics to support asset management with additional health scores and virtual instrumentation enabled by the digital twin model Monitoring and prediction of both base and part-load performance. Many gas turbine tools have been simplified to work only at full load conditions. To be useful and to improve utilization of collected data, part-load conditions should also be considered. Outage and repair impacts, including "what-if" capability to understand and quantify potential root causes of less than expected performance improvement or recovery after outage and repairs. This paper presents current progress in creating an EPRI Digital Twin applicable to gas turbines. The formulation, methodology, and real-world use cases are presented. To date, digital twins have been created and tested for both E and F class frames. This paper describes the process of generating closed-form equations capable of transforming existing, measured historian data into the health parameters and virtual sensors needed to better track unit health and monitor faulted performance. These equations encapsulate the digital twin physical model and provide end-users with a methodology to calibrate to their specific unit and efficiently use their choice of monitoring software. Tests have been performed using operator data and have shown good ac

关键词： Gas turbines Maintenance Stress Physics Sensors Power stations Computer software pattern recognition Virtual instrumentation Emissions Combined cycle gas turbines

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The Implementation of A Crop Diseases APP Based on Deep Transfer Learning 3

The Implementation of A Crop Diseases APP Based on Deep Tran...

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3rd international conference on Artificial Intelligence and Big Data (ICAIBD)

作者： Yang, Mengji Li, Daiwei Chen, Minquan Bouras, Abdelaziz Tang, Yiqian Yu, Xi Chengdu Univ Sch Informat Sci & Engn Chengdu Sichuan Peoples R China Chengdu Univ Informat Technol Dept Software Engn Chengdu Sichuan Peoples R China Qatar Univ Dept Comp Sci ictQATAR Box 2731 Doha Qatar Key Lab Pattern Recognit & Intelligent Informat P Chengdu Sichuan Peoples R China

ISBN: (纸本)9781728197418

Classifying the severity of crop diseases is the staple-basic element of the plant pathology for making disease prevent and control strategies. The diagnosis of disease needs timeliness and accuracy. Thanks to the development and popularity of smart phones and mobile networks, this makes possibly to develop mobile applications that can be widely accepted by users in the agricultural community. This paper provides a system that can detect the severity of crop diseases automatically and intelligently through taking photos. The development of this mobile app is based on deep transfer learning that we proposed an improved method with nearly 92% accuracy based on ResNet 50. The significantly high success rate makes the model a very useful advisory or warning tool. This project provides a new idea and solution for the detection of crop diseases in agriculture.

关键词： mobile app crop disease ResNet 50 deep transfer learning

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Comparative Study between Texture Feature and Local Feature Descriptors for Silk Fabric pattern Image recognition 20

Comparative Study between Texture Feature and Local Feature ...

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3rd international conference on Information Science and System, ICISS 2020

作者： Khamket, Thananchai Surinta, Olarik Applied Informatics Group Department of Information Technology Faculty of Informatics Mahasarakham University Maha Sarakham Thailand Department of Information Technology Faculty of Informatics Mahasarakham University Maha Sarakham Thailand

ISBN: (纸本)9781450377256

Thai silk fabrics have unique patterns in different regions of Thailand. The designers may have been inspired and took ideas from the natural environment to create new silk patterns. Hence, many new silk patterns are modified from the original silk pattern. It is challenging for people to recognize a pattern without any prior knowledge and expertise. This paper aims to present a comparative study between texture feature and local feature descriptor for silk pattern image recognition. First, two feature extraction techniques: texture feature and local feature descriptors are proposed to create robustness features from sub-regions that are divided by the grid-based method. Second, the robust features are then classified using the well-known and effective classifier algorithms: K-nearest neighbor (KNN) and support vector machine (SVM) with the radial basis function. We experimented with silk pattern image recognition on two silk fabric pattern image datasets: the Silk-pattern and Silk-Diff-pattern. The evaluation results show that the texture feature called the local binary pattern (LBP) when combined with the KNN and SVM algorithms outperforms other feature extraction methods, even deep learning architectures. © 2020 ACM.

关键词： Image recognition

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Research on image processing based on pattern recognition 3

Research on image processing based on pattern recognition

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2020 3rd international conference on Advanced Algorithms and Control Engineering, ICAACE 2020

作者： Peng, Kai Chen, Jing Department of Automation Wuhan University of Technology Wuhan Hubei430070 China

Image processing is to transform the image into digital matrix by mathematical formula, then store the data in the computer, and enhance the source bitmap by computer, including gray-scale processing, median filtering and binarization. Then the image is cut, including contour edge processing, seed filling and so on. In this paper, the image is recognized and processed based on pattern recognition, and then a pre classifier is designed to build a system model of fruit intelligent classification. After the fruit is taken by industrial camera, the corresponding image processing and feature value extraction are carried out. Then the extracted eigenvalues are used for classification. Through this method to achieve a variety of fruit classification effect. © Published under licence by IOP Publishing Ltd.

关键词： Fruits

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Forecast of Traffic Vehicle Demand Based on AHP Decision Model 3

Forecast of Traffic Vehicle Demand Based on AHP Decision Mod...

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3rd international conference on Applied Mathematics, Modeling and Simulation (AMMS)

作者： Xia, Jie Tan, Jintao Li, Li Gu, Musong Chengdu Univ Coll Informat Sci & Technol Chengdu Peoples R China Univ Elect Sci & Technol China Sch Elect Sci & Engn Chengdu Peoples R China Chengdu Univ Inst Higher Educ Sichuan Prov Key Lab Pattern Recognit & Intelligent Informat Chengdu Peoples R China

The development of high technology has driven the rapid growth of civil aviation passenger demand. However, most current studies ignore the impact of increased passenger traffic on ground vehicle demand. This paper studies the demand forecast of transportation vehicles in a specific environment. Taxis are under huge service pressure when faced with the characteristics of irregular time distribution and high dynamics. After the taxi driver escorts the passenger to the airport, it is a big problem whether to leave without a load or go to the car pool. This paper uses Analytic Hierarchy Process (AHP) to construct a judgment matrix, establishes an airport taxi demand forecast model based on the best interests. Taking the status of taxi operation in Kunming Changshui international Airport as an example, the results show that the model can predict taxi demand more accurately and has practical guidance significance. Compared with traditional schemes, it is more conducive to improving driver's income.

关键词： Analytic Hierarchy Process Forecast of traffic vehicle demand Target layer-criterion layer Consistency matrix

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Improved pattern recognition of various traditional clothes with Convolutional neural network

Improved pattern recognition of various traditional clothes ...

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Material and Electrical Engineering conference (ISMEE), international Symposium on

作者： Varyl Hasbi Athala Abdul Haris Rangkuti Naufal Fauzi Luthfi Syaugi Vikri Aditama Johan Muliadi Kerta School of Computer Science Bina Nusantara University Jakarta Indonesia School of Computer Science Bina Nusantara University Bandung Indonesia

ISBN: (纸本)9781728177335

This Study would identify regional fabric patterns throughout Indonesia. Traditional clothes patterns come from the handicrafts of remote communities in the regions of Indonesia. A Convolutional neural network is used for Resnet50 V2, VGG16, VGG19, MobileNetV2, and Inception V3 models to support this research. This research was conducted on five types of experiments with supporting parameters: the number of training images and test images, rotation and scale of each image, image class, and CNN parameters. After we conducted experiments on a collection of images found in various scales and degrees, the average classification accuracy using VGG16 was 79.2% in condition 2, but the classification accuracy was between 84.2%-92.5%. Meanwhile, if using VGG19 the classification accuracy is 79.9%, with condition 1 for the highest accuracy between 92%-96%. If we used mobilenetV2, the average classification accuracy is 83.4% for 87%-96%. If we used inceptionV3, the average classification accuracy is 83.7% for the accuracy between 72%-98%, and the last if we used Resnet50 V2, the average classification accuracy is 79.2% for the accuracy between 71%-94%. Traditional clothes pattern recognition research using Deep CNN has succeeded in recognizing some traditional clothes patterns up to more than 97%. We continue the study to recognize other traditional clothes patterns more optimally.

关键词： Training Electrical engineering Image recognition Materials reliability Reliability engineering Fabrics pattern recognition

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