The study focuses on the artificial intelligence empowered road vehicle-train collision risk prediction assessment, which may lead to the development of a road vehicle-train collision avoidance system for unmanned railway level crossings. The study delimits itself around the road vehicle-train collisions at unmanned railway level crossings on single line railroad sections. The first objective of the study revolves around the railroad collision risk evaluation by the development of road vehicle-train collision frequency and severity prediction model using Poisson and Gamma-log regression techniques respectively. Another study objective is the collision modification factor implementation on predicted risk factors, to reduce the road vehicle-train collision risk at the crossings. The collision risk has been predicted to be 3.52 times higher and 77% lower in one direction while in other directions it is 2.95 times higher and 80% lower than average risk at all unmanned railway level crossings. With collision modification factor application on higher risk contributing factors i.e. 'crossing angle' and 'train visibility, it predicts to reduce the road vehicle-train collision risk to 85% approximately.

Nonalcoholic Fatty Liver Disease (NAFLD) is the most common cause of chronic liver disease around the world. Remaining silent in the early stages makes its evaluation a challenge. Liver biopsy is still the gold standard method used to classify NAFLD stages but has important sample error issues and subjectivity in the interpretation. This research is an effort to overcome liver biopsy to a possible extent by forming a non-invasive clinical spectrum. This paper proposed an intelligent scheme using the forward algorithm, Viterbi algorithm, and Baum-welch algorithm for examining the disease, and a new clinical spectrum is introduced that incorporates most likely attributes associated with NAFLD stages. The experimental results verify that our method is efficient in distinguishing the credibility of an attribute being associated with a specific stage in case it is linked with more than one stage. Moreover, the proposed scheme can successfully estimate the likelihood of stage progression and supports medical knowledge more proficiently and realistically. INDEX TERMS Nonalcoholic fatty liver disease, Computational methods, Forward-backward learning, Intelligent systems, healthcare informatics.

Ancient scripts provide a captivating insight into the knowledge of ancestors which needs to be preserved for future generations. Therefore, there is a need to convert the digital script available in degraded format into textual format. To accomplish this model is being proposed in the paper that comprises of binarization using selection encoder decoder techniques. The results indicate the binarization accuracy as 74.24% approximately and F-measure is 75% (approximately) which comes out to be greater than other previously developed model. The binarized images are being further segmented using Seam Carbel method at character level and are manually compared with the vocabulary, the segmentation accuracy (A s) comes out to be 70% approximately. Further, characters are recognized using a three layer Convolutional Neural Network and the recognition accuracy (Ar) is found to be 73% approximately, the recognized images are further converted into text using one to one mapping, to be further used for translation into universally acceptable language like English.

No abstract

Railways are facing a serious problem of road vehicle–train collisions at unmanned railway level crossings. The purpose of the study is the development of a safe stopping sight distance and sight distance from road to rail track model with appropriate computation and analysis. The scope of the study lies in avoiding road vehicle–train collisions at unmanned railway level crossings. An intelligent and autonomous framework is being developed using supervised machine learning regression algorithms. Further, a sight distance from road to rail track model is being developed for road vehicles of 0.5 to 10 m length using the observed geometric characteristics of the route. The model prediction accuracy obtained better results in the development of a stopping sight distance model in comparison to other intelligent algorithms. The developed model suggested an increment of approximately 23% in the current safe stopping sight distance on all unmanned railway level crossings. Further, the feature analysis indicates the ‘approach road gradient’ to be the major contributing parameter for safe stopping sight distance determination. The accident prediction study finally indicates that, as the safe stopping sight distance is increased by following the developed model, it is predicted to decrease road vehicle–train collisions.