has been cited by the following article(s):
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[1]
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A Dual-Branch Incremental Learning Framework for Industrial Production Anomaly Detection
2025 5th International Conference on Machine Learning and Intelligent Systems Engineering (MLISE),
2025
DOI:10.1109/MLISE66443.2025.11100180
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[2]
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What REALLY drives clean energy stocks - Fear or Fundamentals?
Energy Economics,
2025
DOI:10.1016/j.eneco.2025.108558
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[3]
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Two step Outlier Detection to enhance XGBoost Accuracy in Predicting Length of Stay for Type 2 Diabetic Patients
2025 International Conference on Information and Communication Technology (ICoICT),
2025
DOI:10.1109/ICoICT66265.2025.11192918
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[4]
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Forecasting stock market anomalies in emerging markets: An OPTUNA-optimized isolation forest and K-means approach
Machine Learning with Applications,
2025
DOI:10.1016/j.mlwa.2025.100770
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[5]
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Improving Detection Accuracy of Brute-Force Attacks on MariaDB Using Standard Isolation Forest: A Comparative Analysis with RotatedVariant
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer,
2025
DOI:10.30812/matrik.v25i1.5817
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[6]
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Digital banking fortification: a real-time isolation forest architecture for detecting online transaction fraud
Engineering Research Express,
2024
DOI:10.1088/2631-8695/ad4958
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[7]
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Machine Learning Model for Early Detection of COVID-19 by Heart Rhythm Abnormalities
Advanced Engineering Research,
2023
DOI:10.23947/2687-1653-2023-23-1-66-75
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[8]
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6‐2: Potential Failure Detection using Unsupervised Clustering and Anomaly Detection
SID Symposium Digest of Technical Papers,
2023
DOI:10.1002/sdtp.16485
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[9]
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Detecting Anomalies in Financial Data Using Machine Learning Algorithms
Systems,
2022
DOI:10.3390/systems10050130
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[10]
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Short-term prediction of the power of a new wind turbine based on IAO-LSTM
Energy Reports,
2022
DOI:10.1016/j.egyr.2022.07.030
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