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2 2022

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Influence of vehicular frequency On air quality of Delhi, India

Kumar Vijay, Gupta Sunita, Jolli Virat

Ecological Chemistry and Engineering. S

2022

Vol. 29, nr 4

477--485

Vehicular traffic has registered a phenomenal growth in the last few decades on Delhi roads, their increasing number is a cause of concern for city planners and administrators as they not only deteriorated the quality of environment but has also affected the human health. In this context, we studied the influence of traffic i.e., vehicular frequency on air quality of Delhi. Five sites were selected in four cardinal directions i.e., North, South, East, West and Centre for which data of air pollutants were already available in public domain (DPCC online website). Vehicular frequencies were recorded for Light Motor Vehicle (LMV) and Heavy Motor Vehicle (HMV) for both weekdays and weekends. Correlative analysis were carried out to study the relationship between vehicular frequency and air pollutants. The study showed, East of Delhi had the highest traffic load followed by North, West, South and Centre. We found statistically significant positive correlation between dust pollution, PM10 (r = 0.8) and PM2.5 (r = 0.6) with vehicular frequency while negative association with ozone (r = –0.5). A weak positive correlation was found with NOx (r = 0.2) while weak negative correlation with SO2 (r = –0.3). The study revealed that vehicular exhaust and their movement contribute in deteriorating the air quality of Delhi. Our findings suggest promotion of usage of public transport along with implementation of BS-VI stage vehicles and development of vegetation filters along the roads with native tree species.

Dual-modality synthetic mammogram construction for breast lesion detection using U-DARTS

Rautela Kamakshi, Kumar Dinesh, Kumar Vijay

Biocybernetics and Biomedical Engineering

2022

Vol. 42, no. 3

1041--1050

Multimodal image fusion is an emergent research area for cancer detection. It provides a wide variety of visual qualities for the accurate medical diagnosis. However, this process requires accurate registration of each image modality for its efficient and effective use. To address the aforementioned issue, a novel synthetic mammogram construction model is proposed. An image enhancement approach is applied to enhance the image quality. Dual-modality structural feature (DMSF) based mapping function is designed to transform a mammogram from a thermal image segment. This paper also proposes modified Differentiable ARchiTecture Search (DARTS) named as (U-DARTS) to detect and classify the breast lesion. In U-DARTS, a stochastic gradient descent optimizer is used. The proposed approach is tested over DMR and INbreast datasets. The results exhibit a significant improvement in the performance of the proposed model over the existing techniques. The validation and testing accuracies of 98% and 91%, respectively, are achieved. Overall, the proposed approach establishes supremacy in so far as the mammogram construction and further lesion detection are concerned.