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Multimodal Depression recognition Based on Self-Supervised learning

Multimodal Depression Recognition Based on Self-Supervised L...

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Computer Science, Electronic Information Engineering and Intelligent Control Technology (CEI), IEEE international conference on

作者： Guangkai Wang Rui Wang Qin Yang Yong Wu School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Wuhan Second Ship Design and Research Institute Wuhan China Ordnance NCO Academy Army Engineering University of PLA Wuhan China

ISBN: (数字)9798350378542

ISBN: (纸本)9798350378559

Depression is a widespread and severe mental health disorder, characterized by a significant prevalence and a high incidence rate, which presents numerous challenges in diagnosis and treatment. We introduce an innovative multimodal approach for depression recognition utilizing self-supervised learning. the method analyzes audio and video data through three steps: extracting image features, extracting audio features, and performing multimodal temporal fusion, enabling an assessment of depression severity. Extensive experiments conducted on the AVEC2014 dataset demonstrate that the proposed model achieves a Mean Absolute Error (MAE) of 7.013 and a Root Mean Square Error (RMSE) of 8.616 in depression prediction tasks, showcasing strong performance.

关键词： Image recognition Computational modeling Mental health Contrastive learning Predictive models Feature extraction Depression data mining Root mean square Intelligent control

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A Method for Constructing Enterprise Business Portrait Based on Label Layered and Extended Modeling

A Method for Constructing Enterprise Business Portrait Based...

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Electronic Information Engineering and Computer Technology (EIECT), international conference on

作者： Wenlei Chi Wei Jiang Shiyuan Lin Yue Zhang Mengqian Zhang State Grid Fujian Electric Power Co. Ltd. Fuzhou China State Grid Fujian Economic Research Institute Fuzhou China

ISBN: (数字)9798331528850

ISBN: (纸本)9798331528867

the goal of this academic research is to improve the accuracy and timeliness of the description of corporate operating conditions. By integrating in-depth modeling technology of label stratification, as well as cutting-edge data mining and machine learning algorithms, the business characteristics of enterprises are analyzed from multiple angles and in depth, and their dynamic update process is realized. In the deep learning architecture, the EPLLD model is constructed by integrating bidirectional long short-term memory network (BiLSTM) and feature extraction technology. the model has demonstrated excellent performance in processing large corporate text data sets. the research results show that the EPLLD model significantly surpasses the performance of existing models in key performance evaluation criteria such as precision, recall rate and F1 score. Experiments have shown that this strategy can significantly enhance the resolution of corporate portraits and the accuracy of predictions, thereby providing solid data support for enterprises and helping corporate managers make more targeted decisions.

关键词： Performance evaluation Analytical models Accuracy machine learning algorithms Computational modeling Refining Solids data models data mining Business

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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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Predictive analytics and data mining in healthcare 12

Predictive analytics and data mining in healthcare

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12th international conference on Computing Communication and Networking Technologies, ICCCNT 2021

作者： Arjun, A. Srinath, Adithi Chandavarkar, B.R. Department of Computer Science and Engineering National Institute of Technology Karnataka Surathkal Mangalore India

ISBN: (纸本)9781728185958

machine learning and data mining for healthcare. there has been an enormous growth in the field of HIT (health information technology) in the recent years. Be it detection of certain diseases, scanning of organs, finding tumors, these machine oriented operations without human intervention, have certainly increased the quality of medical attention one can get, and the technology required has come a long way. Health data tends to be inherently complex with exceptions in almost all cases. data mining is the technique of converting raw data into a meaningful format. Analysis and prediction on such data, although computationally and algorithmically complex, is an emerging technology that is a small step to more proactive and preventive automated treatment *** are various data mining techniques such as classification, clustering, association, regression,prediction, pattern recognition etc [I]. Even the efficiency of certain medicines can be found using machine learning techniques, which is a life saving and cost effective method. In this paper, we are going to use machine learning as a tool for predictive analysis to predict chronic kidney diseases based on the Chronic disease dataset taken from VCI M L repository. We will be applying machine learning algorithms, specifically decision trees, to build a classifier to predict if a person has the disease or not. this paper shows the issue that specific machine learning algorithms need to be tailor-made to specific nature of medical data. © 2021 IEEE.

关键词： Decision trees

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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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Research on the Method of Constructing Knowledge Graph of Affective Disorder Based on Multi-Source data Fusion 5

Research on the Method of Constructing Knowledge Graph of Af...

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5th international conference on Information Technologies and Electrical Engineering, ICITEE 2022

作者： Wang, Yang Yang, Weihong School of Artificial Intelligence and Big Data Luzhou Vocational and Technical College Sichuan Luzhou646000 China School of Computer Science and Technology Harbin Institute of Technology Heilongjiang Harbin150006 China

ISBN: (纸本)9781450396806

Affective disorder (Depression) has the characteristics of high incidence rate, low recognition rate, high disability and death rate, and has become a serious public health problem that needs to be solved urgently. It is particularly important to use machine learning and big data mining technology to build a complete knowledge graph of affective disorders. In this paper, multi-source data fusion is used to construct the original corpus of the knowledge graph of affective disorders. In the information extraction stage, we designed a machine learning algorithm based on Hidden Markov Model (HMM). the algorithm can extract entities and entity mapping relationships from corpus to form entity database and triplet relationship database. In the knowledge fusion stage, multi-source heterogeneous knowledge fusion algorithm is used to complete entity alignment and relationship deduction. Finally, the open-source graphic database Neo4j is used as the underlying storage structure, so as to realize the visualization of the knowledge graph of affective disorders. © 2022 ACM.

关键词： data fusion

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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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