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Moreover, the Fitness function of HACABM is utilized to calculate the support count of each item and to improve the classification accuracy. Thus the proposed models classify the frequently utilized items accurately and arranged those items in descending order. This helps to run the big data transactional application effectively without any delay. Finally, the key metrics are validated with the existing models and better results are attained by achieving a high accuracy rate of 99.82% and less execution time of 0.0018 ms. <\/jats:p>","DOI":"10.1142\/s1793962323410064","type":"journal-article","created":{"date-parts":[[2022,2,15]],"date-time":"2022-02-15T12:30:02Z","timestamp":1644928202000},"source":"Crossref","is-referenced-by-count":0,"title":["A novel hybrid machine learning-based frequent item extraction for transactional database"],"prefix":"10.1142","volume":"14","author":[{"given":"Divvela","family":"Srinivasa Rao","sequence":"first","affiliation":[{"name":"Computer Science and Engineering Department, Koneru Lakshmaiah Educational Foundation, Guntur, Andhra Pradesh, India"},{"name":"Computer Science and Engineering Department, Lakireddy Bali Reddy College of Engineering, Mylavaram, Andhra Pradesh, India"}]},{"given":"V.","family":"Sucharita","sequence":"additional","affiliation":[{"name":"Computer Science and Engineering Department, Narayana Engineering College, Gudur, Andhra Pradesh, India"}]}],"member":"219","published-online":{"date-parts":[[2022,2,14]]},"reference":[{"key":"S1793962323410064BIB001","first-page":"1","author":"Belhadi A.","year":"2020","journal-title":"Appl. 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