Index

  • Bayesian information criterion (BIC), 30
  • Binary classification, 181
  • Business model, 13, 14
  • Chest x-ray for pneumonia detection,
    • background, 267–268
    • introduction, 266–267
    • research methodology, 268–271
    • results and discussion, 271–272
  • Churn, 122, 124
  • Classification, 5
  • Cloud, 70, 72, 73
  • Cloud computing, 15
  • Clustering, 124, 131, 135, 172, 174, 177
  • CNN, 98–103, 109, 153, 154
  • Collaborative data publishing model with privacy preservation,
    • introduction, 54–56
    • literature survey, 56–58
    • proposed model, 58–61
    • results, 61, 64
  • Collaborative filtering (CF), 114
  • Computers & Electrical Engineering (journal), 267
  • Confusion matrix, 198–201, 267, 272
  • Content-based filtering (CBF), 114
  • Convolutional neural network (CNN), 191, 267
  • Correlation, 79
  • Cosine similarity, 203
  • Coverage, 122, 123, 138–141
  • Crowding, 82
  • Data mining, 54, 57, 58
  • Data noise, 11
  • Data perturbation–based techniques, 58
  • Data pre-processing, 129
  • Data publisher, 54
  • Dataset augmentation, 267
  • Decision making, 3
  • Decision support metrics, 116, 118
  • Decision tree, 132, 134–135
  • Deep learning, 267, 268
  • Demographic, 114, 126, 128, 136
  • Dictionary attack, 88
  • Digital automation, 17
  • Digital communication, 2
  • Digital India, 16
  • Digital representations, 68
  • Dijkstra algorithm, 174
  • Dimensionality reduction, 129–130
  • Distorted, 88
  • Diversity, 122–124, 138–141
  • DRP, 255–257
  • DSDV, 251, 252, 257
  • DSR, 251, 253, 257
  • Edge computing, 15
  • EMGGR, 251, 254, 257
  • European Journal of Radiology, 268
  • False news, 182, 185–186, 188, 191–192
  • Fault tolerant, 69
  • Feature extraction, 185, 196–197
  • Flipkart, 114, 128, 142
  • F-score, 272
  • Fuzzy clustering for categorical multivariate data (FCCM), 58
  • Genetic algorithm (GA) approach, 31, 47–49
  • Genetic algorithm–based rice distribution planning, 41–43
  • Genetic programming (GP), 29, 32
  • Geopolitical, 1
  • GFGD, 251, 254, 257
  • Global positioning system (GPS), 232
  • Google distance matrix API, 177
  • Government, 1, 2, 4, 5, 8, 10, 16, 22, 23
  • GPIO, 147, 150–152
  • GPS, 4
  • Graphical operations, 76
  • Haar wavelet transform, 268
  • Health analysis, 69
  • Heterogeneity of data, 12
  • Heuristic-based optimization approaches, 40
  • HH-VBF, 251, 253, 257
  • Hit ratio (HR), 117
  • HSV, 99
  • Hub count identification, 171–172
  • Image processing, 67
  • Incubators, 3
  • Indian irrigation development project, 31
  • Industrial internet, 70
  • Industry 4.0, 67–69
  • Infiltrates, 266
  • Information providers (IPs), 56
  • Information retrieval, 196
  • Infrastructure, 6–8, 10, 13, 15, 19–21
  • Integer linear programming (ILP), 29, 31, 40
  • Integrated command and control center, 10
  • International Journal of Medical
  • Informatics, 268
  • Internet of Things (IoT), 1, 2, 14, 15, 17-19, 147–149, 152, 229, 230, 232, 235, 247, 249, 251, 259–260
  • Internet of Underwater Things (IoUT), 229–242, 247–253, 255, 257, 259–260
  • IoT sensors, 266
  • “IsRice” platform, 32, 34–40
  • Journal of Healthcare Engineering, 267
  • Journal of the American Medical Informatics Association, 268
  • Key hub identification phase, 172–174
  • Key market hubs, 173
  • K-fold cross-validation, 115, 130
  • K-nearest neighbor, 133, 135, 137
  • Lab experiments, 125–126
  • Linear programming method, 31
  • LISYS method, 268
  • Logistic regression, 132, 181–182, 197–198, 202–204
  • LSTM (long short-term memory) cells, 28–29, 32, 34, 36–40
  • Machine learning (ML), 1, 15, 16, 231, 240–242, 255, 257–260
  • Machine learning algorithms, 2, 266, 268
  • Mask R-CNN, 267
  • Mean absolute error (MAE), 116–117
  • Mean average precision (MAP), 117, 120
  • Mean reciprocal rate (MRR), 117
  • Mean squared error (MSE), 45, 116, 117
  • Mission, 5, 8, 10, 11, 15, 16, 22
  • ML with IoT, 17
  • MovieLens dataset, 116, 135, 137
  • MSE (Mean Absolute Error), 38, 46
  • Naïve Bayes, 132, 135, 182, 198–199
  • National health information network (NHIN), 55
  • Natural language processing (NLP), 182, 204
  • Natural language tool kit (NLTK), 182, 196
  • Netflix, 114, 135, 141–142
  • Network intrusion detection, 268
  • Node java scripting, 69
  • Normalization, 121
  • Normalized discounted cumulative gain (nDCG), 117, 120
  • Novelty, 123–124, 138–143
  • NP-complete problem, 29, 40
  • OLSR, 251–253
  • Online A/B test, 126
  • Optimization techniques, 58
  • Paddy harvest prediction function, 37–39
  • Parabolic, 89
  • Pattern matching, 92
  • Personalization, 122, 123, 128
  • Pi–based, 67, 70
  • Planning and scheduling, 31
  • PLoS Medicine (journal), 267
  • Pneumo-CAD system, 268
  • Polls, 125–126
  • Popularity, 114, 122–123, 127, 138
  • PPDM, 56, 58
  • Precision, 113, 117–120
  • Prediction, 113–117, 122–123, 125, 132
  • Pre-processing, 94
  • Principal components analysis, 268
  • Privacy preservation, 57, 58, 61
  • Privacy preserving data publishing (PPDP), 54, 56–57
  • Probabilistic neural network, 268
  • Production monitoring, 67, 69, 70
  • QID, 55
  • Quality of service (QoS), 250, 258
  • Radiology (journal), 268
  • Random forest, 186, 200, 202
  • Random number, 83
  • Rank aware top-N metrics, 116–117, 120
  • Ranking, 113–114, 117, 143
  • Recall, 113, 117–119
  • Receiver operating characteristic (ROC), 117, 119
  • Recognition, 83
  • Recommender system, 113–115, 127, 129, 143
  • Recurrent neural network (RNN), 28–29, 32, 35–36
  • Relevant, 113, 118–120, 142
  • Remote monitoring, 69
  • Responsiveness, 122, 124
  • RetinaNet, 267
  • Root mean squared error (RMSE), 39, 116, 118
  • Rotation, 83
  • Route optimization for perishable goods transportation system,
    • introduction, 167–168
    • proposed method of routing, 171–174
    • proposed work implementation, 174–175, 177
    • related works, 168–170
  • Sampling, 129
  • SBC, 103, 147, 152, 154, 155, 161
  • SCADA, 4, 23
  • Scaling, 84
  • Secret key, 70
  • Secure data, 69
  • Security frameworks, 69
  • Segmentation, 85
  • Sequential minimal optimization (SMO), 30
  • Serendipity, 122–124
  • Sewage treatment plant, 7
  • Silhouette analysis, 172, 174
  • Single-board computers, 67
  • Smart agri/farm approach for paddy related processes,
    • background, 29–31
    • introduction, 28–29
    • methodology, 31–32, 34–44
    • results and discussion, 45–49
  • Smart city, 1–7, 9–11, 13, 16, 18–22
  • Smart city mission (SCM), 16
  • Smart factories, 69
  • Social media, 182–183, 190–193, 205
  • Soft-max activation function, 271
  • Spam messages, 193
  • Spearman rank correlation coefficient (SRCC), 117, 121
  • Supervised learning, 4
  • Support vector machine (SVM), 16
  • Underwater wireless sensor networks (UWSNs), 29, 229, 230, 234–235, 237–240, 247–248, 251, 258
  • Unsupervised learning, 7
  • Usage log, 125
  • VBF, 251, 253, 257
  • Vehicle route identification process, 167–177
  • Vehicles routing problem, 31
  • Velocity, 19
  • VGG-16, 267
  • WDFAD-DBR, 255, 257 “WEKA” framework, 30
  • Wireless networks, 18
  • WSNs, 149
  • Xception architecture, 268, 271
  • XGBoost, 200–203
  • Zero-day attack, 16
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