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Max pooling in CNNs explained for deep learning

In this video, we will understand what is Max Pooling in Convolutional Neural Network and why do we use it. Max Pooling in ...
IEEE

Deep Image Synthesis, Analysis and Indexing Using Integrated CNN Architectures

Abstract: The excessive use of Internet technology is leading to a massive increase in multimedia content. Fast and effective image retrieval over a wide range of databases is a difficult task in this ...
IEEE

An Efficient Bidirectional Android Translation Prototype for Yemeni Sign Language Using Fuzzy Logic and CNN Transfer Learning Models

Abstract: Hearing-impaired individuals face significant challenges in social interactions due to communication barriers. Advances in technology have introduced numerous assistive tools to bridge this ...
IEEE

A Novel Deep Learning Model for Classifying Power Quality Problems in PV-integrated Microgrids using CNN-LSTM

Abstract: Distributed generation based on photo-voltaic (PV) modules play a vital role to provide continuous power supply for consumers. Power quality (PQ) events caused by integration of distributed ...
IEEE

Comparative Analysis of CNN Architectures With Transfer Learning for Brain Tumor Detection and Classification From MRI Scans

Abstract: In the realm of neuroscience, one of the most profound topics of investigation is brain tumors, as they greatly influence brain operations and overall health. These brain tumors can be ...
TMCnet

Bridging Silence: A Real-Time Sign Language to English Text Translation System Using Python, OpenCV, and Convolutional Neural Networks

Bridging communication gaps between hearing and hearing-impaired individuals is an important challenge in assistive ...

What happened when CNN Travel challenged ChatGPT to come up with guides to our cities

People are increasingly turning to AI-powered tools like ChatGPT for travel-planning advice. Here’s what CNN Travel staff in five major global cities discovered while putting it to the test.
IEEE

A Novel CNN Architecture for Alzheimer's Disease Classification Using MRI Images: Comparison with Traditional Machine Learning Models

Abstract: Automated medical image processing has significantly improved with recent advances in deep learning and imaging technologies, particularly in the area of neuroimaging-based Alzheimer's ...
IEEE

Deep Learning Models for Precision Agriculture: Evaluating CNN Architectures for Accurate Plant Disease Detection

Abstract: Early and precise detection of plant diseases is crucial for enhancing crop yield and minimizing agricultural losses. This paper evaluates the performance of deep learning-based ...
IEEE

Comparison of CNN Model with Different Machine Learning Models for Fake News Detection

Abstract: Fake news continues to be a critical issue in today's era for any citizen concerned about political integrity and the state of governance. The use of internet has experienced exponential ...
IEEE

Epileptic Seizure Prediction Using a Deep Hybrid CNN-GAN Model on EEG Data

Abstract: Epileptic seizure detection from EEG signals is essential for the diagnosis and treatment of neurological conditions. However, this task is difficult because of the complex and fluctuating ...
IEEE

Deep Learning-Based Malaria Detection: A Robust CNN Framework for Accurate and Automated Diagnosis

Abstract: This research suggests a strong framework for automated malaria detection using a Convolutional Neural Network (CNN) model. The dataset, sourced from Kaggle, consists of 27,558 ...