| 1. |
- Dalipi, Fisnik, et al.
(författare)
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An intelligent model for predicting the occurrence of skiing injuries
- 2015
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Ingår i: 2015 5th National Symposium on Information Technology: Towards New Smart World (NSITNSW). - : IEEE. - 9781479976263 - 9781479976256 ; , s. 1-6
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Konferensbidrag (refereegranskat)abstract
- Artificial neural networks offer a unique way to model very complex and innovative systems that can be very effective in anticipating various accident severities. In this article, we propose a neural-network-based model, able to predict the number of severe injuries caused while skiing. The proposed system is intended for use by ski patrol and medical personnel to better prepare themselves in advance for treating ski-injured persons. The ski patrol and any other medical personnel will be able to know the statistics, type and severity of the injuries occurred, and most importantly, will be benefiting from having predictions for each day. Considering that, the number of injured people in a particular place each day was estimated, the results are very promising suggesting that such a system would prove beneficial in accurately predicting skiing injuries.
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| 2. |
- Kastrati, Zenun, 1984-, et al.
(författare)
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A General Framework for Text Document Classification Using SEMCON and ACVSR
- 2015
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Ingår i: Human Interface and the Management of Information. Information and Knowledge Design. - Cham : Springer. - 9783319206110 - 9783319206127 ; , s. 310-319
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Konferensbidrag (refereegranskat)abstract
- The text document classification employs either text based approach or semantic based approach to index and retrieve text documents. The former uses keywords and therefore provides limited capabilities to capture and exploit the conceptualization involved in user information needs and content meanings. The latter aims to solve these limitations using content meanings, rather than keywords. More formally, the semantic based approach uses the domain ontology to exploit the content meanings of a particular domain. This approach however has some drawbacks. It lacks enrichment of ontology concepts with new lexical resources and evaluation of the importance indicated by weights of those concepts. Therefore to address these issues, this paper proposes a new ontology based text document classification framework. The proposed framework incorporates a newly developed objective metric calledSEMCON to enrich the domain ontology with new concepts by combining contextual as well as semantic information of a term within a text document. The framework also introduces a new approach to automatically estimate the importance of ontology concepts which is indicated by the weights of these concepts, and to enhance the concept vector space model using automatically estimated weights.
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| 3. |
- Kastrati, Zenun, et al.
(författare)
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Analysis of Online Social Networks Posts to Investigate Suspects Using SEMCON
- 2015
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Ingår i: Social Computing and Social Media. SCSM 2015. - Los Angeles : Springer. - 9783319203669 ; , s. 148-157
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Konferensbidrag (refereegranskat)abstract
- Analysing users’ behaviour and social activity for investigating suspects is an area of great interest nowadays, particularly investigating the activities of users on Online Social Networks (OSNs) for crimes. The criminal activity analysis provides a useful source of information for law enforcement and intelligence agencies across the globe. Current approaches dealing with the social criminal activity analysis mainly rely on the contextual analysis of data using only co-occurrence of terms appearing in a document to find the relationship between criminal activities in a network. In this paper, we propose a model for automated social network analysis in order to assist law enforcement and intelligence agencies to predict whether a user is a possible suspect or not. The model uses web crawlers suited to retrieve users’ data such as posts, feeds, comments, etc., and exploits them semantically and contextually using an ontology enhancement objective metric SEMCON. The output of the model is a probability value of a user being a suspect which is computed by finding the similarity between the terms obtained from the SEMCON and the concepts of criminal ontology. An experiment on analysing the public information of 20 Facebook users is conducted to evaluate the proposed model.
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| 4. |
- Kastrati, Zenun, 1984-, et al.
(författare)
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Document image classification using SEMCON
- 2015
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Ingår i: 2015 20th Symposium on Signal Processing, Images and Computer Vision (STSIVA). - : IEEE. - 9781467394611 - 9781467394604 ; , s. 1-6
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Konferensbidrag (refereegranskat)abstract
- In this paper, we are proposing a new semantic and contextual based document image classification framework. The framework is composed of two main modules. The first one is the text analysis module (TAM) which processes document images and extracts words from the image, and second one is the SEMCON, which is a semantic and contextual objective metric. From the list of extracted words by TAM, SEMCON finds a list of noun terms, employs contextual and semantic meaning to it and then uses those terms to classify documents. The scope of this paper is limited to the proposed framework and testing the approach presented on a limited test dataset. Our preliminary results are very promising and suggest that the proposed framework can be used effectively to classify document images.
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| 5. |
- Kastrati, Zenun, 1984-, et al.
(författare)
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SEMCON : Semantic and contextual objective metric
- 2015
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Ingår i: Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015). - : IEEE. - 9781479979356 ; , s. 65-68
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Konferensbidrag (refereegranskat)abstract
- This paper proposes a new objective metric called the SEMCON to enrich existing concepts in domain ontologies for describing and organizing multimedia documents. The SEMCON model exploits the document contextually and semantically. The preprocessing module collects a document and partitions that into several passages. Then a morpho-syntatic analysis is performed on the partitioned passages and a list of nouns as part-of-speech (POS) is extracted. An observation matrix based on statistical features is then computed followed by computing the contextual score. The semantics is then incorporated by computing a semantic similarity score between two terms - term (noun) that is extracted from a document and term that already exists in the ontology as a concept Eventually, an overall objective score is computed by adding contextual score with semantic score. Subjective experiments are conducted to evaluate the performance of the SEMCON model. The model is compared with state-of-the-art tf*idf and χ 2 (Chi square) using FI measure. The experimental results show that SEMCON achieved an improved accuracy of 10.64 % over the tf*idf and 13.04 % over the χ 2 .
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| 6. |
- Kastrati, Zenun, 1984-, et al.
(författare)
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Using Context-Aware and Semantic Similarity Based Model to Enrich Ontology Concepts
- 2015
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Ingår i: Natural Language Processing and Information Systems. - Cham : Springer. - 9783319195803 - 9783319195810 ; , s. 137-143
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Konferensbidrag (refereegranskat)abstract
- Domain ontologies are a good starting point to model in a formal way the basic vocabulary of a given domain. However, in order for an ontology to be usable in real applications, it has to be supplemented with lexical resources of this particular domain. The learning process of enriching domain ontologies with new lexical resources employed in the existing approaches takes into account only the contextual aspects of terms and does not consider their semantics. Therefore, this paper proposes a new objective metric namely SEMCON which combines contextual as well as semantic information of terms to enriching the domain ontology with new concepts. The SEMCON defines the context by first computing an observation matrix which exploits the statistical features such as frequency of the occurrence of a term, term’s font type and font size. The semantics is then incorporated by computing a semantic similarity score using lexical database WordNet. Subjective and objective experiments are conducted and results show an improved performance of SEMCON compared with tf*idf and $$\chi ^2$$.
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