{'fraction_non_alphanumeric': 0.013513513513513514, 'fraction_numerical': 0.0, 'mean_word_length': 5.27683615819209, '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}}
Transformation of harmful algae bloom to helpful renewable energy development with artificial intelligence development

The harmful effects of various algae blooms on human and marine life are well-known globally. It is necessary to invent the utilization of such algae bloom for betterment of society and hence proposed research provided multiple benefits. As a supportive tool, Artificial Intelligence (AI) is becoming the high potential area for the fast analysis and predictions in almost all research areas. Also, a microbial fuel cell (MFC) is a relatively new type of bioreactor for wastewater treatment. As there is a need for invention of new renewable energy sources, we invented Sargassum as a new media for energy generation and therefore this research is the first time using Sargassum samples in a single-chambered MFC. This paper presents the development of new deep learning algorithm SargassoCNN to identify the various types of Sargassum bloom samples which further can be analyzed for selection of sample for best electricity generation medium using deep learning image processing algorithm.