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Early recognition of Sudden Cardiac Death Combined with Deep Transfer learning and Superficial ECG Features

Early Recognition of Sudden Cardiac Death Combined with Deep...

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

作者： Yue Zhang Xingzeng Cha Bingqian Cui Dakun Lai School of Electronic Science and Engineering University of Electronic Science and Technology of China Chengdu China

Sudden cardiac death (SCD) is a global public safety issue that poses a serious threat to human life and health. Moreover, the clinical records of SCD data are extremely limited. this paper proposed an early prediction algorithm for SCD based on deep transfer learning. the algorithm used a two-stage training approach, which included pre-training and fine-tuning of convolutional neural networks (CNNs) to automatically capture the deep heart rate features (HRFs) in high-risk electrocardiogram (ECG) signals before SCDs. the aim is to explore automatic early prediction of SCD under limited ECG data training. this paper extracted three HRFs as input sets from 30 second single lead ECG signals of 18 patients with malignant arrhythmia and 18 healthy subjects, including RR interval, dRR interval, and turbulence onset (TO). then, the ten-fold crossover method was used to evaluate the classification performance of this model. Finally, the results showed that the average accuracy (Acc), sensitivity (Se), and specificity (Sp) of predicting SCD within 30 minutes before onset were 91.79%, 87.00%, and 96.63%, respectively. the transfer learning algorithm proposed in this paper effectively addresses the issue of insufficient data in the early prediction models of SCD. It also achieves early and accurate prediction of high-risk signs of SCD by using ECG. this is expected to form an intelligent and effective monitoring and early warning technology for high-risk patients of SCD.

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XGBoost-based student performance prediction in tiered instruction 4

XGBoost-based student performance prediction in tiered instr...

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4th international conference on Computer Science and Educational Informatization, CSEI 2022

作者： Chen, Yawen Zhai, Linbo Shandong Normal University Jinan China

ISBN: (数字)9781839537882

ISBN: (纸本)9781839537882

Student performance prediction is one of the most important subjects in educational data mining. As a modern technology, machine learning has the capabilities of feature extraction and data modeling, which provides necessary guarantee for diversified application scenarios. However, machine learning methods have not been integrated with practical teaching strategies after diverse prediction experiments in modern education. In addition, massive features are directly utilized as input variables for machine learning algorithms, which can cause a certain degree of information redundancy. therefore, it is necessary to select an appropriate model to predict student performance and integrate the prediction results with actual teaching scenarios. In this work, XGBoost is utilized for student performance prediction, since it has been verified as an effective tool in classification task. Specifically, we collect original educational data and utilize feature analysis to select correlated features as input variables, and the selected features are feed into XGBoost model. In experimental part, XGBoost model achieves the better results than five other classic machine learning models. Based on the results of the classification of students, tiered instruction is applied, and different teaching objectives and contents are set for all levels of students. © 2022 IET conference Proceedings. All rights reserved.

关键词： data mining

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Research on intelligent customs declaration generation in Guangdong-Hong Kong cross-border road cargo clearance 4

Research on intelligent customs declaration generation in Gu...

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4th international conference on Computer Science and Communication Technology, ICCSCT 2023

作者： Han, Chengguo Wang, Bin School of Management Guangdong University of Science and Technology Guangdong Dongguan523083 China Shenzhen Taizhou Technology Co. Ltd. Guangdong Shenzhen518031 China

ISBN: (纸本)9781510671232

Due to the uniqueness of "one country, two systems" and "two customs territories" in Guangdong (GD) and Hong Kong (HK), cross-border road cargo clearance between the two regions requires the creation of several documents. these include the Road Cargo Manifest, Vehicle Declaration, Customs Declaration, HK Seamless, HK Bundle, and HK Customs Declaration, as well as additional documents such as Packing List, Contract, and more. the use of Artificial intelligence (AI) can streamline the process of generating these documents and ensure accuracy and efficiency in cross-border road cargo clearance operations. this paper focuses on four aspects: text scanning and recognition, extraction of commodity classification declaration elements, conversion of customs declaration documents, and intelligent commodity classification. these areas of research aim to address the time-consuming and labour-intensive process of document creation and verification. By leveraging AI technologies, such as optical character recognition (OCR), data extraction algorithms, and machine learning (ML), it is possible to automate and optimize the production and review of these documents, thereby reducing time and manpower costs in cross-border road cargo clearance operations between GD and HK. Based on big data analysis of customs declaration documents from both regions, AI technologies such as feature extraction and machine learning can be utilized to achieve automation in the identification of documents like road cargo manifests and customs declarations. this research applied to the China international Trade Single Window (Shenzhen) and significantly improved the efficiency and accuracy of customs declaration in GD and HK. © 2023 SPIE.

关键词： international trade

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Mixed Attention Interleaved Execution Cascade Architecture for Fittings Instance Segmentation in Overhead Transmission Line Images

Mixed Attention Interleaved Execution Cascade Architecture f...

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

作者： Yilong Yang Shangping Zhong Kaizhi Chen College of Computer and Data Science Fuzhou University Fuzhou China

recognition and localization of the fittings in overhead transmission line (OTL) images are the basis of fittings status detection and fault diagnosis. Most of the current recognition and localization of the fittings are based on object detection methods, which cannot accurately locate the fittings, and existing instance segmentation methods have limited accuracy for instance segmentation of fittings in complex OTL scenarios. To solve these problems, in the interleaved execution part, we inherit the idea of Hybrid Task Cascade (HTC) and add a direct information path to the same-stage box and mask branches to reinforce the coupled relationship between detection and segmentation; in the mask branch part, we sequentially apply the efficient channel attention (ECA) module and the dilated spatial attention (DSA) module and then insert them into the mask branch to improve the cross-stage information communication in the cascade architecture and mask prediction. Combining them results in Mixed Attention Interleaved Execution Cascade (MAIEC), a new cascade architecture for instance segmentation. Extensive experiments on the OTL fittings dataset reveal the effectiveness of the proposed method. the proposed MAIEC improves the AP of the box and mask predictions by respectively 2.0% and 1.5% compared to the strong HTC baseline.

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machine learning Based Feature Selection for DDoS Detection in Named data Networking 4

Machine Learning Based Feature Selection for DDoS Detection ...

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4th international conference on Advances in Computing, Communication Control and Networking, ICAC3N 2022

作者： Subasri, I. Emil Selvan, G.S.R. Ramkumar, M.P. Thiagarajar College of Engineering Department of Computer Science and Engineering Madurai India

ISBN: (纸本)9781665474368

With the growing recognition that current Internet protocols have significant security flaws;several ongoing research projects are attempting to design potential next-generation Internet architectures to eliminate flaws made in the past. these projects are attempting to address privacy and security as their essential parameters. NDN (Named data Networking) is a new networking paradigm that is being investigated as a potential alternative for the present host-centric IP-based Internet architecture. It concentrates on content delivery, which is probably underserved by IP, and it prioritizes security and privacy. NDN must be resistant to present and upcoming threats in order to become a feasible Internet framework. DDoS (Distributed Denial of Service) attacks are serious attacks that have the potential to interrupt servers, systems, or application layers. Due to the probability of this attack, the network security environment is made susceptible. the resilience of any new architecture against the DDoS attacks which afflict today's Internet is a critical concern that demands comprehensive consideration. As a result, research on feature selection approaches was conducted in order to use machine learning techniques to identify DDoS attacks in NDN. In this research, features were chosen using the Information Gain and data Reduction approach with the aid of the WEKA machine learning tool to identify DDoS attacks. the dataset was tested using K-Nearest Neighbor (KNN), Decision Table, and Artificial Neural Network (ANN) algorithms to categorize the selected features. Experimental results shows that Decision Table classifier outperforms well when compared to other classification algorithms with the with the accuracy of 85.42% and obtained highest precision and recall score with 0.876 and 0.854 respectively when compared to the other classification techniques. © 2022 IEEE.

关键词： data reduction

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A Framework for Cognitive Load recognition Based on machine learning and Multimodal Physiological Signals by Wearable Sensors

A Framework for Cognitive Load Recognition Based on Machine ...

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

作者： ChenWei Wang XinYu Li JinYang Huang Peng Zhao Meng Wang GuoHang Zhuang Xiao Sun School of Computer and Information Hefei University of Technology Hefei China

Cognitive load recognition is of great significance to the study of emotional health, as excessive cognitive load not only impairs work and learning efficiency but also leads to mental pressure and fatigue, affecting overall well-being. Advances in wearable technology and understanding the role of physiological signals in assessing human health have enabled the possibility of cognitive load recognition using these signals. this work proposes a framework that combines multiple physiological signals (ECG, EDA, and PPG) collected through wearable sensors for cognitive load recognition. Heart rate variability (HRV) features are extracted from ECG signals, while statistical characteristics are extracted from EDA and PPG signals. the RFECV feature selection algorithm is utilized in the feature selection module to achieve optimal feature subset selection and redundant feature removal. the classification of cognitive load states is implemented based on the ASAPSO-LightGBM(Adaptive Simulated Annealing Particle Swarm Optimization-Light Gradient Boosting machine) machine learning model. We validated the effectiveness of the framework on two publicly available datasets (MAUS and WESAD). Experimental comparisons of feature combinations and machine learning models demonstrate that the proposed framework achieves superior performance, with recognition accuracy of 98.52% and 88.38% on the two datasets, respectively.

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Enhancing Blast Design Efficiency for Rock Fragmentation with Gradient Descent and Artificial Neural Networks: An Optimization Study 4

Enhancing Blast Design Efficiency for Rock Fragmentation wit...

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4th international conference on Computers and Artificial Intelligence Technology, CAIT 2023

作者： Saubi, Onalethata Gaopale, Kesalopa Jamisola, Rodrigo S. Suglo, Raymond S. Matsebe, Oduetse Botswana International University of Science and Technology Dept. of Mining and Geological Engineering Palapye Botswana Botswana International University of Science and Technology Dept. of Mechanical Energy and Industrial Engineering Palapye Botswana

ISBN: (纸本)9798350326710

this paper presents a predictive model for blast-induced fragmentation at Jwaneng Diamond Mine in Botswana using a machine learning algorithm namely artificial neural networks (ANN). A dataset consisting of 70 blasts with seven blast design parameters was used. the ANN is optimized based on Monte Carlo method to explore the solution space that is modelled using the input parameters, and the overall fragmentation has been improved to at-least 80%. Root mean square error (RMSE) and determination coefficient (R2) indices were used to validate and compare the performance of the the different ANN models. ANN one model, with architecture 7-10-1 demonstrated superiority over the other ANN models in predicting fragmentation with the highest R2 value of 0.880 and lower RMSE of 0.481. the results of sensitivity analysis showed that spacing has the most influence on fragmentation while hole diameter has the least influence on fragmentation. © 2023 IEEE.

关键词： Optimization

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Detecting Keratoconus using machine learning Models 4

Detecting Keratoconus using Machine Learning Models

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4th international conference on Artificial Intelligence and Speech Technology, AIST 2022

作者： Goyal, Radhika Maity, Priyankar Bhatia, Madhulika Grover, Ashish Amity University Department of Computer Science and Engineering Noida India Manav Rachna International Institute of Research and Studies India

ISBN: (纸本)9781665499026

One of the major progressing sectors due to the introduction of technology has been healthcare. Diagnosis of patients has improved by manifolds. Keratoconus is a rare disease where it affects the patient's cornea. there are ongoing researches around the world to find a solution that is accessible and practical. Our objective is to detect whether a person is suffering from Keratoconus or not. this huge volume of important data cannot be handled manually, hence use of concepts like machine learning, data analysis, data mining, etc. play an important role. To evaluate accuracy of machine learning models like Inception V3, VGG16, MobileNet V2, ResNet 50 using color coded corneal maps. the authors have implemented these models and compared their performances amongst each other and thus select the best fit model. the training set contains of 1050 images and comprising of 1051 Normal eyes and 862 Suspect eyes. the models were implemented in python language on the Google Colab Platform. these models are providing a range of 75-95% accuracies depending on the different models. the highest accuracy was obtained by Inception V3 which was 95%. the dataset were corneal maps recorded using Scheimpflug imaging system. Based on the classification of the parameters of the corneal maps, the input data was sorted on the basis of severity and also predicting how likely the patient is to suffer from keratoconus © 2022 IEEE.

关键词： Convolutional neural networks

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26th international conference on pattern recognition, ICPR 2022

26th International Conference on Pattern Recognition, ICPR 2...

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26th international conference on pattern recognition, ICPR 2022

ISBN: (纸本)9783031377419

the proceedings contain 49 papers. the special focus in this conference is on pattern recognition. the topics include: Towards Content-Aware Pixel-Wise Comic Panel Segmentation;data-Driven Transform Based Adaptive Compressive Sensing Framework for 3D Reconstruction of Forests;Land Cover Classification for Fires Using Sentinel-2 Satellite RGB Images and Deep Transfer learning;landsat 8 data for forest fire monitoring: case of Mediouna forest in Tangier, Morocco;flying Wing Wildfire Tracking Using Visual Servoing and Gimbal Control;An Overview of Tools and Algorithms Used to Classify, Detect, and Monitor Forest Area Using LiDAR data;Towards the Automation of Wildfire Monitoring with Aerial Vehicles: the FIREFRONT Project;real-Time Georeferencing of Fire Front Aerial Images Using Structure from Motion and Iterative Closest Point;forest Fires Identification Using Self-Supervised learning;georeferencing High-Altitude Aerial Imagery;Wildfires Detection and Segmentation Using Deep CNNs and Vision Transformers;a Method to Annotate Who Speaks a Text Line in Manga and Speaker-Line dataset for Manga109;towards Multi-class Forest Floor Analysis;combining Automatic Speaker Verification and Prosody Analysis for Synthetic Speech Detection;towards Unconstrained Audio Splicing Detection and Localization with Neural Networks;comprint: Image Forgery Detection and Localization Using Compression Fingerprints;H4VDM: H.264 Video Device Matching;GBDF: Gender Balanced DeepFake dataset Towards Fair DeepFake Detection;Misalignment Estimation in Non-Aligned Double JPEG Scenario Based on AC Histogram Analysis;attacking and Defending Printer Source Attribution Classifiers in the Physical Domain;descriptive Algorithmic Schemes as Image Analysis machine Programs;application of Artificial Intelligence in Ophthalmology for Coagulate Map Formation to Carry Out Laser Eye Treatment;considering Meanings and Effects of Frames Without Onomatopoeias in Japanese Comics.

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Fourth international conference on Computer Science and Communication Technology, ICCSCT 2023

Fourth International Conference on Computer Science and Comm...

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4th international conference on Computer Science and Communication Technology, ICCSCT 2023

ISBN: (纸本)9781510671232

the proceedings contain 84 papers. the topics discussed include: pose and attention mechanism based behavior recognition method and its application in education;a green vehicle routing problem with pick-up and delivery based on an improved firefly algorithm;an improved RSA encryption algorithm based on Miller-Rabin prime number test;air quality analysis of major Chinese cities based on semi-supervised clustering;research on the influence of Chinese film Weibo topics on film success based on sentiment time curve and clustering;haze prediction research based on SSA-SVR;research on calibration target plate interpretation method based on stencil matching;dynamic path retransmission in named data wireless multi-hop networks;an incremental object detection model based on knowledge distillation;and multi-objective optimization of reconnaissance constellation based on machine learning.

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