Abdelhamid ZAIDI's Books

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

By: Abdelhamid ZAIDI,Renato Racelis Maaliw III,Mrs. K. P. Maheswari,Dr. Haewon Byeon

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

By: Abdelhamid ZAIDI,Renato Racelis Maaliw III,Mrs. K. P. Maheswari,Dr. Haewon Byeon

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.

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201

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Xoffencerpublication

Published Date

ISBN 10

811953445X

ISBN 13

9788119534456

Territorial Integrity in a Globalizing World

International Law and States’ Quest for Survival

By: Abdelhamid El Ouali

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Territorial Integrity in a Globalizing World

International Law and States’ Quest for Survival

By: Abdelhamid El Ouali

This book offers a comprehensive, highly informative and interdisciplinary study on territorial integrity and the challenges globalization, self-determination and external interventions present. This study aims at not only to fill an epistemological gap in this regard, but also answer the question of whether International Law is adequately equipped to help states address these challenges. The author argues that the biggest threat that many states are confronted with today is their disintegration rather than their obsolescence, and that International Law has not often been able to prevent that eventuality. In fact, states, when they were not destroyed by war, managed to survive, thanks to the flexibility of territoriality, i.e. their ability to adjust to difficult situations as they arose. It is this understanding of adaptation that urges an increasing number of states today to revive territorial autonomy and restore an original understanding of self-determination in which democracy is a pivotal factor in establishing congruence between the states and their nations. While this move is endorsed by International Law, it is not the case for globalization; for their own sake, proponents of globalization should recognize that the states are irreplaceable as long as they remain the sole providers of protection for their peoples.

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407

Publisher

Springer Science & Business Media

Published Date

ISBN 10

3642228682

ISBN 13

9783642228681

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

By: Abdelhamid ZAIDI,Renato Racelis Maaliw III,Mrs. K. P. Maheswari,Dr. Haewon Byeon

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

By: Abdelhamid ZAIDI,Renato Racelis Maaliw III,Mrs. K. P. Maheswari,Dr. Haewon Byeon

Average ratings

N/A

Write a review

Solve jigsaw puzzle

Age

Page Count

201

Publisher

Xoffencerpublication

Published Date

ISBN 10

811953445X

ISBN 13

9788119534456

Book's by Abdelhamid ZAIDI

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

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

Territorial Integrity in a Globalizing World

Territorial Integrity in a Globalizing World

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

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

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