Time filter

Source Type

Tirana, Albania

University of New York Tirana is an accredited private higher education institution in Tirana, Albania. Inaugurated in September 2002, it is the first private university in the country. UNYT offers locally Bachelor’s degrees conferred by the State University of New York's Empire State College and study programs through the SUNY Learning Network. Most UNYT faculty members are foreign-educated Albanians. UNYT offers Bachelor, Master, and Doctoral degrees. Since September 2004, UNYT, in collaboration with Institut Universitaire Kurt Bösch in Sion, Switzerland and the University of Sunderland, offers a Master of Business Administration program. As of June 2004, UNYT has become an associate partner of the Cambridge University International Examinations.According to the Webometrics Ranking of World Universities, UNYT is ranked as the seventh university in Albania. The first rector was Prof. Dr. Gramoz Pashko , who died in a helicopter crash in July 2006.There are 510 students enrolled in the academic programs. Wikipedia.

Biba M.,University of New York Tirana | Gjati E.,University of Greenwich
Advances in Intelligent Systems and Computing

Text mining is a knowledge intensive process with the main purpose of effectively and efficiently processing large amounts of unstructured data. Due to the rapidly growing amount of raw text available there is a strong need for methods that are capable of dealing with this in terms of automatic classification or indexing. In this context, an essential task is the semantic processing of natural language in order to provide a sound input to the text classification or categorization task. One of the important tasks is stemming which is the process of reducing a certain word to its root (or stem). When a text is pre-processed for mining purposes, stemming is applied in order to bring words from their current variation to their original root in order to better process the natural language with subsequent steps. A challenging task is that of stemming composite words which in many languages form a large part of the daily used vocabulary. In this paper we develop a novel rule-based algorithm for stemming composite words and we show through extensive experiments that the text classification accuracy greatly improves by stemming composite words. © Springer International Publishing Switzerland 2014 Source

Trandafili E.,Polytechnic University of Tirana | Biba M.,University of New York Tirana
International Journal of e-Business Research

Social networks have an outstanding marketing value and developing data mining methods for viral marketing is a hot topic in the research community. However, most social networks remain impossible to be fully analyzed and understood due to prohibiting sizes and the incapability of traditional machine learning and data mining approaches to deal with the new dimension in the learning process related to the large-scale environment where the data are produced. On one hand, the birth and evolution of such networks has posed outstanding challenges for the learning and mining community, and on the other has opened the possibility for very powerful business applications. However, little understanding exists regarding these business applications and the potential of social network mining to boost marketing. This paper presents a review of the most important state-of-the-art approaches in the machine learning and data mining community regarding analysis of social networks and their business applications. The authors review the problems related to social networks and describe the recent developments in the area discussing important achievements in the analysis of social networks and outlining future work. The focus of the review in not only on the technical aspects of the learning and mining approaches applied to social networks but also on the business potentials of such methods. Copyright © 2013, IGI Global. Source

Biba M.,University of New York Tirana | Xhafa F.,Polytechnic University of Catalonia | Esposito F.,University of Bari | Ferilli S.,University of Bari
Simulation Modelling Practice and Theory

Metabolomics is increasingly becoming an important field. The fundamental task in this area is to measure and interpret complex time and condition dependent parameters such as the activity or flux of metabolites in cells, their concentration, tissues elements and other biosamples. The careful study of all these elements has led to important insights in the functioning of metabolism. Recently, however, there is a growing interest towards an integrated approach to studying biological systems. This is the main goal in Systems Biology where a combined investigation of several components of a biological system is thought to produce a thorough understanding of such systems. Biological circuits are complex to model and simulate and many efforts are being made to develop models that can handle their intrinsic complexity. A significant part of biological networks still remains unknown even though recent technological developments allow simultaneous acquisition of many metabolite measurements. Metabolic networks are not only structurally complex but behave also in a stochastic fashion. Therefore, it is necessary to express structure and handle uncertainty to construct complete dynamics of these networks. In this paper we describe how stochastic modeling and simulation can be performed in a symbolic-statistical machine learning (ML) framework. We show that symbolic ML deal with structural and relational complexity while statistical ML provides principled approaches to uncertainty modeling. Learning is used to analyze traces of biochemical reactions and model the dynamicity through parameter learning, while inference is used to produce stochastic simulation of the network. © 2011 Elsevier B.V. All rights reserved. Source

Ferilli S.,University of Bari | Basile T.M.A.,University of Bari | Esposito F.,University of Bari | Biba M.,University of New York Tirana
Proceedings of the International Conference on Document Analysis and Recognition, ICDAR

Information Retrieval in large digital document repositories is at the same time a hard and crucial task. While the primary type of information available in documents is usually text, images play a very important role because they pictorially describe concepts that are dealt with in the document. Unfortunately, the semantic gap separating such a visual content from the underlying meaning is very wide. Additionally image processing techniques are usually very demanding in computational resources. Hence, only recently the area of Content-Based Image Retrieval has gained more attention. In this paper we describe a new technique to identify known objects in a picture based on a comparison of the shapes to known models. The comparison works by progressive approximations to save computational resources, and relies on novel algorithmic and representational solutions to improve preliminary shape extraction. © 2011 IEEE. Source

Biba M.,University of New York Tirana | Xhafa F.,Polytechnic University of Catalonia
Proceedings - 2nd International Conference on Intelligent Networking and Collaborative Systems, INCOS 2010

The rapidly growing amount of available digital documents of various formats and the possibility to access these through internet-based technologies in distributed environments, have led to the necessity to develop solid methods to properly organize and structure documents in large digital libraries and repositories. Specifically, the extremely large size of document collections make it impossible to manually organize such documents. Additionally, most of the documents exist in an unstructured form and do not follow any schemas. Therefore, research efforts in this direction are being dedicated to automatically infer structure and schemas. This is essential in order to better organize huge collections as well as to effectively and efficiently retrieve documents in heterogeneous domains in networked system. This paper presents a survey of the state-of-the-art methods for inferring structure from documents and schemas in networked environments. The survey is organized around the most important application domains, namely, bio-informatics, sensor networks, social networks, P2P systems, automation and control, transportation and privacy-preserving for which we analyze the recent developments on dealing with unstructured data in such domains. © 2010 IEEE. Source

Discover hidden collaborations