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Author

R. Reddy

Bio: R. Reddy is an academic researcher. The author has co-authored 2 publications.

Papers
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TL;DR: Support Vector Machine (SVM) based Real Time Hand-Written Digit Recognition System to recognize user given handwritten digits in real time is presented.
Abstract: Meanwhile Neural Networks based algorithms have intimated steadfast potential on various visual tasks including the recognition of Digits. This paper presents Support Vector Machine (SVM) based Real Time Hand-Written Digit Recognition System. The system involves two main sections i.e. training and recognition section. SVM classifier is used as the training algorithm and then tested it on MNIST dataset. We achieved a training accuracy of 98.05% and a test accuracy of 97.83% demonstrating that the proposed method can achieve significant and promising performance in digit recognition. Then we implemented our model to recognize user given handwritten digits in real time.
Proceedings ArticleDOI
18 Nov 2022
TL;DR: In this article , the authors presented a machine learning method for classifying the personality pattern based on the Big 5 model and used unsupervised K-means algorithm to generate clusters to recognize the patterns in the dataset and group individuals according to their personality patterns.
Abstract: The personality of an individual is a characteristic of a person's attitude. Identifying a person's personality would be useful in assisting, in choosing the right career or profession. Further, personality has a greater impact on social, personal, and economic dimensions of life. Assessing the personality of a group of people in an organization would enable one to understand the work feel factor and enhance productivity. This paper presents a machine-learning method for classifying the personality pattern based on the Big 5 model. The unsupervised K-Means algorithm is used to generate clusters to recognize the patterns in the dataset and group individuals according to their personality patterns. This would enable recognizing the dominant personality of a country or assessing the personality patterns of an organization. The study suggested how to utilize clustering to extract patterns from a dataset and categorize data points into relevant clusters in this paper. It is also learned how to utilize these clusters to estimate a country's dominant personality and how to expand our dataset with more participants to achieve a more generic model.