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data-Driven Disaster Management in a Smart City 5th

Data-Driven Disaster Management in a Smart City

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5th EAI international conference on Intelligent Transport Systems (INTSYS)

作者： Goncalves, Sandra P. Ferreira, Joao C. Madureira, Ana Inst Univ Lisboa ISCTE IUL ISTAR Lisbon Portugal Politecn Porto 2ISRC Inst Super Engn Porto Porto Portugal

ISBN: (纸本)9783030976033;9783030976026

Disasters, both natural and man-made, are extreme and complex events with consequences that translate into a loss of life and/or destruction of properties. the advances in IT and Big data analysis represent an opportunity for the development of resilient environments once the application of analytical methods allows extracting information from a significant amount of data, optimizing the decision-making processes. this research aims to apply the CRISP-DM methodology to extract information about incidents that occurred in the city of Lisbon with emphasis on occurrences that affected buildings, constituting a tool to assist in the management of the city. through this research, it was verified that there are temporal and spatial patterns of occurrences that affected the city of Lisbon, with some types of occurrences having a higher incidence in certain periods of the year, such as floods and collapses that occur when there are high levels of precipitation. On the other hand, it was verified that the downtown area of the city is the area most affected by occurrences. Finally, machine learning models were applied to the data and the predictive model Random Forest obtained the best result with an accuracy of 58%.

关键词： Disaster management data mining machine learning Smart city

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Spatiotemporal Visual Tracker Based on Deformable Attention

Spatiotemporal Visual Tracker Based on Deformable Attention

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

作者： Zhuo Wu Askar Hamdulla Yucheng Huang School of Computer Science and Technology(School of Cyberspace Security) Xinjiang University Urumqi China School of Intelligence Science and Technology(School of Future Technology) Xinjiang University Urumqi China

ISBN: (数字)9798350355925

ISBN: (纸本)9798350355932

At present, the visual tracking method based on Transformer has made significant progress, and has shown excellent performance on various data sets. However, the traditional Transformer tracker pays too much attention to global features, and its intensive attention calculation will increase the space-time complexity of the tracking algorithm. And the features of the relevant region will be affected by the features of the non-relevant region, resulting in a decrease in tracking accuracy. In addition, the Transformer tracker lacks the ability to utilize the spatiotemporal information between frames, which further hinders the improvement of its tracking performance. To solve these problems, this paper proposes a new tracker SVT-DA based on deformable attention. the tracker captures local and global important features through CNN and deformable attention. In addition, it uses a cross-attention module nested with self-attention to achieve deep semantic information interaction between template features and search features. In order to verify the effectiveness of the proposed method, we conduct experiments on LaSOT, GOT-10K, OTB100 and VAU123 datasets. the result indicators are 4%~5% higher than the baseline method. the experimental results show that our method improves the tracking performance while reducing the number of model parameters, and is highly competitive with other state-of-the-art methods.

关键词： Visualization Target tracking Semantics Transformers Feature extraction Spatiotemporal phenomena pattern recognition Convolutional neural networks Information exchange Resilience

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Knowledge Distillation Scheme for Named Entity recognition Model Based on BERT

Knowledge Distillation Scheme for Named Entity Recognition M...

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international conference on machine learning, Big data and Business Intelligence (MLBDBI)

作者： Chengqiong Ye Alexander A. Hernandez College of Computing and Information Technologies National University Manila Philippines School of Big Data and Artificial Intelligence Anhui Xinhua University Hefei China

In order to improve the applicability of named entity recognition model in industrial application scenarios, this paper studies the BERT named entity recognition task. From the perspective of lightweight model, an adaptive weight distillation method is designed to compress the BERT named entity recognition model. By adjusting the weight hyperparameter of distillation loss to an adaptive function, this method enables the model to automatically adjust the weight value according to the training process, avoiding the influence of manual parameter adjustment. Furthermore, by decreasing the middle hidden layer’s dimension, this method compresses the teacher model from the width and incorporates the distillation of the middle Transformer layer of the model into the distillation process, allowing it to fully utilize the rich semantic information in the deep structure of the model. Ultimately, a light-weight, well-functioning model with a straightforward structure and few parameters is trained. By employing the suggested technique, the model’s inference speed can be increased by seven times while reducing the number of model parameters to 15% of the original model with little loss of accuracy.

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Hierarchical Clustering Approach for Information-Extreme machine learning of Hand Brush Prosthesis 5

Hierarchical Clustering Approach for Information-Extreme Mac...

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5th international conference on Computational Linguistics and Intelligent Systems (COLINS)

作者： Dovbysh, Anatoliy Piatachenko, Vladyslav Sumy State Univ 2 Rymskogo Korsakova St UA-40007 Sumy Ukraine

the article discusses a machine learning method for a control system for a hand limb prosthesis with a non-invasive biosignal reading system, which operates in the mode of agglomerative hierarchical clustering of electromyographic data. the method was developed within the framework of information-extreme intelligent data analysis technology, which is based on maximizing the information capacity of the system in the process of machine learning. In contrast to the existing methods of data mining, the method of information-extreme machine learning is developed as part of a functional approach to modeling cognitive processes inherent in human formation and decision-making. this approach makes it possible to endow the prosthesis control system with the properties of adaptability to arbitrary conditions for the formation of input signals and flexibility in retraining the system due to the expansion of the alphabet of recognition classes. In addition, the decision rules based on the geometric parameters of the hyperspherical containers of the recognition classes obtained in the process of machine learning are invariant to the multidimensionality in the feature space. Based on the proposed categorical model, a machine learning algorithm using an agglomerative hierarchical data structure has been developed. As a criterion for optimizing the parameters of machine learning, a modification of the Kullback information measure is used, which is a functional of the exact characteristics of classification decisions. the results of physical modeling confirm the high functional efficiency for the proposed hierarchical information-extreme machine learning method of the hand with a non-invasive system for reading biosignals prosthesis control system.

关键词： information-extreme intellectual technology hierarchial clustering machine learning information criterion control system prosthesis electromyographic sensor

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Nepal Stock Market Movement Prediction with machine learning 2021

Nepal Stock Market Movement Prediction with Machine Learning

引用

5th international conference on Information System and data mining (ICISDM)

作者： Zhao, Shunan Nankai Univ Coll Comp Sci Tianjin Peoples R China

ISBN: (纸本)9781450389549

Financial market predicting is a popular theme of lots of researches in recent years. However, the majority of previous studies are focus on markets in great countries like China and United States, while some small countries are drawn less attention. To cover this shortage in current literature, we determined to use and compare 17 types of machine learning models to foresee Nepal market in this paper. Based on stock prices, 10 technical indicators were computed as input features. In addition, we also added emotional factors extracted from financial news to improve the prediction performance, which was evaluated by accuracy and F1 score. We predicted whether the closing price would rise or descend after three horizons: 1-day movement, 15-day movement and 30-day movement. From our experiment results, we found that linear SVM and XGBoost perform best and are the best options for further consideration in the trading process.

关键词： Stock Prediction machine learning Nepal Market

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Intracardiac Electrogram Signals Key Feature Points recognition Using Dense Convolutional Network

Intracardiac Electrogram Signals Key Feature Points Recognit...

引用

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

作者： Jiang Yihang Gu Kaihao Wu Xiaomei Department of Biomedical Engineering School of information Science and Technology Fudan University Shanghai China Department of Biomedical Engineering Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention (MICCAI) of Shanghai School of information Science and Technology Fudan University Academy for Engineering and Technology Shanghai Engineering Research Center of Assistive Devices Shanghai China Yiwu Research Institute of Fudan University Zhejiang China

ISBN: (数字)9798350355925

ISBN: (纸本)9798350355932

During radiofrequency ablation procedures for atrial fibrillation patients, physicians can assist in locating ablation targets by analyzing the conduction pathways and low-voltage areas of the intracardiac electrogram signals. the feature points of intracardiac electrogram signals serve as criteria for assessing excitation patterns and low-voltage areas. therefore, extracting feature points from intracardiac signals is of significant value. To identify key points in intracardiac electrogram signals through deep learning, this experiment collected intracardiac electrogram signals from 21 clinical patients, which were then filtered using wavelet transformation. Subsequently, each excitation was treated as a signal group, indicating the key feature points within. Finally, a total of 36,183 groups of intracardiac electrogram signals annotated with key points were obtained. the dataset consisted of data from 17 patients allocated for training, data from 2 patients reserved for testing, and data from the remaining 2 patients designated for validation purposes. Training was conducted using a DenseNet network incorporating both convolutional attention and feature pyramid modules. the final network successfully identifies the feature points of intracardiac electrogram signals. It outputs six feature points, including the starting point, first rising point, first falling point, peak point, valley point, and termination point, with average absolute errors of only 0.34, 0.27, 0.31, 0.01, 0.27, and 0.42 ms, respectively. the final detection results are highly satisfactory, indicating the potential utility of the findings in assisting the analysis of intracardiac electrogram signals. the research outcomes presented in this paper lay a foundation for the analysis of intracardiac electrogram signals.

关键词： Training Wavelet transforms Radio frequency Low voltage Training data Medical services Myocardium Feature extraction pattern recognition Testing

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Automatically mining relevant variable interactions via sparse Bayesian learning 25

Automatically mining relevant variable interactions via spar...

引用

25th international conference on pattern recognition (ICPR)

作者： Yafune, Ryoichiro Sakuma, Daisuke Takayanagi, Mirai Tabei, Yasuo Saito, Noritaka Saigo, Hiroto Kyushu Univ Fukuoka Japan RIKEN AIP Tokyo Japan

ISBN: (纸本)9781728188089

With the rapid increase in the availability of large amount of data, prediction is becoming increasingly popular, and has widespread through our daily life. However, powerful nonlinear prediction methods such as deep learning and SVM suffer from interpretability problem, making it hard to use in domains where the reason for decision making is required. In this paper, we develop an interpretable non-linear model called itemset Sparse Bayes (iSB), which builds a Bayesian probabilistic model, while simultaneously considering variable interactions. In order to suppress the resulting large number of variables, sparsity is imposed on regression weights by a sparsity inducing prior. As a subroutine to search for variable interactions, itemset enumeration algorithm is employed with a novel bounding condition. In computational experiments using real-world dataset, the proposed method performed better than decision tree by 10% in terms of r(2). We also demonstrated the advantage of our method in Bayesian optimization setting, in which the proposed approach could successfully find the maximum of an unknown function while maintaining transparency. Apart from Bayesian optimization with Gaussian process, iSB gives us a clue to understand which variables interactions are important in optimizing an unknown function.

关键词： Decision trees

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the Performance Analysis of Several machine learning Methods in Predicting Diabetes

The Performance Analysis of Several Machine Learning Methods...

引用

pattern recognition and Artificial Intelligence (PRAI), international conference on

作者： Yuanyuan Wu Zhuomin Xia Huizhou Liu Mengxing Huang Yu Zhang College of Information and Communication Engineering Hainan University Haikou P.R. China College of Computer Science and Technology Hainan University Haikou P.R. China

Diabetes is a chronic disease characterized by hyperglycemia. Early screening of diabetes patients will help reduce the incidence of diabetes. Accurately modeling the early symptoms of diabetes to reduce the incidence of diabetes has become an urgent problem. therefore, it is a valuable research direction to mine and study the information of diabetes patients, predict the occurrence of noninfectious diseases, and assist doctors to make correct diagnoses. the precision of different data mining techniques has been compared in this research, employing machine learning techniques to predict the incidence of diabetes. the University of California, Irvine’s open-source dataset has been used as the main database for experiment. Different machine learning classifiers such as random forest, neural network and support vector machine have been employed in this experiment. this research gives a technology review of machine learning algorithms that can be used for diabetes prediction through classification. the experimental results indicate that random forest performs better than other machine learning methods.

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SSL400 - A Comprehensive Word Level dataset for Sinhala Sign Language recognition 5

SSL400 - A Comprehensive Word Level Dataset for Sinhala Sign...

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5th international conference on Advanced Research in Computing, ICARC 2025

作者： Abhishek, Yohan Sumanathilaka, T.G.D.K. Informatics Institute of Technology School of Computing Colombo006 Sri Lanka

ISBN: (纸本)9798331530983

In the context of increasing digital accessibility and the need for inclusive communication technologies, the demand for automated sign language recognition has grown significantly. However, Sinhala Sign Language (SSL), used by the Sri Lankan deaf community, remains underexplored due to the lack of labelled datasets and the challenges of developing recognition models for low-resource languages. this study addresses this gap by introducing an SSL dataset, specifically curated to support real-time recognition of 384 commonly used words in daily and work-related contexts. the methodology for dataset creation involved expert consultations, a structured data collection process, and multi-step labelling. these steps ensured the accurate labeling of video data, which can be used for machine learning tasks like gesture recognition, sentence formation, and speech translation. the proposed dataset lays the foundation for SSL recognition models that are fast, lightweight, and optimized for low-resource environments. Additionally, we explore the performance of initial recognition models using this dataset, demonstrating its applicability in real-world scenarios. the dataset and research outcomes provide valuable resources for the development of inclusive AI-driven communication tools for the Sinhala-speaking deaf community. this contribution addresses the pressing need for better technological support for underrepresented languages in the field of sign language recognition and processing. © 2025 IEEE.

关键词： Multi-task learning

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Vision-Based Human Activity recognition Utilizing DCNN and Stacked Multi-stage Multimodal Fusion Strategy

Vision-Based Human Activity Recognition Utilizing DCNN and S...

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international conference on Mechatronics and machine Vision in Practice (M2VIP)

作者： Wenqin Zhu Wei Meng Nian Peng Kun Chen Quan Liu School of Information Engineering Wuhan University of Technology Wuhan China

ISBN: (数字)9798350391916

ISBN: (纸本)9798350391923

Human Activity recognition (HAR) possesses significant potential in pattern recognition, machine learning, wearable computing, computer vision, and notably in rehabilitation assistance. With the ability to automatically track patients' recovery progress, it addresses the issue of insufficient human resources. Presently, improving recognition accuracy hinges on the fusion of multiple information sources like physiological signals, motion sensors, and visual sensors. In this study, a multi-stage multimodal fusion strategy based on stacked integration with Deep Convolutional Neural Network (DCNN) is proposed. Firstly, the inertial sensor and depth sensor data are converted into image data, and the data enhancement method is used to extract edge information using Prewitt, and features are extracted and spliced by the DCNN to realize multimodality at the feature layer. At the decision layer, the two unimodal decisions are fused using the stacking fusion strategy to obtain the final classification decision. the experimental results show that this strategy exhibits significant advantages in human motion recognition, with accuracy rates of 99.85% and 99.51% on Kinect V2 and UTD-MHAD datasets, respectively, exceeding the state-of-the-art deep learning methods on the same dataset.

关键词： Visualization Accuracy Tracking Stacking Sensor fusion Feature extraction data models Human activity recognition data mining Wearable devices

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