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Ensemble Machine Learning Approaches for Coastal Planetary Boundary Layer Height Estimation Based on Coherent Doppler Wind Lidar

Ensemble Machine Learning Approaches for Coastal Planetary B...

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international conference on Vision, Image and Signal processing (ICVISP)

作者： Yehui Chen Xiaomei Jin Ying Liu Ningquan Weng Wenyue Zhu Qing Liu Key Laboratory of Atmospheric Optics Anhui Institute of Optics and Fine Mechanics HFIPS Chinese Academy of Sciences Hefei China Science Island Branch of Graduate School University of Science and Technology of China Hefei China Advanced Laser Technology Laboratory of Anhui Province Hefei China School of Environmental Science & Optoelectronic Technology Anhui Xinhua University Hefei China School of Environmental Science and Optoelectronic Technology University of Science and Technology of China Hefei China

ISBN: (数字)9798331541460

ISBN: (纸本)9798331541477

The planetary boundary layer is instrumental in mediating the exchange of heat and matter between the Earth's surface and the atmosphere. Using continuous high-resolution wind profiling measurements conducted in Qingdao, an eastern coastal city of China, from December 2019 to April 2020, this study constructs and validates an integrated model that integrates the K-means and Random Forest algorithms. The model estimates the planetary boundary layer height across five real-time weather scenarios:clear, rainy, few clouds, moderate clouds, and cloudy, incorporating a suite of meteorological variables sourced from the ERA5 reanalysis dataset. In particular, the training results for $C2$ (a few clouds) demonstrate better performance, with a value of $R^{2}$ of 0.92, indicating a high degree of robustness. This research offers substantial value for meteorological studies and engineering applications in coastal zones.

关键词： Wind Analytical models Laser radar Atmospheric measurements Atmospheric modeling Clouds Sea measurements Estimation data models Random forests

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Research on credit-risk models via machine-learning algorithms and logistic regression for predicting CBA consumer behaviour

Research on credit-risk models via machine-learning algorith...

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Computers, Information processing and Advanced Education (CIPAE), 2020 international conference on

作者： Ce Fang Te Bu Fang Fang International College Krirk University Bangkok Thailand

Start with the pre-screening of a possible application and continuing through the account's administration during its life and potential write-off, risk assessment techniques are used to handle things a borrower's account. Typically, a logistic regression model is used to estimate how risky it is to lend money to a credit applicant. However, researchers have also looked at a variety of other classifiers, and while early results indicate that support vector machines are the most accurate, practical implementation of these results from the lab may be hampered by issues with data quality. With a regression-based R2 values of forecasted/realized tardiness of 85%, this article generates out-of-sample predictions that greatly increase the categorization rates of credit-card holder defaults and delinquencies. We estimate the cost reductions to range from 6% to 25% of total losses using conservative estimates for the costs and benefits of reducing credit lines based on machine-learning projections.

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A Strategy-based Optimization Algorithm to Design Codes for DNA data Storage System 21st

A Strategy-based Optimization Algorithm to Design Codes for ...

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21st international conference on algorithms and Architectures for Parallel processing (ICA3PP)

作者： Rasool, Abdur Qu, Qiang Jiang, Qingshan Wang, Yang Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Key Lab High Performance Data Min Shenzhen 518055 Peoples R China Univ Chinese Acad Sci Shenzhen Coll Adv Technol Beijing 100049 Peoples R China

ISBN: (纸本)9783030953881;9783030953874

The exponential increase of big data volumes demands a large capacity and high-density storage. Deoxyribonucleic acid (DNA) has recently emerged as a new research trend for data storage in various studies due to its high capacity and durability, where primers and address sequences played a vital role. However, it is a critical biocomputing task to design DNA strands without errors. In the DNA synthesis and sequencing process, each nucleotide is repeated, which is prone to errors during the hybridization reactions. It decreases the lower bounds of DNA coding sets which causes the data storage stability. This study proposes a metaheuristic algorithm to improve the lower bounds of DNA data storage. The proposed algorithm is inspired by a moth-flame optimizer (MFO), which has exploration and exploitation capability in one dimension, and it is enhanced by opposition-based learning (OBL) strategy with three-dimension search space for the optimal solution;hereafter, it is MFOL algorithm. This algorithm is programmed to construct the DNA storage codes by reducing the error rates of DNA coding sets with GC-content, Hamming distance, and No-runlength constraints. In experiments, 13 benchmark functions and Wilcoxon rank-sum test are implemented, and performances are compared with the original MFO and three other algorithms. The generated DNA codewords by MFOL are compared with a state-of-the-art Altruistic algorithm and KMVO algorithm. The proposed algorithm improved 30% DNA coding rates with shorter sequences, reducing errors during DNA synthesis and sequencing.

关键词： DNA data storage Biocomputing DNA coding sets Opposition-based learning MFO algorithm

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Feature Engineering and Missing data Imputation Method of Medical data Analysis 5

Feature Engineering and Missing Data Imputation Method of Me...

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5th international Workshop on Computer Modeling and Intelligent Systems, CMIS 2022

作者： Shakhovska, Nataliya Melnykova, Nataliia Lviv Polytechnic National University S.Bandera str12 Lviv79013 Ukraine

This work provides an alternative way to preprocessing procedure for consolidated data. Two methods are proposed. The first one is used for feature selection based on ensemble of machine learning algorithms. And the second one organizes missing data imputation based on combination of functional dependencies and associative rules. Ensemble methods for processing multimodal data based on a hierarchical classifier, a set of weak classifiers and a number of methods for selecting important characteristics with a much higher value of accuracy on unbalanced data sets compared to existing machine learning methods are developed. The methods are validated on medical dataset. The percentage of recovery data is on 1.2% comparing with associative rules. The proposed missing data imputation method creates additional data values operating a based domain and functional dependencies and includes these values to available training data. The correctness of the filled-in values is proved on the predictor built on the original dataset. The proposed PPD method conducts 12% better than RF and EM models for 30% missing data. © 2020 Copyright for this paper by its authors.

关键词： Classification (of information)

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EGNN-C+: Interpretable Evolving Granular Neural Network and Application in Classification of Weakly-Supervised EEG data Streams

EGNN-C+: Interpretable Evolving Granular Neural Network and ...

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2024 IEEE international conference on Evolving and Adaptive Intelligent Systems, EAIS 2024

作者： Leite, Daniel Silva, Alisson Casalino, Gabriella Sharma, Arnab Fortunato, Danielle Ngomo, Axel-Cyrille Paderborn University Department of Computer Science Germany Federal Center for Technological Education Department of Computer Science Brazil University of Bari Department of Computer Science Italy Federal University of Lavras Department of Automatics Brazil

ISBN: (纸本)9798350366235

We introduce a modified incremental learning algorithm for evolving Granular Neural Network Classifiers (eGNN-C+). We use double-boundary hyper-boxes to represent granules, and customize the adaptation procedures to enhance the robustness of outer boxes for data coverage and noise suppression, while ensuring that inner boxes remain flexible to capture drifts. The classifier evolves from scratch, incorporates new classes on the fly, and performs local incremental feature weighting. As an application, we focus on the classification of emotion-related patterns within electroencephalogram (EEG) signals. Emotion recognition is crucial for enhancing the realism and interactivity of computer systems. The challenge lies exactly in developing high-performance algorithms capable of effectively managing individual differences and non-stationarities in physiological data without relying on subject-specific information. We extract features from the Fourier spectrum of EEG signals obtained from 28 individuals engaged in playing computer games-A public dataset. Each game elicits a different predominant emotion: boredom, calmness, horror, or joy. We analyze individual electrodes, time window lengths, and frequency bands to assess the accuracy and interpretability of resulting user-independent neural models. The findings indicate that both brain hemispheres assist classification, especially electrodes on the temporal (T8) and parietal (P7) areas, alongside contributions from frontal and occipital electrodes. While patterns may manifest in any band, the Alpha (8-13Hz), Delta (1-4Hz), and Theta (4-8Hz) bands, in this order, exhibited higher correspondence with the emotion classes. The eGNN-C+ demonstrates effectiveness in learning EEG data. It achieves an accuracy of 81.7% and a 0.002933 interpretability using 10-second time windows, even in face of a highly-stochastic time-varying 4-class classification problem. © 2024 IEEE.

关键词： Electrodes

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Latent Space Score-based Diffusion Model for Probabilistic Multivariate Time Series Imputation

Latent Space Score-based Diffusion Model for Probabilistic M...

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international conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Guojun Liang Najmeh Abiri Atiye Sadat Hashemi Jens Lundström Stefan Byttner Prayag Tiwari School of Information Technology Halmstad University Halmstad Sweden School of Information Technology Halmstad University Lund University

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Accurate imputation is essential for the reliability and success of downstream tasks. Recently, diffusion models have attracted great attention in this field. However, these models neglect the latent distribution in a lower-dimensional space derived from the observed data, which limits the generative capacity of the diffusion model. Additionally, dealing with the original missing data without labels becomes particularly problematic. To address these issues, we propose the Latent Space Score-Based Diffusion Model (LSSDM) for probabilistic multivariate time series imputation. Observed values are projected onto low-dimensional latent space and coarse values of the missing data are reconstructed without knowing their ground truth values by this unsupervised learning approach. Finally, the reconstructed values are fed into a conditional diffusion model to obtain the precise imputed values of the time series. In this way, LSSDM not only possesses the power to identify the latent distribution but also seamlessly integrates the diffusion model to obtain the high-fidelity imputed values and assess the uncertainty of the dataset. Experimental results demonstrate that LSSDM achieves superior imputation performance while also providing a better explanation and uncertainty analysis of the imputation mechanism. The website of the code is https://***/gorgen2020/LSSDMimputation.

关键词： Uncertainty Accuracy Time series analysis Diffusion models Probabilistic logic Transformers Imputation data models Speech processing Unsupervised learning

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data-Free Network Debiasing for Long-Tailed Visual Recognition

Data-Free Network Debiasing for Long-Tailed Visual Recogniti...

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2022 IEEE international conference on Multimedia and Expo, ICME 2022

作者： Cai, Jinmian Wang, Zheng Fu, Huazhu Chen, Jingjing Jiang, Yu-Gang School of Computer Science Fudan University Shanghai Key Lab of Intelligent Information Processing China Shanghai Collaborative Innovation Center on Intelligent Visual Computing China Biren Technology IHPC A-STAR

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

Real-world data is often unbalanced and exhibits long-tailed distribution over classes. Vanilla classification models trained on imbalanced datasets inherently exhibit bias towards dominant classes. Existing debiasing methods mostly balance the data or the loss during training. Nevertheless, these data-acquiring methods are not suitable for situations where training data are unavailable. In this paper, we appeal to solutions without access to training data and propose a datafree debiasing (Free-D) method that serves as a plug-and-play module for any standard classification model. Specifically, our method adjusts both the feature representation via feature representation shifting and the classifier weight via class prior compensation in a data-free manner. We evaluate and compare our methods on four long-tailed visual recognition datasets, i.e., long-tailed CIFAR-10/-100, ImageNet-LT, and Places-LT. Extensive experiments demonstrate that the proposed data-free method achieves comparable results of other data-acquired methods. © 2022 IEEE.

关键词： Classification (of information)

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Research on Pricing Strategy of Fresh Vegetable Commodities in Grocery Stores Based on Linear Regression and ARIMA Model

Research on Pricing Strategy of Fresh Vegetable Commodities ...

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Computers, Information processing and Advanced Education (CIPAE), 2020 international conference on

作者： Yuanyuan Zheng Zirui He Haipeng He Guangdong University of Technology Guangzhou China

ISBN: (数字)9798331527662

ISBN: (纸本)9798331527679

This study explores the pricing strategy for fresh vegetable commodities in grocery stores using linear regression and ARIMA models. After preprocessing the data and excluding return transactions, Spearman correlation analysis revealed a significant negative correlation between total vegetable sales volume and cost-plus pricing. Further linear regression analysis confirmed this negative linear relationship, demonstrating the model's statistical significance. Subsequently, the ARIMA model was employed to analyze time series data, forecast future replenishment volumes, and pricing strategies to maximize supermarket profits. The ARIMA model captures trends and seasonality in the data through autocorrelation analysis, providing more accurate predictions for vegetable product pricing and replenishment. In the experimental section, stationarity of the time series was ensured through first-order differencing and ADF testing, and ARIMA model parameters were determined using ACF and PACF plots. Finally, the optimized model was used to forecast the replenishment volume and pricing strategy for July 1-7, 2023. The study concludes that the ARIMA model is an effective tool for supermarket managers to make data-driven decisions.

关键词： Analytical models Solid modeling Adaptation models Accuracy Computational modeling Linear regression Time series analysis Pricing Predictive models data models

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Enhancing Cloud Computing Security through AI-Based Intelligent Intrusion Detection Leveraging Neural Networks and Artificial Bee Colony Optimization 8

Enhancing Cloud Computing Security through AI-Based Intellig...

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8th international conference on Digital Technologies in Education, Science and Industry, DTESI 2023

作者： Ali, Mohsin Razaque, Abdul Yeskendirova, Damelya M. Nurlybayev, Talgat A. Askarbekova, Nessibeli Y. Kashaganova, Zarina A. International Information Technology University Manas St. 34/1 Almaty050000 Kazakhstan

This paper presents a novel approach for detecting malicious network traffic by leveraging artificial neural networks, well-suited for deep packet inspection-based intrusion detection systems. Through comprehensive experimentation with diverse benign network traffic data, encompassing images, dynamic link library files, logs, music files, word processing documents, alongside malicious shell code files sourced from the exploit and vulnerability repository exploitdb, our proposed artificial neural network architecture demonstrates exceptional accuracy in distinguishing benign from malicious network traffic. The introduced neural network consistently achieves an average accuracy rate of 99%, an average area under the receiver operator characteristic curve (AUC-ROC) of 0.99, and an average false positive rate of fewer than 2% across multiple 10-fold cross-validation iterations. These findings underscore the robustness, precision, and accuracy of our classification technique. Furthermore, this innovative approach to detecting malicious network traffic holds substantial potential for enhancing intrusion detection systems in both conventional network traffic analysis and cyber-physical systems analysis, such as smart grids. Additionally, we introduce a novel intrusion detection system (IDS) that combines a multilayer perceptron (MLP) network with artificial bee colony (ABC) and fuzzy clustering algorithms. The MLP network identifies normal and abnormal network traffic packets, while the ABC algorithm optimizes linkage weights and biases during MLP training. Validation of our approach employs the CloudSim simulator and the NSL-KDD dataset, with evaluation metrics comprising mean absolute error (MAE), root mean square error (RMSE), and the kappa statistic. The results obtained from our experiments demonstrate the superior performance of our proposed method when compared to state-of-the-art approaches in the field. © 2020 Copyright for this paper by its authors.

关键词： Malware

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Social Media User Behavior Prediction and Personalized Digital Marketing Model Based on Deep Learning

Social Media User Behavior Prediction and Personalized Digit...

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Computers, Information processing and Advanced Education (CIPAE), 2020 international conference on

作者： Tong Zhi Guangzhou City University of Technology Guangzhou China

ISBN: (数字)9798331527662

ISBN: (纸本)9798331527679

This study aims to explore the deep learning technology. By combining the hybrid model of Gradient Boosting Decision Tree (GBDT) and Long Short-Term Memory Network (LSTM), the GBDT model is good at dealing with the nonlinear relationship and high-dimensional features of social media data, and automatically filters out the features most relevant to user behavior, reduces the data dimension, and retains key information. The input selection of the LSTM model was guided to ensure that the model focused on the most relevant behavior patterns. Then the LSTM model was applied to process the time series data of user behavior to capture the dynamic changes and long-term dependence of user behavior. Therefore, a cascade architecture is adopted to generate a concise and informative feature set by using GBDT to preprocess the raw data. These features are then fed into the LSTM model for building a time series prediction model. This architecture generates highly personalized marketing recommendations based on a deep understanding of user behavior so as to improve the marketing effect and user satisfaction.

关键词： Deep learning Analytical models Social networking (online) Architecture Time series analysis Predictive models data models Decision trees Long short term memory Context modeling

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