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Device Action Prediction Based on K-means and Apriori for Smart Home

Device Action Prediction Based on K-means and Apriori for Sm...

引用

2022 ieee asia-pacific conference on Image Processing, Electronics and computers, IPEC 2022

作者： Liu, Qinghua Shi, Tianwei Ren, Ling University of Science and Technology Liaoning School of Computer Science and Software Engineering Anshan China

ISBN: (纸本)9781665409025

This paper proposes to use K-means and Apriori to prediction device action based on time in Smart Home System. In the existing methods, the system provides services to human when conditions are met, such as high temperature and low humidity. However, those methods ignoring the correlation between device action and human activity time. In order to solve this problem, we combine K-means and Apriori algorithm to generate association rules with time information. First, calculate the appropriate K-value for K-means. We select consecutive data in weeks. The data contains the time information of device action. Second, run K-means to classify based on device action time information, and run Apriori to mine association rules based on classification. Finally, decode the associate rules and realize device action prediction. In our work, K-means experiments show that clustering according to the difference of working days is an effective way to process user time information;Apriori experiments proved that the association rules mined with the results of K-means can predict device actions in the Smart Home environment. © 2022 ieee.

关键词： Automation

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Harnessing computer Vision to Identify Pests and Predators: A Comparative Model Analysis

Harnessing Computer Vision to Identify Pests and Predators: ...

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computer science and data engineering (csde), ieee asia-pacific conference on

作者： Mahsa Mohaghegh Lara Jaber James Stratford William Maxwell Connor Tamatea Lachlan Crawford School of Engineering Computer and Mathematical Sciences Auckland University of Technology Auckland New Zealand

This study focuses on developing an intelligent system using machine learning algorithms and motion-sensor camera traps to achieve real-time classification of pest predators in the setting of the bushlands of New Zealand. The primary goal is to provide an optimized tool for ecological monitoring, aiding in the preservation of native Kiwi bird habitats and aligning with the eco-city initiatives. By training and comparing various deep learning models, including Convolutional Neural Networks (CNNs), Multi-Layer Perceptron (MLP), and Vision Transformers (ViT), the system aims to assist in accurately identifying and managing pest populations. We found that our best-performing model on our data was ResNet-50 with an overall accuracy of $98.15{\%}$ and an average f1-score of $0.982$ across the five classes. This was closely followed by DenseNet121 with an overall accuracy of $97.95{\%}$ and an average $F1$ score of 0.978 and our CNN with a Vision Transformer model with an overall accuracy of $97.58{\%}$ and an average $F1$ score of 0.976.

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Food Affection Determination Through Biosignal Based Affective Computing

Food Affection Determination Through Biosignal Based Affecti...

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computer science and data engineering (csde), ieee asia-pacific conference on

作者： Tanvir Islam Anika Rahman Joyita Aisha Rahmot Ananna Rafi Khandoker Fatema Ahsan Md. Golam Rabiul Alam Information and Computer Sciences University of Hawai'i at Mānoa Honolulu Hawaii USA Computer Science and Engineering BRAC University Dhaka Bangladesh

When people seek food, their emotions shift, and not all foods are appealing in all moods. It is incredibly tough to learn people's eating habits and provide advice depending on their emotions at the time. The goal of this study is to create a system that will categorize emotional information from meals based on a person's present emotion. This study proposes a technique for evaluating a person's emotion using an electroencephalogram (EEG) output. To that purpose, we organized trials for EEG data collecting as well as questionnaires. On the raw data, a prominent feature extraction approach known as Discrete Wavelet Transform (DWT) was utilized, and Gradient Boosting Classifier and AdaBoost Classifier were used for affectivity classification. We obtained a notable Accuracy Score and AUC Score from these classifiers in this study.

关键词： computer science Mood Transforms Feature extraction Boosting data engineering Electroencephalography

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Can LLMs Determine Political Leaning of Polish News Articles?

Can LLMs Determine Political Leaning of Polish News Articles...

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computer science and data engineering (csde), ieee asia-pacific conference on

作者： Joanna Szwoch Mateusz Staszkow Rafal Rzepka Kenji Araki Graduate School of Information Science and Technology Hokkaido University Sapporo Japan Mateusz Staszków Software Development Warsaw Poland Faculty of Information Science and Technology Hokkaido University Sapporo Japan

This study explores how Large Language Models (LLMs) can be used to analyze the political and establishment leanings of opening paragraphs in Polish online news articles that discuss controversial topics. This task, often time-consuming and costly when performed by humans, holds promise for enhancing media fairness and balance cost-effectively. It is one of the first attempts to analyze this problem in an under-resourced language like Polish. Findings reveal the challenge of task difficulty for LLMs and human annotators. Accuracy of LLMs varies, with better performance on questions related to political leaning than establishment stance. Notably, the text-davinci-002 model achieves the highest scores in wing orientation detection, whereas gpt-3.5-turbo excels in recognizing pro-/anti-government bias. This study highlights LLMs' limitations for this demanding task in an under-resourced language like Polish, emphasizing the need for alternative detection approaches.

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Investigation of Machine Learning Methods for Oxidation and Tensile Creep Properties of SiC/SiC Composites 2

Investigation of Machine Learning Methods for Oxidation and ...

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2nd asia-pacific conference on Communications Technology and computer science, ACCTCS 2022

作者： Chao, Zhiqin Xiao, Manyu Luan, Xingang School of Mathematics and Statistics Northwestern Polytechnical University Shaanxi Province Xi'an City China Northwestern Polytechnical University Science and Technology on Thermostructural Composite Materials Laboratory Shaanxi Province Xi'an City China

ISBN: (纸本)9781665400343

With the brilliant properties of the low density, high strength and fracture toughness at high temperatures, SiC fiber-reinforced SiC matrix composites are currently being applied for aircraft engine hot-section components. However, both the oxidation and creep resistance of SiC/SiC composites still needed to be improved under different stress, temperature and atmosphere. In order to alleviate the high cost of experimental data collection and the limited data resources, a novel numerical strategy based on machine learning method will be investigated with combination of both the current law of the physical model and the available experimental data. The data-driven analysis results show that in the case of a small amount of training data, neural network has better fitting effect than traditional data fitting methods for univariate fitting;In addition, it is better benefit for reflecting the influence law of multiple variables on the change of material residual strength and quality. © 2022 ieee.

关键词： Numerical methods

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Solar Irradiance Prediction using Deep Learning-Based Approaches

Solar Irradiance Prediction using Deep Learning-Based Approa...

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computer science and data engineering (csde), ieee asia-pacific conference on

作者： Ninu G Hari Jisha G Department of Computer Science and Engineering Rajagiri School of Engineering and Technology Kochi India

The worst effects of pollution from greenhouse gas emissions are increase in atmospheric temperature, sea level rise, and a variety of diseases. The main source of greenhouse gases is the combustion of fossil fuels, especially in thermal power plants. The use of renewable energy sources, such as solar energy, can significantly reduce this pollution. The amount of electricity produced from solar energy can be maximised by placing the solar power plant in an area with high direct horizontal irradiance (DHI). Solar irradiance predictions will help to design and build solar power facilities very efficiently. However, accurate prediction is a challenge. In this study, the deep learning approaches such as single-layered LSTM and stacked LSTM variations like two-layered LSTM and three-layered LSTM are used to forecast DHI.

关键词： Deep learning Renewable energy sources Pollution Greenhouse effect Solar energy Fossil fuels Sea level

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Weight Design for Small data Sample Decision under Attribute Association

Weight Design for Small Data Sample Decision under Attribute...

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computer science and data engineering (csde), ieee asia-pacific conference on

作者： Shixuan Zhang Songpeng Sun Jie Ma Kai Zhang Xiaopeng Zheng Department of Control Science and Engineering Harbin Institute of Technology Harbin China Science and Technology on Space Physics Laboratory Beijing China Research and Development Department China Academy of Launch Vehicle Technology Beijing China

In aircraft test, the sample size of flight test data is small, and the test data returned by telemetry also has the problem of attribute association. A weight design method of small sample decision under attribute association is proposed. The method enhances the small sample data based on the synthetic minority oversampling technique, reduces the dimension of variables through principal component analysis, normalizes the principal components based on the variance contribution rate, and obtains the weight of each principal component after dimensionality reduction. The research results can be applied to the weight design of the guidance and control system of aircraft, and provide a new method support for the subsequent relevant test evaluation.

关键词： Dimensionality reduction Performance evaluation Design methodology Decision making Control systems data models Telemetry

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Risk Prediction of Opioid Dependency Using Machine Learning Based on Electronic Health Record

Risk Prediction of Opioid Dependency Using Machine Learning ...

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computer science and data engineering (csde), ieee asia-pacific conference on

作者： Samaneh Madanian Sherry Dai Priyadarshini Natarajan Hamidreza Rasoulipanah Dept. of Computer Science and Software Engineering Auckland University of Technology Auckland New Zealand

Opioid abuse and dependence have emerged as a pressing global concern, posing significant challenges to public health and society. Early identification and prediction of opioid dependency represent crucial steps in mitigating its abuse impact on individuals and communities. The application of machine learning techniques to analyze medical data has opened new avenues for achieving this goal. While this field and the prediction is still in its infancy, our research explores the potential of several machine learning algorithms including LightGBM for this risk prediction. To tackle the inherent class imbalance in the MIMICIII dataset, we implemented the Synthetic Minority Oversampling Technique (SMOTE). We developed predictive models using four distinct algorithms: decision trees, random forests, support vector machines, and LightGBM. These models were meticulously evaluated to assess their performance. Ultimately, our findings revealed that the LightGBM model outperformed the other algorithms, demonstrating superior accuracy and achieving a higher Area Under the Curve value. This outcome underscores the potential of LightGBM as a valuable algorithm in the early prediction of the risk of opioid dependence, thereby offering substantial benefits to both patients and society at large.

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Research on the Application of computer Big data Multimodal Teaching Mode in College

Research on the Application of Computer Big Data Multimodal ...

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2022 ieee asia-pacific conference on Image Processing, Electronics and computers, IPEC 2022

作者： Bu, Shuang Xi'an Traffic Engineering Institute Xi'an710300 China

ISBN: (纸本)9781665409025

Many applications of computer-assisted English learning in colleges and universities lack the evaluation and feedback of multimodal learning. The article describes a multimodal teaching system of spoken English in colleges and universities using speech recognition technology. In addition to the usual pronunciation scoring, we also provide error detection based on phoneme association and phoneme recognition. Combines improvement suggestions from the Correction Knowledge Base and Prosody Correction Speech. The system can help learners in a timely manner. The experimental results show that the system can correct most of the unintentional mistakes of learners with a certain base. © 2022 ieee.

关键词： Big data

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Application Implementation of K-means Algorithm Based on Wolf Pack Algorithm 2

Application Implementation of K-means Algorithm Based on Wol...

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2nd asia-pacific conference on Communications Technology and computer science, ACCTCS 2022

作者： Xu, Xiaowei Li, Ying College of Computer Science and Technology Jilin University Changchun130012 China

ISBN: (纸本)9781665400343

K-means in non-hierarchical clustering analysis has become the most commonly used clustering algorithm because of its simple implementation and fast convergence speed. However, different selection of clustering center will greatly affect the clustering effect of k-means. If the clustering center is selected randomly, the algorithm will easily fall into the local optimal value and fail to achieve the optimal effect. Therefore, the uncertainty of initial clustering centers makes k-means algorithm lack of good stability. Thus, a K-means algorithm based on AEWPA (KAEWPA) is proposed in this paper to enhance the stability of clustering. © 2022 ieee.

关键词： K-means clustering

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