Mano Ashish Tripathi, Ravikesh Tripathi, Femmy Effendy, Geetha Manoharan, M. John Paul, Mohd Aarif
{"title":"An In-Depth Analysis of the Role That ML and Big Data Play in Driving Digital Marketing’s Paradigm Shift","authors":"Mano Ashish Tripathi, Ravikesh Tripathi, Femmy Effendy, Geetha Manoharan, M. John Paul, Mohd Aarif","doi":"10.1109/ICCCI56745.2023.10128357","DOIUrl":null,"url":null,"abstract":"Machine learning (ML) is an artificial neural network (ANN) that helps developers improve their software’s predictive abilities before they have all the data they need. Because information is so priceless, progress toward fully autonomous agents requires better methods for managing the omnipresent content infrastructures that exist today. All sorts of fields have benefited from advancements in computer vision and AI, from medical diagnosis to data presentation and operations to scientific study, and so on. Learning from polluted or erroneous data may be expensive, much as training for a sport can be dangerous to those who are vulnerable to injury. An organization will incur costs rather than see benefits if its algorithms are improperly taught, as explained in Approaching Data Science. Organizations need to be able to verify the quality and consistency of any large datasets, as well as their sources, to ensure the efficacy of any algorithm.","PeriodicalId":205683,"journal":{"name":"2023 International Conference on Computer Communication and Informatics (ICCCI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 International Conference on Computer Communication and Informatics (ICCCI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCCI56745.2023.10128357","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
Machine learning (ML) is an artificial neural network (ANN) that helps developers improve their software’s predictive abilities before they have all the data they need. Because information is so priceless, progress toward fully autonomous agents requires better methods for managing the omnipresent content infrastructures that exist today. All sorts of fields have benefited from advancements in computer vision and AI, from medical diagnosis to data presentation and operations to scientific study, and so on. Learning from polluted or erroneous data may be expensive, much as training for a sport can be dangerous to those who are vulnerable to injury. An organization will incur costs rather than see benefits if its algorithms are improperly taught, as explained in Approaching Data Science. Organizations need to be able to verify the quality and consistency of any large datasets, as well as their sources, to ensure the efficacy of any algorithm.