Deep Learning Applications in Operations Research

1. Predicting Crop Yield Using Quantum Neural Networks, 2. A Comprehensive Survey on Risk Factor Monitoring Using Deep Learning Methods on Electrocardiogram Data, 3. AI-Powered Data-Centric Approaches: Enhancing Information Quality for Deep Learning Algorithms, 4. Multi-Attribute Decision Modeling, 5. Unmasking Transformations: CNNs for Detecting Land Cover Changes in Satellite Imagery, 6. Leafine: An AI Tool to Recognize and Perceive Leaf Illness with Manure Suggestions, 7. An Expansive Performance Analysis and Comparison between Different Supervised and Unsupervised ML Algorithms for Categorization of ICU Patients at an Indian Hospital, 8. Darknet for Gun and Suspicious Activity Detection and Crime Prediction, 9. Image Edge Detection Using Fireflies to Fine-Tuned Deep Convolution Networks, 10. Application of Machine Learning, Deep Learning, and Econometric Models in Stock Price Movement of Rain Industries: An In-Depth Analysis, 11. Performance Analysis of U-Net and Fully Convolutional Regression Network on Jetson Nano for Real-Time Inventory Analysis, 12. Clinical Decision Support System for Prevention of Puberty Disorders and Fertility Issues due to Noyyal River Pollution using Ensemble Learning Techniques, 13. Obesity Prediction Using Machine Learning, 14. Intuitionistic Fuzzy Dombi-Archimedean Weighted Aggregation Operators and Their Applications in Sustainable Material Selection, 15. Identification of Rice Leaf Disease Using Gaussian Mixture Model: A Machine Learning Approach Using Image Classification Techniques, 16. Multi-Objective Optimization of Economic Development and Environmental Issues in the Yangtze River Basin, China, 17. Qualitative Study on E-Commerce and Brick-and-Mortar Stores: A Machine Learning Approach, 18. Design of Novel Energy Management System in Solar PV Powered EV Charging Station Using Artificial Gorilla Troops Optimization, 19. School Students' Cataract Prediction Using Machine Learning, 20. Minimization of the Threat of Diabetic Kidney Disease through the Lens of Machine Learning, 21. A Novel Segmentation and Feature Extraction-Based Plant Disease Diagnosis Method Based on Stacked Ensemble Learning