Institution
PES University
Education•Bengaluru, Karnataka, India•
About: PES University is a education organization based out in Bengaluru, Karnataka, India. It is known for research contribution in the topics: Facial recognition system & Feature extraction. The organization has 2370 authors who have published 1684 publications receiving 7461 citations. The organization is also known as: P.E.S. Institute of Technology & PES Institute of Technology.
Topics: Facial recognition system, Feature extraction, Cloud computing, Support vector machine, Artificial neural network
Papers published on a yearly basis
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01 Dec 2016
TL;DR: Ries was one of the pioneers of the Lean Startup philosophy as discussed by the authors, based on the Japanese Philosophy of Lean Manufacturing, and he pioneered the philosophy of Lean Startup based on his experience with multiple startups.
Abstract: Eric Ries was born in September 1978. He graduated from Yale University and moved to silicon Valley in the beginning of the millennium. He pioneered the philosophy of Lean Startup, based on his experience with multiple startups, primary being IMVU which he co-founded along with Will Harvey in 2004. Eric Ries originated his Lean Startup philosophy after getting inspired from the Japanese Philosophy of Lean Manufacturing.
776 citations
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TL;DR: This review explores the use of moringa across disciplines for its medicinal value and deals with cultivation, nutrition, commercial and prominent pharmacological properties of this “Miracle Tree”.
700 citations
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TL;DR: This work develops an experimental framework for the classification problem which predicts whether stock prices will increase or decrease with respect to the price prevailing n days earlier, and selects technical indicators and their use as features with high accuracy for medium to long-run prediction of stock price direction.
175 citations
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TL;DR: In this article, a new class of copper based composite material by dispersing both hard and soft reinforcement in appropriate proportions to ensure optimum tribological characteristics was developed by liquid metallurgy route and the results show that the hybrid composites possess higher hardness, higher tensile strength, better wear resistance and lower coefficient of friction when compared to pure copper.
175 citations
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TL;DR: The proposed hybrid approach by combining genetic algorithm (GA) for feature optimization with deep neural network (DNN) for classification and defect prediction performs better when compared with other techniques.
Abstract: In the field of early prediction of software defects, various techniques have been developed such as data mining techniques, machine learning techniques. Still early prediction of defects is a challenging task which needs to be addressed and can be improved by getting higher classification rate of defect prediction. With the aim of addressing this issue, we introduce a hybrid approach by combining genetic algorithm (GA) for feature optimization with deep neural network (DNN) for classification. An improved version of GA is incorporated which includes a new technique for chromosome designing and fitness function computation. DNN technique is also improvised using adaptive auto-encoder which provides better representation of selected software features. The improved efficiency of the proposed hybrid approach due to deployment of optimization technique is demonstrated through case studies. An experimental study is carried out for software defect prediction by considering PROMISE dataset using MATLAB tool. In this study, we have used the proposed novel method for classification and defect prediction. Comparative study shows that the proposed approach of prediction of software defects performs better when compared with other techniques where 97.82% accuracy is obtained for KC1 dataset, 97.59% accuracy is obtained for CM1 dataset, 97.96% accuracy is obtained for PC3 dataset and 98.00% accuracy is obtained for PC4 dataset.
124 citations
Authors
Showing all 2405 results
Name | H-index | Papers | Citations |
---|---|---|---|
Sivaprakasam Radhakrishnan | 36 | 144 | 4949 |
Sami Muhaidat | 33 | 281 | 4034 |
R. K. Somashekar | 30 | 265 | 3215 |
Dinkar Sitaram | 29 | 117 | 3855 |
P.K. Suresh | 28 | 149 | 2037 |
Sriraam Natarajan | 28 | 215 | 3145 |
C.S. Ramesh | 24 | 55 | 1840 |
Ramaiah Keshavamurthy | 24 | 110 | 1729 |
Praveennath G. Koppad | 20 | 43 | 950 |
Archana Mathur | 19 | 73 | 979 |
Syed Khasim | 17 | 56 | 1044 |
Muhammad Faisal | 17 | 118 | 1072 |
K. T. Kashyap | 16 | 40 | 1567 |
Mahua Bhattacharya | 15 | 116 | 692 |
Suraj Srinivas | 14 | 24 | 1252 |