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检索条件"任意字段=11th International Conference on Intelligent Data Engineering and Automated Learning"

共 7263 条记录，以下是1311-1320 订阅

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2023 international conference on Information Technology: Cybersecurity Challenges for Sustainable Cities, ICIT 2023 - Proceeding

2023 International Conference on Information Technology: Cyb...

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11th international conference on Information Technology, ICIT 2023

ISBN: (纸本)9798350320060

the proceedings contain 153 papers. the topics discussed include: improving energy consumption in content-addressable memory through precomputation;a survey: threat hunting for the OT systems;the effectiveness of Nearpod in developing online interactive lesson design skills for mathematics and computer teachers;a proposed technique for business process modeling diagram using natural language processing;intelligent model for optimizing Gantt chart in the planning stage;towards the application of test driven development in big data engineering;ArcaBoard: an overview of the selectromagnetic HoverBoard;sequentially compact and compactly generated groups;FPGA-based radio transceiver using the TV white space for disaster response operation;reliability function of the connected-(2,2)-out-of- (m,n): f linear and circular system using Markov chain;and detection of fabricated survey data using clustering analysis.

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Comparative study of YOLOv8 and YOLO-NAS for agriculture application

Comparative study of YOLOv8 and YOLO-NAS for agriculture app...

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international conference on Signal Processing and Integrated Networks (SPIN)

作者： Yogesh Kumar Pankaj Kumar computer science Engineering UPES Dehradun India computer science Engineering Nirma University Ahmedabad India

Agricultural tasks have significantly improved as a result of ongoing machine learning (ML) improvements. Deep learning (DL), which has a significant capacity for extracting high-dimensional features from fruit images, is widely applied to the automated detection and harvesting of fruits. In the fields of fruit recognition and automated harvesting, Convolutional Neural Networks (CNNs) have demonstrated the ability to attain speed and accuracy levels that rival human performance. this article compares the performance of YOLOv8m with YOLO-NASl for grapes detection. In this research, the YOLOv8 and YOLO-nas object detection models, including their different scales, were trained using a publicly available Embrapa WGISD dataset. the dataset consists of 300 digital images of grapes growing in vineyard settings, and it includes a total of 4,432 annotations. the performance of the YOLOv8m and YOLO-NASl model were evaluated using metrics such as recall, precision, and the mean average precision (mAP@50). In the subset of test data, YOLOv8m achieved the top overall performance, with a precision (0.855), mAP@50 (0.885), and recall (0.827), while best recall was obtained from YOLO-NASl (0.934).

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Machine learning Quantification of Liberica Coffee Morphology in Lipa City 11

Machine Learning Quantification of Liberica Coffee Morpholog...

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11th international conference on Electrical engineering, Computer Science and Informatics, EECSI 2024

作者： Balazon, Francis G. Alvaro, Jonh Vincent M. Fabregas, Mike Clarence F. Sabado, Brian Jerome E. Batangas State University The National Engineering University Lipa College of Informatics and Computing Sciences Batangas Lipa City Philippines

ISBN: (纸本)9798350355314

Investigating the morphological characteristics of Liberica green coffee beans presents challenges due to their irregular shapes and potential overlaps within images. this study aimed to develop a robust image analysis pipeline for quantifying green bean morphology and exploring potential variations between farms within the Lipa City region. A comprehensive coffee bean collection protocol was established, and a diverse image dataset was created. Image preprocessing, including normalization, augmentation, and enhancement techniques, optimized the data for analysis. A U-Net convolutional neural network was developed for semantic segmentation, achieving remarkable performance with an accuracy of 99.83% and an IoU score of 0.985. this enabled the precise extraction of morphological features from individual bean instances. Principal Component Analysis (PCA) revealed the primary contributors to morphological variance, and Multivariate Analysis of Variance (MANDVA) demonstrated statistically significant differences in the overall combination of morphological features between farms. these findings suggest that farm origin, potentially due to variations in environmental conditions, cultivation practices, or bean varieties, influences the physical characteristics of Liberica green coffee beans. this study provides a powerful framework for future investigations into the relationship between morphology, quality assessment, and optimization of processing techniques within the coffee industry. © 2024 IEEE.

关键词： Convolutional neural networks

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A Comparative Study of MNL and Machine learning Methods for Travel Mode Choice of Medical Travel 11th

A Comparative Study of MNL and Machine Learning Methods for ...

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11th international conference on Traffic and Transportation Studies, ICTTS 2024

作者： Huang, Pengpeng Gong, Lei Lei, Tian Wang, Jia Zhu, Cheng Zhang, Zheng College of Urban Transportation and Logistics Shenzhen Technology University Lantian Road 3002 Shenzhen China

ISBN: (纸本)9789819796397

the prevailing disparity in the availability and demand for medical services necessitates an in-depth analysis and projection of the travel behavior patterns of medical travelers. First, this paper screens and processes data on travel to healthcare facilities from the Fifth Tokyo Metropolitan Area Resident Travel Survey. Second, the preliminary descriptive statistics reflected that the medical and health travelers in this dataset are elderly individuals, from smaller households, exhibiting high travel frequency, with a pronounced preference for morning travel. this paper used multinomial logit model, decision tree and random forest model to predict travelers’ choices of public transportation, private transportation, non-motorized vehicles and walking in daily healthcare travel scenarios. the models demonstrate robust fit, achieving commendable predictive accuracies of 63.3%, 65.41%, and 65.54%, respectively. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Nonmotorized transportation

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Effects of Adopting Industry 4.0 on a Manufacturing Plant 11

Effects of Adopting Industry 4.0 on a Manufacturing Plant

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11th international conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Waghanna, Pranav Reddy, Akash Deshpande, Sarthak Chavan, Siddhant Jaiswal, Vishal R. Naranje, Vishal Be It Department of Pune Institute of Computer Technology Pune India It Department of Pune Institute of Computer Technology Pune India School of Engineering Architecture and Interior Design Amity University Dubai United Arab Emirates

ISBN: (纸本)9798350350357

In the era of Industry 4.0, Smart Manufacturing is reshaping how things are made. At the heart of it is the Industrial Internet of things (IIoT), where smart sensors and connections create a digital system for factories. this paper highlights how IIoT facilitates real-time data exchange, aiding quick decision-making and creating adaptable manufacturing setups. Cyber-Physical Systems (CPS) integration is also discussed, showing how the line between physical and digital elements in manufacturing is getting blurred. Big data Analytics is another game-changer, supporting data-driven decisions and predictive maintenance. the paper also digs into Artificial Intelligence and Machine learning, showing how they automate tasks and continuously learn, improving manufacturing processes. Real-world applications are examined, including Digital Twins, advanced robotics like collaborative robots (Cobots), 3D printing, and smart supply chain management. While Smart Manufacturing offers numerous benefits, it's not without challenges. the survey talks about security and privacy concerns, emphasizing the need for strong cybersecurity. Interoperability, ensuring different technologies work seamlessly together, and addressing the workforce skills gap are also explored. the paper ends with examples of successful implementations and looks at future trends, such as the impact of 5G, blockchain in supply chain management, human augmentation, and the push for sustainability in Smart Manufacturing. this survey is a practical guide for anyone interested in understanding the current state and where Smart Manufacturing is headed in the world of Industry 4.0. © 2024 IEEE.

关键词： Cyber Physical System

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FishNet: A Hybrid Deep learning and Machine learning Framework for Precise Fish Species Identification

FishNet: A Hybrid Deep Learning and Machine Learning Framewo...

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international conference on Signal Processing and Integrated Networks (SPIN)

作者： Ankita Suryavanshi Vinay Kukreja Ayush Dogra Priyanshi Aggarwal Manika Manwal Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Computer Science and Engineering Graphic Era Hill University Dehradun Uttarakhand India Computer Science and Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India

this article uses CNNs and Random Forest models to automate fish species identification. the neural network design in Table 3 combines CNN hierarchical feature extraction and Random Forest interpretable ensemble learning, combining their capabilities. the study carefully addresses data gathering and preparation problems, emphasizing the need for a broad, well-prepared dataset. Model optimization in Section C uses hyperparameter tweaking, regularization, and machine learning to create a balanced and effective model. Section D shows the model’s resilience to varied environmental conditions during species recognition and execution. Table 2 displays precision, recall, and Fl scores, demonstrating the model’s versatility across fish species. the findings advance ecological computer vision and offer a viable tool for regulating fisheries, environmental monitoring, and conservation. From the matrix of confusion, the class-specific metrics, and future research, the report suggests that automated identification of fish species systems can have real-world impact and be continuously improved.

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Satellite Image Denoising Using Deep learning: Autoencoder Decoder Application 11

Satellite Image Denoising Using Deep Learning: Autoencoder D...

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11th international conference on Electrical engineering, Computer Science and Informatics, EECSI 2024

作者： Pula, Rolando Advincula Cruz, Sean Chris Kirk Patrick P. Ilagan, Lorena College of Engineering Architecture and Technology University of Perpetual Help System DALTA Las Pinas Philippines

ISBN: (纸本)9798350355314

through deep learning Autoencoder Decoders, it is possible to clean noisy or damaged image data received from satellites. Two models with a PSNR of 25.6 dB and 25.54 dB were generated using a dataset consisting of 610 weather satellite images retrieved from the National Oceanic and Atmospheric Administration (NOAA) Weather (WX) satellites. these images were acquired via the ground station at the University of Perpetual Help System DALTA - Las Pinas campus. Initially, only a few alterations were made to the code in order to work with later Keras versions. However, this led to crashes during training as well as noticeable darkening in the predictions. the original code also used accuracy as the training metric, instead of PSNR. the rewrite rebuilt the model using updated Keras 3 API as well as introduced a PSNR metric. Doing so eliminated the darkening and crashes entirely. © 2024 IEEE.

关键词： Weather satellites

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Root-zone Soil Moisture Nowcasting using Context Aware Machine learning 11

Root-zone Soil Moisture Nowcasting using Context Aware Machi...

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11th international conference on Agro-Geoinformatics, Agro-Geoinformatics 2023

作者： Aktas, Ayda F. Ustundag, Burak Berk Istanbul Technical University Informatics Institute Istanbul Turkey Istanbul Technical University Faculty of Computer and Informatics Engineering Istanbul Turkey

ISBN: (纸本)9798350303513

Estimation of root-zone soil moisture (SM) is crucial for effective agricultural management and water resource planning. However, current methods for soil moisture estimation exhibit several limitations that hinder their practical application. this study introduces a novel nowcasting model, which integrates in-situ and remote sensing data through a Predictive Error Compensated wavelet Neural NETwork (PECNET), addressing the drawbacks of existing *** SM estimation techniques often suffer from limited accuracy, inadequate contextual information, and no real-time monitoring capabilities. Although remote sensing technologies offer promising advantages, such as wide spatial coverage and frequent data acquisition, they are not immune to limitations. Vegetation coverage and density present challenges in accurately estimating root-zone SM using remote sensing techniques. these factors can introduce uncertainties and errors in the estimation process, thereby impacting the reliability of the *** overcome these limitations and enhance the accuracy of root-zone SM estimation, this study proposes the integration of remote sensing data with in-situ measurements. Specifically, Normalized Difference Vegetation Index (NDVI) calculations from Landsat 7 and Landsat 8 satellites are fused with evapotranspiration and rainfall data obtained from agrometeorological stations. Combining these datasets generates an 8-day time series for the target parcels, leveraging the contextual information provided by NDVI and seasonality to improve the accuracy of root-zone soil moisture *** develop a robust and efficient model, we introduce PECNET, which ensures the orthogonality of input features and facilitates the learning of non-linear relationships between variables. Notably, PECNET addresses the challenge of limited labeled training data, minimizing the risk of overfitting and enabling accurate estimation with fewer labeled samples. In addition, this study employs

关键词： Time series

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Screening of Syndrome Elements and Construction of Diagnostic Model of Hyperactivity of Liver-Yang Syndrome Based on Machine learning 11th

Screening of Syndrome Elements and Construction of Diagnosti...

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11th international conference on Signal and Information Processing, Network and Computers, ICSINC 2023

作者： Hu, Sen Li, Zhao-Bing Sun, Yong-Kang Cui, Sin-Yue Xu, Fang-Biao Henan Provincial People’s Hospital Zhengzhou University People’s Hospital Henan Zhengzhou450000 China Henan University of Chinese Medicine Henan Zhengzhou450000 China

ISBN: (纸本)9789819721153

Objective: Based on a variety of machine learning algorithms, screen the hyperactivity syndrome e elements of liver yang syndrome. Methods: Using the classification of hyperactivity of liver yang syndrome standards and searching for relevant standards of hyperactivity of liver yang syndrome, the study constructed a matrix data set containing the syndrome elements of hyperactivity of liver yang syndrome and screened out key syndrome elements with the help of three machine learning algorithms, including XGBoost, SVM, and RF. Based on the results of the expert questionnaire, the key syndrome elements of liver yang syndrome are derived. Results: A total of 10 key elements were screened out through 3 machine learning algorithms. Combined with the results of multi region, multi field and multi-level research, the core syndrome elements of irritability, soreness and softness of the waist and knees, stringy pulse and red tongue were obtained. Conclusion: there is still a need to expand clinical samples and continuously optimize the diagnosis model of hyperactivity of liver-Yang syndrome based on expert opinions because of this study, as it provides a data base for the digitalization and objectification of TCM syndrome differentiation. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Classification (of information)

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7th international conference on Innovative Computing, IC 2024

7th International Conference on Innovative Computing, IC 202...

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7th international conference on Innovative Computing, IC 2024

ISBN: (纸本)9789819741243

the proceedings contain 157 papers. the special focus in this conference is on Innovative Computing. the topics include: Proactive Auto Scaling Based on Marginal Request Change Analysis for Reducing Tail Latency in Kubernetes Cluster;Integration of Generative Adversarial Networks (GAN) and AI Drawing in Criminal Sketches—Applied in Crime Scene Investigation by Law Enforcement;implementation of a High Quality Cardiopulmonary Resuscitation Teaching Intelligence Training and Assessment System;Predicting Twitter Posts from Fake Accounts Using XGBoost Model;the Interplay Between Self-regulated learning and Programming learning Achievement;a Study on the Continued Viewing Intention of YouTuber Videos: Experiential Marketing, Social Capital, and Habit Perspectives;an Exploratory Study on the Design of Human-Computer Interaction Interfaces for Augmented Reality Integrated Museum Exhibitions;communication-Aware Optimization of Microservice Scheduling on Edge Computing;Design of Computer Model for Raw Material Supplier Selection Based on Entropy Weight TOPSIS Method;exploring How Cybersecurity Competitions Can Help Improve Students’ Practical Abilities in Cybersecurity;Adversarially Residual U2Net for COVID-19 Lung Infection Segmentation from CT Images;partnerships Analysis Based on Measurements of Common Research Interest;System Design of Operating Costs of intelligent Healthcare Services for the AHP Algorithm;a Multi-party Private Set Union Protocol Against Malicious Adversary;analysis of the Current Status of Research on Unmanned Collaborative Equipment Above and Below Water;data Mining Analysis of New Energy Vehicles Based on Cluster Analysis Technology;Two-Layer Minimum Variance FIR Filter-Based DPLL Design;fuzzy Proximity Two-Dimension Inductance Gesture Recognition System Analysis and Implement;proximity Capacitive Gesture Recognition for Recursive Neighbor Memory Neural Network;application of Genetic Algorithms in automated Mechanical Design.

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