:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Thilakarathne, Navod Neranjan, Kagita, Mohan Krishna, Lanka, Surekha, Ahmad, Hussain
Format:	Preprint
Published:	2020
Subjects:	Artificial Intelligence Machine Learning
Online Access:	https://arxiv.org/abs/2010.08094
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Information Fusion in Smart Agriculture: Machine Learning Applications and Future Research Directions
by: Katharria, Aashu, et al.
Published: (2024)

Agentic AI: A Comprehensive Survey of Architectures, Applications, and Future Directions
by: Ali, Mohamad Abou, et al.
Published: (2025)

A Comprehensive Survey on Architectural Advances in Deep CNNs: Challenges, Applications, and Emerging Research Directions
by: Khan, Saddam Hussain, et al.
Published: (2025)

Power Plays: Unleashing Machine Learning Magic in Smart Grids
by: Rashid, Abdur, et al.
Published: (2024)

Federated Learning for Big Data: A Survey on Opportunities, Applications, and Future Directions
by: Gadekallu, Thippa Reddy, et al.
Published: (2021)

Deep Generative Models for Offline Policy Learning: Tutorial, Survey, and Perspectives on Future Directions
by: Chen, Jiayu, et al.
Published: (2024)

Structure in Deep Reinforcement Learning: A Survey and Open Problems
by: Mohan, Aditya, et al.
Published: (2023)

A Deep Q-Learning based Smart Scheduling of EVs for Demand Response in Smart Grids
by: Chifu, Viorica Rozina, et al.
Published: (2024)

Inertial Navigation Meets Deep Learning: A Survey of Current Trends and Future Directions
by: Cohen, Nadav, et al.
Published: (2023)

A Survey of Cross-domain Graph Learning: Progress and Future Directions
by: Zhao, Haihong, et al.
Published: (2025)

Deep Reinforcement Learning for Optimizing Energy Consumption in Smart Grid Systems
by: Alsheikhi, Abeer, et al.
Published: (2026)

The Importance of Architecture Choice in Deep Learning for Climate Applications
by: Dräger, Simon, et al.
Published: (2024)

The Role of LLMs in Sustainable Smart Cities: Applications, Challenges, and Future Directions
by: Ullah, Amin, et al.
Published: (2024)

Cyber-Physical Anomaly Detection in IoT-Enabled Smart Grids Using Machine Learning and Metaheuristic Feature Optimization
by: Alihodžić, Adis, et al.
Published: (2026)

DReX: An Explainable Deep Learning-based Multimodal Recommendation Framework
by: Shyam, Adamya, et al.
Published: (2026)

Quantum Machine Learning for Cybersecurity: A Taxonomy and Future Directions
by: Sai, Siva, et al.
Published: (2025)

Future Directions in the Theory of Graph Machine Learning
by: Morris, Christopher, et al.
Published: (2024)

Continual Learning for Smart City: A Survey
by: Yang, Li, et al.
Published: (2024)

Deep Learning within Tabular Data: Foundations, Challenges, Advances and Future Directions
by: Ren, Weijieying, et al.
Published: (2025)

Learning Tennis Strategy Through Curriculum-Based Dueling Double Deep Q-Networks
by: Mohan, Vishnu
Published: (2025)

A Comprehensive Survey on Evidential Deep Learning and Its Applications
by: Gao, Junyu, et al.
Published: (2024)

A Lightweight DL Model for Smart Grid Power Forecasting with Feature and Resolution Mismatch
by: Al-Shareeda, Sarah, et al.
Published: (2025)

Nested Learning: The Illusion of Deep Learning Architectures
by: Behrouz, Ali, et al.
Published: (2025)

Applications of Artificial Intelligence in Smart Grids
by: babaahmadi, shervin
Published: (2025)

ProDER: A Continual Learning Approach for Fault Prediction in Evolving Smart Grids
by: Efatinasab, Emad, et al.
Published: (2025)

A Survey on Learning from Graphs with Heterophily: Recent Advances and Future Directions
by: Gong, Chenghua, et al.
Published: (2024)

A Comprehensive Survey of Deep Learning for Time Series Forecasting: Architectural Diversity and Open Challenges
by: Kim, Jongseon, et al.
Published: (2024)

Efficient Deep Learning Infrastructures for Embedded Computing Systems: A Comprehensive Survey and Future Envision
by: Luo, Xiangzhong, et al.
Published: (2024)

Learning From High-Dimensional Cyber-Physical Data Streams for Diagnosing Faults in Smart Grids
by: Hassani, Hossein, et al.
Published: (2023)

A Heterogeneous Graph-Based Multi-Task Learning for Fault Event Diagnosis in Smart Grid
by: Chanda, Dibaloke, et al.
Published: (2023)

ObitoNet: Multimodal High-Resolution Point Cloud Reconstruction
by: Thapliyal, Apoorv, et al.
Published: (2024)

A Survey on Large Language Models for Communication, Network, and Service Management: Application Insights, Challenges, and Future Directions
by: Boateng, Gordon Owusu, et al.
Published: (2024)

2026 Roadmap on Artificial Intelligence and Machine Learning for Smart Manufacturing
by: Lee, Jay, et al.
Published: (2026)

Advancing Robustness in Deep Reinforcement Learning with an Ensemble Defense Approach
by: Mohan, Adithya, et al.
Published: (2025)

Machine Learning-based Android Intrusion Detection System
by: Tahreem, Madiha, et al.
Published: (2024)

Towards Data-centric Machine Learning on Directed Graphs: a Survey
by: Sun, Henan, et al.
Published: (2024)

A Survey on Deep Tabular Learning
by: Somvanshi, Shriyank, et al.
Published: (2024)

Few-Shot Load Forecasting Under Data Scarcity in Smart Grids: A Meta-Learning Approach
by: Tsoumplekas, Georgios, et al.
Published: (2024)

Fairness in Graph Learning Augmented with Machine Learning: A Survey
by: Luo, Renqiang, et al.
Published: (2025)

Predicting Short Term Energy Demand in Smart Grid: A Deep Learning Approach for Integrating Renewable Energy Sources in Line with SDGs 7, 9, and 13
by: Miah, Md Saef Ullah, et al.
Published: (2023)