The main focus of political debates on digital agriculture has been on environmental sustainability. So far, the literature has primarily ignored social sustainability, notably labor issues. This is worrisome because digitization may fundamentally alter farming techniques and labor processes, potentially affecting rural development, rural communities, and migratory workers. It examines how digital technology affects labor in horticulture and agricultural fields. To incorporate labor into the debates around agriculture and digitalization, this article provides a detailed picture of how digital technologies affect agricultural labor. Results suggest new forms of labor management, intensification of work processes, and risks of working-class fragmentation along age lines. Digitalization has not resulted in worker or farmer deskilling. The claim of greater worker dependency due to reduced agricultural employment possibilities is disputed. The importance of creating agricultural policies that promote fair and equitable working conditions.

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.

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.

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.

Machine Learning is an application of artificial intelligence that allows computers to learn and develop without explicit programming. In other words, the goal of ML is to let computers learn on their own without human involvement and then alter their activities. ML also allows huge data processing. Project management planning and evaluation are vital in project execution. Project management is difficult without a realistic and logical plan. We give a complete overview of works on Machine Learning in Software Project Management. The first category contains software project management research articles. The third category includes research on the phases and tests that are the parameters used in machine-learning management and the final classes of the results from the study, contribution of studies in production, and promotion of machine-learning project prediction. Our contribution also provides a broader viewpoint and context for future project risk management efforts. In conclusion, machine learning is more successful in reducing project failure probabilities, increasing output ratio for growth, and facilitating analysis on software fault prediction based on accuracy.