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Study on machine learning Techniques used for Agricultural Yield Estimation 4

Study on Machine Learning Techniques used for Agricultural Y...

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4th international conference on Inventive Research in Computing Applications, ICIRCA 2022

作者： Manasa, C.M. Prince, Blessed Pavithra, N. Presidency University Dept. of Computer Science and Engineering Bangalore India

ISBN: (数字)9781665497077

ISBN: (纸本)9781665497077

this paper presents the various crop yield prediction algorithms using machine learning techniques. In the Indian economy, agriculture plays an important role and it is the most generally practiced occupation in India. the majority of the land is utilized for agriculture exercises to meet the food necessities of the Indian population. the focus of this paper is on the application of machine learning algorithms in the agricultural domain for crop yield prediction. Various data mining and machine learning techniques, such as K Means, Decision Tree, Random Forest classification, K Nearest neighbor, XGboost, artificial neural network, and SVM have been used for recent data applications. Techniques used in agriculture with the rapid development of data mining technology, it has made significant progress. Artificial intelligence, and computer science data mining is a new research area in crop yield analysis in agriculture. Yield prediction is critical, based on the available data, there is an agricultural problem that needs to be solved. the yield prediction problem can be solved by using data mining and machine learning techniques this paper helps the farmers which are the advancements that assist with forecasting the yield dependent on various parameters. © 2022 IEEE.

关键词： Crops

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Partial Discharge pattern recognition in High Voltage Transmission Cable Project 4

Partial Discharge Pattern Recognition in High Voltage Transm...

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4th IEEE international conference on Electronic Technology, Communication and Information, ICETCI 2024

作者： Zhu, Guangxiuyuan Wang, Shengjie Zhang, Yongsheng Jiang, Ling Li, Qiuyang Zhang, Daping Electric Power Research Institute of State Grid Qinghai Electric Power Company Xining810008 China Qinghai Key Laboratory of High Altitude Electric Power Research Xining810008 China Qinghai High Altitude Power Engineering Technology Research Center Xining810008 China State Grid Qinghai Electric Power Company Xining810001 China Xining Power Supply Company State Grid Qinghai Electric Power Company Xining810003 China

ISBN: (纸本)9798350361643

this paper aims to improve the reliability and safety of cables, prolong the service life of cables and reduce the operating cost of power systems. the paper examines in detail how partial discharge (PD) patterns are identified in high-voltage transmission cable projects. this paper tests the performance of several layers of a convolutional neural network (CNN) to increase recognition efficiency and accuracy. During the experiment, unified network parameters are set, including the use of maximum pooling mode, ReLU activation function, and 32 33-size convolution kernels in each layer. By contrasting the recognition accuracy of 1-layer, 2-layer, 3-layer, 4-layer, 6-layer, and 9-layer CNN, the paper's findings indicate that: (1) CNN's recognition accuracy peaks at 90.77% when there are two network layers. However, as the number of layers increases, the accuracy of recognition gradually declines. this result reveals that moderately increasing the network depth is helpful to improve the performance in PD pattern recognition of high voltage transmission cables, but too deep network may lead to over-fitting. (2) CNN is stable in the detection of most defect types, while Support Vector machine (SVM) is the best in some specific defects. Back propagation neural network (BPNN) performs well in some cases, but its overall performance fluctuates greatly. these results are influenced by many factors, such as defect characteristics, model parameters and data set size. therefore, in practical application, it is necessary to select a suitable model for defect detection according to specific scenarios and requirements. this paper provides a useful reference for the follow-up research in high voltage transmission cable project. © 2024 IEEE.

关键词： Defects

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4th international conference on Dynamic data Driven Applications Systems, DDDAS 2022

4th International Conference on Dynamic Data Driven Applicat...

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4th international conference on Dynamic data Driven Applications Systems, DDDAS 2022

ISBN: (纸本)9783031526695

the proceedings contain 42 papers. the special focus in this conference is on Dynamic data Driven Applications Systems. the topics include: Physics-Aware machine learning for Dynamic, data-Driven Radar Target recognition;DDDAS for Optimized Design and Management of 5G and Beyond 5G (6G) Networks;DDDAS-Based learning for Edge Computing at 5G and Beyond 5G;monitoring and Secure Communications for Small Modular Reactors;data Augmentation of High-Rate Dynamic Testing via a Physics-Informed GAN Approach;unsupervised Wave Physics-Informed Representation learning for Guided Wavefield Reconstruction;passive Radio Frequency-Based 3D Indoor Positioning System via Ensemble learning;deep learning Approach for data and Computing Efficient Situational Assessment and Awareness in Human Assistance and Disaster Response and Battlefield Damage Assessment Applications;SpecAL: Towards Active learning for Semantic Segmentation of Hyperspectral Imagery;generalized Multifidelity Active learning for Gaussian-process-based Reliability Analysis;Multimodal IR and RF Based Sensor System for Real-Time Human Target Detection, Identification, and Geolocation;learning Interacting Dynamic Systems with Neural Ordinary Differential Equations;Relational Active Feature Elicitation for DDDAS;explainable Human-in-the-Loop Dynamic data-Driven Digital Twins;transmission Censoring and Information Fusion for Communication-Efficient Distributed Nonlinear Filtering;distributed Estimation of the Pelagic Scattering Layer Using a Buoyancy Controlled Robotic System;towards a data-Driven Bilinear Koopman Operator for Controlled Nonlinear Systems and Sensitivity Analysis;tracking Dynamic Gaussian Density with a theoretically Optimal Sliding Window Approach;dynamic data-Driven Digital Twins for Blockchain Systems;Adversarial Forecasting through Adversarial Risk Analysis Within a DDDAS Framework;essential Properties of a Multimodal Hypersonic Object Detection and Tracking System;power Grid Resilience: data Gaps for Da

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Research on Food Image recognition of Deep learning Algorithms 4

Research on Food Image Recognition of Deep Learning Algorith...

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4th international conference on Computers, Information Processing and Advanced Education, CIPAE 2023

作者： Luo, Lihua Guangdong University of Science and Technology College of Software Engineering Dongguan523000 China

ISBN: (纸本)9798350342710

In recent years, food image recognition has been a research field that has received much attention. Diet is an essential part of people's daily life. through food image recognition and classification, dietary evaluation is conducted to achieve the goal of healthy eating. Manual features and classification methods in traditional target detection algorithms can no longer meet the requirements of detection accuracy and speed, so it is necessary to introduce depth learning-based techniques into food image recognition. Deep learning is a complex machine learning algorithm that allows computers to simulate the human brain's thinking, judge new problems through data sets (human experience), and improve themselves to obtain relatively accurate models, just like humans. Among them, convolutional neural networks have developed most rapidly. they are applied to image recognition with the expectation of being able to automate recognition through machine learning, thereby reducing the workload of image recognition and providing more objective analysis results. Deep learning has achieved great success in intelligent image processing, image classification, and recognition and has excellent development potential and social value. this article implements automatic recognition of food images based on the recognition algorithm of the YOLOv5 convolutional neural network structure, which can detect food images with high detection efficiency. It explores the combination of deep learning to identify food images, providing a reference for people to eat healthily, especially for obese people to control calorie intake, and providing theoretical support for food image recognition. © 2023 IEEE.

关键词： Image recognition

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Design and Application of the Digital Human Identity Attribute data Synthesis System 4

Design and Application of the Digital Human Identity Attribu...

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4th IEEE international conference on Digital Twins and Parallel Intelligence, DTPI 2024

作者： Ji, Yapeng Li, Yangyang Liang, Yunji Guo, Bin Yang, Yangzhao Northwestern Polytechnical University School of Computer Science Xi'an China Academy of Cyber Beijing China

ISBN: (纸本)9798350349252

In the era of big data, increasing demand for identity data sharing in fields like healthcare and tourism, along with stricter cybersecurity regulations, has made generating realistic digital human identity attribute data and ensuring its efficient use a critical challenge. However, existing methods struggle with complex data types and distributions, often neglecting the social and logical consistency of the synthetic data. To address this, we propose a Conditional Generative Adversarial Network (CGAN)-based system for synthesizing digital human identity attribute data. the system features three modules: data preprocessing, data synthesis, and data verification. the preprocessing module uses a composite encoder to handle discrete, continuous, and mixed data types, with a Variational Gaussian Mixture Model (VGM)-based normalization for continuous variables. the synthesis module introduces conditional vector-based sampling to resolve data imbalance while preserving data distribution characteristics, enhancing the realism of the generated data. the verification module uses association rule mining for secondary validation, ensuring logical consistency. this modular design improves scalability, paving the way for multimodal data synthesis. Experimental results show that the system excels in statistical similarity and machine learning utility, with the verification module boosting rule retention by 15%, significantly improving data quality and practical value. this study provides an effective solution with important theoretical and practical implications for digital human identity attribute data synthesis. © 2024 IEEE.

关键词： Generative adversarial networks

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Proceedings - 2023 4th international conference on Frontiers of Computers and Communication Engineering, FCCE 2023

Proceedings - 2023 4th International Conference on Frontiers...

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4th international conference on Frontiers of Computers and Communication Engineering, FCCE 2023

ISBN: (纸本)9798350333169

the proceedings contain 8 papers. the topics discussed include: running P4 programs on general programmable network interconnection chips;research on flotation level detection based on EIT technology;deadline sensitive cloud computing resource scheduling method for scene rendering;performance characteristics of selected network topology in a software-defined networking QoS testing framework;research on student achievement prediction method based on machine learning;mini-Savi: realistic satellite network simulation platform based on open-source tools;research on key technologies of face recognition data storage security;and analysis on influencing factors of performance of NSM-CFRP sheets reinforcement ancient building timber beams based on digital simulation.

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Accelerating Traditional Object Detection Methods on Sophisticated Embedded Systems 4th

Accelerating Traditional Object Detection Methods on Sophist...

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4th international conference on Intelligent Systems and pattern recognition-ISPR

作者： Sehairi, Kamal Bouhafs, Abdelkader Boucherit, Ibtissam Mama Bouwmans, thierry Chouireb, Fatima Ecole Normale Super Laghouat Dept Phys Lab Appl Sci & Didact Laghouat Algeria Univ Amar Telidji Laghouat Dept Elect Telecommun Signals & Syst Lab Laghouat Algeria Univ La Rochelle Dept Comp Sci Lab Math Image & Applicat La Rochelle France

ISBN: (纸本)9783031821523;9783031821530

Identifying and locating objects in images and videos, including elements like traffic signs, vehicles, buildings, and people, constitutes a fundamental and demanding task in computer vision, known as object detection. Due to the higher computing complexity of this technique and the large amount of data carried by the video signal, it is nearly impossible for ordinary general-purpose processors GPPs or CPUs to run these techniques in real-time, especially for embedded systems applications. therefore, special hardware that can acquire, control, or execute in parallel is required. these specialized hardware systems include Digital Signal Processors DSPs, Field Programmable Gate Arrays FPGAs, Visual Processing Units VPUs, Tensor Processing Units TPUs, Neural Processing Units NPUs or Graphics Processing Units GPUs. this work presents the benefits of accelerating traditional object detection methods on a high-end embedded system, the Jetson Nano Developer Kit. this single computer board is equipped with the Tegra K1 System on Chip SoC, which is composed of a quad-core ARM A15 and 192 cores of Kepler-embedded GPU. Computing acceleration was ensured via the use of the CUDA OpenCV library for both the Histogram of Oriented Gradients HOG and the Haar Cascade Classifier. For VGA resolution, results reveal that the GPU implementation on this embedded system is 1.4x faster than the CPU for the HOG method and 2x for the Haar Cascade Classifier method.

关键词： Object detection machine learning Haar Cascade HOG Local feature descriptors GPU implementation OpenCV CUDA SoC Tegra X1 Jetson Nano

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Computer Vision and pattern recognition Technology on Account of Deep Neural Network 4th

Computer Vision and Pattern Recognition Technology on Accoun...

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4th international conference on machine learning, Image Processing, Network Security and data Sciences (MIND)

作者： Ren, Yiming Han, Jiake Chen, Chongyu Xu, Yang Bao, Tingxuan Shenzhen MSU BIT Univ Shenzhen 518172 Guangdong Peoples R China Shanghai Univ Shanghai 201900 Peoples R China Tianjin Univ Sci & Technol 1038 Dagu South Rd Tianjin Hexi Tianjin 300222 Peoples R China

ISBN: (纸本)9783031243660;9783031243677

With the rapid development of computer, computer vision technology is also making rapid progress. In this paper, deep neural network algorithm is used to improve the technology of computer vision, improve the visual effect, and at the same time, innovative algorithm structure, improve the identification of pattern recognition. Computer vision and pattern recognition is a very cutting edge technology that has given people very advanced tools for vision and recognition. At present, the technology can control more accurate image resolution and improve the ability of pattern recognition. this article mainly explains the process of using deep neural network algorithm to improve computer vision and pattern recognition from the internal mechanism, and reveals the working principle and internal mechanism of computer vision and pattern recognition technology application. data analysis proves that the pattern recognition application established by deep neural network algorithm performs very well in the field of vision and pattern recognition.

关键词： Deep neural network algorithm Computer vision pattern recognition Technical application

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Enhancing the Performance of Multi-Objective Regression for Pelvic Organ Prolapse Prediction via data Augmentation 4

Enhancing the Performance of Multi-Objective Regression for ...

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4th international conference on Big data and Artificial Intelligence and Software Engineering, ICBASE 2023

作者： Mi, Yanlin Tabirca, Sabin O'Reilly, A. University College Cork School of Computer Science and Information Technology Cork Ireland University College Cork Sfi Centre for Research Training in Artificial Intelligence Cork Ireland Transylvania University of Brasov Faculty of Mathematics and Informatics Brasov Romania Cork University Maternity Hospital Department of Obstetrics and Gynecology Cork Ireland

ISBN: (纸本)9798350329490

In healthcare, machine learning has been increasingly applied to predictive models, but the efficacy of these models is often compromised due to limitations in data quality, diversity, and metrics. In other domains, such as image recognition and natural language processing, data augmentation techniques have been successfully applied to mitigate these challenges, but in healthcare such strategies have not been widely applied. therefore, our research actively explores how these data augmentation techniques can be applied to machine learning models for predicting the outcome of pelvic organ prolapse surgery. We first performed in-depth data preprocessing and then tried innovative data enhancement techniques such as noise injection and self-sampling. the results show that the application of data enhancement techniques significantly improves the performance of predictive models and effectively addresses data scarcity and quality issues, which opens up new possibilities for wider application of data enhancement techniques in the medical field in the future. © 2023 IEEE.

关键词： Surgery

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Research on K-means Optimized Cluster Analysis Method for Agricultural Housing Construction data 5

Research on K-means Optimized Cluster Analysis Method for Ag...

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5th IEEE international conference on pattern recognition and machine learning, PRML 2024

作者： Bie, Mu Tang, Lian Lin, Hang School of Information Engineering Chongqing Jianzhu College Chongqing China

ISBN: (纸本)9798350355925

Cluster analysis of agricultural housing construction data plays an important role in understanding the characteristics of rural housing distribution, assessing building quality and planning rural infrastructure construction. Although the traditional K-means clustering method is simple and easy to use, it suffers from the problems of local optimization and sensitivity to the initial center of mass when dealing with complex datasets. In order to solve these problems, the study introduced three optimization algorithms: distributed particle swarm optimization (DPSO), artificial fish swarm optimization (AFSA) and sparrow search algorithm (SSA), to improve the K-means clustering method and compare their effects in the analysis of agricultural housing construction data. the experimental results show that all three optimization algorithms can significantly improve the effect of K-means clustering, especially on complex datasets. Among them, SSA algorithm performs the best in clustering effect, with clear cluster boundaries and accurate data point allocation;although DPSO and AFSA perform well in global search ability, their clustering effectiveness in boundary regions is slightly inferior to SSA. therefore, SSA is the preferred method to improve K-means clustering, and it is suitable for clustering analysis of agricultural housing construction data. Meanwhile, future research directions are proposed, including multi-algorithm fusion, parameter optimization, application scenario extension and intelligent decision support, to further improve the effect and application breadth of clustering analysis. the research provides effective data analysis methods and technical support for rural construction and management, helping to improve the science and efficiency of infrastructure construction and management. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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