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A Logistic-Regression-Based Reduced Model for Predicting Online News Popularity 2

A Logistic-Regression-Based Reduced Model for Predicting Onl...

引用

2nd international conference on Computing, Communication, Perception and Quantum Technology, CCPQT 2023

作者： Lou, Lan Wang, Jingping Liu, Xinya University of Toronto The Department of Computer and Mathematical Sciences Toronto Canada Wuhan University of Technology College of Automation Wuhan China Harbin Institute of Technology The Department of Computer Science and Technology Shen Zhen China

ISBN: (纸本)9798350342697

Consuming news through social media has become an integral part of our lives, thus online news popularity prediction is now a crucial issue for marketing strategies and social media platforms. This paper proposes a real-time prediction model, based on a dataset from Mashable, it uses regression analysis to find out whether an article is of high-popularity or low-popularity. In this paper, we first solve the problem of imbalanced data via Synthetic Minority Oversampling Technique (SMOTE);then various feature selection and engineering methods are employed to extract key features. A comparison experiment is conducted to verify the validity of the data enhancement. And the results show that the reduced logistic model with SMOTE and dimension reduction outperforms other models with an Accuracy of 80.6% and a Recall of 78.6%. Our work can help the software application on evaluating the popularity of online news. © 2023 IEEE.

关键词： Feature Selection

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Deep learning-based blood pressure measurement on a low-power microcontroller 14

Deep learning-based blood pressure measurement on a low-powe...

引用

14th IEEE international conference on Cognitive Infocommunications, CogInfoCom 2023

作者： Tóth, Bálint Torma, Szabolcs Nagy, Ákos Szegletes, Luca Department of Measurement and Information Systems Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Műegyetem rkp. 3. BudapestH-1111 Hungary Department of Automation and Applied Informatics Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Műegyetem rkp. 3. BudapestH-1111 Hungary

ISBN: (纸本)9798350325652

Blood pressure is one of the most vital signals characterizing the health status of the human body. Based on this physiological data, many current and future diseases can be detected. Therefore, having a device that can continuously measure blood pressure without a cuff or medical intervention would be highly beneficial. This study aims to investigate noninvasive blood pressure measurement with the help of a low-power microcontroller, namely STM32F446RE. These measuring methods are based on deep neural networks and utilize externally measured photoplethysmography (PPG) and electrocardiogram (ECG) signals. The performance of the proposed models is compared to similar solutions from the literature and inspected for memory usage and runtime. The results of this work show that a convolutional layer-based model can be used for proper blood pressure estimation in such an energy-efficient device. © 2023 IEEE.

关键词： Blood pressure

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HFT: Lifting Perspective Representations via Hybrid Feature Transformation for BEV Perception

HFT: Lifting Perspective Representations via Hybrid Feature ...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Jiayu Zou Zheng Zhu Junjie Huang Tian Yang Guan Huang Xingang Wang Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China PhiGent Robotics Beijing China

Restoring an accurate Bird's Eye View (BEV) map plays a crucial role in the perception of autonomous driving. The existing works of lifting representations from frontal view to BEV can be classified into two categories, i.e., Camera model-Based Feature Transformation (CBFT) and Camera model-Free Feature Transformation (CFFT). We empirically analyze the significant differences between CBFT and CFFT. The former method lift perspective features based on the flat- world assumption, which often causes distortion of regions lying above the ground plane. The latter method is limited in the perception performance due to the absence of geometric priors and time-consuming computing. In this paper, we propose a novel framework with a Hybrid Feature Transformation module (HFT) to lift perspective representations. Furthermore, we design a mutual learning scheme to augment hybrid transformation. The deformable attention mechanism enables the model to pay more attention to relevant regions and capture features with more semantics. We illustrate the effectiveness of HFT in BEV perception tasks, such as segmentation and object detection. Notably, in the task of semantic segmentation, extensive experiments demonstrate that HFT outperforms the previous state-of-the-art method by relatively 17.9% on the Argoverse and 22.0% on the KITTI 3D Object dataset. With negligible computing budget, HFT outperforms existing image- based methods on 3D object detection. The code will be released soon.

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Total Organic Carbon Detection in Water Based on Attention Mechanism CNN 4

Total Organic Carbon Detection in Water Based on Attention M...

引用

4th international conference on Intelligent Computing and Human-Computer Interaction, ICHCI 2023

作者： Ming, Jiahao Chai, Qinqin Wang, Wu School of Electrical Engineering and Automation Fuzhou University Fuzhou China

ISBN: (纸本)9798350343113

With the increasing demand for dynamic control of sewage quality, developing a fast and accurate detection method that can continuously monitor total organic carbon (TOC) in water has become a crucial issue. However, traditional methods for TOC detection require a high consumption of time and resources. To overcome these drawbacks, in this paper, a rapid and effective TOC detection method is developed based on near-infrared spectroscopy (NIRS) and an improved convolutional neural network (CNN). Firstly, the spectrum of the water samples is collected using a portable near-infrared spectrometer. Then a Convolutional Block Attention Module (CBAM) is integrated with one-dimensional CNN to effectively extract key characteristics of pollutants and achieve accurate quantitative analysis. The feature visualization results demonstrate the effectiveness of the feature extraction. And comparison experimental results shown that, the proposed method model has the greatest coefficient of determination and the smallest root mean square error compared with other models. Thus, the proposed method can accurately detect the total organic carbon in water and is suitable for practical applications. © 2023 IEEE.

关键词： Water quality

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A Digital Decoupling Control Scheme for Synchronous-Frame Current Regulators in High-Speed Motor Drives 26

A Digital Decoupling Control Scheme for Synchronous-Frame Cu...

引用

26th international conference on Electrical Machines and Systems, ICEMS 2023

作者： Mon-Nzongo, Daniel L. Lin, Shengyu Ipoum-Ngome, Paul G. Lai, Chunyan Concordia University Department of Electrical and Computer Engineering Canada Fuzhou University College of Electrical Engineering and Automation Fuzhou China Research and Development Department of Peal Electric China

ISBN: (纸本)9798350317589

This paper introduces a digital current regulator with a dynamic decoupling scheme for three-phase AC motor drives that operate under low sampling to fundamental frequency ratios for high-speed motor drives. Cross-coupling terms introduced by the motor and inverter models in synchronous-frame are the main cause of poor dynamic performances. These disturbances cannot be mitigated due to the bandwidth frequency limitation under high-speed operations. This paper introduces a decoupling matrix emulated in the digital domain aiming to dynamically cancel these effects. In addition, two compensation methods are proposed to reduce the discretization due to low sampling ratio. The proposed approach allows the dynamic cancellation of the plant poles that change their location depending on the synchronous speed. The time delay effect of the plant is compensated using the phase-lead compensation method. Simulation results discussed in this paper demonstrated good decoupling performances with improved dynamic responses. © 2023 IEEE.

关键词： Chemical reactions

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Deep learning Techniques for Identification of Illicit Drug Identification in Youth

Deep learning Techniques for Identification of Illicit Drug ...

引用

Cognitive robotics and Intelligent Systems (ICC - ROBINS), international conference on

作者： Pyingkodi M Parvathavarthini S Suresh Palarimath D Deepa Mohana Prawin. E Praveen. R Kongu Engineering College Erode India IT Department University of Technology and Applied Sciences - Salalah Oman

ISBN: (数字)9798350372748

ISBN: (纸本)9798350372755

Drug consumption poses significant challenges to public health, demanding precise and timely prediction models for efficient intervention and prevention methods. Through the use of deep learning methods combined with ocular image analysis, this work proposes a novel method for predicting drug use. The objective is to develop reliable and understandable model to recognize drug users from their eye pictures. For supervised training, a varied dataset of eye pictures was gathered, including people with various drug use histories. On the dataset, various Convolutional Neural Network designs were chosen, trained, and optimized using the proper hyperparameters. This study proposal compares various Convolutional Neural Network CNN models, including InceptionV3, DenseNet169, and EfficientNetB2, in order to ascertain which one of these models gets the maximum accuracy. These models provide the average accuracy of 93.33%, 97.47% and 85.89% respectively. DenseNet169 outperforms InceptionV3 and EfficientNetB2 with highest accuracy of 97.47%. It demonstrates the highest overall performance among the three models for the given task

关键词： Drugs Deep learning Training Predictive models Convolutional neural networks Reliability Proposals

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Research and Application of Deep Learning-Based Lung Cancer Diagnosis models

Research and Application of Deep Learning-Based Lung Cancer ...

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Artificial Intelligence and automation Control (AIAC), international conference on

作者： Ruijie Huang Kang Liu AI of FST Beijing Normal University-Hong Kong Baptist University United International College Zhuhai China

ISBN: (数字)9798331529147

ISBN: (纸本)9798331529154

Lung cancer remains one of the leading causes of cancer-related deaths globally, primarily due to late-stage diagnosis and the inherent complexities of the disease. Traditional diagnostic methods, while effective, often fall short in accuracy and speed, highlighting the need for innovative approaches. This paper investigates the application of deep learning models in lung cancer diagnosis, focusing on Convolutional Neural Networks (CNNs) and their ability to analyze medical images. We explore the architecture, training methodologies, and evaluation metrics of these models, demonstrating their effectiveness compared to conventional techniques. The results indicate that deep learning approaches significantly enhance diagnostic accuracy, leading to improved patient outcomes. This study not only contributes to the existing body of knowledge but also emphasizes the potential of integrating deep learning in clinical workflows for lung cancer detection.

关键词： Deep learning Training Measurement Accuracy automation Lung cancer Focusing Complexity theory Convolutional neural networks Medical diagnostic imaging

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Anomaly detection of EMU trains based on line-scan image registration 7

Anomaly detection of EMU trains based on line-scan image reg...

引用

7th Global Intelligent Industry conference, GIIC 2024

作者： Fang, Lei Shi, Zelin Xiao, Chuanmin Liu, Yunpeng Pang, Mingqi Zhao, Enbo Faculty of Robot Science and Engineering Northeastern University Shenyang110169 China Key Laboratory of Opto-Electronic Information Processing Chinese Academy of Sciences Shenyang110016 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China Third Military Representative Room in Shenyang Area Air Force Equipment Shenyang110016 China

ISBN: (纸本)9781510682986

Anomaly detection based on template image registration is one of the methods used to detect anomalies of objects with similar structures. However, the imaging device of the Electronic Multiple Units (EMU) train is a line-scan camera, and the geometric transformation law between its image and the area-scan image is different. The conventional image registration method of the area-scan image cannot accurately align the line-scan image. Moreover, the line-scan image of the EMU train collected in uncontrollable environmental scenes exhibits significant grayscale changes, and the registered images cannot detect differences by directly comparing grayscale. To address these two challenges, this paper proposes a two-stage anomaly detection method based on line-scan image registration and edge comparison. In the registration stage, a coarse-to-fine line-scan image registration method was designed. First, the feature-based registration method was used to achieve coarse position of the EMU train template image in the target image. Then, the direct registration method based on the line-scan image geometric transformation model achieved precise geometric alignment. In the anomaly detection stage, edge information is extracted from the template image and the registered target image, and anomaly detection of EMU trains is achieved through edge comparison, edge expansion, and edge connection. The experimental results on the line-scan image of EMU trains show that the two-stage method proposed in this paper can achieve the registration of line-scan images of EMU trains, and on this basis, achieve detection and segmentation of abnormal areas of EMU trains. © 2024 SPIE.

关键词： Image registration

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Few-Shot Steel Defect Detection Method Based on Improved Meta-Learning Model 8

Few-Shot Steel Defect Detection Method Based on Improved Met...

引用

8th international conference on Image, Vision and Computing, ICIVC 2023

作者： Luo, Fengkai Ji, Ruirui Deng, Kehao Sha, Qiliang School of Automation and Information Engineering Xi'An University of Technology Xi'an China

ISBN: (纸本)9798350335231

To address the problem of scarcity of certain defect samples that unable to meet the training requirements of deep learning models, a few-shot steel surface defect detection method based on an improved meta-learning model is proposed in this paper. Meta-learning algorithm MAML is combined with YOLOv7 network to enhance its ability to recognize small target defects in steel surfaces through the SPP module. To fit the scene variations in different steel defect images, domain adaptation optimizer is introduced to endow the model adapt to different scenes for defect detection quickly. In addition, to deal with the issue of high similarity between background and target in certain defect images, label smoothing is incorporated into the YOLOv7 algorithm to prevent model overconfidence, thereby improving its generalization and detection performance. Experimental results on the publicly available NEU-DET dataset demonstrate that compared with other methods, the proposed approach significantly improves the detection accuracy of steel surface defects under few-shot conditions and effectively solves the problem of few sample of surface defects for steel products. © 2023 IEEE.

关键词： Surface defects

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Research on Substation Personnel Fall Detection Method Based on Vision-Language models

Research on Substation Personnel Fall Detection Method Based...

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Artificial Intelligence, robotics, and Communication (ICAIRC), IEEE international conference on

作者： Yongbin Chen Zhijun Zhang Long Yu South China University of Technology Guangzhou China Guangdong Mechanical and Electrical Polytechnic Guangzhou China Guangzhou Baiyun Electric Equipment Co. Ltd. Guangzhou China

ISBN: (数字)9798331531225

ISBN: (纸本)9798331531232

In power substation environments, ensuring personnel safety is critical to maintaining stable and reliable power operations. Detecting falls—a key safety concern—involves overcoming challenges such as low lighting, occlusion by equipment, and visually cluttered backgrounds. Conventional vision-only methods often fail to provide robust performance under these real-world conditions. In this work, we propose a novel fall detection approach leveraging large-scale pre-trained Vision-Language models (VLMs). By integrating textual semantics describing "fall" incidents with visual features from substation surveillance footage and performing incremental fine-tuning on domain-specific annotated datasets, our method achieves high-precision detection under challenging scenarios. Experimental results demonstrate that the proposed approach surpasses traditional CNN-based methods in terms of accuracy, robustness, and generalization. The findings offer a promising solution for intelligent safety monitoring and early warning systems in the power industry, reducing risks associated with personnel injuries and operational disruptions.

关键词： Analytical models Adaptation models Visualization Substations Accuracy Lighting Robustness Power industry Safety Personnel

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