Search results for: Payel Das

Authors: Payel Das, Gnaneshwar Nelakanti

Abstract:

In this paper we analyse the iterated discrete Legendre Galerkin method for Fredholm-Hammerstein integral equations with smooth kernel. Using sufficiently accurate numerical quadrature rule, we obtain superconvergence rates for the iterated discrete Legendre Galerkin solutions in both infinity and $L^2$-norm. Numerical examples are given to illustrate the theoretical results.

Keywords: hammerstein integral equations, spectral method, discrete galerkin, numerical quadrature, superconvergence

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Authors: Saha Saradindu, Das Payel, Somdeb BoseDasgupta

Abstract:

Tuberculosis caused by Mycobacteria tuberculosis is a dreadful disease and more so with the advent of extreme and total drug-resistant species. Mycobacterial pathogenesis is an ever-changing paradigm from phagosome maturation block to phagosomal escape into macrophage cytosol and finally acid tolerance and survival inside the lysosome. Mycobacteria are adept at subverting the host immune response by highjacking host cell signaling and secreting virulence factors. One such virulence factor is a ser/thr kinase; Protein kinase G (PknG), which is known to prevent phagosome maturation. The host substrates of PknG, allowing successful pathogenesis still remain an enigma. Hence we carried out a comparative phosphoproteomic screen and identified a number of substrates phosphorylated by PknG. We characterized some of these substrates in vivo and in vitro and observed that PknG mediated phosphorylation of these substrates leads to reduced TNFa production as well as decreased response to TNFa induced macrophage necroptosis, thus enabling mycobacterial survival and proliferation.

Keywords: mycobacteria, Protein kinase G, phosphoproteomics, necroptosis

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Authors: Payel Datta, Abhishek Das, Abhishek Roychoudhury, Dhiman Chattopadhyay, Tanushyam Chattopadhyay

Abstract:

Machine learning (ML) and deep learning (DL) is most predominantly used in image/video processing, natural language processing (NLP), audio and speech recognition but not that much used in system performance evaluation. In this paper, authors are going to describe the architecture of an abstraction layer constructed using ML/DL to detect the system failure. This proposed system is used to detect the system failure by evaluating the performance metrics of an IoT service deployment under constrained infrastructure environment. This system has been tested on the manually annotated data set containing different metrics of the system, like number of threads, throughput, average response time, CPU usage, memory usage, network input/output captured in different hardware environments like edge (atom based gateway) and cloud (AWS EC2). The main challenge of developing such system is that the accuracy of classification should be 100% as the error in the system has an impact on the degradation of the service performance and thus consequently affect the reliability and high availability which is mandatory for an IoT system. Proposed ML/DL classifiers work with 100% accuracy for the data set of nearly 4,000 samples captured within the organization.

Keywords: machine learning, system performance, performance metrics, IoT, edge

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Authors: Payel Ghosh, Rama Chandra Pradhan, Sabyasachi Mishra

Abstract:

Jamun (Syzygium cuminii L.) is one of the important indigenous minor fruit with high medicinal value. The jamun cultivation is unorganized and there is huge loss of this fruit every year. The perishable nature of the fruit makes its postharvest management further difficult. Due to the strong cell wall structure of pectin-protein bonds and hard seeds, extraction of juice becomes difficult. Enzymatic treatment has been commercially used for improvement of juice quality with high yield. The objective of the study was to optimize the best treatment method for juice extraction. Enzymes (Pectinase and Tannase) from different stains had been used and for each enzyme, best result obtained by using response surface methodology. Optimization had been done on the basis of physicochemical property, nutritional property, sensory quality and cost estimation. According to quality aspect, cost analysis and sensory evaluation, the optimizing enzymatic treatment was obtained by Pectinase from Aspergillus aculeatus strain. The optimum condition for the treatment was 44 ^oC with 80 minute with a concentration of 0.05% (w/w). At these conditions, 75% of yield with turbidity of 32.21NTU, clarity of 74.39%T, polyphenol content of 115.31 mg GAE/g, protein content of 102.43 mg/g have been obtained with a significant difference in overall acceptability.

Keywords: enzymatic treatment, Jamun, optimization, physicochemical property, sensory analysis

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Authors: Brindha Senthil Kumar, Payel Chakraborty, Senthil Kumar Nachimuthu, Arindam Maitra, Prem Nath

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Gastric cancer is predominantly caused by demographic and diet factors as compared to other cancer types. The aim of the study is to predict Early Gastric Cancer (ECG) from diet and lifestyle factors using supervised machine learning algorithms. For this study, 160 healthy individual and 80 cases were selected who had been followed for 3 years (2016-2019), at Civil Hospital, Aizawl, Mizoram. A dataset containing 11 features that are core risk factors for the gastric cancer were extracted. Supervised machine algorithms: Logistic Regression, Naive Bayes, Support Vector Machine (SVM), Multilayer perceptron, and Random Forest were used to analyze the dataset using Python Jupyter Notebook Version 3. The obtained classified results had been evaluated using metrics parameters: minimum_false_positives, brier_score, accuracy, precision, recall, F1_score, and Receiver Operating Characteristics (ROC) curve. Data analysis results showed Naive Bayes - 88, 0.11; Random Forest - 83, 0.16; SVM - 77, 0.22; Logistic Regression - 75, 0.25 and Multilayer perceptron - 72, 0.27 with respect to accuracy and brier_score in percent. Naive Bayes algorithm out performs with very low false positive rates as well as brier_score and good accuracy. Naive Bayes algorithm classification results in predicting ECG showed very satisfactory results using only diet cum lifestyle factors which will be very helpful for the physicians to educate the patients and public, thereby mortality of gastric cancer can be reduced/avoided with this knowledge mining work.

Keywords: Early Gastric cancer, Machine Learning, Diet, Lifestyle Characteristics

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Authors: Payel Saha

Abstract:

Social media is playing an important role for dissemination of information in society. In 21st century most people have a smart phone and used different social media tools like Facebook, Twitter, Instagram, WhatsApp, Skype etc. in day to day life. It is rapidly growing web-based tool for everyone to share thoughts, ideas and knowledge globally using internet. The study highlights the current use of social media tools for promoting library and information services of Library and Information Professionals of India, which are working in Library. The study was conducted during November, 2017. A structured questionnaire was prepared using google docs and shared using different mailing list, sent to individual email IDs and sharing with other social media tools. Only 90 responses received from the different states of India and analyzed via MS-Excel. The data receive from 17 states and 3 union territories of India; however most of the respondents has come from the states Odisha 23, Himachal Pradesh 14 and Assam 10. The results revealed that out 90 respondents 37 Female and 53 male categories and also majority of respondents 71 have come from academic library followed by special library 15, Public library 3 and corporate library 1 respondent. The study indicates that, out of 90 respondent’s majority of 53 of respondents said that their Library have a social media account while 39 of respondents have not their Library social media account. The study also inform that Facebook, YouTube, Google+, LinkedIn, Twitter and Instagram are using by the LIS professional of India and Facebook 86 was popular social media tool among the other social media tools. Furthermore, respondent reported that they are using social media tools for sharing photos of events and programs of library 72, followed by tips for using different services 64, posting of new arrivals 56, tutorials of database 35 and send brief updates to patrons 32, announcement of library holidays 22. It was also reported by respondents that they are sharing information about scholarships training programs and marketing of library events etc. The study furthermore identify that lack of time is the major problem while using social media with 53 of respondents followed by low speed of internet 35, too many social media tools to learn 17 and some 3 respondents reported that there is no problem while using social media tools. The results also revealed that, majority of the respondents reported that they are using social media tools in daily basis 71 followed by weekly basis 16. It was followed by monthly 1 respondent and other 2 of the respondents. In summary, this study is expected to be useful in further promoting the social media for dissemination of library and information services to the general public.

Keywords: application of social media, India, promoting library services, library professionals

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