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AUGMENTING TRANSFERRED REPRESENTATIONS FOR STOCK CLASSIFICATION

AUGMENTING TRANSFERRED REPRESENTATIONS FOR STOCK CLASSIFICAT...

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IEEE international conference on Acoustics, Speech and Signal processing (ICASSP)

作者： Fons, Elizabeth Dawson, Paula Zeng, Xiao-jun Keane, John Iosifidis, Alexandros Univ Manchester Dept Comp Sci Manchester Lancs England AllianceBernstein London England Aarhus Univ Dept Elect & Comp Engn Aarhus Denmark

ISBN: (纸本)9781728176055

Stock classification is a challenging task due to high levels of noise and volatility of stocks returns. In this paper we show that using transfer learning can help with this task, by pre-training a model to extract universal features on the full universe of stocks of the S&P500 index and then transferring it to another model to directly learn a trading rule. Transferred models present more than double the risk-adjusted returns than their counterparts trained from zero. In addition, we propose the use of data augmentation on the feature space defined as the output of a pre-trained model (i.e. augmenting the aggregated time-series representation). We compare this augmentation approach with the standard one, i.e. augmenting the time-series in the input space. We show that augmentation methods on the feature space leads to 20% increase in risk-adjusted return compared to a model trained with transfer learning but without augmentation.

关键词： Transfer learning data augmentation deep learning financial signal processing stock classification

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Lung Cancer Diagnosis and Classification Using Hybrid Deep Feature Extraction

Lung Cancer Diagnosis and Classification Using Hybrid Deep F...

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Computing and Information Technology (ICCIT-1441), 2020 international conference on

作者： Muhammad Hassan Shoaib Hassan Aniqa Mujahid Muhammad Umair Muhammad Zubair Faculty of Information Technology & Computer Science University of Central Punjab Lahore Pakistan School of Computer Science & Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (数字)9798350353839

ISBN: (纸本)9798350353846

Lung cancer, a leading cause of mortality, necessitates prompt detection for improved patient outcomes. Recent strides in diagnostic technologies, including biomarkers, genetic testing, and advanced imaging like CT and MRI, have revolutionized lung cancer detection. Computer-aided diagnosis (CAD) tools offer invaluable insights, rapidly analyzing imaging data for early identification. Integration of machine learning, deep learning, and CNNs has further transformed lung cancer diagnosis, empowering pulmonologists with highly accurate and efficient tools for interpreting medical imaging. Feature extraction plays the most vital role in the detection of lung cancer. In this research, a hybrid approach is proposed for better feature extraction, and then different classifiers are used to classify it for efficient detection of lung cancer. The dataset is taken from IQ-OTH/NCCD and is publicly available on Kaggle. Median and Gaussian filters are used for data preprocessing. To enlarge the dataset and avoid the chances of overfitting, data augmentation is used. Then, feature extraction is done on the preprocessed dataset using handcrafted methods HOG, GLCM, and pretrained automated models DenseNet201 and VGG-16. Features extracted from these algorithms are concatenated into a single array. Different combinations are tested with SVM, XGBoost, Artificial Neural Network (ANN) classifiers, and Logistic Regression. Results are compared in accuracy, precision, recall, and score. The results obtained from these combinations after classifying them with SVM, ANN, XGBoost, and Logistic regression give efficient results, and one of the combinations outperforms the previous results, providing an accuracy of up to 99.3% after being classified with SVM.

关键词： Support vector machines Logistic regression Accuracy Filters Biological system modeling Lung cancer Artificial neural networks Feature extraction data augmentation Classification algorithms

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A time series data augmentation method based on dynamic time warping

A time series data augmentation method based on dynamic time...

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2021 international conference on Computer Communication and Artificial Intelligence, CCAI 2021

作者： Yang, Xinyu Zhang, Zhenguo Cui, Xu Cui, Rongyi Department of Computer Science and Technology Yanbian University 977 Gongyuan Road Yanji133002 China

ISBN: (纸本)9781728194011

Deep learning has become a hot research topic in the field of time series analysis and data mining. Training models often requires balanced and large data sets, but on the one hand, the number of different types of data in time series data sets is often extremely imbalanced, on the other hand, some time series data are often difficult to collect. Therefore, it is necessary to augment the training data before training the deep learning model. SMOTE is a data augmentation method widely used in preprocessing imbalanced data sets, but the classical SMOTE method does not satisfy the characteristics of time series when processing time series, so it is not effective when applied to time series data sets. To address this point, we propose an oversampling data augmentation method based on dynamic time warping - DTW-SMOTE. For the possible phase shifts of time series with the same characteristics, this method uses dynamic time warping to obtain more reasonable similarity between different time series, which improves the classical SMOTE method and achieves data augmentation for time series data. We conducted comparison experiments using two models with different architectures: ResNet and LSTM. The experimental results show that the performance of the above models is significantly improved after training with the DTW-SMOTE method pre-processed dataset. © 2021 IEEE.

关键词： Time series analysis

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Sentiment Analysis of StockTwits Using Transformer models 20

Sentiment Analysis of StockTwits Using Transformer Models

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20th IEEE international conference on Machine Learning and Applications (ICMLA)

作者： Bozanta, Aysun Angco, Sabrina Cevik, Mucahit Basar, Ayse Ryerson Univ Data Sci Lab Toronto ON Canada

ISBN: (纸本)9781665443371

Forecasting stock price movements is an important task for investors and traders, though still very difficult due to the unstable nature and complex behavior of the stock market. Accordingly, each piece of information related to stock prices can be deemed useful. In this regard, social media platforms provide a vast amount of information that can be used to predict stock movements. In this study, we compare the performances of various traditional, deep learning, and state-of-art pre-trained transformer models for text classification of tweets related to the stock market, which are obtained through a financial microblog, StockTwits. For this purpose, we collected 100,000 labeled messages of five stocks, namely, Apple Inc. (AAPL), Amazon (AMZN), Boeing Co, (BA), Walt Disney Co. (DIS), and the SPDR S&P 500 ETF Trust (SPY) for a period between December 2019 and June 2020. We used logistic regression and random forest as traditional classifiers and Long Short Term Memory and Gated Recurrent Unit as the deep learning algorithms, and BERT, DistillBERT, RoBERTa, and XLNet as the state-of-art transformer models to classify tweets as either "bearish" or "bullish" Our numerical study showed that RoBERTa outperformed traditional classifiers and deep learning algorithms in terms of average Flscores.

关键词： stocktwits text classification deep learning BERT DistillBERT Roberta XLNet

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Research on Hubei Tourism Talent Demand and University Tourism Education Training Model Based on Computer Big data Mining Technology

Research on Hubei Tourism Talent Demand and University Touri...

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2020 international conference on Mechanical Automation and Computer Engineering, MACE 2020

作者： Fan, Huanhuan Wuhan Donghu University Hubei430070 China

The data processing technology in the training and construction of tourism talents can well integrate the complex data, make it more concrete and visualized and effectively reflect the real situation of campus construction. The use of computers can organically link the parents of students with the school. Parents of students can understand the learning situation of the students at any time and carry out education work for the students in time, which is conducive to the improvement of student performance. At the same time, data processing technology still has certain drawbacks in the actual use process and relevant educators need to continuously explore new processing schemes and improve education. The data processing technology of the campus should also fully integrate the actual situation of the school, implement the data processing work seriously and build the training of tourism talents. The scope of tourism talent training is becoming wider and wider and data processing technology plays an important role in it. Therefore, in the process of tourism talent training and construction, it is necessary to accurately grasp the data processing technology and explore more efficient office models. In the actual construction process of tourism talent training, it can be found that there are still many problems, because the construction of tourism talent training sites still lacks certain theoretical guidance and the construction of tourism talent training is blind, which causes the construction of tourism talent training to not give full play. Its theoretical effect. © Published under licence by IOP Publishing Ltd.

关键词： Big data

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data-Driven Self-calibration Control Strategy for Accuracy Enhancement of Micromachined Gyroscope with Two Orders of Magnitude

Data-Driven Self-calibration Control Strategy for Accuracy E...

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international conference on Guidance, Navigation and Control, ICGNC 2020

作者： Yang, Dengfeng Shen, Qiang Ren, Zhaozhi MOE Key Laboratory of Micro/Nano Systems for Aerospace Yangtze River Delta Research Institute Northwestern Polytechnical University Xi’an China

ISBN: (纸本)9789811581540

This paper presents data-driven self-calibration control strategy based on variable expectation-maximum (VEM) algorithm to improve accuracy of MEMS gyroscope under unknown stochastic disturbances in harsh environment. Disturbances sourcing from device insides with slow-varying, long-duration characteristic and external environment with drastic-magnitude, short-duration disturbances characteristic are successively constructed in the two measurement models of the gyroscope, respectively. The controller of the presented method is dynamically chosen to identify these disturbances to reduce the bias variance of the gyroscope. Numerical calculation results based on experimental test shows that the bias variances are reduced to 48.654°/s, 3.981°/s, 0.587°/s by Kalman filter (KF), least mean square (LMS), standard EM (SEM) methods, respectively. Comparing with these algorithms, bias variance of the proposed method is improved by 287.9, 23.6 and 3.5 times, respectively. It is proved that the proposed method can improve the accuracy by two orders of magnitude. © 2022, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Gyroscopes

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data-driven ship propulsion modeling with artificial neural networks 7

Data-driven ship propulsion modeling with artificial neural ...

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SNAME 7th international Symposium on Ship Operations, Management and Economics, SOME 2021

作者： Karagiannidis, P. Themelis, N. School of Naval Architecture and Marine Engineering National Technical University of Athens Greece

The paper examines data-driven techniques for the modeling of ship propulsion that could support a strategy for the reduction of emissions and be utilized for the optimization of a fleet’s operations. A large, high-frequency and automated collected data set is exploited for producing models that estimate the required shaft power or main engine’s fuel consumption of a container ship sailing under arbitrary conditions. A variety of statistical calculations and algorithms for data processing are implemented and state-of-the-art techniques for training and optimizing Feed-Forward Neural Networks (FNNs) are applied. Emphasis is given in the pre-processing of the data and the results indicate that with a proper filtering and preparation stage it is possible to significantly increase the model’s accuracy. Thus, increase our prediction ability and our awareness regarding the ship's hull and propeller actual condition. Copyright © 2020 Society of Naval Architects and Marine Engineers (SNAME)

关键词： data handling

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Secure Authentication and Authorization of IoT Devices in Smart Home Networks

Secure Authentication and Authorization of IoT Devices in Sm...

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Electronics and Sustainable Communication Systems (ICESC), 2020 international conference on

作者： Pawan Hete Swanand Deo Pradyumn Waghmare Rajeshwar Balla Sudhanshu Maurya Nitin Rakesh Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune India

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

Continuous growth and implementation of the popular Internet of Things (loT) in smart home systems devices have innovated and automated its usage, significantly integrating it. The development of the internet has produced both pros and cons. Indeed, this has led to security challenges that need solutions. Here, the author considers how IoT devices, digital certificates,API keys, and authorization technologies can safeguard smart home network technologies' authentication and user access processes. The proposed study relies on evidence-based techniques that involve data collection, pre-processing, feature extraction, model development, and evaluation of performance outputs to address research problems such as class imbalances and feature extraction. This study contributes to the security trend in smart home technologies by analyzing the appropriate use of digital certificates, API keys, and access controls. The delivery of smart home technologies, security enhancements, and trust in those systems will become possible.

关键词： Authorization Surveys Home appliances Technological innovation Authentication Smart homes Feature extraction data models Internet of Things Testing

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Classification and Analysis of Water Quality Using Machine Learning algorithms

Classification and Analysis of Water Quality Using Machine L...

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international conference on Communication, Circuits, and Systems, IC3S 2020

作者： Kaur, Amandeep Khurana, Meenu Kaur, Preetinder Kaur, Manpreet Chitkara University Institute of Engineering and Technology Chitkara University Rajpura Punjab India Western Sydney University International College Sydney Australia

ISBN: (纸本)9789813348653

This research work revolves around the development of supervised machine-learning models that can automatically classify the quality of river water. The original dataset is transformed and binned into two (swimming, boating) class types. Using this data, an exploratory study of the machine-learning models has been done as to construct a generic water quality classifier. At the same time, it was found that there is an imbalance in dataset. To overcome this problem, SMOTE algorithm was applied and the exploratory analysis of the machine-learning algorithms was done. The performance analysis of the various classifier algorithms show initially that there is a need for customization of the machine learning so that generalized classifier can be build. Deeper analysis of the study showed that the correlation-based features are helpful in RF and CART. At the same time, the PCA data projection shows a higher level of accuracy (0.989) with the neural network algorithm © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Water quality

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6VecLM: Language Modeling in Vector Space for IPv6 Target Generation

6VecLM: Language Modeling in Vector Space for IPv6 Target Ge...

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European conference on Machine Learning and Principles and Practice of Knowledge Discovery in databases (ECML PKDD)

作者： Cui, Tianyu Xiong, Gang Gou, Gaopeng Shi, Junzheng Xia, Wei Chinese Acad Sci Inst Informat Engn Beijing Peoples R China Univ Chinese Acad Sci Sch Cyber Secur Beijing Peoples R China

ISBN: (纸本)9783030676674;9783030676667

Fast IPv6 scanning is challenging in the field of network measurement as it requires exploring the whole IPv6 address space but limited by current computational power. Researchers propose to obtain possible active target candidate sets to probe by algorithmically analyzing the active seed sets. However, IPv6 addresses lack semantic information and contain numerous addressing schemes, leading to the difficulty of designing effective algorithms. In this paper, we introduce our approach 6VecLM to explore achieving such target generation algorithms. The architecture can map addresses into a vector space to interpret semantic relationships and uses a Transformer network to build IPv6 language models for predicting address sequence. Experiments indicate that our approach can perform semantic classification on address space. By adding a new generation approach, our model possesses a controllable word innovation capability compared to conventional language models. The work outperformed the state-of-the-art target generation algorithms on two active address datasets by reaching more quality candidate sets.

关键词： IPv6 target generation Deep learning data mining Network measurement Natural language processing

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