RFMVDA: An Enhanced Deep Learning Approach for Customer Behavior Classification in E-Commerce Environments by Kwanhee Kim, Mingyu Jo, Ilkyeun Ra, Sangoh Park

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EXPLORING THE INFLUENCES ON DISPLACED CITIZENS' DECISION TO REPATRIATE: A CLASSIFICATION OF POSITIVE AND NEGATIVE FACTORS by Volodymyr Filippov, Iryna Bashynska, Elvin Yangulov

“…The innovation of the work lies in the creation of a novel classification of factors that considers both the Ukrainian context and international experience. …”
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An improved method to determine basic probability assignment with interval number and its application in classification by Bowen Qin, Fuyuan Xiao

“…The experiments on Iris data set that is widely used in classification problem illustrated that the proposed method is effective in determining basic probability assignment and classification problem, and the proposed method shows more accurate results in which the classification accuracy reaches 96.7%.…”
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Deep learning for the classification of atrial fibrillation using wavelet transform-based visual images by Ling-Chun Sun, Chia-Chiang Lee, Hung-Yen Ke, Chih-Yuan Wei, Ke-Feng Lin, Shih-Sung Lin, Hsin Hsiu, Ping-Nan Chen

“…In our study, we explore the employment of MsCWT in the classification of AF with ECG signals in a continuum. …”
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Option Volatility Investment Strategy: The Combination of Neural Network and Classical Volatility Prediction Model by Yuanyang Teng, Yicun Li, Xiaobo Wu

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How Scientists Obtain Approval to Release Organisms for Classical Biological Control of Invasive Weeds by John C. Scoles, James P. Cuda, William A. Overholt

“…ENY-828/IN607: How Scientists Obtain Approval to Release Organisms for Classical Biological Control of Invasive Weeds (ufl.edu) …”
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Determining diabetic kidney disease severity using traditional Chinese medicine syndrome classification by Amanda Yun Rui Lam, Huang Fang Zheng, Kok Keong Teo, Xiao Hui Xin, Chieh Suai Tan, Jean-Paul Kovalik, Sujoy Ghosh, Hong Chang Tan

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Nephrotic and Non-Nephrotic Focal Segmental Glomerulosclerosis: Clinical Characteristics, Etiology, and Columbia Classification by Gabriel Figueiredo, Luis Yu, Lectícia Barbosa Jorge, Viktoria Woronik, Cristiane Bitencourt Dias

“…<b>Introduction</b>: Focal segmental glomerulosclerosis (FSGS) is a pattern of kidney injury with diverse causes and pathogeneses, resulting in podocyte injury and depletion. It can be classified as primary, genetic, or secondary. Because FSGS classically has a worse prognosis in patients with nephrotic syndrome, most studies have focused on the treatment and evolution of these patients, resulting in a lack of data related to patients without nephrotic syndrome. …”
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Enhanced ResNet-50 for garbage classification: Feature fusion and depth-separable convolutions. by Lingbo Li, Runpu Wang, Miaojie Zou, Fusen Guo, Yuheng Ren

“…However, existing deep learning-based garbage image classification models generally suffer from low classification accuracy, insufficient robustness, and slow detection speed due to the large number of model parameters. …”
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Handling Imbalance Classification Virtual Screening Big Data Using Machine Learning Algorithms by Sahar K. Hussin, Salah M. Abdelmageid, Adel Alkhalil, Yasser M. Omar, Mahmoud I. Marie, Rabie A. Ramadan

“…For a dataset identified without considering the data’s imbalanced nature, most classification methods tend to have high predictive accuracy for the majority category. …”
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An Adaptive Scalable Data Pipeline for Multiclass Attack Classification in Large-Scale IoT Networks by Selvam Saravanan, Uma Maheswari Balasubramanian

“…To meet this requirement, in this work, we propose a data pipeline with Apache Kafka, Apache Spark structured streaming, and MongoDB that can adapt to the ever-changing attack patterns in real time and classify attacks in large-scale IoT networks. When concept drift is detected, the proposed system retrains the classifier with the instances that cause the drift and a representative subsample instances from the previous training of the model. …”
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Prediabetes risk classification algorithm via carotid bodies and K-means clustering technique by Rafael F. Pinheiro, Maria P. Guarino, Marlene Lages, Rui Fonseca-Pinto

“…In the search for methods to support early diagnosis, this article introduces a novel prediabetes risk classification algorithm (PRCA) for type-2 diabetes mellitus (T2DM), utilizing the chemosensitivity of carotid bodies (CB) and K-means clustering technique from the field of machine learning. …”
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Classification of RTV-coated porcelain insulator condition under different profiles and levels of pollution by Ali Ahmed Salem, Samir Ahmed Al-Gailani, Abdulrahman Ahmed Ghaleb Amer, Mohammad Alsharef, Mohit Bajaj, Ievgen Zaitsev, Razali Ngah, Sherif S. M. Ghoneim

Subjects: “…Classification models…”
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Corrigendum to “Hardware Accelerators Targeting a Novel Group Based Packet Classification Algorithm” by O. Ahmed, S. Areibi, G. Grewal

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Robust Forest Sound Classification Using Pareto-Mordukhovich Optimized MFCC in Environmental Monitoring by Ahmad Qurthobi, Robertas Damasevicius, Vytautas Barzdaitis, Rytis Maskeliunas

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APPLICATION OF MACHINE LEARNING ALGORITHMS TO EVALUATE THE UCI DATABASE IN THE CLASSIFICATION OF AUTISM SPECTRUM DISORDERS by Phạm Quang Thuận, Nguyễn Đình Thuân

“…In this article, we present the results of an evaluation of the autism spectrum disorder classification (ASD) of children in the UCI database. …”
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Utility of complexity analysis in electroencephalography and electromyography for automated classification of sleep-wake states in mice by Naoki Furutani, Yuki C. Saito, Yasutaka Niwa, Yu Katsuyama, Yuta Nariya, Mitsuru Kikuchi, Tetsuya Takahashi, Takeshi Sakurai

“…We introduced a novel methodology for sleep stage classification based on two types of complexity analysis, namely multiscale entropy and detrended fluctuation analysis. …”
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A Comparative Study Evaluated the Performance of Two-class Classification Algorithms in Machine Learning by Shilan Abdullah Hassan, Maha Sabah Saeed

“…A comparative study evaluated the performance of five well-known two-class classification algorithms: two-class boosted decision trees, two-class decision forests, two-class locally deep SVMs, two-class neural networks, and two-class logistic regression. …”
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Evaluation of Sanandaj city vulnerability to earthquake using classification tree analysis (CTA) model by shahrivar rostaei, rahim heydari chyaneh, ayoub zoghi

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Mining versatile feruloyl esterases: phylogenetic classification, structural features, and deep learning model by Liang Guo, Yuxin Dong, Deyong Zhang, Xinrong Pan, Xinjie Jin, Xinyu Yan, Yin Lu

“…Subsequent phylogenetic analysis of these clusters unveiled a correlation between phylogenetic classification and substrate promiscuity, and enzymes with broad substrate scope tended to locate within specific branches of the phylogenetic tree. …”
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