Deep Learning and Neural Networks: Decision-Making Implications
Abstract
:1. Introduction
- RQ1: What are the decision-making implications of deep learning and neural networks?
- RQ2: How can deep learning and neural networks be used in decision support systems?
- RQ3: What are future directions and research opportunities of deep learning and neural networks for decision-making implications?
2. Overview of Deep Learning and Neural Networks
2.1. Key Concepts
2.2. Preprocessing Stages and Caveats in AI Decision Making
2.3. Types of Neural Networks Used in Decision Making
2.4. Deep Learning Algorithms and Architectures
2.5. Applications of Deep Learning and Neural Networks in Decision Making
3. Methodology
4. Results
4.1. Subject Area
4.2. Publishing Year
5. Decision-Making Models and Frameworks
5.1. Improved Accuracy and Performance in Decision Making
5.2. Challenges and Limitations of Deep Learning and Neural Networks
6. Interdisciplinary Synergy in Deep Learning for Decision Making
6.1. Medical Diagnosis
6.2. Post-Disaster Decision Making
6.3. Financial Analysis
6.4. Affective Computing
6.5. Clinical Decision Support
6.6. Other Applications
7. Future Directions and Research Opportunities
7.1. Emerging Trends and Technologies
7.2. Interdisciplinary Collaboration and Integration
8. Conclusions
Funding
Data Availability Statement
Conflicts of Interest
Abbreviations
MCDM | Multi-Criteria Decision Making |
AI | Artificial Intelligence |
RL | Reinforcement Learning |
FNN | Feedforward Neural Network |
RNN | Recurrent Neural Network |
CNN | Convolutional Neural Network |
NLP | Natural Language Processing |
LSTM | Long Short-Term Memory |
GAN | Generative Adversarial Network |
GRU | Gated Recurrent Unit |
RBM | Restricted Boltzmann Machine |
PTT | Pulse Transition Time |
SVM | Support Vector Machine |
k-NN | k-nearest Neighbors |
T2DM | Type 2 Diabetic Mellitus |
EVCT | Explaining and Visualizing CNNs for Text Information |
CDSS-T | Cancer Treatment Response Assessment |
VAE | Variational Autoencoder |
XAI | Explainable AI |
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Application | Description | Technical Aspect |
---|---|---|
Image and Object Recognition | Deep learning models for image classification, object detection, and facial recognition. | CNNs and Transfer Learning |
NLP | Neural networks for language translation, sentiment analysis, and question-answering systems. | RNNs, Transformers, and Word Embeddings |
Recommender Systems | Personalized recommendations in e-commerce, streaming services, and social media platforms. | Collaborative Filtering and Matrix Factorization |
Financial Decision Making | Stock market prediction, fraud detection, credit scoring, and algorithmic trading. | Time Series Analysis and Reinforcement Learning |
Healthcare and Medicine | Medical diagnosis, disease prediction, and treatment planning using medical data and images. | Medical Imaging Analysis and Clinical Data Integration |
Autonomous Systems | Decision making in self-driving cars, drones, and robots for navigation and task execution. | Sensor Fusion and Path Planning |
Anomaly Detection | Identifying anomalies or outliers in network security, fraud detection, and predictive maintenance. | Autoencoders and Isolation Forests |
Gaming and Strategy | Deep learning models trained through RL for game playing and strategy. | RL and Deep Q-Networks (DQN) |
Field | Number of Papers |
---|---|
Computer Science | 14 |
Decision Sciences | 6 |
Engineering | 6 |
Medicine and Dentistry | 6 |
Business, Management, and Accounting | 5 |
Biochemistry, Genetics, and Molecular Biology | 2 |
Physics and Astronomy | 2 |
Agricultural and Biological Sciences | 1 |
Earth and Planetary Sciences | 1 |
Energy | 1 |
Decision-Making Framework | Domain | Reference |
---|---|---|
CNN | Healthcare: Pressure ulcer assessment system | [84] |
Transportation: Intelligent decision making for automated vehicles | [86] | |
General: Flash-flood hazard prediction | [89] | |
General: Socially responsible investments and portfolio optimization | [90] | |
PANN | Healthcare: Pattern recognition in medical diagnostics decision making | [85] |
Fuzzy Logic | General: Flash-flood hazard prediction | [89] |
General: Micro-grid energy management | [88] | |
Opinion mining: Integration with CNN | [91] | |
MCDM Analysis | General: Flash-flood hazard prediction | [89] |
H2O R Package | General: Flash-flood hazard prediction | [89] |
LSTM | Energy: Intelligent energy management for micro-grid | [88] |
Dimension-Reduction Method | Healthcare: Medical decision making | [92] |
Deep Learning | Healthcare: Deep neural networks for health decision making | [93] |
Transportation: Automated highway pavement maintenance | [87] | |
General: Socially responsible investments and portfolio optimization | [90] |
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© 2023 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
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Taherdoost, H. Deep Learning and Neural Networks: Decision-Making Implications. Symmetry 2023, 15, 1723. https://doi.org/10.3390/sym15091723
Taherdoost H. Deep Learning and Neural Networks: Decision-Making Implications. Symmetry. 2023; 15(9):1723. https://doi.org/10.3390/sym15091723
Chicago/Turabian StyleTaherdoost, Hamed. 2023. "Deep Learning and Neural Networks: Decision-Making Implications" Symmetry 15, no. 9: 1723. https://doi.org/10.3390/sym15091723