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To overcome the unsatisfying trend prediction results of network public opinion in the present research, this paper put forward a method of Levenberg-Marquardt-based Back-Propagation (LM-BP) neural network algorithm to predict the network public opinion trend. Taking the microblog as the research object, the effectiveness and reliability of the method are proved with some real data in this article...
Deep learning has gained considerable attention in the scientific community, breaking benchmark records in many fields such as speech and visual recognition [1]. Motivated by extending advancement of deep learning approaches to brain imaging classification, we propose a framework, called “deep neural network (DNN)+ layer-wise relevance propagation (LRP)”, to distinguish schizophrenia patients (SZ)...
Software Defined Networking (SDN) is a new promising networking concept which has a centralized control over the network and separates the data and control planes. This new approach provides abstraction of lower-level functionality and allows the network administrators to initialize, control, change, and manage network behavior programmatically. The centralized control, being the major advantage of...
Ransomware is one type of malware that covertly installs and executes a cryptovirology attack on a victims computer to demand a ransom payment for restoration of the infected resources. This kind of malware has been growing largely in recent days and causes tens of millions of dollars losses to consumers. In this paper, we evaluate shallow and deep networks for the detection and classification of...
Network traffic prediction aims at predicting the subsequent network traffic by using the previous network traffic data. This can serve as a proactive approach for network management and planning tasks. The family of recurrent neural network (RNN) approaches is known for time series data modeling which aims to predict the future time series based on the past information with long time lags of unrevealed...
This paper proposes a low-cost video-based Real-Time Pupil-Tracking embedded system which will allow people with reduced mobility to control a wheelchair through their eyes. The main aspect of the method is its capacity to be implemented in a portable computing system, reduced both in computing power and in RAM memory. The Pupil-Tracking system is based on Feedforward Neural Networks-using offline...
This paper proposes an optimized pedestrian and vehicle detection method based on deep learning technique. We optimize the convolutional neural network architecture by three mainly methods. The first one is the choice of the learning policy. The second one is to simplify the convolutional neural network architecture. The last one is careful choice of training samples. With limited loss of accuracy,...
Spiking Neural Networks offer low precision communication, robustness, and low power consumption and are attractive for autonomous applications. One of the well accepted learning rules for these networks is spike time dependent plasticity which is governed by the pre- and postsynaptic spike timings. To stabilize the plasticity and avoid saturation in these learning rules, synaptic normalization is...
In this paper, an extraction and classification of steady state-visual evoked potentials using the IIR Chebyshev I of 4 order and the adaptive feed-forward Neural Networks algorithm, respectively are applied. The classification results of the extracted signals is directly used to make a user able of controlling the directions (stop, forward, right, and left with stimuli frequencies of 7.5, 10, 15,...
In this work, we propose a regularized learning method that is able to take into account the deviation of the memristor-mapped synaptic weights from the target values determined during the training process. Experimental results obtained when utilizing the MNIST data set show that compared to the conventional learning method which considers the learning and mapping processes separately, our learning...
Millions of people around the world suffer from epilepsy. It is very important to provide a method to efficiently monitor the seizures and alert the caregivers to help patients. It is proven that EEG signals are the best markers for diagnosis of the epileptic seizures. In this paper, we used the frequency domain features (normalized in-band power spectral density) to extract information from EEG signals...
Recent studies suggest that epidural stimulation of the spinal cord could increase the motor pattern both in motor and sensory complete spinal cord injury (SCI) patients. However, choosing the optimal epidural stimulation variables, such as the frequency, intensity, and location of the stimulation, significantly affects maximal motor functionality. This paper presents a novel technique using machine...
As a popular deep learning technique, convolutional neural network has been widely used in many tasks such as image classification and object recognition. Convolutional neural network exploits spatial correlations in the images by performing convolution operations in local receptive fields. Convolutional neural networks are preferred over fully connected neural networks because they have fewer weights...
In this paper a Siamese-Twin Random Projection Neural Network (ST-RPNN) is proposed for unsupervised binary hashing of images. ST-RPNN is made of two identical random projection neural networks with hard threshold neurons where the binary code is taken as the neuron outputs. The learning objective is to produce similar binary codes for similar input image pairs and different binary codes otherwise...
Spiking Neural Networks (SNNs) are the third generation of artificial neural networks that closely mimic the time encoding and information processing aspects of the human brain. It has been postulated that these networks are more efficient for realizing cognitive computing systems compared to second generation networks that are widely used in machine learning algorithms today. In this paper, we review...
The paper presents the results of research on the use of Deep Neural Networks (DNN) for automatic classification of the skin lesions. The authors have focused on the most effective kind of DNNs for image processing, namely Convolutional Neural Networks (CNN). In particular, three kinds of CNN were analyzed: VGG19, Residual Networks (ResNet) and the hybrid of VGG19 CNN with the Support Vector Machine...
The impressive evolution of neural networks and deep learning techniques during the last few years has offered new incomparable routes to solve many complex problems. Moreover, the fact that neural networks are structured and supervised has made it possible to perform automatic parameter tuning that guarantees convergence to the best expressive model for the problem assessed. In this work, we investigated...
This paper presents an artificial neural network (ANN) model based design for Hénon chaotic systems, and its equivalent hardware model for hardware co-simulation using Field Programmable Gate Arrays (FPGA). Chaotic generators can be used for the study of chaotic behaviors of brain activities captured by Electroencephalogram (EEG). The ANN model is designed with different fixed-point data format and...
Acoustic analysis is a non-invasive technique that supports voice disease screening, especially the detection and diagnosis of distinction between chosen voice pathologies and healthy control group. This work put en effort on creation of efficient and accurate system for automatic detection and differentiation of normal and three different voice pathologies. This system ensures non-invasive and fully...
Temperature resolution is a key factor for the performance of a Distributed Temperature Sensor (DTS). One can define the resolution as the degree of uncertainty in the temperature information. Thus, the temperature measured in a steady-state condition at a given point in the fiber will vary between successive measurements and between adjacent points that are at the same temperature. Temperature resolution...
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