Advancements in Deep Learning Algorithms

Techniques from artificial intelligence have been widely applied in optical communication and networks, evolving from early machine learning (ML) to the recent deep learning (DL). This paper focuses on state-of-the-art DL algorithms and aims to highlight the contributions of DL to optical communications. Considering the characteristics of different DL algorithms and data types, we review multiple DL-enabled solutions to optical communication. First, a convolutional neural network (CNN) is used for image recognition and a recurrent neural network (RNN) is applied for sequential data analysis. A variety of functions can be achieved by the corresponding DL algorithms through processing the different image data and sequential data collected from optical communication. A data-driven channel modeling method is also proposed to replace the conventional block-based modeling method and improve the end-to-end learning performance. Additionally, a generative adversarial network (GAN) is introduced for data augmentation to expand the training dataset from rare experimental data. Finally, deep reinforcement learning (DRL) is applied to perform self-configuration and adaptive allocation for optical networks.

In recent years, Deep Learning (DL), such as the algorithms of Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN) and Generative Adversarial Networks (GAN), has been widely studied and applied in various fields including agriculture. Researchers in the fields of agriculture often use software frameworks without sufficiently examining the ideas and mechanisms of a technique. This article provides a concise summary of major DL algorithms, including concepts, limitations, implementation, training processes, and example codes, to help researchers in agriculture to gain a holistic picture of major DL techniques quickly. Research on DL applications in agriculture is summarized and analyzed, and future opportunities are discussed in this paper, which is expected to help researchers in agriculture to better understand DL algorithms and learn major DL techniques quickly, and further to facilitate data analysis, enhance related research in agriculture, and thus promote DL applications effectively. Keywords: deep learning, smart agriculture, neural network, convolutional neural networks, recurrent neural networks, generative adversarial networks, artificial intelligence, image processing, pattern recognition DOI: 10.25165/j.ijabe.20181104.4475 Citation: Zhu N Y, Liu X, Liu Z Q, Hu K, Wang Y K, Tan J L, et al. Deep learning for smart agriculture: Concepts, tools, applications, and opportunities. Int J Agric & Biol Eng, 2018; 11(4): 32-44.

Citation (2023), "Index", Sudhir, K. and Toubia, O. (Ed.) Artificial Intelligence in Marketing (Review of Marketing Research, Vol. 20), Emerald Publishing Limited, Bingley, pp. 309-318. https://doi.org/10.1108/S1548-643520230000020016 Publisher: Emerald Publishing Limited Copyright © 2023 K. Sudhir and Olivier Toubia. Published under exclusive licence by Emerald Publishing Limited INDEX Activation functions, 246 Advertising, 89–90 context of, 96 Agent-based simulation model, 183 Airbnb context, 117 Smart Pricing algorithm, 231 smart pricing tool, 107 Airlines, 105, 107 Alexa, 289 Algorithmic bias, 117 Algorithmic collusion, 33, 118–119 Algorithmic sellers, 109–110 Amazon Mechanical Turk, 163 Amazon’s current business model, 32–33 Anthropomorphism, 185, 274–275 in AI, 277–286 beneficial and harmful effects, 287–290 conceptual framework, 286–298 conditions, 290–293 cumulative distribution of articles, 275 future research directions, 298–302 individual characteristics of AI users, 293–296 insights emerging from literature, 284–286 journals included in literature search, 279 limitations, 302 literature review procedures, 278, 280, 283–284 related to context of employing AI anthropomorphism, 299–300 related to effects of AI anthropomorphism, 298–299 related to individual characteristics of AI users, 300–302 relationship perspective, 297–298 Apple (technology company), 13–14 Application programming interfaces (APIs), 159–160 Area Under the Curve (AUC), 87–88 Artificial intelligence (AI), 1–2, 13–14, 104–105, 125–126, 147–148, 170, 218, 274 advertising, persuasion, and communication, 153 agenda for future work, 34 AI-based algorithm, 29 AI-based innovation, 1–2 AI-based model selection tools, 28–29 AI-based queries, 154 AI-based solutions, 133 AI-supported content generation, 139–140 aiding marketing decisions, 4–6 algorithmic collusion, 118–119 anthropomorphism in, 277–286 applications of AI-powered VOC, 150–153 challenges in use of UGC, 149 consequences for pricing, 115–119 considerations for use of, 139–140 consumer reactions, 139 data available for AI and VOC, 154–156 decisions, 30–32 dynamic pricing, 115–116 economic framework of, 14–27 firms implementing AI for pricing, 104–115 identifying and organizing customer needs, 150 impact on consumers and society, 8–9 level of impact of, 27–28, 34 market research, 6 marketing purpose of, 4–6 opportunity identification for AI research, 10 personalized pricing, 117 potential abuse and need for regulation, 139–140 prediction, 28–30 price algorithms, 111 promise of, 149–150 promise of AI and Machine Learning, 149–150 promise of user-generated content, 149 reflection of branding through users, 151–153 research in marketing on, 16, 26, 40, 76 society, 33–34 strategy, 32–33 tools, 32, 136, 140 understanding and forecasting demand, 150–151 and VOC, 148–150 VOC practice before, 148–149 workforce implications, 140 Artificial Intelligence Assistants (AIAs), 289 Artificial neural networks (ANNs), 227, 240–241 Attenuation bias, 170, 175 Augmented reality (AR), 7, 228–229 Autocompletion for email and text messaging, 139 Autoencoders, 257 Autoencoding models, 203 Automated content generation, 129 Automation, 104–105 Autoregressive models, 203 Average treatment effect (ATE), 84 Azure’s Face API, 222–223 “Bag-of-words”–based methods, 180 Behavioral experiments, 229–230 Berry–Levinsohn–Pakes–type random coefficient choice model, 178 Bias mitigation, 230–231 Bibliometric network, 173 Bidirectional Encoder Representations from Transformers (BERT), 125–126, 157, 159, 180–181, 198–199, 202–203 Big data (see also Data), 104 VOC practice before, 148–149 Big GAN (BigGAN), 132 Binary Robust Independent Elementary Features, 221 Brand logos, 220 Brand perception, 162 Brand selfies concept, 153 Brand-related social tags, 30 Branding, 148 brand perception, 151–152 brand positioning, 152–153 reflection of branding through users, 151–153 user–brand interaction, 152–153 Brands marketing strategies, 154 Business leaders, 13–14 Business-to-consumer (B2C), 275 Canny edge detector, 221 Causal inference, 268 Causality, 194–195 Charge supracompetitive prices, 118 Classical ML models, 242 Click-through rate (CTR), 89–90 Clustering, 158 algorithms, 150 Co-citation analysis, 173 Collusive algorithm, 118–119 Color histogram descriptor, 221 Colors, 221 Common method bias, 170 Company-level topics, 223–224 Computer vision, 7–8 application domain, 223–224 data format, 219–221 future research, 228–231 in marketing research, 219–224 model structure, 221–223 techniques, 218 Conditional average treatment effect (CATE), 82 Conditional GAN (CGAN), 131–132 Conjoint analysis, 149 Construct validity, 164 Consumer reactions, 139 Consumer silence, 170–171 Consumer-centric perspective, 276 Consumer-level topics, 223 Content generation considerations for use of AI-supported content generation, 139–140 generating synthetic images, 131–133 generating textual content with language models, 129–131 potential for, 129–133 potential for AI throughout customer journey, 126–129 potential for content generation, 129–133 supporting customer equity management with content generation, 133–139 Content selection method, 130 Content-related marketing tasks, 140 Contextual bandit, 82–83 Contour, 221 Convolutional neural networks (CNNs), 28, 129, 157, 163, 202, 220–221, 246, 249, 254 Convolutional-LSTM, 157 Counterfactual explanations, 227–228 Counterfactual policy evaluation, 84–85 Counterfactual validity, 81–82 Criticisms, 227–228 Cross-entropy, 247 CTR prediction problems, 81 Customer acquisition, 134–136 Customer equity framework, 126–127 Customer equity management with content generation customer acquisition, 134–136 customer retention, 138–139 relationship development, 136–138 supporting, 133–139 Customer feedback, 170 future of customer feedback research, 183–185 online customer feedback, 175–183 publication count by journal, 172 publication count by year, 172 review methodology, 171–174 from user-generated content, 176 Customer relationship management (CRM), 8 Customer retention, 138–139 Customer reviews, 154 Customer satisfaction research, 174–175 DALL-E, 137 Data, 14, 154 available for AI AND VOC, 154–156 customer reviews, 154 data-generating process, 87–88 direct queries to customers, 154 images, 155 preprocessing, 156–157 social media, 154 sources, 154 text, 155 trading, 1 transformation, 160 types, 155–156 user engagement, 155–156 De-bias pricing algorithms, 117 Decision trees, 82 Decision types, 15–27 Decision-makers in marketing, 218 Deep learning (DL), 8, 82, 192, 230, 239–240 algorithms, 248, 250, 265 architectures for NLP, 200–202 causal inference, 268 combine unstructured data with structured data, 265–266 common testbeds, 268–269 customized algorithm development, 265–266 customized constraint, 266 deep learning–based language model, 174 future directions, 266–269 in marketing, 239–240, 243–244 model efficiency improvement, 267 models, 126, 220–221, 224 multimodal, five senses, and networks, 267 neural networks, 242–248 plug and play, 265 problems, 265–266 properties, 241–242 reinforcement learning, 267–268 theory-driven architecture design, 266 theory-driven initialization, 266 Deep neural networks (DNN), 81, 242 Deep Q-Network (DQN), 248–249 Deep reinforcement learning (DRL), 262–265 Deepfakes, 139–140 Demand, real-time swings in, 105–110 Diachronic word embeddings, 199–200 Dictionary and word frequency–based text mining, 179–180 Difference-in-difference estimation approach, 194–195 Digital cameras, 150 Digital exhaust of individual behavior, 1 Digital footprints, 147–148 Digital voice assistants, 139 Direct marketing context, 138 Direct method, 85 Direct queries to customers, 154 Discriminative deep learning models, 249–258 CNNs, 249–254 RNN, 254–255 transformers, 255–258 Discriminative models, 248–249 Discriminator network, 131 Disney, 88 Distributional hypothesis, 199 doc2vec, 200 Dominant color descriptor, 221 Double machine learning (DML), 178 Doubly Robust estimator (DR estimator), 86 Doubly robust method, 86 Dropout method, 248 Dynamic methods, 83–84 Dynamic models update customers, 138 Dynamic pricing, 105, 110, 115–116 E-commerce, 104 Emails, 132–133 Embedded Topic Model (ETM), 204 Embeddings, 157–158, 198–200, 204 Entropy, 87–88 Equilibrium analysis, strategic behavior and, 95–96 ERNIE 3.0, 203–204 European Union (EU), 33 Evaluation of AI methods, 161–162 Evaluative Lexicon 2.0, 197 Evidence lower bound (ELBO), 259 Example-based explanation techniques, 227–228 eXplainable Artificial Intelligence (XAI), 219 External validity, 164 Facebook (technology company), 13–14 engagement data, 156 news feed algorithm, 93 user-engagement data, 155–156 Fairness in marketing, 224 Fake reviews, 183 fastText, 198–199 Feature-level models, 221 Feedback data, 14 Field experimentation, 84 Field experiments, 229–230 Financial Times Top 50 journals (FT50 journals), 278–279, 284 Fine-tuning, 130 Firms, 3–4, 131 First-order methods, 247 Flexible supervised learning algorithms, 81 Frames, 219 Fuzzy SVM, 159 Gated recurrent unit (GRU), 255 GauGAN, 137 Gender differences, 294 General Data Protection Regulation (GDPR), 33 Generalizability, 81–82 Generative adversarial networks (GAN), 8, 131, 248–249, 260, 262 Generative deep learning models, 258–262 GAN, 260–262 VAE, 258–260 Generative models, 140, 248–249 Generative Pre-trained Transformer 3 (GPT-3), 125–126, 180–181, 198–199 Generative video models, 139 Generator network, 131 Global interpretability, 225 GLOVE, 180, 198–199 Google (technology company), 13–14 search engine algorithm, 134 search personalization, 93 Gradient-weighted class activation mapping (Grad-CAM), 161, 226–227 Heatmap method, 228 Hidden Markov Model (HMM), 165 Hulu, 88 Human–machine collaboration, 230 ImageNet, 268 Images, 155, 218 data, 220 image-based social media, 218 image/post clusters, 153 tagging, 157 Incentive-aware personalization, 96 Individual-level personalization, 77–78 InferNER approach, 195 Influence methods, 227 Input data, 14 Insight generation, 170–171 Instagram, 171 Instrumental variable approach (IV approach), 175 Interactive methods, 82–83 Internet, 151 Interpretability, 219, 228 issues, 224–228 Inverse Propensity Score estimator (IPS estimator), 85–86 ISI Web of Science, 171–172 Judgment, 14–15 Knowledge extraction, 227 LambdaMART ranking algorithm, 29 Language models, 131, 198, 204 generating textual content with, 129–131 marketing applications of, 204–205 Language structure and deep learning–based text mining, 180 Large language models, 140 Large-scale pretrained language models, 129–130 LDA, 195 Learning from audio visual data, 184 from interactive two-sided feedback, 185 Lexicons, 197–198 and word frequency–based methods, 180 Linguistic Inquiry and Word Count (LIWC), 197 Local interpretability, 226 Local Interpretable Model-Agnostic Explanations (LIME), 226 Long short-term memory (LSTM), 129, 157, 201–202, 255 Low response rates, 170 Lyft, 107 Machine learning (ML) (see also Deep learning (DL)), 14–15, 29–30, 32, 147–148, 170, 219, 240 algorithms, 150 methods, 82 promise of AI and, 149–150 Manual encoding, 219–220 Manual inspection, 160 Mapping methods to research questions, 162–165 posteriori–identified phenomena and constructs, 162–163 priori–defined constructs, 163–164 validation, 164–165 Market fairness, 224 research, 6 Marketers, 1–2 Marketing, 274 AI’s impact on consumers and society and vice versa, 8–9 algorithms and methods, 7–8 applications of language models, 204–206 communications, 132–133 data, 6–7 marketing-AI ecosystem, 2–4 modelers, 242–246 novel approaches for established tasks, 204 novel approaches for novel tasks, 204–205 opportunity identification for AI research, 10 purpose of AI, 4–6 research in marketing on Artificial Intelligence, 40–76 scholars, 1–2, 14, 28–30, 34, 193 Markov decision process (MDP), 263 Matrix factorization approaches, 81 Maximum likelihood estimation (MLE), 247 Mean Average Error (MAE), 87–88 Measurement error, 180 Megatron-Turing NLG, 125–126 Menu costs, 103 Metaphor elicitation technique, 151–152 Methodological approaches to personalization, 79–84 dynamic methods, 83–84 generalizability and counterfactual validity, 81–82 online and interactive methods, 82–83 scalability, 80–81 Metric-based evaluation, 87–88 Mind perception, 299 mini-Xception, 222–223 Model interpretability, 219 Model interpretation, 160–161 with manual inspection and data transformation, 160 post hoc model explanation, 161 Model-agnostic interpretability, 227 Model-agnostic techniques, 226 Model-specific interpretability, 226–227 Multi-armed bandit (MAB), 113 Multiarmed bandit models (MAB models), 5 Multihead attention, 202 Naive Bayes classifier, 178 Named entity extraction (NER), 195 Natural language generation models (NLG models), 125–126, 129–130 Natural language inference task (NLI task), 205 Natural language processing (NLP), 3, 7, 150, 172–173, 192 applications, 192 challenges, biases, and potential harms, 208–209 concept and topic extraction, 195–197 current state of NLP in marketing, 195–198 embeddings, language models, transfer learning, 198–204 established and novel tools for diverse text-based marketing applications, 196 marketing applications of language models, 204–205 relationship extraction, 197 roadmap and future trends, 206–207 sentiment and writing style extraction, 197–198 text in marketing, 193–195 Netflix, 88 Network embeddings, 163 Neural networks (NN), 242, 248 activation functions, 246 architecture, 246 objective function, 247 optimizer, 247–248 regularization, 248 News personalization, 83 Nonconvergence, 262 Nonparametric approach, 115 Nontech firms, 267 Nontextual data, 194 Objective function, 247 Offline beacons, 1 Online customer feedback, 175–183 AI and machine learning in analyzing unstructured review data, 178–181 challenges in learning from, 181–183 economic impact of online reviews, 177–178 Online forum discussions, 30 Online methods, 82–83 Online platforms, 147–148 Online reviews, 30 OpenCV, 222–223 Optimal algorithm, 115 Optimizer, 247–248 Overlap assumption, 82 Peer influence, 182–183 Personality, 295 Personalization algorithms, 82–83, 91 alternative approaches, 87–88 direct method, 85 doubly robust method, 86 evaluation, 84–88 extensions to special settings, 86–87 IPS estimator, 85–86 methodological approaches to personalization, 79–84 models, 94–95 multiple objectives and long-term outcomes, 94–95 problem definition, 78–79 returns to personalization, 88–90 signal-to-noise ratio, 94 strategic behavior and equilibrium analysis, 95–96 time drifts, 95 and welfare, 90–93 Personalized policy design, 78–79 Personalized pricing, 89–90, 110, 113, 117 Personification, 278 Photorealistic images, 131 Pix2pix approach, 132 Pixel-level models, 222 Plug and play language models (PPLM), 130 Poisson factorization, 197 Polarization, 93 Position encoding, 255, 257 Post hoc model explanation, 161 Posteriori–identified phenomena and constructs, 162–163 Prediction Machines , 14, 32–33 Predictions, 4, 28, 30 prediction-based algorithms, 158–159 process, 14 Predictive ML algorithms, 163 Preprocessing images, 157 Price discrimination, 110–113 Price experimentation, 113–115 Pricing automation, 105–107 consequences of AI for pricing, 115–119 dynamic pricing, 105–110 firms implementing AI for pricing, 104–115 personalized pricing, 110–113 price experimentation, 113–115 Primary data, 149 Prime Video, 88 Principal component analysis (PCA), 258 Priori–defined constructs, 163–164 Privacy, personalization and welfare, 91–92 Probabilistic content generation process, 130 Product development, 150 Propensity-based approaches, 87 Prospective customers, 126 Prototypes, 227–228 Q-learning algorithm, 115 models, 118 Q-value function approximator, 264–265 Quantitative marketers, 1 Racist language, 130 Random Forests, 81–82 Recency Frequency Monetary value (RFM value), 115 Rectified linear units (ReLu), 246 Recurrent neural networks (RNNs), 129, 159, 200–201, 248–249, 254–255 Recursive neural networks, 129 Regression models, 225 Regularization, 248 Regulators, 4 Reinforcement learning (RL), 248–249, 267–268 Relationship development, 136–138 Relationship extraction, 197 Relative Information Gain (RIG), 87–88 Representation learning, 240–241 Reputation platforms, 171 systems, 175 ResNet-50, 222 Restricted Boltzmann machine (RBM), 242 Ride-hailing platforms, 107 RoBERTa, 157, 198–199 Robots, 289–290 Rule-based learners, 226–227 Scalability, methodological approaches to personalization, 80–81 Scale-Invariant Feature Transform (SIFT), 221 SCImago Journal & Country Rank, 278–279 SE-ResNet-50, 222 Search engine optimization (SEO), 30, 133, 205 Second-order methods, 247 Seeded LDA, 195–196 Selection bias, 183 Self-attention, 255 Self-selection, 182 Self-supervised representation learning, 200 Semantic network analysis, 179–180 Sentence-based LDA, 195–196 SentenceBERT, 200 Sentiment analysis, 151, 198 Sentiment and writing style extraction, 197–198 Sequence-to-sequence models, 203 SHapley Additive exPlanations (SHAP), 161, 226 algorithm, 226 values, 163 “Shipping then shopping” strategy, 32–33 “Shop, then ship” model, 4 Short-term rental market, 107 Signal-to-noise ratio, 94 Small-and medium-sized enterprises (SMEs), 267 “Smart pricing” tool, 117 Social media, 147–148, 154, 218 messages, 135–136 messaging, 135 posts, 132–133 Social Sciences Citation Index (SSCI), 171–172 Speeded-Up Robust Features, 221 Standard reinforcement learning algorithm, 118 Stanford Named Entity recognizer, 195 “Stick-and carrot” strategies, 118 Stochastic gradient descent (SGD), 247–248 Stochastic parrots, 140 Stroop test performance, 293 Structural models, 225 Style-based GAN (StyleGAN), 131–132 Subnetworks, 131 Subscription-based “shipping-then-shopping” business model, 32–33 Supervised learning algorithms, 81 Supervised ML models, 151 Supply, real-time swings in, 105–110 Support vector machines (SVM), 29, 159, 178 Surge pricing algorithms, 107 Survey-based perceptual maps, 152 Synthetic images, generating, 131–133 Technology, 274 companies, 13–14 Text data, 7, 155 Text in marketing, 193–195 causality, 194–195 dependent variable, 194 dual role of language, 193 independent variables, 194 Text mining, 192 algorithms, 240 Textual analysis in marketing, 192–193 Textual consumer feedback, 179–181 Textual content with language models, generating, 129–131 Textures, 221 3D convolutional neural network, 220–221 TikTok, 218 Time drifts, 95 Topic modeling, 158, 192 Traditional LDA approach, 195–196 Training data, 14 Training process, 131 Transaction data, 149 Transfer learning, 198, 202, 204, 222 Transform data, 157 Transformer-based models, 157, 202, 204 Transformers, 202, 255, 258 Twitter, 171 Uber, 107 Unconditional counterfactual explanations, 227–228 Unconfoundedness assumption, 82 Underspecification, 209 Uniform policy, 79 Unstructured data, 170, 192, 218 Unsupervised learning, 157–158 clustering, 158 embeddings, 157–158 topic modeling, 158 Upper confidence bound algorithm (UCB algorithm), 115 US Congress, 116 User clusters, 153 User engagement, 155–156 User-generated content (UGC), 30, 147–149, 170–171 challenges in use of, 149 customer feedback from, 176 data preprocessing, 156–157 evaluation, 161–162 hybrid of unsupervised and supervised learning, 159–160 model interpretation, 160–161 prediction-based algorithms, 158–159 promise of, 149 tools and methods to understand, 156–162 unsupervised learning, 157–158 User-generated text, 156 User–brand interaction, 152–153 VADER, 197 Validation, 164–165 Value functions, 263 Variational autoencoders (VAE), 8, 160, 248–249, 258, 260 Vector semantics, 199–200 VGG-16 algorithm, 159 Video analytics, 7 Video content, 137 Video data, 220 Video platforms, 218 Virtual reality (VR), 7, 228–229 Visual consumer feedback, 181 Visual content, 137 Visual data, 7 Visualization techniques, 227 Voice of the Customer (VOC), 6, 147–148, 150 data available for AI AND, 154–156 importance of, 148 practice before artificial intelligence and big data, 148–149 Volume, velocity, variety (3Vs), 3 VOSviewer software, 173 Welfare fairness, 92–93 personalization and, 90–93 polarization, 93 privacy, 91–92 search cost, 91 White House’s Council of Economic Advisors (White House’s CEA), 117 Word embeddings, 198–199 Word-of-mouth (WOM), 172–173 Word2Vec (language embedding algorithm), 157, 180, 198–200 XAI methods, 224–228 model specificity, 226–228 model transparency, 224–225 scope of explanation, 225–226 XGBoost, 81, 159 Yelp, 171 YouTube, 88, 93, 218 ZIP codes, 111 Book Chapters Prelims The State of AI Research in Marketing: Active, Fertile, and Ready for Explosive Growth The Economics of Artificial Intelligence: A Marketing Perspective AI and Personalization Artificial Intelligence and Pricing Leveraging AI for Content Generation: A Customer Equity Perspective Artificial Intelligence and User-Generated Data Are Transforming How Firms Come to Understand Customer Needs Artificial Intelligence Applications to Customer Feedback Research: A Review Natural Language Processing in Marketing Marketing Through the Machine's Eyes: Image Analytics and Interpretability Deep Learning in Marketing: A Review and Research Agenda Anthropomorphism in Artificial Intelligence: A Review of Empirical Work Across Domains and Insights for Future Research Index

Industrial processes contribute significantly to environmental degradation through emissions, waste, and resource depletion. The need for real-time monitoring and mitigation strategies has led to the adoption of deep learning (DL) models for predictive analytics and automated decision-making. This study explores the application of deep learning techniques in predicting and mitigating the environmental impact of industrial activities. We review state-of-the-art deep learning architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM) networks, and transformers, in processing large-scale environmental data. These models analyze real-time sensor data, satellite imagery, and industrial parameters to forecast pollution levels, detect anomalies, and optimize industrial operations for sustainability. Key advancements in deep learning, such as hybrid architectures integrating deep reinforcement learning (DRL) and generative adversarial networks (GANs), enhance predictive accuracy and robustness in environmental monitoring systems. Transfer learning and federated learning approaches facilitate scalable and adaptive solutions across diverse industrial sectors. The study highlights the role of DL in early detection of air and water pollution, energy consumption optimization, and emission control through predictive maintenance and process adjustments. Moreover, integrating explainable artificial intelligence (XAI) ensures model interpretability, fostering trust among policymakers and industry stakeholders. Challenges in deploying deep learning models include data heterogeneity, computational complexity, and model interpretability. To address these issues, we discuss techniques such as data augmentation, adversarial training, and edge AI implementation for real-time processing. Ethical and regulatory considerations surrounding AI-driven environmental monitoring are also examined to ensure compliance with sustainability standards. This research underscores the transformative potential of deep learning in industrial sustainability, emphasizing its role in real-time decision support systems. Future directions involve integrating quantum computing and neuromorphic computing for enhanced model efficiency and expanding interdisciplinary collaborations for AI-driven environmental governance. By leveraging deep learning for predictive environmental impact assessment, industries can transition toward greener and more efficient operational frameworks.

This paper presents a detailed review of existing and emerging deep learning algorithms for time series forecasting in geotechnics and geoscience applications. Deep learning has shown promising results in addressing complex prediction problems involving large datasets and multiple interacting variables without requiring extensive feature extraction. This study provides an in-depth description of prominent deep learning methods, including recurrent neural networks (RNNs), convolutional neural networks (CNNs), generative adversarial network, deep belief network, reinforcement learning, attention and transformer algorithms as well as hybrid networks using a combination of these architectures. In addition, this paper summarizes the applications of these models in various fields, including mining and tunnelling, railway and road construction, seismology, slope stability, earth retaining and stabilizing structures, remote sensing, as well as scour and erosion. This review reveals that RNN-based models, particularly Long Short-Term Memory networks, are the most commonly used models for time series forecasting. The advantages of deep learning models over traditional machine learning, including their superior ability to handle complex patterns and process large-scale data more effectively, are discussed. Furthermore, in time series forecasting within the fields of geotechnics and geosciences, studies frequently reveal that deep learning methods tend to surpass traditional machine learning techniques in effectiveness.

Deep learning, which is originated from an artificial neural network (ANN), is one of the major technologies of today’s smart cybersecurity systems or policies to function in an intelligent manner. Popular deep learning techniques, such as multi-layer perceptron, convolutional neural network, recurrent neural network or long short-term memory, self-organizing map, auto-encoder, restricted Boltzmann machine, deep belief networks, generative adversarial network, deep transfer learning, as well as deep reinforcement learning, or their ensembles and hybrid approaches can be used to intelligently tackle the diverse cybersecurity issues. In this paper, we aim to present a comprehensive overview from the perspective of these neural networks and deep learning techniques according to today’s diverse needs. We also discuss the applicability of these techniques in various cybersecurity tasks such as intrusion detection, identification of malware or botnets, phishing, predicting cyberattacks, e.g. denial of service, fraud detection or cyberanomalies, etc. Finally, we highlight several research issues and future directions within the scope of our study in the field. Overall, the ultimate goal of this paper is to serve as a reference point and guidelines for the academia and professionals in the cyber industries, especially from the deep learning point of view.

1 - Congruence of deep learning in biomedical engineering: future prospects and challenges

Deep Learning and Beyond: AI’s New Horizons” is an expansive monograph that delves into the dynamic and rapidly evolving field of deep learning, a key area of artificial intelligence. This comprehensive work provides an insightful exploration of the revolutionary developments, foundational concepts, and future directions of deep learning, positioning itself as a crucial resource for understanding this transformative technology. The monograph commences with an introductory chapter that lays the groundwork by elucidating the background of machine learning and chronicling the ascent of deep learning. This foundational perspective is vital for comprehending the underlying principles and methodologies that have propelled deep learning to the forefront of technological innovation. Subsequent chapters delve into the “Historical Evolution” of AI, tracing the origins of neural networks from their early inception to the Renaissance period of deep learning. This historical analysis is pivotal in understanding the field’s trajectory and significant milestones. The “Foundations of Deep Learning” chapter presents an in-depth analysis of neural networks, covering their basic architecture, activation functions, layers, and the critical process of backpropagation and gradient descent. This is followed by examining “Advanced Neural Network Architectures,” where the focus shifts to state-of-the-art models such as convolutional neural networks, recurrent neural networks, and groundbreaking transformers and attention mechanisms. In addressing the “Training Deep Models,” the monograph explores regularization techniques, optimization strategies, and the increasingly significant role of transfer learning and pre-trained models. The application-oriented chapter, “Applications of Deep Learning,” showcases real-world implementations in domains like image recognition, natural language viii processing, and autonomous systems, illustrating deep learning technologies’ vast potential and versatility. The monograph also tackles the “Challenges in Deep Learning,” including overfitting, interpretability, and the ethical considerations surrounding AI deployment. It then ventures into the “Future of Deep Learning,” contemplating emerging trends such as integrating quantum computing, neural architecture search, and the convergence of neuroscience and deep learning. “Case Studies” provide practical insights and empirical evidence of the impact of deep learning. At the same time, the concluding chapter synthesizes the current state of deep learning, offers predictions for the future, and provides reflective thoughts on this transformative field. “Deep Learning and Beyond: AI’s New Horizons” educates and informs readers about the vast potential and future possibilities of deep learning and artificial intelligence. Keywords: Deep Learning, Artificial Intelligence, Neural Networks, Machine Learning, Convolutional Neural Networks, Recurrent Neural Networks, Transformers, Quantum Computing, Ethical AI, Neural Architecture Search, Natural Language Processing, Autonomous Systems, AI in Healthcare, Future of AI, AI Applications, AI Challenges.

What can classic Atari video games tell us about the human brain?

“NEURAL NETWORKS AND DEEP LEARNING: THEORITICAL INSIGHTS AND FRAMEWORKS” is a comprehensive guide that dives deep into the world of neural networks and their applications in modern technology. From foundational theories to cutting-edge advancements, this book provides readers with a comprehensive understanding of deep learning and its potential impact on various fields. In Chapter 1: Introduction to Neural Networks and Deep Learning, readers are introduced to the theoretical underpinnings of deep learning and its real-world applications. The chapter explores key concepts, navigates through neural network architectures, and discusses the current landscape of deep learning research. It also addresses ethical considerations and social implications, highlighting the intersection of deep learning with other disciplines. Chapter 2: Mathematical Foundations of Neural Networks lays the groundwork by covering essential mathematical concepts relevant to deep learning. From linear algebra to calculus, probability, and statistics, readers gain insights into the mathematical rigor behind neural network operations. The chapter also delves into optimization techniques and advanced mathematical concepts crucial for understanding deep learning models. Chapter 3: Single-Layer Perceptrons and Feedforward Networks explores the building blocks of neural networks, including perceptrons and activation functions. It discusses universal approximation theorems, backpropagation algorithms, and weight initialization techniques. Additionally, the chapter addresses challenges such as vanishing and exploding gradient problems, along with evolutionary algorithms and self-organizing maps. Chapter 4: Convolutional Neural Networks (CNNs) focuses on specialized architectures designed for image processing tasks. Readers learn about convolutional layers, pooling operations, and hierarchical feature learning. The chapter also covers object localization, transfer learning, and interpretability of CNNs, along with advanced architectures like capsule networks. Chapter 5: Recurrent Neural Networks (RNNs) delves into sequential data processing, temporal dependencies, and architectures like Long Short-Term Memory (LSTM) networks and Gated Recurrent Unit (GRU) networks. It addresses training challenges and explores real-world applications of recurrent networks. Chapter 6: Generative Adversarial Networks (GANs) introduces readers to the innovative concept of GANs and their applications in image generation. The chapter discusses training dynamics, challenges, and ethical considerations surrounding GANs. It also explores future developments and applications in creativity and adversarial robustness. Chapter 7: Autoencoders and Variational Autoencoders (VAEs) explores unsupervised learning techniques for representation learning and anomaly detection. Readers learn about various types of autoencoders, including adversarial autoencoders and quantum autoencoders. Chapter 8: Reinforcement Learning and Deep Q Networks (DQNs) provides insights into reinforcement learning fundamentals, Markov decision processes, and deep Q networks. It discusses policy gradient methods and their applications in real-world scenarios. Chapter 9: Transfer Learning in Deep Neural Networks explores transfer learning paradigms, domain adaptation techniques, and the role of transfer learning in achieving explainable AI. Readers gain insights into evaluating performance and generalization in transfer learning, along with applications in various domains. “NEURAL NETWORKS AND DEEP LEARNING: THEORITICAL INSIGHTS AND FRAMEWORKS” is an invaluable resource for researchers, practitioners, and enthusiasts looking to deepen their understanding of neural networks and harness the power of deep learning in diverse applications.

Comprehensive Study for Breast Cancer Using Deep Learning and Traditional Machine Learning

Focus issue: Artificial intelligence in medical physics.

Artificial Intelligence (AI) has emerged as a defining force in the current era, shaping the contours of technology and deeply permeating our everyday lives. From autonomous vehicles to predictive analytics and personalized recommendations, AI continues to revolutionize various facets of human existence, progressively becoming the invisible hand guiding our decisions. Simultaneously, its growing influence necessitates the need for a nuanced understanding of AI, thereby providing the impetus for this book, “Introduction to Artificial Intelligence and Neural Networks.” This book aims to equip its readers with a comprehensive understanding of AI and its subsets, machine learning and deep learning, with a particular emphasis on neural networks. It is designed for novices venturing into the field, as well as experienced learners who desire to solidify their knowledge base or delve deeper into advanced topics. In Chapter 1, we provide a thorough introduction to the world of AI, exploring its definition, historical trajectory, and categories. We delve into the applications of AI, and underscore the ethical implications associated with its proliferation. Chapter 2 introduces machine learning, elucidating its types and basic algorithms. We examine the practical applications of machine learning and delve into challenges such as overfitting, underfitting, and model validation. Deep learning and neural networks, an integral part of AI, form the crux of Chapter 3. We provide a lucid introduction to deep learning, describe the structure of neural networks, and explore forward and backward propagation. This chapter also delves into the specifics of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). In Chapter 4, we outline the steps to train neural networks, including data preprocessing, cost functions, gradient descent, and various optimizers. We also delve into regularization techniques and methods for evaluating a neural network model. Chapter 5 focuses on specialized topics in neural networks such as autoencoders, Generative Adversarial Networks (GANs), Long Short-Term Memory Networks (LSTMs), and Neural Architecture Search (NAS). In Chapter 6, we illustrate the practical applications of neural networks, examining their role in computer vision, natural language processing, predictive analytics, autonomous vehicles, and the healthcare industry. Chapter 7 gazes into the future of AI and neural networks. It discusses the current challenges in these fields, emerging trends, and future ethical considerations. It also examines the potential impacts of AI and neural networks on society. Finally, Chapter 8 concludes the book with a recap of key learnings, implications for readers, and resources for further study. This book aims not only to provide a robust theoretical foundation but also to kindle a sense of curiosity and excitement about the endless possibilities AI and neural networks offer. The journ

Deep learning (DL) has become a core component of modern artificial intelligence (AI), driving significant advancements across diverse fields by facilitating the analysis of complex systems, from protein folding in biology to molecular discovery in chemistry and particle interactions in physics. However, the field of deep learning is constantly evolving, with recent innovations in both architectures and applications. Therefore, this paper provides a comprehensive review of recent DL advances, covering the evolution and applications of foundational models like convolutional neural networks (CNNs) and Recurrent Neural Networks (RNNs), as well as recent architectures such as transformers, generative adversarial networks (GANs), capsule networks, and graph neural networks (GNNs). Additionally, the paper discusses novel training techniques, including self-supervised learning, federated learning, and deep reinforcement learning, which further enhance the capabilities of deep learning models. By synthesizing recent developments and identifying current challenges, this paper provides insights into the state of the art and future directions of DL research, offering valuable guidance for both researchers and industry experts.

The arrival of intelligent, general-purpose robots that can learn to perform new tasks autonomously has been promised for a long time now. Deep reinforcement learning, which combines reinforcement learning with deep neural network function approximation, has the potential to enable robots to learn to perform a wide range of new tasks while requiring very little prior knowledge or human help. This framework might therefore help to finally make general purpose robots a reality. However, the biggest successes of deep reinforcement learning have so far been in simulated game settings. To translate these successes to the real world, significant improvements are needed in the ability of these methods to learn quickly and safely. This thesis investigates what is needed to make this possible and makes contributions towards this goal. <br/><br/>Before deep reinforcement learning methods can be successfully applied in the robotics domain, an understanding is needed of how, when, and why deep learning and reinforcement learning work well together. This thesis therefore starts with a literature review, which is presented in Chapter 2. While the field is still in some regards in its infancy, it can already be noted that there are important components that are shared by successful algorithms. These components help to reconcile the differences between classical reinforcement learning methods and the training procedures used to successfully train deep neural networks. The main challenges in combining deep learning with reinforcement learning center around the interdependencies of the policy, the training data, and the training targets. Commonly used tools for managing the detrimental effects caused by these interdependencies include target networks, trust region updates, and experience replay buffers. Besides reviewing these components, a number of the more popular and historically relevant deep reinforcement learning methods are discussed.<br/><br/>Reinforcement learning involves learning through trial and error. However, robots (and their surroundings) are fragile, which makes these trials---and especially errors---very costly. Therefore, the amount of exploration that is performed will often need to be drastically reduced over time, especially once a reasonable behavior has already been found. We demonstrate how, using common experience replay techniques, this can quickly lead to forgetting previously learned successful behaviors. This problem is investigated in Chapter 3. Experiments are conducted to investigate what distribution of the experiences over the state-action space leads to desirable learning behavior and what distributions can cause problems. It is shown how actor-critic algorithms are especially sensitive to the lack of diversity in the action space that can result form reducing the amount of exploration over time. Further relations between the properties of the control problem at hand and the required data distributions are also shown. These include a larger need for diversity in the action space when control frequencies are high and a reduced importance of data diversity for problems where generalizing the control strategy across the state-space is more difficult.<br/><br/>While Chapter 3 investigates what data distributions are most beneficial, Chapter 4 instead proposes practical algorithms to {select} useful experiences from a stream of experiences. We do not assume to have any control over the stream of experiences, which makes it possible to learn from additional sources of experience like other robots, experiences obtained while learning different tasks, and experiences obtained using predefined controllers. We make two separate judgments on the utility of individual experiences. The first judgment is on the long term utility of experiences, which is used to determine which experiences to keep in memory once the experience buffer is full. The second judgment is on the instantaneous utility of the experience to the learning agent. This judgment is used to determine which experiences should be sampled from the buffer to be learned from. To estimate the short and long term utility of the experiences we propose proxies based on the age, surprise, and the exploration intensity associated with the experiences. It is shown how prior knowledge of the control problem at hand can be used to decide which proxies to use. We additionally show how the knowledge of the control problem can be used to estimate the optimal size of the experience buffer and whether or not to use importance sampling to compensate for the bias introduced by the selection procedure. Together, these choices can lead to a more stable learning procedure and better performing controllers. <br/><br/>In Chapter 5 we look at what to learn form the collected data. The high price of data in the robotics domain makes it crucial to extract as much knowledge as possible from each and every datum. Reinforcement learning, by default, does not do so. We therefore supplement reinforcement learning with explicit state representation learning objectives. These objectives are based on the assumption that the neural network controller that is to be learned can be seen as consisting of two consecutive parts. The first part (referred to as the state encoder) maps the observed sensor data to a compact and concise representation of the state of the robot and its environment. The second part determines which actions to take based on this state representation. As the representation of the state of the world is useful for more than just completing the task at hand, it can also be trained with more general (state representation learning) objectives than just the reinforcement learning objective associated with the current task. We show how including these additional training objectives allows for learning a much more general state representation, which in turn makes it possible to learn broadly applicable control strategies more quickly. We also introduce a training method that ensures that the added learning objectives further the goal of reinforcement learning, without destabilizing the learning process through their changes to the state encoder. <br/><br/>The final contribution of this thesis, presented in Chapter 6, focuses on the optimization procedure used to train the second part of the policy; the mapping from the state representation to the actions. While we show that the state encoder can be efficiently trained with standard gradient-based optimization techniques, perfecting this second mapping is more difficult. Obtaining high quality estimates of the gradients of the policy performance with respect to the parameters of this part of the neural network is usually not feasible. This means that while a reasonable policy can be obtained relatively quickly using gradient-based optimization approaches, this speed comes at the cost of the stability of the learning process as well as the final performance of the controller. Additionally, the unstable nature of this learning process brings with it an extreme sensitivity to the values of the hyper-parameters of the training method. This places an unfortunate emphasis on hyper-parameter tuning for getting deep reinforcement learning algorithms to work well. Gradient-free optimization algorithms can be more simple and stable, but tend to be much less sample efficient. We show how the desirable aspects of both methods can be combined by first training the entire network through gradient-based optimization and subsequently fine-tuning the final part of the network in a gradient-free manner. We demonstrate how this enables the policy to improve in a stable manner to a performance level not obtained by gradient-based optimization alone, using many fewer trials than methods using only gradient-free optimization.<br/>