{'fraction_non_alphanumeric': 0.031007751937984496, 'fraction_numerical': 0.0, 'mean_word_length': 5.396039603960396, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Application of least-squares support vector machine (LS-SVM) to determination of deep level defect centers parameters in semi-insulating GaAs

The purpose of this paper is to present the Least Squares Support Vector Machine (LS-SVM) applied to investigation of deep level defects in semi-insulating gallium arsenide (SI GaAs). LS-SVM was used for spectral surface approximation, computed as a result of Photo Induced Transient Spectroscopy (HRPITS). Deep defects level parameters were extracted based on the spectral surface approximation and Arrhenius equation. Diverse LS-SVM modification was implemented to achieve good quality of estimation.