Index

  • Adha questionnaires dataset collection, 26, 31
  • Adult Adha self-report scale, 28
  • Advantage of AI implementation in farming, 237–238
    • intelligent agriculture cloud
      • platform, 238–239
      • consultation of remote experts, 238
      • remote control and administration in real time, 238
  • Agent-oriented software engineering, 249
  • Agriculture, 83–91, 93–98
  • Agriculture, application of AI, 11
  • Alexa, 16, 20
  • Algorithms, defined, 24
  • Alpha-Go, 16
  • Amazon, 20–21, 27
  • Amyotrophic lateral sclerosis (ALS), 209
  • Anxiety, 26
  • AnyWare, 32
  • Applications of artificial intelligence in agriculture, 231–234
    • AI farming bots, 233
    • AI-based irrigation system, 234
    • AI-based monitoring systems, 233
    • crop and soil quality surveillance, 231
    • forecasting weather details, 231
    • pesticide use reduction, 233
  • Artificial intelligence (AI), 16–17, 69, 83–94, 96–98, 171–172, 174
    • AI in agriculture, 83, 93, 96, 97
    • AI startups in agriculture, 88
    • applications of AI, 8, 10–12, 83, 93
    • branches of, 18–21
      • deep learning, 19–20
      • expert systems, 21
      • fuzzy logic, 21
      • machine learning, 19, 20
      • NLP, 20–21
      • robotics, 21
    • categories, 17–18
      • limited memory AI, 17–18
      • reactive machine AI, 17
      • self-aware AI, 18
      • theory of mind AI, 18
    • components of artificial intelligence, 86
    • defined, 8
    • domains of, 28
    • elements of intelligence, 12–14
      • learning, 13–14
      • linguistic intelligence, 14
      • perception, 14
      • problem solving, 14
      • reasoning, 12, 13
    • explosive growth of, 15
    • future in 2035, 15
    • history of, 8–10
    • humanoid robot and, 15
    • in innovative engineering,
      • defined, 2
      • guiding principles for, 4–7
      • overview, 2–3
      • process flow for, 3–4
    • learning, types, 16–17
      • AGI, 16–17
      • ANI, 16
      • ASI, 17
    • need for, 8
    • overview, 7–15
    • scope of artificial intelligence in agriculture, 91
    • tools, 14
    • WSN privacy through. see Wireless sensor network (WSN) privacy through AI technique
  • Artificial intelligence effect on farming, 229–231
    • agriculture lifecycle, 229–230
    • problems with traditional methods of farming, 232–233
  • Artificial intelligence in healthcare,
    • advancements, 117–118
    • benefits, 117
    • discussion, 118–119
    • expert systems, 106–107
    • future challenges, 118
    • fuzzy logic, 108–109
    • in medicine, 115–116
    • in rehabilitation, 116–117
    • in surgery, 116
    • introduction, 102
    • machine learning, 103–104
    • natural language processing, 109–110
    • neural interface for sensors, intrusion devices in artificial intelligence, 113–115
    • neural networks, 105
    • robots, 107–108
    • sensor network technology, 110–112
    • sensory devices in healthcare, implantable devices, 112–113 wearable devices, 112
  • Artificial narrow intelligence (ANI), 16
  • Artificial neural networks (ANNs), 164, 174–175, 216, 248
  • Artificial super intelligence (ASI), 17
  • Assembly robotics, 9
  • Astronomy, application of AI, 8
  • Auditory learning, 13
  • Augmented reality (AR), 210–212
  • Auto regression integrated moving average (ARIMA), 183
  • Autoencoders, 26
  • Automotive fabrication, application of AI, 11
  • Bagging, 29
  • Base station, 157
  • BDI model, 252, 253
  • Berkeley Motes, 246
  • Best matching unit (BMU), 25
  • Biological intelligence, 15
  • Blue River Technology, 88, 94
  • Boosting, 29
  • Bootstrap aggregating, 29
  • BTnode, 246
  • Capturing agent, 253
  • Cellular networks, 156
  • Challenges in AI adoption, 89
  • Classification accuracy, 30
  • Cleansing of data, 31–33
  • CLEF eRisk, 27
  • Climate change, 83–84, 86, 88, 93, 94
  • Cluster algorithm, 104
  • Clustering,
    • K-means clustering, 71, 72
  • Clustering algorithms, 164
  • Competitive edge, 33
  • Condition-based maintenance (CBM), 246
  • ConvNets, 25
  • Convolutional neural networks (CNNs), 25, 27, 41, 45, 47, 48, 52, 53, 62, 215–216
    • layers in CNN, 53
  • Coordinator agent (CoA), 253
  • COVID, 189
  • Data,
    • cleansing of, 31–33
    • collection, 33–37
    • extraction, 34
    • scrubbing, 31, 32
  • Data security, application of AI, 11
  • Dataset,
    • data collection from, 34–37
    • data extraction from, 31
    • pre-processing, 27–28
  • Decision making, improved, 32
  • Decision tree, 29, 189, 192, 199, 200, 202, 205
  • Decision tree algorithm, 26, 27
  • Deductive reasoning, 13
  • Deep belief networks (DBNs), 25, 41, 47–49
  • Deep Blue Chess Program, 10
  • Deep learning, 19–20, 41–44, 47, 48, 52, 53, 57, 61, 62, 172, 174–176, 215, 217
  • Deep learning algorithms for stress prediction,
    • dataset pre-processing, 27–28
    • literature review, 26–27
    • machine learning techniques used, 28–29
      • bagging, 29
      • boosting, 29
      • decision tree, 29
      • KNN classifier, 29
      • logistic regression, 29
      • random forest classifier, 29
    • overview, 24–26
    • performance parameter, 30
    • proposed methodology, 31–34
    • result and experiment, 34–37
    • types of algorithms, 25
  • Deep mind, 17
  • Deep neural network, 41–44, 48, 50, 52, 61
  • Deliberative agent (DA), 253
  • Depression, 23, 26, 27
  • Distributed AI, 247, 248
  • Distributed independent reinforcement learning, 248
  • Domino’s Pizza, 32
  • Drones, 83, 86, 90, 93–95, 97
  • Drones for agriculture, 236
  • E-commerce, application of AI, 11
  • Education, application of AI, 11
  • Effect, 70, 74
  • Eigen value decomposition, 164
  • Elements of intelligence, 12–14
    • learning, 13–14
      • auditory, 13
      • episodic, 13
      • motor, 13
      • observational, 13
      • perceptual, 13
      • relational, 13–14
      • spatial, 14 stimulus response, 14
    • linguistic intelligence, 14
    • perception, 14
    • problem solving, 14
    • reasoning, 12, 13
  • Elements of neural networks, 43
  • Elephant intrusion detection system (EIDS), 133
    • challenges, 134
    • existing approaches, 133–134
  • ELIZA, 9
  • Emotional intelligence, 18
  • Endoskeleton, 117
  • Entertainment sector, application of AI, 11
  • Environment, 189–191, 203–204, 206–207
  • Environmental stress, 24
  • Episodic learning, 13
  • Exoskeleton, 117
  • Experimental results,
    • dataset preparation, 144–146
    • performance analysis of DL algorithms, 146–151
  • Expert systems, 106–107
    • AI, branch of, 21
    • application of AI, 10
  • External stress, 24
  • Extraction, data, 34
  • Eye accessing cues (EAC), 209, 211
  • F – measure, 30
  • Face verification algorithmic program, 20
  • Facebook, 20
  • Facebook prophet model, 183
  • False positive, 30, 34
  • Farm bot, 88
  • Feature, 191, 199
  • Feature engineering, 174
  • Feed forward neural networks, 164
  • Feedback loop, in reinforcement learning, 167
  • Feed-forward ANN, 115
  • Finance, application of AI, 10
  • Freddy, 9
  • F-score, 30, 34
  • Fundamental architecture of GAN, 57
    • discriminative-network, 57
    • generative-network, 57
  • Fuzzy logic, 21, 108–109, 248
  • Gaming, application of AI, 10
  • Generative adversarial networks (GANs), 25
  • Genetic algorithms intrusion devices, 115
  • Gmail, 27
  • G-node, 246
  • Google drive, 27
  • Google refine, 32
  • Google survey form, 27–28
  • Gradient adversarial network (GAN), 41, 48–50, 57, 62
  • Handwriting recognition, application of AI, 11
  • Hanson artificial intelligence, 15
  • Hardware specifications,
    • GSM module, 142
    • night vision OV5647 camera module, 141–142
    • PIR sensor, 142
    • raspberry-Pi 3 model B, 141
  • Hawking, Leslie Stephen, Sir, 18
  • Hawking, Stephen William, 16
  • Healthcare industries, application of AI, 10
  • Heuristic classifications, application of AI, 12
  • Hidden layer, 43, 48–51, 59
  • Hidden Markov model (HMM), 27
  • Hog algorithm, 209–210, 213–217, 220
  • Human visual system, 41, 60
  • Humanoid robot and AI, 15
  • Hybrid intelligent system, 116
  • IBM chess program, 17
  • Image processing, 209–211, 217
  • Implantable devices, 112–113
  • Inductive reasoning, 13
  • Innovative engineering,
    • defined, 2
    • guiding principles for, 4–7
      • agile increments, 6
      • breaking proposed system, 5
      • downside risk, reducing, 6
      • effectuation principals, 5
      • insight in technical story, 5–6
      • keeping design simple, 6
      • measurable objectives, 6
      • minimal viable system architecture, 6
      • scaling phase, 5
      • story, start with, 4–5
      • support ecosystem, creating, 7
      • user’s viewpoint first, 5
    • overview, 2–3
    • process flow for, 3–4
  • Innovative engineering with AI applications,
    • AI and multi-agent systems, 246–247
    • introduction, 244–245
    • model plan,
      • application layer, 253
      • hardware layer, 251
      • middle layer, 252–253
    • multi-agent constructed simulation, 248–249
    • multi-agent model plan, 249–250
    • simulation models on behalf of wireless sensor network, 250
    • wireless sensor network (WSNs), 245–246
      • and AI, 247–248
  • Intelligence,
    • defined, 7
    • elements of, 12–14
      • learning, 13–14
      • linguistic intelligence, 14
      • perception, 14
      • problem solving, 14
      • reasoning, 12, 13
  • Intelligent robots, application of AI, 11–12
  • Intrusion devices in artificial intelligence, 113–115
  • Keras, 41, 42, 44–47, 54, 62
    • functional API model, 45
    • sequential API model, 44
  • Kismet, 10
  • K-nearest neighbor (KNN) classifier, 29
  • KNN, 189
  • Labelled training set, 163
  • Learning, 13–14
    • auditory, 13
    • episodic, 13
    • motor, 13
    • observational, 13
    • perceptual, 13
    • relational, 13–14
    • spatial, 14
    • stimulus response, 14
    • types, 16–17
      • AGI, 16–17
      • ANI, 16
      • ASI, 17
  • Limited memory AI, 17–18
  • Linear regression, 189–193, 199–200, 202, 204
  • Linguistic intelligence, 14
  • LISP programming language, 9
  • Logistic regression, 29
  • Long short term memory networks (LSTMs), 25
  • Low tech demo, 3, 4
  • Machine learning, 19, 20, 84, 89, 91, 93, 94, 96, 103–104, 177–181, 195–196, 209–211, 223
  • Machine learning (ML), in WSN, 156, 160–168
    • algorithms, types, 161
    • applications, 157, 158
    • reinforcement learning, 166–168
    • supervised learning, 161–164 process of, 162–164
    • topology formation, 161
    • unsupervised learning, 164–166
      • benefits, 165
      • drawbacks, 165
  • Machine learning technique(s),
    • types, 26
    • used, 28–29
      • bagging, 29
      • boosting, 29
      • decision tree, 29
      • KNN classifier, 29
      • logistic regression, 29
      • random forest classifier, 29
  • Manager capital (MA), 252
  • MANET, 160
  • Matthews’s correlation coefficient (MCC), 30, 34
  • Mechanical Turk (MTurk), 27
  • Mental illness, 23
  • Meteorological, 189, 192–193
  • Mica Mote, 246
  • Minimum viable product (MVP), 6
  • MIT manus, 117
  • MNIST, 41, 47, 49, 51, 53, 57
  • Mobile agents, 250
  • Mobile applications, 117–118
  • Motor learning, 13
  • Multiagent, 41, 62
  • Multiagent networks, 244–245
  • Multi-hop routing algorithms, 245
  • Multilayer perceptrons (MLPs), 25
  • Natural language processing (NLP), 109–110, 116, 180
    • AI, branch of, 20–21
    • application of AI, 10
  • NCR, 189, 202–203
  • Neural interface for sensors,
    • intrusion devices in artificial intelligence, 113–115
  • Neural network model, 24–27
  • Neural networks (NNs), 19, 105, 163–164, 172, 174–175, 189–193, 195, 198–199, 203–204
  • Neuro-fuzzy, 248
  • NO2, 192, 194, 196, 198
  • Nomad, 10
  • North India, 189
  • NS2, 160
  • Observational learning, 13
  • OpenRefine, 32
  • OutWit Hub, 34
  • OWL-S, 72–73
  • PAPNET, 116
  • Particle, 189, 193
  • PDDL, 73–75
  • Perceived stress scale (PSS), 26, 28, 31
  • Perception, 14
  • Perceptual learning, 13
  • Phonology, 109
  • Physical robots, 107
  • Physical stress, 24
  • Physical vapor deposition technique, 113
  • Physiological stressor, 24
  • Piezo nano-wires, 113
  • Planning,
  • PM, 189–192, 194, 196, 198, 200, 204
  • Pollution, 189–193, 197–199, 203–206
  • Post traumatic stress disorder (PTSD), 27
  • Precision, 30, 34
  • Precondition, 70, 74
  • Pre-processing, 193, 198, 203
  • Pre-processing, dataset, 27–28
  • Preserving land, 168
  • Principal component analysis (PCA), 164, 166
  • Problem solving, 14
  • Python, 42, 46, 47, 62
  • Radial basis function networks (RBFNs), 25
  • Random forest, 189
  • Random forest classifier, 27, 29
  • Rational agents, 246–247
  • Reactive machine AI, 17
  • Reasoning,
    • categories, 12, 13
    • deductive, 13
    • inductive, 13
  • Recall, 30, 34
  • Recommender systems, 104
  • Recurrent neural networks (RNNs), 25, 41, 45, 47, 48, 50, 51, 62
  • Regression, 183
  • Reinforcement learning, 166–168, 178–179
  • Relational learning, 13–14
  • Research perspective of deep learning, 61
    • argumentation, 61
    • phenotyping, 61
    • visualization, 61
  • Restricted Boltzmann machines (RBMs), 26, 48
  • Robotics, 21, 84, 87–88, 93–96
  • Robots, 9, 10, 107–108
  • Robots in agriculture, 235
  • Rossum’s universal robots (RUR), 9
  • Saliency mapping, 44, 62
  • See and spray model, 90, 91
  • Self-aware AI, 18
  • Self-awareness, 93
  • Self-calibration, 248
  • Self-driving cars, 16, 18, 20
  • Self-organizing function map, 168
  • Self-organizing maps (SOMs), 25, 168
  • Self-supervised deep learning model, 26
  • Semi-supervised deep learning model, 26
  • Sensitivity, 30
  • Sensor network technology, 110–112
  • Sensor nodes, 245
  • Sensors, 156–157, 159, 161
  • Sensory devices in healthcare,
    • implantable devices, 112–113
    • wearable devices, 112
  • Shakey, 9
  • Sink, 157
  • Siri, 16, 20
  • SO2, 192, 196
  • Social media, application of AI, 11
  • Social robots, 107
  • Social stress, 24
  • Sophia, 15, 16, 21
  • Spatial learning, 14
  • Specificity, 30
  • Speech recognition, application of AI, 11
  • Stanford cart, 9
  • Stimulus response learning, 14
  • Stress, 23, 26
  • Stressors, kinds of, 24
  • Strong AI, 16–17
  • Stunning discoveries of AI, 89
  • Suicide, 27
  • Sun spot, 246
  • Supervised learning, 161–164, 178–179
    • process of, 162–164
  • Supervised learning algorithm, 29
  • Support ecosystem, creating, 7
  • Support vector, 189–190, 192, 203
  • Support vector machine (SVM), 27, 210
  • Tasks agent (TA), 253
  • Teaching professionals, stress prediction in. see Deep learning algorithms for stress prediction
  • TensorFlow, 41, 42, 44, 46, 47, 62
  • Tesla, 5
  • Theoretical framework,
    • deep learning models for EIDS, 134
      • fast RCNN, 135
      • faster RCNN, 135–136
        • anchors, 136
        • loss function, 136
        • region proposal network (RPN), 136–137
      • single-shot multibox detector (SSD), 137–138
        • architecture, 138
        • non-maximum suppression (NMS), 139
      • you only look once (YOLO), 139 bounding box predictions, 140–141
  • Theory of mind AI, 18
  • TIP sequence mote, 246
  • Tmote sky, 246
  • Transport, application of AI, 11
  • Travel domain, 66–67, 71, 76–80
  • True positive rate, 30, 34
  • Trusted third parties (TTPs), 159
  • Turing testing, 9
  • Twitter, 20, 27
  • Unsupervised learning, 178–180
  • Unsupervised learning algorithm, 164–166
    • benefits, 165
    • drawbacks, 165
  • Visible layer, 48, 49
  • Vision systems, application of AI, 10–11
  • Zinc oxygen single wire generator, 113
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