MACHINE LEARNING EXPLAINED: A PRACTICAL GUIDE TO DATA-DRIVEN DECISION MAKING

Abdelhamid ZAIDI · Renato Racelis Maaliw III · Mrs. K. P. Maheswari · Dr. Haewon Byeon

Oct 2023 · Xoffencerpublication

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201

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During the course of the process of making a choice, we rely on a variety of presumptions, premises, and the circumstances; all of this is directed by the goal that is related with the decision itself. However, the premises and the knowledge of the corporation are dependent on our data since they are an essential component of our organization as a system. The context and the assumptions are both external factors that are beyond the control of any decision maker. Both the background and the assumptions represent outside forces that are not within the control of any decision maker. A prominent example of a conceptual error is the misunderstanding that exists between data and information, which in reality correspond to entirely distinct ideas. This misunderstanding is a common occurrence. In point of fact, information and data cannot in any way be substituted for one another in any context. To put this another way, there is no guarantee that the data will be consistent, comparable, or traceable, despite the fact that we are able to collect data from a broad variety of distinct data sources. This is because there are so many diverse data sources. Because of this, in order for us to make a decision, we need to have a good comprehension of both the component that is presently being examined and the data that is linked with it at the present time. Only then will we be able to make an informed choice. The identification of the system itself is the first step that must be taken before any other aspects of the system, such as its boundaries, context, subsystems, feedback, inputs, and outputs, can be determined. Because of this, it is significant because, according to the point of view connected with general system theory, it is necessary to identify the system that is being discussed. In order to get a more in-depth understanding of the system, we must first begin by defining it. After that, we may proceed to quantifying each associated quality in order to achieve this goal. This would make it possible for us to have a better understanding of the system. Because of this, in order for us to collect information on the topic of the research, we will initially need to measure it in order to quantify the characteristics that are associated with it. For this, we will need to perform certain measurements on the subject. After that, we will establish the indicators that will be applied for the purpose of determining the value of each measure, and we will do so by utilizing the results of the stage that came before it. Within the context of this method, the Measurement and Evaluation (M&E) process can gain an advantage from making use of a conceptual framework that is built on top of an underlying ontology. The M&E framework makes it possible to describe the basic ideas, which prepares the way for a measurement process to be carried out in a manner that is consistent and repeatable. This is made possible by the fact that the framework makes it possible to specify the essential concepts. The ability of a measuring process to be automated is of the utmost significance, even if it is required for a measuring process to give findings that are consistent, comparable, and traceable. The ability of a measuring process to be automated is of the utmost relevance. Because the activities that take place in today's economy take place in real time, we need to pay considerable attention to the use of online monitoring in order to notice and avoid a variety of different scenarios while they are happening. Because of this, we will be able to reduce risk while maximizing our efficiency. In this regard, the functionality of the measurement and evaluation frameworks is an extremely valuable asset, as they make it possible to organize and automate the process of measuring in a manner that is consistent. This makes the frameworks an exceptionally helpful asset. As a result of this, the frameworks are a very useful asset. As soon as it is feasible to guarantee that the measurements are comparable, consistent, and traceable, the method of decision-making will naturally be based on their history, which will consist of the measurements collected throughout the years. This will be the case as soon as it is possible to guarantee that the measurements are comparable, consistent, and traceable. This will take place as soon as it is practical to assure that the measurements are comparable, consistent, and traceable. In this regard, the organizational memory is of special importance due to the fact that it makes it possible to store prior organizational experience and knowledge in order to get ready for future proposals (that is, as the foundation for a range of different assumptions and premises, among other things). In this regard, the organizational memory is of particular use. Because of this, the organizational memory is a component that is of very high importance. Measurements and the experiences that are associated with them provide continuous nourishment for the organizational memory, and the organizational memory provides the foundation for the feedback that is utilized in the process of decision making.

About the author

Abdelhamid ZAIDI is an Associate Professor in the College of Science at Qassim University in Saudi Arabia. He has a PhD in Statistics and Stochastic Modeling from University Grenoble-Alpes (France) and an Engineering degree in Computer Science and Applied Mathematics from ENSIMAG Grenoble (France). He works mainly on the development of computational methods applied to various subjects of signal and image processing and artificial intelligence. He also has many contributions in the field of artificial intelligence. His research work was published in many top ranked journals. He is also the author of three books covering numerical analysis, algorithmic, probability, and statistics.

Renato Racelis Maaliw III is an Associate Professor and Researcher at the College of Engineering in Southern Luzon State University, Lucban, Quezon, Philippines. He has a doctorate degree in Information Technology with specialization in Machine Learning, a Master's degree in Information Technology with specialization in Web Technologies, and a Bachelor’s degree in Computer Engineering. His area of interest is in artificial intelligence, computer engineering, web technologies, software engineering, data mining, machine learning, and analytics. He has published original research articles, a multiple time best paper awardee for various IEEE sanctioned conferences; served as technical program committee for world-class conferences, author, editor and peer reviewer for reputable high-impact research journals.

Mrs. K. P. Maheswari MCA., M.Phil., NET (Computer Science) is Assistant Professor of Computer Applications at Fatima College, Madurai. She has rich experience in teaching. She has received “Women Transforming Nation Awards 2023 – Certificate of Appreciation for Dedication” from Women Lead. Her research interests include Machine Learning, Deep Learning, Artificial Intelligence and Network Security. She has authored book chapters and several publications in a reputed journals. She has presented papers in various Conferences and seminars at National and International level. She has also served as a subject matter expert for workshops and seminars.Her professional achievements have included obtaining Microsoft (MTA - Microsoft Technology Associate) International certifications in Python Programming, HTML 5, and Security Fundamentals.

Dr. Haewon Byeon received the Dr Sc degree in Biomedical Science from Ajou University School of Medicine. Haewon Byeon currently works at the Department of Medical Big Data, Inje University. His recent interests focus on health promotion, AImedicine, and biostatistics. He is currently a member of international committee for a Frontiers in Psychiatry, and an editorial board for World Journal of Psychiatry. Also, He were worked on 4 projects (Principal Investigator) from the Ministry of Education, the Korea Research Foundation, and the Ministry of Health and Welfare. Byeon has published more than 343 articles and 19 books.

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