Kalpdrum Passi received his Ph.D. in Parallel Numerical Algorithms from Indian Institute of Technology, Delhi, India in 1993. He is an Associate Professor, Department of Mathematics
The explosive growth in the amount of data in the field of biology, education, environmental rese... more The explosive growth in the amount of data in the field of biology, education, environmental research, sensor network, stock market, weather forecasting and many more due to vast use of internet in distributed environment has generated an urgent need for new techniques and tools that can intelligently automatically transform the processed data into useful information and knowledge. Hence data mining has become a research are with increasing importance. Since continuation in collection of more data at this scale, formalizing the process of big data analysis will become paramount. Given the vast amount of data are geographically spread across the globe, this means a very large number of models is generated, which raises problems on how to generalize knowledge in order to have a global view of the phenomena across the organization. This is applicable to web-based educational data. In this chapter, the new dynamic and scalable data mining approach has been discussed with educational data.
Sign gesture recognition is an important problem in human-computer interaction with significant s... more Sign gesture recognition is an important problem in human-computer interaction with significant societal influence. However, it is a very complex task, since sign gestures are naturally deformable objects. Gesture recognition contains unsolved problems for the last two decades, such as low accuracy or low speed, and despite many proposed methods, no perfect result has been found to explain these unsolved problems. In this paper, we propose a deep learning approach to translating sign gesture language into text. In this study, we have introduced a self-generated image data set for American Sign language (ASL). This dataset is a collection of 36 characters containing A to Z alphabets and 0 to 9 number digits. The proposed system can recognize static gestures. This system can learn and classify specific sign gestures of any person. A convolutional neural network (CNN) algorithm is proposed for classifying ASL images to text. An accuracy of 99% on the alphabet gestures and 100% accuracy on digits was achieved. This is the best accuracy compared to existing systems.
The explosive growth in the amount of data in the field of biology, education, environmental rese... more The explosive growth in the amount of data in the field of biology, education, environmental research, sensor network, stock market, weather forecasting and many more due to vast use of internet in distributed environment has generated an urgent need for new techniques and tools that can intelligently automatically transform the processed data into useful information and knowledge. Hence data mining has become a research are with increasing importance. Since continuation in collection of more data at this scale, formalizing the process of big data analysis will become paramount. Given the vast amount of data are geographically spread across the globe, this means a very large number of models is generated, which raises problems on how to generalize knowledge in order to have a global view of the phenomena across the organization. This is applicable to web-based educational data. In this chapter, the new dynamic and scalable data mining approach has been discussed with educational data.
Sign gesture recognition is an important problem in human-computer interaction with significant s... more Sign gesture recognition is an important problem in human-computer interaction with significant societal influence. However, it is a very complex task, since sign gestures are naturally deformable objects. Gesture recognition contains unsolved problems for the last two decades, such as low accuracy or low speed, and despite many proposed methods, no perfect result has been found to explain these unsolved problems. In this paper, we propose a deep learning approach to translating sign gesture language into text. In this study, we have introduced a self-generated image data set for American Sign language (ASL). This dataset is a collection of 36 characters containing A to Z alphabets and 0 to 9 number digits. The proposed system can recognize static gestures. This system can learn and classify specific sign gestures of any person. A convolutional neural network (CNN) algorithm is proposed for classifying ASL images to text. An accuracy of 99% on the alphabet gestures and 100% accuracy on digits was achieved. This is the best accuracy compared to existing systems.
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