Search results for "Intelligence"
showing 10 items of 6959 documents
Reliability of Virtual Screening Methods in Prediction of PDE4Binhibitor Activity
2015
Identification of active ligands using computational methods is a challenging task. For example, molecular docking, pharmacophore modeling, and three dimensional quantitative structure-activity relationship models (3D-QSAR) are widely used methods to identify novel small molecules. However, all these methods have, in addition to advantages, also significant pitfalls. The aim of this study was to compare some commonly used computational methods to estimate their ability to separate highly active PDE4B-inhibitors from less active and inactive ones. Here, 152 molecules with pIC 50 -range of 3.4-10.5, originating from six original studies were used. High correlation coefficients by using dockin…
Special issue on architectures of smart cameras for real-time applications
2016
Smart cameras are embedded vision systems whose primary function is to produce a semantic understanding of the scene and generate a response in the form of application-specific signals and data. They are autonomous vision systems themselves and can be the building blocks of a more complex smart camera network. They are built around high-performance on-chip and on-board computing and communication infrastructure, combining image sensing, real-time image and video processing, and communications into a single embedded device. They can also be interconnected in networks and cooperate to provide access to many views, enabling more challenging applications in fields like visual control, surveilla…
Computational Offloading in Mobile Edge with Comprehensive and Energy Efficient Cost Function: A Deep Learning Approach
2021
In mobile edge computing (MEC), partial computational offloading can be intelligently investigated to reduce the energy consumption and service delay of user equipment (UE) by dividing a single task into different components. Some of the components execute locally on the UE while the remaining are offloaded to a mobile edge server (MES). In this paper, we investigate the partial offloading technique in MEC using a supervised deep learning approach. The proposed technique, comprehensive and energy efficient deep learning-based offloading technique (CEDOT), intelligently selects the partial offloading policy and also the size of each component of a task to reduce the service delay and energy …
Joint Usage of Dynamic Sensitivity Control and Time Division Multiple Access in Dense 802.11ax Networks
2016
It is well known that in case of high density deployments, Wi-Fi networks suffer from serious performance impairments due to hid- den and exposed nodes. The problem is explicitly considered by the IEEE 802.11ax developers in order to improve spectrum efficiency. In this pa- per, we propose and evaluate the joint usage of dynamic sensitivity con- trol (DSC) and time division multiple access (TDMA) for improving the spectrum allocation among overlapping 802.11ax BSSs. To validate the solution, apart from simulation, we used a testbed based on the Wireless MAC Processor (WMP), a prototype of a programmable wireless card.
A naive relevance feedback model for content-based image retrieval using multiple similarity measures
2010
This paper presents a novel probabilistic framework to process multiple sample queries in content based image retrieval (CBIR). This framework is independent from the underlying distance or (dis)similarity measures which support the retrieval system, and only assumes mutual independence among their outcomes. The proposed framework gives rise to a relevance feedback mechanism in which positive and negative data are combined in order to optimally retrieve images according to the available information. A particular setting in which users interactively supply feedback and iteratively retrieve images is set both to model the system and to perform some objective performance measures. Several repo…
Modeling Snow Dynamics Using a Bayesian Network
2015
In this paper we propose a novel snow accumulation and melt model, formulated as a Dynamic Bayesian Network DBN. We encode uncertainty explicitly and train the DBN using Monte Carlo analysis, carried out with a deterministic hydrology model under a wide range of plausible parameter configurations. The trained DBN was tested against field observations of snow water equivalents SWE. The results indicate that our DBN can be used to reason about uncertainty, without doing resampling from the deterministic model. In all brevity, the DBN's ability to reproduce the mean of the observations was similar to what could be obtained with the deterministic hydrology model, but with a more realistic repre…
Steerable wavelet transform for atlas based retinal lesion segmentation
2013
International audience; Computer aided diagnosis and follow up can help in prevention and treatment of diabetes and its related complications. Screening of diabetes related disease in the eyes is done by a special low cost fundus camera. A follow up of the patients visiting at di fferent time intervals for screening brings us to the problem of image analysis for change detection and its cost per patient. It is very likely that human annotations for the lesions may be erroneous and often time consuming. Since the ethnic background plays a signi cant role in retinal pigment epithelium, visibility of the choroidal vasculature and overall retinal luminance in patients and retinal images, an eth…
Deep Convolutional Neural Network for HEp-2 fluorescence intensity classification
2019
Indirect ImmunoFluorescence (IIF) assays are recommended as the gold standard method for detection of antinuclear antibodies (ANAs), which are of considerable importance in the diagnosis of autoimmune diseases. Fluorescence intensity analysis is very often complex, and depending on the capabilities of the operator, the association with incorrect classes is statistically easy. In this paper, we present a Convolutional Neural Network (CNN) system to classify positive/negative fluorescence intensity of HEp-2 IIF images, which is important for autoimmune diseases diagnosis. The method uses the best known pre-trained CNNs to extract features and a support vector machine (SVM) classifier for the …
A multi-process system for HEp-2 cells classification based on SVM
2016
An automatic system for pre-segmented IIF images analysis was developed.A non-standard pipeline for supervised image classification was adopted.The system uses a two-level pyramid to retain some spatial information.From each cell image 216 features are extracted.15 SVM classifiers one-against-one have been implemented. This study addresses the classification problem of the HEp-2 cells using indirect immunofluorescence (IIF) image analysis, which can indicate the presence of autoimmune diseases by finding antibodies in the patient serum. Recently, studies have shown that it is possible to identify the cell patterns using IIF image analysis and machine learning techniques. In this paper we de…
Automated detection of microaneurysms using robust blob descriptors
2013
International audience; Microaneurysms (MAs) are among the first signs of diabetic retinopathy (DR) that can be seen as round dark-red structures in digital color fundus photographs of retina. In recent years, automated computer-aided detection and diagnosis (CAD) of MAs has attracted many researchers due to its low-cost and versatile nature. In this paper, the MA detection problem is modeled as finding interest points from a given image and several interest point descriptors are introduced and integrated with machine learning techniques to detect MAs. The proposed approach starts by applying a novel fundus image contrast enhancement technique using Singular Value Decomposition (SVD) of fun…