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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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Stimuli-induced modulation of white light emission in colloidal polymer particles: Design of chemosensors and dynamic chromism toward unclonable patterning and its classification based on machine learning

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CHEMICAL ENGINEERING JOURNAL 2024年 490卷

作者： Mardani, Hanieh Herizchi, Ata Roghani-Mamaqani, Hossein Sahand Univ Technol Fac Polymer Engn POB 513351996 Tabriz Iran Sahand Univ Technol Inst Polymer Mat Tabriz Iran

Stimuli-responsive light-emitting materials have gained significant attention due to their potential applications in optoelectronic devices, chemosensors, anticounterfeiting inks, and unclonable encryption. Coumarincontaining fluorescent polymer particles with light-, pH-, and thermo-responsiveness were prepared by distillation-precipitation polymerization of hydroxyethyl methacrylate and 2-(dimethylamino)ethyl methacrylate. White light emission was observed by physical incorporation of hydroxyl-functionalized spiropyran (SPOH) into the colloidal polymer particles in a certain ratio of the colloidal polymer particles and SPOH content. The white light emission was obtained by modulating pH and temperature to finely tune the Fo rster resonance energy transfer process and cover the entire visible spectra from the emissive lights. This phenomenon was successfully used to prepare pH sensors, dynamic anticounterfeiting inks, unclonable patterning, and also white lightemitting colloids, films, coatings, and diodes. As an example, a white light-emitting diode was fabricated using the SPOH-doped colloidal polymer particles in poly(vinyl alcohol) matrix having Commission Internationale del ' Eclairage color coordinates of (0.31, 0.33). Dynamic chromism and preparation of randomly distributed blue, red, and white light-emitting patterns are examples of advanced anticounterfeiting and encryption methods that were generated via the prepared low-cost environmentally friendly waterborne ink. For the authentication purpose of the unclonable patterns, the classification algorithms were designed based on machine learning to classify the true patterns from the false ones.

关键词： White light emission Fo rster resonance energy transfer Unclonable patterning Dynamic anticounterfeiting ink machine learning classification

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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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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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Federated learning-driven IoT system for automated freshness monitoring in resource-constrained vending carts

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JOURNAL OF BIG DATA 2025年第1期12卷 1-34页

作者： Stephan, Thompson Paramana, Padma Priya Dharishini Lin, Chia-Chen Agarwal, Saurabh Verma, Rajan Gulf Med Univ Thumbay Coll Management & AI Healthcare Ajman U Arab Emirates MS Ramaiah Univ Appl Sci Fac Engn & Technol Dept Comp Sci & Engn Bangalore Karnataka India Natl Chin Yi Univ Technol Dept Comp Sci & Informat Engn Taichung 411 Taiwan Yeungnam Univ Dept Informat & Commun Engn Gyongsan 38541 South Korea Chandigarh Univ Univ Ctr Res & Dev Mohali 140413 Punjab India

Street vendors in developing regions often lack access to portable and affordable cold storage, leading to accelerated food spoilage, financial losses, and health risks. Traditional refrigeration solutions are bulky and costly, while manual freshness assessment is error-prone. This study proposes a smart vending cart integrating IoT sensors and federated learning (FL) to address these challenges, offering real-time environmental monitoring, freshness classification, and privacy-preserving data handling. The smart vending cart incorporates IoT sensors to monitor temperature, humidity, and gas emissions. A Peltier cooling module and a humidifier maintain optimal conditions. machine learning models classify food freshness, while federated learning ensures vendor privacy by training models locally on each cart. The study explores nine federated learning approaches to train machine learning models across multiple carts without sharing raw data, thus preserving vendor privacy. The Stacking Ensemble approach outperformed all other methods, achieving the highest accuracy, F1-Score, and Cohen's Kappa (0.99964), with the lowest log loss (0.0022). Metalearning and Weighted Aggregation also demonstrated high performance but with marginally higher log loss values. Personalized models performed well in heterogeneous data environments but were less effective than ensemble methods. The developed smart vending cart system effectively reduces food spoilage and enhances vendor profitability through automated freshness classification and real-time environmental control. The integration of federated learning ensures privacy, while ensemble techniques improve robustness in resource-constrained settings, offering a scalable solution for street vendors.

关键词： Smart vending cart Federated learning IoT-based freshness detection Data privacy machine learning classification Cold storage solutions

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A new dimensionality reduction technique based on the Wavelet Transform for cancer classification

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JOURNAL OF BIG DATA 2025年第1期12卷 1-23页

作者： Fernandez, Lisardo Perez, Mariano Orduna, Juan M. Alcaraz, Jose M. Univ Valencia Dept Informat Burjassot Valencia Spain Univ West Scotland Sch Comp Engn & Phys Sci Glasgow Scotland

ProblemDNA methylation and hydroxymethylation have become important epigenetic markers for early detection of cancer. In recent years, there has been a significant increase in both the number of research works on this topic and the number and size of labeled databases with some type of cancer. Although the advent of methylation microarrays such as the HumanMethylation450 platform has greatly reduced the dimensionality of the problem from billions to 450K positions, this data size is still too large to be processed by machine learning algorithms for cancer prediction and *** the particular case of methylation, an efficient dimensionality reduction technique should also preserve the spatial information of the original data in order to properly predict and classify *** work proposes a new approach for data dimensionality reduction technique based on the Discrete Wavelet Transform (DWT), which preserves spatial information. We have evaluated the proposed technique with a dataset collected from the most important cancer databases according to their social impact, and we have compared our proposal to five well-known dimensionality reduction techniques: PCA, ReliefF, Isomap, LLE and *** performance evaluation results show that the proposed technique significantly reduces both the computational resources and the execution time required for dimensionality reduction. In addition, it significantly improves the accuracy achieved in the classification by a support vector machine when it uses as input data the resulting dataset yielded by each *** proposed approach based on the DWT can be considered as an efficient alternative for those cases where dimensionality reduction must preserve spatial information.

关键词： Dimensionality reduction Cancer classification DNA methylation analysis Wavelet Transform machine learning classification

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Active Vibrational Achilles Tendon Sensing for Identifying and Characterizing Inflammatory Symptomatology in Enthesitis Related Arthritis

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IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING 2025年第2期72卷 645-654页

作者： Goossens, Quentin Locsin, Miguel Ponder, Lori A. Chan, Michael Ozmen, Goktug C. Prahalad, Sampath Inan, Omer T. Georgia Inst Technol Sch Elect & Comp Engn Atlanta GA 30332 USA Emory Univ Dept Pediat Sch Med Atlanta GA USA Childrens Healthcare Atlanta Atlanta GA USA Emory Univ Sch Med Dept Pediat Childrens Healthcare Atlanta Atlanta GA USA Emory Univ Sch Med Dept Human Genet Atlanta GA USA Georgia Inst Technol Wallace H Coulter Dept Biomed Engn Atlanta GA 30332 USA

Objective: This study explores the potential of active vibrational sensing as a digital biomarker to identify and characterize inflammatory symptomatology in the Achilles tendon and its entheses in juvenile idiopathic arthritis (JIA), particularly enthesitis related arthritis (ERA), a subcategory of JIA. Methods: Active vibrational data were non-invasively recorded using a miniature coin vibration motor and accelerometer. Twenty active vibration recordings from children diagnosed with JIA were used in the analysis. machine learning algorithms were leveraged to classify the vibrational signatures according to the corresponding subject groups. Subjects were classified into symptomatic ERA (sxERA), asymptomatic ERA (asxERA), and asymptomatic JIA (non-ERA) (asxNERA) groups based on clinical evaluations and ILAR criteria. Results: Distinct vibrational signatures were observed during tiptoe standing, providing differentiation between subject groups. Feature-based and waveform-based approaches effectively classified the sxERA group against asxNERA and asxERA groups using leave-one-subject-out (LOSO-CV) and 3-fold cross-validation. For the 3-fold cross-validation, the mean accuracies for distinguishing sxERA from asxNERA were 81% (feature-based) and 81% (waveform-based), while the accuracies for discriminating sxERA against asxERA were 73% (feature-based) and 74% (waveform-based). Conclusion: Active vibrational sensing demonstrates promise as a tool for identifying Achilles tendon inflammation in JIA, potentially aiding in early diagnosis and disease monitoring. Significance: Developing active vibrational sensing as a diagnostic modality could address challenges in diagnosing ERA and facilitate timely intervention and personalized care for JIA, potentially enhancing long-term patient outcomes.

关键词： Achilles tendon Ankle Sensors Vibrations Diseases Medical services Recording Juvenile idiopathic arthritis (JIA) machine learning classification non-invasive digital biomarker pediatric rheumatology vibration analysis

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Innovative integration of machine learning and colorimetry for precise potential of hydrogen monitoring in printed hydrogel sensors

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2025年 146卷

作者： Sakr, Abdelrahman El Shamy, Ahmed R. Butt, Haider Khalifa Univ Sci & Technol Dept Mech & Nucl Engn POB 127788 Abu Dhabi U Arab Emirates Khalifa Univ Sci & Technol Dept Elect Engn POB 127788 Abu Dhabi U Arab Emirates

Proper potential of hydrogen (pH) monitoring finds wide applications in environmental monitoring, clinical diagnostics, and a variety of industrial processes. However, traditional pH sensors normally present several challenges related to adaptability, portability, and environmental compatibility. In addition, the recently developed hydrogel-based sensors have manifested several advantages due to the flexibility and biocompatibility of the material in a wide variety of applications. While much advancement has been made in integration techniques, further advances need improvement in precision and reliability. The present work describes a novel methodology of pH sensing through integration of hydrogel-based sensors with machine learning algorithms. pHsensitive dye-impregnated hydrogel sensors have been fabricated using three-Dimensional (3D) printing technology, whereby colorimetric data analysis is combined with five machine learning models, namely Decision Trees, eXtreme Gradient Boosting, K-Nearest Neighbours, Random Forests, and Neural Networks, in the classification of pH based on Red, Green, Blue (RGB) data. The sensor designed can detect pH between 4 and 10 pH with high speed, stability, and reversibility. With precision, recall, and F1-scores all above 99%, this shows how efficient the classification approach is based on RGB and gives weight to the potential of the developed sensors for real-time applications in monitoring and diagnostics, hence making a big contribution to the evolution of pH sensing and paving the way for smarter, more adaptable sensor solutions.

关键词： machine learning classification Potential of hydrogen-sensitive hydrogel sensors Red Green Blue colorimetric analysis Three-dimensional printing fabrication

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