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2nd IEEE International Conference on Electronic Technology, Communication and Information, ICETCI 2022

作者： Ni, Shangyuan Ng, Taishing Xue, Ling Zhang, Jiawen Data Science New York University Shanghai Shanghai China Paul G. Allen School of Computer Science & Engineering University of Washington Seattle United States School of Mathematics and Statistics Henan University of Science and Technology Luoyang China Applied & Computational Mathematical Sciences University of Washington Seattle United States

ISBN: (数字)9781728181158

ISBN: (纸本)9781728181158

Event relation extraction is an important research direction in the field of information extraction. Compared with named entity recognition, entity relation extraction, and event extraction. Event relation extraction can extract common-sense knowledge that is more in line with human intuition from unstructured texts and can be organized together in the form of logical interaction. In this paper, the current event relation extraction works are reviewed in detail. This paper is organized according to different types of event relations. In addition, this paper also introduces two kinds of event relation data sets. Finally, we discuss the challenges and prospects of event relation extraction. © 2022 IEEE.

关键词： Deep learning

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LSTM-XGB: A New Deep Learning Model for Human Activity Recognition based on LSTM and XGBoost 7

LSTM-XGB: A New Deep Learning Model for Human Activity Recog...

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7th International Conference on Digital Arts, Media and Technology, DAMT 2022 and 5th ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering, NCON 2022

作者： Mekruksavanich, Sakorn Jantawong, Ponnipa Jitpattanakul, Anuchit University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao Thailand King Mongkut's University of Technology North Bangkok Faculty of Applied Science Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Bangkok Thailand

ISBN: (纸本)9781665495103

Recognition of human activity focuses on identifying various human motions and behaviors using data acquired from multiple types of sensors, a branch of computer science. Considering time series data, deep learning algorithms show promise compared to laborious handcrafted feature extraction methods, which are highly dependent on the quality of the used domain parameters. This paper proposes a novel deep learning model for HAR issues called the Long Short Term Memory Neural network with eXtreme Gradient Boosting (LSTM-XGB), developed on recurrent neural networks with eXtreme Gradient Boosting (XG-Boost). The LSTM-XGB comprises LSTM layers to understand the characteristics of the input and is capable of learning features automatically, followed by XGBoost in the final layer to forecast the class labels, which serves to enhance detection capability. When sufficient circumstances are satisfied, the LSTM-XGB model can be simpler by lowering the number of parameters. It is not essential to readjust the weighting factors throughout a backpropagation phase. Investigations using smartphone sensor data were carried out using a publicly available standardized HAR dataset called the UniMiB-SHAR dataset, which contains activities associated with everyday life and activities related to falls. The experiments show that the LSTM-XGB provides much superior identification capabilities than other standard deep learning techniques, with the highest accuracy of 92.59%. Furthermore, the LSTM-XGB models surpass the existing best-practice models on the same dataset. © 2022 IEEE.

关键词： Long short-term memory

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A Novel Perspective on Brain Tumor Classification Using Hybrid Algorithm

A Novel Perspective on Brain Tumor Classification Using Hybr...

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computer, Electronics & Electrical engineering & their Applications (IC2E3), International Conference on

作者： R. Jayanthi A. Hepzibah Christinal R. Hephzibah T. Shekinah Chandrajit Bajaj D. Abraham Chandy Department of Mathematics Karunya University Coimbatore India Department of Computer Science and Engineering Loyola – ICAM College of Engineering and Technology Chennai Computational Applied Mathematics Chair in Visualization University of Texas Austin Department of Electronics and Communication Engineering Karunya University Coimbatore India

Brain tumor classification is a critical task in medical imaging, where the early and accurate detection of brain tumors is crucial for patient treatment and survival. In recent years, Machine Learning techniques have emerged as a promising approach for automated brain tumor classification. In this paper, we investigate the performance of three ML classifiers – Support Vector Machine, Naïve Bayes, and an ensemble of SVM and Naïve Bayes – for the two-class classification of brain tumor data into a tumor and no tumor classes. Our experiments demonstrate that the ensemble of SVM and Naïve Bayes classifier achieved an accuracy of 97% outperforming the other classifiers. These results indicate that our ensemble classifier can be a powerful tool for the accurate and efficient classification of brain tumor data, potentially leading to improved patient outcomes.

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Pre-Impact Fall Detection Based on Wearable Inertial Sensors using Hybrid Deep Residual Neural Network 6

Pre-Impact Fall Detection Based on Wearable Inertial Sensors...

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6th International Conference on Information Technology, InCIT 2022

作者： Mekruksavanich, Sakorn Jantawong, Ponnipa Jitpattanakul, Anuchit University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science Bangkok Thailand

ISBN: (纸本)9781665489126

Falls are the primary cause of fatal and nonfatal injuries among the elderly. Consequently, pre-impact fall detection that identifies a fall before the body's collision with the ground is of essential importance. In the latest days, academics have moved their attention from post-impact fall detection to pre-impact fall detection. Pre-impact fall detection systems typically use either a threshold-based or machine learning-based approach, and the thresholding is challenging to determine accurately for threshold-based methods. In addition, while additional features can sometimes assist in categorizing falls and non-falls more precisely, the value computation of prominent features will be too time-intensive, using too much of the algorithm's operating time. We proposed a pre-impact fall detection method employing wearable inertial sensors and a deep residual model to address the limitations of feature extraction, threshold definition, and algorithm sophistication. After training on a large-scale motion dataset known as the KFall, the suggested deep learning model could identify with 91.87% accuracy in 0.5 second. © 2022 IEEE.

关键词： Fall detection

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Comparative Analysis of CNN-based Deep Learning Approaches on Complex Activity Recognition 7

Comparative Analysis of CNN-based Deep Learning Approaches o...

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作者： Mekruksavanich, Sakorn Jantawong, Ponnipa Jitpattanakul, Anuchit University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Faculty of Applied Science Department of Mathematics Bangkok Thailand

ISBN: (纸本)9781665495103

For those with Parkinson's disease, freezing of gait (FOG) could be a distressing condition. In individuals with diabetes, FOG could have a significant influence on their quality of life and cause falls, which can be dangerous. A questionnaire has traditionally been used to measure the severity of FOG symptoms;nevertheless, this technique is subjective and can not accurately reflect the intensity of this illness. The adoption of sensor-based devices may give reliable and objective information to follow the progression of symptoms, allowing for better Parkinson's disease management and therapy. A compact deep convolutional neural network comprising squeeze and excitation components, which we called SE-DeepConV model, was designed to identify FOG utilizing wearable data from the sensors. The SE-DeepConV model was constructed and evaluated using a publicly accessible standard FOG dataset, particularly the Daphnet dataset. The SE-DeepConV model outperforms other benchmark deep learning models in terms of accuracy, with the most outstanding accuracy of 95.65%, according to research observations. © 2022 IEEE.

关键词： Deep neural networks

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Mathematical modeling and analysis of schistosomiasis transmission dynamics

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International Journal of Modeling, Simulation, and Scientific Computing 2021年第4期12卷 58-76页

作者： Umar T.Mustapha Salihu S.Musa Muhammad A.Lawan Aliyu Abba Evren Hincal Musa D.Mohammed Bashir D.Garba Rabiu B.Yunus Shehu A.Adamu Daihai He Department of Mathematics Near East University TRNC Mersin 10 Turkey Department of Mathematics Federal University Dutse Nigeria Department of Applied Mathematics The Hong Kong Polytechnic University Hong Kong Department of Mathematics Kano University of Science and Technology Wudil Nigeria Department of Statistics Jigawa State Polytechnic DutseNigeria Department of Computer Engineering Institute of Continuing Education Kano University of Science and Technology Wudil Nigeria Department of Mathematics and Statistics Kano State Polytechnic Nigeria

Schistosomiasis is a parasitic disease from the family of Schistosomatidae and genus Schistosoma,which is caused by blood *** disease is endemic in many countries and still a serious threat to global public health and *** this paper,a new deterministic model is designed and analyzed qualitatively to explore the dynamics of schistosomiasis transmission in human,cattle and snail *** from our mathematical analysis show that the model has a disease-free equilibrium(DFE)which is locally asymptotically stable(LAS)whenever a particular epidemiological threshold quantity,also known as basic reproduction number(R0)is less than *** analysis shows that the model has a unique endemic equilibrium(EE)which is globally asymptotically stable whenever R0>1 and unstable when R0<***,we adopt partial rank correlation coefficient for sensitivity analysis to reveal the most important parameters for effective control and mitigation of schistosomiasis disease in a ***,we obtain some numerical results by simulating the entire dynamics of the model,which show that the infections in the compartments of each population decrease with respect to *** further indicates that avoiding contact with infected human,cattle or infested water is vital to prevent the spread of schistosomiasis disease infection.

关键词： Schistosomiasis disease basic reproduction number stability analysis sensitivity analysis

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Deep Learning-based Action Recognition for Pedestrian Indoor Localization using Smartphone Inertial Sensors 7

Deep Learning-based Action Recognition for Pedestrian Indoor...

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ISBN: (纸本)9781665495103

For decades, the identification of human action has been one of the most critical study areas in artificial intelligence research. The study in human activity recognition aims to classify people's behavior based on data that is already accessible, such as pictures, visual, sensing, or stream recorded data, among other things. A common distinction made by most human activity recognition methods is between daily tasks such as walking and specific behaviors like standing or sitting. More specialized functions targeted to the use-case of pedestrian interior navigation are investigated in this study. These activities include several types of stationary and movement activities and various other activities. A deep learning-based human activity recognition framework is presented that can be used to recognize people's actions inside an indoor localization situation by analyzing smartphone inertial sensor data. Furthermore, we introduced a 1D-ResNet-SE model in the framework and assessed it using a publicly available dataset known as the Activity Logs dataset. According to the experimental measurements, the 1D-ResNet-SE surpasses other standard deep learning techniques in terms of accuracy, with a maximum accuracy of 84.11% in the general scenario. © 2022 IEEE.

关键词： Smartphones

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Accuracy Improvement of Complex Sensor-based Activity Recognition Using Hybrid CNN 6

Accuracy Improvement of Complex Sensor-based Activity Recogn...

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6th International Conference on Information Technology, InCIT 2022

作者： Mekruksavanich, Sakorn Jantawong, Ponnipa Jitpattanakul, Anuchit University of Phayao Department of Computer Engineering School of Information and Communication Technology Phayao Thailand King Mongkut's University of Technology North Bangkok Faculty of Applied Science Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Bangkok Thailand

ISBN: (纸本)9781665489126

Applying smartphones and smartwatches for Human ActivitiesRecognition (HAR) is beneficial for analyzing the participant's dailybehavior and identifying mishaps, such as fall detection, enabling early healthcare monitoring and fall detection. Numerous HAR studies have investigated different recognition models based on sensor data. Nonetheless, this research mainly focused on basic motions such as sitting, standing and walking. In actual settings, human actions are often accomplished in complex ways. This research will examine the recognition of complicated activities using CNN and LSTM networks as deep learning classifiers. Furthermore, this study presented hybrid CNN for enhancing the identification of complicated activities. We evaluated these deep learning classifiers using WISDM-HARB, a publically available benchmark HAR. This dataset used accelerometer and gyroscope data from smartphones and smartwatches to collect sensor data of complicated activities. The experiment demonstrates that all deep learning classifiers recognize basic actions more accurately than complicated ones. Also, it was observed that the hybrid CNN classifier presented had the most fantastic accuracy with 96.25%. According to the research, these classifiers are effective at recognizing basic activities, but their performance decreases as the sophistication of the actions increases. Nonetheless, hybrid CNN models are competent for handling complicated tasks. © 2022 IEEE.

关键词： Smartphones

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Sequential Solution Strategies for the Cahn-Hilliard-Biot Model

Sequential Solution Strategies for the Cahn-Hilliard-Biot ...

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European Conference on Numerical mathematics and Advanced Applications, ENUMATH 2023

作者： Storvik, Erlend Riethmüller, Cedric Both, Jakub W. Radu, Florin Adrian Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Bergen Norway Institute of Applied Analysis and Numerical Simulation University of Stuttgart Stuttgart Germany Centre for Modeling of Coupled Subsurface Dynamics Department of Mathematics University of Bergen Bergen Norway

ISBN: (纸本)9783031861680

This paper presents a study of solution strategies for the Cahn-Hilliard-Biot equations, a complex mathematical model for understanding flow in deformable porous media with changing solid phases. Solving the Cahn-Hilliard-Biot system poses significant challenges due to its coupled, nonlinear and non-convex nature. We explore various solution algorithms, comparing monolithic and splitting strategies, focusing on both their computational efficiency and robustness. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Computational efficiency

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Understanding complex crowd dynamics with generative neural simulators

arXiv

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arXiv 2024年

作者： Minartz, Koen Hendriks, Fleur Koop, Simon Martinus Corbetta, Alessandro Menkovski, Vlado Department of Mathematics and Computer Science Eindhoven University of Technology Netherlands Department of Mechanical Engineering Eindhoven University of Technology Netherlands Department of Applied Physics and Science Education Eindhoven University of Technology Netherlands Eindhoven Artificial Intelligence Systems Institute Netherlands

Understanding the dynamics of pedestrian crowds is an outstanding challenge crucial for designing efficient urban infrastructure and ensuring safe crowd management. To this end, both small-scale laboratory and large-scale real-world measurements have been used. However, these approaches respectively lack statistical resolution and parametric controllability, both essential to discovering physical relationships underlying the complex stochastic dynamics of crowds. Here, we establish an investigation paradigm that offers laboratory-like controllability, while ensuring the statistical resolution of large-scale real-world datasets. Using our data-driven Neural Crowd Simulator (NeCS), which we train on largescale data and validate against key statistical features of crowd dynamics, we show that we can perform effective surrogate crowd dynamics experiments without training on specific scenarios. We not only reproduce known experimental results on pairwise avoidance, but also uncover the vision-guided and topological nature of N-body interactions. These findings show how virtual experiments based on neural simulation enable data-driven scientific discovery. Copyright © 2024, The Authors. All rights reserved.

关键词： Stochastic systems

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