Renato Racelis Maaliw III'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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ISBN 10

811953445X

ISBN 13

9788119534456

MACHINE LEARNING IN HEALTHCARE

By: Dr. Anand Ashok Khatri,Dr. Ashok Kumar,Miss. Namrata Gohel,Renato Racelis Maaliw III

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MACHINE LEARNING IN HEALTHCARE

By: Dr. Anand Ashok Khatri,Dr. Ashok Kumar,Miss. Namrata Gohel,Renato Racelis Maaliw III

The study of how data pertaining to healthcare may be gathered, transferred, processed, stored, and retrieved is what is known as the field of healthcare informatics. Early illness prevention, early disease detection, early disease diagnosis, and early disease therapy are all essential components of this field of research. Within the realm of healthcare informatics, the only types of data that are considered reliable are those that pertain to illnesses, patient histories, and the computing procedures that are required to interpret this data. Conventional medical practices throughout the United States have made significant investments in state-of-the-art technological and computational infrastructure over the course of the last two decades in order to improve their ability to support academics, medical professionals, and patients. Significant resources have been invested in order to raise the quality of medical treatment that can be provided by using these approaches. The aim to offer patients with healthcare that is not only reasonably priced and of good quality, but also completely free of any and all anxiety served as the impetus for these many projects. As a direct result of these efforts, the advantages and significance of utilizing computational tools to help with referrals and prescriptions, to set up and manage electronic health records (EHR), and to make technological advancements in digital medical imaging have become more obvious. These tools can also assist with setting up and managing electronic health records (EHR). It has been shown that computerized physician order entry, commonly known as CPOE, may improve the quality of care that is provided to patients while simultaneously lowering the number of prescription mistakes and adverse drug reactions. When a doctor uses CPOE, they are able to swiftly get pertinent patient data without having to leave the screen where they are entering prescriptions. The history of the patient provides the treating physician with advance notice of any possibly dangerous responses. Moreover, the CPOE offers the physician the ability to monitor the order's development as it moves through the system.

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235

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Xoffencerpublication

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ISBN 10

9394707999

ISBN 13

9789394707993

THEORY AND APPLICATIONS IN DEEP LEARNING

By: Dr. Shashi,Rashi Rastogi,Renato Racelis Maaliw III,Dr. Ashok Kumar

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THEORY AND APPLICATIONS IN DEEP LEARNING

By: Dr. Shashi,Rashi Rastogi,Renato Racelis Maaliw III,Dr. Ashok Kumar

Deep learning is a subfield of computer science that is currently focusing the majority of its attention on the areas of video, picture, text, and speech recognition, in addition to autonomous driving, robotics, healthcare, and other areas. This is in addition to other areas such as robotics and healthcare. Academics and academicians are currently showing a significant amount of interest in the field of deep learning. This is because it is a subfield of study that focuses a significant emphasis on achieving outcomes, which explains why this is the case. Rina Dechter was the first person to use the phrase "deep learning" in 1986, and the building of an intelligent computer that could emulate the functioning of the human brain was the driving force behind the expansion of this field of study. The term "deep learning" was coined by Rina Dechter, who was also the first person to use the term. The human brain, which is in charge of decision-making, is the most important organ in the body. In order for the brain to arrive at its conclusions, it takes in data through its five senses: sight, smell, touch, and hearing. Memory is another item that is stored in the brain, and it is this memory that may be used to solve complicated problems by drawing on experiences that have been gained in the past. Throughout the course of the past few decades, scientists have kept alive the dream that they may one day be able to design a computer with intellect comparable to that of our own brains. In order to make progress towards achieving this goal, they have initiated research into the fundamental make-up and operation of the human brain. One of the primary motivations behind the development of autonomous vehicles as well as robots that are capable of performing a variety of functions is the reduction in the number of collisions that take place along roadways. This can be accomplished through the use of robots that are multi-functional. Because it is estimated by the World Health Organization (WHO) that 1.35 million people lose their lives on the roads of the world each year, and since it is estimated that more than 90 percent of those deaths are the result of human errors that could have been avoided.

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215

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Xoffencerpublication

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ISBN 10

9394707778

ISBN 13

9789394707771

DEEP LEARNING FOR COMPUTER VISION

By: Mr. Amol Dattatray Dhaygude,Dr. Pushpendra Kumar Verma,Dr. Sheshang D. Degadwala,Renato Racelis Maaliw III

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DEEP LEARNING FOR COMPUTER VISION

By: Mr. Amol Dattatray Dhaygude,Dr. Pushpendra Kumar Verma,Dr. Sheshang D. Degadwala,Renato Racelis Maaliw III

In the most recent few years, tremendous technical progress has been made in the creation of high-throughput graphics processing units in addition to parallel processing. Processing in parallel has allowed for the realisation of these recent advancements. (GPUs). The amount of computational power that is now accessible has significantly risen, yet the needed amount of power consumption has stayed the same. Highperformance parallel processing units are now accessible at a price that is affordable for almost everyone. This is because many of these systems are developed for the consumer market to deliver high-definition gaming experiences. Although they have been considerably altered to make graphical calculations more effective, they are broad enough to be utilised in a range of different jobs that may be completed concurrently. This is despite the fact that they have been adjusted to make graphical calculations more effective. This new advancement will have a tremendous impact on the whole area of study that focuses on deep learning. At this point in time, it is feasible for anybody to use the most recent techniques of deep learning to their work, regardless of whether they are doing their study in conventional laboratories or at home. Deep learning is a subfield of machine learning that has shown its usefulness for a variety of activities that are deemed simple for humans but too tough for computers to handle on their own. Image recognition, natural language processing, and voice recognition are all examples of the tasks that fall under this category. Natural language processing and image analysis are two examples of this kind of technology. Both of these include the categorization, identification, and segmentation of various items inside pictures. This paves the way for the development of autonomous systems, which in turn paves the way for an infinite number of additional possibilities.

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220

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ISBN 10

9394707794

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9789394707795

UNLEASHING THE POWER OF AI: A COMPREHENSIVE GUIDE TO ADVANTAGES AND DISADVANTAGES IN DIFFERENT INDUSTRIES

By: Renato Racelis Maaliw III,Mr. Gunawan Widjaja,Berdjouh Chafik ,Dr. Haewon Byeon

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UNLEASHING THE POWER OF AI: A COMPREHENSIVE GUIDE TO ADVANTAGES AND DISADVANTAGES IN DIFFERENT INDUSTRIES

By: Renato Racelis Maaliw III,Mr. Gunawan Widjaja,Berdjouh Chafik ,Dr. Haewon Byeon

The term "artificial intelligence" (AI) refers to a category of computing technologies that have become increasingly advanced in recent years. This study presents an overview that is easily understandable of how it works, why it is important, and what we can do as a reaction to the difficulties that it poses. Since the beginning of the field of artificial intelligence, the capacity to behave in a way that gives the impression of intelligence has been the primary emphasis of the concept of AI. Several variations of the 'Turing test' determine that computers are intelligent when people are unable to distinguish between their behaviors and those of a person. The disruptive power of artificial intelligence (AI) Once considered an idea from the far future, artificial intelligence (AI) has now developed into a disruptive force that has impacted every facet of our life. A revolution has been triggered as a result of its rapid breakthroughs, with artificial intelligence altering the way we live, work, and interact with technology. The significance of artificial intelligence cannot be emphasized; its applications range from driverless automobiles to AI-assisted medical diagnosis. This article will investigate the myriad of ways in which AI is causing a revolution in technology, diving into its applications and the ramifications it has across a variety of industries. We will investigate the ways in which artificial intelligence is influencing areas such as management, human resources, digital marketing, creativity, the future of work, careers, startups, social media, marketing, economics, branding, personal development, investing, job interviews, money, motivation, healthcare, education, productivity, travel, and entrepreneurship.

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227

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Xoffencerpublication

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ISBN 10

8197370850

ISBN 13

9788197370854

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

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Solve jigsaw puzzle

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Page Count

201

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Xoffencerpublication

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ISBN 10

811953445X

ISBN 13

9788119534456

UNLEASHING THE POWER OF AI: A COMPREHENSIVE GUIDE TO ADVANTAGES AND DISADVANTAGES IN DIFFERENT INDUSTRIES

By: Renato Racelis Maaliw III,Mr. Gunawan Widjaja,Berdjouh Chafik ,Dr. Haewon Byeon

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UNLEASHING THE POWER OF AI: A COMPREHENSIVE GUIDE TO ADVANTAGES AND DISADVANTAGES IN DIFFERENT INDUSTRIES

By: Renato Racelis Maaliw III,Mr. Gunawan Widjaja,Berdjouh Chafik ,Dr. Haewon Byeon

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8197370850

ISBN 13

9788197370854

Book's by Renato Racelis Maaliw III

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

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

MACHINE LEARNING IN HEALTHCARE

MACHINE LEARNING IN HEALTHCARE

THEORY AND APPLICATIONS IN DEEP LEARNING

THEORY AND APPLICATIONS IN DEEP LEARNING

DEEP LEARNING FOR COMPUTER VISION

DEEP LEARNING FOR COMPUTER VISION

UNLEASHING THE POWER OF AI: A COMPREHENSIVE GUIDE TO ADVANTAGES AND DISADVANTAGES IN DIFFERENT INDUSTRIES

UNLEASHING THE POWER OF AI: A COMPREHENSIVE GUIDE TO ADVANTAGES AND DISADVANTAGES IN DIFFERENT INDUSTRIES

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

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

UNLEASHING THE POWER OF AI: A COMPREHENSIVE GUIDE TO ADVANTAGES AND DISADVANTAGES IN DIFFERENT INDUSTRIES

UNLEASHING THE POWER OF AI: A COMPREHENSIVE GUIDE TO ADVANTAGES AND DISADVANTAGES IN DIFFERENT INDUSTRIES

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