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检索条件"任意字段=6th International Conference on Signal Processing and Machine Learning, SPML 2023"

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machine learning-based study of eye features under the emotion of anger 22

Machine learning-based study of eye features under the emoti...

引用

Proceedings of the 2022 6th international conference on Electronic Information Technology and Computer Engineering

作者： Zhuoluo Huang Yafang Li Key Laboratory of Traditional Chinese Medicine Psychology and Brain Science Jiangxi Provincial Administration of Traditional Chinese Medicine China

ISBN: (纸本)9781450397148

Based on the theory of "the liver is open to the eyes", this study uses a machine learning approach to explore methods and models that can be applied to the identification of eye features for angry emotions, in the hope of providing some ideas for objective identification of angry emotions. We used the angry emotion faces in the China Affective Picture System (CAPS) as the research object. Using multiple feature point inspection, we use openface software to obtain the 3D coordinate point coordinate information of the eye features of the angry emotion images. the aggregated data were then statistically analyzed using matlab software to calculate the eye feature values to be used, and then continued to process them using the classifier function to build a face eye feature point model based on Support Vector machine (SVM), Linear Discriminant Analysis(LDA), K-Neares Neighbor, Ensemble Subspace Discriminant (ESD), Decision Tree(DT) and other classifiers, and the model was comprehensively evaluated in terms of accuracy, area under the ROC curve and other evaluation metrics. For the results of the measured classification tests, the correct rate obtained by DT classification reached 83.1%, KNN reached 84.7%, ESD reached 88.1%, SVM reached 84.7% correct rate and LDA reached 88.1%. the effect of the eye feature description parameters on the classification effect of anger emotion was verified through experiments, and the best performance of ESD and LDA classification was achieved. Based on the above results we can draw the following conclusions. "the eye is the heart's ambassador", and the classifier constructed based on the eye diagnosis information can play the role of classification, which has certain scientific value and can provide objective diagnostic guidance for clinically relevant psychosomatic problems.

关键词： machine learning Anger emotions Eye features Image processing

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Identification and Classification of Human Mental Stress using Physiological Data: A Low-Power Hybrid Approach

Identification and Classification of Human Mental Stress usi...

引用

international conference on Condition Assessment Techniques in Electrical Systems (CATCON)

作者： Ayush Roy Arkaprovo Acharya Sabarno Biswas Susanta Ray Biswarup Ganguly Department of Electrical Engineering Jadavpur University Kolkata India Department of Electrical Engineering Meghnad Saha Institute of Technology Kolkata India

this paper proposes a framework using artificial intelligence for recognizing human mental stress based on physiological data. It comprises three stages; at first, emotion detection has been carried out from the facial image using a deep learning-based framework employing convolutional neural networks (CNN). Secondly, electrocardiogram (ECG) signals have been acquired with the help of the developed sensor-based system integrating Arduino UNO with AD8232 sensor. Finally, preprocessing has been performed and extracted features from the ECG signals are fed to support vector machines (SVM) for identification of arrhythmia and hence diagnosis of mental stress. All three aspects are connected together via the overall output calculation. A score is added in accordance to the output for each of these three stages. the range of the final score (i.e., the overall output) decides the level of mental stress suffered by the person. the proposed method has been trained on FER-2013 for emotion detection and on MIT-BIH for arrhythmia detection. It is found that the performance outperforms other machine learning and deep learning models without much compromise in accuracy. the proposed method can perform classification based on the patient’s current condition and gives an insight of the degree and time duration of stress of the patient.

关键词： Support vector machines Deep learning Emotion recognition Arrhythmia Human factors Electrocardiography signal processing

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Hybrid Sentiment Analysis: Majority Voting with Multinomial Naive Bayes and Logistic Regression on IMDB Dataset

Hybrid Sentiment Analysis: Majority Voting with Multinomial ...

引用

international conference on Information and Communications Technology (ICOIACT)

作者： Zakaria Andi Sunyoto Magister of Informatics Engineering Universitas Amikom Yogyakarta Yogyakarta Indonesia

ISBN: (数字)9798350315639

ISBN: (纸本)9798350315646

Sentiment analysis plays a critical role in natural language processing as it seeks to identify and categorize the emotions expressed in texts. In this study, we conducted a case analysis using the IMDB dataset to analyze sentiments in movie reviews. We propose an approach that employs a Majority Voting method that merges two effective classification techniques: Multinomial Naive Bayes (MNB) and Logistic Regression (LR). the Majority Voting method we employed considers the decisions made by both MNB and LR, selecting the sentiment that appears most frequently as the outcome. Our objective is to enhance sentiment analysis performance by leveraging the strengths of both methods. We divided the IMDB dataset into an $80 \%$ training set and a $20 \%$ testing set to evaluate the approach. Performance assessment included metrics such as accuracy, precision, and recall. the experimental outcomes significantly improved sentiment analysis performance by combining MNB and LR using Majority Voting. the achieved accuracy was $0.89 \%$ higher compared to using the methods individually. the Majority Voting approach allowed us to integrate the advantages of both classification methods, resulting in more accurate and consistent sentiment predictions in analyzing movie reviews on the IMDB dataset. these findings indicate that the proposed hybrid approach holds great potential in enhancing the performance of sentiment analysis on the IMDB dataset and can be applied to various applications of sentiment analysis.

关键词： Training Sentiment analysis Logistic regression machine learning algorithms Reviews Motion pictures Transformers

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Automatic Metadata Extraction From Iranian theses And Dissertations 6

Automatic Metadata Extraction From Iranian Theses And Disser...

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6th international conference on signal processing and Intelligent Systems (ICSPIS)

作者： Rahnama, Mohadese Hasheminejad, Seyed Mohammad Hossein Nasiri, Jalal A. Alzahra Univ Fac Engn Dept Comp Engn Tehran Iran Iranian Res Inst Informat Scienceand Technol Iran Tehran Iran

ISBN: (纸本)9781728186290

Metadata extraction is a critical part of scholarly information management. this paper outlines an approach to extract metadata information from Persian thesis documents. the proposed method comprises of two subtasks: In the initial stage of the process, the boundary of the thesis's front matter and the body is detected automatically and the main body is discarded. Once Body detection has been conducted, the remaining paragraphs are classified into desired metadata. the classification tasks are carried out using Support Vector machine technique. In an experiment with thesis documents, novel features are introduced, compatible with Iranian thesis/dissertation documents. the findings confirm the suggested method achieves 91.36% F1-score.

关键词： Information extraction Metadata extraction Scientific literature analysis Content classification Support Vector machines (SVM) Persian text processing

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Trading Decision Making Based on Hybrid Neural Network

Trading Decision Making Based on Hybrid Neural Network

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6th international conference on Intelligent Computing and signal processing (ICSP)

作者： Wu Haotian Beijing Jiaotong University Beijing China

Now, with the dominance of electronic stock trading, it is possible to find and make profit from the price difference in real time. machine learning has been applied in stock trading for years by companies. Yet as the rising of deep learning, price forecasting models become more accurate, which create more opportunities to gain higher profits. In this work, a novel hybrid neural network is proposed to deal with the stock trading decision making challenge. After properly training with labeled stock trading data, the hybrid neural network model proposed in this paper has been proved to be able to assist stock trading decisions better and achieve higher profits. the proposed hybrid neural network is evaluated on the stock trading data of Jane Street data set provided by the Kaggle competition. It is shown in the experiments that the proposed hybrid neural network outperforms other neural networks. Our neural network achieves as high profit value as 11417, which reveals the efficiency of the proposed method.

关键词： Training Deep learning Neural networks Decision making signal processing algorithms Stochastic processes signal processing

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Jane Street Stock prediction model based on LightGBM

Jane Street Stock prediction model based on LightGBM

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6th international conference on Intelligent Computing and signal processing (ICSP)

作者： Fangzhou Ye Jingru Wang Zhenning Li Zheng Jihan Congmin Yang Fudan University Shanghai China University of Maryland Maryland United States CentraleSupélec Paris France Jiangsu University of Science and Technology Zhenjiang China The University of Sheffield Sheffield United Kingdom

In recent years, the application of deep learning prediction models in predicting stock price trends has attracted extensive attention from researchers, which can help investors make better investment decisions. therefore, we aims to use the data set provided by Jane Street to establish a stock forecast model and develop a trading strategy. In this paper, we proposed a stock prediction model based on LightGBM. Experiments show that our model has better predictive power and higher returns compared with other machine learning models. Specifically, our LightGBM-based stock prediction model has a return rate of 24.7% higher than that of the Xgboost model and 8.9% higher than that of the Nerual Network.

关键词： Deep learning Computational modeling signal processing algorithms Standardization Predictive models signal processing Market research

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Research on Evaluation Method of Image Blending Based Image Simulation

Research on Evaluation Method of Image Blending Based Image ...

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6th international conference on Intelligent Computing and signal processing (ICSP)

作者： Yu Zhang Yan Zhang Zhiguang Shi Xiaoran Shi Di Liu Yuchang Suo National Key Laboratory of Science and Technology on Automatic Target Recognition College of Electronic Science and Technology National University of Defense Technology Changsha Hunan Province China

ISBN: (纸本)9781665404136;9781665447300

Object detection and tracking algorithms for specific targets are important technologies in the fields of autonomous driving and video surveillance. the verification of related algorithms and the training of data-driven machine learning methods often rely on high-quality and large-scale data support. therefore, data simulation methods are usually used to supplement the quantity and richness of data. this paper proposes an image simulation performance evaluation method based on the Probably Approximately Correct (PAC) learnable theory: experience consistency loss, which is used to quantitatively evaluate simulated images. Compared with the traditional performance evaluation method based on expert score and Turing test, this evaluation method can objectively and quantitatively reflect the characteristic consistency between the simulated image and the real image. the experimental results show that the evaluation results of this method are consistent with the qualitative analysis results, which can directly reflect the performance improvement of the simulation image for the machine learning model.

关键词： Performance evaluation Training Analytical models machine learning algorithms Target tracking Computational modeling machine learning

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Deep learning Assisted Relay Matching in Multi-user Pair and Multi-relay Untrusted Networks

Deep Learning Assisted Relay Matching in Multi-user Pair and...

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6th international conference on Intelligent Computing and signal processing (ICSP)

作者： Rugui Yao Qiannan Qin Yanyuan Hu Ye Fan Nan Qi Xiaoya Zuo School of Electronics and Information Northwestern Polytechnical University Xian Shanxi Nanjing University of Aeronautics and Astronautics Nanjing Jiangsu

ISBN: (纸本)9781665447300

In this paper, we study the relay matching based on deep learning in multiple user-pair and multiple untrusted-relay networks. In our previous work, Kuhn-Munkras (KM) algorithm was utilized with the goal of maximizing the system secrecy rate based on the weight design. However, the computational complexity of KM algorithm is very high with the number of user pairs and relays increased. Due to the fact that deep neural network (DNN) is capable of dealing with the complex nonlinear relationship and reducing computational complexity, the deep learning assisted maximum weight matching between user pairs and untrusted relays is adopted to address this issue. And batch normalization is used to accelerate the network convergence. the simulation results show that the proposed approach almost obtain the same average user secrecy rate as the conventional KM algorithm, while the system complexity is reduced.

关键词： Deep learning machine learning algorithms Simulation Neurons signal processing algorithms signal processing Relays

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DDoS Detection Method Based on Instance Transfer learning

DDoS Detection Method Based on Instance Transfer Learning

引用

IEEE international conference on signal and Image processing (ICSIP)

作者： Dongqi Wang Xin Chen Dongming Chen Software College Northeastern University Shenyang China

ISBN: (纸本)9781665446006

As a significant threat to network security, Distributed Denial of Service Attack (DDoS) is relatively simple to launch and difficult to prevent. Over the year, machine learning-based methods are widely used for DDoS detection. While, along with the evolution of attacking skill, machine learning-based detection technology’s general applicability faces severe challenges. Transfer learning is a machine learning method that seeks to overcome isolated learning tasks and use knowledge learned by one job to solve related ones. In this paper, we propose an instance-based transfer learning algorithm for DDoS attack detection. the proposed method treats publicly accessible DDoS datasets as the source domain and selects top m information gain instances for dimension reconstruction. the reconstructed selected features and target are integrated as input of the TrAdaBoost algorithm through dimension reconstruction. It provides a DDoS detection method with good general applicability for unseen attack categories detection.

关键词： learning systems machine learning algorithms conferences Transfer learning Network security Denial-of-service attack Computer crime

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A Novel Zero-velocity Detector for Pedestrian Inertial Navigation based on Deep learning

A Novel Zero-velocity Detector for Pedestrian Inertial Navig...

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6th international conference on Intelligent Computing and signal processing (ICSP)

作者： Ze Chen Xianfei Pan College of Intelligence Science and Technology National University of Defense Technology Changsha China

ISBN: (纸本)9781665447300

Due to the poor accuracy of the MIMU (Miniature Inertial Measurement Unit), ZUPT (zero-velocity update algorithm) is essential for foot-mounted inertial pedestrian navigation. Zero-velocity detection is the premise of ZUPT. Although conventional threshold-based zero-velocity detectors have achieved nice results in detection, it needs to tune thresholds and is not available in all cases. Modeling the precise mathematical relationship between thresholds and foot motion is hard, because lots of factors can affect the human foot motion. the zero-velocity detection is actually the binary classification which can realized by machine learning. We designed a novel zero-velocity detector based on deep learning. the convolutional neural network is used to train the detector based on the preprocessed inertial data. this detector does not need a motion classifier and have a better generality. Experiments are conducted and results show that this novel zero-velocity detector can perform better in combined motion than the threshold-based zero-velocity detector.

关键词： Deep learning Training Neural networks signal processing algorithms Detectors signal processing Convolutional neural networks

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