The present study encompasses a new method to simultaneous analysis, design and optimization of Water Distribution Systems (WDSs). In this method, analysis procedure is carried out using Charged System Search (CSS) optimization algorithm. Besides design and cost optimization of WDSs are performed simultaneous with analysis process using a new objective function in order to satisfying the analys...

• A novel heterogeneous multiview bayesian model for factor analysis. Include Sparse capabilities to automatically select the most relevant features. Work in a Semisupervised way handle unlabelled data as well missing values. It outperforms of state-of-the-art algorithms. Results show great versatility and an interpretability gain. The Bayesian approach feature extraction, known analysis (FA), ...

In opinion mining, there has been only very little work investigating semi-supervised machine learning on document-level polarity classification. We show that semi-supervised learning performs significantly better than supervised learning when only few labeled data are available. Semi-supervised polarity classifiers rely on a predictive feature set. (Semi-)Manually built polarity lexicons are o...

We present a newly collected data set of 8,868 gold-standard annotated Arabic twitter feeds. The corpus is manually labelled for subjectivity and sentiment analysis (SSA) (κ = 0.816). In addition, the corpus is annotated with a variety of linguistically motivated feature-sets that have previously shown positive impact on classification performance. The paper highlights issues posed by twitter a...

as the information carried in a high spatial resolution image is not represented by single pixels but by meaningful image objects, which include the association of multiple pixels and their mutual relations, the object based method has become one of the most commonly used strategies for the processing of high resolution imagery. this processing comprises two fundamental and critical steps towar...

In recent years supervised representation learning has provided state of the art or close to the state of the art results in semantic analysis tasks including ranking and information retrieval. The core idea is to learn how to embed items into a latent space such that they optimize a supervised objective in that latent space. The dimensions of the latent space have no clear semantics, and this ...