Analytical tools and algorithms are essential in business data and information systems. Efficient economic and financial forecasting in machine learning techniques increases gains while reducing risks. Providing research on predictive models with high accuracy, stability, and ease of interpretation is important in improving data preparation, analysis, and implementation processes in business organizations. Machine Learning Techniques for Improved Business Analytics is a collection of innovative research on the methods and applications of artificial intelligence in strategic business decisions and management. Featuring coverage on a broad range of topics such as data mining, portfolio optimization, and social network analysis, this book is ideally designed for business managers and practitioners, upper-level business students, and researchers seeking current research on large-scale information control and evaluation technologies that exceed the functionality of conventional data processing techniques.

This book gathers selected high-quality research papers presented at the International Conference on Paradigms of Communication, Computing and Data Sciences (PCCDS 2021), held at the National Institute of Technology, Kurukshetra, India, during May 07–09, 2021. It discusses high-quality and cutting-edge research in the areas of advanced computing, communications, and data science techniques. The book is a collection of latest research articles in computation algorithm, communication, and data sciences, intertwined with each other for efficiency.

Based on current literature and cutting-edge advances in the machine learning field, there are four algorithms whose usage in new application domains must be explored: neural networks, rule induction algorithms, tree-based algorithms, and density-based algorithms. A number of machine learning related algorithms have been derived from these four algorithms. Consequently, they represent excellent underlying methods for extracting hidden knowledge from unstructured data, as essential data mining tasks. Implementation of Machine Learning Algorithms Using Control-Flow and Dataflow Paradigms presents widely used data-mining algorithms and explains their advantages and disadvantages, their mathematical treatment, applications, energy efficient implementations, and more. It presents research of energy efficient accelerators for machine learning algorithms. Covering topics such as control-flow implementation, approximate computing, and decision tree algorithms, this book is an essential resource for computer scientists, engineers, students and educators of higher education, researchers, and academicians.

The relentless growth of data in financial markets has boosted the demand for more advanced analytical tools to facilitate and improve financial planning. The ability to constructively use this data is limited for managers and investors without the proper theoretical support. Within this context, there is an unmet demand for combining analytical finance methods with business analytics topics to inform better investment decisions. Advancement in Business Analytics Tools for Higher Financial Performance explores the financial applications of business analytics tools that can help financial managers and investors to better understand financial theory and improve institutional investment practices. This book explores the value extraction process using more accurate financial data via business analytical tools to help investors and portfolio managers develop more modern financial planning processes. Covering topics such as financial markets, investment analysis, and statistical tools, this book is ideal for accountants, data analysts, researchers, students, business professionals, academicians, and more.

Learn modern-day technologies from modern-day technical giants.KEY FEATURES1. Real-world success and failure stories of artificial intelligence explained2. Understand concepts of artificial intelligence and deep learning methods 3. Learn how to use artificial intelligence and deep learning methods4. Know how to prepare dataset and implement models using industry leading Python packages 5. You'll be able to apply and analyze the results produced by the models for predictionDESCRIPTION The aim of this book is to help the readers understand the concept of artificial intelligence and deep learning methods and implement them into their businesses and organizations. The first two chapters describe the introduction of the artificial intelligence and deep learning methods. In the first chapter, the concept of human thinking process, starting from the biochemical responses within the structure of neurons to the problem-solving steps through computational thinking skills are discussed. All chapters after the first two should be considered as the study of different technological and Artificial Intelligence giants of current age. These chapters are placed in a way that each chapter could be considered a separate study of a separate company, which includes the achievements of intelligent services currently provided by the company, discussion on the business model of the company towards the use of the deep learning technologies, the advancement of the web services which are incorporated with intelligent capability introduced by company, the efforts of the company in contributing to the development of the artificial intelligence and deep learning research. WHAT WILL YOU LEARN How to use the algorithms written in the Python programming language to design models and perform predictions in general datasetsUnderstand use cases in different industries related to the implementation of artificial intelligence and deep learning methodsLearn the use of potential ideas in artificial intelligence and deep learning methods to improve the operational processes or new products and how services can be produced based on the methodsWHO THIS BOOK IS FORThis book is targeted to business and organization leaders, technology enthusiasts, professionals, and managers who seek knowledge of artificial intelligence and deep learning methods.Table of Contents1. Artificial Intelligence and Deep Learning2. Data Science for Business Analysis3. Decision Making4. Intelligent Computing Strategies By Google 5. Cognitive Learning Services in IBM Watson6. Advancement web services by Baidu 7. Improved Social Business by Facebook8. Personalized Intelligent Computing by Apple9. Cloud Computing Intelligent by MicrosoftAbout the AuthorDr. Jagreet KaurDr. Jagreet Kaur is a doctorate in computer science and engineering. Her topic of thesis was "e;ARTIFICIAL INTELLIGENCE BASED ANALYTICAL PLATFORM FOR PREDICTIVE ANALYSIS IN HEALTH CARE."e; With more than 12 years of experience in academics and research, she is working in data wrangling, machine learning and deeplearning algorithms on large datasets, real-time data often in production environments for data science solutions and data products to get actionable insights for the last four years. She also possesses ten international publications and five national publications under her name.Her skill set includes data engineering skills (Hadoop, Apache Spark, Apache Kafka, Cassandra, Hive, Flume, Scoop, and Elasticsearch), programming skills (Python, Angularjs, D3.js , Machine Learning, and R), data science skills (Statistics, Machine Learning, NLP, NLTK, Artificial Intelligence, R, Python, Pandas, Sklearn, Hadoop, SQL, Statistical Modeling, Data Munging, Decision Science, Machine Learning, Graph Analysis, Text Mining and Optimization, and Web Scraping, Deep learning packages:- Theano, Keras, Tensorflow, Pytorch, Julia) and Algorithms Specialization (Regression Algorithms: Linear Regression, Random Forest Regressor, XGBoost, SVR, Ridge Regression, Lasso Regression, Neural Networks Classification Algorithms: Decision Trees, Random Forest Classifier, Support Vector Machines(SVM), Logistic Regression, KNN Classifier, Neural Network, Clustering Algorithms: K-Means, DBSCAN, Deep Learning Algorithms: Simple RNN, LSTM Network, GRU)Currently, she works as a Chief Operating Officer (COO) and Chief Data Scientist in Xenonstack. Under her Guidance, more than 400 projects are already developed and productionized which also includes more than 200 AI and data science projects. Navdeep Singh GillNaveed Singh Gill is a technology and solution architect having more than 15 years of experience in the IT and Telecom industry. For the past six years, he is working in big data analytics, automation and advanced analytics using machine learning and deep learning for planning and architecting of data science solutions and data products. He's also working in 3 As (Analytics, Automation, and AI), more focused on writing software for building data lake, analytics platform , NoSQL deployments, data migration, data modelling tasks, ML/DL on real-time data often in production environments.He started his career with HFCL Infotel as a network engineer for managing the technical network of Broadband Customers with Linux servers and Cisco routers. He also worked in Ericsson, where he handled the synchronization plan and implementation for synchronization of Microwave Network and Media Gateway, MSS, and Core Network. SSU Implementation Planning and Optimization with respect to IP RAN, Mobile Backhaul Solution- Optimization of Existing Microwave Network to Ethernet, Microwave Hybrid Solution, Convergence to all IP, SIU Implementation for conversion to IP of Existing BTS,GB over IP.His area of expertise includes Hadoop, Openstack, DevOps, Kubernetes, Dockers, Amazon web services, Apache Spark, Apache Storm, Apache Kafka, Hbase, Solr, Apache FlinkNutch, Mapreduce, Pig, Hive, Flume, Scoop, ElasticSearch, and programming expertise includes Python, Angular.js, and Node.js.

This book comprehensively discusses how information technology can be utilized in business planning and development. With a practical approach based on current business theories, readers will be guided through the fundamental concepts of business planning and sustainable business development strategies. Each chapter provides an in-depth understanding of how information technology can strengthen the business planning and development processes. From using data for strategic decision-making to integrating information technology into business models, readers will be encouraged to understand how to optimize the potential of information technology for maximum business advantage. Furthermore, the book also addresses the challenges and opportunities faced in the utilization of information technology in the modern era. By including relevant case studies from various industries, readers will gain clearer insights into the real-world applications of the discussed concepts. Thus, this book becomes an essential guide for business professionals, managers, and students who seek to understand and effectively utilize the potential of information technology in business planning and development in the digital era.

Offering innovative ideas that explore how strategy and management methodology can be developed, Methods to Improve Our Field considers approaches that range from the re-imagining of secondary data in the digital age and Interpretive Phenomenological Analysis (IPA) to Machine Learning and Artificial Intelligence.

This book is a transformative guide catering to undergraduate and graduate students and research scholars, providing a comprehensive understanding of critical concepts in modern analytics. In today’s fast-paced business landscape, data utilization is paramount for success. This book delves into tools and techniques facilitating the conversion of raw data into actionable insights, covering descriptive, predictive, and prescriptive analytics. Beginning with foundational principles, it ensures accessibility for readers of all backgrounds. Real-world case studies seamlessly woven throughout the text illustrate successful business analytics implementations, showcasing how organizations make strategic decisions. This precise and insightful guide equips readers with the knowledge to optimize processes, making it an indispensable resource for navigating the dynamic realm of business analytics.

Unveiling the Future: Your Portal to Artificial Intelligence Proficiency In the epoch of digital metamorphosis, Artificial Intelligence (AI) stands as the vanguard of a new dawn, a nexus where human ingenuity intertwines with machine precision. As we delve deeper into this uncharted realm, the boundary between the conceivable and the fantastical continually blurs, heralding a new era of endless possibilities. The Dictionary of Artificial Intelligence, embracing a compendium of 3,300 meticulously curated titles, endeavors to be the torchbearer in this journey of discovery, offering a wellspring of knowledge to both the uninitiated and the adept. Embarking on the pages of this dictionary is akin to embarking on a voyage through the vast and often turbulent seas of AI. Each entry serves as a beacon, illuminating complex terminologies, core principles, and the avant-garde advancements that characterize this dynamic domain. The dictionary is more than a mere compilation of terms; it's a labyrinth of understanding waiting to be traversed. The Dictionary of Artificial Intelligence is an endeavor to demystify the arcane, to foster a shared lexicon that enhances collaboration, innovation, and comprehension across the AI community. It's a mission to bridge the chasm between ignorance and insight, to unravel the intricacies of AI that often seem enigmatic to the outsiders. This profound reference material transcends being a passive repository of terms; it’s an engagement with the multifaceted domain of artificial intelligence. Each title encapsulated within these pages is a testament to the audacity of human curiosity and the unyielding quest for advancement that propels the AI domain forward. The Dictionary of Artificial Intelligence is an invitation to delve deeper, to grapple with the lexicon of a field that stands at the cusp of redefining the very fabric of society. It's a conduit through which the curious become enlightened, the proficient become masters, and the innovators find inspiration. As you traverse through the entries of The Dictionary of Artificial Intelligence, you are embarking on a journey of discovery. A journey that not only augments your understanding but also ignites the spark of curiosity and the drive for innovation that are quintessential in navigating the realms of AI. We beckon you to commence this educational expedition, to explore the breadth and depth of AI lexicon, and to emerge with a boundless understanding and an unyielding resolve to contribute to the ever-evolving narrative of artificial intelligence. Through The Dictionary of Artificial Intelligence, may your quest for knowledge be as boundless and exhilarating as the domain it explores.