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  • 1.
    Bashir, Shariq
    et al.
    Mohammad Ali Jinnah University, Islamabad, Pakistan.
    Afzal, Wasif
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Baig, Rauf
    Al Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia.
    Opinion-based entity ranking using learning to rank2016Inngår i: Applied Soft Computing, ISSN 1568-4946, E-ISSN 1872-9681, Vol. 38, nr 1, s. 151-163Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    As social media and e-commerce on the Internet continue to grow, opinions have become one of the most important sources of information for users to base their future decisions on. Unfortunately, the large quantities of opinions make it difficult for an individual to comprehend and evaluate them all in a reasonable amount of time. The users have to read a large number of opinions of different entities before making any decision. Recently a new retrieval task in information retrieval known as Opinion-Based Entity Ranking (OpER) has emerged. OpER directly ranks relevantentities based on how well opinions on them are matched with a user's preferences that are given in the form of queries. With such a capability, users do not need to read a large number of opinions available for the entities. Previous research on OpER does not take into account the importance and subjectivity of query keywords in individual opinions of an entity. Entity relevance scores are computed primarily on the basis of occurrences of query keywords match, by assuming all opinions of an entity as a single field of text. Intuitively, entities that have positive judgments and strong relevance with query keywords should be ranked higher than those entities that have poor relevance and negative judgments. This paper outlines several ranking features and develops an intuitive framework for OpER in which entities are ranked according to how well individual opinions of entities are matched with the user's query keywords. As a useful ranking model may be constructed from many rankingfeatures, we apply learning to rank approach based on genetic programming (GP) to combine features in order to develop an effective retrieval model for OpER task. The proposed approach is evaluated on two collections and is found to be significantly more effective than the standard OpER approach.

  • 2.
    Ghareh Baghi, Arash
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Lindén, Maria
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Babic, A.
    University of Bergen, Norway.
    An artificial intelligent-based model for detecting systolic pathological patterns of phonocardiogram based on time-growing neural network2019Inngår i: Applied Soft Computing, ISSN 1568-4946, E-ISSN 1872-9681, Vol. 83, artikkel-id 105615Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    This paper presents a novel hybrid model for classifying time series of heart sound signal using time-growing neural network. The proposed hybrid model takes segmental behaviour of heart sound signal into account by combining two different deep learning methods, the Static and the Moving Time-Growing Neural Network, which we call STGNN and MTGNN, respectively. Flexibility of the model in learning both deterministic and stochastic segments of signal allows it to learn those complicated characteristics of heart sound signal caused by any obstruction on semilunar heart valve. The model is trained to distinguish between a patient group and a reference group. The patient group is comprised of the subjects with the semilunar heart valve abnormalities including aortic stenosis, pulmonary stenosis and bicuspid aortic valve, whereas the reference group which is composed of the individuals with the heart abnormalities other than those of the reference group or the healthy ones. The model is validated using two different databases: one comprised of 140 children with various heart conditions, and the other one constituted of 50 elderly patients with aortic stenosis. Both the datasets were collected from the referrals to the University hospitals. The overall accuracy and sensitivity of the model are estimated to be 84.2% and 82.8%, respectively. The results show that the model exhibits sufficient capability to distinguish between the patient and the reference group in such a demanding clinical application. 

  • 3.
    Xiong, Ning
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Fuzzy Rule-Based Similarity Model Enables Learning from Small Case Bases2013Inngår i: Applied Soft Computing, ISSN 1568-4946, E-ISSN 1872-9681, Vol. 13, nr 4, s. 2057-2064Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The concept of similarity plays a fundamental role in case-based reasoning. However, the meaning of “similarity” can vary in situations and is largely domain dependent. This paper proposes a novel similarity model consisting of linguistic fuzzy rules as the knowledge container. We believe that fuzzy rules representation offers a more flexible means to express the knowledge and criteria for similarity assessment than traditional similarity metrics. The learning of fuzzy similarity rules is performed by exploiting the case base, which is utilized as a valuable resource with hidden knowledge for similarity learning. A sample of similarity is created from a pair of known cases in which the vicinity of case solutions reveals the similarity of case problems. We do pair-wise comparisons of cases in the case base to derive adequate training examples for learning fuzzy similarity rules. The empirical studies have demonstrated that the proposed approach is capable of discovering fuzzy similarity knowledge from a rather low number of cases, giving rise to the competence of CBR systems to work on a small case library.

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