An artificial intelligence (AI) model's performance is strongly influenced by the input features. Therefore, it is vital to find the optimal feature set. It is more crucial for the survival prediction of the glioblastoma multiforme (GBM) type of brain tumor. In this study, we identify the best feature set for predicting the survival days (SD) of GBM patients that outranks the state-of-the-art methodologies currently in use. 

The proposed approach is an end-to-end AI model. This model first segments tumors from healthy brain parts in patients' MRI images, extract features from the segmented results, performs feature selection, and makes predictions about patients' survival days based on the features selected. The extracted features are primarily shape based, location-based, and radiomics-based features. Additionally, patient metadata is also included as a feature. The methods used for selecting features include recursive feature elimination (RFE), permutation importance (PI), and finding the correlation between the features. Finally, we examined features behavior at local (single sample) and global (all the samples) levels. In this study, we find that out of 1265 extracted features, only 29 dominant features play a crucial role in predicting patients' survival days (SD). Furthermore, we find explanations of these features using post-hoc interpretability methods to validate the model's robust prediction. Finally, we analysed the behavioural impact of the top six features on survival prediction, and the findings drawn from the explanations were coherent with medical facts. We find that after the Age of 50 years, the likelihood of survival of a patient deteriorates, and survival after 80 years is scarce. Again, for location-based features, the SD is less if the tumor location is in the central or back part of the brain. The results show an overall 33% improvement in the accuracy of SD prediction compared to the top-performing methods of the BraTS-2020 challenge

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The detection of seat belts is an essential aspect of vehicle safety. It is crucial in providing protection in the event of an accident. Seat belt detection devices are installed into many automobiles, although they may be easily manipulated or disregarded. As a result, the existing approaches and algorithms for seat belt detection are insufficient. Using various external methods and algorithms, it is required to determine if the seat belt is fastened or not. This paper proposes an approach to identify seat belt fastness using the concepts of image processing and deep learning. Our proposed approach can be deployed in any organizational setup to aid the concerned authorities in identifying whether or not the drivers of the vehicles passing through the entrance have buckled their seat belts up. If a seat belt is not detected in a vehicle, the number plate recognition module records the vehicle number. The concerned authorities might use this record to take further necessary actions. This way, the organization authorities can keep track of all the vehicles entering the premises and ensure that all drivers/shotgun seat passengers are wearing seat belts.