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检索条件"任意字段=25th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2024"

共 168 条记录，以下是61-70 订阅

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EC-Net: Efficient Compound Network with Dendritic learning for Cataract Eye Detection 13

EC-Net: Efficient Compound Network with Dendritic Learning f...

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Joint 13th international conference on Soft Computing and intelligent Systems and 25th international Symposium on Advanced intelligent Systems, SCIS and ISIS 2024

作者： Wang, Haiqing Ju, Zeyuan Chen, Chouyu Liu, Zhipeng Fu, Qiong Gao, Shangce University of Toyama Faculty of Engineering Toyama930-8555 Japan

ISBN: (纸本)9798350373332

As the leading cause of visual impairment and blindness, automatic methods for diagnosing cataracts from retinal images are desired. With the advancement of computer technology, deep learning-based approaches have demonstrated superior performance compared to traditional manual methods. these algorithms are particularly advantageous in terms of conserving human and material resources and minimizing the dependency on clinical expertise. However, existing methods often struggle with extracting the non-linear characteristics inherent in cataract data. To alleviate this challenge, we propose a novel Efficient-dendritic Cataract-detection Network (EC-Net), which integrates the Dendritic Neuron Model for efficient and accurate detection of cataracts. this network leverages the nonlinear properties of dendritic neurons in conjunction with advanced deep learning feature extraction techniques to enhance the accuracy of cataract identification. Experimental results on publicly available datasets demonstrate that EC-Net significantly outperforms traditional models in the classification of cataract fundus images, providing a promising solution for medical image classification and supporting auxiliary diagnosis. © 2024 IEEE.

关键词： Classification Deep learning Dendritic neuron model EfficientNet

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DeepFuzzLoc Positioning: A Unified Fusion of Fuzzy Clustering and Deep learning for Scalable Indoor Localization Using Wi-Fi RSSI 13

DeepFuzzLoc Positioning: A Unified Fusion of Fuzzy Clusterin...

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Joint 13th international conference on Soft Computing and intelligent Systems and 25th international Symposium on Advanced intelligent Systems, SCIS and ISIS 2024

作者： Mahali, Muhammad Izzuddin Leu, Jenq-Shiou Achmad Sulistyo Putro, Nur Widyawan Prakosa, Setya Avian, Cries National Taiwan University of Science and Technology Dept. of Electronic and Computer Engineering Taipei Taiwan Universitas Negeri Yogyakarta Dept. of Electronics and Informatics Engineering Education Yogyakarta Indonesia Universitas Gadjah Mada Dept. of Computer Science and Electronics Yogyakarta Indonesia

ISBN: (纸本)9798350373332

this research introduces a novel method for indoor positioning by integrating Fuzzy C-Means clustering with deep learning autoencoders, leveraging Wi-Fi RSSI fingerprinting for precise location prediction and building and floor level classification within a unified framework. the approach combines the comprehensibility of Fuzzy C-Means (FCM) with the robust feature extraction capabilities of deep autoencoders through a dual-phase strategy: extensive offline training utilizing RSSI fingerprint data followed by real-time online position prediction. the model's performance was rigorously tested using 5-Fold cross-validation, examining its effectiveness both with and without the integration of FCM on various cluster numbers. Evaluation metrics, including Euclidean distance error for regression and accuracy, F1-score, precision, and specificity for classification effectiveness, were used for comparative analysis. the results were visualized through regression plots, confusion matrices, and learning curves, supplemented by t-SNE visualizations that provide profound insights into the feature space. these detailed analyses highlight the considerable promise of this integrated approach in enhancing the accuracy and reliability of indoor positioning systems. © 2024 IEEE.

关键词： Fuzzy clustering

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Integrating servopress acoustic monitoring with Machine learning for enhanced predictive maintenance: opportunities and limitations 25

Integrating servopress acoustic monitoring with Machine Lear...

引用

25th international Carpathian Control conference (ICCC)

作者： Aradi, Attila Varga, Attila Karoly Takacs, Peter Univ Miskolc Hatvany Jozsef Doctoral Sch Miskolc Hungary Univ Miskolc Inst Automat & Infocommun Miskolc Miskolc Hungary HunReN Balaton Limnol Res Inst Tihany Hungary

ISBN: (纸本)9798350350708;9798350350715

this paper explores the innovative integration of acoustic monitoring of servo presses with machine learning algorithms to improve predictive maintenance strategies in industrial environments. Our research focuses on the application of advanced acoustic sensors to capture and analyze servo press acoustic data, which is critical in forming and pressing operations. this acoustic data serves as a key indicator of machine health, potentially revealing signs of wear or failure before they occur. We present the implementation of machine learning models trained on historical acoustic data, highlighting how these models can identify patterns indicative of machine degradation. the goal of this predictive approach is to optimize maintenance schedules, reduce downtime and extend the lifetime of industrial equipment. It also addresses the inherent challenges and limitations of applying artificial intelligence in this field. these include issues related to the quality and quantity of data required for accurate modelling, the complexity of real industrial environments, the ongoing need for AI system maintenance, and the risks associated with over-reliance on automated systems. Our findings suggest that while integrating servo press acoustic monitoring with machine learning offers significant potential for improving predictive maintenance, it requires a balanced approach that combines technological innovation with human expertise. the paper concludes with recommendations for future research and practical implementation strategies in industrial settings.

关键词： Artificial Intelligence machine learning predictive maintenance acoustic monitoring servopress SCADA sensor system engineering fault diagnosis Industry 4.0

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Optimizing Remaining Useful Life Prediction: A Feature engineering Approach 25

Optimizing Remaining Useful Life Prediction: A Feature Engin...

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25th international Carpathian Control conference (ICCC)

作者： Knap, Pawel Jachymczyk, Urszula Lalik, Krzysztof AGH Univ Krakow Fac Mech Engn & Robot Krakow Poland

ISBN: (纸本)9798350350708;9798350350715

Machine learning techniques have recently brought many improvements in the field of machine's Prognostics and Health Management (PHM). the main objectives include identification of degradation patterns indicating potential issues, forecasting Remaining Useful Life (RUL), optimizing undertaking maintenance actions, and minimizing downtime. In this paper, we focused on creating a successful RUL model, which allows predicting the amount of time when a machine, or its element, will function effectively before failure. the success of such an intelligent health assessment model depends not only on a chosen algorithm but also thorough data preprocessing and feature selection. Dimensionality reduction algorithms can help prevent the so-called 'curse of dimensionality,' which refers to the challenges when working with high-dimensional data. A large number of features create the risk of being very sparse, instances are far away from each other, which affects the reliability of predictions. We have chosen one of the most popular dimensionality reduction techniques - Principal Component Analysis (PCA), which projects data down to a determined number of dimensions. Gathering data for RUL is quite challenging because it requires measurements while running the machine to failure;therefore, it has been decided to use bearing open-source dataset prepared in an environment well-adjusted for this demanding task.

关键词： Vibration Analysis Condition Monitoring Systems Predictive Maintenance Remaining Useful Life Prediction Machine learning Algorithms

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Analysis of Placental Oxygenation in BOLD MRI for Fetal Growth Restriction Estimation 13

Analysis of Placental Oxygenation in BOLD MRI for Fetal Grow...

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Joint 13th international conference on Soft Computing and intelligent Systems and 25th international Symposium on Advanced intelligent Systems, SCIS and ISIS 2024

作者： Nishida, Kentaro Morita, Kento Enomoto, Naosuke Magawa, Shoichi Nii, Masafumi Wakabayashi, Tetsushi Graduate School of Engineering Mie University Department of Information Engineering Japan Graduate School of Medicine Mie University Department of Obstetrics and Gynecology Japan

ISBN: (纸本)9798350373332

Fetal Growth Restriction (FGR) is an obstetric and gynecological disease, resulting in low fetal weight at gestational age. It causes fetal growth delay during gestation and increases the risk of prenatal and postnatal health problems. Recently, some studies have reported that fetal growth is related to placental oxygenation that can be visualized using BOLD MRI. the BOLD MRI image analysis using machine learning requires the placental region mask, but its manual annotation requires a heavy burden on experts. In this paper, we apply image registration in 2D+t images to reduce the annotation workload from 2D+t times to one time, and extract statistical image features in the auto-annotated placental regions to validate the image features related to the clinical data of FGR and non-FGR patients. the experiment on BOLD MRI of 8 FGR and 15 non-FGR patients showed that the proposed method recognized the placental region in dice coefficient of 0.779. Experiments using the extracted feature analysis found the correlation to the clinical data in placental weight and fetal birth weight. From these results, the proposed method is expected to be used for the evaluation of placental oxygenation and FGR diagnosis. © 2024 IEEE.

关键词： Dimensionality reduction

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Super Resolution of Remote Sensing Image Using Improved SwinIR 13

Super Resolution of Remote Sensing Image Using Improved Swin...

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Joint 13th international conference on Soft Computing and intelligent Systems and 25th international Symposium on Advanced intelligent Systems, SCIS and ISIS 2024

作者： Matsumoto, Shun Kamiya, Tohru Kyushu Institute of Technology Department of Mechanical and Controll Engineering 1-1 Sensui Tobata Kitakyushu Fukuoka Japan

ISBN: (纸本)9798350373332

In recent years, the number of satellite launches has been increasing. One of the reasons for this is the growing market for space development, which has led to the release of a large amount of satellite data. Among them are satellite images, which capture a wide range of ground conditions and are used in various fields, such as the extraction of building areas. However, human analysis of these images is time-consuming and inefficient, so deep learning is expected to be used for image analysis. the images that can be used to recognize objects in satellite images are high-resolution images taken by high-performance satellites, but such images are expensive, and the cost of acquiring a large number of images is high. In this paper, we propose a method to reconstruct a high-resolution satellite image by super-resolution from a low-resolution satellite image using the Vision Transformer model. Image Restoration Using Transformer (SwinIR) is adopted as the base model. To improve the accuracy, Knowledge Distillation and Multi-Layer Perceptron with Gating Unit are incorporated into the base model, and good results are obtained. © 2024 IEEE.

关键词： Satellite imagery

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Health Classification on Oil Palm UAV Images Using Machine learning with Features Reduction 13

Health Classification on Oil Palm UAV Images Using Machine L...

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Joint 13th international conference on Soft Computing and intelligent Systems and 25th international Symposium on Advanced intelligent Systems, SCIS and ISIS 2024

作者： Tee, Zi Choong Tay, Lee Choo Lai, Weng Kin Lee, Sheng Siang Tunku Abdul Rahman University of Management and Tehnology Department of Mechanical Engineering Kuala Lumpur Malaysia Centre of Multi-modal Signal Processing Tunku Abdul Rahman University of Management and Tehnology Kuala Lumpur Malaysia Aonic Sdn Bhd Subang Jaya Malaysia

ISBN: (纸本)9798350373332

the sheer size of oil palm plantations is causing challenges in managing the plantation efficiently and effectively. Among the many challenges affecting the palms, timely disease control is crucial in maintaining high yield and sustainable agriculture practice. this paper investigates the health monitoring of oil palms using images taken with unmanned aerial vehicles (UAVs), enabling proactive intervention and targeted disease management. Based on 2002 real-world oil palm images provided by the industry, support vector machine (SVM) optimized with a radial basis function (RBF) kernel was able to achieve an Fl score of 0.975 on test data and a macro Fl score of 0.88 on inference data, paving the way for its application in large scale diseased tree identification in the plantation. © 2024 IEEE.

关键词： Support vector machines

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Energy-Based Continuous Time Series Probability Model with Latent Stochastic Process 13

Energy-Based Continuous Time Series Probability Model with L...

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Joint 13th international conference on Soft Computing and intelligent Systems and 25th international Symposium on Advanced intelligent Systems, SCIS and ISIS 2024

作者： Shibata, Hiroki Obo, Takenori Anzai, Yohei Sato-Shimokawara, Eri Ho, Yishin Takama, Yasufumi Kobayashi, Ichiro Graduate School of System Design Tokyo Metropolitan University Tokyo Japan Exevita Inc. Kanagawa Japan Takushoku University Faculty of Engineering Tokyo Japan

ISBN: (纸本)9798350373332

Astract- this paper presents a new energy-based continuous time series probability model that has stochastic process in its latent dynamics. Because the model assuming its output is used for connecting a vector space such as embedding space and latent dynamics, one of the possible application of the present study is to predict sequence of segments such as in the human motion behavior recognition. the present model gives joint distribution of all variable, therefore, it has the property to generate learned sequence without conditioning any values including initial state. this property overcomes existing problem in initial value condition for frequently applied time series model such as recurrent neural network. Furthermore, energy-based continuous formulation gives us consistent method for both learning and estimating the state by the gradient descent and its derivative methods. Such consistent method is not proposed in existing study. In this paper, the model formulation, gradient estimator, and its learning method is shown. Experiments on synthetic data is given, and the validity of the proposed model and method discussed. © 2024 IEEE.

关键词： Vector spaces

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Deep learning-Based Analysis of Neuronal Signal Streams: Estimating the Activity Range of Cultured Neuronal Networks in Consecutive Stimulus Inputs Using CNN 13

Deep Learning-Based Analysis of Neuronal Signal Streams: Est...

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Joint 13th international conference on Soft Computing and intelligent Systems and 25th international Symposium on Advanced intelligent Systems, SCIS and ISIS 2024

作者： Asada, Hiroki Kudoh, Suguru N. Dept. of Human System Interaction Sanda Japan Artificial Intelligence and Mechanical Engineering Course Dept. of Engineering Sanda Japan

ISBN: (纸本)9798350373332

We utilized a Convolutional Neural Network (CNN) -that incorporates a structure designed to integrate local relationship features. the CNN-based deep learning approach enabled us to identify response patterns following stimulus input, using images of neural activity that depict the activity of individual neurons. the feature of CNN-based neural network, integrating local features, considered to contribute to decipher the relationship within time-series data, which are interpreted as streams of signals passing through a single neuron. Utilizing this feature, we attempted to determine the scope of activity that preserves the history of ongoing stimulation within a cultured living neuronal network. We repeatedly subjected the network to the same stimulus, and from the post-stimulus response activity, we extracted spatial combinations of neurons that fired simultaneously within brief 1 ms time windows. We calculated the firing rates for each of these 1 ms intervals in response to 10 different stimuli. By arranging these firing rates into a distinctive configuration that sustains temporal continuity, we generated images that illustrate neuronal activity. We then attempted to discriminate response images obtained at various stimulus intervals, and compared the accuracy of the discrimination to gauge the scope and degree of history retention in sequential activation. © 2024 IEEE.

关键词： Convolutional neural networks

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Development of Gait Classification System Using Six-Axis Inertial Sensors by CNN-LSTM 13

Development of Gait Classification System Using Six-Axis Ine...

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Joint 13th international conference on Soft Computing and intelligent Systems and 25th international Symposium on Advanced intelligent Systems, SCIS and ISIS 2024

作者： Ohno, Shotaro Nagamune, Kouki Graduated School of Engineering University of Fukui 3-9-1 Bunkyo Fukui Fukui Japan Graduated School of Engineering University of Hyogo Hyogo Japan

ISBN: (纸本)9798350373332

Restoration of walking function is the most important goal of rehabilitation. However, the evaluation of post-stroke rehabilitation lacks quantitative elements. In this study, we used an IMU to acquire the angles of three axes (rotation of x-axis, rotation of y-axis, and rotation of z-axis) of the knee during walking motion as gait parameters. Using this data, machine learning analysis is performed. In this article, we discuss the gait parameters, the angular data of the three axes of the knee, and examine which of the three angular parameters increases or de-creases the generalization ability of the model. As a result, we identify the gait parameters to be used as a dataset. Time series data of the knee angle of one step in the gait measurement was used as the dataset. CNN - LSTM algorithm was used to construct the model. Five subjects were measured with the knee joint motion measurement device attached to both knees. A total of four types of gait movements, including normal gait and three types of gait movements that reproduce hemiplegia, were meas-ured for analysis. Out of one hundred thirty-six steps of data from five subjects, four subjects were used as training data and one unknown subject's data, which was not used during training, was applied to the test data. the results of the prediction of the estimated data show that rotation of z-axis was a parameter that reduced the generalization ability of the model, while rotation of y-axis was a parameter that increased the generalization ability of the model. Prediction results for the rotation of x-axis data alone showed poor accuracy, but when combined with the rotation of y-axis data, the generalization ability was improved. © 2024 IEEE.

关键词： Joints (anatomy)

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