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检索条件"任意字段=5th Asia Conference on Machine Learning and Computing, ACMLC 2022"

共 595 条记录，以下是41-50 订阅

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An Analysis on Balance Model of Exploration and Exploitation Under Decoupled-learning Pattern for Large-Scale Particle Swarm Optimizers 5th

An Analysis on Balance Model of Exploration and Exploitation...

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5th International conference on Neural computing for Advanced Applications (NCAA)

作者： Ni, Wenke Guo, Weian Li, Dongyang Tongji Univ Coll Elect & Informat Engn Shanghai Peoples R China Tongji Univ Sino German Coll Appl Sci Shanghai Peoples R China

ISBN: (纸本)9789819770007;9789819770014

To effectively tackle large-scale optimization problems, numerous efforts have been made by various swarm intelligence methodologies to strike a balance between two critical functions: exploration and exploitation. However, in the majority of existing particle swarm optimizers, these two functions often remain indistinguishable throughout the optimization process. To mitigate the mutual interference between exploration and exploitation, designers of optimizers have attempted to design these functions separately. Nonetheless, there has been limited research into analyzing the roles and the relationship between exploration and exploitation. Building upon our previous work, we have redesigned several state-of-the-art large-scale particle swarm optimizers using a decoupled learning pattern. through comprehensive experimental analysis on the synergistic effect of exploitation and exploration, we delve into the optimization performance and introduce a mathematical model to illustrate their interrelationship. Our investigation and analysis reveal that most existing algorithms exhibit sensitivity in the parameter settings for exploration and exploitation, an aspect that has not been adequately addressed before. Based on the proposed mathematical analysis of the parameters, we provide clear balance model for algorithm designers and users on how to balance these two factors when solving large-scale optimization problems.

关键词： Large scale optimization problems Exploration and exploitation Decoupled learning pattern Balance model

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Rapid Detection of Formaldehyde Emission from Wood-Based Panels Based on the IPOA-XGBoost 5th

Rapid Detection of Formaldehyde Emission from Wood-Based Pan...

引用

5th International conference on Neural computing for Advanced Applications (NCAA)

作者： Wang, Yinuo Zheng, Huanqi Wu, Qiang Zhou, Yucheng Shandong Jianzhu Univ Sch Informat & Elect Engn Jinan 250101 Shandong Peoples R China Shandong Jianzhu Univ Sch Architecture & Urban Planning Jinan 250101 Shandong Peoples R China Natl Ctr Qual Inspect & Test Decorat Mat Jinan 250102 Peoples R China

ISBN: (纸本)9789819770038;9789819770045

Aiming at the complexity of the process of analyzing formaldehyde composition by physicochemical methods in the process of formaldehyde emission detection by the full-scale chamber method, and the problem of bias in the accuracy of sensor method, the IPOA-XGBoost formaldehyde emission modified deviation model for a class of panels is proposed for the fast and accurate detection of formaldehyde emission from wood-based panels. the model utilizes data obtained from the electrochemical sensor as input. the output is the formaldehyde emission measured by the full-scale chamber method. the model is fitted using the extreme gradient boosting (XGBoost). To address the weak optimization ability in the pelican optimization algorithm (POA), random opposition-based learning is employed to establish the initial population. Additionally, nonlinear weighting factors and a sparrow alert mechanism perturbation strategy are introduced to enhance the ability of global optimization and escape local optimal solution. the improved POA (IPOA) is used to optimize the key parameters of XGBoost, and then the modified deviation model of formaldehyde emission of the IPOA-XGBoost wood-based panel is constructed, and compared with four machine learning models. Experimental results indicate that the proposed model achieves a coefficient of determination of 0.9701 and a root mean square error of 2.7648e-03. Furthermore, it exhibits lower prediction errors compared to other models tested herein. this provides an effective and reliable solution for rapid detection of formaldehyde emission in wood-based panels.

关键词： the full-scale chamber Improved pelican optimization algorithm Extreme gradient boosting Formaldehyde emission detection

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Advanced machine learning Framework for Efficient Plant Disease Prediction

Advanced Machine Learning Framework for Efficient Plant Dise...

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machine learning and computing (acmlc), asia conference on

作者： M Aswath S Sowdeshwar M Saravanan Satheesh K Perepu Dept. of Electronics and Communication Engineering VIT Chennai India Ericsson India Global Services Pvt. Ltd. Ericsson Research Chennai India

Recently, machine learning (ML) methods are built-in as an important component in many smart agriculture platforms. In this paper, we explore the new combination of advanced ML methods for creating a smart agriculture platform where farmers could reach out for assistance from the public, or a closed circle of experts. Specifically, we focus on an easy way to assist the farmers in understanding plant diseases where the farmers can get help to solve the issues from the members of the community. the proposed system utilizes deep learning techniques for identifying the disease of the plant from the affected image, which acts as an initial identifier. Further, Natural Language Processing techniques are employed for ranking the solutions posted by the user community. In this paper, a message channel is built on top of Twitter, a popular social media platform to establish proper communication among farmers. Since the effect of the solutions can differ based on various other parameters, we extend the use of the concept drift approach and come up with a good solution and propose it to the farmer. We tested the proposed framework on the benchmark dataset, and it produces accurate and reliable results.

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Epileptic Seizure Detection using EEG Signals 5

Epileptic Seizure Detection using EEG Signals

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5th International conference on computing and Informatics (IEEE ICCI)

作者： Khan, Irfan Mabood Khan, Mohd Maaz Farooq, Omar Aligarh Muslim Univ ZHCET Dept Elect Engn Aligarh Uttar Pradesh India

ISBN: (纸本)9781665499736;9781665499729

Epilepsy is a common neurological disorder which can be diagnosed by neurologists or physicians by using electroencephalogram or EEG signals. Since the manual examination of EEG for this purpose is very time consuming and requires trained professionals, it calls for the need of an automatic seizure detection method. In this study, time and frequency domain features are extracted from the EEG signals after preprocessing the raw EEG data and then using machine learning algorithms such as Logistic Regression, Decision Tree, Support Vector machines, etc. to detect generalized seizures in the Temple University Hospital (TUH) corpus. A detailed account of the TUH dataset is also given. this work summarizes and compares the results of each of the algorithm trained, in terms of the performance metrics. Using the proposed approach, SVM obtained the highest accuracy of 92.7% in binary classification.

关键词： EEG Epileptic Seizure machine learning Feature Engineering TUH Binary Classification

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A Method of Predicting Occupancy in Commercial Building Based on machine learning

A Method of Predicting Occupancy in Commercial Building Base...

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machine learning and computing (acmlc), asia conference on

作者： Qin Zou Nan Li Baowei Xu Xintong Li School of civil engineering Chongqing University Chongqing China Cloud Data Center Mgmt Dept Huawei Cloud Computing Technologies Co. Ltd. Dongguan China China Merchants Shekou Industrial Zone Holdings CO. Ltd Chongqing China

As a reference value, the occupancy guides the building Automation System (BAS) operation, which can significantly reduce energy consumption. However, the occupancy counts of commercials fluctuate dynamically with time, and how to gain the occupancy clusters and accurately predict the occupancy counts has yet to be well solved. To solve the above problems, this research proposes a prediction method for the occupancy counts of commercial buildings based on the integration of Wi-Fi connection counts data, categories of weekdays and holidays, outdoor climate data sets, and the combination of K-Means and decision tree algorithm. First, the K-means algorithm was used to cluster to obtain the representative occupancy daily clusters. Subsequently, the decision tree algorithm recognizes the clusters’ generation rules and constructs the prediction model. the validation experiments were conducted in a commercial building in Chongqing, China. the results showed that the prediction model had an accuracy of 95.24%, with better robustness than independent data sources. the prediction results can provide a practical reference for formulating BAS’s operation control and commercial operation scheme in the low carbon emission reduction environment.

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Predictive Models for Preventive Medicine based on Deep learning 5

Predictive Models for Preventive Medicine based on Deep Lear...

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5th asia-Pacific conference on Image Processing, Electronics and Computers, IPEC 2024

作者： Chen, Yuanling Deng, Yinghui Qujing Medical College Yunan Qujing655000 China

ISBN: (纸本)9798350374407

Deep learning (DL) algorithms have characteristics such as multi-level, multi perspective, and adaptive learning. With the improvement of computing power and the explosive growth of data volume, deep learning has achieved rapid development. With the development of modern biomedical and information technology, health management is gradually shifting from 'passive' to 'active', and personalized health management and disease prevention are gradually becoming the focus of research in the field of public health. Traditional preventive medicine is mostly based on natural data, which makes it difficult to meet personalized prediction needs. It can obtain potential information through deep mining of data features, thereby improving the accuracy and clinical application value of disease risk prediction models. this article designs a predictive model for preventive medicine based on DL, and compares it with traditional preventive medicine through data analysis to evaluate the preventive learning predictive model designed in this article, and proposes relevant improvement measures. the research results indicate that the highest prediction accuracy of the preventive medicine prediction model based on DL can reach $\mathbf{9 6. 2 \%}$. © 2024 IEEE.

关键词： Prediction models

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machine learning Study on Cross-border E-commerce Products Price Prediction: Evidence from Platform of Lazada 24

Machine Learning Study on Cross-border E-commerce Products P...

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5th International conference on Artificial Intelligence and Computer Engineering, ICAICE 2024

作者： Wang, Qingxiao Hu, Shanfeng Zha, Xiaoyu Business School Henan University of Science and Technology Henan Luoyang China

ISBN: (纸本)9798400718007

In the wave of increasingly digital and globalized trade activities, cross-border e-commerce (CBEC) has emerged as an indispensable trade form, and CBEC platforms have grown rapidly in this process and become a key force to promote consumption levels. Product pricing is widely considered to be one of the key determinants of the development of the CBEC industry. therefore, CBEC platforms enable many platform merchants to flourish through unique pricing strategies. A comprehensive understanding of these pricing strategies is essential for exploring the nature of innovative business models. Taking Lazada, a leading CBEC platform in Southeast asia, as an example, this paper analyzes the commodity information data set of electronic products as of November 8,2024, and develops a multi-factor price prediction model by using natural language processing and machine learning technology. the study used three different prediction methods: linear regression, random forest and XGBoost. By comparing the prediction performance of the three methods, the random forest method has the strongest prediction ability. In addition, the random forest model is used for attribution analysis to help platform merchants formulate appropriate prices. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Costs

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Research of Data-driven Iterative Optimization Scheme for machine learning Models 5

Research of Data-driven Iterative Optimization Scheme for Ma...

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5th International conference on computing and Big Data, ICCBD 2022

作者： Yang, Jinsen Zou, Zhijian Xiu, Jiapeng Wang, Peinian Yang, Yiting Beijing University of Posts and Telecommunications Software Systems and Engineering Center Beijing China Beijing Qiyuan Capital Market Development Service Co. Ltd. Beijing China Law School Zhengzhou University Zhengzhou China

ISBN: (纸本)9781665457163

With the development of big data technology providing massive data information for machine learning, more researchers are focusing on data methods in big data technology, by constructing better data set to improve machine learning model's performance. this research based on the data-driven and human-machine collaboration methods in big data technology, continuously build data set to optimize machine learning models. We take the growth and risk assessment model of technology companies as an example, design and implement a data-driven machine learning model pipeline. the experiments show that the pipeline can effectively and continuously optimize these machine learning models, and ensure the stability of machine learning application. © 2022 IEEE.

关键词： Big data

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Artificial Intelligence Aspect Of Transportation Analysis Using Large Scale Systems 6

Artificial Intelligence Aspect Of Transportation Analysis Us...

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6th Artificial Intelligence and Cloud computing conference (AICCC) / 5th asia Digital Image Processing conference

作者： Hu, Tiechuan Zhu, Wenbo Yan, Yuqi Johns Hopkins Univ Dept Comp Sci Baltimore MD 21218 USA Univ Chicago Dept Comp Sci Chicago IL USA Washington Univ Olin Business Sch St Louis MO USA

ISBN: (纸本)9798400716225

Problem: the problem of the finalized exploration revolved around the inadequacy of current traffic forecasting models. Despite decades of examination in many fields, existing approaches, often relying on linear models and stationary time series assumptions, struggle to accurately predict traffic under chaotic events. the identified limitation has profound consequences, evident in the significant economic losses and time inefficiencies incurred due to traffic congestion, as exemplified by the $144 billion in losses and the 34% increase in travel time for drivers in Los Angeles County in 2013. the challenge lies in the inherently unpredictable nature of traffic events, ranging from regular rush hours causing sharp declines in traffic speed to unpredictable accidents leading to unforeseen delays. Consequently, there is a pressing need for a more effective and adaptive traffic forecasting model that can reliably operate under both normal and abnormal traffic conditions, addressing the shortcomings of traditional linear models and stationary time series assumptions. Purpose: the purpose of the completed investigation was to determine whether a traffic forecasting model that incorporates machine learning and deep learning technologies can yield effective traffic forecasts based on real-time weather and traffic data. Method: the study involved the development of a traffic forecasting model as informed by existing literature. Data collection was done through simulating data similar to the traffic data from Los Angeles County that was utilized in Yu et al.'s research on deep learning and traffic prediction in extreme weather scenarios [1]. Data analysis was done through MAE and t-test. Results: the findings demonstrated that the created traffic forecasting model outperformed the current methods in its ability to provide traffic forecasts with a better degree of accuracy. Conclusion: Regardless of the traffic volume, weather, or time of day, the developed traffic forecasting

关键词： Performance Analysis Sentiment Analysis machine learning Transfer learning

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A Predictive Maintenance Platform for a Conveyor Motor Sensor System Using Recurrent Neural Networks 5th

A Predictive Maintenance Platform for a Conveyor Motor Senso...

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5th International conference on Neural computing for Advanced Applications (NCAA)

作者： Kiangala, Kahiomba Sonia Wang, Zenghui Univ South Africa UNISA Dept Elect & Smart Syst Engn ZA-1709 Johannesburg Gauteng South Africa

ISBN: (纸本)9789819770007;9789819770014

Since the effusion of Industry 4.0 (I40) and smart manufacturing, predictive maintenance (PdM) has become critical to prevent severe system breakdowns and costly production downtime in various industries. Several state-of-the-art Artificial Intelligence (AI) approaches, such as machine learning models (ML), empower the PdM design concept to produce more accurate outcomes. In this study, we propose the development of a PdM platform that effectively detects future possible warnings and failures in the vibration speed sensor of a conveyor motor. We build the platform backend using recurrent neural networks (RNN) regression ML models to predict vibration speed values and their trends. We train our RNN models using univariate time-series historical vibration sensor data recorded over the years. the experimental results demonstrate the robustness of our platform built on RNN models compared to other traditional regression machine learning models such as Extreme Gradient Boosting (XGboost). We compare the effectiveness of the PdM platform with two RNN models: a basic RNN model and a long short-term memory (LSTM) model. Unlike other regression PdM systems focusing on predicting devices Remaining Useful Life (RUL), our PdM platform forecasts and generates new vibration sensor data based on the desired future time range. It also pinpoints set warning and failure values and indicates when these faults are more likely to occur. Factories can utilize this feature for analytics purposes.

关键词： Conveyor Motor Sensor Predictive Maintenance (PdM) Recurrent Neural Networks (RNN) Regression Models time-series data

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