Abstract Glaucoma is an eye disease that damages the optic nerve as a result of vision loss, it is the leading cause of blindness worldwide. Due to the time‐consuming, inaccurate, and manual nature of traditional methods, automation in glaucoma detection is important. This paper proposes an explainable artificial intelligence (XAI) based model for automatic glaucoma detection using pre‐trained convolutional neural networks (PCNNs) and machine learning classifiers (MLCs). PCNNs are used as feature extractors to obtain deep features that can capture the important visual patterns and characteristics from fundus images. Using extracted features MLCs then classify glaucoma and healthy images. An empirical selection of the CNN and MLC parameters has been made in the performance evaluation. In this work, a total of 1,865 healthy and 1,590 glaucoma images from different fundus datasets were used. The results on the ACRIMA dataset show an accuracy, precision, and recall of 98.03%, 97.61%, and 99%, respectively. Explainable artificial intelligence aims to create a model to increase the user's trust in the model's decision‐making process in a transparent and interpretable manner. An assessment of image misclassification has been carried out to facilitate future investigations.

The primary goal of this article is to find out best current excitation for controlling maximum torque per amp (MTPA) in internal permanent magnet synchronous motors (IPMSM) with non-sinusoidal back emf. The optimum current elation for the mean torque in the IPMSM is presented in this work in closed form. The suggested work seeks to furnish a universal method for choosing the appropriate current for MTPA control for IPMSM used in EV drive train better than operating vector control for current harmonic injection. Hysteresis current control is also taken into account for current injection in IPMSM. Various examples from experimental investigations are shown, including the optimal current, an FFT analysis of the torque and current profile, and a back emf profile that takes into account the first and third order current harmonics. Finally, this investigation claims an efficient MTPA control strategy under specific operating environment employed in energy-efficient EV drive train.

In this study, a combination of adaptive vector median filter (VMF) and weighted mean filter is proposed for removal of high‐density impulse noise from colour images. In the proposed filtering scheme, the noisy and non‐noisy pixels are classified based on the non‐causal linear prediction error. For a noisy pixel, the adaptive VMF is processed over the pixel where the window size is adapted based on the availability of good pixels. Whereas, a non‐noisy pixel is substituted with the weighted mean of the good pixels of the processing window. The experiments have been carried out on a large database for different classes of images, and the performance is measured in terms of peak signal‐to‐noise ratio, mean squared error, structural similarity and feature similarity index. It is observed from the experiments that the proposed filter outperforms (∼1.5 to 6 dB improvement) some of the existing noise removal techniques not only at low density impulse noise but also at high‐density impulse noise.