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An Integrated Model for Time Series Price Prediction Based on MODWT and BP Neural Network 5

An Integrated Model for Time Series Price Prediction Based o...

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5th international conference on Frontiers Technology of Information and Computer, ICFTIC 2023

作者： Wang, Jin Sinounited Investment Group Corporation Limited Postdoctoral Programme Beijing China

ISBN: (纸本)9798350309034

Time series exist widely in the real world and are often used to study price forecasting. By modeling the time series, it is possible to predict the future values of specific research objectives. With the continuous improvement of people's income and financial literacy, they are increasingly emphasizing the mining and analysis of data in the financial market. therefore, the stock price time series is often used for quantitative investment modeling to identify investment opportunities. However, the time series in the financial market is relatively complex, and the relevant data has characteristics such as nonlinearity and non-stationary. these characteristics make it difficult to achieve research objectives using general statistical econometric methods. While machine learning methods can effectively process complex data. In fact, with the continuous development of machine learning technology, it has also been used for related research in the financial market. Considering this, this paper uses BP neural network to model the time series. Furthermore, considering that the original time series and the new subsequences obtained by decomposing the original time series based on specific techniques have different characteristics. By studying new subsequences and modeling them, better research results can be achieved. therefore, this paper further constructs an integrated model based on MODWT and BP neural network to forecast the time series price. It is found that the integrated model based on MODWT and BP neural network shows better prediction effect, with smaller root mean square error and mean absolute error, and the fluctuation range of error is smaller and more concentrated. © 2023 IEEE.

关键词： BP neural network machine learning MODWT price prediction time Series wavelet transform

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IProceedings of the 4th international conference on Artificial Intelligence and Computer Engineering, ICAICE 2023

IProceedings of the 4th International Conference on Artifici...

引用

4th international conference on Artificial Intelligence and Computer Engineering, ICAICE 2023

ISBN: (纸本)9798400708831

the proceedings contain 200 papers. the topics discussed include: heat-aware graph data placement strategy for NVM;parameters estimation of photovoltaic models via an improved differential evolution algorithm;study on the extraction of law enforcement relationships in administrative law enforcement instrument data;online algorithm for exploring a grid polygon with two robots;research on gesture recognition method by improving dung beetle algorithm to optimize BP neural network;an improved algorithm for frequent sequence pattern mining based on PrefixSpan-ComplexPrefixSpan;machine learning-based research on reserve prediction of natural-gas-hydrates;enhancing coal mine safety monitoring algorithm using graph computing techniques;reverse distillation support vector data description for unsupervised anomaly detection;and few-shot object counting model based on self-support matching and attention mechanism.

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learning a Latent Space with Triplet Network for Few-Shot Image Classification 26

Learning a Latent Space with Triplet Network for Few-Shot Im...

引用

26th international conference on pattern recognition / 8th international Workshop on Image mining - theory and Applications (IMTA)

作者： Wu, Jiaying Hu, Jinglu Waseda Univ Grad Sch Informat Prod & Syst Fukuoka 8080135 Japan

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Few-shot image classification has attracted much attention due to its requirement of limited training data for target classes. Existing methods usually pretrain a network with images from the base set as feature extractor to obtain features of images from novel set. However, the pretrained feature extractor cannot extract accurate representation for categories have never seen, making images from novel set difficult to distinguish. To be specific, in the pretrained feature space, there exist a large number of overlapped areas between novel categories. To address this issue, it is crucial to acquire a space, where features from same class are gathering together and features from different classes are far away from each other. Since lots of experiments have proved that the triplet network is effective to achieve this goal, in this paper, we base our network on the Maximum a posteriori (MAP), learning a latent space with triplet network to project features from pretrained feature space into a more discriminative one. Experimental results on four few-shot benchmarks show that it significantly outperforms the baseline methods, improves around 1.09% similar to 13.09% than the best results in each dataset on both 1- and 5-shot tasks.

关键词： Training data Benchmark testing Feature extraction pattern recognition Task analysis Image classification

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A Multimodal Framework for Automated Teaching Quality Assessment of One-to-many Online Instruction Videos 26

A Multimodal Framework for Automated Teaching Quality Assess...

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26th international conference on pattern recognition / 8th international Workshop on Image mining - theory and Applications (IMTA)

作者： Pan, Yueran Wu, Jiaxin Ju, Ran Zhou, Ziang Gu, Jiayue Zeng, Songtian Yuan, Lynn Li, Ming Wuhan Univ Sch Comp Sci Wuhan Hubei Peoples R China Duke Kunshan Univ Ctr Teaching & Learning Kunshan Jiangsu Peoples R China Duke Kunshan Univ Data Sci Res Ctr Kunshan Jiangsu Peoples R China Fumi Hlth & Technol LLC Hunan Fujian Peoples R China

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

In the post-pandemic era, online courses have been adopted universally. Manually assessing online course teaching quality requires significant time and professional pedagogy experience. To address this problem, we design an evaluation protocol and propose a multimodal machine learning framework1 for automated teaching quality assessment of one-to-many online instruction videos. Our framework evaluates online teaching quality from five aspects, namely Clarity, Classroom interaction, Technical management of online teaching, Empathy, and Time management. Our method includes mid-level behavior feature extraction, high-level interpretable feature extraction, and supervised learning prediction. Our automated multimodal teaching quality assessment system achieves comparable performance to human annotators on our one-to-many online instruction videos. For binary classification, the best average accuracy of five aspects is 0.898. For regression, the best average means square error is 0.527 on a 0-10 scale.

关键词： Teaching Quality Assessment Multimodal Behavior Coding Interptretable Feature Extraction Speaker Diarization Emotion recognition

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Grid Investment Performance Portfolio Forecasting Model Based on PLS-VIP-GA-ELM 5th

Grid Investment Performance Portfolio Forecasting Model Base...

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Proceedings of the 5th international conference on Clean Energy and Electrical Systems - Proceedings of CEES 2023

作者： Li, Yizheng Peng, Dong Zhao, Lang Liu, Cong Xue, Yawei State Grid Economic and Technological Research Institute Co. Ltd. Beijing102209 China Economic and Technological Research Institute of State Grid Hunan Electric Power Co. Ltd. Changsha410004 China

ISBN: (纸本)9789819938872

this paper conducts data mining on the basic data of Hunan power grid investment performance indicators and 170 influencing factors from 2010 to 2020, compares and determines the ga-elm algorithm through a variety of machine learning prediction methods, processes the data of various influencing factors by using PLS-VIP and GA-ELM combined machine learning algorithm, and obtains the prediction results of power grid investment performance in 2020. By analyzing the final predicted values of all performance indicators, the overall prediction effect is good and the prediction accuracy is high. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： machine learning

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Federated learning for Lung Sound Analysis 5th

Federated Learning for Lung Sound Analysis

引用

5th international conference on Recent Trends in Image Processing and pattern recognition (RTIP2R)

作者： Farjana, Afia Makkar, Aaisha Univ South Dakota Dept Comp Sci Appl Res Lab 414 E Clark St Vermillion SD 57069 USA

ISBN: (纸本)9783031235986;9783031235993

Despite the general success of employing artificial intelligence (AI) to help radiologists perform computer-aided patient diagnosis, creating good models with tiny datasets at different sites is still tough. Medical image analysis is crucial for the quick and precise detection of lung disease and helps clinicians treat patients effectively while averting more fatalities. this is why real-time medical data management is becoming essential in the healthcare industry, especially for systems that monitor patients from a distance. To overcome this challenge, we propose a different approach by utilizing a relatively new learning framework. Individual sites may jointly train a global model using this approach, referred to as federated learning. Without explicitly sharing datasets, federated learning combines training results from various sites to produce a global model. this makes sure that patient confidentiality is upheld across all sites. Additionally, the additional supervision gained from partner sites' results enhances the global model's overall detection capabilities. this study's primary goal is to determine how the federated learning (FL) approachmay offer amachine learning average model that is robust, accurate, and unbiased in detecting lung disorders. For this aim, we analyze 325 Lung Sound audio recordings collected from https://***/and, transform this audio signal into Melspectrograms. Once the labeling and preprocessing steps were carried out, a Convolutional Neural Network (CNN)(FederatedNet) model was used to classify the respiratory sounds into healthy and unhealthy. We achieved the result of almost 88% validation accuracy. Furthermore, this paper discusses the application of FL and its overview. Lastly, we discuss the main challenges to federated learning adoption and potential future benefits.

关键词： Federated learning Lung sound machine learning

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Training Point-Based Deep learning Networks for Forest Segmentation with Synthetic data 27th

Training Point-Based Deep Learning Networks for Forest Segm...

引用

27th international conference on pattern recognition, ICPR 2024

作者： Raverta Capua, Francisco Schandin, Juan De Cristóforis, Pablo Facultad de Ciencias Exactas y Naturales Departamento de Computación Universidad de Buenos Aires Buenos Aires Argentina CONICET-Universidad de Buenos Aires Buenos Aires Argentina

ISBN: (纸本)9783031781278

Remote sensing through unmanned aerial systems (UAS) has been increasing in forestry in recent years, along with using machine learning for data processing. Deep learning architectures, extensively applied in natural language and image processing, have recently been extended to the point cloud domain. However, the availability of point cloud datasets for training and testing remains limited. Creating forested environment point cloud datasets is expensive, requires high-precision sensors, and is time-consuming as manual point classification is required. Moreover, forest areas could be inaccessible or dangerous for humans, further complicating data collection. then, a question arises whether it is possible to use synthetic data to train deep learning networks without the need to rely on large volumes of real forest data. To answer this question, we developed a realistic simulator that procedurally generates synthetic forest scenes. thanks to this, we have conducted a comparative study of different state-of-the-art point-based deep learning networks for forest segmentation. Using created datasets, we determined the feasibility of using synthetic data to train deep learning networks to classify point clouds from real forest datasets. Both the simulator and the datasets are released as part of this work. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Autoencoder Attractors for Uncertainty Estimation 26

Autoencoder Attractors for Uncertainty Estimation

引用

26th international conference on pattern recognition / 8th international Workshop on Image mining - theory and Applications (IMTA)

作者： Da Cruz, Steve Dias Taetz, Bertram Stifter, thomas Stricker, Didier IEE SA Bissen Luxembourg Univ Kaiserslautern Kaiserslautern Germany German Res Ctr Artificial Intelligence DFKI Darmstadt Germany

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

the reliability assessment of a machine learning model's prediction is an important quantity for the deployment in safety critical applications. Not only can it be used to detect novel sceneries, either as out-of-distribution or anomaly sample, but it also helps to determine deficiencies in the training data distribution. A lot of promising research directions have either proposed traditional methods like Gaussian processes or extended deep learning based approaches, for example, by interpreting them from a Bayesian point of view. In this work we propose a novel approach for uncertainty estimation based on autoencoder models: the recursive application of a previously trained autoencoder model can be interpreted as a dynamical system storing training examples as attractors. While input images close to known samples will converge to the same or similar attractor, input samples containing unknown features are unstable and converge to different training samples by potentially removing or changing characteristic features. the use of dropout during training and inference leads to a family of similar dynamical systems, each one being robust on samples close to the training distribution but unstable on new features. Either the model reliably removes these features or the resulting instability can be exploited to detect problematic input samples. We evaluate our approach on several dataset combinations as well as on an industrial application for occupant classification in the vehicle interior for which we additionally release a new synthetic dataset.

关键词： Training Visualization Uncertainty Computational modeling Estimation Training data Safety

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Application of Artificial Neural Network on South African Sign Language recognition System 5

Application of Artificial Neural Network on South African Si...

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5th international conference on Artificial Intelligence, Big data, Computing and data Communication Systems (ICABCD)

作者： Ndlovu, Wonder Alatise, Mary Tshwane Univ Technol Comp Syst Engn Dept City Of Tshwane South Africa

ISBN: (数字)9781665484220

ISBN: (纸本)9781665484220

Sign language assumes a significant part in filling the oral communication void for non-hearing or hard of hearing people. Despite the use of sign language, most people might not understand sign language clearly. Globally, research has been ongoing to explore and recommend various sign language recognition systems for different sign languages. On the other hand, researchers are still faced with challenges of practically implementing recognition systems. though, machine learning has emerged as one of the advanced sign language recognition systems. Artificial neural network (ANN) models and techniques are machine learning models which are widely utilised to improve the accuracy level thus offering better results when recognising sign language gestures. However, there are few or no actively existing South African Sign Language (SASL) automatic recognition systems readily available to facilitate communication seamlessly and instantly between non-hearing and hearing people. this research concluded that application of finger tracking technique significantly improved feature extraction, training and prediction of hand gestures produced under different conditions, i.e. skin color and background. While the application of artificial neural network on SASL recognition system led to better performance with 98.77% and 97.5% accuracy scores during training and prediction, respectively. In this paper, the proposed ANN utilised finger tracking techniques and forward and backward algorithms to implement a system that recognizes fingerspelling in SASL.

关键词： SASL Finger Tracking machine learning

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Application of Multilingual OCR Algorithm for Converting Text from Images and PDFs 5th

Application of Multilingual OCR Algorithm for Converting Tex...

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5th international conference on data Science, machine learning and Applications, ICDSMLA 2023

作者： Venkatesh, Maganti Chiranjeevi, S.V. Kumar, M. Siva Alam, S. Shiek Davanam, Ganesh Malchi, Sunil Kumar Department of AI & ML Aditya Engineering College Surampalem India Department of CSE N.B.K.R Institute of Science and Technology Vidyanagar AP Nellore India TN Chennai India AP Tirupati India

ISBN: (纸本)9789819780426

Multilingual OCR with audio output is an innovative approach that aims to convert text from imagesand PDFs into audio representations in multiple languages. this method leverages advancedtechniques in OCR, language identification, text-to-speech (TTS), and graph analysis to provide accurate and comprehensive results. the process begins with preprocessing the input images or PDFsto enhance their quality and remove any noise or distortions. Next, sophisticated OCR algorithms andmachine learning techniques are employed to extract text from the documents. this step involves character recognition, word segmentation, and sentence parsing to ensure precise and reliable text extraction. Language identification models are then utilized to determine the languages present in the extracted text, enabling multilingual support. the method also incorporates computer vision techniques and pattern recognition algorithms for graph analysis. Graphsand charts within the documents are identified and their components, such as labels, axes, and datapoints, are analyzed and interpreted. the textual and graphical information is combined to generate the final audio representation. By integrating cutting-edge OCR algorithms, language identification, language-specific TTS models, and graph analysis techniques, this novel method facilitates accurate multilingual OCR with audio output. It enhances accessibility and comprehension of textual and graphical information, catering to a diverse range of users with different language needs. this technology has the potential to benefit various domains, including education, accessibility services, and information dissemination in multilingual contexts. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep learning

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