Over the last decade, one of the most significant areas under focus in process safety management was developing an inherently safer design. The main objective of having an inherently safer design is to avoid hazards and risks from developing in the first place, rather than to reduce them after they have already occurred. A number of strategies, including index‐based and other types, are used in today's process industries. This paper provides a brief overview of the current inherent design methods used in the process industries. This study also details how new technologies such as fuzzy logic and machine learning are used in the improvement of inherently safer designs. Traditional safety evaluation methods have flaws such as poor accuracy, large human element influence, which can affect the degree of safety. Inherently safer design prediction was modeled using various machine learning techniques like random forest, support vector machine (SVM), and K‐neighborhood algorithm. Accuracy obtained for the sample prediction of upper flammability limit while using random forest algorithm was found to be more efficient while comparing with K‐neighborhood and support vector machine algorithms. Accuracy obtained was in the range of 90%–95% for each epoch. The accuracy of the model will always be dependent on the type of parameters that we select for prediction. By considering more safety parameters and efficient machine learning algorithms for training models, we can develop systems with high accuracy predictions for inherently safer process plants.

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This paper proposes a method for enhancing the performance of Content Based Image Retrieval, employing a shape feature along with color and texture of Walsh wavelet. The color and texture features of the images are extracted using Walsh Wavelet and the shape feature is extracted by edge detection using any of Roberts, Sobel, Prewitt or Canny Operator. The performance of the approach is tested based on the precision values on a database containing 44 images. The results show that the precision of retrieval is increased when a shape feature is employed in the second stage of a two-stage retrieval process. Adding the shape as a third feature in a single stage retrieval process does not provide any improvement in retrieval performance with respect to precision and recall. Performance comparison was also carried out with other existing approaches, namely Walshlet and Walsh transform. The experimental results show that Walsh Wavelet has higher precision than Walshlet and Walsh transform. Also, shape extraction with Sobel and Prewitt operators provides better performance when compared to Canny and Roberts.