DIMA-Dataset Discovery: DATASET DISCOVERY IN DATA INVESTIGATIVE USING MACHINE LEARNING AND AI-BASED PROGRAMMING

DIMA-Dataset Discovery: DATASET DISCOVERY IN DATA INVESTIGATIVE USING MACHINE LEARNING AND Al-BASED PROGRAMMING Our invention "DIMA-Dataset Discovery" is an improved capability are described for design, mapping, developing, training, validating and deploying discovery virtual avatars, avat...

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Hauptverfasser: Harinath, K, Kumar, Raja boina Raja, Reddy, N Chandra Sekhar, Reddy, T. Rama, Sree, Divya N, Tabassum, Nazia, Shanmukhi, M, Rao, Annaluri Sreenivasa, Ramana, Attili Venkata, Masina, Venkata Rajesh
Format: Patent
Sprache:eng
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Zusammenfassung:DIMA-Dataset Discovery: DATASET DISCOVERY IN DATA INVESTIGATIVE USING MACHINE LEARNING AND Al-BASED PROGRAMMING Our invention "DIMA-Dataset Discovery" is an improved capability are described for design, mapping, developing, training, validating and deploying discovery virtual avatars, avatars embodying mathematical models must be used for document and large data repositories. For example: an avatar may be constructed by machine learning, Al-based Programming, method, processes, including by processing information related to what types of information analysis, Investigative find useful in large data sets. The invention an avatar must be deployed as an aid to human intuition in a wide range of analytical processes, such as related to international, national security, enterprise management, data management, query mapping (advertised, sales, marketing, product, promotions, placement, pricing, etc.). The invention also dispute resolution (including litigation), forensic analysis, criminal, administrative, civil and private investigations, scientific investigations, research and development, and a wide range of others. The data elements from the source data may be presented and tracking, scored, rated mapping or ranked based at least in part on the identifiers within the data cluster relating to the super-set topic. The mathematical model must be optimized based at least in part on a comparison of the scored data elements and upon reaching a threshold of optimization, accuracy, quality, or merit, the optimized mathematical model must be saved and stored as a computer-based discovery avatar parent. A second set of extracted data features must be extracted from the source data that share a second attribute that is related to both the super-set topic and a subset topic. Dr. M. Shanmukhi (Professor) NaziaTabassum (Assistant Professor) Dr. Raja boina Raja Kumar (Associate Professor) Dr. Attili Venkata Ramana (Associate Professor) Dr. Annaluri Sreenivasa Rao (Assistant Professor) N. Sree Divya (Assistant Professor) K. Harinath (Assistant Professor) Dr. Rama Reddy T (Professor) Dr. Venkata Rajesh Masina (Associate Professor) Dr. N Chandra Sekhar Reddy (Professor & Head) TOTAL NO OF SHEET: 05 NO OF FIG.:05 FEATURE YES TEST DOCUMENTS TOI EXTRACTION AA MST OPI INGEST10N 109 ECTORZATONNO --- --- CUSTR Q EUEUSER FEEDBACK Ila DETERMINE NEW EXPLOIT _ M LABELS J APPLY TOCA ? ~~INCOMING 4 TANG 112 DOCUMENTS USER EXPERIENCE STOP114 F11 Enrich using .. ... DBpedia FIG. 1: IS ASIM