When evaluating computer vision systems, we are often concerned with performance on a task-specific evaluation measure such as the Intersection-Over-Union score used in the PASCAL VOC image segmentation challenge. Ideally, our systems would be tuned specifically to these evaluation measures. However, despite much work on loss-aware structured prediction, top performing systems do not use these ...

tissue level myocardial perfusion is one of the most important prognostic factors after successful recanalisation of the occluded coronary artery in patients suffering acute st elevation myocardial infarction (stemi). the primary objective of the present study was to examine the relationship between videodensitometric myocardial perfusion parameters as assessed on coronary angiograms directly f...

the use of neural networks methodology is not as common in the investigation and pre-diction noise as statistical analysis. the application of artificial neural networks for pre-diction of power tiller noise is set out in the present paper. the sound pressure signals for noise analysis were obtained in a field experiment using a 13-hp power tiller. during measurement and recording of the sound ...

the available methods for predicting mechanical characteristics of pavement layers are categorized into two general groups, destructive and non-destructive. in destructive method, using coring and pavement subgrade and performing necessary experiments on them, the quantities of layers properties will be identified. in non-destructive method, the attained deflection is measured by applying the l...

statistical methods, and especially machine learning, have been increasingly used in nanofluid modeling. this paper presents some of the interesting and applicable methods for thermal conductivity prediction and compares them with each other according to results and errors that are defined. the thermal conductivity of nanofluids increases with the volume fraction and temperature. machine learni...

abstract nowadays, due to the environmental uncertainty and rapid development of new technologies, economic variables are often predicted by using less data and short-term timeframes. therefore, prediction methods which require fewer amounts of data are needed. auto regressive integrated moving average (arima) model and artificial neural networks (anns) need large amounts of data to achieve acc...

A large number of recent observational data strongly suggest that we live in a flat, accelerating Universe composed of ∼ 1/3 of matter (baryonic + dark) and ∼ 2/3 of an exotic component with large negative pressure, usually named Dark Energy or Quintessence. The basic set of experiments includes: observations from SNe Ia, CMB anisotropies, large scale structure, X-ray data from galaxy clusters,...

Query performance prediction aims to determine in advance whether a user’s search request will return a useful answer set. The success of such prediction attempts are currently evaluated by calculating the correlation between the predicted performance and standard information retrieval metrics of system performance such as average precision. However, recent work suggests that there is little re...

AbstrAct The spatio-temporal prediction problem requires that one or more future values be predicted for time series input data obtained from sensors at multiple physical locations. Examples of this type of problem include weather prediction, flood prediction, network traffic flow, and so forth. In this chapter we provide an overview of this problem, highlighting the principles and issues that ...

Spectrum scarcity due to inefficient utilisation has ignited a plethora of dynamic spectrum access solutions to accommodate the expanding demand for future wireless networks. Dynamic spectrum access systems allow secondary users to utilise spectrum bands owned by primary users if the resulting interference is kept below a pre-designated threshold. Primary and secondary user spectrum occupancy p...