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.
The main purpose of the research is an assessment of utilization efficiency of artificial intelligence methods for an assessment of effectiveness and feasibility of scientific and technical decisions and technologies for the purpose of implementation of the learning and plug-and-play information analysis system capable of suggesting alternatives of efficiency assessment concerning scientific and technical...
Histopathology image classification can provide automated support towards cancer diagnosis. In this paper, we present a transfer learning-based approach for histopathology image classification. We first represent the image feature by Fisher Vector (FV) encoding of local features that are extracted using the Convolutional Neural Network (CNN) model pretrained on ImageNet. Next, to better transfer the...
Mining drug targets from database is a problem of challenge but significance, which has received much attention from academia and industry. As a main source of drug targets, proteins have abundant sequence properties analyzed by the modern measuring techniques and those properties provide a new perspective for detecting the potential proteins as drug targets. In our research, two typical ensemble...
This study explores the application of artificial intelligence on the causal relationship between mining production index and electricity load. The data used is the total mining production index and total electricity consumption in the mining sector sampled on a monthly basis from January 1985 to December 2011 in South Africa. Optimally-pruned and basic extreme learning machines were used to develop...
Automated cell detection is a critical step for a number of computer-assisted pathology related image analysis algorithm. However, automated cell detection is complicated due to the variable cytomorphological and histological factors associated with each cell. In order to efficiently resolve the challenge of automated cell detection, deep learning strategies are widely applied and have recently been...
The need for energy-awareness in current data centers has encouraged the use of power modeling to estimate their power consumption. However, existing models present noticeable limitations, which make them application-dependent, platform-dependent, inaccurate, or computationally complex. In this paper, we propose a platform-and application-agnostic methodology for full-system power modeling in heterogeneous...
With the rapid development of biology, its relevant literature will be more and more important for researchers to dig out more valuable knowledge. Named Entity Recognition (NER) for biology literature is a very common and important task in these works. With the increase of the literature amount, the recognition speed becomes slower and less accurate. In order to tackle this problem, a MapReduce based...
We analyze the theoretical vulnerability of maximum a posteriori(MAP) speaker adaptation, which is widely used in practical speaker recognition systems. First, we proved that there exist a set of feature vectors, what are called wolves, which can impersonate almost all the registered speakers with probability asymptotically close to 1 with at most two trials. Second, our experiment shows that the...
Learning acoustic models directly from the raw waveform data with minimal processing is challenging. Current waveform-based models have generally used very few (∼2) convolutional layers, which might be insufficient for building high-level discriminative features. In this work, we propose very deep convolutional neural networks (CNNs) that directly use time-domain waveforms as inputs. Our CNNs, with...
Human body analysis raises special interest because it enables a wide range of interactive applications. In this paper we present a gesture estimator that discriminates body poses in depth images. A novel collaborative method is proposed to learn 3D features of the human body and, later, to estimate specific gestures. The collaborative estimation framework is inspired by decision forests, where each...
Biometric is a pattern recognition system that automatically identifies people according to their physiologic and behavioral properties. Among the physiologic properties, hand has a special place so that all features of hand like palm lines, inner knuckles, external knuckles and geometry could be used. More recently, the usage of blood vessels pattern in the palm, in addition to the high acceptability,...
The article discusses spiking neural networks, their uniqueness, their ability to training, architecture, and the possibility of a hardware implementation. Special attention is given to reveal the prospects for the development and application of spiking neural networks for the implementation in robotics and control systems.
Accurate prediction of clinical changes of Mild Cognitive Impairment (MCI) patients at future time points is important for early diagnosis and possible prevention of Alzheimer's disease (AD). In this paper, future clinical changes in Neuropsychological Measures (NM) of MCI patients are estimated via three different predictive models employing linear regression and extrapolation. The completed time...
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...
In this study, an ultrasensitive biological boron nitride-based nanotube (Bio-BNNT) sensor is modeled and investigated by means of neural approach. The type of configuration studied is a cantilevered BNNT resonator sensor with an attached mass at the tip. The idea behind our resonator sensor is based on the determination of the natural BNNT frequency shift induced by added biological mass. A multilayer...
System identification is the process of developing a mathematical model of a system using input and output knowledge of system. Identification of nonlinear system is well known problem due to its unpredictability and complexity. The nonlinear system for identification is Inverted Pendulum in this work which is well known benchmark system in control system theory due to it's highly nonlinear and unstable...
In this work we provide details on a new and effective approach able to generate Gaussian Mixture Models (GMMs) for the classification of aggregated time series. More specifically, our procedure can be applied to time series that are aggregated together by adding their features. The procedure takes advantage of the additive property of the Gaussians that complies with the additive property of the...
Ocular biometrics in the visible spectrum has emerged as an area of significant research activity. In this paper, we propose two convolution-based models for verifying a pair of periocular images containing the iris, and compare the two approaches amongst each other as well as with a baseline model. In the first approach, we perform deep learning in an unsupervised manner using a stacked convolutional...
In the area of Non-Intrusive Load Monitoring (NILM), many approaches need a supervised procedure of appliance modelling, in order to provide the informations about the appliances to the disaggregation algorithm and to obtain the disaggregated consumptions related to each one of them. In many approaches, the appliance modelling relies on the consumption footprint, which is a typical working cycle of...
We live in the era of big data with dataset sizes growing steadily over the past decades. In addition, obtaining expert labels for all the instances is time-consuming and in many cases may not even be possible. This necessitates the development of advanced semi-supervised models that can learn from both labeled and unlabeled data points and also scale at worst linearly with the number of examples...
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.