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Python Final year CSE projects in Bangalore
About Deep Learning (DL)
Deep Learning (DL) is a type of artificial intelligence that teaches computers to learn from large amounts of data, similar to how humans learn. It uses structures called neural networks, which are layers of algorithms modeled after the human brain. By analyzing patterns and features in the data, deep learning systems can recognize images, understand speech, make decisions, and even generate text. This technology powers many applications we use daily, such as virtual assistants, translation services, and recommendation systems. Essentially, deep learning helps computers perform complex tasks by learning from examples, improving their accuracy over time.
IEEE DL projects Bangalore |TOP IEEE DL projects Bangalore | Best DL IOT projects Bangalore
DL FEATURES:
DL futures, or Deep Learning futures, refer to the potential advancements and applications of deep learning technology in the future. This includes improvements in artificial intelligence systems for tasks like image recognition, language translation, and autonomous driving. These advancements could lead to more efficient, accurate, and intelligent systems across various industries, enhancing daily life and technological progress.
DL for Image Processing Projects – DL for image processing uses neural networks to analyze and understand images. It helps in tasks like recognizing objects, enhancing image quality, and detecting patterns. By learning from large datasets, these models can make accurate predictions and automate complex image-based tasks.
DL for Text Processing Projects – DL for text processing involves using advanced algorithms to understand and generate human language. It helps in tasks like translating languages, summarizing articles, and answering questions. DL models, like neural networks, learn from vast amounts of text data to make accurate predictions and perform complex language tasks.
DL for Tabular Data Projects – DL for tabular data projects involves using advanced neural networks to analyze and make predictions from structured data in rows and columns, like spreadsheets. It’s useful for complex patterns that simpler models might miss, offering powerful insights for business, science, and more.
DL with Transfer Learning Projects – DL with Transfer Learning means using a pre-trained model on a new task. It’s like teaching a student with prior knowledge, making learning faster and easier. For projects, this approach helps quickly build accurate models, especially when you don’t have a lot of data.
DL with Transformers Projects – DL with Transformers Projects involves using advanced neural networks, called Transformers, to handle tasks like language translation, text summarization, and image processing. These projects showcase how Transformers can learn from large amounts of data to perform complex tasks efficiently and accurately, making them a powerful tool in AI development.
DL with GAN Projects – DL with GAN (Generative Adversarial Networks) projects involve training two neural networks against each other. One creates fake data (generator), while the other tries to detect fake from real data (discriminator). This competition improves both, leading to realistic images, videos, or sounds created by the AI.
DL for Image Processing Projects
Abstract: “Predicting Poverty Levels from Space” explores using Convolutional Neural Networks (CNNs) on satellite imagery to understand poverty distribution. With over a billion people living below the international poverty line, efficient methods are needed to map poverty. Traditional census-based approaches are slow and costly. Leveraging the abundance of high-resolution satellitedata, this study demonstrates the effectiveness of CNNs in predicting poverty levels. Focusing on Ethiopia, Malawi, and Nigeria, our CNN models, achieved promising accuracies, with the custom CNN model achieving 90% accuracy. This innovative approach aids in resource allocation and policy formulation to fight against poverty effectively.
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This project focuses on the essential task of classifying Alzheimer’s Disease (AD) using two distinct deep learning approaches: a Convolutional Neural Network (CNN) and Transfer Learning with DenseNet201. Utilizing a comprehensive dataset with four categories – Non-Demented, Very Mild Demented, Mild Demented, and Moderate Demented – from open-access sources, we aim to assess the effectiveness of these models in accurately identifying different stages of AD. The CNN will be constructed from the ground up, while DenseNet201, a pre-trained model, will undergo fine-tuning to enhance its performance. By evaluating and comparing these models, our study will uncover their individual strengths and weaknesses, providing valuable insights into the most effective method for AD classification. This research contributes to the advancement of diagnostic techniques, potentially improving the early detection of Alzheimer’s Disease.
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cardiovascular diseases (heart diseases) are the leading cause of death worldwide. The earlier they can be predicted and classified; the more lives can be saved. Electrocardiogram (ECG) is a common, inexpensive, and noninvasive tool for measuring the electrical activity of the heart and is used to detect cardiovascular disease. In this work, the power of deep learning techniques was used to predict the four major cardiac abnormalities: abnormal heartbeat, myocardial infarction, history of myocardial infarction, and normal person classes using the public ECG images dataset of cardiac patients. First, the transfer learning approach was investigated using the low-scale pretrained deep neural networks SqueezeNet and AlexNet. Second, a new Convolutional Neural Network (CNN) architecture was proposed for cardiac abnormality prediction. Third, the aforementioned pretrained models and our proposed CNN model were used as feature extraction tools for traditional machine learning algorithms, namely Support Vector Machine (SVM), K-Nearest Neighbors (K-NN), Decision Tree (DT), Random Forest (RF), and Naïve Bayes (NB). According to the experimental results, the performance metrics of the proposed CNN model outperform the exiting works; it achieves 98.23% accuracy, 98.22% recall, 98.31% precision, and 98.21% F1 score. Moreover, when the proposed CNN model is used for feature extraction, it achieves the best score of 99.79% using the NB algorithm.