{'fraction_non_alphanumeric': 0.023408239700374533, 'fraction_numerical': 0.0, 'mean_word_length': 5.6397515527950315, '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}}
Optimization of fuzzy neural network using APSO for predicting strength of Malaysian SMEs

Despite their significant contribution to the country's economy, Malaysian SMEs have not been given adequate attention by researchers. The researchers have been mostly biased towards larger and listed firms. Moreover, they also have put more focus on financial factors, whereas, in case of SMEs, financial factors will not show appreciable figures unless non-financial factors are considered. Utilizing these non-financial factors, this research proposes a strength prediction model for Malaysian SMEs using Adaptive Neuro Fuzzy Interference System (ANFIS). This paper concentrates on optimizing ANFIS by choosing the best rule-base, training antecedent and consequent parameters by Accelerated Particle Swarm Optimization (APSO). For accuracy validation, results of the proposed model are compared with SCORE; a system developed by SME Corporation Malaysia for ranking SMEs. The model will also help reduce financial losses by providing pre-warning to investors and creditors.