{'fraction_non_alphanumeric': 0.023496240601503758, 'fraction_numerical': 0.0009398496240601503, 'mean_word_length': 5.3017751479289945, '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}}
Impact of Selected Input Features for Lightpath Feasibility Validation Using Artificial Neural Networks

The new advents of 5G and Internet of Things (IoT) will impact the traffic, both in volume and dynamicity, at unprecedented rates. As a result, optical transport networks should become more responsive to the traffic changes as well as to operate more closely to optimality. Therefore, the implementation of a self-driving network is being proposed as a way to achieve these targets. One of the key challenges in this environment is the automatic provisioning of lightpaths. In order to provision a lightpath, Quality of Transmission (QoT) needs to be estimated, which involves complex and time consuming computations. This work proposes the use of artificial neural networks (ANN) to speed up lightpath feasibility validation without performing full validation per request (slow) nor keeping a full database of feasible lightpaths (memory consuming). Moreover, we evaluate the impact of input features selection and number of neurons in the obtained accuracy.