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Southwest Pacific tropical cyclone development classification utilizing machine learning and synoptic composites

INTERNATIONAL JOURNAL OF CLIMATOLOGY 2022年第8期42卷 4187-4213页

作者： Bhowmick, Rupsa Trepanier, Jill C. Haberlie, Alex M. Louisiana State Univ Dept Geog & Anthropol Baton Rouge LA 70803 USA

This study evaluates the ability of machine learning algorithms to classify tropical depressions (TDs) and tropical storms (TSs) in the western region of the southwest Pacific Ocean (SWPO). Decision rules are generated to predict the environment required for a depression to fully develop into a mature storm, and the most influential predictors in the classification decision are ranked. TD and TS are discriminated based on a maximum sustained wind speed threshold (>= 17 ms(-1)). Various aerosol, thermodynamic, and dynamic parameters are extracted closest to the initiation point of each non-developing and developing sample. The covariates associated with each labelled sample are used to train a decision tree and random forest model. Results using a testing dataset suggest the random forest approach more accurately distinguishes between non-developing and developing samples. The classification accuracy of the decision tree and random forest are 72% and 91%, respectively. Random forest outperformed the decision tree by providing higher accuracy in test data. The most important variables for binary classification are sea salt aerosol optical depth (AOD), 1,000 mb relative humidity, and sea surface temperature. AOD is a quantitative estimate of the aerosols presents in the air through the extinction of a ray of light as it passes through the atmosphere. Mean composite maps constructed in an unsupervised manner have been created for the most important variables identified by the random forest classifier during TD and TS events to highlight the difference in geophysical and aerosol variables' climatology during the two different classifications. This work will advance the risk management strategies for northeastern Australia and other SWPO basin islands to control their tropical cyclone related losses through prioritizing forecasting variables that are the strongest predictors of the strengthening of tropical depressions into tropical cyclones.

关键词： composite maps machine learning classification Southwest Pacific Ocean basin Tropical cyclone development

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Big data analytics and classification of cardiovascular disease using machine learning

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022年第2期43卷 2025-2033页

作者： Narejo, Sanam Shaikh, Anoud Memon, Mehak Maqbool Mahar, Kainat Aleem, Zonera Zardari, Bisharat Mehran Univ Engn & Technol Dept Comp Syst Engn Jamshoro Pakistan Mehran Univ Engn & Technol Dept Software Engn Jamshoro Pakistan Univ Teknol Petronas Dept Comp & Informat Sci Seri Iskandar Perak Malaysia

Hundreds of people dying from heart disease almost every day that is how terrific a delayed diagnosis can be. Living in an advanced era full of intelligent systems, the increasing number of deaths can be reduced. This research paper focuses on the development of a cardiovascular disease prediction system particularly a heart disease, by developing machine learning classifiers, for instance, Support Vector machine (SVM), Decision Tree, and XGBoost Classifiers. We also scaled the features to standardize unconstrained features in data, available in a fixed range for better optimization of models. For efficiency, the classification of features was also done in two categories, Independent features, and dependent features. Furthermore, the performance measures helped with best practices for model assessment & classifier performance. Eventually, after tuning hyper-parameters, the results exhibit high accuracy for XGBoost among other trained classifiers. After a comparative analysis, the best-suited algorithm can be utilized for heart disease detection, in the medical field, and regarding the economy, as costly treatments are taken into consideration. This indicates that a non-expert can also attempt for diagnosis without fretting over expensive treatments.

关键词： Medical diagnosis heart disease exploratory data analysis machine learning classification support vector machines decision tree XGBoost classifiers

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Addressing contingency in algorithmic (mis)information classification: Toward a responsible machine learning agenda 23

Addressing contingency in algorithmic (mis)information class...

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6th ACM Conference on Fairness, Accountability, and Transparency (FAccT)

作者： Hernandez, Andres Dominguez Owen, Richard Nielsen, Dan Saattrup Mcconville, Ryan Univ Bristol Bristol England Alexandra Inst Aarhus Denmark

ISBN: (纸本)9781450372527

machine learning (ML) enabled classification models are becoming increasingly popular for tackling the sheer volume and speed of online misinformation and other content that could be identified as harmful. In building these models, data scientists need to take a stance on the legitimacy, authoritativeness and objectivity of the sources of “truth” used for model training and testing. This has political, ethical and epistemic implications which are rarely addressed in technical papers. Despite (and due to) their reported high accuracy and performance, ML-driven moderation systems have the potential to shape online public debate and create downstream negative impacts such as undue censorship and the reinforcing of false beliefs. Using collaborative ethnography and theoretical insights from social studies of science and expertise, we offer a critical analysis of the process of building ML models for (mis)information classification: we identify a series of algorithmic contingencies—key moments during model development that could lead to different future outcomes, uncertainty and harmful effects as these tools are deployed by social media platforms. We conclude by offering a tentative path toward reflexive and responsible development of ML tools for moderating misinformation and other harmful content online.

关键词： Misinformation Content Moderation Fact-checking machine learning classification Responsible ML

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Customer Churn Prediction by classification Models in machine learning 9

Customer Churn Prediction by Classification Models in Machin...

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9th International Conference on Electrical and Electronics Engineering (ICEEE)

作者： Zhao, Heng Zuo, Xumin Xie, Yuanyuan Shenzhen Technol Univ Coll Big Data & Internet Shenzhen Peoples R China

ISBN: (纸本)9781665467544

The classification model in machine learning has been employed to address different problems. machine learning classification is an effective method to realize customer churn prediction. This article provides a comparative study of machine learning from the perspective of predicting customer churn. Customer churn is one of the obstacles hindering the development of companies. Through classification approaches based on machine learning, customer churn can be predicted precisely, thus providing decision-making capabilities to these companies. Customer churn prediction is a typical classification problem and can be addressed by the use of a decision tree or random forest model. In this paper, decision tree and random forest models are employed to predict customer churn, using sales data provided by a chemical company from 2012 to 2020. We analyze the underlying risk of customer churn and the customer churn factors, including the low-priced count (LC), total amount of money (TM), and creation time (CT). The prediction results of the two models are evaluated by calculating various metrics. The experimental results indicate that the low-priced count (LC) is the most essential factor for customer churn. The results of the comparison, considering the training error and generalization error including: confusion matrix, ROC curve, AUC, precision, recall, and F1 score, reveal that the random forest model has better prediction accuracy than the decision tree model.

关键词： machine learning classification prediction customer churn customer churn factors decision tree random forest

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machine learning Based Abnormal Gait classification with IMU Considering Joint Impairment

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SENSORS 2024年第17期24卷 5571页

作者： Hwang, Soree Kim, Jongman Yang, Sumin Moon, Hyuk-June Cho, Kyung-Hee Youn, Inchan Sung, Joon-Kyung Han, Sungmin Korea Inst Sci & Technol KIST Res Ctr B Biomed Res Div Seoul 02792 South Korea Korea Univ Sch Biomed Engn Seoul 02841 South Korea Korea Univ Coll Med Dept Neurol Anam Hosp Seoul 02841 South Korea Korea Univ Sci & Technol KIST Sch Div Biomed Sci & Technol Seoul 02792 South Korea Kyung Hee Univ KHU KIST Dept Converging Sci & Technol Seoul 02447 South Korea

Gait analysis systems are critical for assessing motor function in rehabilitation and elderly care. This study aimed to develop and optimize an abnormal gait classification algorithm considering joint impairments using inertial measurement units (IMUs) and walkway systems. Ten healthy male participants simulated normal walking, walking with knee impairment, and walking with ankle impairment under three conditions: without joint braces, with a knee brace, and with an ankle brace. Based on these simulated gaits, we developed classification models: distinguishing abnormal gait due to joint impairments, identifying specific joint disorders, and a combined model for both tasks. Recursive Feature Elimination with Cross-Validation (RFECV) was used for feature extraction, and models were fine-tuned using support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGB). The IMU-based system achieved over 91% accuracy in classifying the three types of gait. In contrast, the walkway system achieved less than 77% accuracy in classifying the three types of gait, primarily due to high misclassification rates between knee and ankle joint impairments. The IMU-based system shows promise for accurate gait assessment in patients with joint impairments, suggesting future research for clinical application improvements in rehabilitation and patient management.

关键词： abnormal gait joint impairment IMU-based system walkway system RFECV machine learning classification

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Ganoderma boninense classification based on near-infrared spectral data using machine learning techniques

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2023年 232卷

作者： Tan, Mas Ira Syafila Mohd Hilmi Jamlos, Mohd Faizal Omar, Ahmad Fairuz Kamarudin, Kamarulzaman Jamlos, Mohd Aminudin Univ Malaysia Pahang Fac Elect & Elect Engn Technol Pekan 26600 Malaysia Univ Sains Malaysia Sch Phys George Town 11800 Penang Malaysia Univ Malaysia Perlis Fac Elect Engn Technol Pauh Putra 02600 Perlis Malaysia Univ Malaysia Perlis Fac Elect Engn Technol Pauh Putra 02600 Perlis Malaysia

Ganoderma boninense (G. boninense) infection reduces the productivity of oil palms and causes a severe threat to the palm oil industry. Early detection of G. boninense is vital since there is no effective treatment to stop the continuing spread of the disease unless ergosterol, a biomarker of G. boninense can be detected. There is yet a non-destructive and in-situ technique explored to detect ergosterol. Capability of NIR to detect few biomarkers such as mycotoxin and zearalenone (ZEN) has been proven to pave the way an effort to explore NIR's sensitivity towards detecting ergosterol, as discussed in this paper. A compact hand-held NIR with a measurement range of 900-1700 nm is utilized by scanning the leaves of three oil palm seedlings inoculated with G. boninense while the other three were non-inoculated from 16-weeks-old to 32-weeks-old. Significant changes of spectral reflectance have been notified occur at the wavelength of similar to 1450 nm which reflectance of infected sample is higher 0.2-0.4 than healthy sample which 0.1-0.19. The diminishing of the spectral curve at approximately 1450 nm is strongly suspected to happened due to the loss of water content from the leaves since G. boninense attacks the roots and causes the disruption of water supply to the other part of plant. However, a few overlapped NIRs' spectral data between healthy and infected samples require for further validation which chemometric and machine learning (ML) classification technique are chosen. It is found the spectra of healthy samples are scattered on the negative sides of PC-1 while infected samples tend to be on a positive side with large loading coefficients marked significant discriminatory effect on healthy and infected samples at the wavelength of 1310 and 1452 nm. A PLS regression is used on NIR spectra to implement the prediction of ergosterol concentration which shows good corelation of R = 0.861 between the ergosterol concentration and oil palm NIR spectra. Four different

关键词： Oil palms Near-infrared spectroscopy machine learning classification

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Environmental sustainability: A machine learning approach for cost analysis in plastic recycling classification

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RESOURCES CONSERVATION AND RECYCLING 2023年第1期197卷

作者： Carrera, Berny Mata, Judit Bazin Pinol, Victor Luid Kim, Kwanho Incheon Natl Univ Ind & Management Engn Songdodong 119 Acad ro Incheon 22012 South Korea Univ Politecn Cataluna Ind Engn Avinguda Diagonal 647 Barcelona 08028 Spain

Recycling plastics can reduce waste generation and improve waste management, but the recycling industry needs both cost reduction and increased revenue to be economically viable. Recently, recycling plastic classification techniques with Artificial Intelligence have gained popularity, as they can avoid manual sorting, which is timeconsuming and economically less profitable than automatidc processing. In this paper, we provide an economic framework for quality sorting control by classifying plastics based on the infrared spectrum of polymers and machine learning algorithms. In addition, the suggested framework offers a method for selecting the algorithm according to the polymer's income class and the highest economic advantages. Furthermore, our experiments probe that Fourier-transform infrared (FTIR) and near-infrared (NIR) spectroscopies combined with machine learning algorithms are suitable for plastic classification as four datasets and seven machine learning algorithms have been tested to classify Polyethylene (PE), Polypropylene (PP), Polyethylene terephthalate (PET), polystyrene (PS), and Polyvinyl chloride (PVC).

关键词： Plastic recycling revenue Profitable plastics Plastic recycling industry Waste management Optimization machine learning classification Environment Sustainable cities Quality sorting

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Enhanced Binary Kepler Optimization Algorithm for effective feature selection of supervised learning classification

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JOURNAL OF BIG DATA 2025年第1期12卷 1-67页

作者： Abd El-Mageed, Amr A. Abohany, Amr A. Hosny, Khalid M. Sohag Univ Dept Informat Syst Sohag 82511 Egypt Damanhour Univ Fac Comp & Informat Damanhour Egypt Zagazig Univ Fac Comp & Informat Dept Informat Technol Zagazig 44519 Egypt

This study proposes an Enhanced Binary Kepler Optimization Algorithm (BKOA-MUT) improves feature selection (FS) by integrating Kepler's planetary motion laws with DE/rand and DE/best Mutation Approach. BKOA-MUT balances exploration and exploitation, effectively guiding search for optimal feature subsets. BKOA-MUT was evaluated using k-Nearest Neighbors (k-NN) and Support Vector machine (SVM) on 25 UCI benchmarks, including three large-scale ones. It outperformed recent Meta-heuristic Algorithms (MHAs) in accuracy, feature reduction, and computational efficiency. The algorithm showed rapid convergence, minimal feature selection, and scalability, making it a robust and adaptable tool for enhancing FS in machine learning, validated through the Wilcoxon rank-sum test.

关键词： Feature selection Meta-heuristic Algorithms Kepler Optimization Algorithm DE/rand and DE/best Mutation Approach machine learning classification

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Binary classification of Fractal Time Series by machine learning Methods 15th

Binary Classification of Fractal Time Series by Machine Lear...

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15th International Scientific Conference on Intellectual Systems of Decision Making and Problems of Computational Intelligence (ISDMCI)

作者： Kirichenko, Lyudmyla Radivilova, Tamara Bulakh, Vitalii Kharkiv Natl Univ Radioelect Kharkiv Ukraine

ISBN: (纸本)9783030264741

The paper considers the binary classification of time series based on their fractal properties by machine learning. This approach is applied to the realizations of normal and attacked network traffic, which allows to detect DDoS-attacks. A comparative analysis of the results of the classification by the random forest and neural network - fully connected multi-layer perceptron is carried out. The statistical, fractal and recurrence characteristics calculated from each time series were used as features for classification. The analysis showed that both methods provide highly accurate of classification and can be used to detect attacks in intrusion detection systems.

关键词： Fractal time series Random forest Neural network machine learning classification Hurst exponent Recurrence plot

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classification of Time Realizations Using machine learning Recognition of Recurrence Plots

Classification of Time Realizations Using Machine Learning R...

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International Scientific Conference on Intellectual Systems of Decision-Making and Problems of Computational Intelligence (ISDMCI)

作者： Kirichenko, Lyudmyla Zinchenko, Petro Radivilova, Tamara Kharkiv Natl Univ Radio Elect UA-61166 Kharkiv Ukraine

ISBN: (纸本)9783030542153;9783030542146

In the article, the machine learning classification of time realizations using the recurrence plot visualization is considered. Every time realization is converted into a matrix of recurrence states and it is presented as a black-and-white image. The resulting images of realizations are classified using deep neural networks. A deep residual neural network is used as an image classifier. The binary classification of EEG realizations is carried out. The result of the binary classification is the detection of an epileptic seizure. The data for the experiment are records of brain activity containing 178 values. The results of the study show that the considered method has a high classification accuracy. The proposed classification approach can be readily used in practice.

关键词： machine learning classification Time series classification Recurrence plot EEG realizations Deep residual networks

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