Название: Machine Learning: Algorithms, Models and ApplicationsАвтор: Jaydip Sen
Издательство: ITexLi
Год: 2021
Страниц: 131
Язык: английский
Формат: pdf (true)
Размер: 10.26 MB
The chapters in the book illustrate how Machine Learning and Deep Learning algorithms and models are designed, optimized, and deployed. The volume will be useful for advanced graduate and doctoral students, researchers, faculty members of universities, practicing data scientists and data engineers, professionals, and consultants working on the broad areas of Machine Learning, Deep Learning, and Artificial Intelligence.
Recent times are witnessing rapid development in Machine Learning algorithm systems, especially in reinforcement learning, natural language processing (NLP), computer and robot vision, image processing, speech, and emotional processing and understanding. In tune with the increasing importance and relevance of Machine Learning models, algorithms, and their applications, and with the emergence of more innovative uses–cases of Deep Learning and Artificial Intelligence, the current volume presents a few innovative research works and their applications in real-world, such as stock trading, medical and healthcare systems, and software automation.
The increase in the amount of big data and the emergence of analytics technologies has created the opportunity for applying algorithm development techniques using Machine Learning (ML) languages to predict future events. To conduct inclusive analyses of contemporary literature of existing relevant narratives with a focus on program management themes, including state-of-the art methodologies on current plausible predictive analytics models. The methodology used is the review and applications of relevant programming platforms available. Program management requires the utilization of the existing ML languages in understanding future events. Enabling decision makers to make strategic - goals, objectives, and missions. The use of Predictive Analytics Algorithms (PAAs) has gained thematic significance in automotive industries, energy sector, financial organizations, industrial operations, medical services, governments, and more. PAAs are important in promoting the management of future events such as workflow or operational activities in a way that institutions can schedule their activities in order to optimize performance. It enables organizations to use existing Big Data to predict future performance and mitigate risks. The improvements in information technology and data analytics procedures have resulted in the ability of businesses to make effective use of historical data in making predictions. This enables evidence-based planning, mitigating risks, and optimizing production.
The Chapter 6 examines the current knowledge and scholarly information about Predictive Analytics Algorithms (PAAs) by focusing on the concept of working principles on which they are used to predict future events and the procedures followed in creating them. The PAAs have been used extensively in predicting future events in healthcare practice, manufacturing companies, businesses, education, sports, and agriculture. The main programming languages used to create PAAs are Java, C, and Python amongst others. The forms of algorithms that are commonly used are brute force algorithm, simple recursive algorithm, backtracking algorithm, randomized algorithm, and dynamic programming algorithms.
Contents:
1. Machine Learning in Finance-Emerging Trends and Challenges
2. Design and Analysis of Robust Deep Learning Models for Stock Price Prediction
3. Articulated Human Pose Estimation Using Greedy Approach
4. Ensemble Machine Learning Algorithms for Prediction and Classification of Medical Images
5. Delivering Precision Medicine to Patients with Spinal Cord Disorders; Insights into Applications of Bioinformatics and Machine Learning from Studies of Degenerative Cervical Myelopathy
6. Enhancing Program Management with Predictive Analytics Algorithms (PAAs)
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