Journal of Intelligent & Fuzzy Systems - Volume 37, issue 6

Article Type: Other

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 6, pp. 7111-7111, 2019

Authors: Nguyen, Ngoc Thanh | Szczerbicki, Edward | Trawiński, Bogdan | Nguyen, Van Du

Article Type: Editorial

Abstract: In recent years, advances in information technologies have also facilitated the use of distributed knowledge from different autonomous sources for finding solutions to some common problems in the real world. This approach can be considered as an efficient approach to tap into collective intelligence, which is often considered as the intelligence emerging from the collaboration and competition of many individuals in a group. This paper aims to present the application of collective intelligence in information systems briefly. Apart from these, we also introduce papers in this issue.

Keywords: Collective intelligence, intelligent systems, collective knowledge

DOI: 10.3233/JIFS-179324

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 6, pp. 7113-7115, 2019

Authors: Hoang, Dinh Tuyen | Nguyen, Ngoc Thanh | Hwang, Dosam

Article Type: Research Article

Abstract: Question-and-answering (Q&A) sites are information systems that allow users to ask and answer questions. Users can learn by frequently discussing, answering questions, or exchanging opinions with other experts using Q&A systems. In addition, they can arrange the existing top answers using a number of upvotes and downvotes from experts and crowd wisdom. The number of knowledge-sharing sites has increased significantly in recent years. However, some Q&A sites began to shrink (Yahoo Answers) or were shut down (Google Answers). The main reason is low-quality answers because they do not connect visitors and experts with the right questions. In addition, a question …may contain several subtopics with which the expert is unfamiliar. The recommendation of a list of experts closest to the question will lead to a long-tail problem. In this paper, we propose an expert group recommendation method for Q&A systems by taking into consideration users’ behaviors and diversity criteria in the group. Users’ behavior is analyzed to determine a group of experts or non-experts on specific topics. Diversity is an important factor in promoting the sustained comprehensible growth of Q&A sites and avoid following the crowd. Experiments on a Quora dataset show that our method achieves better results in terms of accuracy in comparison with other methods. Show more

Keywords: Expert group, group recommendation, diversity criteria, user behaviors

DOI: 10.3233/JIFS-179325

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 6, pp. 7117-7129, 2019

Authors: Nguyen, Loan T.T. | Vo, Bay | Nguyen, Thanh-Ngo | Nguyen, Anh

Article Type: Research Article

Abstract: The task of discovering sets of good rules from imbalanced class datasets may not come easy for existing class association rule mining algorithms. The reason is that they often generate rules belonging to the dominant classes. For example, in medical applications, some symptoms of illness are not popular, and the doctors are very interested in the rules associated with these symptoms. This paper proposes a novel approach for mining class association rules (CARs) in imbalanced class datasets. Firstly, assuming there are n given classes, the training dataset is split into n corresponding groups. For each group, the data is …clustered by the k -means algorithm into k groups where the value of k is equal to the number of records of the smallest group. Secondly, we combine all records from the groups after clustering and use the CAR-Miner-Diff algorithm to mine all CARs. We also propose an iterative method to get a highly accurate classifier. From experiments, we show that the proposed approach outperforms existing algorithms while maintaining a large number of useful rules in the classifier. Show more

Keywords: Class association rules, associative classification, imbalanced class dataset, clustering, data mining

DOI: 10.3233/JIFS-179326

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 6, pp. 7131-7139, 2019

Authors: Zanni-Merk, Cecilia | Szczerbicki, Edward

Article Type: Research Article

Abstract: This article presents a survey on the use of KREM, a generic knowledge-based framework for building collective intelligence through experience. After a discussion on the disadvantages of the traditional architecture used to deploy intelligent systems, the KREM architecture (Knowledge, Rules, Experience, Meta-Knowledge) is presented. The novelty of the proposal comes from the inclusion of the capitalisation of experience and the use of meta-knowledge in the traditional architecture previously discussed. KREM improves the efficiency of traditional intelligent systems by allowing incomplete expert knowledge models to be used, gradually completing them, learning with experience. In addition, the use of meta-knowledge can guide …their execution more effectively. This framework has been successfully used in various projects in different application areas, which are presented and discussed. Show more

Keywords: Knowledge technologies, ontologies, reasoning, experience, meta-knowledge

DOI: 10.3233/JIFS-179327

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 6, pp. 7141-7153, 2019

Authors: Shafiq, Syed Imran | Szczerbicki, Edward | Sanin, Cesar

Article Type: Research Article

Abstract: Modeling an effective mechanism for design and control strategies for the implementation of a flexible manufacturing system (FMS) has been a challenge. Consequently, to overcome this issue various techniques have applied in the past but most of these models are effective only for some specific situation or an element of FMS. In this study, the knowledge representation technique of Decisional DNA (DDNA) is applied to FMS to develop a generic model to achieve effective scheduling and manufacturing flexibility. Decisional DNA based Virtual Engineering Objects (VEO) are used as communicating media between machines, equipment and works pieces. The concept of Virtual …Engineering Process (VEP) is applied for modeling routing flexibility. VEOs combined with VEPs form FMS-DDNA model, which facilitates in enhancing the performance of FMS, by inducing intelligence based on its own previous experience thus making it practical and smart. Show more

Keywords: Set of experience knowledge structure (SOEKS), Decisional DNA, Virtual engineering object (VEO), Virtual engineering process (VEP).

DOI: 10.3233/JIFS-179328

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 6, pp. 7155-7167, 2019

Authors: Waris, Mohammad Maqbool | Sanin, Cesar | Szczerbicki, Edward

Article Type: Research Article

Abstract: This paper presents the idea of implementing the virtual Community of Practice for Product Innovation processes towards the establishment of intelligent enterprise. Since the fourth industrial revolution is passing through the developing phase, implementation of Cyber-Physical Production Systems require more realistic approach. Knowledge Management and Engineering plays an important role in manufacturing industries facing global competition. One of the most promising areas where Knowledge Management is studied and applied is product innovation. This paper explains the efficient and systematic methodology for Knowledge Management through Community of Practice for product innovation. Manufacturing industries can connect with similar industries at global level, …sharing and using technical and experiential knowledge in decision making thus converting them into intelligent enterprises. Show more

Keywords: Smart Innovation Engineering, product innovation, set of experience, community of practice, industry 4.0.

DOI: 10.3233/JIFS-179329

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 6, pp. 7169-7178, 2019

Authors: Bilal Ahmed, Muhammad | Sanin, Cesar | Shafiq, Syed Imran | Szczerbicki, Edward

Article Type: Research Article

Abstract: This paper presents the idea of Smart Virtual Product Development (SVPD) system to support product design. The foundations of the system are based upon smart knowledge management techniques called Set of Experience Knowledge Structure (SOEKS) and Decisional DNA (DDNA). It enhances the industrial product development process by using the previous experiential knowledge gathered from the formal decisional activities. This experiential knowledge is collected from the group of similar products having some common functions and features. The developed system comprises of three modules: design knowledge management (DKM), manufacturing capability analysis and process planning (MCAPP), and product inspection planning (PIP). The working …of design knowledge management module is presented in this study and is validated by using an industrial case study, which suggests that it is capable of capturing and reusing the required design knowledge for material selection process. The developed system has the capability to facilitate decision making and mistake proofing during early stages of product design. It can be beneficial for small and medium enterprises (SMEs) involved in product development. Show more

Keywords: Product development, product design, set of experience knowledge structure, decisional DNA, material selection process

DOI: 10.3233/JIFS-179330

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 6, pp. 7179-7187, 2019

Authors: Alshammari, Gharbi | Jorro-Aragoneses, Jose L. | Polatidis, Nikolaos | Kapetanakis, Stelios | Pimenidis, Elias | Petridis, Miltos

Article Type: Research Article

Abstract: Recommender systems are decision support systems that play an important part in generating a list of product or service recommendations for users based on the past experiences and interactions. The most popular recommendation method is Collaborative Filtering (CF) that is based on the users’ rating history to generate the recommendation. Although, recommender systems have been applied successfully in different areas such as e-Commerce and Social Networks, the popularity bias is still one of the challenges that needs to be further researched. Therefore, we propose a multi-level method that is based on a switching approach which solves the long tail recommendation …problem (LTRP) when CF fails to find the target case. We have evaluated our method using two public datasets and the results show that it outperforms a number of bases lines and state-of-the-art alternatives with a further reduce of the recommendation error rates for items found in the long tail. Show more

DOI: 10.3233/JIFS-179331

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 6, pp. 7189-7198, 2019

Authors: D’Acunto, Mario | Martinelli, Massimo | Moroni, Davide

Article Type: Research Article

Abstract: Early diagnosis of cancer often allows for a more vast choice of therapy opportunities. After a cancer diagnosis, staging provides essential information about the extent of disease in the body and the expected response to a particular treatment. The leading importance of classifying cancer patients at the early stage into high or low-risk groups has led many research teams, both from the biomedical and bioinformatics field, to study the application of Deep Learning (DL) methods. The ability of DL to detect critical features from complex datasets is a significant achievement in early diagnosis and cell cancer progression. In this paper, …we focus the attention on osteosarcoma. Osteosarcoma is one of the primary malignant bone tumors which usually afflicts people in adolescence. Our contribution to classification of osteosarcoma cells is made as follows: a DL approach is applied to discriminate human Mesenchymal Stromal Cells (MSCs) from osteosarcoma cells and to classify the different cell populations under investigation. Glass slides of different cell populations were cultured including MSCs, differentiated in healthy bone cells (osteoblasts) and osteosarcoma cells, both single cell populations or mixed. Images of such samples of isolated cells (single-type of mixed) are recorded with traditional optical microscopy. DL is then applied to identify and classify single cells. Proper data augmentation techniques and cross-fold validation are used to appreciate the capabilities of a convolutional neural network to address the cell detection and classification problem. Based on the results obtained on individual cells, and to the versatility and scalability of our DL approach, the next step will be its application to discriminate and classify healthy or cancer tissues to advance digital pathology. Show more

Keywords: Human mesenchymal stromal cells, Osteosarcoma cells, deep learning, convolutional neural networks, convolutional object detection systems, cell classification

DOI: 10.3233/JIFS-179332

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 6, pp. 7199-7206, 2019