Intelligent Techniques for Predictive Data Analytics
Description
Comprehensive resource covering tools and techniques used for predictive analytics with practical applications across various industries
Intelligent Techniques for Predictive Data Analytics provides an in-depth introduction of the tools and techniques used for predictive analytics, covering applications in cyber security, network security, data mining, and machine learning across various industries. Each chapter offers a brief introduction on the subject to make the text accessible regardless of background knowledge.
Readers will gain a clear understanding of how to use data processing, classification, and analysis to support strategic decisions, such as optimizing marketing strategies and customer relationship management and recommendation systems, improving general business operations, and predicting occurrence of chronic diseases for better patient management.
Traditional data analytics uses dashboards to illustrate trends and outliers, but with large data sets, this process is labor-intensive and time-consuming. This book provides everything readers need to save time by performing deep, efficient analysis without human bias and time constraints. A section on current challenges in the field is also included.
Intelligent Techniques for Predictive Data Analytics covers sample topics such as:
- Models to choose from in predictive modeling, including classification, clustering, forecast, outlier, and time series models
- Price forecasting, quality optimization, and insect and disease plant and monitoring in agriculture
- Fraud detection and prevention, credit scoring, financial planning, and customer analytics
- Big data in smart grids, smart grid analytics, and predictive smart grid quality monitoring, maintenance, and load forecasting
- Management of uncertainty in predictive data analytics and probable future developments in the field
Intelligent Techniques for Predictive Data Analytics is an essential resource on the subject for professionals and researchers working in data science or data management seeking to understand the different models of predictive analytics, along with graduate students studying data science courses and professionals and academics new to the field.
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Persons
Dr. Neha Singh is an Assistant Professor in the Electronics & Communication Engineering Department at Manipal University Jaipur, India.
Dr. Shilpi Birla is an Associate Professor in the Electronics & Communication Department at Manipal University Jaipur, India.
Dr. Mohd Dilshad Ansari is an Associate Professor in the Computer Science & Engineering Department at SRM University Delhi-NCR, Sonepat, Haryana, India.
Dr. Neeraj Kumar Shukla is an Associate Professor in the Electrical Engineering Department at King Khalid University, Saudi Arabia.
Content
About the Editors xiii
List of Contributors xv
Preface xix
Acknowledgments xxi
1 Data Mining for Predictive Analytics 1
Prakash Kuppuswamy, Mohd Dilshad Ansari, M. Mohan, and Sayed Q.Y. Al Khalidi
1.1 Introduction 1
1.2 Background Study 3
1.3 Applications of Data Mining 4
1.4 Challenges of Data Analytics in Data Mining 7
1.5 Significance of Data Analytics Tools for Data Mining 7
1.6 Life Cycle of Data Analytics 8
1.7 Predictive Analytics Model 11
1.8 Data Analytics Tools 14
1.9 Benefits of Predictive Analytics Techniques 18
1.10 Applications of Predictive Analytics Model 18
1.11 Conclusion 20
2 Challenges in Building Predictive Models 25
Rakesh Nayak, Ch. Rajaramesh, and Umashankar Ghugar
2.1 Introduction 25
2.2 Literature Survey 30
2.3 Few Suggestions to Overcome the Above Challenges 42
2.4 Conclusion and Future Directions 44
3 AI-driven Digital Twin and Resource Optimization in Industry 4.0 Ecosystem 47
Pankaj Bhambri, Sita Rani, and Alex Khang
3.1 Introduction 47
3.2 Digital Twin Technology 50
3.3 Industry 4.0 Ecosystem 53
3.4 AI in Digital Twins 56
3.5 Resource Optimization 57
3.6 AI-driven Resource Allocation 59
3.7 Challenges and Consideration 62
3.8 Future Trends 62
3.9 Conclusion 63
4 Predictive Analytics in Healthcare 71
N. Venkateswarulu, P. Pavan Kumar, and O. Obulesu
4.1 Predictive Analytics 71
4.2 Predictive Analysis in Medical Imaging 73
4.3 Predictive Analytics in the Pharmaceutical Industry 75
4.4 Predictive Analytics in Clinical Research 78
4.5 AI for Disease Prediction 81
4.6 Medical Image Classification for Disease Prediction 83
5 A Review of Automated Sleep Stage Scoring Using Machine Learning Techniques Based on Physiological Signals 89
Santosh Kumar Satapathy, Poojan Agrawal, Namra Shah, Ranjit Panigrahi, Bidita Khandelwal, Paolo Barsocchi, and Akash Kumar Bhoi
5.1 Introduction 89
5.2 Review of Related Works 91
5.3 Methodology 98
5.4 Conclusion 105
5.5 Future Work 105
6 Predictive Analytics for Marketing and Sales of Products Using Smart Trolley with Automated Billing System in Shopping Malls Using LBPH and Faster R-CNN 111
Balla Adi Narayana Raju, Deepika Ghai, Suman Lata Tripathi, Sunpreet Kaur Nanda, and Sardar M.N. Islam
6.1 Introduction 111
6.2 Major Contributions 112
6.3 Related Work 113
6.4 Proposed Methodology 119
6.5 Experimental Results and Discussions 126
6.6 Conclusion 130
7 Enhancing Stock Market Predictions Through Predictive Analytics 135
Ameya Patil, Shantanu Saha, and Rajeev Sengupta
7.1 Introduction 135
7.2 Factors Influencing Stock Prices 137
7.3 Can Markets Be Predicted? 138
7.4 Using Predictive Analytics for Stock Prediction 140
7.5 Neural Networks 141
7.6 Conclusion 146
8 Predictive Analytics and Cybersecurity 151
Mohammed Sayeeduddin Habeeb
8.1 Introduction 151
8.2 Cybersecurity and Predictive Analysis 152
8.3 Machine Learning 153
8.4 Proactive Cybersecurity and Real-Time Threat Detection 156
8.5 Network Security Analytics 159
8.6 Cyber Risk Analytics 160
8.7 Impact of Predictive Analytics on the Cybersecurity Landscape 162
8.8 Challenges in Applying Predictive Analytics to Cybersecurity 162
8.9 Conclusion 164
9 Precision Agriculture and Predictive Analytics: Enhancing Agricultural Efficiency and Yield 171
Nafees Akhter Farooqui, Mohd. Haleem, Wasim Khan, and Mohammad Ishrat
9.1 Introduction 171
9.2 Background 173
9.3 Precision Agriculture Technologies and Methods 178
9.4 Smart Agriculture Cultivation Recommender System 183
9.5 Conclusion 184
10 A Simple Way to Comprehend the Difference and the Significance of Artificial Intelligence in Agriculture 189
Karan Aggarwal, Ruchi Doshi, Maad M. Mijwil, Kamal Kant Hiran, Murat Gök, and Indu Bala
10.1 Introduction 189
10.2 Machine Learning 191
10.3 Deep Learning 192
10.4 Data Science 193
10.5 AI in the Agriculture Industry 194
10.6 Conclusions 198
11 An Overview of Predictive Maintenance and Load Forecasting 203
Nand Kishor Gupta, Vivek Upadhyaya, and Vijay Gali
11.1 Introduction 203
11.2 PdM: Revolutionizing Asset Management 204
11.3 Load Forecasting: Illuminating the Path Ahead 216
11.4 Synergies and Future Prospects 222
11.5 Conclusion 225
12 Predictive Analytics: A Tool for Strategic Decision of Employee Turnover 231
SMD Azash, Potala Venkata Subbaiah, and Lucia Vilcekova
12.1 Introduction 231
12.2 Literature Review 232
12.3 Need and Importance of the Study 233
12.4 Objectives of the Study 235
12.5 Hypothesis of the Study 235
12.6 Research Method 235
12.7 Data Analysis Procedures and Discussion 236
12.8 Recommendations 240
12.9 Conclusion 241
References 242
Index 245
1
Data Mining for Predictive Analytics
Prakash Kuppuswamy1, Mohd Dilshad Ansari1, M. Mohan1, and Sayed Q.Y. Al Khalidi2
1 Department of Computer Science and Engineering, SRM University Delhi-NCR, Sonepat, India
2 Department of Information Technology, King Khalid University, Abha, Kingdom of Saudi Arabia
1.1 Introduction
Data mining is the process of discovering meaningful patterns, correlations, and relationships in large datasets using computational algorithms [1-3]. It involves various steps, including data preprocessing, pattern discovery, and result interpretation, extracting knowledge, and making informed decisions from large and complex datasets [4-6]. It combines techniques from various domains such as statistics, machine learning, artificial intelligence, and database systems to explore and analyze structured and unstructured data to gain insights and generate actionable information [7-9]. This chapter aims to delve into the concept of data mining, its methods, applications, challenges, and future research scope.
The primary goals of data mining are prediction, classification, association, clustering, and anomaly detection [10, 11]. These techniques help businesses and researchers derive valuable insights, support decision-making processes, and uncover hidden patterns within data [12, 13]. In Figure 1.1, the process of data mining typically includes data collection, data preprocessing, feature selection, algorithm selection, pattern discovery, interpretation, and evaluation. Each step plays a crucial role in extracting meaningful information from the data [14-16].
Figure 1.1 Stages of data mining process.
1.1.1 Data Mining Process
Data mining involves a systematic process to extract knowledge or patterns from large datasets. The data mining process typically consists of the following stages:
- Data Collection: Collecting relevant and comprehensive datasets is crucial for accurate predictions. This data can be obtained from internal databases, third-party sources, or through data scraping techniques [17, 18].
- Data Cleaning: Raw data often contains inconsistencies, missing values, and anomalies. Data cleaning involves removing or correcting these errors to ensure the accuracy and reliability of the data [19, 20].
- Data Integration: In many cases, data is obtained from multiple sources and requires to be integrated into a unified dataset. This stage involves consolidating and merging data from different sources to establish a comprehensive dataset for analysis [21, 22].
- Data Transformation: Data transformation involves converting raw data into a suitable format for analysis. This process includes data normalization, standardization, and feature engineering [13, 23].
- Data Mining Algorithms: Selecting the appropriate algorithms based on the nature of the data and the desired outcome is crucial. Various data mining algorithms such as decision trees, neural networks, and association rules can be employed for predictive analytics [24, 25].
- Model Development and Evaluation: Once the data mining algorithms have been applied, models are developed using historical data. These models are subsequently evaluated using performance metrics to assess their accuracy and effectiveness [26, 27].
- Model Deployment: Once the models are considered reliable, they can be deployed in real-world scenarios to make predictions based on new data [28, 29].
Data analytics tools encompass a range of software and platforms designed to enable the extraction, analysis, and visualization of data. These tools utilize various techniques, including data mining, statistical analysis, machine learning, and visualization, to transform raw data into actionable insights. Data analytics tools come with a variety of features that enable efficient data mining, including data integration, data preprocessing, exploratory data analysis, statistical modeling, machine learning algorithms, and visualization capabilities. Using data analytics tools for data mining offers several advantages, including improved decision-making, enhanced productivity, increased efficiency, greater accuracy, and the ability to uncover hidden patterns and trends in data [12, 30, 31]. The most popular data mining techniques are as follows:
- Classification
- Clustering
- Association Rule Mining
- Regression
- Anomaly Detection
1.2 Background Study
There is a great deal of data being generated worldwide in the modern era, which is the age of data. An analysis of small, limited-dimensional data can be conducted using a basic computer. In the medical sciences, the utilization of data mining techniques has emerged as an emerging trend. However, traditional text-mining approaches are not adequate to manage the current surge in published data [32]. To find hidden features and correlations in large volumes of data, artificial intelligence-based text mining tools are being developed and used. In the field of industrial data mining, large-scale data and predictive analytics present the greatest challenges [33]. In the context of extracting and analyzing underlying knowledge from a data repository, it is necessary to use data mining and machine learning techniques due to the rapid changes in the industrial environment and the availability of huge customer data. Better administration will enable better decisions in the future to manage resources and improve efficiency [34]. With the aim of creating business value, business analytics enable organizations to make better, quicker, and more intelligent decisions. At present, descriptive and predictive analytics are dominant in academic and industrial research. The field of prescriptive analytics, which seeks to determine the best course of action, has been attracting more and more attention in recent years. Often, predictive analytics is regarded as the next step in improving business performance through improved decision-making ahead of time [35]. Data mining technique that gets insights into what has happened in the past using descriptive analytics. Forecasting and statistical techniques are used in predictive analytics to know what's likely to happen in the future. A predictive analytics technique uses algorithms to determine what actions should be taken to change what will likely happen in the future. Starting with an introduction to analytics, it examines data mining and the role of machine learning and artificial intelligence in analytics. Comparisons are drawn based on the purposes for which the techniques are used. In addition to having advantages and disadvantages, these techniques are unique in their own way.
1.3 Applications of Data Mining
The data mining process has transformed decision-making processes across various industries, enabling organizations to anticipate trends, make accurate predictions, and optimize their operations - from marketing and healthcare to finance and supply chain management. With the ongoing advancements in technology, data mining is poised to have an even more profound impact on decision-making processes, unlocking new opportunities, and driving innovation across sectors in the foreseeable future.
1.3.1 Healthcare Industry
Data mining plays a crucial role in the healthcare sector, where the analysis of patient records, medical images, clinical trial data, and genomic data provides valuable insights. Also, it plays a vital role in disease diagnosis, patient monitoring, drug discovery, and personalized medicine. It assists healthcare professionals identify correlations between symptoms, predict disease outcomes, and optimize treatment plans. The key applications of data mining in healthcare include the following:
- Disease Prediction and Diagnosis: By analyzing large-scale patient records, data mining algorithms can identify patterns, risk factors, and early indicators of diseases. This helps in predicting disease outcomes, improving diagnostic accuracy, and facilitating personalized treatments.
- Drug Discovery and Development: Data mining techniques can analyze vast databases of chemical compounds to identify potential drug candidates, accelerating the drug discovery process by reducing the time and cost involved in traditional laboratory-based approaches.
- Healthcare Management and Cost Reduction: Data mining enables healthcare providers to analyze patient data, optimize resource allocation, and identify potential areas for cost reduction. It helps in streamlining operations, improving efficiency, and enhancing patient care.
1.3.2 Finance and Banking
Data mining applications in the finance and banking sectors have revolutionized the way financial institutions operate. The utilization of vast amounts of financial data combined with data mining techniques offers several advantages:
- Fraud Detection: Data mining algorithms can identify patterns of fraudulent activities, such as credit card fraud, money laundering, or identity theft. By analyzing transactional data, anomalies, and suspicious patterns can be detected, enabling...
