scholarly journals Survey on Early Detection of Alzheimer's Disease using Different Types of Neural Network Architecture

2021 ◽  
Vol 8 (1) ◽  
pp. 25-32
Author(s):  
Deepthi Kamath ◽  
Misba Firdose Fathima ◽  
Monica K. P. ◽  
M. Kusuma
Keyword(s):  
Neural Network ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Network Architecture ◽  
Network Architectures ◽  
Neural Network Architecture ◽  
Brain Disorder ◽  
Different Types ◽  
Promising Treatment ◽  
Diagnostic Symptoms

Alzheimer’s disease is a condition that leads to, progressive neurological brain disorder and destroys cells of the brain thereby causing an individual to lose their ability to continue daily activities and also hampers their mentality. Diagnostic symptoms are experienced by patients usually at later stages after irreversible neural damage occurs. Detection of AD is challenging because sometimes the signs that distinguish AD MRI data, can be found in MRI data of normal healthy brains of older people. Even though this disease is not completely curable, earlier detection can aid in promising treatment and prevent permanent damage to brain tissues. Age and genetics are the greatest risk factors for this disease. This paper presents the latest reports on AD detection based on different types of Neural Network Architectures.

scholarly journals Efficient Semantic Segmentation Using Multi-Path Decoder

2020 ◽  
Vol 10 (18) ◽  
pp. 6386
Author(s):  
Xing Bai ◽  
Jun Zhou
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Network Architecture ◽  
Resource Constraints ◽  
Cost Effective ◽  
Semantic Segmentation ◽  
Classification Model ◽  
Neural Network Architecture ◽  
Great Progress ◽  
Different Types

Benefiting from the booming of deep learning, the state-of-the-art models achieved great progress. But they are huge in terms of parameters and floating point operations, which makes it hard to apply them to real-time applications. In this paper, we propose a novel deep neural network architecture, named MPDNet, for fast and efficient semantic segmentation under resource constraints. First, we use a light-weight classification model pretrained on ImageNet as the encoder. Second, we use a cost-effective upsampling datapath to restore prediction resolution and convert features for classification into features for segmentation. Finally, we propose to use a multi-path decoder to extract different types of features, which are not ideal to process inside only one convolutional neural network. The experimental results of our model outperform other models aiming at real-time semantic segmentation on Cityscapes. Based on our proposed MPDNet, we achieve 76.7% mean IoU on Cityscapes test set with only 118.84GFLOPs and achieves 37.6 Hz on 768 × 1536 images on a standard GPU.

scholarly journals Preintegration Lateral Inhibition Enhances Unsupervised Learning

2002 ◽  
Vol 14 (9) ◽  
pp. 2157-2179 ◽  
Author(s):  
M. W. Spratling ◽  
M. H. Johnson
Keyword(s):  
Neural Network ◽  
Lateral Inhibition ◽  
Network Architecture ◽  
Network Architectures ◽  
Neural Network Architecture ◽  
Neurophysiological Data ◽  
Perceptual Representations ◽  
Neural Network Architectures ◽  
The Right ◽  
To Receive

A large and influential class of neural network architectures uses postintegration lateral inhibition as a mechanism for competition. We argue that these algorithms are computationally deficient in that they fail to generate, or learn, appropriate perceptual representations under certain circumstances. An alternative neural network architecture is presented here in which nodes compete for the right to receive inputs rather than for the right to generate outputs. This form of competition, implemented through preintegration lateral inhibition, does provide appropriate coding properties and can be used to learn such representations efficiently. Furthermore, this architecture is consistent with both neuroanatomical and neurophysiological data. We thus argue that preintegration lateral inhibition has computational advantages over conventional neural network architectures while remaining equally biologically plausible.

scholarly journals Early Detection of Alzheimer’s Disease using Convolutional Neural Network Architecture

2021 ◽  
Vol 8 (2) ◽  
pp. 48-57
Author(s):  
Deepthi Kamath ◽  
Misba Firdose Fathima ◽  
Monica K. P ◽  
Kusuma Mohanchandra
Keyword(s):  
Neural Network ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Brain Mri ◽  
Memory Loss ◽  
Neural Network Approach ◽  
The Neural Network ◽  
Mri Scans

Alzheimer's disease is an extremely popular cause of dementia which leads to memory loss, problem-solving and other thinking abilities that are severe enough to interfere with daily life. Detection of Alzheimer’s at a prior stage is crucial as it can prevent significant damage to the patient’s brain. In this paper, a method to detect Alzheimer’s  Disease from Brain MRI images is proposed. The proposed approach extracts shape features and texture of the Hippocampus region from the MRI scans and a Neural Network is used as a Multi-Class Classifier for detection of AD. The proposed approach is implemented and it gives better accuracy as compared to conventional approaches. In this paper, Convolutional Neural Network is the Neural Network approach used for the detection of AD at a prodromal stage.

scholarly journals Rumor Detection Based on SAGNN: Simplified Aggregation Graph Neural Networks

2021 ◽  
Vol 3 (1) ◽  
pp. 84-94
Author(s):  
Liang Zhang ◽  
Jingqun Li ◽  
Bin Zhou ◽  
Yan Jia
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Architecture ◽  
Network Architectures ◽  
Neural Network Architecture ◽  
Convolutional Networks ◽  
Effective Manner ◽  
Deep Learning Network ◽  
Graph Neural Networks ◽  
Rumor Detection

Identifying fake news on media has been an important issue. This is especially true considering the wide spread of rumors on popular social networks such as Twitter. Various kinds of techniques have been proposed for automatic rumor detection. In this work, we study the application of graph neural networks for rumor classification at a lower level, instead of applying existing neural network architectures to detect rumors. The responses to true rumors and false rumors display distinct characteristics. This suggests that it is essential to capture such interactions in an effective manner for a deep learning network to achieve better rumor detection performance. To this end we present a simplified aggregation graph neural network architecture. Experiments on publicly available Twitter datasets demonstrate that the proposed network has performance on a par with or even better than that of state-of-the-art graph convolutional networks, while significantly reducing the computational complexity.

scholarly journals Neural Networks based solution for Door Automation

2021 ◽  
Author(s):  
Monalika Padma Reddy
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Network Architecture ◽  
Senior Citizens ◽  
Convolution Neural Network ◽  
Network Architectures ◽  
Neural Network Architecture ◽  
The Face ◽  
User Friendly ◽  
Gain Access

Face Recognition is one of the most common biometric strategies which has gained popularity because of the accuracy and security. This paper presents the implementation of a Convolution Neural Network architecture for door automation. This model is devised to overcome the disadvantages of a traditional door system and other methods such as door automation using Bluetooth, figure prints, passwords, or retinal scans. It allows the authorized people to gain access to the house by face recognition. The proposed system makes use of convolution neural network architectures and RaspberryPi. The ResNet architecture [6] is used to implement face recognition and runs on RaspberryPi. The images of the residents of the house will be used to train the model. If the person is a resident of the house, the face will be recognized and the lock will open, else it will be recognized as a human and an alarm will ring and an email alert consisting of the image of the person in front of the door will be sent to the owner. It has numerous advantages as it is user-friendly especially for senior citizens, lesser maintenance, does not require the residents to carry the keys and reduces the threat of robbery.

The adaptive control of a four-degrees-of-freedom stereo camera head

1992 ◽  
Vol 337 (1281) ◽  
pp. 315-326 ◽  
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Network Architecture ◽  
Degrees Of Freedom ◽  
Ground Plane ◽  
High Dimensional ◽  
Network Architectures ◽  
Neural Network Architecture ◽  
Subsumption Architecture ◽  
Head Positions

The paper describes the use of biologically plausible neural network architectures to address some of the issues associated with the use of stereopsis under variable camera geometry. We report an implementation of a layered (subsumption) architecture for the adaptive control of microsaccadic tracking, and show experimental results demonstrating the use of lattice filter predictors for trajectory modelling. A rather simple, but seemingly adequate, neural network architecture for representing high-dimensional surface approximations ( piluts) is evaluated as a method of encoding the predictive stereo mapping of the ground plane for different head positions.

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