Ahmed M. Elshewey

Accurate house price prediction is essential for real estate valuation, investment planning, and intelligent property decision-support systems. This study proposes an optimized hybrid deep learning framework that integrates a Gated Recurrent Unit and Multilayer Perceptron model with the Binary Whale Optimization Algorithm for feature selection and Ant Colony Optimization for hyperparameter tuning. The proposed framework was evaluated using a publicly available Kaggle house price regression dataset containing 500 housing records with structural, locational, and amenity-related attributes. The dataset was divided into training, validation, and testing subsets using a 70:20:10 ratio, and leakage-free normalization was applied using only the training data. Experimental results show that the proposed BWOA–ACO–GRU–MLP model outperformed standalone GRU, MLP, CNN, LSTM, and BiLSTM models. It achieved an MSE of 0.0146, MAE of 0.1051, RMSE of 0.1208, MAPE of 0.0112, MedAE of 0.0969, and an R ² of 99.04%. These results demonstrate that combining feature selection, hyperparameter optimization, and hybrid neural regression improves prediction accuracy and model stability for house price estimation. The proposed framework provides a reliable data-driven approach for smart real estate valuation applications.

Air quality prediction is an important environmental task that supports pollution monitoring, public health protection, and sustainable urban decision-making. This study proposes a hybrid BiGRU-MLP prediction framework enhanced using Binary Ant Colony Optimization (BACO) for feature selection and the Firefly Algorithm (FA) for hyperparameter tuning. The proposed model was evaluated using the public Air Quality dataset from Kaggle, which contains 9,357 records and multiple pollutant and meteorological attributes, including CO(GT), NOx(GT), NO2(GT), temperature, relative humidity, and sensor-based air quality indicators. The dataset was divided into 70% training, 20% validation, and 10% testing subsets to ensure reliable model development and unbiased performance evaluation. Several baseline models were implemented for comparison, including BiGRU, MLP, Neural Network (NN), LSTM, and BiLSTM. The experimental results demonstrate that the proposed BiGRU-MLP model achieved the best predictive performance, obtaining an MSE of 0.0001, RMSE of 0.0101, MAE of 0.0081, MedAE of 0.0070, MAPE of 0.0008, and R² of 99.99%. In comparison, the standalone BiGRU achieved an R² of 96.87%, while MLP, NN, LSTM, and BiLSTM achieved R² values of 95.17%, 92.71%, 90.88%, and 89.29%, respectively. These results confirm that combining BiGRU’s temporal learning capability with MLP’s nonlinear feature representation significantly improves prediction accuracy. The integration of BACO and FA further enhances the model by selecting informative features and optimizing model parameters. Therefore, the proposed framework provides an accurate and efficient solution for air quality prediction and can support intelligent environmental monitoring systems.

Accurate prediction of the Remaining Useful Life of lithium-ion batteries is essential for enhancing reliability, safety, and maintenance planning in energy storage systems. This study proposes an optimized hybrid deep learning framework that integrates Convolutional Neural Networks with Deep Neural Networks for effective battery remaining useful life prediction. The model leverages convolutional neural network layers to automatically extract discriminative features from multivariate battery degradation data, while deep neural network layers model complex nonlinear relationships for precise regression estimation. To further enhance predictive performance and reduce feature redundancy, Binary Particle Swarm Optimization is employed for optimal feature selection. Experiments were conducted using a publicly available lithium-ion battery degradation dataset comprising 680 samples with electrical, thermal, and health-related parameters, including State of Health and remaining useful life indicators. The dataset was divided into training, validation, and testing subsets with proportions of 70%, 15%, and 15%, respectively. A comprehensive performance evaluation was performed using Mean Squared Error, Mean Absolute Percentage Error, Median Absolute Error, Mean Absolute Error, and the coefficient of determination. The proposed hybrid model achieved superior performance with a Mean Squared Error of 0.0141, Mean Absolute Error of 0.0931, Mean Absolute Percentage Error of 0.0142, Median Absolute Error of 0.0739, and a coefficient of determination of 99.01%, significantly outperforming comparative deep learning models. These results demonstrate that the proposed framework provides a robust and accurate solution for lithium-ion battery remaining useful life prediction and supports its potential deployment in intelligent battery management systems.