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317 results on '"Singh, Prateek"'

8. 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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9. 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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12. 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

13. 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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15. 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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37. 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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40. 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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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. 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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49. 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

Subjects
INDUCED pluripotent stem cells, GENE expression, ENDOTHELIAL cells, GENOME editing, SHEARING force
Abstract

Induced pluripotent stem cell (iPSC) derived endothelial cells (iECs) have emerged as a promising tool for studying vascular biology and providing a platform for modelling various vascular diseases, including those with genetic origins. Currently, primary ECs are the main source for disease modelling in this field. However, they are difficult to edit and have a limited lifespan. To study the effects of targeted mutations on an endogenous level, we generated and characterized an iPSC derived model for venous malformations (VMs). CRISPR-Cas9 technology was used to generate a novel human iPSC line with an amino acid substitution L914F in the TIE2 receptor, known to cause VMs. This enabled us to study the differential effects of VM causative mutations in iECs in multiple in vitro models and assess their ability to form vessels in vivo. The analysis of TIE2 expression levels in TIE2L914F iECs showed a significantly lower expression of TIE2 on mRNA and protein level, which has not been observed before due to a lack of models with endogenous edited TIE2L914F and sparse patient data. Interestingly, the TIE2 pathway was still significantly upregulated and TIE2 showed high levels of phosphorylation. TIE2L914F iECs exhibited dysregulated angiogenesis markers and upregulated migration capability, while proliferation was not affected. Under shear stress TIE2L914F iECs showed reduced alignment in the flow direction and a larger cell area than TIE2WT iECs. In summary, we developed a novel TIE2L914F iPSC-derived iEC model and characterized it in multiple in vitro models. The model can be used in future work for drug screening for novel treatments for VMs. [ABSTRACT FROM AUTHOR]

Published
2024
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