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Anil Kumar Saini
Researcher at Council of Scientific and Industrial Research
Publications - 54
Citations - 363
Anil Kumar Saini is an academic researcher from Council of Scientific and Industrial Research. The author has contributed to research in topics: Genetic programming & Support vector machine. The author has an hindex of 8, co-authored 47 publications receiving 245 citations. Previous affiliations of Anil Kumar Saini include Malaviya National Institute of Technology, Jaipur & University of Massachusetts Amherst.
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Book ChapterDOI
Lexicase Selection Beyond Genetic Programming
TL;DR: This chapter presents a framework for solving Boolean constraint satisfaction problems using a traditional genetic algorithm, with linear genomes of fixed length, and shows that when lexicase selection is used, more solutions are found, fewer generations are required to find those solutions, and more diverse populations are maintained.
Proceedings ArticleDOI
NTIRE 2019 Challenge on Image Colorization: Report
Seungjun Nah,Radu Timofte,Richard Zhang,Maitreya Suin,Kuldeep Purohit,A. N. Rajagopalan,S Athi Narayanan,Jameer Babu Pinjari,Zhiwei Xiong,Zhan Shi,Chang Chen,Dong Liu,Manish Sharma,Megh Makwana,Anuj Badhwar,Ajay Pratap Singh,Avinash Upadhyay,Akkshita Trivedi,Anil Kumar Saini,Santanu Chaudhury,Prasen Kumar Sharma,Priyankar Jain,Arijit Sur,Gokhan Ozbulak +23 more
TL;DR: This paper reviews the NTIRE challenge on image colorization (estimating color information from the corresponding gray image) with focus on proposed solutions and results.
Proceedings ArticleDOI
An IoT Instrumented Smart Agricultural Monitoring and Irrigation System
TL;DR: An IoT platform based on ThingSpeak and NodeMCU is demonstrated, which will help the farmer to control the irrigation by using a PC or smartphone from anywhere and anytime, to monitoring the moisture and temperature parameter and reduce his efforts and also to optimize the use of water.
Journal ArticleDOI
A novel real-time resource efficient implementation of Sobel operator-based edge detection on FPGA
TL;DR: The proposed architecture uses single processing element for both horizontal and vertical gradient computation for Sobel operator and utilised approximately 38% less FPGA resources as compared to standard Sobel edge detection architecture while maintaining real-time frame rates for high definition videos.
Proceedings ArticleDOI
Analyzing impact of image scaling algorithms on viola-jones face detection framework
TL;DR: This paper has analyzed the effect of different image scaling algorithms existing in literature on the performance of the Viola and Jones face detection framework and has tried to find out the optimal algorithm significant in performance.