H.3.15.3. Evolutionary computing and genetic algorithms
Homa Mehtarizadeh; Najme Mansouri; Behnam Mohammad Hasani Zade; Mohammad Mehdi Hosseini
Abstract
Accurate and reliable stock price prediction is both a formidable and essential task in financial markets, requiring the use of advanced techniques. This paper presents an innovative approach that integrates Long Short-Term Memory (LSTM) networks with Modified Complex Variational Mode Decomposition (MCVMD) ...
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Accurate and reliable stock price prediction is both a formidable and essential task in financial markets, requiring the use of advanced techniques. This paper presents an innovative approach that integrates Long Short-Term Memory (LSTM) networks with Modified Complex Variational Mode Decomposition (MCVMD) for preprocessing and the Secretary Bird Optimization Algorithm (SBOA) for hyperparameter optimization. In the preprocessing phase, MCVMD decomposes stock price time series into intrinsic mode functions, effectively capturing complex patterns and reducing noise. To enhance predictive performance, SBOA optimizes both the hyperparameters of the LSTM network and the decomposition parameters of MCVMD. The proposed methodology is evaluated on datasets from six companies: Ferrari NV (RACE) and Intesa Sanpaolo (ISP) from Italy, Amadeus IT (AMA) and Repsol (REP) from Spain, and Hitachi Ltd (6501) and Chugai Pharmaceutical Co., Ltd. (4519) from Japan. Results show that the LSTM-MCVMD-SBOA model achieves lower error values compared with conventional benchmarks including ARIMA-GARCH, vanilla LSTM, Long Short-Term Memory-Particle Swarm Optimization (LSTM-PSO), and Long Short-Term Memory-Sine Cosine Algorithm (LSTM-SCA). Compared with these alternatives, SBOA was selected because of its superior balance between exploration and exploitation, inspired by secretary bird hunting and evasion behavior, which enables efficient search in complex optimization landscapes. Overall, the proposed model demonstrates significantly improved predictive accuracy over conventional methods, highlighting the efficacy of combining advanced decomposition with nature-inspired optimization for stock market forecasting.
H.3.2.3. Decision support
Fatemeh Iranmanesh; Najme Mansouri; Behnam Mohammad Hasani Zade
Abstract
The diagnosis of Alzheimer's Disease (AD) remains a significant challenge in medical research. To address the limitations of static models in capturing dynamic brain changes, this paper proposes a novel GNN-xLSTM model that integrates Graph Neural Networks (GNN) with an extended Long Short-Term Memory ...
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The diagnosis of Alzheimer's Disease (AD) remains a significant challenge in medical research. To address the limitations of static models in capturing dynamic brain changes, this paper proposes a novel GNN-xLSTM model that integrates Graph Neural Networks (GNN) with an extended Long Short-Term Memory (xLSTM) architecture. The key innovation lies in combining GNN’s ability to model spatial relationships in brain imaging data with xLSTM’s enhanced sequential learning via matrix-based memory representation and exponential gate stabilization. In the proposed approach, brain images are divided into regions, with each region represented as a graph node connected in a grid structure, and feature vectors are extracted for each node. The proposed architecture incorporates Graph Convolutional Network (GCN) layers, xLSTM cells, residual connections, batch normalization, and dropout to jointly capture global, local, and temporal dependencies. Evaluated on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, the GNN-xLSTM model outperforms baseline models in terms of accuracy, precision, recall, and F1-score. These results demonstrate the model’s effectiveness in identifying critical brain regions and improving AD classification performance.
