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检索条件"任意字段=3rd International Conference on Model and Data Engineering, MEDI 2013"

共 10584 条记录，以下是1-10 订阅

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A Hybrid model for Parameter Tuning using SSO and CNN 3

A Hybrid Model for Parameter Tuning using SSO and CNN

引用

3rd international conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Patidar, Anil Bandhu, Kailash Chandra Department of Computer Applications Medi-Caps University Indore India Department of Computer Science and Engineering Medi-Caps University Indore India

ISBN: (纸本)9798331527549

Social Spider Optimization (SSO) and Particle Swarm Optimization (PSO) algorithms applied to Intrusion Detection Systems (IDS) across small and large datasets. The study aims to evaluate and compare both PSO and SSO and find best optimal parameters in order to improve the accuracy of IDS. Additionally, the SSO algorithm find the best hyperparameter settings which are kernel, filter, padding, learning rate, and stride to achieve optimal results that directly impact IDS security. The evaluation of algorithms in terms of accuracy, precision, recall, F1 score, and AUC score. Results indicate that, on small datasets, PSO comes under overfitting but not SSO. Even on larger datasets, SSO crosses PSO significantly in terms of accuracy. This finding shows the importance of improvement in scalability, efficiency, and robustness of IDS with help of Social Spider Optimization (SSO). © 2025 IEEE.

关键词： Particle swarm optimization (PSO)

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Mini-SCXception: Balancing Accuracy and Efficiency in Real-Time Facial Expression Recognition 24

Mini-SCXception: Balancing Accuracy and Efficiency in Real-T...

引用

3rd international conference on Artificial Intelligence and Intelligent Information Processing, AIIIP 2024

作者： Shao, Wenhao Huang, Xiaoning Xue, Bohuan Tang, Xiaoyu School of Data Science and Engineering Xingzhi College South China Normal University Guangdong Guangzhou China School of Electronic and Information Engineering Xingzhi College South China Normal University Guangdong Guangzhou China

ISBN: (纸本)9798400707308

In Facial Expression Recognition (FER), existing models often encounter challenges such as high parameter counts and significant computational demands, making real-time application on resource-constrained devices difficult. To address this issue, this paper proposes an improved Mini-SCXception model based on the Mini-Xception architecture. By integrating Spatial and Channel Reconstruction Convolution (SCConv), the proposed model reduces redundant computations while preserving the efficiency of Mini-Xception and enhancing feature extraction capabilities. On the FER2013 dataset, Mini-SCXception achieved an accuracy of 64.50%, representing a 1.05% improvement over the baseline model’s 63.83%. Experimental results demonstrate that Mini-SCXception balances accuracy and computational efficiency, providing a robust approach for real-time FER. © 2024 Copyright held by the owner/author(s).

关键词： Feature extraction

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Construction and Validation of Big data Analytics in Air Traffic Control Flow Prediction models 24

Construction and Validation of Big Data Analytics in Air Tra...

引用

3rd international conference on Algorithms, data Mining, and Information Technology, ADMIT 2024

作者： Wang, Minghua Qiao, Jiantong Air Force Engineering University ATC&GCL College Shaanxi Xi'an China

ISBN: (纸本)9798400718120

Air traffic control flow prediction is one of the key issues in aviation transportation management, crucial for optimizing resource allocation, enhancing control efficiency, and ensuring flight operation safety. This paper proposes a solution for air traffic control flow prediction based on big data analysis and machine learning techniques. Through extensive feature engineering and model construction using large-scale historical data, multiple prediction models including Support Vector Regression, Random Forest, Gradient Boosting Decision Tree, and an ensemble model were trained. Comprehensive performance evaluations using cross-validation and multiple metrics were conducted. Experimental results demonstrate that the proposed ensemble model effectively combines the strengths of multiple base models, significantly outperforming individual models in terms of prediction accuracy, stability, and generalization ability. This provides an efficient and reliable technical path for air traffic control flow prediction, with broad application prospects. © 2024 Copyright held by the owner/author(s).

关键词： Resource allocation

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Cross-domain Person Search data Augmentation Based on Style Transfer 24

Cross-domain Person Search Data Augmentation Based on Style ...

引用

3rd international conference on Algorithms, data Mining, and Information Technology, ADMIT 2024

作者： Liu, Zhen Zhang, Yaohui Lai, Guiyu Institute of Computer Application China Academy of Engineering Physics Sichuan Mianyang China

ISBN: (纸本)9798400718120

As a computer vision technology that combines pedestrian detection and person re-identification, person search focuses on its application in practical scenarios. However, at present, the academic community pays more attention to the performance of person search algorithm on a single data domain, and the generalization performance of person search models across data domain is less considered. In order to improve the cross-domain performance of person search model, we design a person search image style transfer network suitable for the characteristics of person search task. The proposed style transfer network is able to generate the source domain training image of the target domain style and reduce the overfitting of the person search model to the source domain style. Experiments show that the proposed method can generate qualified source domain training images of the target domain style. Further, the generalization performance of person search model on the target domain can be effectively improved by using the generated style transferred images as the data augmentation in the source domain pre-training stage. © 2024 Copyright held by the owner/author(s).

关键词： person search

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Design and Analysis of a Time-Sensitive Networking Simulation model 24

Design and Analysis of a Time-Sensitive Networking Simulatio...

引用

3rd international conference on Artificial Intelligence and Intelligent Information Processing, AIIIP 2024

作者： Yuan, Yuhao Cheng, Jing School of Computer Science and Engineering Xi’an Technological University Shaanxi Xi’an China

ISBN: (纸本)9798400707308

As a new communication technology for Industry 4.0, Time-Sensitive Networking (TSN) provides reliable support for real-time data amidst massive data transmissions. However, manually verifying the overall performance mechanisms of such a network is challenging. Therefore, this article models and simulates the TSN network on the OPNET platform. First, the key mechanisms and current development status of Time-Sensitive Networking are introduced. Next, the simulation modeling of the network switches and end systems is described, highlighting the key technologies within the model;where the end systems are responsible for data generation, transmission, and processing, and the switches are responsible for data forwarding and routing. Finally, this article designed the initial data of the TSN model and completed the testing of the model under a certain network bandwidth, with an almost zero packet loss rate for high-priority data packets, and the end-to-end delay meets expectations. Although the packet loss rate and delay for medium and low-priority data are slightly higher than those for high-priority data, they still meet expectations. © 2024 Copyright held by the owner/author(s).

关键词： Packet loss

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Enhanced Predictive modeling for Myocardial Infarction Risk Assessment through Multimodal data Integration 3

Enhanced Predictive Modeling for Myocardial Infarction Risk ...

引用

3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Abdulkadar, S. Dharun, S. Kirthivashan, V. Janani, B. K.S. Rangasamy College of Technology Department of Computer Science and Engineering Tiruchengode India

ISBN: (纸本)9798331509675

Since myocardial infarction (MI) continues to be a major cause of death worldwide, precise risk prediction is essential. In order to improve MI risk assessment, this work presents an improved predictive model that makes use of multimodal data, including clinical, genetic, imaging, and lifestyle information. By utilizing sophisticated machine learning methods, specifically the XGBClassifier, the model combines various datasets to reveal intricate patterns, attaining exceptional sensitivity and specificity. Robust reliability testing, a comparison with current approaches, and the use of interpretability tools such as SHAP guarantee that the suggested model provides useful information for clinical decision-making. Addressing dataset biases and extending feature integration to improve generalizability are two potential future steps. © 2025 IEEE.

关键词： AI model and Accuracy Myocardial infarction streamlit metrics Train XGBClassifier

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data Augmentation for Technical Standard Relation Extraction 24

Data Augmentation for Technical Standard Relation Extraction

引用

3rd international conference on Algorithms, data Mining, and Information Technology, ADMIT 2024

作者： Jia, Quanye Liang, Xiao Zhang, Qiang Zheng, Huamingzhou Fan, Xiaoxuan China Electric Power Research Institute Co. Ltd Beijing China State Grid Wuxi Power Supply Company of Jiangsu Electric Power Co. Ltd Wuxi China

ISBN: (纸本)9798400718120

The paper introduces a method for fine-grained relation extraction in grid technology standards, addressing challenges in manual annotation due to complex guidelines and large-scale dataset requirements. data augmentation techniques, specifically word-level perturbation and sentence template methods, are applied to a limited annotated dataset to improve model generalization and efficiency. Experiments on Q/GDW 1168-2013 annotated data show that the GPLinker model has demonstrated its higher efficiency and accuracy in relation extraction, with the integrated use of word-level perturbation and sentence template augmentation significantly boosts the model’s F1-score to 0.582925, indicating improved precision and recall in relation extraction from technical standards. © 2024 Copyright held by the owner/author(s).

关键词： data accuracy

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Streamlined Machine Learning Workflow for Accurate Rice Yield Estimation Using Remote Sensing and Meteorological data 3

Streamlined Machine Learning Workflow for Accurate Rice Yiel...

引用

3rd international conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Sinchana, H.S. Pallavi, N.R. Bgs Institute of Technology Adichunchanagiri University Department of Computer Science Engineering B.G Nagar Mandya District India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515911

The goal of this project is to use machine learning (ML), meteorological data, and remote sensing data to create an effective methodology for accurate rice yield estimation. The project entails creating a customized machine learning model with the aim of forecasting rice yields while accounting for the unique agricultural characteristics of the target region. The goal of the research is to determine the best approach for estimating rice yields by utilizing sophisticated data processing techniques and carefully assessing various machine learning models. By combining these strategies, crop production forecasts should become more accurate and provide insightful information for agricultural planning and management in a variety of geographical areas. © 2025 IEEE.

关键词： Adversarial machine learning

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Reversible data Hiding using Histogram Mapping and Level-3 Embedding in Encrypted Images 3

Reversible Data Hiding using Histogram Mapping and Level-3 E...

引用

3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Geetha, R. Rani, D. Jenila Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Biomedical Engineering Chennai India

ISBN: (纸本)9798331509675

Work carried out in this article is about reversible data-hiding (rdH) method for cloud environments. It is based on three-levels of ordered embedding in encrypted images along with histogram mapping. The suggested method employs a multi-level (3) of hierarchical embedding procedure after two-levels of encryption. An intra block and inter block transformation using an XOR operation is done at the encryption stage. Within the three-level nested embedding Two estimated predictors are present: a weak predictor and a strong predictor. In first level of embedding the robust predicting model is utilized, whereas in second level of embedding makes advantage of the weak predicting model. Next, a histogram mapping is applied to the acquired location map after being embedded at levels 1 and 2, and finally compressed by means of Huffman coding. The level three embedding procedure is then used for further embedding the compressed location map data. The compressed location map data is extracted first in the extraction phase, followed by the level 2 and level 1 data. The suggested data embedding technique is assessed using the various datasets, yielding, in turn, embedding rates of approximately 3.556 bpp. © 2025 IEEE.

关键词： encrypted image Histogram mapping Location map PSNR Reversible data Hiding

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Quantifying Effectiveness of Governmental Agriculture Policies using RNN-Transformer based Sentiment Analysis 3

Quantifying Effectiveness of Governmental Agriculture Polici...

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3rd international conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Joshi, Vibhor Yede, Navinya Parihar, Prajwal Dhule, Chetan Agrawal, Rahul Morris, Nekita Chavhan Department of DIC G H Raisoni College of Engineering Maharashtra Nagpur India

ISBN: (纸本)9798331527549

The objective of this research paper is to understand the impact of governmental agricultural policies through a sentiment analysis approach, using advanced neural networks like RNNs and Transformer models. The study seeks to understand public sentiment on different policies and practices by analyzing emotional reactions from various stakeholders in the agricultural sector. The methodology employs farmer-targeted surveys for data collection, preprocessing of the tokens or the data, and the creation of a high-performance sentiment analysis model. This model uses RNN and Transformer techniques together to boost the predictive accuracy. The results are aimed at providing the policymakers with actionable information, thus, improving the decision-making process and the communication strategies in the agricultural community. © 2025 IEEE.

关键词： ensemble techniques Government agricultural policies machine learning policy evaluation Recurrent Neural Networks (RNN) sentiment analysis survey data collection textual data preprocessing Transformer models

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