Manish Kumar
Delhi Technological University
17 Papers
42 Citations
Manish Kumar is an academic researcher from Delhi Technological University. The author has contributed to research in topics: Combustion & Amperometry. The author has an hindex of 3, co-authored 15 publications.
Chat about Author
Papers
Nanostructured SnO2 encapsulated guar-gum hybrid nanocomposites for electrocatalytic determination of hydrazine.
TL;DR: The obtained results suggest that SnO2-GG nanocomposites electrode provides a favorable sensing platform for the electrochemical studies, and shows a good response time, reproducibility, and long-term stability.
48
Optimization of engine performance parameters and exhaust emissions in compression ignition engine fueled with biodiesel-alcohol blends using taguchi method, multiple regression and artificial neural network
Tanmaya Agrawal,Raghvendra Gautam,Sudeekcha Agrawal,Vishal Singh,Manish Kumar,Saket Kumar +5 more
- 01 Jan 2020
TL;DR: In this paper, a study engine performance in terms of brake thermal efficiency (BTE), brake specific fuel consumption (BPSC) and CO, HC, CO2, and NOx emissions are optimized for Kusum Oil Ethyl Ester (KOEE) with butanol blend using Taguchi's method, to obtain an optimized dataset of the input parameters viz. engine rpm, fuel properties and lower heating value of fuel which are then fed into the two prediction techniques viz. Artificial Neural Network (ANN) and multiple regression.
46
Melt-quenched vanadium pentoxide-stabilized chitosan nanohybrids for efficient hydrazine detection
Jay Singh,Kshitij Rb Singh,Manish Kumar,Rahul Verma,Ranjana Verma,Priya Malik,Saurabh Srivastava,Ravindra Pratap Singh,Devendra Kumar +8 more
- 18 Oct 2021
TL;DR: In this paper, a nanostructured vanadium pentoxide (n-V2O5) nanoparticles were synthesized using a hydrothermal and melt-quenching approach without using any reducing agent, acids/bases, and hazardous solvents.
Predicting Customer Churn Using Artificial Neural Network
Sanjay Kumar,Manish Kumar +1 more
- 24 May 2019
TL;DR: An effective solution to this challenging problem of customer churn prediction is presented using the data set of telecommunication industry and Artificial Neural Networks to determine the factors influencing the customer churn and optimize the solutions by experimenting with different activation functions.
29
Controlling Mouse Motions Using Eye Tracking Using Computer Vision
Kshitij Meena,Manish Kumar,Mohit Jangra +2 more
- 13 May 2020
TL;DR: An algorithm to carry out the functions of a mouse by providing a hands-free interaction between humans and computers by using different expressions of a face using computer vision and matching it with already stored expression and execute actions as per the move is introduced.
25