Intersection of Machine Learning and Computational Social Sciences

Cover....1

Half Title....2

Series Page....3

Title Page....4

Copyright Page....5

Table of Contents....6

Preface....8

Editors....9

List of Contributors....11

Chapter 1: Leveraging artificial intelligence for educational transformation: A critical study of the Indian context....14

1.1 Introduction....14

1.2 Review of literature....18

1.3 Application of AI in education....25

1.4 Application of AI in Indian education system....27

1.5 Critical analysis of AI in Indian education system....34

1.6 Discussion....37

1.7 Conclusion....42

References....43

Chapter 2: A study on artificial intelligence and its role in medical image analysis....47

2.1 Introduction....47

2.1.1 Deep learning in medical imaging....50

2.1.2 Limitations and challenges....54

2.1.3 Proposed solutions....58

2.1.4 Future directions....61

2.2 Conclusion....61

References....62

Chapter 3: An overview of machine learning: Concepts, algorithms, and applications....68

3.1 Introduction....68

3.2 Motivation....69

3.3 Novel contributions in the chapter....69

3.4 Chapter organization....70

3.5 Categorization of machine learning....70

3.6 Machine learning algorithms....73

3.7 Machine learning application....76

3.8 Ethical considerations in machine learning....78

3.9 Model evaluation and selection....79

3.10 Feature engineering and selection....80

3.11 Case studies and future directions and trends in machine learning....81

3.12 Conclusion....83

References....84

Chapter 4: Natural language processing: Food habits-based disease prediction using large language models....88

4.1 Introduction....88

4.2 Food style and healthcare....88

4.3 Large language models (LLMs)....89

4.4 Proposed methodology....92

4.5 Experimental model development....95

4.6 Result and discussion....99

4.7 Conclusion....106

References....106

Chapter 5: Convolutional neural network-based plant leaf disease classification: Implications for society and agriculture....109

5.1 Introduction....109

5.2 Precision agriculture....110

5.3 Literature review....111

5.4 Datasets....113

5.4.1 PlantVillage dataset....113

5.4.2 Rice leaf dataset....113

5.5 Results and analysis....115

5.5.1 Evaluation metrics....115

5.5.2 Experiments on the PlantVillage dataset....116

5.5.3 Experiments on rice dataset....120

5.6 Conclusions....125

References....125

Chapter 6: Classifying the social world: Algorithms and applications in computational social science....128

6.1 Introduction....128

6.1.1 The challenge of understanding the social world....128

6.1.2 The rise of computational social science....129

6.1.3 The power of classification by unveiling patterns in social data....129

6.2 Applications of classification algorithms in social science....130

6.2.1 Mapping social networks....130

6.2.2 Understanding public opinion through sentiment analysis of textual data....131

6.2.3 Beyond words: image classification for social research....132

6.2.3.1 Facial expression or emotion recognition....132

6.2.3.2 Scene analysis....132

6.2.4 Classifying economic behavior through consumer trends and market analysis....133

6.2.4.1 Consumer trend analysis....133

6.2.4.2 Customer churn prediction....133

6.2.4.3 Market analysis and fraud detection....133

6.2.5 Applications across disciplines....134

6.3 Common classification algorithms in computational social science....134

6.3.1 Supervised learning techniques....134

6.3.1.1 Logistic regression....135

6.3.1.2 Decision trees....135

6.3.1.3 Support vector machines (SVMs)....136

6.3.1.4 Neural networks....137

6.3.2 Unsupervised learning techniques....138

6.3.2.1 K-means clustering....138

6.3.2.2 Hierarchical clustering....139

6.3.3 Choosing the right algorithm....140

6.4 Validation and beyond....141

6.4.1 The need for validation and ensuring model accuracy and generalizability....141

6.4.2 Evaluating classification performance through various metrics....141

6.4.3 Cross-validation techniques: testing models on unseen data....142

6.4.4 Addressing bias and fairness....143

6.5 Case studies in computational social science classification....143

6.5.1 Predicting customer churn in the retail industry....144

6.6 Challenges and ethical considerations in computational social sciences....148

6.6.1 Challenges in computational social sciences....148

6.6.2 Ethical considerations in computational social sciences....149

6.7 Future of computational social science....149

6.8 Conclusion....150

References....151

Chapter 7: Social network analysis: Need, data collection, APIs, data preprocessing, feature engineering techniques, etc.....154

7.1 Introduction....154

7.2 Initial network design....155

7.2.1 Modeling of the network....156

7.2.2 Network characteristics....156

7.2.2.1 Structural characteristics....157

7.2.2.1.1 Density....157

7.2.2.1.2 Size....157

7.2.2.1.3 Diversity....157

7.2.2.1.4 Structural hole....157

7.2.2.1.5 Clique....157

7.2.2.1.6 Degree....157

7.2.2.1.7 Betweenness....157

7.2.2.1.8 Closeness....158

7.2.2.1.9 Clustering coefficients....158

7.2.2.1.10 Isolates....158

7.2.2.1.11 PageRank....158

7.2.2.2 Relational characteristics....158

7.2.2.2.1 Strength of tie....158

7.2.2.2.2 Availability....159

7.2.2.2.3 Appealability....159

7.2.2.2.4 Trust....159

7.2.2.2.5 Reputation....159

7.2.2.2.6 Reliance....159

7.2.2.2.7 Expected mutual aid....159

7.2.2.2.8 Rivalry....160

7.2.2.3 Individual characteristics....160

7.2.2.3.1 Character....160

7.2.2.3.2 Emotional intelligence....160

7.2.2.3.3 Purposefulness....161

7.2.2.3.4 Prior experience....161

7.2.2.3.5 Emotional dissection....161

7.3 SNA process....161

7.3.1 Design....163

7.3.2 Data collection....163

7.3.3 Data analysis and visualization....163

7.3.4 Data review....163

7.3.5 Evaluation....164

7.3.6 Take actions....164

7.3.7 Summary....164

7.4 SNA software and tools....165

7.4.1 SocNetV....166

7.4.2 NetworkX....167

7.4.3 Pajek....167

7.4.4 Cytoscape....167

7.4.5 Apache Spark GraphX....167

7.4.6 JGraphT....167

7.4.7 UCINET....167

7.4.8 igraph....168

7.4.9 JUNG....168

7.5 Using APIs (application programming interfaces) to retrieve data....168

7.5.1 How to retrieve data using APIs....169

7.5.1.1 Choose the appropriate API....169

7.5.1.2 Authentication and access....169

7.5.1.3 Requesting and retrieving data....169

7.5.1.4 Data storage and parsing....169

7.5.1.5 Data management....169

7.6 Feature engineering....170

7.6.1 Customized feature engineering techniques for network data....170

7.6.1.1 Hole structures....170

7.6.1.2 Clustering parameters....170

7.6.1.3 Algorithms for community identification....170

7.6.1.4 Centrality indicators....171

7.6.2 Advanced methods: node embeddings and graph neural networks....171

7.7 Anticipated research findings....172

7.7.1 Information distribution....172

7.7.1.1 The threshold model....172

7.7.1.2 The cascade models....172

7.7.1.3 The epidemiological model....172

7.7.1.4 Model of triggering....173

7.7.1.5 Time-aware model....173

7.7.2 Link prediction....173

7.7.2.1 Predicated on resemblance....173

7.7.2.2 Based on maximum likelihood and probability....173

7.7.2.3 Reduction of dimensionality....174

7.7.3 Influence maximization....174

7.7.3.1 Based on approximations....174

7.7.3.2 Heuristics....174

7.7.3.3 Based in the community....175

7.7.3.4 Heuristics in meta-form....175

7.7.4 Community detection....175

7.7.4.1 Partitioning graphs....176

7.7.4.2 Dynamic approaches....176

7.7.4.3 Grouping in hierarchies....176

7.7.4.4 Methods based on density....176

7.7.5 Big data using SNA....177

7.7.6 Summary....178

7.8 Social network insights platform....178

7.8.1 Education....179

7.8.1.1 Identifying experts in the field....179

7.8.1.2 Examining learner performance....179

7.8.1.3 Evaluation of institutions and women researchers relevance....180

7.8.1.4 Recognizing new interest-based communities across the faculty....180

7.8.2 Sports....180

7.8.2.1 Football....180

7.8.2.2 Cricket....180

7.8.3 Society and culture....181

7.8.3.1 Gathering social network information for writing....181

7.8.3.2 Examining storylines and adapted films....181

7.8.3.3 Investigating media and news....181

7.8.4 Patterns and knowledge discovery....182

7.8.5 Politics....182

7.8.5.1 False information and fake news....182

7.8.5.2 Campaigning for elections....182

7.8.6 Tourism and hospitality....182

7.8.7 Promotion and branding....183

7.8.8 Disaster management....183

7.8.9 Transport....183

7.8.10 Cyber security....184

7.8.11 Multimedia....184

7.9 Challenges and future directions....184

7.9.1 Variability....184

7.9.2 Flexibility....184

7.9.3 Relation type and weight....184

7.9.4 Features of the node....185

7.9.5 Restrictions on data....185

7.9.6 Insufficient public datasets....185

7.9.7 Analysis in context....185

7.9.8 Maintaining privacy....185

7.9.9 Including topological network architecture....185

7.9.10 The spread and evolution of opinions....185

7.10 Conclusion....186

References....186

Chapter 8: Feature selection in DNA microarray data: Insights for healthcare and social science applications through machine learning....195

8.1 Introduction....195

8.2 Microarray data....197

8.2.1 What is a microarray dataset?....197

8.2.2 Essential properties inherent to microarray data....197

8.2.2.1 Small sample size....198

8.2.2.2 Class imbalance....198

8.2.2.3 Data shift....198

8.2.2.4 Outliers....199

8.2.3 Datasets and repositories....199

8.3 Feature selection on microarray data....200

8.3.1 Filter-based algorithms....201

8.3.1.1 ReliefF....201

8.3.1.2 Correlation-based feature selection (CFS)....202

8.3.1.3 Information gain (IG)....202

8.3.1.4 Minimal-redundancy-maximal-relevance (mRMR)....203

8.3.1.5 Rough sets and fuzzy-rough sets-based feature selection....204

8.3.2 Wrapper-based algorithms....205

8.3.3 Embedded algorithms....206

8.3.4 Other algorithms....207

8.4 An empirical setup....208

8.4.1 Selected datasets and feature selection methods....209

8.4.2 Evaluation metrics....209

8.5 Results analysis and discussion....212

8.5.1 Cancer RNA-seq dataset....212

8.5.1.1 Results analysis....212

8.5.1.2 Discussion....214

8.5.2 Leukemia dataset....216

8.5.2.1 Results analysis....216

8.5.2.2 Discussion....217

8.5.3 Ovarian dataset....221

8.5.3.1 Results analysis....221

8.5.3.2 Discussion....223

8.5.4 Limitations and challenges....227

8.6 Conclusion....229

References....230

Chapter 9: Self-supervised learning for pathological speech detection....234

9.1 Introduction....234

9.2 Contextual embeddings....236

9.3 Methodology....237

9.3.1 Dataset....237

9.3.2 Implementation....237

9.3.3 Embedding extraction....238

9.4 Results and discussion....238

9.5 Conclusion....240

References....241

Chapter 10: Analyzing the social consequences of lung cancer risk prediction with lifestyle data: A comparative study of machine learning techniques....244

10.1 Introduction....244

10.1.1 Contribution....245

10.2 Review and related literature....245

10.3 Methodology....246

10.3.1 Parameter information....247

10.3.2 Dataset description....247

10.3.3 Exploratory data analysis....248

10.4 Findings and discussion....251

10.4.1 Confusion matrix....251

10.4.2 Accuracy of the model....254

10.4.3 Other statistical measurements....255

10.4.4 AUC-ROC curve....255

10.4.5 Comparative analysis....258

10.4.6 Social impact....259

10.5 Conclusion and future work....259

Disclosure of correspondence....260

Data accessibility declaration....260

Note....260

References....260

Chapter 11: Adversarial learning for enhancing security in Cobot-driven industries: A machine learning approach to risk mitigation....262

11.1 Introduction to Cobots....262

11.2 Application of Cobots....264

11.2.1 Advantages of adopting Cobots in industries....266

11.3 Security challenges in collaborative robots....268

11.3.1 Examining the hazards of collaborative robot environments....269

11.4 Literature review....271

11.4.1 Types of cooperative situations....272

11.4.2 Examples of real businesses....273

11.4.3 Various applications of HRC....274

11.4.4 Programming in Cobots....276

11.5 Adversarial attacks on Cobots....280

11.5.1 Understanding adversarial attacks....280

11.5.2 Impacts of adversarial attacks on Cobots....280

11.5.3 Adversarial attack mitigation strategies....280

11.6 Adversarial learning for Cobots security....281

11.7 Challenges and limitations....283

11.7.1 Addressing challenges and limitations....284

11.8 Future trends and research directions....284

11.9 Conclusion....287

References....287

Chapter 12: Cybersecurity challenges in energy harvesting systems: A machine learning approach to safeguarding industrial IoT networks....292

12.1 Introduction....292

12.2 Basic concepts....294

12.2.1 Energy harvesting techniques....294

12.2.2 Energy harvesting via solar energy....296

12.2.3 Energy harvesting via radio frequency....297

12.3 Cyberattacks and threats in EH network....298

12.4 Wiretapping....299

12.5 Denial of service (DoS)....299

12.5.1 Side-channel attack....300

12.5.2 Spoofing....301

12.5.3 De-authentication....301

12.6 Physical-layer data secrecy attack....302

12.6.1 Untrusted relays....304

12.6.2 Internal adversaries....304

12.6.3 Lightweight cryptography technique....304

12.7 Literature survey....305

12.8 Current cybersecurity methods in energy harvesting....307

12.8.1 Security methods....307

12.9 Probable attacks on energy harvesting....308

12.10 Future research directions....309

12.11 Conclusion....310

References....310

Chapter 13: Integrating transfer learning techniques for automated recognition of medicinal plant leaves in computational social science....314

13.1 Introduction and importance of medicinal plants....314

13.1.1 Manual plant identification challenges....314

13.1.2 Machine learning and deep learning potential for automated plant identification....314

13.1.3 Contribution of this study....315

13.2 Related work....316

13.3 Proposed design....320

13.4 Dataset description....320

13.5 Data preprocessing....321

13.6 Feature extraction and machine learning approaches....321

13.7 Machine learning classifiers....322

13.7.1 Logistic regression....322

13.7.2 Random Forest....322

13.7.3 Support vector machine....323

13.8 Classification....324

13.8.1 Evaluation criteria....324

13.8.2 Experiment and results....325

13.8.3 Discussion....331

13.8.4 Challenges and proposed solutions....332

13.9 Conclusion....332

References....333

Chapter 14: Deep learning-based approach for combating fake news....336

14.1 Introduction....336

14.2 Literature review....337

14.3 Methodology....338

14.3.1 Data collection....338

14.3.2 Data preprocessing....339

14.3.3 Data augmentation....340

14.3.4 Tokenization....341

14.3.5 Normalization....341

14.3.6 Removal of noise, URLs, hashtags, and user mentions....341

14.3.7 Word segmentation....341

14.3.8 Replacing emoticons and emojis....342

14.3.9 Abbreviations and slang....342

14.3.10 Punctuation removal....343

14.3.11 Stopword removal....343

14.3.12 Stemming and lemmatization....343

14.4 Feature engineering....344

14.4.1 TFIDF....344

14.4.2 Bag of words....345

14.4.3 Sentiment analysis....345

14.4.4 News length....345

14.5 Pseudocode 1 EFEFI: hybrid feature engineering approach for FakeNews identification....346

14.6 Deep learning methods used....347

14.6.1 Convolutional neural networks (CNNs)....347

14.6.2 Long short-term memory (LSTM) networks....347

14.7 Results and analysis....348

14.7.1 Accuracy....348

14.7.2 Precision....349

14.7.3 Recall....349

14.7.4 F1-score....350

14.7.4.1 LSTM....350

14.7.4.2 CNN....350

14.7.4.3 Ensemble (CNN and LSTM)....350

14.8 Conclusion....357

References....357

Index....360

The text employs computational techniques and large-scale data analysis to study complex social phenomena and human behavior. It discusses diverse methodologies, including agent-based modeling, network analysis, natural language processing, and machine learning, to gain insights into topics ranging from social network dynamics and opinion formation to economic trends and public health crises.

Features:

• Discusses the theoretical background of each algorithm in detail and presents the applications of each method.
• Presents artificial intelligence implications, sustainable artificial intelligence, and the importance of artificial intelligence in agriculture, and energy.
• Explains the use of predictive modeling in computational social science and applications of computational social science.
• Showcases the framework for social network analysis, application program interface, data collection methods, and data preprocessing.
• Covers topics such as density-based spatial clustering of applications with noise, the role of clustering in computational social science, and clustering in network structure.

The text is primarily written for senior undergraduates, graduate students, and academic researchers in the fields of electrical engineering, electronics and communications engineering, computer science and engineering, and information technology.