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Performance of Domain Adaptation Schemes in Video Action recognition using Synthetic data 2022

Performance of Domain Adaptation Schemes in Video Action Rec...

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4th international conference on Image, Video and Signal Processing, IVSP 2022

作者： Isoi, Hana Takefusa, Atsuko Nakada, Hidemoto Oguchi, Masato Ochanomizu University Japan National Institute of Informatics Japan National Institute of Advanced Industrial Science and Technology Japan

ISBN: (纸本)9781450387415

the videos obtained from surveillance cameras are expected to be processed in neural networks and utilized for indoor monitoring and surveillance services. However, collecting training data is generally an issue in machine learning, obtaining sufficient training data is very difficult from the viewpoint of privacy protection, especially for the abovementioned services. To address this issue, synthetic data instead of real data might be employed for training. However, the domain shift of video has not been sufficiently investigated, and therefore high-accuracy, unsupervised learning of real video using synthetic video has not been achieved. In this study, we create a synthetic video dataset for action classification and show that action classification can be performed with high accuracy without labeling real video data by learning with this synthetic dataset. First, we create an OchaHouse dataset for action classification for indoor monitoring and surveillance. this dataset consists of a real video dataset OchaHouse-Real that records the behavior of one person in a room and a synthetic video dataset OchaHouse-Syn that resembles it. Second, video action recognition of real videos is performed using synthetic data and various unsupervised learning methods with domain adaptation schemes. the results show that learning with synthetic data enables highly accurate action classification of real data without a real data label. the dataset will be published. © 2022 Association for Computing machinery.

关键词： Classification (of information)

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A Novel Model for Recognising Handwritten Devanagari Numerals using machine learning

A Novel Model for Recognising Handwritten Devanagari Numeral...

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Pervasive Computing and Social Networking (ICPCSN), international conference on

作者： Ch. Prathima Ramprakash Reddy Arava K Sevitha G Manikanth D Vinay C Surya School of Computing Mohan Babu University (formerly Sree Vidyanikethan Engineering College) Tirupati India Department of CSE KSRM College of Engineering Department of CSSE Sree Vidyanikethan Engineering College Tirupati India

ISBN: (数字)9798350386349

ISBN: (纸本)9798350386356

this research study performs a comprehensive comparative analysis aimed at developing effective machine learning models for classifying handwritten Devanagari numerals. the research focuses on evaluating the performance of various models to determine the most accurate classification approach. Initiating with data pre-processing, including feature extraction and normalization, the data is prepared for model training and assessment. A diverse range of machine learning models, from traditional methods like Support Vector machines (SVM) to advanced techniques such as Random Forests, K-Nearest Neighbors, and Convolutional Neural Networks, are considered for the comparative analysis, ensuring a thorough assessment of classification capabilities. Cross-validation techniques are employed during model training and testing to enhance reliability. Statistical tests are utilized to assess the performance variations among models, enhancing the robustness of the analysis. Visual representations of performance metrics and comparison results offer clear insights. this research study aims to identify the most suitable machine learning model for handwritten Devanagari numeral classification, potentially advancing character recognition systems and linguistic applications.

关键词： Support vector machines Training Handwriting recognition Analytical models machine learning algorithms Image recognition Nearest neighbor methods

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Comparative Analysis of machine learning Techniques for Concrete Compressive Strength Prediction

Comparative Analysis of Machine Learning Techniques for Conc...

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data Analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), international conference on

作者： Fuad Musleh Ranyah Taha Abdel Rahman Musleh Civil Engineering Department University of Bahrain Isa Town Kingdom of Bahrain Computer Department Al Iman School Isa Town Kingdom of Bahrain Electrical Engineering Department University of Bahrain Isa Town Kingdom of Bahrain

ISBN: (数字)9798350369786

ISBN: (纸本)9798350369793

the compressive strength of concrete is of utmost importance in ensuring the structural stability and integrity of construction projects. With increasing importance placed on this aspect, there is a demand for intelligent and efficient techniques to accurately predict and evaluate compressive strength. machine learning methods (ML) present a promising approach to developing accurate models for assessing the compressive strength of concrete. By leveraging the power of data analysis and pattern recognition, these techniques enable the construction industry to obtain reliable and robust concrete for a wide range of applications. ML models can identify the key factors influencing compressive strength, allowing for more informed decision-making, and ensuring the construction of structurally sound and durable projects. this article explores the assessment and comparison of four machine learning (ML) methods, namely Gradient Boosting, Categorical Gradient Boosting CatBoost, Random Forest (RF), and extreme gradient boosting (XGBoost) regressors, for predicting concrete compressive strength. Among these techniques, the CatBoost Regressor demonstrated superior performance in terms of accuracy, R-squared coefficient of determination, and Root Mean Square Error RMSE, surpassing the other three methods.

关键词： Radio frequency data analysis Accuracy Boosting Concrete Structural engineering pattern recognition

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A learning Model of Evoked EEG Signals Based on PCA and Semi-supervised SVM 4

A Learning Model of Evoked EEG Signals Based on PCA and Semi...

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4th international conference on Robotics, Control and Automation Engineering (RCAE) / 4th international conference on Advanced Mechanical and Electrical Engineering (AMEE)

作者： Xie, Zixing Yu, Baocheng Xu, Wenxia Wuhan Inst Technol Sch Comp Sci & Engn Engn Res Ctr Intelligent Prod Line Equipment Hube Wuhan Peoples R China

ISBN: (纸本)9781665427302

Since in the recognition of Evoked electroencephalograph ( EEG) signal (P300 signal), there are many problems such as a large amount of data to be collected, multiple acquisition channels, complex calculation, difficulty in obtaining label samples by traditional recognition model and long training time, a learning model based on principal component analysis (PCA) and semi-supervised support vector machine (SVM) was proposed. In this model, the EEG signal is preprocessed first, and then the PCA method is used to reduce the dimensionality of the collected brain channels to obtain a set of optimal channel subsets that have a good response to the P300 signal. On this basis, the semi-supervised classification model based on SVM is designed, and improves on the traditional discriminant analysis method, through a Euclidean distance between tag data and no labels to obtain the relationship of them, and, in turn, to optimize the objective function, to solve the projection vector, feature extraction, and then vote classification approach to the final recognition. Finally, experimental verification is carried out. the results show that the method can obtain 84.3% of the correct classification results under 40% of the label data. It has a good application prospect in the context of only a small amount of label data and reduces the time complexity. It has greater application value for online systems.

关键词： P300 EEG signal channel selection optimization PCA semi-supervised classification model SVM

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Stock Market Prediction Model Based on LSTM Deep learning: the Case of Top Corporate Company in China 4

Stock Market Prediction Model Based on LSTM Deep Learning: T...

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4th international conference on Artificial Intelligence and Advanced Manufacturing, AIAM 2022

作者： Zhang, Yaojun Tumibay, Gilbert M. Angeles University Foundation Manila Philippines

ISBN: (纸本)9781665463997

the fluctuation of stock market price is the basis of stock market operation. the big fluctuation of stock market price will cause huge losses to investors. Stock price fluctuation is a highly complex nonlinear system, the price adjustment is not in accordance with the uniform time process, has its own process, has high risk, is very difficult to predict. the stock prediction topic is one of the most explored area of research. However, due to the characteristics of large quantity, non-linearity and long memory of stock price data, the traditional machine learning methods have some problems, such as few model parameters, impractical, time-consuming prediction and so on. therefore, how to study effective deep learning methods to improve the recognition efficiency and prediction accuracy of forecasting models is an urgent problem to be solved in the field of stock forecasting. Based on the theory of deep learning, this paper constructs a stock price prediction index system according to the characteristics of China's top enterprises. In this paper, the stock price trend of Build Your Dreams (BYD), the top new energy vehicle company in China, is taken as the main data set, and the Long and Short-term Memory (LSTM) recurrent neural network is used to predict BYD's stock price. this paper solves the long sequence dependence problem in general recursive neural networks. Using the characteristics of recurrent neural network and stock market, the data are preprocessed by interpolation and wavelet denoising, and then pushed to different LSTM layers. Training and testing were carried out in LSTM network models with different numbers of hidden neurons at the same level. the evaluation indexes and prediction effects were compared to find the appropriate LSTM layers and hidden neurons to improve the prediction accuracy © 2022 IEEE.

关键词： Financial markets

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machine learning Method Evaluates Upper-Limb Motor Function by fNIRS in Stroke Patients

Machine Learning Method Evaluates Upper-Limb Motor Function ...

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pattern recognition and machine learning (PRML), IEEE international conference on

作者： Weiwei Xu Yi Tao Jialin Zhu Ziwen Yuan Jing Guo Gang Wang Key Laboratory of Biomedical Information Engineering of Ministry of Education School of Life Science and Technology Xi’an Jiaotong University Xi’an China Department of Rehabilitation First Affiliated Hospital of Xi’an Jiaotong University Xi’an China

the upper limb motor impairment after stroke seriously affects patients’ quality of life, and the evaluation of upper limb motor function depends on professionals, which is subjective, complicated, and time-consuming. Neurophysiological information evaluation Fugl-Meyer assessment of the upper limb (FMA-UL) is expected to solve this problem. Our study aims to estimate FMA-UL using the functional connectivity measures and network properties of task state functional near-infrared spectroscopy (fNIRS). FNIRS was recorded when the subjects performed the hand-grasping task, representing the oxyhemoglobin concentration changes. Functional connectivity was gauged via Pearson correlation coefficients, selectively retained within a 10% to 50% threshold. We calculated two macroscale and two mesoscale network properties based on graph theory. then the support vector regression (SVR) was applied to estimate the FMA-UL. Cross-validation with leave-one-out, resulted in an R$^{\mathbfth}$ of 0.49, with a root mean square error of 4.96, 5 of the 15 subjects had absolute errors less than 0.75, when the sparsity of functional connectivity is 40%. Functional connectivity and network properties by fNIRS is a promising method to predict motor impairment. Our study demonstrated that functional connectivity and network properties are correlated with motor impairment and brain reorganization in stroke patients. the method proposed reflects prospects for finding neural biomarkers to predict FMA-UL and is feasible for using fNIRS to predict the motor function of stroke.

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Imagined Object recognition Using EEG-Based Neurological Brain Signals 4th

Imagined Object Recognition Using EEG-Based Neurological Bra...

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4th international conference on Recent Trends in Image Processing and pattern recognition, RTIP2R 2021

作者： Saini, Rajkumar Prabhu, Sameer Upadhyay, Richa Rakesh, Sumit Chippa, Prakash Chandra Mokayed, Hamam Liwicki, Marcus Liwicki, Foteini Luleå Tekniska Universitet Luleå Sweden Data Ductus AB Luleå Sweden

ISBN: (纸本)9783031070044

Researchers have been using Electroencephalography (EEG) to build Brain-Computer Interfaces (BCIs) systems. they have had a lot of success modeling brain signals for applications, including emotion detection, user identification, authentication, and control. the goal of this study is to employ EEG-based neurological brain signals to recognize imagined objects. the user imagines the object after looking at the same on the monitor screen. the EEG signal is recorded when the user thinks up about the object. these EEG signals were processed using signal processing methods, and machine learning algorithms were trained to classify the EEG signals. the study involves coarse and fine level EEG signal classification. the coarse-level classification categorizes the signals into three classes (Char, Digit, Object), whereas the fine-level classification categorizes the EEG signals into 30 classes. the recognition rates of 97.30%, and 93.64% were recorded at coarse and fine level classification, respectively. Experiments indicate the proposed work outperforms the previous methods. © 2022, Springer Nature Switzerland AG.

关键词： Electroencephalography

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A survey of parallel clustering algorithms based on vertical scaling platforms for big data 4

A survey of parallel clustering algorithms based on vertical...

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4th international conference on pattern Analysis and Intelligent Systems, PAIS 2022

作者： Zemmouri, Hadjir Labed, Said Kout, Akram University of Abdelhamid Mehri Constantine 2 Misc Laboratory Constantine25000 Algeria University of Ferhat Abbas Setif 1 Misc Laboratory Setif Algeria

ISBN: (数字)9781665461610

ISBN: (纸本)9781665461610

Clustering, or cluster analysis, is an important unsupervised task in machine learning that determines how the observed data naturally clusters. Many efficient traditional clustering methods based on different behaviours, such as partitioning' hierarchical, grid, model, and density based, have been proposed in recent decades. However, clustering itself is considered an NP-hard problem, and it becomes more challenging when the clustered data is large. the classical clustering techniques cannot handle big data problems due to their large volume, fast generation, significant heterogeneity and complexity. therefore, more effective, flexible, and efficient clustering approaches are required. Recently, the parallel and distributed computing concepts gives birth to the parallel clustering algorithms. Nowadays, the researches focus on scalable clustering methods based on different acceleration platforms to deal with big data problems. the acceleration platforms can be classified into horizontal and vertical-scaling platforms. In this paper we present a recent overview of the latest parallel and distributed clustering algorithms based on vertical scaling platforms. Otherwise, the paper gives a discussion that will be useful for researchers to propose more effective and efficient algorithms for Big data clustering. © 2022 IEEE.

关键词： Big data

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Deep learning-Based Modulation recognition for Imbalanced Classification 23

Deep Learning-Based Modulation Recognition for Imbalanced Cl...

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Proceedings of the 2023 4th international conference on machine learning and Computer Application

作者： Runqing Luo Jian Sun Yanan Guo Beijing Information Science and Technology University China Key Laboratory of Information and Communication Systems Beijing Information Science and Technology University China Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument Beijing Information Science and Technology University China

ISBN: (纸本)9798400709449

Automatic modulation recognition (AMR) is a crucial task in intelligent receiver systems, with significant applications in both civilian and military scenarios. Deep learning-based modulation recognition algorithms have achieved high recognition accuracy and good robustness. However, the performance of DL-AMR heavily relies on a large amount of training data. When the training dataset has a limited number of samples or an imbalanced distribution across different classes, the accuracy of automatic modulation recognition for minority class samples may significantly decrease. therefore, a CNN model with a focal loss as the output layer's loss function is proposed to address the issue of decreased recognition accuracy for minority classes in the deep learning model when faced with imbalanced training data. Simulation results demonstrate that this model improves the recognition accuracy of classes with a limited number of samples.

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4th international conference on Innovative Computing, IC 2021

4th International Conference on Innovative Computing, IC 202...

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4th international conference on Innovative Computing, IC 2021

ISBN: (纸本)9789811642579

the proceedings contain 241 papers. the special focus in this conference is on Innovative Computing. the topics include: Inertial Navigation Method for Spacecraft Based on General Relativity;influencing Mechanism of Live Broadcasting Marketing pattern on Consumers’ Purchase Decision Internet Background;Reinventing the Wheel of Marketing: Assessing the Impact of Artificial Intelligence (AI) on Digital Marketing and Consumer Buying Behavior;Fraud Identification Model of Profit and Loss Adjustment Financial Report Based on Lib SVM Algorithm;stock Price Prediction Based on Deep Reinforcement learning;space Design of Medical and Maintenance Building Under the Background of Computer;application of Computer Technology in Concrete;design and Implementation of Educational Information System Based on Cloud Computing;Evaluation Model of English–Chinese Cross-Language Initiation Oral Teaching Based on SOFMNN;exploration of Optimized Teaching Mode in Applied Technology Colleges Under the Background of Big data;efficient-net Speaker recognition Master—the New Speaker recognition System Built Base on Efficient-net;intelligent Invoice Identification Technology Based on Zxing Technology;author Identification of Handwritten Numbers Based on Transfer learning and Multi-task learning;influence of Oil Price on Corn Price Based on Multiple Linear Regression Model;An Empirical Analysis of the VAR Model of Consumption Structure, Industrial Structure and Economic Growth in the Internet Age: Taking Henan Province as an Example;a Research on the Influence of Raising the threshold of Individual Income Tax on Urban Residents’ Consumption Based on Eviews Software—A Case Study of Shanghai;effects of Minimum Wage on Enterprise Productivity—Empirical Analysis Based on database of Industrial Enterprises.

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