Achyutha N Prasad
Biographic Data
| ID | 10314139 |
|---|---|
| NAME | Achyutha N Prasad |
| GIVEN NAMES | Achyutha N |
| FAMILY NAME | Prasad |
| SIGNATURE | PRASAD A N |
| VERIFIED | No |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2022 |
| LATEST PUBLICATION YEAR | 2022 |
| H-INDEX | 0 |
Human activity recognition using CNN and long term short term memory
Human activity recognition aims to work out the activities performed by someone in a picture or video. Examples of actions are running, sitting, sleeping, and standing. Complex movement patterns and harmful occurrences like falling may be a part of these activities. The suggested ConvLSTM network can be created by successively combining fully connected layers, long immediate memory (LSTM) networks, and convolutional neural networks (CNN). The acq…
Recognition efficiency enhancement of control chart pattern using ensemble MLP neural network
chart pattern for six different fundamental patterns have been proposed. The multilayer perceptron based neural network has applied as an entity to form the ensemble. The loss of generalization property of an individual neural network has been fulfilled by providing the diversity in the training data for the individual entity of ensemble. The outcome of individual recognizer has combined over their decision value to include the outcome knowledge …
No prominent works on this page.
Human activity recognition using CNN and long term short term memory
Human activity recognition aims to work out the activities performed by someone in a picture or video. Examples of actions are running, sitting, sleeping, and standing. Complex movement patterns and harmful occurrences like falling may be a part of these activities. The suggested ConvLSTM network can be created by successively combining fully connected layers, long immediate memory (LSTM) networks, and convolutional neural networks (CNN). The acq…
Recognition efficiency enhancement of control chart pattern using ensemble MLP neural network
chart pattern for six different fundamental patterns have been proposed. The multilayer perceptron based neural network has applied as an entity to form the ensemble. The loss of generalization property of an individual neural network has been fulfilled by providing the diversity in the training data for the individual entity of ensemble. The outcome of individual recognizer has combined over their decision value to include the outcome knowledge …
Artificial Intelligence (2 works) · Computer Science (2 works) · Pattern recognition (psychology (2 works) · Advanced Statistical Process Monitoring (1 work) · Anomaly Detection Techniques and Applications (1 work) · Artificial neural network (1 work) · Chart (1 work) · Computer vision (1 work) · Context-Aware Activity Recognition Systems (1 work) · Control chart (1 work)