In industrial piping systems, turbomachinery, heat exchangers etc., pipe bends are essential components. Computational fluid dynamics (CFD), which is frequently used to analyse the flow behaviour in such systems, provides extremely precise estimates but is computationally expensive. As a result, a computationally efficient method is developed in this paper by leveraging machine learning for such computationally expensive CFD problems. Random forest regression (RFR) is used as the machine learning algorithm in this work. Four different fluid flow characteristics (i.e., axial velocity, x-velocity, y-velocity and z-velocity) are studied in this work. The accuracy of the RFR models is assessed by using a number of statistical metrics such as mean-absolute error (MAE), mean-squared-error (MSE), root-mean-squared-error (RMSE), maximum error (Max.Error) and median error (Med.Error) etc. It is observed that the RFR models can produce considerable cost reductions in computing by surrogating the CFD model. Minor loss in estimation accuracy as compared to the CFD models is observed. While the magnitude of intricate flow characteristics such as the additional vortices are correctly predicted, some error in their location is observed.

As the volume of data generated by information systems continues to increase, machine learning (ML) techniques have become essential for the extraction of meaningful insights. However, the sheer volume of data often causes these techniques to become sluggish. To overcome this, feature selection is a vital step in the pre-processing of data. In this paper, we introduce a novel K-nearest neighborhood (KNN)-based wrapper system for feature selection that leverages the iterative improvement ability of the weighted superposition attraction (WSA). We evaluate the performance of WSA against seven well-known metaheuristic algorithms, i.e., differential evolution (DE), genetic algorithm (GA), particle swarm optimization (PSO), flower pollination algorithm (FPA), symbiotic organisms search (SOS), marine predators’ algorithm (MPA) and manta ray foraging optimization (MRFO). Our extensive numerical experiments demonstrate that WSA is highly effective for feature selection, achieving a decrease of up to 99% in the number of features for large datasets without sacrificing classification accuracy. In fact, WSA-KNN outperforms traditional ML methods by about 18% and ensemble ML algorithms by 9%. Moreover, WSA-KNN achieves comparable or slightly better solutions when compared with neural networks hybridized with metaheuristics. These findings highlight the importance and potential of WSA for feature selection in modern-day data processing systems.

In this research, the effectiveness of a novel optimizer dubbed as decomposition-based multi-objective symbiotic organism search (MOSOS/D) for multi-objective problems was explored. The proposed optimizer was based on the symbiotic organisms’ search (SOS), which is a star-rising metaheuristic inspired by the natural phenomenon of symbioses among living organisms. A decomposition framework was incorporated in SOS for stagnation prevention and its deep performance analysis in real-world applications. The investigation included both qualitative and quantitative analyses of the MOSOS/D metaheuristic. For quantitative analysis, the MOSOS/D was statistically examined by using it to solve the unconstrained DTLZ test suite for real-parameter continuous optimizations. Next, two constrained structural benchmarks for real-world optimization scenario were also tackled. The qualitative analysis was performed based on the characteristics of the Pareto fronts, boxplots, and dimension curves. To check the robustness of the proposed optimizer, comparative analysis was carried out with four state-of-the-art optimizers, viz., MOEA/D, NSGA-II, MOMPA and MOEO, grounded on six widely accepted performance measures. The feasibility test and Friedman’s rank test demonstrates the dominance of MOSOS/D over other compared techniques and exhibited its effectiveness in solving large complex multi-objective problems.

Movie recommender systems are meant to give suggestions to the users based on the features they love the most. A highly performing movie recommendation will suggest movies that match the similarities with the highest degree of performance. This study conducts a systematic literature review on movie recommender systems. It highlights the filtering criteria in the recommender systems, algorithms implemented in movie recommender systems, the performance measurement criteria, the challenges in implementation, and recommendations for future research. Some of the most popular machine learning algorithms used in movie recommender systems such as K-means clustering, principal component analysis, and self-organizing maps with principal component analysis are discussed in detail. Special emphasis is given to research works performed using metaheuristic-based recommendation systems. The research aims to bring to light the advances made in developing the movie recommender systems, and what needs to be performed to reduce the current challenges in implementing the feasible solutions. The article will be helpful to researchers in the broad area of recommender systems as well as practicing data scientists involved in the implementation of such systems.

One of the most complex areas of image processing is image classification, which is heavily relied upon in clinical care and educational activities. However, conventional models have reached their limits in effectiveness and require extensive time and effort to extract and choose classification variables. In addition, the large volume of medical image data being produced makes manual procedures ineffective and prone to errors. Deep learning has shown promise for many classification problems. In this study, a deep learning-based classification model is developed to decrease misclassifications and handle large amounts of data. The Adaptive Guided Bilateral Filter is used to filter images, and texture and edge attributes are gathered using the Spectral Gabor Wavelet Transform. The Black Widow Optimization method is used to choose the best features, which are then input into the Red Deer Optimization-enhanced Gated Deep Reinforcement Learning network model for classification. The brain tumor MRI dataset was used to test the model on the MATLAB platform, and the results showed an accuracy of 98.8%.