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Artificial Intelligence (AI) and Machine Learning (ML) transforming the Insurance industry by improving efficiency, reducing costs, and providing a better customer experience. As these technologies continue to evolve, more innovation can be expected in the Insurance industry. A Bibliographic analysis is conducted for scientific mapping based on 1,084 SCOPUS-indexed publications between the year 2000-2022 using VOSviewer Application. The Analysis was conducted based on Publications by year, Source, Author, Affiliation, Country, Type, Subject Area and Funding Sponsors. The research found the result, the year 2022 had the highest publication of 203, through documents per year by the source of Lecture Notes In Computer Science Including Subseries Lecture Notes In Artificial Intelligence And Lecture Notes In Bioinformatics at 43 publications, Bauder, R.A. author is the maximum contributor, Harvard Medical School has been the major affiliate, United States of America, has the maximum 341 publications, Conference paper has the majority participation at 412 documents at 38 percent and Keywords "Artificial intelligence", Machine Learning" and "Insurance" has the highest occurrence. The maximum number of publications inthe field of computer science at 28 percent. Overall, this bibliographic analysis provides a comprehensive overview of the current state of research in AI and Machine Learning in the Insurance industry and highlights the potential for further innovation and development in this field. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) and Machine Learning (ML) is a branches of computer science applications that develop system-related tasks performed by programs and mathematical algorithms providing computers able to learn with or without human intervention. The perspective of AI and ML includes looking at exceptionally expansive space if conceivable speculations to decide one that best fits with the watched information and any earlier information held by the learner for this purpose so many algorithms are used in different types. Artificial intelligence is something that will produce our lives easier by performing different high-level computations and understanding complex issues. This can be a straightforward definition of what precisely artificial intelligence has done. The paper presents the supporting ability of artificial intelligence and machine learning in different applications, such as education, smartphones, medical care, and space technologies. However, this review studies the present status of machine learning, deep learning, and artificial intelligence technologies applied to the above technologies. [ABSTRACT FROM AUTHOR]

In last few years, music is unresolved to computerization anyplace can produce a music robotically in the nonappearance of human participation or minutest human participation. Music alignment is a very big task, which disclosures the originality and competence of composer, no matter it is computer or human being. There have been different theories and ideas from different researchers on music generation, regulations, alterations, compositions...etc. The extraordinary progress of machine learning in computer science has also re-joined the questions in music generation. Deep learning methods are accomplished of making programmed music generation with correct data and algorithms and that would wide-ranging human. Masses of Neural Networks models took place in the progress of programmed music generation. It is noticeable that tuneful ideas can be generated from Deep Learning approaches and it hopes to kind new pieces of music. The study deals with programmed music generation using deep learning methods and the superlative model in use. [ABSTRACT FROM AUTHOR]

Predictive modeling is a method that utilizes data mining and other recent techniques to estimate results for use of data security. Every model is created with different predictors to manage upcoming outcomes for future security of the data. Once information is collected for significant predictors, a statistical model is designed and prediction has been done with the help composite neural network designed by sophisticated software. As extra information becomes available, the statistical analysis model is authenticated or modified accordingly. Machine learning (ML), on the other hand as a subfield of computer science enabling the computers to study with no unambiguously plan. Machine learning developed from the study of the concepts that heuristics can learn from and build predictions on information. As they start to grow more intellectually, these heuristics can conquer program instructions to create closely perfect, data-driven results. Predictive analytics is driven by predictive modeling. Predictive analytics and machine learning are tightly attached, as predictive models usually incorporate a machine learning heuristic. These models can be skilled over time to react to new data or values and supply the outcomes according to which we can provide security on the data. [ABSTRACT FROM AUTHOR]