The Use of Big Data Analytics in Medical Applications

As a fundamental design feature of their digital archives, this approach was applied to the project. It has so demonstrated considerable potential in terms of defining and condensing crucial data pieces that support the presumption of authenticity across a wide range of record formats. The purpose of this study is to offer a conceptual technique, which will be referred to as object-oriented diplomacy in this work. This methodology focuses on creating digital records that can maintain their authenticity over time and when they are removed from their original systems. This is accomplished through the extension of archival diplomatic theory and the application of object-oriented programming (OOP) principles. A new way to support the presumption of the authenticity of digital records is presented in this study, which makes use of concepts from archival diplomatic theory, which are combined with OOP principles. It is the author's goal that this work may spark a more fruitful collaboration between archivists and records managers and software developers in the development and implementation of digital repositories in the future.

Section: Discussionmentioning

confidence: 99%

Software Application Development to Backing the Legitimacy of Digital Annals: Use of the Diplomatic Archives

Adusumalli

2017

“…Data assets become essential competitiveness, and small businesses must have Big Data as well. Traditional quality data include variables, attributes, faults, internal and external failure costs, etc., obtained by data collection, data processing, statistical analysis, root cause analysis, etc (Pasupuleti, 2016b). Quality practitioners used to rely on these so-called professional qualities to get a job.…”

Section: Big Data Thinkingmentioning

confidence: 99%

“…So they can't see how the overall wisdom is generated since they are concerned with several allegedly powerful algorithms. I'm afraid this is a researcher in the AI field, future issues (Pasupuleti, 2016b). The three waves of AI, so the authors can visualize the above-mentioned AI novel scenario (Figure 1).…”

Section: Introductionmentioning

confidence: 99%

Applications of Artificial Intelligence in Quality Assurance and Assurance of Productivity

Chisty¹,

Adusumalli²

2022

Probabilistic intelligence is vital in current management and technology. It is simpler to persuade readers when a management or engineer reports connected difficulties with objective statistical data. Statistical data support the evaluation of the true status, and cause and effect can be induced. The rationale is proven using deductive logic and statistical data verification and induction. Quality practitioners should develop statistical thinking skills and fully grasp the three quality principles: “essence of substance,” “process of business,” and “psychology.” Traditional quality data include variables, attributes, faults, internal and external failure costs, etc., obtained by data collection, data processing, statistical analysis, root cause analysis, etc. Quality practitioners used to rely on these so-called professional qualities to get a job. If quality practitioners do not keep up with the steps of times, quality data collection, organization, analysis, and monitoring will be confusing or challenging. Increasingly, precision tool machines are embedded in various IoTs, gathering machine operation data, component diagnostic and life estimation, consumables monitoring and utilization monitoring, and various data analyses. Data mining and forecasting have steadily been combined into Data Science, which is the future of quality field worth worrying about.

“…There is also omission of data. During preprocessing, you identify unimportant data properties and remove them from further consideration (Pasupuleti, 2016). Identifying and reporting erroneous portions of the data collection is also required.…”

Section: Picking Datamentioning

confidence: 99%

Big Data as a Driving Tool of Digital Transformation

Adusumalli¹

2016

As a result of this research, it was discovered how Big Data is characterized by the five Vs: Velocity, Volume, Variety, Veracity, and Value; and how Hadoop and other tools, in conjunction with distributed computing capacity, are utilized to meet the needs of Big Data. The research defines the abilities necessary to analyze Big Data, as well as the method of Data Mining and how it generates results, and it also includes recommendations. Physicians may use data science to give the best care possible for their patients, and meteorologists can use it to anticipate the scope of local meteorological occurrences. Data science can even be used to predict natural disasters such as earthquakes and tornadoes. Capturing data is an excellent way for businesses to begin their data science journeys. They can begin evaluating the data as soon as they obtain it. Here are some examples of how people produce data and how corporations such as Netflix, Amazon, United Parcel Service (UPS), Google, and Apple exploit the data generated by their customers and workers. When a Data Science project is completed, the final output should be used to communicate new information and insights gained from the data analysis to important decision-makers.