Cognitive Computing Systems

Applications and Technological Advancements

Vishal Jain, Akash Tayal, Jaspreet Singh, Arun Solanki, Vishal Jain, Akash Tayal, Jaspreet Singh, Arun Solanki

• Verlag: Apple Academic Press
• Jahr: 2021
• ISBN:
  9781000164404
• Auflage: 1
• Thema: Informatik
• Thema: Künstliche Intelligenz (KI) & Semantik

This new volume, Cognitive Computing Systems: Applications and Technological Advancements, explores the emerging area of artificial intelligence that encompasses machine self-learning, human-computer interaction, natural language processing, data mining and more. It introduces cognitive computing systems, highlights their key applications, discusses the technologies used in cognitive systems, and explains underlying models and architectures.

Focusing on scientific work for real-world applications, each chapter presents the use of cognitive computing and machine learning in specific application areas. These include the use of speech recognition technology, application of neural networks in construction management, elevating competency in education, comprehensive health monitoring systems, predicting type 2 diabetes, applications for smart agricultural technology, human resource management, and more.

With chapters from knowledgeable researchers in the area of artificial intelligence, cognitive computing, and allied areas, this book will be an asset for researchers, faculty, advances students, and industry professionals in many fields.

PART I Using Assistive Learning to Solve Computationally Intense Problems

CHAPTER 1 High-Frequency Stochastic Data Analysis Using a Machine Learning Framework: A Comparative Study

LOKESH KUMAR SHRIVASTAV¹ and RAVINDER KUMAR^2*
1University School of Information, Communication and Technology, Guru Gobind Singh Indraprastha University, New Delhi, Delhi 110078, India
²Skill Faculty of Engineering and Technology, Shri Vishwakarma Skill University, Gurugram, Haryana 122003, India
*Corresponding author. E-mail: [email protected]

ABSTRACT

High-frequency stochastic data analysis and prediction are challenging and exciting problems if we aim to maintain high level of accuracy. The stock market dataset is selected randomly for the experimental investigation of the study. Historical datasets of a few stock markets have been collected and used for this purpose. The model is trained, and the results are compared with the real data. In the past few years, specialists have tried to build computationally efficient techniques and algorithms, which predict and capture the nature of the stock market accurately. This chapter presents a comparative analysis of the literature on applications of machine learning tools on the financial market dataset. This chapter provides a comparative and brief study of some relevant existing tools and techniques used in financial market analysis. The main objective of this chapter is to provide a comparative study of novel and appropriate methods of stock market prediction. A brief explanation of advanced and recent tools and techniques available for the analysis and a generalized and fundamental model in R-language for the stock market analysis and prediction are also provided in this chapter. In addition, this chapter presents a review of significant challenges and futuristic challenges of the field.

1.1 INTRODUCTION

In recent years, high-speed data acquisition technology has demanded an appropriate and advanced analysis and prediction mechanism. With easy access and advancement of the storage system, a massive amount of data are generated every second. It forced the demand for a high-speed data analytics processing system. The development of information communication technology and complex computational algorithms and the collection, analysis, and prediction using high frequency are possible [1]. In recent years, machine learning frameworks play an important role in providing an excellent forecast and fast computation on a massive amount of high-frequency data. High-frequency data mean that datasets are collected in a fine regular or irregular interval of time and are referred to as high-frequency stochastic time-series datasets [2]. Problem-solving technology may be classified into two parts: hard computational method and soft computational method. The hard computational method can deal with precise models to achieve the solution accurately and quickly. Prof. L. Zadeh introduced the soft computational method in 1994. It is a mechanism that deals with uncertainty, robustness, and better tolerance objectives of the model. The soft computing method is a hybrid mechanism that is a combination of fuzzy logic, evolutionary computation, probabilistic reasoning, and machine learning. The fundamental constituent of soft computing is machine learning that was introduced by A. Samuel in 1959, which explores the study and development of appropriate technologies and tools that can learn and adopt the nature from the data and predict its futuristic nature. The application of machine learning has gradually grown and now reached on its optimum and very advanced level, where it can examine and forecast the...