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26th International Conference on computer Supported Cooperative Work in Design, CSCWD 2023

作者： Ma, Yuntao Wang, Lihe Luo, Junzhou Fang, Xiaolin Southeast University School of Computer Science and Engineering NanJing China China Information Consulting & Designing Institute CO Ltd NanJing China

ISBN: (纸本)9798350331684

Traditional Chinese Medicine (TCM) is the crystallization of Chinese medical heritage for thousands of years and plays a huge role in human health. How to integrate TCM products with modern information intelligent production means to efficiently obtain standardized Chinese medicine services is a very worthy research direction. In this paper, the decocting service of TCM is taken as the research object, and combined with artificial intelligence, blockchain and other information technologies, an intelligent service system for trusted collaboration in the whole process of decocting TCM is innovatively proposed and designed. Under the intelligent collaboration of a variety of decocting devices, key links, such as automated prescription business management, intelligent dispensing and decocting, and blockchain-based quality monitoring, were studied, and collaborative decision-making optimization and information trusted traceability of the whole process of decocting Chinese medicine were realized. The service system was applied in a Chinese medicine piece processing company, realizing the ability to handle 880,000 prescriptions annually, 144 decocting stations with intelligent collaboration, and 35 indicators with full process traceability. The practice shows that the system can realize the trusted intelligent cooperation of the whole process of Chinese herbal decoction. © 2023 IEEE.

关键词： Artificial intelligence

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Forecasting of Stock Volatility using Deep Learning Model with Likelihood-Based Loss Function 5

Forecasting of Stock Volatility using Deep Learning Model wi...

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5th IEEE International Conference on Advances in Electronics, computers and Communications, ICAECC 2023

作者： Boopalan, K. Yamunadevi, S. Chanthirasekaran, K. Ali, Ahmed Mudassar Anbunathan, R. Reddy, Pundru Chandra Shaker Computer Science and Engineering Annamacharya Institute of Technology and Sciences AP Rajampet India Panimalar Engineering College Computer Science and Engineering Tamilnadu Chennai India Electronics and Communication Engineering Saveetha Engineering College Tamilnadu Chennai India Computer Science and Engineering MEASI Institute of Information Technology Chennai India Department of Mathematics Tamilnadu Chennai India School of Computer Science and Engineering Lovely Professional University Punjab Phagwara India

ISBN: (纸本)9798350305449

Predicting the volatility of financial assets can be helpful, and volatility is employed in a variety of financial contexts. Volatility in the stock sector is a widely used indicator of overall market risk. Time-series anticipating in the economic sector is exceedingly difficult because of the intricacy and fluctuation of financial fields. In this paper, we anticipate stock index volatility using a deep-neural-network (DNN) and a long-short-term-memory (LSTM) model. Distance loss function is widely used in related research to train machine-learning (ML) strategies, although this has two drawbacks. The initial is that their designs cannot be reasonably contrasted to econometric ones, and the second is that they create mistakes when utilizing predicted volatility as the estimated aim. We also incorporate a probability-based loss-function (LF) to train the deep-learning (DL) approaches and evaluate the models based on the likelihood of the test sample, which allows us to address the aforementioned two issues. In comparison to the econometric and the DL strategies with distance LF, our results show that the volatility can be predicted more accurately by the LSTM model in the two DL approaches with likelihood-based LF. © 2023 IEEE.

关键词： Forecasting

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Predicting the Emergence of Hyperglycaemia through the Application of Machine Learning Methodologies 5

Predicting the Emergence of Hyperglycaemia through the Appli...

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5th International Conference on Image Processing and Capsule Networks, ICIPCN 2024

作者： Pattnayak, Parthasarathi Patnaik, Sanghamitra Gourisaria, Mahendra Kumar Singh, Satyendr Goje, Nitin S. Patra, Sudhansu Shekhar KIIT Deemed to be University School of Computer Applications Bhubaneswar India KIIT Deemed to be University School of Law Bhubaneswar India KIIT Deemed to be University School of Computer Engineering Bhubaneswar India BML Munjal University Computer Science & Engg Department Gurugram India Webster University Computer and Information Sciences Department Tashkent Uzbekistan

ISBN: (纸本)9798350367171

Hyperglycaemia is a lifelong digestive condition that causes excessive blood sugar levels. Early detection along with treatment can avoid or prolong the problems in their beginning. Earlier studies have used machine learning to predict pathology. This study suggests that an artificially intelligent neural network could be a beneficial tool for managing and preventing diabetes. Hyperglycaemia can lead to both acute and chronic consequences, including fatalities. Chronic problems can lead to various impairments and organ deterioration. Failure to provide comprehensive remedies and preventions for diabetes patients can result in increased medical costs and a reduction in society's overall quality of life. Eight subject parameters were examined in this study: age, body mass index, plasma glucose level, insulin level, diastolic blood pressure, sebum thickness, pregnancies' number, and diabetes pedigree function. This study used to build neural network models and assess their accuracy in predicting diabetes. Results showed that some models performed better than others. © 2024 IEEE.

关键词： Self-supervised learning

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Model of Visualization and Analytics for Open Data (Case: Election Voters & Kids Disability Category) 1

Model of Visualization and Analytics for Open Data (Case: El...

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1st International Conference in Advanced Innovation on Smart City, ICAISC 2023

作者： Wibowo, Ripto Mukti Fakieh, Bahjat Ramzan, Muhammad Sher Alzahrani, Ahmed Saeed Siddiqui, Muazzam Alzahrani, Bander King Abdulaziz University Faculty of Computing and Information Technology Department of Information Systems Saudi Arabia King Abdulaziz University Faculty of Computing and Information Technology Department of Computer Science Saudi Arabia Indiana University Luddy School of Informatics Computing and Engineering Bloomington United States

ISBN: (纸本)9781665472753

Several regional head elections had to be postponed due to the pandemic, including in Indonesia because of the COVID-19 pandemic. Several big cities in Indonesia are of concern because of their large population and GDP. This study conducts analysis and testing of datasets taken from Open Data in a city in Indonesia. In addition to conducting research on regional head elections, we also present information on voters from the category of kids with disabilities. The steps used in this research are using regional mapping data of the city of Surabaya in the Election of the Regional Head. Download the data or dataset for the Regional Head Election ampersand Categories of kids with disabilities. Based on the dataset voters from the category of children with disabilities are more than 5 *** this research, we use Python to process our datasets & Big Data technology. Data cleaning or cleansing, Exploratory Data Analysis, and Empirical Cumulative Distribution Functions (ECDF) in python are also needed. Result from ECDF chart with steady increase (increment of 0.1). The highest variance value is in Electoral District 5 = 6.090 and the lowest value is in Electoral District 4 = 0.90. The result of Open Data is graphical data visualization and candidate scores to help as an alternative for the 2024 Regional Head Election and the Category of kids with disabilities. © 2023 IEEE.

关键词： Open Data

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Competitive Multi-Verse Optimization with Deep Learning Based Sleep Stage Classification

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computer systems science & engineering 2023年第5期45卷 1249-1263页

作者： Anwer Mustafa Hilal Amal Al-Rasheed Jaber SAlzahrani Majdy M.Eltahir Mesfer Al Duhayyim Nermin M.Salem Ishfaq Yaseen Abdelwahed Motwakel Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam Bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Information Systems College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam Bin Abdulaziz UniversitySaudi Arabia Department of Electrical Engineering Faculty of Engineering and TechnologyFuture University in EgyptNew Cairo11835Egypt

Sleep plays a vital role in optimum working of the brain and the *** people suffer from sleep-oriented illnesses like apnea,insomnia,*** stage classification is a primary process in the quantitative examination of polysomnographic *** stage scoring is mainly based on experts’knowledge which is laborious and time ***,it can be essential to design automated sleep stage classification model using machine learning(ML)and deep learning(DL)*** this view,this study focuses on the design of Competitive Multi-verse Optimization with Deep Learning Based Sleep Stage Classification(CMVODL-SSC)model using Electroencephalogram(EEG)*** proposed CMVODL-SSC model intends to effectively categorize different sleep stages on EEG ***,data pre-processing is performed to convert the actual data into useful ***,a cascaded long short term memory(CLSTM)model is employed to perform classification *** last,the CMVO algorithm is utilized for optimally tuning the hyperparameters involved in the CLSTM *** order to report the enhancements of the CMVODL-SSC model,a wide range of simulations was carried out and the results ensured the better performance of the CMVODL-SSC model with average accuracy of 96.90%.

关键词： Signal processing EEG signals sleep stage classification clstm model deep learning cmvo algorithm

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Self-Supervised ECG Anomaly Detection Based on Time-Frequency Specific Waveform Mask Feature Fusion

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IEEE Access 2025年 13卷 97585-97596页

作者： Chongrui Tian Fengbin Zhang School of Computer Science and Technology Harbin University of Science and Technology Harbin China School of Information Engineering East University of Heilongjiang Harbin China

The imbalance of ECG signal data and the complexity of labeling pose significant challenges for deep learning-based anomaly detection. Traditional contrastive learning approaches for ECG anomaly detection often rely on reconstruction or generation; however, normal signals that resemble abnormal ECG samples may be incorrectly clustered, leading to suboptimal performance. To address this issue, we propose an anomaly detection framework TFMAD that integrates ECG signal mask reconstruction with time-frequency contrastive learning, leveraging the correlation between time- and frequency-domain features for anomaly detection. Specifically, the proposed method incorporates an auto-encoder module, a time-frequency mask module, and a contrastive learning module to extract masked time-frequency domain features of ECG signals. The model then reconstructs the signal using time-frequency feature fusion and employs contrastive learning to structure the feature space, ensuring abnormal distributions are effectively learned. We evaluated this method on six datasets, and the results demonstrate that TFMAD outperforms nine state-of-the-art methods.

关键词： Electrocardiography Anomaly detection Time-frequency analysis Feature extraction Contrastive learning Image reconstruction Arrhythmia Autoencoders Training Time series analysis

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Advances in Convolutional Neural Networks for Object Detection and Recognition

Advances in Convolutional Neural Networks for Object Detecti...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Yadav, Dhananjay Kumar Kumari, Neeraj Harron, Syed Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Teerthanker Mahaveer University Department of Computing Sciences and Information Technology Uttar Pradesh Moradabad India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350383348

Convolutional neural networks (CNNs) have emerged as a powerful tool for object detection and recognition. Recent advances in CNNs have improved their performance on object detection by incorporating innovative convolutional layers and architectures. These advances include the inception architecture, region proposal networks (RPNs), and fully convolutional networks (FCNs). Additionally, these architectures have enabled object detection and recognition with significant improvements in accuracy and speed. Furthermore, recent research has focused on applying deep transfer learning techniques to CNNs for object detection and recognition, which have shown promising results in terms of precision and accuracy. Overall, these ongoing advancements have further improved the state of the art in object detection and recognition tasks. © 2024 IEEE.

关键词： Additionally Architectures Convolutional Improvements Layers Promising Recognition

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Moving Object Segmentation by Combing Deep Learning and Geometric Constraints for Autonomous Driving

Moving Object Segmentation by Combing Deep Learning and Geom...

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2023 China Automation Congress, CAC 2023

作者： Yuan, Jianying Wu, Sidong Tsotsos, John K. School of Automation Chengdu University of Information Technology Chengdu China York University Department of Electrical Engineering and Computer Science Toronto Canada

ISBN: (纸本)9798350303759

Moving object segmentation is an important and challenging task in the field of autonomous driving. This paper presents a novel and effective method that combines deep learning and geometric constraints for moving object segmentation. First, potential moving objects in the traffic scene are segmented using SOLOV2. Next, the residual optical flows (ROFs) of feature points on the potential moving objects are calculated. Lastly, a strategy considering randomness of ROF angles and ROF magnitude thresholding is developed to estimate the motion status of these potential moving objects. The effectiveness of the proposed method is evaluated on the KITTI and KITTI MOD datasets, and the experimental results demonstrate that the proposed method can achieve higher precision and recall compared to the existing algorithms. © 2023 IEEE.

关键词： Autonomous vehicles

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Applying Johnson’s Rule in Scheduling Multiple Parallel Two-Stage Flowshops 1

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17th International Joint Conference on Theoretical computer science - Frontier of Algorithmic Wisdom, IJTCS-FAW 2023

作者： Wu, Guangwei Zuo, Fu Shi, Feng Wang, Jianxin College of Computer and Information Engineering Central South University of Forestry and Technology Changsha China School of Computer Science and Engineering Central South University Changsha China Xiangjiang Laboratory Changsha China

ISBN: (数字)9783031393440

ISBN: (纸本)9783031393433

It is well-known that the classical Johnson’s Rule leads to optimal schedules on a two-stage flowshop. However, it is still unclear how Johnson’s Rule would help in scheduling multiple parallel two-stage flowshops with the objective of minimizing the makespan. Thus within the paper, we study the problem and propose a new efficient algorithm that incorporates Johnson’s Rule applied on each individual flowshop with a carefully designed job assignment process to flowshops. The algorithm is successfully shown to have a runtime O(nlog n) and an approximation ratio 7/3, where n is the number of jobs. Compared with the recent PTAS result for the problem, our algorithm has a larger approximation ratio, but it is more efficient in practice from the perspective of runtime. © 2023, Springer Nature Switzerland AG.

关键词： Approximation algorithms

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Conditional LS-GAN Based Skylight Polarization Image Restoration and Application in Meridian Localization 48

Conditional LS-GAN Based Skylight Polarization Image Restora...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Yang, Tian Bo, Hongbo Yang, Xinyu Gao, Jun Shi, Zijian Hefei University of Technology School of Computer Science and Information Engineering Hefei China University of Bristol Department of Computer Science Bristol United Kingdom Lancaster University 3School of Computing and Communications Lancaster United Kingdom

ISBN: (纸本)9781728163277

Skylight polarization images (SPIs) contain crucial spatial information that can be used for navigation purposes. Under most circumstances, the quality of the images becomes a major concern, especially when there is blocking between the perception equipment and the sky. This paper introduces a deep learning-based methodology for restoring SPIs and utilizing the restored images for navigation. First, an adversarial training paradigm is adopted to restore the SPIs collected in a severe blocking environment. We utilize a self-developed simulation method that uses solar altitude and azimuth angle to generate ground truth, and no prior knowledge of the masking information for noise is used. Second, we show how to locate the meridian based on the restored SPIs using a residual neural network. In experiments, we demonstrate the superiority of the proposed model in restoring SPIs, and the enhanced meridian localization precision by using the restored SPIs. © 2023 IEEE.

关键词： Image reconstruction

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