@article{MTMT:34152518,
	title = {EFFICIENCY IMPROVEMENT OF ADAPTIVE RANDOM FOREST USING PRINCIPAL COMPONENT ANALYSIS FOR MINING DATA STREAM},
	url = {https://m2.mtmt.hu/api/publication/34152518},
	author = {Hayder, Fatlawi and Kiss, Attila},
	journal-iso = {ANN UNIV SCI BP R EÖTVÖS NOM SECT COMPUT},
	journal = {ANNALES UNIVERSITATIS SCIENTIARUM BUDAPESTINENSIS DE ROLANDO EOTVOS NOMINATAE SECTIO COMPUTATORICA},
	volume = {55},
	unique-id = {34152518},
	issn = {0138-9491},
	year = {2023},
	pages = {39-48},
	orcid-numbers = {Kiss, Attila/0000-0001-8174-6194}
}

@{MTMT:34088397,
	title = {Handling Delayed Labeling of EEG Data Stream Using Semi-Supervised Label Propagation},
	url = {https://m2.mtmt.hu/api/publication/34088397},
	author = {Hayder, Fatlawi and Kiss, Attila},
	booktitle = {2023 15th International Conference on Electronics, Computers and Artificial Intelligence (ECAI)},
	doi = {10.1109/ECAI58194.2023.10193922},
	unique-id = {34088397},
	year = {2023},
	pages = {01-05},
	orcid-numbers = {Kiss, Attila/0000-0001-8174-6194}
}

@article{MTMT:33634899,
	title = {An Elastic Self-Adjusting Technique for Rare-Class Synthetic Oversampling Based on Cluster Distortion Minimization in Data Stream},
	url = {https://m2.mtmt.hu/api/publication/33634899},
	author = {Hayder, Fatlawi and Kiss, Attila},
	doi = {10.3390/s23042061},
	journal-iso = {SENSORS-BASEL},
	journal = {SENSORS},
	volume = {23},
	unique-id = {33634899},
	abstract = {Adaptive machine learning has increasing importance due to its ability to classify a data stream and handle the changes in the data distribution. Various resources, such as wearable sensors and medical devices, can generate a data stream with an imbalanced distribution of classes. Many popular oversampling techniques have been designed for imbalanced batch data rather than a continuous stream. This work proposes a self-adjusting window to improve the adaptive classification of an imbalanced data stream based on minimizing cluster distortion. It includes two models; the first chooses only the previous data instances that preserve the coherence of the current chunk’s samples. The second model relaxes the strict filter by excluding the examples of the last chunk. Both models include generating synthetic points for oversampling rather than the actual data points. The evaluation of the proposed models using the Siena EEG dataset showed their ability to improve the performance of several adaptive classifiers. The best results have been obtained using Adaptive Random Forest in which Sensitivity reached 96.83% and Precision reached 99.96%.},
	keywords = {Cluster Analysis; SMOTE; adaptive machine learning; imbalance data stream},
	year = {2023},
	eissn = {1424-8220},
	orcid-numbers = {Kiss, Attila/0000-0001-8174-6194}
}

@article{MTMT:33227006,
	title = {Similarity-Based Adaptive Window for Improving Classification of Epileptic Seizures with Imbalance EEG Data Stream},
	url = {https://m2.mtmt.hu/api/publication/33227006},
	author = {Hayder, Fatlawi and Kiss, Attila},
	doi = {10.3390/e24111641},
	journal-iso = {ENTROPY-SWITZ},
	journal = {ENTROPY},
	volume = {24},
	unique-id = {33227006},
	abstract = {Data stream mining techniques have recently received increasing research interest, especially in medical data classification. An unbalanced representation of the classification’s targets in these data is a common challenge because classification techniques are biased toward the major class. Many methods have attempted to address this problem but have been exaggeratedly biased toward the minor class. In this work, we propose a method for balancing the presence of the minor class within the current window of the data stream while preserving the data’s original majority as much as possible. The proposed method utilized similarity analysis for selecting specific instances from the previous window. This group of minor-class was then added to the current window’s instances. Implementing the proposed method using the Siena dataset showed promising results compared to the Skew ensemble method and some other research methods.},
	year = {2022},
	eissn = {1099-4300},
	pages = {1-22},
	orcid-numbers = {Kiss, Attila/0000-0001-8174-6194}
}

@article{MTMT:33034049,
	title = {Prediction of COVID-19 Patients Recovery using Ensemble Machine Learning and Vital Signs Data Collected by Novel Wearable Device},
	url = {https://m2.mtmt.hu/api/publication/33034049},
	author = {Hasan, K. Naji and Hayder, Fatlawi and Ammar, J. M. Karkar and Nicolae, GOGA and Kiss, Attila and Abdullah, T. Al-Rawi},
	doi = {10.14569/IJACSA.2022.0130792},
	journal-iso = {IJACSA},
	journal = {INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS},
	volume = {13},
	unique-id = {33034049},
	issn = {2158-107X},
	year = {2022},
	eissn = {2156-5570},
	pages = {792-800},
	orcid-numbers = {Kiss, Attila/0000-0001-8174-6194}
}

@article{MTMT:32759187,
	title = {An Adaptive Classification Model for Predicting Epileptic Seizures Using Cloud Computing Service Architecture},
	url = {https://m2.mtmt.hu/api/publication/32759187},
	author = {Hayder, Fatlawi and Kiss, Attila},
	doi = {10.3390/app12073408},
	journal-iso = {APPL SCI-BASEL},
	journal = {APPLIED SCIENCES-BASEL},
	volume = {12},
	unique-id = {32759187},
	abstract = {Data science techniques have increasing importance in medical data analysis, including detecting and predicting the probability of contracting a disease. A large amount of medical data is generated close to the patients in the form of a stream, such as data from sensors and medical devices. The distribution of these kinds of data may change from time to time; adaptive Machine Learning (ML) consists of a continuous training process responding to the distribution’s change. Adaptive ML models require high computational resources, which can be provided by cloud computing. In this work, a classification model is proposed to utilize the advantages of cloud computing, edge computing, and adaptive ML. It aims to precisely and efficiently classify EEG signal data, thereby detecting the seizures of epileptic patients using Adaptive Random Forest (ARF). It includes a global adaptive classifier in the cloud master node and a local light classifier in each edge node. In this model, the delayed labels consider missing values, and the Model-based imputation method is used to handle them in the global classifier. Implementing the proposed model on a real huge dataset (CHB-MIT) showed an accurate performance. It has a 0.998 True Negative Rate, a 0.785 True Positive Rate, and a 0.0017 False Positive Rate, which overcomes much of the research in the state-of-the-art.},
	year = {2022},
	eissn = {2076-3417},
	orcid-numbers = {Kiss, Attila/0000-0001-8174-6194}
}

@article{MTMT:31905533,
	title = {Differential privacy based classification model for mining medical data stream using adaptive random forest},
	url = {https://m2.mtmt.hu/api/publication/31905533},
	author = {Hayder, Fatlawi and Kiss, Attila},
	doi = {10.2478/ausi-2021-0001},
	journal-iso = {ACTA UNIV SAP INFORM},
	journal = {ACTA UNIVERSITATIS SAPIENTIAE INFORMATICA},
	volume = {13},
	unique-id = {31905533},
	issn = {1844-6086},
	year = {2021},
	eissn = {2066-7760},
	pages = {1-20},
	orcid-numbers = {Kiss, Attila/0000-0001-8174-6194}
}

@CONFERENCE{MTMT:32128882,
	title = {Efficiency Improvement of Adaptive Random Forest using Principal Component Analysis for Mining Data Stream},
	url = {https://m2.mtmt.hu/api/publication/32128882},
	author = {Hayder, Fatlawi and Kiss, Attila},
	booktitle = {Collection of Abstracts},
	unique-id = {32128882},
	year = {2020},
	pages = {1-8},
	orcid-numbers = {Kiss, Attila/0000-0001-8174-6194}
}

@CONFERENCE{MTMT:31362493,
	title = {Activity Recognition Model for Patients Data Stream using Adaptive Random Forest and Deep Learning Techniques},
	url = {https://m2.mtmt.hu/api/publication/31362493},
	author = {Hayder, Fatlawi and Kiss, Attila},
	booktitle = {The 12th Conference of PhD Students in Computer Science},
	unique-id = {31362493},
	abstract = {Precise detection of the current activity status for chronic diseases patients could play a significant role for protect their lives against sudden decline in health. Combining the information form various data resources present a reasonable challenge.On the other hand, stream classification techniques have a privilege of low computational time but they need a feedback for adapting the classifier. This work proposes a model for providing efficient automatic feedback for adaptive random forest classifier using deep learning classifying of video stream from surveillance systems.},
	year = {2020},
	pages = {88-91},
	orcid-numbers = {Kiss, Attila/0000-0001-8174-6194}
}

@article{MTMT:31283521,
	title = {Evaluation of Deep Learning and Data Mining Techniques for Audio Data Stream Classification},
	url = {https://m2.mtmt.hu/api/publication/31283521},
	author = {Hayder, Fatlawi and Kiss, Attila},
	journal-iso = {IJMSTA},
	journal = {INTERNATIONAL JOURNAL OF MUSIC SCIENCE, TECHNOLOGY AND ART},
	volume = {2},
	unique-id = {31283521},
	year = {2020},
	eissn = {2612-2146},
	pages = {10-18},
	orcid-numbers = {Kiss, Attila/0000-0001-8174-6194}
}