Ai-Driven Volatility Prediction In Gcc Financial Markets: A Cnn-Lstm-Attention Approach

It develops a hybrid AI framework combining CNN LSTM, and an attention mechanism to predict stock market volatility in the GCC, focusing on the Saudi Tadawul

AI-Enabled Optimisation for Renewable Energy Grids: A Hybrid CNN–LSTM A Hybrid CNN-LSTM Deep Learning Approach for Data-Driven Crop Yield Prediction

Over the last decade, neural network-based deep learning has sparked heated competition in the AI world. Cutting-edge technologies like CNN, LSTM, and the

May 27, 2022 Finally, the study included six deep learning models, MLP, GRU, LSTM, Bi-LSTM, CNN, and CNN-LSTM, to predict the adjusted closing price of the

Aug 25, 2025 Khaled Alkhateeb, “Financial Time Series Forecasting using Deep Learning: A Focus on. Emerging Markets M.Sc. Thesis, MSc in Computational Data

May 19, 2026 The digital era has brought unprecedented change to financial systems, business practices, education, and social life. At the heart of this

The PINN approach combines data-driven learning with physics-based modeling, while deep learning architectures depend on data-driven learning through CNN–LSTM

This ap- proach integrates AI-driven forecasting with climate finance cludes a network with CNN, LSTM/GRU, attention(A and. Normalization(N)

From smarter energy grids and climate- resilient infrastructure to growth finance innovation and personalized education for sustainability, AI is opening new

Modern machine learning models are able to forecast time series data, including that of financial applications in a purely data driven manner [43 2.2.1.1 LSTM

financial operations. The application of AI in fintech has revolutionized the global banking, financial ser- vices, and insurance (BFSI) industry. AI-driven

Jul 21, 2025 Abstract - textAuthor is a data-driven, artificial-intelligence (AI predict in the short term due to high market volatility, changes caused by

Volatility, and Herding: Insights from the Chinese Shanghai Stock Market CNN-LSTM Hybrid Approach for Reliable Component Failure. Prediction

Also covered are developments stemming from new market demands in systems, products, and technologies such as per sonal communications services, multimedia

Examples are generative AI, medical AI, agriculture, autonomous maneuvering, financial technologies, cyber security, office applications and many more. Related

Apr 11, 2026 Elmousalami developed a hybrid CNN-LSTM algorithm for sustainable. AI-driven solar power forecasting, achieving mean absolute percentage errors

Dec 31, 2025 SARIMA–CNN–LSTM approach to forecast daily tourism demand. Journal including computational biology and AI-driven drug and protein structure

approach for stock market prediction. The Jour- nal of Supercomputing, 76(3 2098 2118, March 2020. CODEN JO SUED. ISSN 0920-8542 (print 1573 0484

Also covered are developments stemming from new market demands in systems, products, and technologies such as per- sonal communications services, multimedia

Approaches. Starts 3 November. Project Execution for Aerospace Professionals. Starts 3 November. Generative AI for Code Generation and Evaluation: From