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Cognitive Load Classification with 2D-CNN Model in Mental Arithmetic Task

Cognitive load is crucial in assessing mental effort in tasks. This paper discusses using EEG signals and a 2D-CNN model to classify cognitive load during mental arithmetic tasks, aiming to optimize performance. EEG signals help evaluate mental workload, although they can be sensitive to noise. The

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Recent Advances in RNN and CNN Models: CS886 Lecture Highlights

Explore the fundamentals of recurrent neural networks (RNNs) and convolutional neural networks (CNNs) in the context of downstream applications. Delve into LSTM, GRU, and RNN variants, alongside CNN architectures like ConvNext, ResNet, and more. Understand the mathematical formulations of RNNs and c

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Understanding Convolutional Neural Networks: Architectural Characterizations for Accuracy Inference

This presentation by Duc Hoang from Rhodes College explores inferring the accuracy of Convolutional Neural Networks (CNNs) based on their architectural characterizations. The talk covers the MINERvA experiment, deep learning concepts including CNNs, and the significance of predicting CNN accuracy be

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Real-Time Cough and Sneeze Detection Project Overview

This project focuses on real-time cough and sneeze detection for assessing disease likelihood and individual well-being. Deep learning, particularly CNN and CRNN models, is utilized for efficient detection and classification. The team conducted a literature survey on keyword spotting techniques and

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CNN-based Multi-task Learning for Crowd Counting: A Novel Approach

This paper presents a novel end-to-end cascaded network of Convolutional Neural Networks (CNNs) for crowd counting, incorporating high-level prior and density estimation. The proposed model addresses the challenge of non-uniform large variations in scale and appearance of objects in crowd analysis.

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Exploring DRONET: Learning to Fly by Driving

DRONET presents a novel approach to safe and reliable outdoor navigation for Autonomous Underwater Vehicles (AUVs), addressing challenges such as obstacle avoidance and adherence to traffic laws. By utilizing a Residual Convolutional Neural Network (CNN) and a custom outdoor dataset, DRONET achieves

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Wavelet-based Scaleograms and CNN for Anomaly Detection in Nuclear Reactors

This study utilizes wavelet-based scaleograms and a convolutional neural network (CNN) for anomaly detection in nuclear reactors. By analyzing neutron flux signals from in-core and ex-core sensors, the proposed methodology aims to identify perturbations such as fuel assembly vibrations, synchronized

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Real-Time Cough and Sneeze Detection Using Deep Learning Models

Detection of coughs and sneezes plays a crucial role in assessing an individual's health condition. This project by Group 71 focuses on real-time detection using deep learning techniques to analyze audio data from various datasets. The use of deep learning models like CNN and CRNN showcases improved

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Convolutional Neural Networks for Sentence Classification: A Deep Learning Approach

Deep learning models, originally designed for computer vision, have shown remarkable success in various Natural Language Processing (NLP) tasks. This paper presents a simple Convolutional Neural Network (CNN) architecture for sentence classification, utilizing word vectors from an unsupervised neura

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Evolution of Sentiment Analysis in Tweets and Aspect-Based Sentiment Analysis

The evolution of sentiment analysis on tweets from SemEval competitions in 2013 to 2017 is discussed, showcasing advancements in technology and the shift from SVM and sentiment lexicons to CNN with word embeddings. Aspect-Based Sentiment Analysis, as explored in SemEval2014, involves determining asp

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Convolutional Neural Networks for Sentence Classification

Experiments show that a simple CNN with minimal hyperparameter tuning and static vectors achieves excellent results for sentence-level classification tasks. Fine-tuning task-specific vectors further improves performance. A dataset from Rotten Tomatoes is used for the experiments, showcasing results

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Advanced Artificial Intelligence for Adventitious Lung Sound Detection

This research initiative by Suraj Vathsa focuses on using transfer learning and hybridization techniques to detect adventitious lung sounds such as wheezes and crackles from patient lung sound recordings. By developing an AI system that combines deep learning models and generative modeling for data

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Guide to Setting Up Neural Network Models with CIFAR-10 and RBM Datasets

Learn how to install Apache Singa, prepare data using SINGA recognizable records, and convert programs for DataShard for efficient handling of CIFAR-10 and MNIST datasets. Explore examples on creating shards, generating records, and implementing CNN layers for effective deep learning.

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Assistive System Design for Disabilities with Multi-Recognition Integration

Our project aims to create an assistive system for individuals with disabilities by combining IMU action recognition, speech recognition, and image recognition to understand intentions and perform corresponding actions. We use deep learning for intent recognition, gesture identification, and object

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Enhancing UI Display Issue Detection with Visual Understanding

The research presents a method utilizing visual understanding to detect UI display issues in mobile devices. By recruiting testers and employing visual techniques, the severity of issues like component occlusion, text overlap, and missing images was confirmed. CNN-based models aid in issue detection

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Multimodal Recurrent Attention CNN for Image Aesthetic Prediction

Using a multimodal recurrent attention neural network, MRACNN, this study proposes a unified approach for image aesthetic prediction by jointly learning visual and textual features. Inspired by human attention mechanisms, the network utilizes datasets like AVA and photo.net comments to enhance multi

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Neural Image Caption Generation: Show and Tell with NIC Model Architecture

This presentation delves into the intricacies of Neural Image Captioning, focusing on a model known as Neural Image Caption (NIC). The NIC's primary goal is to automatically generate descriptive English sentences for images. Leveraging the Encoder-Decoder structure, the NIC uses a deep CNN as the en

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