TY - JOUR AU - Hayder, Fatlawi AU - Kiss, Attila TI - EFFICIENCY IMPROVEMENT OF ADAPTIVE RANDOM FOREST USING PRINCIPAL COMPONENT ANALYSIS FOR MINING DATA STREAM JF - ANNALES UNIVERSITATIS SCIENTIARUM BUDAPESTINENSIS DE ROLANDO EOTVOS NOMINATAE SECTIO COMPUTATORICA J2 - ANN UNIV SCI BP R EÖTVÖS NOM SECT COMPUT VL - 55 PY - 2023 IS - Ovtober SP - 39 EP - 48 PG - 10 SN - 0138-9491 UR - https://m2.mtmt.hu/api/publication/34152518 ID - 34152518 LA - English DB - MTMT ER - TY - CHAP AU - Hayder, Fatlawi AU - Kiss, Attila TI - Handling Delayed Labeling of EEG Data Stream Using Semi-Supervised Label Propagation T2 - 2023 15th International Conference on Electronics, Computers and Artificial Intelligence (ECAI) PB - IEEE CY - Danvers (MA) SN - 9798350321388 PY - 2023 SP - 01 EP - 05 PG - 5 DO - 10.1109/ECAI58194.2023.10193922 UR - https://m2.mtmt.hu/api/publication/34088397 ID - 34088397 LA - English DB - MTMT ER - TY - JOUR AU - Hayder, Fatlawi AU - Kiss, Attila TI - An Elastic Self-Adjusting Technique for Rare-Class Synthetic Oversampling Based on Cluster Distortion Minimization in Data Stream JF - SENSORS J2 - SENSORS-BASEL VL - 23 PY - 2023 IS - 4 PG - 19 SN - 1424-8220 DO - 10.3390/s23042061 UR - https://m2.mtmt.hu/api/publication/33634899 ID - 33634899 N1 - ISSN:1424-8220 AB - 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%. LA - English DB - MTMT ER - TY - JOUR AU - Hayder, Fatlawi AU - Kiss, Attila TI - Similarity-Based Adaptive Window for Improving Classification of Epileptic Seizures with Imbalance EEG Data Stream JF - ENTROPY J2 - ENTROPY-SWITZ VL - 24 PY - 2022 IS - 11 SP - 1 EP - 22 PG - 22 SN - 1099-4300 DO - 10.3390/e24111641 UR - https://m2.mtmt.hu/api/publication/33227006 ID - 33227006 AB - 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. LA - English DB - MTMT ER - TY - JOUR AU - Hasan, K. Naji AU - Hayder, Fatlawi AU - Ammar, J. M. Karkar AU - Nicolae, GOGA AU - Kiss, Attila AU - Abdullah, T. Al-Rawi TI - Prediction of COVID-19 Patients Recovery using Ensemble Machine Learning and Vital Signs Data Collected by Novel Wearable Device JF - INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS J2 - IJACSA VL - 13 PY - 2022 IS - 7 SP - 792 EP - 800 PG - 9 SN - 2158-107X DO - 10.14569/IJACSA.2022.0130792 UR - https://m2.mtmt.hu/api/publication/33034049 ID - 33034049 LA - English DB - MTMT ER - TY - JOUR AU - Hayder, Fatlawi AU - Kiss, Attila TI - An Adaptive Classification Model for Predicting Epileptic Seizures Using Cloud Computing Service Architecture JF - APPLIED SCIENCES-BASEL J2 - APPL SCI-BASEL VL - 12 PY - 2022 IS - 7 PG - 22 SN - 2076-3417 DO - 10.3390/app12073408 UR - https://m2.mtmt.hu/api/publication/32759187 ID - 32759187 AB - 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. LA - English DB - MTMT ER - TY - JOUR AU - Hayder, Fatlawi AU - Kiss, Attila TI - Differential privacy based classification model for mining medical data stream using adaptive random forest JF - ACTA UNIVERSITATIS SAPIENTIAE INFORMATICA J2 - ACTA UNIV SAP INFORM VL - 13 PY - 2021 IS - 1 SP - 1 EP - 20 PG - 20 SN - 1844-6086 DO - 10.2478/ausi-2021-0001 UR - https://m2.mtmt.hu/api/publication/31905533 ID - 31905533 LA - English DB - MTMT ER - TY - CONF AU - Hayder, Fatlawi AU - Kiss, Attila ED - Horváth, Zoltán ED - Adrian, Petruşel TI - Efficiency Improvement of Adaptive Random Forest using Principal Component Analysis for Mining Data Stream T2 - Collection of Abstracts PB - Babes-Bolyai Tudományegyetem C1 - Budapest PY - 2020 SP - 1 EP - 8 PG - 8 UR - https://m2.mtmt.hu/api/publication/32128882 ID - 32128882 LA - English DB - MTMT ER - TY - CONF AU - Hayder, Fatlawi AU - Kiss, Attila TI - Activity Recognition Model for Patients Data Stream using Adaptive Random Forest and Deep Learning Techniques T2 - The 12th Conference of PhD Students in Computer Science PB - Szegedi Tudományegyetem (SZTE) C1 - Szeged PY - 2020 SP - 88 EP - 91 PG - 4 UR - https://m2.mtmt.hu/api/publication/31362493 ID - 31362493 AB - 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. LA - English DB - MTMT ER - TY - JOUR AU - Hayder, Fatlawi AU - Kiss, Attila TI - Evaluation of Deep Learning and Data Mining Techniques for Audio Data Stream Classification JF - INTERNATIONAL JOURNAL OF MUSIC SCIENCE, TECHNOLOGY AND ART J2 - IJMSTA VL - 2 PY - 2020 IS - 1 SP - 10 EP - 18 PG - 9 SN - 2612-2146 UR - https://m2.mtmt.hu/api/publication/31283521 ID - 31283521 LA - English DB - MTMT ER -