At present usage of computational intelligence became the ultimate need of the heavy engineering industries. Digitization can be achieved in these sectors by scanning the hard copy images. When older documents are digitized are not of very high fidelity and therefore the accuracy, reliability of the estimates of components such as equipment and materials after digitization are remarkably low since (Piping and Instrumentation Diagrams) P&IDs come in various shapes and sizes, with varying levels of quality along with myriad smaller challenges such as low resolution of images, high intra project diagram variation along with no standardization in the engineering sector for diagram representation to name a few, digitizing P&IDs remains a challenging problem. In this study an end to end pipeline is proposed for automatically digitizing engineering diagrams which would involve automatic recognition, classification and extraction of diagram components from images and scans of engineering drawings such as P&IDs and automatically generating digitized drawings automatically from this obtained data. This would be done using image processing algorithms such as template matching, canny edge detection and the sliding window method. Then the lines would be obtained from the P&ID using canny edge detection and sliding window approach, the text would be recognized using an aspect ratio calculation. Finally, all the extracted components of the P&ID are associated with the closest texts present and the components mapped to each other. By the way of using such pipelines as proposed the diagrams are consistently of high quality, other smaller problems such as mis-spelling and valuable time churn are solved or minimized to large extent and paving the way for application of big data technologies such as machine learning analytics on these diagrams resulting in further efficiencies in operational processes.

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Classification generally assigns objects to enormous predefined categories and it is pervasive crisis that covers various application. Preparing the data for Classification and Prediction is the major problem in classification. In order to rectify this issue, an approximate function is proposed using Interpretable intuitive and Correlated-contours Fuzzy Neural Network (IC-FNN). For acquiring cor- related fuzzy rules and non-separable rules that comes under proper optimization problem. The extracted fuzzy rule’s parameter was fine-tuned sourced on hierarchical Levenberg Marquardt (LM) learning method for enhancing performance. But here parameters of fuzzy rules aren’t tuned per- fectly. Hybridization of Ant Colony Optimization Genetic Algorithm (HACOGA) is proposed here to rectify these issues. It tunes the parameters of the extracted fuzzy rules. Hybridization is enforced to certain factors and ACO and GA variables that share same characteristics in the computation. Experimental results shows that proposed HACOGA assist in enhancing the performance of FNN with recall, precision, accuracy and F -measure for the Abalone age prediction dataset.