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323 results on '"Singh, Prateek"'

9. A Survey on Recent Progress in Blockchain Technology

Author

Khan, Naseem Ahmad, Singh, Prateek, Ambesh, Radiant, Ansari, Md Tarique Jamal, Bansal, Jagdish Chand, Series Editor, Deep, Kusum, Series Editor, Nagar, Atulya K., Series Editor, Roy, Bimal Kumar, editor, Chaturvedi, Atul, editor, Tsaban, Boaz, editor, and Hasan, Sartaj Ul, editor

Published
2024
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11. Correction: Human iPSC and CRISPR targeted gene knock-in strategy for studying the somatic TIE2L914F mutation in endothelial cells

Author

Lazovic, Bojana, Nguyen, Hoang-Tuan, Ansarizadeh, Mohammadhassan, Wigge, Leif, Kohl, Franziska, Li, Songyuan, Carracedo, Miguel, Kettunen, Jere, Krimpenfort, Luc, Elgendy, Ramy, Richter, Kati, De Silva, Laknee, Bilican, Bilada, Singh, Prateek, Saxena, Pratik, Jakobsson, Lars, Hong, Xuechong, Eklund, Lauri, and Hicks, Ryan

Published
2024
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14. Automated Atrial Fibrillation Classification Based on Denoising Stacked Autoencoder and Optimized Deep Network

Author

Singh, Prateek, Sharma, Ambalika, and Maiya, Shreesha

Subjects
Electrical Engineering and Systems Science - Signal Processing, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

The incidences of atrial fibrillation (AFib) are increasing at a daunting rate worldwide. For the early detection of the risk of AFib, we have developed an automatic detection system based on deep neural networks. For achieving better classification, it is mandatory to have good pre-processing of physiological signals. Keeping this in mind, we have proposed a two-fold study. First, an end-to-end model is proposed to denoise the electrocardiogram signals using denoising autoencoders (DAE). To achieve denoising, we have used three networks including, convolutional neural network (CNN), dense neural network (DNN), and recurrent neural networks (RNN). Compared the three models and CNN based DAE performance is found to be better than the other two. Therefore, the signals denoised by the CNN based DAE were used to train the deep neural networks for classification. Three neural networks' performance has been evaluated using accuracy, specificity, sensitivity, and signal to noise ratio (SNR) as the evaluation criteria. The proposed end-to-end deep learning model for detecting atrial fibrillation in this study has achieved an accuracy rate of 99.20%, a specificity of 99.50%, a sensitivity of 99.50%, and a true positive rate of 99.00%. The average accuracy of the algorithms we compared is 96.26%, and our algorithm's accuracy is 3.2% higher than this average of the other algorithms. The CNN classification network performed better as compared to the other two. Additionally, the model is computationally efficient for real-time applications, and it takes approx 1.3 seconds to process 24 hours ECG signal. The proposed model was also tested on unseen dataset with different proportions of arrhythmias to examine the model's robustness, which resulted in 99.10% of recall and 98.50% of precision.

Published
2022

15. Influence of E-Learning to Support Learning Process in Higher Education

Author

Sriram, K. V., Singh, Prateek, Kamath, Giridhar B., and Mathew, Asish Oommen

Abstract

The purpose of this paper is to identify the critical factors of e-learning and examine the influence of these factors on learning success among undergraduate engineering students in India. Exploratory research is carried out to determine the critical success factors of e-learning. The data from 307 respondents was collected using an online survey with the snowball technique. The causal relationship between the factors of e-learning and the success of e-learning was tested using structural equation modelling (SEM). The exploratory study identifies control, quality of education, platform design, online peer support, perceived usefulness, diversity of assessment, pricing, effort expectancy, and awareness as the critical success factors. The result indicates control, perceived usefulness, platform design, pricing, and quality of education support e-learning success. The theoretical model presented in this paper is the first of its kind and would enable researchers to understand e-learning from a holistic perspective. The factors developed would act as a guideline for future researchers who would like to analyse e-learning in different contextual settings. This study serves as a potential aid to higher education institutions that encourage e-learning in their curriculums.

Published
2023
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23. Human-derived air–liquid interface cultures decipher Alzheimer’s disease–SARS-CoV-2 crosstalk in the olfactory mucosa

Author

Shahbaz, Muhammad Ali, Kuivanen, Suvi, Lampinen, Riikka, Mussalo, Laura, Hron, Tomáš, Závodná, Táňa, Ojha, Ravi, Krejčík, Zdeněk, Saveleva, Liudmila, Tahir, Numan Ahmad, Kalapudas, Juho, Koivisto, Anne M., Penttilä, Elina, Löppönen, Heikki, Singh, Prateek, Topinka, Jan, Vapalahti, Olli, Chew, Sweelin, Balistreri, Giuseppe, and Kanninen, Katja M.

Published
2023
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38. A machine learning-based approach to determine infection status in recipients of BBV152 (Covaxin) whole-virion inactivated SARS-CoV-2 vaccine for serological surveys

Author

Singh, Prateek, Ujjainiya, Rajat, Prakash, Satyartha, Naushin, Salwa, Sardana, Viren, Bhatheja, Nitin, Singh, Ajay Pratap, Barman, Joydeb, Kumar, Kartik, Gayali, Saurabh, Khan, Raju, Rawat, Birendra Singh, Tallapaka, Karthik Bharadwaj, Anumalla, Mahesh, Lahiri, Amit, Kar, Susanta, Bhosale, Vivek, Srivastava, Mrigank, Mugale, Madhav Nilakanth, Pandey, C.P., Khan, Shaziya, Katiyar, Shivani, Raj, Desh, Ishteyaque, Sharmeen, Khanka, Sonu, Rani, Ankita, Promila, Sharma, Jyotsna, Seth, Anuradha, Dutta, Mukul, Saurabh, Nishant, Veerapandian, Murugan, Venkatachalam, Ganesh, Bansal, Deepak, Gupta, Dinesh, Halami, Prakash M., Peddha, Muthukumar Serva, Veeranna, Ravindra P., Pal, Anirban, Singh, Ranvijay Kumar, Anandasadagopan, Suresh Kumar, Karuppanan, Parimala, Rahman, Syed Nasar, Selvakumar, Gopika, Venkatesan, Subramanian, Karmakar, Malay Kumar, Sardana, Harish Kumar, Kothari, Anamika, Parihar, Devendra Singh, Thakur, Anupma, Saifi, Anas, Gupta, Naman, Singh, Yogita, Reddu, Ritu, Gautam, Rizul, Mishra, Anuj, Mishra, Avinash, Gogeri, Iranna, Rayasam, Geethavani, Padwad, Yogendra, Patial, Vikram, Hallan, Vipin, Singh, Damanpreet, Tirpude, Narendra, Chakrabarti, Partha, Maity, Sujay Krishna, Ganguly, Dipyaman, Sistla, Ramakrishna, Balthu, Narender Kumar, A, Kiran Kumar, Ranjith, Siva, Kumar, B. Vijay, Jamwal, Piyush Singh, Wali, Anshu, Ahmed, Sajad, Chouhan, Rekha, Gandhi, Sumit G., Sharma, Nancy, Rai, Garima, Irshad, Faisal, Jamwal, Vijay Lakshmi, Paddar, Masroor Ahmad, Khan, Sameer Ullah, Malik, Fayaz, Ghosh, Debashish, Thakkar, Ghanshyam, Barik, S.K., Tripathi, Prabhanshu, Satija, Yatendra Kumar, Mohanty, Sneha, Khan, Md. Tauseef, Subudhi, Umakanta, Sen, Pradip, Kumar, Rashmi, Bhardwaj, Anshu, Gupta, Pawan, Sharma, Deepak, Tuli, Amit, Ray chaudhuri, Saumya, Krishnamurthi, Srinivasan, Prakash, L., Rao, Ch V., Singh, B.N., Chaurasiya, Arvindkumar, Chaurasiyar, Meera, Bhadange, Mayuri, Likhitkar, Bhagyashree, Mohite, Sharada, Patil, Yogita, Kulkarni, Mahesh, Joshi, Rakesh, Pandya, Vaibhav, Mahajan, Sachin, Patil, Amita, Samson, Rachel, Vare, Tejas, Dharne, Mahesh, Giri, Ashok, Paranjape, Shilpa, Sastry, G. Narahari, Kalita, Jatin, Phukan, Tridip, Manna, Prasenjit, Romi, Wahengbam, Bharali, Pankaj, Ozah, Dibyajyoti, Sahu, Ravi Kumar, Dutta, Prachurjya, Singh, Moirangthem Goutam, Gogoi, Gayatri, Tapadar, Yasmin Begam, Babu, Elapavalooru VSSK., Sukumaran, Rajeev K., Nair, Aishwarya R., Puthiyamadam, Anoop, Valappil, Prajeesh Kooloth, Pillai Prasannakumari, Adrash Velayudhan, Chodankar, Kalpana, Damare, Samir, Agrawal, Ved Varun, Chaudhary, Kumardeep, Agrawal, Anurag, Sengupta, Shantanu, and Dash, Debasis

Published
2022
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41. Highly scalable and standardized organ-on-chip platform with TEER for biological barrier modeling.

Author

Nguyen, Hoang-Tuan, Rissanen, Siiri-Liisa, Peltokangas, Mimosa, Laakkonen, Tino, Kettunen, Jere, Barthod, Lara, Sivakumar, Ragul, Palojärvi, Anniina, Junttila, Pauliina, Talvitie, Jussi, Bassis, Michele, Nickels, Sarah L., Kalvala, Sara, Ilina, Polina, Tammela, Päivi, Lehtonen, Sarka, Schwamborn, Jens C., Mosser, Sebastien, and Singh, Prateek

Subjects
HIGH throughput screening (Drug development), DRUG discovery, MICROPHYSIOLOGICAL systems, SHEARING force, INDIVIDUALIZED medicine
Abstract

The development of new therapies is hampered by the lack of predictive, and patient-relevant in vitro models. Organ-on-chip (OOC) technologies can potentially recreate physiological features and hold great promise for tissue and disease modeling. However, the non-standardized design of these chips and perfusion control systems has been a barrier to quantitative high-throughput screening (HTS). Here we present a scalable OOC microfluidic platform for applied kinetic in vitro assays (AKITA) that is applicable for high, medium, and low throughput. Its standard 96-well plate and 384-well plate layouts ensure compatibility with existing laboratory workflows and high-throughput data collection and analysis tools. The AKITA plate is optimized for the modeling of vascularized biological barriers, primarily the blood-brain barrier, skin, and lung, with precise flow control on a custom rocker. The integration of trans-epithelial electrical resistance (TEER) sensors allows rapid and repeated monitoring of barrier integrity over long time periods. Together with automated liquid handling and compound permeability testing analyses, we demonstrate the flexibility of the AKITA platform for establishing human-relevant models for preclinical drug and precision medicine's efficacy, toxicity, and permeability under near-physiological conditions. [ABSTRACT FROM AUTHOR]

Published
2024
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42. Deep Network for Network Intrusion with Concept Drift

Author

Prasad, Shivam, Agyeya, Osho, Singh, Prateek, Krishnakumar, Shridevi S., Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Ranganathan, G., editor, Chen, Joy, editor, and Rocha, Álvaro, editor

Published
2021
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43. 'VISIO': An IoT Device for Assistance of Visually Challenged

Author

Singh, Rashbir, Singh, Prateek, Chahal, Deepak, Kharb, Latika, Cavas-Martínez, Francisco, Series Editor, Chaari, Fakher, Series Editor, Gherardini, Francesco, Series Editor, Haddar, Mohamed, Series Editor, Ivanov, Vitalii, Series Editor, Kwon, Young W., Series Editor, Trojanowska, Justyna, Series Editor, Pandey, V. C., editor, Pandey, P. M., editor, and Garg, S. K., editor

Published
2021
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44. Stock Market Prediction Using Machine Learning

Author

Shaikh, Ashfaq, Panuganti, Ajay, Husain, Maaz, Singh, Prateek, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Pandian, A. Pasumpon, editor, Palanisamy, Ram, editor, and Ntalianis, Klimis, editor

Published
2021
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46. High failure rate of ChAdOx1-nCoV19 immunization against asymptomatic infection in healthcare workers during a Delta variant surge

Author

Ujjainiya, Rajat, Tyagi, Akansha, Sardana, Viren, Naushin, Salwa, Bhatheja, Nitin, Kumar, Kartik, Barman, Joydeb, Prakash, Satyartha, Kutum, Rintu, Bhaskar, Akash Kumar, Singh, Prateek, Chaudhary, Kumardeep, Loomba, Menka, Khanna, Yukti, Walecha, Chestha, Ahmed, Rizwan, Yadav, Ashutosh, Bajaj, Archana, Malik, Gaurav, Qureshi, Sahar, Waghdhare, Swati, Siddiqui, Samreen, Trehan, Kamal Krishan, Mani, Manju, Dang, Rajiv, Das, Poonam, Dougall, Pankaj, Mahajan, Monica, Sonar, Sudipta, Jakhar, Kamini, Kumar, Reema, Tiwari, Mahima, Mani, Shailendra, Bhattacharyya, Sankar, Budhiraja, Sandeep, Agrawal, Anurag, Dash, Debasis, Jha, Sujeet, and Sengupta, Shantanu

Published
2022
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49. Predictive Strength of Selected Classification Algorithms for Diagnosis of Liver Disease

Author

Singh, Prateek, Chahal, Deepak, Kharb, Latika, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martin, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Singh, Pradeep Kumar, editor, Kar, Arpan Kumar, editor, Singh, Yashwant, editor, Kolekar, Maheshkumar H., editor, and Tanwar, Sudeep, editor

Published
2020
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