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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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| Paper Title: | Deep Image Learning Using ADAM Classifier |
| Authors Name: | P.Sreenivasulu , S.Vamsi , G. Gopi Krishna , E. Manoj , P. Sai Bhupathi |
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IJNRD_219863
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| Published Paper Id: | IJNRD2404840 |
| Published In: | Volume 9 Issue 4, April-2024 |
| DOI: | |
| Abstract: | Deep image classification has seen significant advancements with the advent of deep learning techniques. In this study, we explore the efficacy of the Adam optimization algorithm for training deep neural networks (DNNs) in image classification tasks. Adam, an adaptive learning rate optimization algorithm, has gained popularity for its ability to efficiently optimize large-scale neural networks. We propose a methodology that utilizes Adam to train a deep convolutional neural network (CNN) for image classification tasks. Convolutional Neural Networks (CNNs) have revolutionized the field of computer vision, particularly in image classification tasks. In Data Preparation, Collect a labeled dataset of images suitable for the classification task. Split the dataset into training, validation, and testing sets. In Data Augmentation, Augment the training dataset using techniques such as rotation, flipping, scaling, and cropping to increase variability and improve generalization. In Model Training, Initialize the model parameters (weights and biases) randomly or using pre-trained weights. Train the CNN on the training dataset using back propagation and an optimization algorithm (e.g., Adam, SGD). In Hyperparameter Tuning, Tune hyperparameters such as learning rate, batch size, dropout rate, and regularization strength to optimize model performance. In Model Evaluation, Evaluate the trained model on the test set to assess its performance in terms of accuracy, precision, recall, and F1 score. Visualize performance metrics and analysis any misclassifications or errors. This proposal results demonstrate that Adam offers superior performance in terms of convergence speed and accuracy, making it a promising choice for training deep neural networks in image classification tasks. |
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| Cite Article: | "Deep Image Learning Using ADAM Classifier ", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.9, Issue 4, page no.i303-i306, April-2024, Available :http://www.ijnrd.org/papers/IJNRD2404840.pdf |
| Downloads: | 00047 |
| ISSN: |
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID:IJNRD2404840 Registration ID: 219863 Published In: Volume 9 Issue 4, April-2024 DOI (Digital Object Identifier): Page No: i303-i306 Country: Kadapa, Andhra Pradesh, India Research Area: Electronics & Communication Engg. Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2404840 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2404840 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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