Sensor fusion is a commonly used technique to fuse information from multiple information sources in such a way that synergistic effects are used and the result gets more reliable. Sensor fusion is needed by robotic systems which have to navigate safely and autonomously in a noisy and dynamic environment. The robot’s system state in the environment is usually estimated by mathematical and stocha...

To investigate the mechanisms of water hyacinth (Eichhornia crassipes) control, an efficient Baysian model system is required. Although deterministic models have been used to predict organism control, such models suffer from the inability to account for stochasticity in a system Entomologists and conservationists in related fields have offered multidisciplinary and multi‐institutional computer ...

Dropout has recently emerged as a powerful and simple method for training neural networks preventing co-adaptation by stochastically omitting neurons. is currently not grounded in explicit modelling assumptions which so far precluded its adoption Bayesian modelling. Using entropic reasoning we show that dropout can be interpreted optimal inference under constraints. We demonstrate this on an an...

We present a comprehensive census of the AGNs in GOODS-S/HUDF region from X-ray to radio, covering both obscured and unobscured populations. This work includes robust analysis source optical-to-mid-IR SEDs featuring (semi-)empirical AGN galaxy dust emission models Baysian fitting techniques, ultra-deep VLA 3 6 GHz observations, an integrated various selection including properties, UV-to-MIR SED...

In this study, accurate and convenient prediction models of tubular solar still performance, expressed as hourly production, were developed by utilizing machine learning. Based on experimental data, the compared, such classical artificial neural network with/without Baysian optimization, random forest traditional multilinear regression. Before applying Bayesian both predict production. But supe...

Due to the increasing demand of a faster and more reliable target tracking ability specifically for some critical applications such as military navigation and Robotics, using prediction and estimation concepts have been comprehensively studied. As a result of this demand, old and modern estimation algorithms based on probabilistic Baysian algorithms have been employed. In the case of visual obj...

Feature selection is an important data preprocessing step in data mining and pattern recognition. Many algorithms have been proposed in the past for simple patterns that can be characterised by a single feature vector. Unfortunately, these algorithms are hardly applicable to what are referred as complex patterns that have to be described by a finite set of feature vectors. This paper addresses ...

In the semantic web, ontology plays an important role to provide formal definitions of concepts and relationships. Due to the presence of several similar ontologies in the same domain, there might be several definitions for a given concept. Ontology alignment overcomes these difficulties by exploring a map between similar entities that refer to the same concept in two different ontologies. This...