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4th Congress on intelligent systems, CIS 2023

4th Congress on Intelligent Systems, CIS 2023

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4th Congress on intelligent systems, CIS 2023

ISBN: (纸本)9789819990399

the proceedings contain 35 papers. the special focus in this conference is on intelligent systems. the topics include: Track Learning Agent Using Multi-objective Reinforcement Learning;performance Assessment of Gaussian Filter-Based Image Fusion Algorithm;a Cognitive Comparative Analysis of Geometric Shape-Based Cryptosystem;a Lesion Feature Engineering Technique Based on Gaussian Mixture Model to Detect Cervical Cancer;energy Optimization of Electronic Vehicle Using Blockchain Method;pattern recognition: An Outline of Literature Review that Taps into Machine Learning to Achieve Sustainable Development Goals;novel Approach for Stock Prediction Using Technical Analysis and Sentiment Analysis;visualizing and Exploring the Dynamics of Optimization via Circular Swap Mutations in Constraint-Based Problem Spaces;An Approach to Increase the Lifetime of Traditional LEACH Protocol Using CHME-LEACH and CHP-LEACH;denseMammoNet: An Approach for Breast Cancer Classification in Mammograms;aspect-Based Sentiment Classification Using Supervised Classifiers and Polarity Prediction Using Sentiment Analyzer for Mobile Phone Tweets;microbial Metabolites and Recent Advancement;design of a 3D-Printed Accessible and Affordable Robotic Arm and a User-Friendly Graphical User Interface;profit Maximization of a Wind-Integrated System by V2G Method;detection of Partially Occluded Area in Images Using Image Segmentation Technique;Application of IP Network Modeling Platforms for Cyber-Attack Research;enhancing Information Integrity: Machine Learning Methods for Fake News Detection;Optimum Selection of Virtual Machine in Cloud Using Improved ACO;data Imputation Using Artificial Neural Network for a Reservoir System;depth Multi-modal Integration of Image and Clinical Data Using Fusion of Decision Method for Enhanced Kidney Disease Prediction in Medical Cloud;an Efficient Prediction of Obstructive Sleep Apnea Using Hybrid Convolutional Neural Network.

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4th Congress on intelligent systems, CIS 2023

4th Congress on Intelligent Systems, CIS 2023

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4th Congress on intelligent systems, CIS 2023

ISBN: (纸本)9789819990429

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4th Congress on intelligent systems, CIS 2023

4th Congress on Intelligent Systems, CIS 2023

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4th Congress on intelligent systems, CIS 2023

ISBN: (纸本)9789819990368

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Surface segmentation method based on multi-feature fusion for regionally grown workpiece point clouds 4

Surface segmentation method based on multi-feature fusion fo...

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4th international conference on Image Processing and intelligent Control, IPIC 2024

作者： Zhao, Haoxi Zhang, Zhijia Wang, Hongyu School of artificial Intelligence Shenyang University of Technology Shenyang China

ISBN: (纸本)9781510682313

In order to improve the segmentation accuracy of 3D point cloud model in feature ambiguous region, the unsupervised clustering algorithm based on surface fusion features combines the depth residuals with the normal deviation angle to form the fusion features with good descriptive properties and significant differentiation. Adopting the point cloud local density median proximity points as the candidate points for point cloud region growth, using point cloud cluster features instead of individual point features, for the problem of feature ambiguity in the edge region, the unattributed points are reclustered, so as to improve the cluster segmentation in the way of extended growth;experiments are conducted on the selected model dataset, and the results show that the method can achieve the reasonable segmentation of the complex point cloud model, and the under-segmentation situation has a obvious improvement on the under-segmentation situation. © 2024 SPIE.

关键词： Point clouds Image segmentation 3D modeling Feature fusion Fuzzy logic Spherical lenses Evolutionary algorithms Feature extraction Image processing algorithms and systems Object recognition

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Deep Neural Networks for Automated Metadata Extraction 4th

Deep Neural Networks for Automated Metadata Extraction

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4th international conference on Digital Technologies and Applications (ICDTA)

作者： El Omari, Abdellah Antari, Jilali Elkina, Hamza Ibnou Zohr Univ Polydisciplinary Fac Taroudant Lab Comp Syst Engn Math & Applicat ISIMA Agadir 80000 Morocco Ibnou Zohr Univ Fac Appl Sci Innovat Math & Intelligent Syst Agadir Morocco

ISBN: (纸本)9783031686740;9783031686757

Metadata extraction is essential for the discovery of scientific information. However, it can be tedious and requires a lot of time and effort, given that the number of scientific publications is increasing rapidly. In this work, we developed a solution based on deep learning to automatically extract information relating to metadata from scientific articles. the architecture based on this solution permits segmenting and labeling the main areas of scientific documents, such as title, abstract, sections, image, etc., while simultaneously recognizing the text of the detected areas. the proposed solution is based on object detection models, specifically Faster Region-based Convolutional Neural Networks (Faster R-CNN), Mask Region-based Convolutional Neural Networks (Mask R-CNN), and Cascade Mask Region-based Convolutional Neural Networks (Cascade Mask R-CNN), in order to detect the layout. the areas of interest found are then passed through an OCR (Optical Character recognition) for text recognition. the model was trained using annotated data from the SciBank dataset. During this study, we had the opportunity to test three different models and compare the results obtained. the results from the proposed solution show promise.

关键词： Metadata extraction Deep Learning Object detection

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Traffic flow tensor prediction framework integrating nonlocal self-similarity features and global consistency 4

Traffic flow tensor prediction framework integrating nonloca...

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4th international conference on Internet of things and Smart City, IoTSC 2024

作者： Hu, Yaxin Sun, Qiuxia Li, Qing Sun, Lu Jia, Xiuyan Zhao, Jianli Shandong University of Science and Technology Qingdao266590 China Qingdao University of Technology Qingdao266520 China

ISBN: (纸本)9781510681767

With the vigorous development of intelligent transportation systems, data-driven traffic flow forecasting has played a crucial role in alleviating traffic congestion and reducing traffic accidents. In recent years, the completion algorithm based on tensor has been widely used as an effective traffic prediction method. However, most of the methods only consider the global low rank structure of data and ignore the nonlocal spatio-temporal self-similarity prior characteristics, resulting in low prediction accuracy. this paper proposes a traffic flow tensor prediction framework(NSS-TENSOR) that integrates nonlocal self-similarity features and global consistency of traffic data. A novel traffic flow tensor with strong spatiotemporal correlation is reconstructed by using the dual advantages of nonlocal spatiotemporal self-similarity prior and tensor structure can automatically learn the global consistency of traffic data. Firstly, the nonlocal self-similarity prior characteristics of traffic flow data are captured by clustering algorithm and K-nearest neighbor (KNN) algorithm. then the pattern information blocks are matched based on the prior features of nonlocal self-similarity, considering the global consistency information simultaneously, a new brief tensor is reconstructed. through experiments on two real-world datasets, the results show that the proposed method in this paper has higher traffic flow prediction accuracy compared to the original tensor algorithm model. © 2024 SPIE.

关键词： intelligent systems

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Applications of convolutional neural networks in fault diagnosis of mechanical-electrical-hydraulic systems: a review 4

Applications of convolutional neural networks in fault diagn...

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4th international conference on Mechanical Engineering, intelligent Manufacturing, and Automation Technology, MEMAT 2023

作者： Liu, Zengguang Zhou, Yan Yue, Daling Zhang, Benguo Feng, Ke Zhang, Shuaidi Energy and Power Engineering College Lanzhou University of Technology Lanzhou China

ISBN: (纸本)9781510674820

the mechanical-electrical-hydraulic systems are developing towards automation and intelligence, which raise higher requirements for accurate and smart fault diagnosis of the core component in these systems. Traditional diagnostic methods not only rely too much on a priori knowledge for feature extraction but also have limited computational ability for massive data, which can’t satisfy the demand for intelligent fault diagnosis. thanks to the deep network structure, deep learning (DL) can automatically extract the deep implicit feature information from multiple data without a priori knowledge and has an immense advantage in realizing complicated fault diagnosis. As a typical representative of deep learning, Convolutional neural network (CNN) can directly process images and has made significant achievements in image recognition and classification, therefore scholars have conducted extensive research on CNN for fault diagnosis. Based on the above-mentioned content, this review sorts out the current research works employing the 1DCNN model, 2DCNN model and hybrid model of CNN for fault diagnosis in mechanical-electrical-hydraulic systems, and then summarizes the advantages and disadvantages of the three CNN models, respectively. Finally, we discuss the future development directions and challenges of fault diagnosis with CNN. © 2024 SPIE · 0277-786X.

关键词： Failure analysis

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Joint Transmit and Receive Antenna Selection Based on Cat Swarm Optimization 4

Joint Transmit and Receive Antenna Selection Based on Cat Sw...

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4th IEEE international conference on pattern recognition and Machine Learning, PRML 2023

作者： Xie, Lin Ni, Wenqing Lei, Guowei Zheng, Shuicao Jimei University School of Science Xiamen China

ISBN: (纸本)9798350324303

In large-scale multiple antenna systems, the complexity of detection and signal processing will get unbearable high. Although antenna selection at transceiver may decrease the complexity of system, exhaustive searching for optimal combination of antennas will bring higher complexity as the number of antennas increases. In the paper, cat swarm optimization (CSO) is employed in the joint transmit and receive antenna selection. the capacity of MIMO systems is considered as the fitness function of the algorithm, which is used to calculate the fitness of each cat and output as the final result. Meanwhile, in the cat swarm algorithm, each cat is composed of position, velocity, flag and fitness. Simulated results shows that it can have lower computational complexity without much loss of performance. © 2023 IEEE.

关键词： MIMO systems

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Construction of Topological Navigation Map Based on Model Fusion 4

Construction of Topological Navigation Map Based on Model Fu...

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

作者： Li, Weishi Yi, Fanxiao Peng, Yong Zhang, Miao Liu, Jiyuan College of Systems Engineering National University of Defense Technology Changsha China College of Intelligence Science and Technology National University of Defense Technology Changsha China

ISBN: (纸本)9798350343113

With the advancements in artificial intelligence technology and the field of mobile robotics, small robots like drones and unmanned cars have become increasingly visible. However, the trajectory maps created by unmanned vehicles in complex environments pose challenges for planning and navigation tasks, often leading to significant misidentification issues during the mapping process. To tackle these problems, we propose a method for constructing topological navigation maps based on model fusion. this approach aims to reduce the misidentification rate through model fusion, thereby enhancing the efficiency of constructing topological maps. In this study, we utilized the ORB-SLAM2 framework to extract keyframe information from a dataset of unmanned driving videos. these keyframes were fed into a deep learning neural network for intersection recognition. To improve recognition accuracy, we employed model fusion by averaging multiple points on the SGD trajectory using SWA. As a result, we were able to generate a topological map. Our experiments confirmed the effectiveness of the model fusion-based intersection recognition network and the construction of the topological navigation map. Furthermore, we evaluated the fusion method on common benchmark datasets and found that our results were competitive compared to the current state-of-the-Art approaches. © 2023 IEEE.

关键词： Navigation

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Identification of Alzheimer's Disease Associated Genes through Explicable Deep Learning and Bioinformatic 4

Identification of Alzheimer's Disease Associated Genes throu...

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4th IEEE international conference on pattern recognition and Machine Learning, PRML 2023

作者： Sun, Xiangrong Shu, Wei Zhang, Yuxin Huang, Xiansheng Liu, Juxia Liu, Yuan Yang, Tiejun Guilin Medical University Department of Intelligent Medicine College of Intelligent Medicine and Biotechnology Key Laboratory of Medical Biotechnology and Translational Medicine of Guangxi Education Department Guilin China Guilin Medical University College of Intelligent Medicine and Biotechnology Department of Intelligent Medicine Guilin China

ISBN: (纸本)9798350324303

We utilized interpretable deep learning to identify key genes and potential biomarkers associated with Alzheimer's disease (AD). AD-related gene expression data from GEO datasets and AlzData were collected. To create a training set, we performed differential expression analysis to delete low-expressed genes. To explore disease-related genes and potential biomarkers, we employed a pathway-centric deep learning network, which integrated gene expression data with established biological pathways, building upon the foundation of PASNet. Our study employed a pathway-related deep learning model based on PASNet combined with bioinformatic analysis for AD risk prediction, and achieved a good performance (AUC = 0.82, F1 score = 0.73). the model accurately classified AD patients based on gene expression data from the brain tissue while providing interpretability deep neural networks. Potential biomarkers (DYNC1I1, DNAJC1, SCRIB, TFEB, MAPKAPK3) and biological pathways (Senescence-Associated Secretory Phenotype, Transcriptional activity of SMAD2/SMAD3:SMAD4 heterotrimer, Toll-like Receptor Cascades) associated with AD were identified. We have employed a combination of bioinformatics analysis and deep learning models to identify a series of candidate genes associated with the diagnosis of Alzheimer's disease (AD). Moreover, we have validated their potential as biomarkers through the utilization of external datasets, and finally focus on the five genes DYNC1I1, DNAJC1, SCRIB, TFEB, and MAPKAPK3. © 2023 IEEE.

关键词： Risk assessment

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