Ali Khan

Cardiac disease is the leading cause of death worldwide. Cardiovascular diseases can be prevented if an effective diagnostic is made at the initial stages. The ECG test is referred to as the diagnostic assistant tool for screening of cardiac disorder. The research purposes of a cardiac disorder detection system from 12-lead-based ECG Images. The healthcare institutes used various ECG equipment that present results in nonuniform formats of ECG images. The research study proposes a generalized methodology to process all formats of ECG. Single Shoot Detection MobileNet v2-based Deep Neural Network architecture was used to detect cardiovascular disease detection. The study focused on detecting the four major cardiac abnormalities with 98% accuracy results were calculated. The work is relatively rare based on their dataset; a collection of 11,148 standard 12-lead-based ECG images used in this study were manually collected from health care institutes and annotated by the domain experts. The study achieved high accuracy results to differentiate and detect four major cardiac abnormalities. Several cardiologists manually verified the proposed system’s accuracy result and recommended that the proposed system can be used to screen for a cardiac disorder.

Half a million species of plants could be existing in the world. Classification of plants based on leaf features is a critical job as feature extraction from binary images of leaves may result in duplicate identification. However, leaves are an effective means of differentiating plant species because of their unique characteristics like area, diameter, perimeter, circularity, aspect ratio, solidity, eccentricity, and narrow factor. This paper presents the extraction of plant leaf gas alongside other features from the camera images or a dataset of images by applying a convolutional neural network. The extraction of leaf gas enables identification of the actual level of chlorophyll and nitrogen which may help to interpret future predictions. Our contribution includes the study of texture and geometric features, analyzing ratio of Ch and N in both healthy and dead leaves, and the study of color-based methods via CNN. Several steps are included to obtain the results: image preprocessing, testing, training, enhancement, segmentation, feature extraction, and aggregation of results. A vital contrast of the results can be seen by considering the kind of image, whether a healthy or dead leaf.

Plastic consumption is an important aspect of contemporary living, and studies that systematically examine consumers’ plastic waste recycling intentions from an ethical perspective are scarce. Considering the severity of plastic waste recycling problems globally based on the stimulus-organism-response paradigm, this study analyses how the information publicity influences consumers’ plastic waste recycling intentions from an ethical perspective in an emerging market economy. We investigate this link by focusing on the indirect effect of perceived social pressure and the moderating role of media disposition (i.e., media richness and content trustworthiness). Data from a three-wave field survey of 469 household consumers from China showed that social pressure mediated the positive relationship between information publicity and ethical recycling intentions. Additionally, media richness and content trustworthiness moderate the link between information publicity and consumers’ perceptions of social pressure. These findings provide encouraging evidence for applying the SOR paradigm to explain the ethical recycling intention of consumers. The findings may assist policymakers in motivating consumers to think ethically about plastic waste recycling.