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Objective: Our aim is to identify the damaged tablets from the manufacturing line using image processing techniques and remove them before packaging. Methods:The various problems posed during inspection are broken tablets, corner chips, black or other color spots in tablets, empty blisters (without one or more tablets or capsules), foreign particles/color variation in the tablets/capsules, improper sealing, etc., Image processing techniques will be used for defect detection. Results:Tablets are available in packed forms that are usually transparent, semi-transparent or opaque. Euclidean distance was employed for detecting defects, during testing that had a similarity of 100 for tablets with no defects, for defective blisters had similarity ranging from 98 to 41. Empty blisters had a similarity of 0 on comparing with trained images. Conclusion:Similarity measuring based technique can accurately detect defects in the pharmaceutical tablets, hence can be adopted for removing such blisters from the manufacturing line itself.

Objective: Our aim is to detect printing defects in pharmaceutical tablets from the manufacturing line using image processing techniques.Methods: The printed labels contain the details of the chemical composition, date of manufacture, date of expiry, manufacturing location, etc., images of the labels are obtained and processed using image processing algorithms to detect any defects on the labels before dispatch.Results: The printing defects on the labels such as missing letters, words, lines, and disorientation of alignments.Conclusion: Euclidean distance method was used for comparison that yielded 95% accuracy in removing tablets with printing defects.

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Road accidents are a major cause of death and disabilities. The aim of the traffic accident analysis for a region is to investigate the cause for accidents and to determine dangerous locations in a region. Multivariate analysis of traffic accidents data is critical to identify major causes for fatal accidents. In this work, accident dataset is analysed using algorithmic approach, as an attempt to address this problem. The relationship between fatal rate and other attributes including collision manner, weather, surface condition, light condition, mobile users and drunken driving are considered. Prediction model using various data mining classifiers such as Bayesian, J48, Random Forest will be constructed to enhance safety regulations for a region.