R
R. Mahammad Shafi
Researcher at Mizan–Tepi University
Publications - 6
Citations - 94
R. Mahammad Shafi is an academic researcher from Mizan–Tepi University. The author has contributed to research in topics: Computer science & Random forest. The author has an hindex of 1, co-authored 2 publications receiving 7 citations.
Papers
More filters
Journal ArticleDOI
Real-Time Twitter Spam Detection and Sentiment Analysis using Machine Learning and Deep Learning Techniques
Anisha P. Rodrigues,Roshan Fernandes,Aakash A,Abhishek Lal B,Adarsh Shetty,Atul K,Kuruva Lakshmanna,R. Mahammad Shafi +7 more
TL;DR: The main purpose of this proposed work is to develop a system that can determine whether a tweet is “spam” or “ham” and evaluate the emotion of the tweet and create a learning model that will associate tweets with a particular sentiment.
Journal ArticleDOI
An efficient and secure data storage in cloud computing using modified RSA public key cryptosystem
TL;DR: A method for providing data storage and security in cloud using public key Cryptosystem, which uses the concept of the modified RSA algorithm to provide better security for the data stored in the cloud is recommended.
Journal ArticleDOI
Perimeter Degree Technique for the Reduction of Routing Congestion during Placement in Physical Design of VLSI Circuits
Kuruva Lakshmanna,Fahimuddin Shaik,Vinit Kumar Gunjan,Ninni Singh,Gautam Kumar,R. Mahammad Shafi +5 more
TL;DR: In this paper , the impact of placement and routing congestion on the performance of integrated circuits is investigated using the Improved Harmonic Search Optimization (IHOSO) algorithm and the perimeter degree technique (PDT).
Journal ArticleDOI
Early Detection of Forest Fire Using Mixed Learning Techniques and UAV
Varanasi L. V. S. K. B. Kasyap,D. Sumathi,Kumarraju Alluri,Pradeep Reddy CH,Navod Neranjan Thilakarathne,R. Mahammad Shafi +5 more
TL;DR: The purpose of this work is to propose deep learning techniques to predict forest fires, which would be cost-effective and outperforms the traditional methods such as Bayesian classifiers, random forest, and support vector machines.