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27th International Conference on Advanced Communications technology, ICACT 2025

作者： Ngoc, Lan Dong Thi Ngo-Thi-Thu-Trang Kim, Kyoung-Mok Pham, Vung Nguyen, Ha-Nam Department of Economic Information System Academy of Finance Hanoi Viet Nam Faculty of Information Technology University of Engineering and Technology Vietnam National University Hanoi Viet Nam Posts and Telecommunications Institute of Technology Hanoi122300 Viet Nam Computer Science Department Sam Houston State University HuntsvilleTX77340 United States Department of Medical Information Sahmyook Health University Seoul Korea Republic of Faculty of Information Technology Electric Power University Hanoi Viet Nam

ISBN: (纸本)9791188428137

This study explores the application of machine learning models in forecasting macro-economic indicators, including GDP, inflation rate, unemployment rate, and exchange rate across 11 Southeast Asian countries. The models used include Linear Regression, ARIMA, Random Forest, XGBoost, LSTM, and SVM. We conducted a performance comparison of each model based on MAE, RMSE, and R2 metrics to evaluate the accuracy of the forecasts. The experimental results indicate that Random Forest and XGBoost models excel in predicting nonlinear and complex indicators such as GDP and unemployment rate, while ARIMA and Linear Regression models perform better in time series with clear regular patterns, like inflation rate. The LSTM model shows inconsistent effective-ness, requiring large data volumes and complex optimization processes. SVM demonstrates potential in handling nonlinear data but requires careful tuning. This study concludes that using machine learning models presents significant potential for improving the accuracy of macroeconomic forecasting. However, model tuning and optimization are essential to match the characteristics of each type of economic indicator. Future research directions include developing hybrid models and integrating additional factors such as market sentiment, social and environmental indicators (ESG) to enhance forecasting outcomes. Copyright 2025 Global IT Research Institute (GIRI). All rights reserved.

关键词： Inflation

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A survey on deep learning for polyp segmentation: techniques, challenges and future trends

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Visual Intelligence 2025年第1期3卷 1-20页

作者： Mei, Jiaxin Zhou, Tao Huang, Kaiwen Zhang, Yizhe Zhou, Yi Wu, Ye Fu, Huazhu PCA Lab Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Nanjing China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China School of Computer Science and Engineering Southeast University Nanjing China Institute of High Performance Computing A*STAR Singapore Singapore

Early detection and assessment of polyps play a crucial role in the prevention and treatment of colorectal cancer (CRC). Polyp segmentation provides an effective solution to assist clinicians in accurately locating and segmenting polyp regions. In the past, people often relied on manually extracted lower-level features such as color, texture, and shape, which often had problems capturing global context and lacked robustness to complex scenarios. With the advent of deep learning, more and more medical image segmentation algorithms based on deep learning networks have emerged, making significant progress in the field. This paper provides a comprehensive review of polyp segmentation algorithms. We first review some traditional algorithms based on manually extracted features and deep segmentation algorithms, and then describe benchmark datasets related to the topic. Specifically, we carry out a comprehensive evaluation of recent deep learning models and results based on polyp size, taking into account the focus of research topics and differences in network structures. Finally, we discuss the challenges of polyp segmentation and future trends in the field. © The Author(s) 2025.

关键词： Comprehensive evaluation Deep learning Medical imaging Polyp segmentation

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Human Behavior Recognition for Substation Operation and Maintenance 4

Human Behavior Recognition for Substation Operation and Main...

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4th International Conference on Computer engineering and Application, ICCEA 2023

作者： Tang, Qiongping Liu, Guozhong Lin, Chunlong Lin, Changnian Ruan, Mengyu Jin, Wei School of Instrument Science and Optoelectronic Engineering Beijing Information Science and Technology University Beijing China Beijing Kedong Electric Power Control System Co. Ltd. Beijing100192 China

ISBN: (纸本)9798350347548

In the field of substation operation and maintenance, recognizing the behaviors of substation operation and maintenance personnel is crucial for maintaining the safe operation of the power grid and ensuring the safety of workers. To enhance the accuracy of human behavior recognition in substation operation and maintenance, this paper proposes an ECA-MSG3D algorithm model for human behavior recognition of work apparatus based on the Multi-Scale Pointwise Convolutional Neural Network for 3D Object Detection (MSG3D) model. The MSG3D module, which utilizes a multi-scale aggregation scheme for unified spatiotemporal graph convolution, is used for feature learning across time and space. Then, channel features are learned through the efficient channel attention (ECA) module, and the final action is obtained through global average pooling with the softmax classifier. Results show that the ECA-MSG3D model with the ECA attention module outperforms the MSG3D model with other attention modules, achieving 89.06% and 89.26% accuracy and precision on the NTU RGB+D 60 Skeleton dataset. This model provides a highly accurate human behavior recognition method for substation operation and maintenance. © 2023 IEEE.

关键词： Object detection

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Perioperative Anesthesia Data: Visualization, Effects, Analysis with Artificial Intelligence 4

Perioperative Anesthesia Data: Visualization, Effects, Analy...

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4th IEEE International Conference on Electronic Communications, Internet of Things and Big Data, ICEIB 2024

作者： Liu, Xiaoxiao McGrath, Sean Flanagan, Colin Lei, Yiming Zeng, Liaoyuan Electronic and Computer Engineering University of Limerick Limerick Ireland School of Mathematics and Information HeBei University HeBei China Mobile Digital Hospital System School of Electronics Peking University Ministry of Education Engineering Research Center Beijing China University of Electronic Science and Technology of China Department of Information and Communication Engineering Chengdu China

ISBN: (纸本)9798350360721

A comprehensive understanding of physiological data is essential for anesthesiologists to monitor and maintain the health of surgical patients. Understanding anesthetic signals is essential to improve over the past ten decades to comprehend a variety of diseases, ensure the safety of patients, and accelerate their recovery. We identified and summarized four themes of artificial intelligence (AI) research in anesthesia: applications of AI in anesthesiology, signals monitored in different anesthesia stages, commonly monitored signs, and data standardization. The effect of various AI technologies on data analysis was studied, and the types, functions, sensors, characteristics, and intelligent analysis of physiological signals monitored during anesthesia are presented in this article. AI influences anesthesia practices in many ways, including preoperative data evaluation, anesthetic depth monitoring, and postoperative event prediction. © 2024 IEEE.

关键词： Machine learning

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Factor Graph Localization Algorithm Based on Robust Estimation 5

Factor Graph Localization Algorithm Based on Robust Estimati...

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5th IEEE International Conference on Electronic information and Communication technology, ICEICT 2022

作者： Ma, Jing Mao, Ying Chen, Hangyu Tian, Yuan Science and Technology on Special System Simulation Laboratory Beijing China College of Information and Communication Engineering Harbin Engineering University Harbin China

ISBN: (数字)9781665472111

ISBN: (纸本)9781665472111

In recent years, with the continuous development of unmanned ground vehicles (UGVs), the performance requirements of unmanned vehicle positioning system are also gradually improved. Existing unmanned ground vehicles need to use a variety of sensors to provide measurement information, and the available positioning information is dynamically combined in the positioning system and the positioning results are calculated. In this paper, multi-source information fusion positioning system of unmanned ground vehicles is established based on factor graph model. In order to solve the problem that the factor graph algorithm is difficult to identify and exclude abnormal observations autonomously, this paper combines the robust estimation with the factor graph to improve the factor graph method. The reliability factor is added in the construction of the factor node, which can be adjusted adaptively according to the current measured value to reduce the weight of abnormal observations and ensure the normal operation of the system. Simulation results show that the improved factor graph algorithm proposed in this paper can effectively reduce the influence of abnormal observations on positioning accuracy. © 2022 IEEE.

关键词： information fusion

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Can Language Models Serve as Temporal Knowledge Bases?

Can Language Models Serve as Temporal Knowledge Bases?

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2022 Findings of the Association for Computational Linguistics: EMNLP 2022

作者： Zhao, Ruilin Zhao, Feng Xu, Guandong Zhang, Sixiao Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China Data Science and Machine Intelligence Lab University of Technology Sydney Sydney Australia

Recent progress regarding the use of language models (LMs) as knowledge bases (KBs) has shown that language models can act as structured knowledge bases for storing relational facts. However, most existing works only considered the LM-as-KB paradigm in a static setting, which ignores the analysis of temporal dynamics of world knowledge. Furthermore, a basic function of KBs, i.e., the ability to store conflicting information (i.e., 1-N, N-1 and N-M relations), is underexplored. In this paper, we formulate two practical requirements for treating LMs as temporal KBs: (i) the capacity to store temporally-scoped knowledge that contains conflicting information and (ii) the ability to use stored knowledge for temporally-scoped knowledge queries. We introduce a new dataset called LAMA-TK which is aimed at probing temporally-scoped knowledge, and investigate the two above requirements to explore the LM-as-KB paradigm in the temporal domain. On the one hand, experiments show that LMs can memorize millions of temporally-scoped facts with relatively high accuracy and transfer stored knowledge to temporal knowledge queries, thereby expanding the LM-as-KB paradigm to the temporal domain. On the other hand, we show that memorizing conflicting information, which has been neglected by previous works, is still challenging for LMs and hinders the memorization of other unrelated one-to-one relationships. © 2022 Association for Computational Linguistics.

关键词： Computational linguistics

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Transcriptomic and Macroscopic Architectures of Multimodal Covariance Network Reveal Molecular–Structural–Functional Co-alterations

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Research 2024年第1期2023卷 471-487页

作者： Lin Jiang Yueheng Peng Runyang He Qingqing Yang Chanlin Yi Yuqin Li Bin Zhu Yajing Si Tao Zhang Bharat B.Biswal Dezhong Yao Lan Xiong Fali Li Peng Xu The Clinical Hospital of Chengdu Brain Science Institute MOE Key Lab for NeuroinformationUniversity of Electronic Science and Technology of ChinaChengdu 611731China School of Life Science and Technology Center for Information in BioMedicineUniversity of Electronic Science and Technology of ChinaChengdu 611731China School of Psychology Xinxiang Medical UniversityXinxiang 453003China School of Science Xihua UniversityChengdu 610039China Department of Biomedical Engineering New Jersey Institute of TechnologyNewarkNJUSA School of Electrical Engineering Zhengzhou UniversityZhengzhou 450001China Research Unit of NeuroInformation Chinese Academy of Medical Sciences2019RU035ChengduChina Montreal Neurological Institute and Hospital McGill UniversityMontrealQCCanada Department of Electrical and Computer Engineering Faculty of Science and TechnologyUniversity of MacaoMacaoChina Radiation Oncology Key Laboratory of Sichuan Province 610041 ChengduChina Rehabilitation Center Qilu Hospital of Shandong UniversityJinan 250012China

Human cognition is usually underpinned by intrinsic structure and functional neural co-activation in spatially distributed brain *** to lacking an effective approach to quantifying the covarying of structure and functional responses,how the structural–functional circuits interact and how genes encode the relationships,to deepen our knowledge of human cognition and disease,are still ***,we propose a multimodal covariance network(MCN)construction approach to capture interregional covarying of the structural skeleton and transient functional activities for a single *** further explored the potential association between brain-wide gene expression patterns and structural–functional covarying in individuals involved in a gambling task and individuals with major depression disorder(MDD),adopting multimodal data from a publicly available human brain transcriptomic atlas and 2 independent *** analysis showed a replicable cortical structural–functional fine map in healthy individuals,and the expression of cognition-and disease phenotype-related genes was found to be spatially correlated with the corresponding MCN *** analysis of cell type-specific signature genes suggests that the excitatory and inhibitory neuron transcriptomic changes could account for most of the observed correlation with task-evoked MCN differences.

关键词： alterations evoked spatially

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High Precision Temperature Sensor Based on p-PhaseShifted FBG and OFDR system

High Precision Temperature Sensor Based on p-PhaseShifted FB...

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CLEO: Applications and technology in CLEO 2024, CLEO: A and T 2024 - Part of Conference on Lasers and Electro-Optics

作者： Sun, Zhengqi Lv, Yuejuan Zou, Meng Sun, Qizhen Yan, Zhijun School of Optical and Electronic Information Wuhan National Laboratory for Optoelectronics National Engineering Research Center of Next Generation Internet Access-system Huazhong University of Science and Technology Wuhan430074 China Wuxi Research Institute Huazhong University of Science and Technology Jiangsu Wuxi214174 China

We proposed a high precision temperature sensing system which is based on phase-shifted fiber grating interrogated by an OFDR system. The temperature sensing system showed 0.1°C temperature precision with ±0... 详细信息

关键词： Fiber Bragg gratings

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An SIE Formulation with Loop Analysis for Large-Scale Arbitrarily Shaped Interconnects

An SIE Formulation with Loop Analysis for Large-Scale Arbitr...

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2023 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio science Meeting, AP-S/URSI 2023

作者： Zhu, Zekun Gong, Ningbo Diao, Guijie Yang, Shunchuan School of Electronic and Information Engineering Beihang University Beijing China Beijing Electro-mechanical Engineering Institute Beijing China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory China

ISBN: (纸本)9781665442282

A magneto-quasi-static (MQS) surface integral equation (SIE) formulation with loop analysis is proposed to extract the impedance of interconnects in packages. The loop analysis is developed with an independent and complete set of unknowns according to the graph theory, which can significantly reduce the dimension of the final matrix. With the pre-corrected fast Fourier Transform (pFFT), large-scale interconnects can be efficiently modelled. A practical example in packages is carried out to validate the effectiveness and scalability of the proposed formulation. Results show that the proposed formulation is accurate and flexible to model complex interconnects in packages compared with the industrial solver. © 2023 IEEE.

关键词： Fast Fourier transforms

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2.45 GHz Frequency- Tracking Electro-Optic Imaging Using Polarization CMOS Image Sensor

2.45 GHz Frequency- Tracking Electro-Optic Imaging Using Pol...

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2024 IEEE Asia-Pacific Microwave Conference, APMC 2024

作者： Okada, Ryoma Mizuno, Maya Takehara, Hironari Haruta, Makito Tashiro, Hiroyuki Ohta, Jun Sasagawa, Kiyotaka Graduate School of Science and Technology Nara Institute of Science and Technology Nara Ikoma Japan Radio Research Institute National Institute of Information and Communications Technology Koganei Tokyo Japan Graduate School of Science and Technology Nara Institute of Science and Technology Chitose Institute of Science and Technology Department of Opto-Electronic System Engineering Nara Hokkaido Chitose Ikoma Japan Graduate School of Science and Technology Nara Institute of Science and Technology Kyushu University Faculty of Medical Sciences Fukuoka Maidashi Ikoma Japan Institute for Research Initiatives Nara Institute of Science and Technology Nara Ikoma Japan

ISBN: (纸本)9798350363548

In this study, we developed an optical local oscillator signal generation system that tracks the frequency of the electric field to be observed. This system was applied to our highly sensitive electric field imaging system using the electro-optic (EO) effect. It enables electric field imaging by tracking a time-varying frequency wave source, such as frequency modulation (FM). Using this configuration, we successfully imaged at the edge of a PCB antenna with a signal modulated at 2.45 GHz, FM deviation of 100 kHz, and sweep frequency of 100 kHz. © 2024 IEEE.

关键词： CMOS integrated circuits

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