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IEEE Canadian Journal of Electrical and Computer Engineering 2025年第2期48卷 109-114页

作者： Sevtap Turk Vocational School of Technical Sciences İstanbul University-Cerrahpaşa İstanbul Türkiye

Weather conditions directly affect sectors such as agriculture and transport. With climate change, unpredictability is increasing and traditional calculation methods may not be sufficient. In addition to some statistical methods, machine learning algorithms are also used for weather forecasting. This study attempts to classify precipitation using machine learning algorithms on selected meteorological data. The models used are K-nearest neighbors (KNNs), support vector machine (SVM), and multilayer perceptron (MLP). These models were implemented on four different open-source and free data mining platforms. These platforms are Altair AI Studio (formerly Rapidminer), Knime, Orange, and Weka. The dataset includes parameters such as pressure, temperature, humidity, number of rainy days, cloudiness rate, and year and month information. According to the values of these parameters, the data were classified as less rainy, rainy, and very rainy.

关键词： Support vector machines Nearest neighbor methods machine learning algorithms Classification algorithms Rain Data mining Weather forecasting Prediction algorithms Training Humidity Precipitation Climate change

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An efficient strategy for early sex identification in Litsea cubeba based on portable Raman technology combined with machine learning algorithms

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JOURNAL OF FOOD COMPOSITION AND ANALYSIS 2025年 140卷

作者： Wang, Chuyi Xu, Peipei Wang, Haonan Luo, Feili Tu, Qianmin Fang, Yulin You, Ruiyun Yang, Yan Lu, Yudong Fujian Normal Univ Coll Chem & Mat Sci Fujian Prov Key Lab Adv Oriented Chem Engineer Fujian Key Lab Polymer Mat Fuzhou 350117 Fujian Peoples R China Fujian Normal Univ Coll Life Sci Fuzhou 350117 Peoples R China Hunan Acad Forestry State Key Lab Utilizat Woody Oil Resource Changsha 410004 Peoples R China

Early sex identification of the dioecious and medicinal spice plant, Litsea cubeba (Lour.) Pers (LC), is necessary for expanding the production and application of LC. We describe a Raman spectroscopy (RS) and surface-enhanced Raman spectroscopy (SERS) method combined with machine learning techniques. Based on the RS and SERS features, we combined principal component analysis and linear discriminant analysis (PCA-LDA) to dimension reduction for the data. And we combined seven machine learning algorithms, including logistic regression (LR), Naive Bayes (NB), decision tree (DT), random forest (RF), k-nearest neighbor (KNN), support vector machines (SVM), and extreme gradient boosting (XGBoost) algorithms to construct a gender prediction model for LC. The results demonstrated that the surface reinforcement treatment with drops of silver sol combined with LR, SVM, and XGBoost models achieved an accuracy of 84.62 % in distinguishing male and female leaves. Furthermore, compared to the discrimination effect of scanning the leaf surface only, the gender recognition accuracy of the surface reinforcement treatment increased by 17.31 %, 19.24 % and 19.24 %, respectively. Portable Raman spectroscopy combined with machine learning algorithms can be promoted for use as a tool for early sex identification in most plants, which can be applied to large-scale plant cultivation and breeding programmes.

关键词： Litsea cubeba Surface-enhanced Raman spectroscopy Sex identification Leaves machine learning algorithms

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Determining the key meteorological factors affecting atmospheric CO2 and CH4 using machine learning algorithms at a suburban site in China

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URBAN CLIMATE 2025年 59卷

作者： Liu, Wanyu Niu, Zhenchuan Feng, Xue Zhou, Weijian Liang, Dan Wang, Guowei Liu, Lin Beijing Normal Univ Interdisciplinary Res Ctr Earth Sci Frontier Beijing 100875 Peoples R China Chinese Acad Sci Inst Earth Environm State Key Lab Loess & Quaternary Geol Xian 710061 Peoples R China Xi An Jiao Tong Univ Inst Global Environm Change Xian 710049 Peoples R China Natl Observat & Res Stn Reg Ecol Environm Change & Xian Shaanxi Peoples R China Xian Inst Innovat Earth Environm Res Xian 710061 Peoples R China Joint Xian AMS Ctr IEECAS & Xian Jiaotong Univ Shaanxi Prov Key Lab Accelerator Mass Spectrometry Xian 710061 Peoples R China

Atmospheric CO2 and CH4 were measured from March 2023 to February 2024 at a suburban site near the northern foot of the Qinling Mountains, China, aiming to determine the key meteorological factors that influencing the seasonal CO2 and CH4 dynamics using machine learning (ML) algorithms. Yearly average atmospheric CO2 and CH4 were 446.1 +/- 10.0 ppm and 2118.9 +/- 50.5 ppb, respectively. Anthropogenic emissions dominated atmospheric CO2 and CH4 changes in winter, and the excess CO2 (Delta CO2) and CH4 (Delta CH4) above background levels during the heating period were mainly from combustion emissions. We selected seven ML algorithms to determine the variable importance of meteorological factors, among which eXtreme Gradient Boosting (XGBoost) and Random Forest (RF) demonstrating the best performance and ranking consistency of the factors. Humidity and temperature of the atmosphere and soil had the higher effect (XGBoost: 80.4 %;RF: 78.1 %) on CO2 in spring, while humidity was crucial in winter, with variable importance of 69.3 % for XGBoost and RF. In summer and autumn, photosynthetic photon flux density and wind speed (WS) (totaling 35.2 % similar to 50.7 %) dominated CO2 dynamics. For CH4, atmospheric and soil humidity (totaling around 40.0 %) were key factors in spring, whereas atmospheric humidity was important in winter. WS had the largest effect in summer (XGBoost: 26.3 %;RF: 33.3 %) and autumn (XGBoost: 19.8 %;RF: 28.8 %). Meteorological processes like cold front passage significantly reduced CO2 and CH4 concentrations during haze events. XGBoost and RF have emerged as powerful tools for determining the key meteorological factor that favour seasonal GHGs evolution.

关键词： Dominant emission sources Variable importance of meteorological factor machine learning algorithms Random forest eXtreme gradient boosting

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Detecting Anxiety via machine learning algorithms: A Literature Review

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2025年

作者： Tayarani-N., M. -H. Shahid, Shamim Ibne Univ Hertfordshire Sch Phys Engn & Comp Sci HATFIELD Hatfield AL10 9AB England

Recent machine learning (ML) advances have opened up new possibilities for addressing various challenges. Given their ability to tackle complex problems, the use of ML algorithms in diagnosing mental health disorders has seen substantial growth in both the number and scope of studies. Anxiety, a major health concern in today's world, affects a significant portion of the population. Individuals with anxiety often exhibit distinct characteristics compared to those without the disorder. These differences can be observed in their outward appearance-such as voice, facial expressions, gestures, and movements-and in less visible factors like heart rate, blood test results, and brain imaging data. In this context, numerous studies have utilized ML algorithms to extract a diverse range of features from individuals with anxiety, aiming to build predictive models capable of accurately identifying those affected by the disorder. This paper performs a comprehensive literature review on the state-of-the-art studies that employ machine learning algorithms to identify anxiety. This paper aims to cover a wide range of studies and categorize them based on their methodologies and data types used.

关键词： Anxiety disorders machine learning algorithms Mental health Prediction algorithms Feature extraction Depression Accuracy Systematic literature review Sleep Signal processing algorithms Anxiety disorder machine learning artificial intelligence mental disorder social signal processing affective computing

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Unmasking Hypertension Through a Fusion of Human Lifestyle Data Using machine learning algorithms

Unmasking Hypertension Through a Fusion of Human Lifestyle D...

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Mobile Computing and Sustainable Informatics (ICMCSI), International Conference on

作者： V. Vishnu Priya Nisanth G Nivethaa R P Srinidhi Vijay Department of Computer Science and Engineering Kongu Engineering College Erode India Department of Computer Applications Kongu Engineering College Erode India Department of Computer Technology-UG Kongu Engineering College Erode India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

Hypertension, also referred to as high blood pressure, is a condition arising from the consistently high blood pressure against artery walls. The volume and output of blood from the heart primarily control blood pressure under stiff or tight arterial conditions. High pulse pressure leads to increased blood pressure, since the pumping of more blood makes the arteries inactive. The World Health Organization has a comprehensive guideline in which an evidence-based pharmacological approach has been recommended for managing adult patients with hypertension, not neglecting to mention lifestyle modification in order to support medicinal treatment. machine learning may revolutionize the way hypertension treatment is projected for the management of personalized treatment routines for patients based on their characteristics. ML provides great promise in seeking ideal medication and predicting responses thereof against massive sets of data for hypertensive patients. The techniques allow for the determination of the individual risk for hypertension and ensure that treatment protocols are optimal. Compared to methods used to predict hypertension in the past, ML algorithms show a marked improvement in performance.

关键词： Hypertension Deep learning machine learning algorithms Time series analysis Prediction algorithms Ensemble learning Blood Random forests Medical diagnostic imaging Arteries

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Comparative Study Between Traditional SARIMA Model And machine learning algorithms To Forecast Monthly Coconut Production In Sri Lanka

Comparative Study Between Traditional SARIMA Model And Machi...

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Advanced Research in Computing (ICARC), International Conference on

作者： A.P.A. Fernando J.B. DonPaul D.D.A. Arthanayake Department of Data Science National Institute of Business Management Colombo Sri Lanka

ISBN: (数字)9798331530983

ISBN: (纸本)9798331530990

This research sought to forecast monthly coconut production for the next three months using a univariate time series approach by utilizing the best-fitted model out of Seasonal Autoregressive Integrated Moving Average (SARIMA), eXtreme gradient boosting (XGBoost), and Prophet models. Two univariate XGBoost time-series models were created: one with raw data and the other utilizing log-transformed data. The performance of these models was compared to that of the SARIMA and Prophet models, using Root mean squared error (RMSE) and Mean absolute percentage error (MAPE) metrics for evaluation. The log-transformed XGBoost model showed better accuracy, achieving an RMSE of 3.517 and MAPE of 1.195%, surpassing the models based on raw data and the Prophet model. This study highlighted the effectiveness of XGBoost, especially with log transformation, in forecasting short-term monthly coconut production. The results offer actionable insights for industry stakeholders, which could improve decision-making in supply chain management and policy planning.

关键词： Measurement Industries Supply chain management machine learning algorithms Time series analysis Production Predictive models Data models Planning Stakeholders

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A Review of machine learning algorithms in Diabetes Management

A Review of Machine Learning Algorithms in Diabetes Manageme...

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Sentiment Analysis and Deep learning (ICSADL), International Conference on

作者： Unnati Gayaki Subodh Daronde Abhay Tale Aditya Barhate Department of Computer Science and Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India Department of Biomedical Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (Deemed to be University)) Wardha Maharashtra India Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Diabetes is a prevalent and chronic disease affecting millions worldwide, posing significant challenges in its management and treatment. This review article aims to explore the current and potential future roles of machine learning (ML) in diabetes management, focusing on progress, applications, and challenges. It discusses various ML techniques, including support vector machines (SVM), logistic regression, decision trees, ensemble methods, and advanced deep learning approaches such as long short-term memory (LSTM) networks and convolutional neural networks (CNN). These methods have demonstrated benefits in analytics, patient monitoring, decision support systems, personalised treatment, and patient engagement. However, significant challenges persist, such as computational complexity, data quality, limited interoperability of systems, and ethical concerns regarding AI use in healthcare. The article addresses these challenges by fostering collaboration between AI implementers and health care professionals. This collaboration is essential to develop long-term, legally compliant, and solution-oriented frameworks that enhance patient care and diabetes management and pave the way for more efficient and ethical AI applications in healthcare.

关键词： Deep learning Support vector machines Ethics machine learning algorithms Reviews Neural networks Medical services Diabetes Convolutional neural networks Long short term memory

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Data-Driven E-Waste Management: Leveraging machine learning algorithms for Efficient Collection and Recycling Systems

Data-Driven E-Waste Management: Leveraging Machine Learning ...

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Intelligent Systems and Computational Networks (ICISCN), International Conference on

作者： Varsha K. S Anju Mohan Murugan Ramu Saveetha School of Law Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai India

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

Quick growth of e-waste poses a substantial risk environment and health concerns; thereby, there is an imminent need for efficient management solutions. This paper explains the utilization of ML-based algorithms in improving e-waste collection and recycling capabilities. It employs superior techniques like CNN and YOLOv8 along with image recognition to identify e-waste in real time. The CNN-VGG model uses deep convolutional layers to extract features in the E-Waste Dataset to classify e-wastes. YOLOv8 assists in the location and detection of e-waste objects. The new approach is developed in Python and demonstrates an accuracy of nearly 99%, better than other models such as FrHHGO-based ShCNN and SNN by a margin of 10.44%. This shows how CNN-VGG is good for the classification of e-waste. Adding these ML models into automation systems in recycling helps recycle more material and sustain the environment in e-waste management. The work published here advances more effective, ecologically sound e-waste recycling practice.

关键词： machine learning algorithms Image recognition Biological system modeling Object detection machine learning Electronic waste Feature extraction Real-time systems Recycling Intelligent systems

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Predicting Port Congestion at Busan Port Using machine learning algorithms and Temporal Variables

Predicting Port Congestion at Busan Port Using Machine Learn...

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International Conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Sang-Hyun Ha Ki-Hwan Kim Young-Jin Kang Ji Yeon Kim Seok-Chan Jeong AI Grand ICT Research Center Dong-Eui University Busan Republic of Korea Department of Artificial Intelligence Dong-Eui University Busan Republic of Korea Dept. of e-Business AI Grand ICT Research Center Dong-Eui University Busan Republic of Korea

ISBN: (数字)9798331507022

ISBN: (纸本)9798331507039

Ports are vital infrastructures for global logistics and trade, with South Korea handling approximately 99.7 % of its import and export cargo via maritime routes. Busan Port, the largest in the country and the seventh-largest container port worldwide, plays a crucial role in both national logistics and regional economic growth. However, congestion in port operations leads to delays, increased costs, and reduced efficiency. This study employs actual data from Busan Port (February to September 2024) to analyze the impact of temporal variables-such as day of the week, time of day, and monthly factors-on port congestion. machine learning algorithms, including Random Forest, XGBoost, and LightGBM, were utilized to predict congestion levels using a refined dataset of approximately 1.4 million samples. Results indicate that Extra Trees and CatBoost classifiers achieved high accuracy (0.9654) and AUC (0.9952), while Extreme Gradient Boosting reached an AUC of 0.9989, demonstrating exceptional performance. These findings highlight the effectiveness of machine learning in capturing the nonlinear dynamics of port operations. Future research will aim to enhance model performance by expanding data collection and integrating external factors such as weather conditions and cargo characteristics. This study underscores the potential of machine learning tools in optimizing port management and improving logistical efficiency.

关键词： machine learning algorithms Costs Seaports Data collection Data models Nonlinear dynamical systems Delays Random forests Logistics Meteorology

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Credit Card Fraud Detection Using KNC, SVC, and Decision Tree machine learning algorithms

Credit Card Fraud Detection Using KNC, SVC, and Decision Tre...

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AI in Cybersecurity (ICAIC), International Conference on

作者： Pushpanjali Pandey K K Garg Department of Management Lingaya’s Vidyapeeth Faridabad India

ISBN: (数字)9798331518882

ISBN: (纸本)9798331518899

Credit cards are replacing cash in shopping. With the increase in popularity of plastic money like credit cards, hackers are misusing this as an opportunity to commit fraud. In this research work, we have used three different machine-learning techniques to detect credit card fraud. We have taken a credit card fraud dataset from Kaggle and applied K Neighbor Classifiers (KNC), Support Vector Classifiers (SVC), and decision tree machine-learning methods to this dataset. We have achieved $\mathbf{9 9. 9 4 \%}$ accuracy using both KNN and Decision Tree machine learning algorithms. We have got $\mathbf{9 9. 8 4 \%}$ accuracy using the Support vector classifier (SVC) machine learning Algorithm.

关键词： machine learning algorithms Accuracy Static VAr compensators machine learning Nearest neighbor methods Credit cards Vectors Fraud Decision trees Plastics

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