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Start Over Topic machine learning Topic feature selection Publication Year Range This year
388 results

1. Empirical Analysis of Machine Learning Algorithms for Predicting Thyroidism

Author

Dholakia, Neel H., Kshatriya, Teesha, Ladva, Vipul, Shukla, Madhu, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Rajagopal, Sridaran, editor, Popat, Kalpesh, editor, Meva, Divyakant, editor, and Bajeja, Sunil, editor

Published
2024
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2. An Efficient Method for Heart Failure Diagnosis

Author

Sachdeva, Ravi Kumar, Singla, Anshika, Bathla, Priyanka, Jain, Anurag, Choudhury, Tanupriya, Kotecha, Ketan, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Garg, Deepak, editor, Rodrigues, Joel J. P. C., editor, Gupta, Suneet Kumar, editor, Cheng, Xiaochun, editor, Sarao, Pushpender, editor, and Patel, Govind Singh, editor

Published
2024
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3. A Study on Optimizing the Personalization of Recommendations and Customer Services Using Artificially Intelligent Neural Networks to Improve Electronic Commerce

Author

Arora, Anudeep, Kaur, Ranjeeta, Vats, Prashant, Gupta, Mamta, Chopra, Gayatri, Mehmi, Shikha, Khanna, Trishali, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Nagar, Atulya K., editor, Jat, Dharm Singh, editor, Mishra, Durgesh, editor, and Joshi, Amit, editor

Published
2024
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6. SmartBuoy: A Machine Learning-Based Detection Method for Interest Flooding Attacks in VNDN

Author

Xu, Yuwei, Zhang, Tiantian, Zeng, Junyu, Wang, Rongrong, Song, Kehui, Xu, Jingdong, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Wang, Guojun, editor, Wang, Haozhe, editor, Min, Geyong, editor, Georgalas, Nektarios, editor, and Meng, Weizhi, editor

Published
2024
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8. Anomaly Detection for Intrusion Detection Systems Using Machine Learning: Experimental Study and Feature Reduction Approach

Author

Krivchenkov, Aleksandr, Grakovski, Alexander, Misnevs, Boriss, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Kabashkin, Igor, editor, Yatskiv, Irina, editor, and Prentkovskis, Olegas, editor

Published
2024
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9. A Study and Analysis of Predicting College Students’ Final Exam Scores by Integrating Physical Fitness Test Data and Poverty Level Information

Author

Xu, Nuo, Zhang, Xiaoli, Zhu, Guifu, Wen, Jun, Nie, Jialei, Yang, Can, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Gan, Jianhou, editor, Pan, Yi, editor, Zhou, Juxiang, editor, Liu, Dong, editor, Song, Xianhua, editor, and Lu, Zeguang, editor

Published
2024
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10. Classifying breast cancer using multi-view graph neural network based on multi-omics data.

Author

Yanjiao Ren, Yimeng Gao, Wei Du, Weibo Qiao, Wei Li, Qianqian Yang, Yanchun Liang, and Gaoyang Li

Subjects
GRAPH neural networks, DEEP learning, MACHINE learning, FEATURE selection, BREAST cancer, TUMOR classification
Abstract

Introduction: As the evaluation indices, cancer grading and subtyping have diverse clinical, pathological, and molecular characteristics with prognostic and therapeutic implications. Although researchers have begun to study cancer differentiation and subtype prediction, most of relevant methods are based on traditional machine learning and rely on single omics data. It is necessary to explore a deep learning algorithm that integrates multi-omics data to achieve classification prediction of cancer differentiation and subtypes. Methods: This paper proposes a multi-omics data fusion algorithm based on a multi-view graph neural network (MVGNN) for predicting cancer differentiation and subtype classification. The model framework consists of a graph convolutional network (GCN) module for learning features from different omics data and an attention module for integrating multi-omics data. Three different types of omics data are used. For each type of omics data, feature selection is performed using methods such as the chi-square test and minimum redundancy maximum relevance (mRMR). Weighted patient similarity networks are constructed based on the selected omics features, and GCN is trained using omics features and corresponding similarity networks. Finally, an attention module integrates different types of omics features and performs the final cancer classification prediction. Results: To validate the cancer classification predictive performance of the MVGNN model, we conducted experimental comparisons with traditional machine learning models and currently popular methods based on integrating multi-omics data using 5-fold cross-validation. Additionally, we performed comparative experiments on cancer differentiation and its subtypes based on single omics data, two omics data, and three omics data. Discussion: This paper proposed the MVGNN model and it performed well in cancer classification prediction based on multiple omics data. [ABSTRACT FROM AUTHOR]

Published
2024
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