Enhancing Spam Filtering: A Comparative Study of Modern Advanced Machine Learning Techniques by Zhang Chenwei

“…With an emphasis on Naïve Bayes (NB), Decision Trees (DT), and Support Vector Machines (SVM), this study offers a thorough analysis of the major machine learning techniques utilized in contemporary spam filtering. …”
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Feature importance analysis of solar flares and prediction research with ensemble machine learning models by Yun Yang, Yun Yang

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Emerging trends in the optimization of organic synthesis through high-throughput tools and machine learning by Pablo Quijano Velasco, Kedar Hippalgaonkar, Balamurugan Ramalingam

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Predicting the risk of cardiovascular disease in adults exposed to heavy metals: Interpretable machine learning by Meiyue Shen, Yine Zhang, Runqing Zhan, Tingwei Du, Peixuan Shen, Xiaochuan Lu, Shengnan Liu, Rongrong Guo, Xiaoli Shen

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Relaxing the confirmatory factor model in the Big Five: Exploratory, Bayesian, and machine learning approaches by Jacob S. Gray

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Machine learning of Antarctic firn density by combining radiometer and scatterometer remote-sensing data by W. Li, S. B. M. Veldhuijsen, S. Lhermitte, S. Lhermitte

“…Hence, we investigate the potential of a non-linear random forest (RF) machine learning approach trained on radiometer and scatterometer data to derive the spatial and temporal variations in dry-firn density. …”
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Machine Learning-Driven Adaptive Modulation for VLC-Enabled Medical Body Sensor Networks by Reza Bayat Rizi, Amir R. Forouzan, Farshad Miramirkhani, Mohamad F. Sabahi

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Context aware machine learning techniques for brain tumor classification and detection – A review by Usman Amjad, Asif Raza, Muhammad Fahad, Doaa Farid, Adnan Akhunzada, Muhammad Abubakar, Hira Beenish

“…Background: Machine learning has tremendous potential in acute medical care, particularly in the field of precise medical diagnosis, prediction, and classification of brain tumors. …”
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Screening of serum biomarkers in patients with PCOS through lipid omics and ensemble machine learning by Ji-ying Chen, Wu-jie Chen, Zhi-ying Zhu, Shi Xu, Li-lan Huang, Wen-qing Tan, Yong-gang Zhang, Yan-li Zhao

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A machine learning based variable selection algorithm for binary classification of perinatal mortality by Maryam Sadiq, Ramla Shah

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Predictive Modeling of Volume and Biomass in Pinus pseudostrobus Using Machine Learning and Allometric Approaches by Pablo Antúnez, Christian Wehenkel, Erickson Basave-Villalobos, Celi Gloria Calixto-Valencia, César Valenzuela-Encinas, Faustino Ruiz-Aquino, David Sarmiento-Bustos

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Predictive modelling of sustainable concrete compressive strength using advanced machine learning algorithms by Tejas Joshi, Pulkit Mathur, Parita Oza, Smita Agrawal

Subjects: “…machine learning…”
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A Machine Learning Method to improve Supplier Delivery Appointments in Supply Chain Industries by Anitha Palakshappa, Sumana Maradithaya, Charunayana V

“…The objective of this study is to investigate whether the machine learning algorithms can be used to predict the delivery dates of the products based on the trained data. …”
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Prototype of Swiftlet Nest Moisture Content Measurement Using Resistance Sensor and Machine Learning by Ratu Anggriani Tangke Parung, Hanna Arini Parhusip, Suryasatriya Trihandaru

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Machine learning boosts wind turbine efficiency with smart failure detection and strategic placement by Sekar Kidambi Raju, Muthusamy Periyasamy, Amel Ali Alhussan, Subhash Kannan, Srikanth Raghavendran, El-Sayed M. El-kenawy

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Detection and diagnosis of unknown threats in power equipment using machine learning and Spark technology by Li Di, Cen Chen, Zhuo Lv, Mingyan Li, Nuannuan Li, Hao Chang

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Application of Machine Learning to Background Rejection in Very-high-energy Gamma-Ray Observation by Jie Li, Hongkui Lv, Yang Liu, Jiajun Huang, Yu Wang, Wenbin Lin

“…Based on the simulated data from the square kilometer array (KM2A) of LHAASO, eight high-level features were extracted for the gamma/hadron classification. Machine learning (ML) models, including logistic regression, support vector machines, decision trees, random forests, XGBoost, CatBoost, and deep neural networks (DNN) were constructed and trained using data sets of four energy bands ranging from 10 ^12 to 10 ^16 eV, and finally fused using the stacking ensemble algorithm. …”
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The dynamic duo: cryo-EM teams up with machine learning to visualize biomolecules in motion by Beatrice Bowlby

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A machine learning based algorithm accurately stages liver disease by quantification of arteries by Zhengxin Li, Xin Sun, Zhimin Zhao, Qiang Yang, Yayun Ren, Xiao Teng, Dean C. S. Tai, Ian R. Wanless, Jörn M. Schattenberg, Chenghai Liu

Subjects: “…Machine learning…”
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Prediction of cardiovascular disease from factors associated with waist hip ratio by machine learning by Zeynep Kucukakcali, Ipek Balikci Cicek

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