A Deep Learning Approach for AR-based Adaptive Simulation using Wearables
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Poster presented in Research Symposium 2019 by Faculty of Engineering and Applied Science at Queen's University, Canada.
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This paper proposes a Capsules Exploration Module (Caps-EM) that uses a Capsule Networks architecture paired with an Advantage Actor Critic algorithm for autonomous agent navigation. The Caps-EM is tested in sparse reward environments from the game ViZDoom and achieves better performance than previous approaches that used intrinsic reward modules, requiring fewer parameters and training time. Specifically, Caps-EM uses 44% and 83% fewer parameters and achieves 1141% and 437% average time improvement over the Intrinsic Curiosity Module and Depth-Augmented Curiosity Module respectively for converging navigation policies across test scenarios.
Review on classification based on artificial
Artificial neural networks (ANN) consider classification as one of the most dynamic research and
application areas. ANN is the branch of Artificial Intelligence (AI). The neural network was trained by
back propagation algorithm. The different combinations of functions and its effect while using ANN as a
classifier is studied and the correctness of these functions are analyzed for various kinds of datasets. The
back propagation neural network (BPNN) can be used as a highly successful tool for dataset classification
with suitable combination of training, learning and transfer functions. When the maximum likelihood
method was compared with backpropagation neural network method, the BPNN was more accurate than
maximum likelihood method. A high predictive ability with stable and well functioning BPNN is possible.
Multilayer feed-forward neural network algorithm is also used for classification. However BPNN proves to
be more effective than other classification algorithms.
Using Artificial Neural Networks to Detect Multiple Cancers from a Blood Test
Research paper drafted during my 2 year internship with Oak Ridge National Laboratory illustrating the potential of artificial intelligence in cancer research.
Capsule Network Performance with Autonomous Navigation
Capsule Networks (CapsNets) have been proposed as an alternative to Convolutional Neural Networks
(CNNs). This paper showcases how CapsNets are more capable than CNNs for autonomous agent
exploration of realistic scenarios. In real world navigation, rewards external to agents may be rare. In
turn, reinforcement learning algorithms can struggle to form meaningful policy functions. This paper’s
approach Capsules Exploration Module (Caps-EM) pairs a CapsNets architecture with an Advantage
Actor Critic algorithm. Other approaches for navigating sparse environments require intrinsic reward
generators, such as the Intrinsic Curiosity Module (ICM) and Augmented Curiosity Modules (ACM). CapsEM uses a more compact architecture without need for intrinsic rewards. Tested using ViZDoom, the CapsEM uses 44% and 83% fewer trainable network parameters than the ICM and Depth-Augmented Curiosity
Module (D-ACM), respectively, for 1141% and 437% average time improvement over the ICM and DACM, respectively, for converging to a policy function across "My Way Home" scenarios.
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BLOOD TISSUE IMAGE TO IDENTIFY MALARIA DISEASE CLASSIFICATION
This document describes using convolutional neural networks to classify malaria in blood tissue images. The researchers collected a dataset of blood tissue images from malaria-positive and negative patients. They preprocessed the data and developed convolutional neural network models like AlexNet and Lenet to classify the images. The models were trained on the dataset and evaluated based on metrics like accuracy, precision and recall. The goal is to create an automated method for malaria diagnosis that can help improve early detection and treatment in areas with limited resources.
researchpaper_2023_Lungs_Cancer.pdfdfgdgfhdf
The document describes using a convolutional neural network with the VGG16 architecture to classify lung cancer CT scan images into 4 classes: large cell carcinoma, squamous cell carcinoma, adenocarcinoma, and normal lungs. The model is trained on a dataset of 1000 CT scan images from Kaggle and achieves an AUC of 0.94, indicating high accuracy in identifying different types of lung cancer. This CNN model with pre-trained VGG16 weights provides an effective approach for classifying lung cancer images and could help enable early diagnosis and treatment of lung cancer.
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Deep Learning aplicado al diagnostico de Alzheimer | BioInformaticsGRX
El Alzheimer es una de las enfermedades que más afecta a pacientes de edad avanzada en todo el mundo. Su cura se desconoce, por lo que un diagnóstico precoz puede ayudar a mejorar notablemente la vida de los pacientes. Son muchos los avances en Deep Learning dentro del área de visión por ordenador y clasificación de imágenes. Estas nuevas técnicas de clasificación en imágenes cerebrales de pacientes, puede suponer una ayuda notable para diagnosticar si un paciente está comenzando a desarrollar la enfermedad. En esta charla daremos un repaso a la bibliografía existente sobre la clasificación de imágenes en resonancia magnética para la detección de Alzheimer. Además, si con las nuevas técnicas de Deep Learning podemos clasificar correctamente imágenes cerebrales 2D de pacientes, y de ser así, cuáles serían las capas del cerebro más recomendables para realizar esta clasificación.
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Evolutionary Algorithm for Optimal Connection Weights in Artificial Neural Ne...
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heart final last sem.pptx
This document proposes using machine learning algorithms to predict heart disease at early stages. It discusses problems with current diagnosis methods and the need for an automated system. The proposed system would use a dataset of 779 individuals and various machine learning algorithms to predict the likelihood of heart disease for new individuals. It describes preprocessing the data, training models like logistic regression, random forest, SVM and comparing their performance. The system architecture involves preprocessing, training models, testing them and predicting heart disease risk. Modules like SVM, decision trees, random forest and naive Bayes are explained. The document concludes by discussing implementation and outputs like algorithm accuracies for training and test sets.
Performance Comparision of Machine Learning Algorithms
In this paper Compare the performance of two
classification algorithm. I t is useful to differentiate
algorithms based on computational performance rather
than classification accuracy alone. As although
classification accuracy between the algorithms is similar,
computational performance can differ significantly and it
can affect to the final results. So the objective of this paper
is to perform a comparative analysis of two machine
learning algorithms namely, K Nearest neighbor,
classification and Logistic Regression. In this paper it
was considered a large dataset of 7981 data points and 112
features. Then the performance of the above mentioned
machine learning algorithms are examined. In this paper
the processing time and accuracy of the different machine
learning techniques are being estimated by considering the
collected data set, over a 60% for train and remaining
40% for testing. The paper is organized as follows. In
Section I, introduction and background analysis of the
research is included and in section II, problem statement.
In Section III, our application and data analyze Process,
the testing environment, and the Methodology of our
analysis are being described briefly. Section IV comprises
the results of two algorithms. Finally, the paper concludes
with a discussion of future directions for research by
eliminating the problems existing with the current
research methodology.
0 eeg based 3 d visual fatigue evaluation using cnn
This document describes a new multi-scale convolutional neural network (CNN) called MorletInceptionNet that is designed to detect visual fatigue levels using electroencephalogram (EEG) signals as input. The network uses Morlet wavelet-like kernels to extract time-frequency features from the EEG data. It then uses inception modules of different kernel sizes to extract multi-scale temporal features, which are concatenated and fed into fully connected layers for classification. An evaluation of the network on EEG data collected during a visual fatigue experiment showed it achieved better classification accuracy and kappa values than five state-of-the-art methods, demonstrating it is effective at automatically evaluating visual fatigue levels from EEG signals.
Ensemble learning approach for multi-class classification of Alzheimer’s stag...
The document presents an ensemble learning approach for multi-class classification of Alzheimer's disease stages using magnetic resonance imaging (MRI). The proposed algorithm concatenates the output layers of three pre-trained models - Xception, InceptionV3, and MobileNet. The algorithm is tested on MRI images from the Alzheimer's Disease Neuroimaging Initiative database. The middle slice is extracted from each 3D MRI and data augmentation is applied. The ensemble model provides 85% accuracy for multi-class classification of Alzheimer's, mild cognitive impairment convertible, mild cognitive impairment non-convertible, and cognitively normal. It also achieves 85% accuracy for classifying mild cognitive impairment convertible vs non-convertible, 94% for Alzheimer's vs
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An improved modulation technique suitable for a three level flying capacitor ...
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A Deep Learning Approach for AR-based Adaptive Simulation using Wearables
- 1. RESEARCH POSTER PRESENTATION DESIGN © 2015 www.PosterPresentations.com TimeDomain Features FrequencyDomain Features Simulations enable a risk-free way for healthcare practitioners to learn and enhance their skills. Such simulations should be tailored to the needs of the individual learners. However, most current simulations do not consider the learner’s existing skills, expertise, and ability to handle cognitive load. We propose an end-to-end framework for trauma simulation that actively classifies participant's cognitive load and level of expertise and dynamically modulates the complexity of a simulation by altering symptom severity of a patient using augmented reality (AR) to ensure an optimum amount of cognitive load is maintained. A deep learning approach was taken to utilize electrocardiogram (ECG) and galvanic skin response (GSR) collected using wearable devices. The model was developed to classify high vs low cognitive load, as well as level of expertise, and validated using “leave one out” scheme. Finally, the intelligent dynamically adaptive AR simulation was successfully tested in real time on two participants of each class. Abstract Motivation Data Pre-processing & Feature Extraction Experiment Setup Result and Analysis Conclusion We were able to accurately estimate cognitive load and expertise in real-time and use the classifications to alter a simulation with augmented reality to maintain optimal cognitive load. Pritam Sarkar1, Kyle Ross1, Dirk Rodenburg3, Aaron Ruberto4, Paul Hungler2, Dan Howes4, Adam Szulewski4, Ali Etemad1 1Dept. of Electrical and Computer Engineering, 2Dept. Of Chemical Engineering, 3Faculty of Engineering and Applied Science, 4Dept. of Emergency Medicine Queen’s University A Deep Learning Approach for AR-based Adaptive Simulation using Wearables Cognitive Load Learning Rate Microsoft HoloLens ECG Shimmer Sensor ProposedDynamic SimulationFramework Data Collection Data Visualization DataCollection AR-based Adaptive Simulation ECG based Convolutional Neural Network Feature Extraction from ECG FusedDecision (Probability) Feature Extraction GSR GSR based Neural Network ECG GSR ❑ Many learning systems are one size fits all regarding levels of expertise. ❑ The development of expertise is characterized by the ability to execute increasingly complex tasks with lowered cognitive load. ❑ To be effective, simulations should match complexity with the cognitive abilities of the learner. ❑ Goal: To develop a learning system that dynamically adjusts complexity to match learner expertise and cognitive capacity. GSR Shimmer Sensor TSNE plot of GSR feature-space without (left) and with (right) baseline correction TSNE plot of ECG feature-space without (left) and with (right) baseline correction Expertise Cognitive Load Testing (Offline) 91% 74% Realtime Testing 2 participantsof each class were successfully tested to classify level of expertise and cognitive load. We used an augmented reality overlayon top of a mannequinto change the difficulty of the simulationthrough patient symptomology Participants during simulation anne u n stra tor View from HoloLens during simulation RR peaks for ECG feature extraction SCR response for GSR feature extraction Features Publications ➢ Conference: Classification of Expertise and Cognitive Load in Adaptive AR-Based Simulation using Deep Learning, Affective Computing & Intelligent Interaction (ACII), 2019 ➢ Journal: Toward Dynamically Adaptive Simulation: Multimodal Classification of User Expertise using Wearable Devices, Sensors 2019, 19, 4270 ➢ Expert participants (Experienced in emergency medicine) ➢ Nov e part pants (Queen’s 4th year medical students) (Left) Plot of the likelihood for expertise vs cognitive load. The results show an inverse relationship between level of expertise and cognitive load, where the top left corner shows high level of expertise and low cognitive load, and the bottom right corner indicates low expertise and high cognitive load. Reach us at: pritam.sarkar@queensu.ca, 12kjr1@queensu.ca