A
Akhilesh Kumar Sharma
Researcher at Manipal University Jaipur
Publications - 52
Citations - 431
Akhilesh Kumar Sharma is an academic researcher from Manipal University Jaipur. The author has contributed to research in topics: Computer science & Deep learning. The author has an hindex of 5, co-authored 41 publications receiving 158 citations. Previous affiliations of Akhilesh Kumar Sharma include Manipal University & Indian Institute of Technology Kanpur.
Papers
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Journal ArticleDOI
Dermatologist-Level Classification of Skin Cancer Using Cascaded Ensembling of Convolutional Neural Network and Handcrafted Features Based Deep Neural Network
Akhilesh Kumar Sharma,Shamik Tiwari,Gaurav Aggarwal,Nitika Goenka,Anil Kumar,Prasun Chakrabarti,Tulika Chakrabarti,Radomir Gono,Zbigniew Leonowicz,Michal Jasinski +9 more
TL;DR: A cascaded ensembled network that uses an integration of ConvNet and handcrafted features based multi-layer perceptron is proposed in this work and it is demonstrated that accuracy of ensembleled deep learning model is improved to 98.3% from 85.3%.
Book
Mobile Ad-Hoc Networks (MANET)
TL;DR: Several issues are explained and discussed in this book to establish the connection with the mobile adhoc networks in optimized way and to cover different topological and network study.
Journal ArticleDOI
Classification of Indian Classical Music With Time-Series Matching Deep Learning Approach
Akhilesh Kumar Sharma,Gaurav Aggarwal,Sachit Bhardwaj,Prasun Chakrabarti,Tulika Chakrabarti,Jemal H. Abawajy,Siddhartha Bhattacharyya,Mishra Richa,Anirban Das,Hairulnizam Mahdin +9 more
TL;DR: In this paper, two approaches are used to implement classification models, i.e. 3-layer CNN and RNN-LSTM, and SVM (Sigmoid, Polynomial & Gaussian Kernel).
Journal ArticleDOI
WB-CPI: Weather Based Crop Prediction in India Using Big Data Analytics
Rishi Gupta,Akhilesh Kumar Sharma,Oorja Garg,Krishna Modi,Shahreen Kasim,Zirawani Baharum,Hairulnizam Mahdin,Salama A. Mostafa +7 more
TL;DR: In this paper, the authors collected and analyzed temperature, rainfall, soil, seed, crop production, humidity and wind speed data (in a few regions), which will help the farmers improve the produce of their crops.
Journal ArticleDOI
Phonocardiogram Signal Based Multi-Class Cardiac Diagnostic Decision Support System
TL;DR: Shamiktiwari et al. as discussed by the authors proposed a ConvNet model trained by Hybrid Constant-Q Transform (HCQT) for heart sound beat classification, which achieved 96% in multi-class classification.