Prof. Dr. Masun Nabhan Homsi
My research is in the areas of machine learning, deep learning, text and sequence mining. Now I am putting the focus on predicting site binding and future trends in microbiology by applying big data computing methods.
Prior to joining the UFZ, I was working as associate professor of Data Mining, Artificial Intelligence and Algorithms and Programming at Computer Science and Information Technology Department of Simon Bolivar University, Caracas- Venezuela, where I directed many researches focused on extracting actionable information from medical data, such as patient information, medical record, examination data, laboratory tests and biosignals readings: phonocardiogram (PCG), photoplethysmogram (PPG), electrocardiology (EKG), electroencephalography (EEG), electrooculography (EOG), electromyography (EMG) and oxygen saturation (SaO2).
- Warrick, P. A., Lostanlen, V., & Homsi, M. N. (2019). Hybrid scattering-LSTM networks for automated detection of sleep arousals. Physiological measurement, 40(7), 074001.
- Warrick, P. A., & Homsi, M. N. (2018). Ensembling convolutional and long short-term memory networks for electrocardiogram arrhythmia detection. Physiological measurement, 39(11), 114002.
- Warrick, P., & Homsi, M. N. (2018, September). Sleep arousal detection from polysomnography using the scattering transform and recurrent neural networks. In 2018 Computing in Cardiology Conference (CinC) (Vol. 45, pp. 1-4). IEEE.
- Ismail, W. S., & Homsi, M. N. (2018). Dawqas: A dataset for arabic why question answering system. Procedia computer science, 142, 123-131.
- Tur, G., & Homsi, M. N. (2017, September). Cost-sensitive classifier for spam detection on news media Twitter accounts. In 2017 XLIII Latin American Computer Conference (CLEI) (pp. 1-6). IEEE.
- Warrick, P., & Homsi, M. N. (2017, September). Cardiac arrhythmia detection from ECG combining convolutional and long short-term memory networks. In 2017 Computing in Cardiology (CinC) (pp. 1-4). IEEE.
- Homsi, M. N., & Warrick, P. (2017). Ensemble methods with outliers for phonocardiogram classification. Physiological measurement, 38(8), 1631.
- Homsi, M. N., et al, (2016, September). Automatic heart sound recording classification using a nested set of ensemble algorithms. In 2016 Computing in Cardiology Conference (CinC) (pp. 817-820). IEEE.
- Delgado, J. A., Altuve, M., & Homsi, M. N. (2015, December). Detection of segments with fetal QRS complex from abdominal maternal ECG recordings using support vector machine. In 11th International Symposium on Medical Information Processing and Analysis (Vol. 9681, p. 96811A). International Society for Optics and Photonics.
- Delgado, J. A., Altuve, M., & Homsi, M. N. (2015, September). Haar wavelet transform and principal component analysis for fetal QRS classification from abdominal maternal ECG recordings. In 2015 20th Symposium on Signal Processing, Images and Computer Vision (STSIVA) (pp. 1-6). IEEE.
- Homsi, M. N. (2014). Multi-class sentiment analysis using a hierarchical logistic model tree approach. Maskana, 5.