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2022 international conference on machine learning and Knowledge engineering, MLKE 2022

作者： Zhang, Shujie University of California Mathematics Santa BarbaraCA United States

ISBN: (纸本)9781665495677

With the increase in the number of all megacities and large cities, urban traffic has become an important issue in urban life. Urban traffic accidents are occurring more and more frequently, which has a negative impact on the economy and safety. The existing research shows that sunlight intensity or rainy days can increase the probability of accidents in urban traffic. This paper proposes multiple machine learning methods to compare the accuracy of different machine learning algorithms for New York traffic accident prediction on weather data from 2015 to 2020. The experimental results show that both OVR and OVO methods have high accuracy except for the lower accuracy in 2020. © 2022 IEEE.

关键词： learning algorithms

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Mapping the Dynamic Changes of LULC Around the Lake Systems Using machine learning Approaches for Hanamkonda District 28th

Mapping the Dynamic Changes of LULC Around the Lake Systems ...

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28th international conference on Hydraulics, Water Resources, River and Coastal engineering, HYDRO 2023

作者： Tejaswi, Vangala Venkata Reddy, Keesara Loukika, K.N. National Institute of Technology Warangal Telangana Warangal India

ISBN: (纸本)9789819774661

Land Use and Land Cover (LULC) change refer to the loss of natural areas, particularly forests, agricultural areas, or water bodies, to urban or exurban development. Understanding how LULC will impact the district of Hanamkonda’s water resource availability is crucial. In order to conduct tasks like change detection analysis and theme mapping, baseline data on land cover must be determined. Expanding urban areas affects natural resources and makes them vulnerable. As it is observed that rapid changes are occurring in LULC around the water bodies, this will badly affect the quantity and quality of water resources, increasing the pressure on water availability in urban areas. It also creates flood hazards in the surrounding areas of the water bodies due to not protecting the boundaries of the water bodies. Any loss in the water surface area will also impact the groundwater resources in the region. The Hanamkonda district of Telangana state, India, has many water bodies. Over the period, the surroundings of some of the water bodies are highly urbanised, causing stress on water resource availability and flood-related problems during monsoon season. The land use and land cover changes for the four lake systems in the Hanamkonda district over a ten-year period, from 2013 to 2022, are presented in this paper using machine learning algorithms in the Google Earth Engine site. The accuracy assessment is used to compare the performance of the two machine learning algorithms such as Random Forest (RF) and Support Vector machine (SVM) in the classification of LULC. For the years 2013, 2016, 2019, and 2022, Landsat-8 data is used, and the major LULC classes are ‘water bodies’, ‘urban’, ‘vegetation’, and ‘barren’. The average overall accuracy of RF and SVM classifiers is 88.47% and 91.92%, respectively. The results suggest that the support vector machine classifier outperforms the random forest classifier in terms of accuracy. The findings revealed that from 2013 to 2022, water bo

关键词： Lakes

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Comparative Approach to Detect Nocturnal Frontal Lobe Epilepsy Sleep Disorder through Frequency spectrum and its Energy Levels

Comparative Approach to Detect Nocturnal Frontal Lobe Epilep...

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2022 international conference on machine learning and data engineering, icmlde 2022

作者： Tiwari, Shivam Arora, Deepak Nagar, Vishal Department of Computer Science and Engineering Amity University Lucknow Campus 226010 India Department of Computer Science and Engineering Pranveer Singh Institute of Technology Kanpur209305 India

EEG data has proved to reflect the activities of the brain over all the sections with respect to human activities. It is useful in both cases that are in awakening states and in the sleep stage. Most of brain disease is due to the deterioration of brain cells. In this research work, EEG data is used to generate some frequency-based energy level features for NFLE (Nocturnal Frontal Lobe Epilepsy) patients and normal persons. After experimentation, it is found that the percentage of energy levels may be considered as a parameter to distinguish healthily and defected EEG data due to sleep disorder. During this study, it is also found that S0 is proved to be helpful in the diagnosis of NFLE sleep disorder because in this stage the percentage energy level is to be high for alpha waves for patients having a sleep disorder. The results show that the average percentage energy level in NFLE patient is 0.009872 while in normal cases it is 0.0010148. © 2023 The Authors. Published by Elsevier B.V.

关键词： data mining

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A review of sequential three-way decision and multi-granularity learning

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international JOURNAL OF APPROXIMATE REASONING 2023年 152卷 414-433页

作者： Yang, Xin Li, Yanhua Li, Tianrui Southwestern Univ Finance & Econ Sch Comp & Artificial Intelligence Chengdu 611130 Peoples R China Southwestern Univ Finance & Econ Financial Intelligence & Financial Engn Key Lab Si Chengdu 611130 Peoples R China Southwest Jiaotong Univ Sch Comp & Artificial Intelligence Chengdu 611756 Peoples R China

The concept of three-way decision, interpreted and described as thinking, problem solving, and information processing in "threes", has been widely studied and applied in machine learning and data engineering in recent years. In open-world environment, the connection and interaction of dynamic and uncertainty by multi-granularity learning gives more vitality to three-way decision. In this paper, we investigate and summarize the initial and development models of three-way decision. Then we revisit the historical line of sequential three-way decision from rough set to granular computing. Besides, we focus on exploring a unified framework of three-way multi-granularity learning with four crucial problems on mining uncertain region continually. Finally, we give some proposals on three-way decision associated with open-continual learning.(c) 2022 Elsevier Inc. All rights reserved.

关键词： Three-way decision Granular computing Sequential three-way decision Three-way multi-granularity learning

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Research on Course Recommendation Model Based on MLP and Attention Mechanism 24

Research on Course Recommendation Model Based on MLP and Att...

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2024 international conference on Intelligent Education and Computer Technology, IECT 2024

作者： Yuan, Hongli Zhang, Fucheng Liu, Liqin Big Data and Artificial Intelligence College Anhui Xinhua University Hefei230088 China

ISBN: (纸本)9798400709920

Course recommendation plays a significant role in the field of education. Through personalized recommendation systems, students can more quickly and effectively find course resources that match their interests and learning needs, thereby improving learning efficiency, academic performance, and personal development. The multilayer perceptron (MLP) is a commonly used feedforward neural network structure, widely applied in tasks such as classification, regression, and feature extraction, learning complex representations of data through multiple fully connected hidden layers. The attention mechanism, which simulates human attention, can assign different attention weights to different parts when processing sequential data. It is widely used in machine translation, speech recognition, image captioning, and recommendation systems, improving model performance and effectiveness. This paper combines MLP and the attention mechanism in a model that better captures students' learning interests and behavior patterns, thereby enhancing recommendation accuracy and effectiveness. The attention mechanism dynamically adjusts the recommended course list based on the student's current learning state and needs, making the recommendations more aligned with the student's actual requirements. Experimental validation has shown that this model achieves significant improvements in recommendation effectiveness on real-world datasets, demonstrating the efficacy and superiority of the MLP and attention mechanism in the course recommendation task. © 2024 ACM.

关键词： Multilayer neural networks

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Comparative Analysis of Medium and Short Term Power Load Forecasting machine learning Methods

Comparative Analysis of Medium and Short Term Power Load For...

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2022 international conference on Computer Graphics, Artificial Intelligence, and data Processing, ICCAID 2022

作者： Wang, Jing Yao, Jian Chen, Huayi Li, Xiaoqiang Lin, Jianbing School of Artificial Intelligence The Open University of Guangdong Guangzhou510091 China School of Remote Sensing and Information Engineering Wuhan University Wuhan430070 China

ISBN: (纸本)9781510663350

High precision medium and short term load forecasting is a reliable guarantee for optimizing power grid operation strategy and improving power grid operation efficiency. This paper introduces the machine learning algorithms commonly used in power load forecasting. In order to improve the prediction accuracy of these algorithms, this paper applies parameter optimization to these models. The experimental results show that the prediction accuracy of the optimized model is better. In addition, the prediction accuracy of LSTM model outperforms than XGBoost and LightGBM models greatly. © 2023 SPIE.

关键词： Forecasting

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Self-supervised representation learning with artificial fish swarm optimization for machine anomalous sound detection 4

Self-supervised representation learning with artificial fish...

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4th international conference on Internet of Things and Smart City, IoTSC 2024

作者： Chen, Shengbing Wang, Jiajun Wang, Junjie Xu, Zhiqi School of Artificial Intelligence and Big Data Hefei University Hefei China

ISBN: (纸本)9781510681767

machine Anomalous Sound Detection is crucial for artificial intelligence automation in the context of the fourth industrial revolution. Recent approaches employ self-supervised representation learning, which combines representations extracted through training models on normal and pseudo-anomaly data in the upstream phase with anomaly detection algorithms in the downstream phase. Although effective in extracting representations and demonstrating some robustness, this approach overlooks the complexity and intrinsic relevance of the individual tasks in the upstream models. To address these challenges, this paper introduces the Self-Supervised Multi-Task Representation learning with Artificial Fish Swarm Optimization. This network integrates two task classifiers into the upstream model, specifically designed to distinguish the sound origin of the target machine and discern attribute information related to the machines. We assign weights to individual tasks and optimize these weights using the artificial fish swarm algorithm. Our approach was evaluated on the DCASE2022 Task 2 evaluation dataset, where the method outperformed integrated models by an average of 6.8% in terms of AUC and pAUC. © 2024 SPIE.

关键词： Self-supervised learning

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Success and failure rate prediction of Android Application using machine learning

Success and failure rate prediction of Android Application u...

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2022 OPJU international Technology conference on Emerging Technologies for Sustainable Development, OTCON 2022

作者： Sankar, Swagatam Jay Singh, Utkrisht Ali, Israj Naskar, M.Nazma Gourisaria, Mahendra Kumar Kiit Deemed to Be University School of Electronic Engineering Odisha Bhubaneswar751024 India School of Computer Engineering Kiit Deemed to Be University Odisha Bhubaneswar751024 India

ISBN: (纸本)9781665492942

This paper reports a machine learning model to predict the likelihood of success of android applications. As the android applications are play an important role with in the software industry, it would be a beneficial to study the field. There is currently no archived collection or method for predictingthe success rate of these Android applications. In this research work, we establish a dataset consisting of 30, 000 apps taken from the google play store, third-party apps, and apple storeapps. The dataset is complex and contains about 184 features ofa single application. The data are distributed into two classes malware application and benign applications. The redundant information are dropped from the dataset, following that data cleaning, dimension reduction, and mathematical analysis on the dataset is performed. There are three challenges arise i.e. dataset contain missing value, outliers and class distribution is imbalance. Using the standard techniques the missing value and outliers are treated. For imbalance class distribution various sampling method and cost-sensitive approach are considered. The machine learning algorithms like Logistic Regression (LR), Decision Tree (DT), Support Vector machine(SVM), and ExtremeGradient Boosting (XG-Boost) are used. It is observed that highest accuracy of 84.44% achieved using ADASYN sampling technique using XGBoost classifier. © 2023 IEEE.

关键词： Forecasting

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Fairness in Graph machine learning: Recent Advances and Future Prospectives 23

Fairness in Graph Machine Learning: Recent Advances and Futu...

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29th ACM SIGKDD conference on Knowledge Discovery and data Mining (KDD)

作者： Dong, Yushun Kose, Oyku Deniz Shen, Yanning Li, Jundong Univ Virginia Charlottesville VA 22903 USA Univ Calif Irvine Irvine CA USA

ISBN: (纸本)9798400701030

Graph machine learning algorithms have become popular tools in helping us gain a deeper understanding of the ubiquitous graph data. Despite their effectiveness, most graph machine learning algorithms lack considerations for fairness, which can result in discriminatory outcomes against certain demographic subgroups or individuals. As a result, there is a growing societal concern about mitigating the bias exhibited in these algorithms. To tackle the problem of algorithmic bias in graph machine learning algorithms, this tutorial aims to provide a comprehensive overview of recent research progress in measuring and mitigating the bias in machine learning algorithms on graphs. Specifically, this tutorial first introduces several widely-used fairness notions and the corresponding metrics. Then, we present a well-organized review of the theoretical understanding of bias in graph machine learning algorithms, followed by a summary of existing techniques to debias graph machine learning algorithms. Furthermore, we demonstrate how different real-world applications benefit from these graph machine learning algorithms after debiasing. Finally, we provide insights on current research challenges and open questions to encourage further advances.

关键词： algorithmic fairness graph machine learning algorithms

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A bibliometric analysis of research on remote sensing-based monitoring of soil organic matter conducted between 2003 and 2023

Artificial Intelligence in Agriculture

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Artificial Intelligence in Agriculture 2025年第1期15卷 26-38页

作者： Xionghai Chen Fei Yuan Syed Tahir Ata-Ul-Karim Xiaojun Liu Yongchao Tian Yan Zhu Weixing Cao Qiang Cao National Engineering and Technology Center for Information Agriculture MOE Engineering and Research Center for Smart AgricultureMARA Key Laboratory for Crop System Analysis and Decision MakingJiangsu Key Laboratory for Information AgricultureCollaborative Innovation Centre for Modern Crop Production Co-sponsored by Province and MinistryNanjing Agricultural UniversityNanjing 210095China Department of Geography Minnesota State UniversityMankatoMN 56001USA Department of Agroecology Climate and WaterAarhus UniversityTjele 8830Denmark

Soil organic matter(SOM)is a key metric for assessing soil quality and crop yield *** plays a vital role in maintaining the ecological balance environment and promoting sustainable farming *** review examines the evolving trends in remote sensing(RS)-based SOM monitoring by analyzing 739 scholarly publications from the Web of Science database from 2003 to 2023 using a bibliometric *** study reveals that research on RS-based SOM monitoring has entered a rapid growth phase since 2018,with China and the United States as the main contributors and an extensive international cooperation *** model construction,high frequency covariates such as soil pH,precipitation,temperature,and topography significantly improved the prediction *** preprocessing methods such as Standard Normal Variables(SNV),Principal Component Analysis(PCA),and Multiple Scattering Correction(MSC)enhanced data *** statistical models are gradually being replaced by nonlinear machine learning and deep learning methods(CNN,XGBoost,andStacking),which are particularly good at handling complex high-dimensional *** spectral libraries(OzSoil and AfSIS)excel in local accuracy,while global spectral libraries(ISRIC and LUCAS)are more suitable for cross-region modeling,and the migration learning technique effectively improves the model generalization ability in low data *** models(CNN-LSTM and GAN)have significant advantages in capturing the spatial and temporal dynamics of SOMs,and uncertainty quantification methods(Bayesian inference,Monte Carlo simulation)enhance the reliability of the models in multi-source data and data-scarce *** research should focus on further optimization of multi-source data fusion and uncertainty quantification to promote the development and applicability of RS-based SOM monitoring techniques for precision soil management and sustainable agriculture.

关键词： data fusion Modeling methods Transfer learning Spatial mapping Spectral libraries

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