Faculty Publications
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Publications by NITK Faculty
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Item A sustainable bioprocess for lipase production using seawater and the byproduct obtained from coconut oil industries(CRC Press/Balkema, 2019) Raval, R.; Verma, A.; Raval, K.Globally lipases are the most attractive source of research, as it has numerous applications in various industries like food industry, paper and pulp industry, preparation of beverages etc. A lipase producing bacterium, Pseudomonas stutzeri, was isolated from sea water. The bacterial culture was introduced to the physical and chemical mutagens and then allowed to grow on the solid media. A number of mutated clones were produced which were further followed by examining their lipase activity. There was a significant increase in the extracellular lipase activity i.e. 13, 56 and 14 folds increase in the case of UV mutation, sodium azide, and NTG respectively. Further, the mutants were subcultured and stability was observed in NTG mutants. The lipase production from the NTG mutants was optimized using Response Surface Methodology (RSM). The maximum lipase activity of 1132.6 U/ml was obtained which was about 7 folds higher than the parent strain using the process which utilized the residual coconut cake, a byprtoduct of coconut oil industries and the sea water which makes the process sustainable. © 2020 Taylor & Francis Group, London, ISBN 978-0-367-33737-7Item Semantic Segmentation on Low Resolution Cytology Images of Pleural and Peritoneal Effusion(Institute of Electrical and Electronics Engineers Inc., 2022) Aboobacker, S.; Verma, A.; Vijayasenan, D.; Sumam David, S.; Suresh, P.K.; Sreeram, S.Automation in the detection of malignancy in effusion cytology helps to save time and workload for cytopathologists. Cytopathologists typically consider a low-resolution image to identify the malignant regions. The identified regions are scanned at a higher resolution to confirm malignancy by investigating the cell level behaviour. Scanning and processing time can be saved by zooming only the identified malignant regions instead of entire low-resolution images. This work predicts malignancy in cytology images at a very low resolution (4X). Annotation of cytology images at a very low resolution is challenging due to the blurring of features such as nuclei and texture. We address this issue by upsampling the very low-resolution images using adversarial training. This work develops a semantic segmentation model trained on 10X images and reuse the network to utilize the 4X images. The prediction results of low resolution images improved by 15% in average F-score for adversarial based upsampling compared to a bicubic filter. The high resolution model gives a 95% average F-score for high resolution images. Also, the sub-area of the whole slide that requires to be scanned at high magnification is reduced by approximately 61% while using adversarial based upsampling compared to a bicubic filter. © 2022 IEEE.Item Automated Detection of Maize Leaf Diseases in Agricultural Cyber-Physical Systems(Institute of Electrical and Electronics Engineers Inc., 2022) Verma, A.; Bhowmik, B.R.Agricultural cyber-physical systems (ACPS) are an ever-increasing sector that affects the quality and quantity of agricultural products as the population increases rapidly. Maize, also known as 'corn,' is one of the world's old food crops, consumed every part of Bharat with 1.4 billion masses across the globe. But a disease, whether on seeds, leaves, or other parts of a crop plant, poses a significant risk to food security. For example, a Maize leaf experiences three diseases-blight, common rust, and gray leaf spot. Early detection and correct identification of these diseases can help restrict the spread of infection and ensure crop quality for long-Term health. This paper proposes a deep convolutional neural network (DCNN) framework for Maize leaves named "MDCNN"that detects these diseases. The proposed MDCNN model undergoes training and is tuned to detect four prevalent classes of the conditions. The proposed model exercises a voluminous dataset of the diseases. Experimental results demonstrate that the proposed model achieves a training and test accuracy up to 95.51% and 99.54%, respectively. Furthermore, it outperforms many existing methods and delivers a superior disease control solution for Maize leaf diseases. © 2022 IEEE.Item LEASE: Leveraging Energy-Awareness in Serverless Edge for Latency-Sensitive IoT Services(Institute of Electrical and Electronics Engineers Inc., 2024) Verma, A.; Satpathy, A.; Das, S.K.; Addya, S.K.Resource scheduling catering to real-time IoT services in a serverless-enabled edge network is particularly challenging owing to the workload variability, strict constraints on tolerable latency, and unpredictability in the energy sources powering the edge devices. This paper proposes a framework LEASE that dynamically schedules resources in serverless functions catering to different microservices and adhering to their deadline constraint. To assist the scheduler in making effective scheduling decisions, we introduce a priority-based approach that offloads functions from over-provisioned edge nodes to under-provisioned peer nodes, considering the expended energy in the process without compromising the completion time of microservices. For real-world implementations, we consider a testbed comprising a Raspberry Pi cluster serving as edge nodes, equipped with container orchestrator tools such as Kubernetes and powered by OpenFaaS, an open-source serverless platform. Experimental results demonstrate that compared to the benchmarking algorithm, LEASE achieves a 23.34% reduction in the overall completion time, with 97.64% of microservices meeting their deadline. LEASE also attains a 30.10% reduction in failure rates. © 2024 IEEE.