The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
With a similar capability of processing spikes as biological neural systems, networks of spiking neurons are expected to achieve a performance similar to that of living brains. In recent days, many great achievements have been made in spiking neuron models and its learning algorithm. We build an integrated model using spiking neural networks (SNNs), which performs phase encoding method and delay learning-based...
Recently, more neuroscience researches focus on the role of dendritic structure during the neural computation. Inspired by the specified topologies of numerous dendritic trees, we proposed a single neural model with a particular dendritic structure. The dendrites are composed of several branches, and these branches correspond to three distributions in coordinate, which are used to classify the training...
Clustering is the organization of a set of data in homogeneous classes. It aims to classify the representation of the initial data. The automatic classification recovers all the methods allowing the automatic construction of such groups. This paper describes how to classify data using a new design of neural classifiers with radial basis function (RBF) based on a new algorithm for characterizing the...
The requirement for data privacy is limiting to exploit the full potential of what modern data analytic capability could offer. To address such privacy concern, a number of techniques based on homomorphic encryption (HE) have been proposed to allow analytic computation, such as classification based on machine learning techniques, to run on encrypted data. However, these HE-based techniques suffer...
Image classification is a vital technology many people in all arenas of human life utilize. It is pervasive in every facet of the social, economic, and corporate spheres of influence, worldwide. This need for more accurate, detail-oriented classification increases the need for modifications, adaptations, and innovations to Deep learning algorithms. This paper uses Convolutional Neural Networks (CNN)...
Data mining techniques and multi criteria decision making techniques have been used widely in many areas, such as customer relationship management, medicine, engineering, education, geographic information systems, and recommendation systems. The present study aims to design a hybrid approach based on Deep Neural Networks (DNNs) and multi criteria decision making. DNNs and multi criteria decision making...
Classification involves the learning of the mapping function that associates input samples to corresponding target label. There are two major categories of classification problems: Single-label classification and Multi-label classification. Traditional binary and multi-class classifications are sub-categories of single-label classification. Several classifiers are developed for binary, multi-class...
No one knows yet how to organize, in a simple yet predictive form, the knowledge concerning the anatomical, biophysical, and molecular properties of neurons that are accumulating in thousands of publications every year. The situation is not dissimilar to the state of Chemistry prior to Mendeleev's tabulation of the elements. We propose that the patterns of presence or absence of axons and dendrites...
The deep learning is a growing multi-layer neural network learning algorithm in the field of machine learning in recent years. Firstly, this paper analyzes the superiority of the deep learning at the aspect of feature extraction. Aimed at the lack of feature expression capacity and curse of dimensionality results from excessive feature dimensions of shallow learning, this paper proposes that using...
Extreme learning machine (ELM) is a learning method for training feedforward neural networks with randomized hidden layer(s). It initializes the weights of hidden neurons in a random manner and determines the output weights in an analytic manner by making use of Moore-Penrose (MP) generalized inverse. No-Prop algorithm is recently proposed training algorithm for feedforward neural networks in which...
Handwriting disability is one type of learning disability that is difficult to be detected as it may requires experts and professionals to diagnose. Owing to this matter, a computerized character recognition application is very much in need to ease the process of detecting children with learning disability based on handwriting. For any character recognition method, it's critical to extract the class...
Creating a neural network based classification model is commonly accomplished using the trial and error technique. However, this technique has several difficulties in terms of time wasted and the availability of experts. In this article, an algorithm that simplifies structuring neural network classification models is proposed. The algorithm aims at creating a large enough structure to learn models...
This paper proposes a custom convolutional deep belief network for polarimetric synthetic aperture radar (PolSAR) data feature extraction. The proposed architecture stands out through the interesting features it shows, starting with the fact that it is adapted to fully polarimetric SAR data. Then, the multilayer approach allows the stepwise discovery of higher-level features. The convolutional approach...
When a person learns, they observe and interact with their surroundings, and monitor the outcome of these interactions. During this process, the brain only examines single snapshots of information. It does not need to continuously revisit past instances of time to retain learned information. Supervised neural networks, as much as they resemble the human brain, do not learn well incrementally. The...
This work proposes to learn autoencoders with sparse connections. Prior studies on autoencoders enforced sparsity on the neuronal activity; these are different from our proposed approach - we learn sparse connections. Sparsity in connections helps in learning (and keeping) the important relations while trimming the irrelevant ones. We have tested the performance of our proposed method on two tasks...
This paper presents the results of a study developing artificial neural network system (ANN) for classification of Alzheimer's disease (AD) and healthy patients. The classification is done using biomarkers, from cerebrospinal fluid: albumin ratio (CSF/Serum and/or Plasma), Aβ40 (CSF), Aβ42 (CSF), tau-total (CSF) and tau-phospho (CSF). Neural network input parameters are datasets from Alzbiomarkers...
This paper presents the results of a study developing expert system to support stress recognition based on Artificial Neural Network (ANN). Developed ANN is trained using data from Physionet database and collected data from other researchers. The implemented system for stress recognition uses drivers ECG signal, Galvanic Skin Response and Respiration Rate as parameters. Developed neural network was...
Functional magnetic resonance imaging (fMRI) is one of the most popular and reliable modality to measure brain activities. The quality of fMRI data is best among other modalities such as Electroencephalography (EEG) and Magnetoencephalography (MEG). In fMRI, normally number of features are more than the number of instances so it is necessary to select the features and do dimension reduction to remove...
Dendritic spines, membranous protrusions of neurons, are one of the few prominent characteristics of neurons. Their shapes change with variations in neuron activity. Spine shape analysis plays a significant role in inferring the inherent relationship between neuron activity and spine morphology variations. First step towards integrating rich shape information is to classify spines into four shape...
The aim of this work is to develop an accurate method for pattern recognition of human hand motions. Eight surface EMG electrodes (dual type) were placed on the forearm of healthy subjects while performing individual wrist and finger motions. A total of 1080 signals that incorporated all the selected nine hand motions were acquired from 12 volunteers, preprocessed, and then time-domain features were...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.