Browsing by Author "Shukla, A."

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Data-Driven Stillbirth Prediction and Analysis of Risk Factors in Pregnancy
(Springer Science and Business Media Deutschland GmbH, 2021) Unnikrishnan, A.; Chandrasekaran, K.; Shukla, A.
One of the main issues in developing countries is the lack of policies for ensuring good public health conditions in rural areas. Maternal and child health care is one such area that has not improved in developing countries. Although child health has improved noticeably over the years, infant or under-5-mortality has not become any better. There remain major knowledge gaps in our understanding of how factors such as prenatal care, antenatal care, social and economic backgrounds, living conditions and lifestyle of pregnant women and their family members affect the pregnancy outcomes. Understanding such factors that affect the poor pregnancy outcome helps in formulating plans to prevent such issues and to treat them effectively. Determining health policies will be easier from a deeper analysis of such factors involved. This paper discusses some of the key machine learning techniques to predict the pregnancy outcome as a stillbirth or not and analyze some of the factors that majorly cause stillbirth. Â© 2021, Springer Nature Singapore Pte Ltd.
Intelligent Data Mining for Collaborative Information Seeking
(Springer Nature, 2020) Kumar, A.; Chandrasekaran, K.; Shukla, A.; Usha, D.
World Wide Web (WWW) contains different kinds of information whether it be social, educational, historical, sports, news, financial, weather, technology, politics etc. Most of the people spend time on the internet to access data for information seeking purposes. Information provided on the web is available in different formats like in text format, image format or video format, and they can be accessed through different access interfaces. Accessing information from such a large place i.e. World Wide Web through so many websites would become a very cumbersome process, therefore, in this paper, we present a new method which will produce information based on the user input using appropriate keywords. The data will be retrieved from the internet using Data Mining approach without the need for rules and training of pages. The main focus will be to extract or retrieve data of a person like educational qualifications, gender, contact information, contributions in his work, his/her social nature, etc. The query to be searched on the platform or model should have meaningful keywords attached to it best describing the person or else data of some different person might be fetched. Â© 2020, Springer Nature Switzerland AG.
Multifunctional conjugated 1,6-heptadiynes and its derivatives stimulated molecular electronics: Future moletronics
(Elsevier Ltd, 2020) Magisetty, R.; Hemanth, N.R.; Kumar, P.; Shukla, A.; Shunmugam, R.; Balasubramanian, B.
Over the past decade conducting polymers have been studied for electronic applications, among them, molecular electronics: the study and investigation of molecular building blocks is a next-generation demanded area of research. Hence, the moletronic equivalent multi-functional advantages of cyclopolymerized 1,6-heptadiyne (HD) systems explored in this review. Further, this report elucidates physical properties via conditional cyclopolymerization methodologies, it describes the chemistry of tethering molecular-chains facilitated intrinsic-conductivity. HD and its derivatives induce superior conductivity characteristics via doping elements, wherein, significant electronic conductivity mechanism is attributable to the solitons and anti-solitons, which was described in this context. HD and their derivatives molecular mechanism, its compatibility are expounded for moletronic application, which is new insight of the article. Moreover, required inherent characteristics, for e.g., thermal-stability, chemical-resistance, mechanical properties, magnetic, and electronic properties have been discussed. Furthermore, failures, physical limitations, and its realizable similarity solutions for moletronics have described. Though electronic or moletronic components having failures and other physical limitations, HDs offers excellent conductivity with wide functional and physical properties that could lead to potential candidates to deliver efficient and low-cost moletronic devices. © 2019 Elsevier Ltd
Poly(1,6-heptadiyne)/NiFe2O4 composite as capacitor for miniaturized electronics
(Bellwether Publishing, Ltd., 2020) Magisetty, R.; N R, H.; Shukla, A.; Shunmugam, R.; Balasubramanian, B.
Impedance spectroscopy-based electrical measurements were conducted on different molecular weight (MW) Poly(1,6-heptadiyne)s (PHDs) embedded PHD/NiFe2O4 nanocomposites. Nanocomposites conductivity result demonstrated the conductivities of around (Formula presented.) (nanocomposite Root mean square (RMS) current is 12–15 times greater than DC current of PHDs at 27° C). Additionally, dielectric loss and capacitance characteristics suggested the nanocomposite (4500 MW PHD) device quality factor is 35.7 at 1 kHz, which is ~13.89 times superior than that of NiFe2O4 alone sample, also ‘Q’ value for highest MW PHD nanocomposite is 50% enhanced than NiFe2O4. Moreover, the capacitance result suggested the 12400 MW PHD nanocomposite nearly frequency-independent capacitance (15–20pF) over a frequency range of 500 Hz–500 kHz. © 2020 Taylor & Francis.