Papers by Mohammad Nematbakhsh
Recently, the approach towards mining various opinions on weblogs, forums and websites has gained... more Recently, the approach towards mining various opinions on weblogs, forums and websites has gained attentions and interests of numerous researchers. In this regard, feature-based opinion mining has been extensively studied in English documents in order to identify implicit and explicit product features and relevant opinions. However, in case of texts written in Persian language, this task faces serious challenges. The objective of this research is to present an unsupervised method for feature-based opinion mining in Persian; an approach which does not require a labeled training dataset. The proposed method in this paper involves extracting explicit product features. Previous studies dealing with extraction of explicit features often focus on lexical roles of words; the approach which cannot be used in distinguishing between an adjective as a part of a noun or a sentiment word. In this study, in addition to lexical roles, syntactic roles are also considered to extract more relevant explicit features. The results demonstrate that the proposed method has got higher recall and precision values compared to prior studies.
Physica D: Nonlinear Phenomena, Nov 1, 2017
ï‚· The goal is to identify and rank influential users in social networks. ï‚· We determine the role ... more ï‚· The goal is to identify and rank influential users in social networks. ï‚· We determine the role of each node's neighbors in the network and apply it to rank the nodes. ï‚· We measure relation strength between nodes based on action logs. ï‚· We propose a two-level method to rank the nodes according to the neighbors' role and relation strength among them.
Identification of multi-spreader users in social networks for viral marketing
Journal of Information Science, Apr 1, 2016
Identifying high spreading power nodes is an interesting problem in social networks. Finding supe... more Identifying high spreading power nodes is an interesting problem in social networks. Finding super spreader nodes becomes an arduous task when the nodes appear in large numbers, and the number of existing links becomes enormous among them. One of the methods that is used for identifying the nodes is to rank them based on k-shell decomposition. Nevertheless, one of the disadvantages of this method is that it assigns the same rank to the nodes of a shell. Another disadvantage of this method is that only one indicator is fairly used to rank the nodes. k-Shell is an approach that is used for ranking separate spreaders, yet it does not have enough efficiency when a group of nodes with maximum spreading needs to be selected; therefore, this method, alone, does not have enough efficiency. Accordingly, in this study a hybrid method is presented to identify the super spreaders based on k-shell measure. Afterwards, a suitable method is presented to select a group of superior nodes in order to maximize the spread of influence. Experimental results on seven complex networks show that our proposed methods outperforms other well-known measures and represents comparatively more accurate performance in identifying the super spreader nodes.
Design and Performance Evaluation of a High Speed Fiber Optic Integrated Computer Network for Picture Archiving and Communications System
Proceedings of SPIE, May 25, 1989
In recent years, a growing number of diagnostic examinations in a hospital are being generated by... more In recent years, a growing number of diagnostic examinations in a hospital are being generated by digitally formatted imaging modalities. The evolution of these systems has led to the development of a totally digitized imaging system, which is called Picture Archiving and Communication System (PACS). A high speed computer network plays a very important role in the design of a Picture Archiving and Communication System. The computer network must not only offer a high data rate, but also it must be structured to satisfy the PACS requirements efficiently. In this dissertation, a computer network, called PACnet, is proposed for PACS. The PACnet is designed to carry image, voice, image pointing overlay, and intermittent data over a 200 Mbps dual fiber optic ring network. The PACnet provides a data packet channel and image and voice channels based on Time Division Multiple Access (TDMA) technique. The intermittent data is transmitted over a data packet channel using a modified token passing scheme. The voice and image pointing overlay are transferred between two stations in real-time to support the consultive nature of a radiology department using circuit switching techniques. Typical 50 mega-bit images are transmitted over the image channel in less than a second using circuit switching techniques. A technique, called adaptive variable frame size, is developed for PACnet to achieve high network utilization and short response time. This technique allows the data packet traffic to use any residual voice or image traffic momentarily available due to variation in voice traffic or absence of images. To achieve optimal design parameters for network and interfaces, the PACnet is also simulated under different conditions.
Zenodo (CERN European Organization for Nuclear Research), Oct 9, 2012
Clustering is an active research topic in data mining and different methods have been proposed in... more Clustering is an active research topic in data mining and different methods have been proposed in the literature. Most of these methods are based on numerical attributes. Recently, there have been several proposals to develop clustering methods that support mixed attributes. There are three basic groups of clustering methods: partitional methods, hierarchical methods and densitybased methods. This paper proposes a hybrid clustering algorithm that combines the advantages of hierarchical clustering and fuzzy clustering techniques and considers mixed attributes. The proposed algorithms improve the fuzzy algorithm by making it less dependent on the initial parameters such as randomly chosen initial cluster centers, and it can determine the number of clusters based on the complexity of cluster structure. Our approach is organized in two phases: first, the division of data in two clusters; then the determination of the worst cluster and splitting. The number of clusters is unknown, but our algorithms can find this parameter based on the complexity of cluster structure. We demonstrate the effectiveness of the clustering approach by evaluating datasets of linked data. We applied the proposed algorithms on three different datasets. Experimental results the proposed algorithm is suitable for link discovery between datasets of linked data. Clustering can decrease the number of comparisons before link discovery.
Journal of Universal Computer Science, 2013
This paper introduces the use of WordNet as a resource for RDF web resources sense disambiguation... more This paper introduces the use of WordNet as a resource for RDF web resources sense disambiguation in Web of Data and shows the role of designed system in interlinking datasets in Web of Data and word sense disambiguation scope. We specify the core labelling properties in semantic web to identify the name of entities which are described in web resources and use them to identify the candidate senses for a web resource. Moreover, we define the web resource's context to identify the most appropriate sense for each of the input web resources. Evaluation of the system shows the high coverage of the core labelling properties and the high performance of the sense disambiguation algorithm.
International journal of sciences, Jul 1, 2000
Most of the ontology alignment tools use terminological techniques as the initial step and then a... more Most of the ontology alignment tools use terminological techniques as the initial step and then apply the structural techniques to refine the results. Since each terminological similarity measure considers some features of similarity, ontology alignment systems require exploiting different measures. While a great deal of effort has been devoted to developing various terminological similarity measures and also developing various ontology alignment systems, little attention has been paid to develop similarity search algorithms which exploit different similarity measures in order to gain benefits and avoid limitations. We propose a novel terminological search algorithm which tries to find an entity similar to an input search string in a given ontology. This algorithm extends the search string by creating a matrix from its synonym and hypernyms. The algorithm employs and combines different kind of similarity measures in different situations to achieve a higher performance, accuracy, and stability in comparison with previous methods which either use one measure or combine more measures in a naive ways such as averaging. We evaluated the algorithm using a subset of OAEI Bench mark data set. Results showed the superiority of proposed algorithm and effectiveness of different applied techniques such as word sense disambiguation and semantic filtering mechanism.
Clustering is an active research topic in data mining and different methods have been proposed in... more Clustering is an active research topic in data mining and different methods have been proposed in the literature. Most of these methods are based on numerical attributes. Recently, there have been several proposals to develop clustering methods that support mixed attributes. There are three basic groups of clustering methods: partitional methods, hierarchical methods and densitybased methods. This paper proposes a hybrid clustering algorithm that combines the advantages of hierarchical clustering and fuzzy clustering techniques and considers mixed attributes. The proposed algorithms improve the fuzzy algorithm by making it less dependent on the initial parameters such as randomly chosen initial cluster centers, and it can determine the number of clusters based on the complexity of cluster structure. Our approach is organized in two phases: first, the division of data in two clusters; then the determination of the worst cluster and splitting. The number of clusters is unknown, but ou...
Collecting Scholars' Background Knowledge for Profiling
Collecting precise knowledge from scholars' context for profiling is crucial in recommender s... more Collecting precise knowledge from scholars' context for profiling is crucial in recommender systems as profiles provide foundational information for successful recommendation. However, acquiring of scholars' knowledge is often a challenging task since it is associated with difficulties including: what are the appropriate knowledge resources, how knowledge items can be unobtrusively captured, and how heterogeneity among different knowledge sources should be resolved. Despite the availability of various knowledge resources, identification and collecting comprehensive knowledge in an unobtrusive manner is not straightforward. To address these issues, we analyze the scholar academic behaviors and collect various scholars' knowledge diffused over the Web. The result of empirical evaluation shows the efficiency of our approach in terms of completeness and accuracy.
Expert Systems with Applications, 2020
This is a PDF file of an article that has undergone enhancements after acceptance, such as the ad... more This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Query-independent learning to rank RDF entity results of SPARQL queries
2014 4th International Conference on Computer and Knowledge Engineering (ICCKE), 2014
RDF is a data model to represent structured data on the web. SPARQL is a query language for RDF d... more RDF is a data model to represent structured data on the web. SPARQL is a query language for RDF data that returns exactly matching results. Number of these results may be very high. By rapid growth of web of data the need for efficient ranking methods for results of this kind of queries is increased. Because of exactly matching results in SPARQL queries, the focus is on the query independent features for ranking them. We use a learning to rank approach with four sets of query independent features to rank entity results of SPARQL queries over DBpedia. These features include: features extracted from RDF graph, weighted LinkCount, search engine based and information content of the RDF resource. We investigate the performance of individual features and the combination of them in learning to rank entity results. Experiments show that the complete feature set has the best performance in rankings. As an individual feature, the proposed information content of the RDF resource is a good choice based on its performance in ranking and the elapsed time for extracting this feature.
Computers & Electrical Engineering, 2015
Mobile Cloud Computing (MCC) augments capabilities of mobile devices by offloading applications t... more Mobile Cloud Computing (MCC) augments capabilities of mobile devices by offloading applications to cloud. Resource allocation is one of the most challenging issues in MCC which is investigated in this paper considering neighboring mobile devices as service providers. The objective of the resource allocation is to select service providers minimizing the completion time of the offloading along maximizing lifetime of mobile devices satisfying deadline constraint. The paper proposes a two-stage approach to solve the problem: first, Non-dominated Sorting Genetic Algorithm II (NSGA-II) is applied to obtain the Pareto solution set; second, entropy weight and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method are employed to specify the best compromise solution. Furthermore, a context-aware offloading middleware is developed to collect contextual information and handle offloading process. Moreover, to stimulate selfish users, a virtual credit based incentive mechanism is exploited in offloading decision. The experimental results demonstrate the ability of the proposed resource allocation approach to manage the trade-off between time and energy comparing to traditional algorithms.
Examination of the online reputation systems problems and provide solution
Electronic commerce communities are considered to be communities that provide opportunities for t... more Electronic commerce communities are considered to be communities that provide opportunities for the sellers on the one hand and include threats for the purchasers on the other hand. One of the ways to reduce such threats in these open communities is to use transaction-based reputations. These pieces of reputation information may assist estimation of the trustworthiness and evaluation of the future behaviors of peer. Many of the presented reputation evaluation models for such communities are merely based upon transaction-based feedbacks. We are going to open our discussion that exclusively feedback-based reputation models are inaccurate and ineffective. Then, we will introduce parameters for the more accurate measurement of reputation, namely: feedback, credibility of feedback source, time of feedback, number and value of transaction and based on these parameters, we will offer an evaluation model and finally provide a report on all the initial experiments showing the possibility of this model.
International Journal of Electronic Marketing and Retailing, 2012
Preventing customer churn and trying to retain customers is the main object of customer churn man... more Preventing customer churn and trying to retain customers is the main object of customer churn management. This paper proposes a model to measure churn probability and introduces a policy to retain customers. Using existing datasets of customers, we calculated CLV and used the C5.0 technique to predict churn probability for each customer. We also used process mining to find a policy to retain each customer separately. The model was simulated using a super market chain (Refah) and the results show the model is performing much better than previous proposed models. A computer result is shown.
Group recommendation systems can be very challenging when the datasets are sparse and there are n... more Group recommendation systems can be very challenging when the datasets are sparse and there are not many available ratings for items. In this paper, by enhancing basic memorybased techniques we resolve the data sparsity problem for users in the group. The results have shown that by conducting our techniques for the users in the group we have a higher group satisfaction and lower group dissatisfaction.
Development and application of an optimal COVID-19 screening scale utilizing an interpretable machine learning algorithm
Engineering Applications of Artificial Intelligence, Nov 1, 2023
World Academy of Science, Engineering and Technology, International Journal of Computer, Electrical, Automation, Control and Information Engineering, Oct 21, 2008
To overcome the product overload of Internet shoppers, we introduce a semantic recommendation pro... more To overcome the product overload of Internet shoppers, we introduce a semantic recommendation procedure which is more efficient when applied to Internet shopping malls. The suggested procedure recommends the semantic products to the customers and is originally based on Web usage mining, product classification, association rule mining, and frequently purchasing. We applied the procedure to the data set of MovieLens Company for performance evaluation, and some experimental results are provided. The experimental results have shown superior performance in terms of coverage and precision.
FarsNewsQA: a deep learning-based question answering system for the Persian news articles
Information Retrieval Journal
Span-prediction of Unknown Values for Long-sequence Dialogue State Tracking
2022 12th International Conference on Computer and Knowledge Engineering (ICCKE)
Representation-Centric Approach for Classification of Consumer Health Questions
SSRN Electronic Journal
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Papers by Mohammad Nematbakhsh