Browsing by Author "Kumar, Basant"

Now showing 1 - 5 of 5
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    Automatic Detection of Hard Exudates Shadow Region within Retinal Layers of OCT Images
    (Hindawi Limited, 2022) Singh, Maninder; Gupta, Vishal; Singh, Pramod Kumar; Gupta, Rajeev; Kumar, Basant; Alenezi, Fayadh; Alhudhaif, Adi; Althubiti, Sara A.; Polat, Kemal
    The optical coherence tomography (OCT) is useful in viewing cross-sectional retinal images and detecting various forms of retinal disorders from those images. Image processing methods and computational algorithms underlying this paper try to detect the shadowing region beneath exudates automatically. This paper presents a novel method for detecting hard exudates from retinal OCT images, often associated with macular edema near or within the outer plexiform layer. In this paper, an algorithm can automatically detect the presence of hard exudates in retinal OCT images, and these exudates appear as highly reflective spots. Still, they do not appear as noticeable bright spots because of their minute sizes in predevelopment phases. In the proposed work, we are using a method to detect the presence of hard exudates by analyzing their shadowing effect instead of focusing on brightness spots. The raster scanning operation is performed by traversing the retina horizontally, and noting up any change in normalized summation of brightness intensity (summing up the intensity from top to bottom retinal layers and normalized concerning retinal width) leads to the detection of minute as well as the presence for the detection of large exudates detection by differentiating this brightness intensity graph. The shadow region helps identify the hard exudates; in our proposed method, the output for three input images has been shown. There is an excellent agreement between the results generated by the proposed algorithm and the diagnostic opinion made by the ophthalmologist. The proposed method automatically detects the hard exudates using shadow regions, and it does not need any parameter settings or manual intervention. It can yield significant results by giving the position of shadow regions, which indicates the presence of exudates. � 2022 Maninder Singh et al.
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    Contrastive Learning Embedded Siamese Neural Network for the Assessment of Fatty Liver
    (Institute of Electrical and Electronics Engineers Inc., 2023) Mohit, Kumar; Shukla, Ankit; Gupta, Rajeev; Singh, Pramod Kumar; Agarwal, Kushagra; Kumar, Basant
    This paper presents an self-supervised Siamese neural network (SNN) for identification and classification of fatty liver severity. SNN is used for self-supervision tasks for being influenced from model optimization property of supervised and manual annotation property of unsupervised learning. This technique is based on contrastive learning of the joint embedding network which can learn more subtle representations from the medical images for classification task, with just one or few number of labelled images required from each class for training. The efficiency of the proposed model is validated on our dataset of liver ultrasound to classify them into three stages of the fatty liver disease and normal liver. A two-class classifier (normal/grade-I, normal/grade-II and normal/grade-III fatty liver) and four-class classifier (normal, grade-I, grade-II, grade-III fatty liver disease) were trained by minimizing contrastive loss to obtain classification accuracy of 98.91% and 96.84% respectively. � 2023 IEEE.
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    Nanopriming in sustainable agriculture: recent advances, emerging challenges and future prospective
    (Elsevier, 2022) Kumar, Basant; Indu; Singhal, Rajesh Kumar; Chand, Subhash; Chauhan, Jyoti; Kumar, Vivek; Mishra, Udit Nandan; Hidangmayum, Akash; Singh, Ankita; Bose, Bandana
    Diversified anthropogenic activities including intense farming (farm mechanization, high dose of fertilizer, and agrochemicals) increase the productivity and production of the agriculture sector. However, it alters the quantity and grain quality of agricultural products and also dwindles the soil and environment sustainability. Therefore, it is imperative necessity to explore novel, emerging, highly efficient, and ecofriendly approaches, which can provide a balanced diet to the next generation. In this context, nanotechnology in the agriculture sector is moving towards achieving the goal of future sustainability. Seed priming with nanochemicals or green chemicals such as plant growth promoting rhizobacteria (PGPR) represents one of the emerging, cost-effective, and environmentally friendly approaches to enhance the crop production with the use of minimum inputs and higher utilization of primary resources (light, water, and nutrients) under adverse climatic and soil conditions. The nanopriming [seed treatment with nanoparticles (NPs) before sowing] with green chemicals such as chitosan, micronutrients (Fe, Zn, Cu), and inorganic and organic compounds (PGPR hormones) has shown dynamic and excellent results during seed germination, vegetative phase, reproductive phase and in grain quality attributes. This approach is very efficient in improving nutrient use efficiency (via enhancing nutrient absorption, transport, and partitioning to reproductive parts), water use efficiency (via enhanced water uptake and utilization), assimilated partitioning, and other resource use efficiencies, which reduces the extra load of fertilizers, pesticides, and other agrochemicals used in the cultivation of crops. Moreover, nanopriming improves the plant antioxidant defense via improving stress genes and proteins, antioxidant chemicals, and signaling compounds, which ultimately reduce the energy load of the crop during adverse situations. To realize the importance of nanopriming, this chapter explores the recent advances and achievements made under unfavorable situations, future challenges, and prospects to improve the depth of knowledge regarding NP mechanism�s to accomplish the central goal of sustainability for farmers, scientific communities, and our future generation. � 2022 Elsevier Inc. All rights reserved.
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    Plant Phenolics: A Dynamic Compound Family Under Unfavorable Environment and Multiple Abiotic Stresses
    (Springer Nature, 2023) Chauhan, Jyoti; Kumar, Vivek; Kumar, Basant; Indu; Chand, Subhash; Anuragi, Hirdayesh; Patel, Richa; Singhal, Rajesh Kumar
    Expeditious progress ie and tolerance mechanism by PCs under these circumstances. � Springer Nature Singapore Pte Ltd. 2023.
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    Plant-Environment Interaction in Developing Crop Species Resilient to Climate Change
    (Apple Academic Press, 2022) Chand, Subhash; Indu, B.; Chauhan, Jyoti; Kumar, Basant; Kumar, Vivek; Dey, Prjjal; Mishra, Udit Nandan; Sahu, Chandrasekhar; Singhal, Rajesh Kumar
    Plants are sessile in nature and phenotypic developmental plasticity has an important role in plant-environment interactions under fluctuating climatic situations. Plants are very sensitive to these circumstances and endeavor to acclimatize or adapt via modulating their phenotypic and genotypic characteristics. In this aspect, the development of climate-resilient crops is essential to accelerate production and meet global food security. These goals can be achieved by the application of high-throughput phenotyping, molecular breeding, and new advance biotechnological approaches, which will hasten the breeding cycle of a crop plant. Recent researches suggested that high-throughput phenotyping, micro-RNA-mediated developmental plasticity, phytohormones signaling, circadian clock, the molecular basis of eco-evolutionary phenological diversified functional traits, prediction of genotypic � environment interactions through next-generation sequencing and molecular markers, growth and development modeling, bioinformatics and omics approaches accelerates can be useful for deep understanding of plant-environment interaction. Therefore, considering before mentioned this chapter elaborates on crucial mechanisms (plant physiological, biochemical, genetical, molecular, and evolutionary) for further understanding of plant- environment interactions and developing climate resilient and smart crop varieties under extreme climatic events. � 2022 by Apple Academic Press, Inc.