Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

Browse

Filters

20222

Settings

Author: search.filters.author.Anand Kumar, A.M.Subject: search.filters.subject.CNN

Search Results

Now showing 1 - 2 of 2
  • No Thumbnail Available
    Item
    Price Prediction of Agricultural Products Using Deep Learning
    (Springer Science and Business Media Deutschland GmbH, 2022) Kankar, M.; Anand Kumar, A.M.
    Every field in the world is undergoing a significant change because of the influence of technology. The agricultural sector of the Indian economy needs more technological support for its development and growth in India. Price prediction of agricultural products helps ensure that the farmers either get good returns or recover their investments. Hence, the characteristics of deep neural networks such as CNN and deep learning models can be used in predicting prices. A convolution neural network-based model can indirectly predict fruits and vegetable prices by classifying images to their variety. Deep learning models such as long short-term memory (LSTM) and bidirectional LSTM (BiLSTM) can also help predict the market price of agricultural products. Fruits and vegetable prices mainly depend on a few things, variety, quality, and market rate. We use the CNN model to deal with variety and quality, different varieties of a single fruit or vegetable having different prices, followed by prediction using LSTM and bidirectional LSTM to deal with market price prediction in a volatile market. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
  • No Thumbnail Available
    Item
    Speaker Identification and Verification using Deep Learning
    (Institute of Electrical and Electronics Engineers Inc., 2022) Recharla, R.; Jeevan Reddy, C.; Tanguturu, R.; Anand Kumar, A.M.
    Many voice assistants gained importance across globe in the recent times, for example, Cortana, Siri, Ok Google. These assistants are part of everyone's life these days. The main motive behind the proposed system is to improve recognition assistant system. The speaker prediction model is trained using features MFCC, Chroma, Tonnetz, Mel spectrogram, and Spectral contrast extracted from audio samples. The proposed system has numerous real-world applications, such as meeting transcription, unlocking smart devices using voice, and online viva voice verification. It can replace the existing biometric system for faculty attendance and traditional fingerprint recognition. A Dense Neural Network was created for each audio feature and finally concatenated using a concatenation layer which fetched the best performance output compared to LSTM. Dense Neural Network successfully predicted the speaker with an accuracy of more than 95% most of the times. In the case of LSTM, due to fewer samples, the accuracy of speaker prediction is around 79%. In the case of CNN, the accuracy of speaker prediction is around 86%; this behavior can be attributed to the noise environment. When an unknown speaker tries to speak, the Dense Neural network can manage the task by placing them in an anonymous class. © 2022 IEEE.