Optimized Machine Learning Approach Research Articles

Image processing based automatic recognition of asphalt pavement patch using a metaheuristic optimized machine learning approach

Patch detection is an important task in pavement condition survey. This study establishes an automatic approach for asphalt pavement patch recognition based on image texture analysis and hybrid machine learning algorithms. Features based on image texture that employs statistical properties of color channels and the gray-scale co-occurrence matrix are used by the Least Squares Support Vector Machine (LSSVM) for discriminating patched areas from non-patch ones. In addition, to optimize the LSSVM training phase, the Differential Flower Pollination (DFP) metaheuristic is used. A data set constructed from a set of 1000 image samples has been utilized to train and verify the proposed integration of image texture analysis techniques, LSSVM, and DFP. Experimental results show that the new model can achieve a good prediction result with Classification Accuracy Rate = 95.30%, Positive Predictive Value = 0.96, and the Negative Predictive Value = 0.95. Additionally, a patch detection program has been developed and compiled in Visual C# .NET to ease the implementation of the hybrid model. Thus, the newly developed method can be a potential tool for traffic management agencies during the phase of pavement condition evaluation.

Optimized Machine Learning Approach for Software Defect Prediction using K-means with Genetic Algorithms

Optimized Machine Learning Approach for Software Defect Prediction using K-means with Genetic Algorithms

Distribution Grids Fault Location employing ST based Optimized Machine Learning Approach

Precise information of fault location plays a vital role in expediting the restoration process, after being subjected to any kind of fault in power distribution grids. This paper proposed the Stockwell transform (ST) based optimized machine learning approach, to locate the faults and to identify the faulty sections in the distribution grids. This research employed the ST to extract useful features from the recorded three-phase current signals and fetches them as inputs to different machine learning tools (MLT), including the multilayer perceptron neural networks (MLP-NN), support vector machines (SVM), and extreme learning machines (ELM). The proposed approach employed the constriction-factor particle swarm optimization (CF-PSO) technique, to optimize the parameters of the SVM and ELM for their better generalization performance. Hence, it compared the obtained results of the test datasets in terms of the selected statistical performance indices, including the root mean squared error (RMSE), mean absolute percentage error (MAPE), percent bias (PBIAS), RMSE-observations to standard deviation ratio (RSR), coefficient of determination (R2), Willmott’s index of agreement (WIA), and Nash–Sutcliffe model efficiency coefficient (NSEC) to confirm the effectiveness of the developed fault location scheme. The satisfactory values of the statistical performance indices, indicated the superiority of the optimized machine learning tools over the non-optimized tools in locating faults. In addition, this research confirmed the efficacy of the faulty section identification scheme based on overall accuracy. Furthermore, the presented results validated the robustness of the developed approach against the measurement noise and uncertainties associated with pre-fault loading condition, fault resistance, and inception angle.

Machine Learning Approach for Optimization of Automated Fiber Placement Processes

Automated Fiber Placement (AFP) processes are commonly deployed in manufacturing of lightweight structures made of carbon fibre reinforced polymer. In general, AFP is connected to individual manufacturing knowledge during process planning and time consuming manual quality inspections. In both cases, automatic solutions provide a high economic potential. Therefore, a machine learning approach for planning, optimizing and inspection of AFP processes is presented. Process data from planning, CNC and online process monitoring is aggregated for the documentation of the part specific manufacturing history and the automated generation of manufacturing knowledge. Within this approach a complete automation of data capturing, data storing, modeling and optimizing is achieved.

A predictive machine learning approach for microstructure optimization and materials design.

This paper addresses an important materials engineering question: How can one identify the complete space (or as much of it as possible) of microstructures that are theoretically predicted to yield the desired combination of properties demanded by a selected application? We present a problem involving design of magnetoelastic Fe-Ga alloy microstructure for enhanced elastic, plastic and magnetostrictive properties. While theoretical models for computing properties given the microstructure are known for this alloy, inversion of these relationships to obtain microstructures that lead to desired properties is challenging, primarily due to the high dimensionality of microstructure space, multi-objective design requirement and non-uniqueness of solutions. These challenges render traditional search-based optimization methods incompetent in terms of both searching efficiency and result optimality. In this paper, a route to address these challenges using a machine learning methodology is proposed. A systematic framework consisting of random data generation, feature selection and classification algorithms is developed. Experiments with five design problems that involve identification of microstructures that satisfy both linear and nonlinear property constraints show that our framework outperforms traditional optimization methods with the average running time reduced by as much as 80% and with optimality that would not be achieved otherwise.