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This study was conducted on the presentation of a method to improve the forecast of urban gas consumption based on the weather variables including temperature, pressure, humidity, wind speed and also the gas price. The diversity of input variables as well as investigating a short-term (daily) scale, led to creation complex and nonlinear relationships between the variables, which makes its solving difficult. To this end, the categorical boosting (CatBoost) method is combined with some meta-heuristic algorithms to create hybrid models. These meta-heuristic algorithms include Phasor Particle Swarm Optimization, Artificial Bee Colony, Battle Royale Optimizer, Grey Wolf Optimizer, Satin Bowerbird algorithm, and Fruit Fly Optimization Algorithm. During the network training, the K-Fold cross-validation has also been used to prevent overfitting. Finally, using an actual dataset, the performance of the proposed method is investigated. The results showed that the proposed method can predict the value of short-term urban gas consumption. The results showed that the hybrid Catboost-PPSO model had the best performance among all presented hybrid models. Therefore, using the PPSO algorithm to optimize the hyper-parameters of the CatBoost network is recommended for predicting gas consumption. • The study was performed on the forecast of urban gas consumption. • Meta-heuristic algorithms help CatBoost network to perform more efficiently. • The results showed that the hybrid Catboost-PPSO model has the best performance. • The results showed that the hybrid Catboost-FOA model performs weaker than others. • In training and testing, the TIC values of the hybrid Catboost-PPSO model are 0.0013 and 0.0540. [ABSTRACT FROM AUTHOR]

• We proposed a novel model hyOPTXg to identify cardiovascular disease. • The proposed model hyOPTXg utilized the XGBoost and OPTUNA techniques. • Data pre-processing and hyper-parameter tuning techniques were applied. Cardiovascular disease is a dangerous disorder that causes the most significant number of deaths across the world. In the past years, researchers proposed several automated systems to identify heart disease early so that there is a chance to enhance the diagnosis process. This research paper proposes an expert model called hyOPTXg, which predicts heart disease using an optimized XGBoost classifier. To produce a better system using a classifier, we need decent hyper-parameter tuning. So, we tuned the hyper-parameters of XGBoost and trained the model using tuned parameters. The framework used for hyper-parameter tuning is OPTUNA (hyper-parameter optimization technique). This system was tested on three datasets: The Cleveland dataset and the Heart Failure prediction dataset from Kaggle and heart disease UCI from Kaggle. We used various metrics to assess the system's efficiency, including recall, precision, f1-score, accuracy, and the ROC chart's area under the curve (AUC). And we got better results compared to other systems which other authors propose in recent times. We got 94.7% in the Cleveland dataset,89.3% in the Kaggle heart failure dataset, and 88.5% in the heart disease UCI Kaggle dataset. Our prediction result says that we got better results than other systems proposed by the other authors. And we did a comparison study of our model with other authors' models. [ABSTRACT FROM AUTHOR]

• The NASA dataset is trained with a tuned 2-layer LSTM and GRU models. • The results are compared to existing outputs and inference shown. • Introduce a semi-novel optimization algorithm using genetic algorithms. • Hyper-parameters of the model – learning rate and batch size are self-tuned by a genetic algorithm. The application of remaining useful life (RUL) prediction is very important in terms of energy optimization, cost-effectiveness, and risk mitigation. The existing RUL prediction algorithms mostly constitute deep learning frameworks. In this paper, we implement LSTM and GRU models and compare the obtained results with a proposed genetically trained neural network. The current models solely depend on ADAM and SGD for optimization and learning. Although the models have worked well with these optimizers, even little uncertainties in prognostics prediction can result in huge losses. We hope to improve the consistency of the predictions by adding another layer of optimization using Genetic Algorithms. The hyper-parameters – learning rate and batch size are optimized beyond manual capacity. These models and the proposed architecture are tested on the NASA Turbofan Jet Engine dataset. The optimized architecture can predict the given hyper-parameters autonomously and provide superior results. [ABSTRACT FROM AUTHOR]

Deep Belief Networks (DBN) have become a powerful tools to deal with a wide range of applications. On complex tasks like image reconstruction, DBN's performance is highly sensitive to parameter settings. Manually trying out different parameters is tedious and time consuming however often required in practice as there are not many better options. This work proposes an evolutionary hyper-heuristic framework for automatic parameter optimisation of DBN. The hyper-heuristic framework introduced here is the first of its kind in this domain. It involves a high level strategy and a pool of evolutionary operators such as crossover and mutation to generates DBN parameter settings by perturbing or modifying the current setting of a DBN. Providing a large set of operators could be beneficial to form a more effective high level strategy, but in the same time would increase the search space hence make it more difficulty to form a good strategy. To address this issue, a non-parametric statistical test is introduced to identify a subset of effective operators for different phases of the hyper-heuristic search. Three well-known image reconstruction datasets were used to evaluate the performance of the proposed framework. The results reveal that the proposed hyper-heuristic framework is very competitive when compared to the state of art methods. • We propose a hyper-heuristic to optimise Deep Belief Networks. • We use a large set of heuristic with in the hyper-heuristic. • We propose a non-parametric statistical test to identify the best heuristics. • We tested the proposed hyper-heuristics on three different datasets. • The results demonstrated the effectiveness of the hyper-heuristic. [ABSTRACT FROM AUTHOR]