| 1. |
- Bandaru, Sunith, et al.
(författare)
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Development, analysis and applications of a quantitative methodology for assessing customer satisfaction using evolutionary optimization
- 2015
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Ingår i: Applied Soft Computing. - : Elsevier. - 1568-4946 .- 1872-9681. ; 30, s. 265-278
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Tidskriftsartikel (refereegranskat)abstract
- Consumer-oriented companies are getting increasingly more sensitive about customer's perception of their products, not only to get a feedback on their popularity, but also to improve the quality and service through a better understanding of design issues for further development. However, a consumer's perception is often qualitative and is achieved through third party surveys or the company's recording of after-sale feedback through explicit surveys or warranty based commitments. In this paper, we consider an automobile company's warranty records for different vehicle models and suggest a data mining procedure to assign a customer satisfaction index (CSI) to each vehicle model based on the perceived notion of the level of satisfaction of customers. Based on the developed CSI function, customers are then divided into satisfied and dissatisfied customer groups. The warranty data are then clustered separately for each group and analyzed to find possible causes (field failures) and their relative effects on customer's satisfaction (or dissatisfaction) for a vehicle model. Finally, speculative introspection has been made to identify the amount of improvement in CSI that can be achieved by the reduction of some critical field failures through better design practices. Thus, this paper shows how warranty data from customers can be utilized to have a better perception of ranking of a product compared to its competitors in the market and also to identify possible causes for making some customers dissatisfied and eventually to help percolate these issues at the design level. This closes the design cycle loop in which after a design is converted into a product, its perceived level of satisfaction by customers can also provide valuable information to help make the design better in an iterative manner. The proposed methodology is generic and novel, and can be applied to other consumer products as well.
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| 2. |
- Bashir, Shariq, et al.
(författare)
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Opinion-based entity ranking using learning to rank
- 2016
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Ingår i: Applied Soft Computing. - : Elsevier BV. - 1568-4946 .- 1872-9681. ; 38:1, s. 151-163
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 3. |
- dos Santos Neto, Pedro de Alcântara, et al.
(författare)
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A hybrid approach to suggest software product line portfolios
- 2016
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Ingår i: Applied Soft Computing. - : Elsevier. - 1568-4946 .- 1872-9681. ; 49, s. 1243-1255
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Tidskriftsartikel (refereegranskat)abstract
- Software product line (SPL) development is a new approach to software engineering which aims at the development of a whole range of products. However, as long as SPL can be useful, there are many challenges regarding the use of that approach. One of the main problems which hinders the adoption of software product line (SPL) is the complexity regarding product management. In that context, we can remark the scoping problem. One of the existent ways to deal with scoping is the product portfolio scoping (PPS). PPS aims to define the products that should be developed as well as their key features. In general, that approach is driven by marketing aspects, like cost of the product and customer satisfaction. Defining a product portfolio by using the many different available aspects is a NP-hard problem. This work presents an improved hybrid approach to solve the feature model selection problem, aiming at supporting product portfolio scoping. The proposal is based in a hybrid approach not dependent on any particular algorithm/technology. We have evaluated the usefulness and scalability of our approach using one real SPL (ArgoUML-SPL) and synthetic SPLs. As per the evaluation results, our approach is both useful from a practitioner's perspective and scalable. © 2016 Elsevier B.V.
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| 4. |
- Gharehbaghi, Arash, et al.
(författare)
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An artificial intelligent-based model for detecting systolic pathological patterns of phonocardiogram based on time-growing neural network
- 2019
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Ingår i: Applied Soft Computing. - : ELSEVIER. - 1568-4946 .- 1872-9681. ; 83
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Tidskriftsartikel (refereegranskat)abstract
- 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. (C) 2019 Elsevier B.V. All rights reserved.
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| 5. |
- Golzadeh, M., et al.
(författare)
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A new Ensemble based multi-agent system for prediction problems : Case study of modeling coal free swelling index
- 2018
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Ingår i: Applied Soft Computing. - : Elsevier. - 1568-4946 .- 1872-9681. ; 64, s. 109-125
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Tidskriftsartikel (refereegranskat)abstract
- In this article, a new ensemble based multi-agent system called “EMAS” is introduced for prediction of problems in data mining. The EMAS is constructed using a four-layer multi-agent system architecture to generate a data mining process based on the coordination of intelligent agents. The EMAS performance is based on data preprocessing and prediction. The first layer is dedicated to clean and normalize data. The second layer is designed for data preprocessing by using intelligent variable ranking to select the most effective agents (select the most important input variables to model an output variable). In the third layer, a negative correlation learning (NCL) algorithm is used to train a neural network ensemble (NNE). Fourth layer is dedicated to do three different subtasks including; knowledge discovery, prediction and data presentation. The ability of the EMAS is evaluated by using a robust coal database (3238 records) for prediction of Free Swelling Index (FSI) as an important problem in coke making industry, and comparing the outcomes with the results of other conventional modeling methods Coal particles have complex structures and EMAS can explore complicated relationships between their structural parameters and select the most important ones for FSI modeling. The results show that the EMAS outperforms all presented modeling methods; therefore, it can be considered as a suitable tool for prediction of problems. Moreover, the results indicated that the EMAS can be further employed as a reliable tool to select important variables, predict complicated problems, model, control, and optimize fuel consumption in iron making plants and other energy facilities.
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| 6. |
- Kusetogullari, Hüseyin, 1981-, et al.
(författare)
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Evolutionary multiobjective multiple description wavelet based image coding in the presence of mixed noise in images
- 2018
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Ingår i: Applied Soft Computing. - : Elsevier Ltd. - 1568-4946 .- 1872-9681. ; 73, s. 1039-1052
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Tidskriftsartikel (refereegranskat)abstract
- In this paper, a novel method for generation of multiple description (MD) wavelet based image coding is proposed by using Multi-Objective Evolutionary Algorithms (MOEAs). Complexity of the multimedia transmission problem has been increased for MD coders if an input image is affected by any type of noise. In this case, it is necessary to solve two different problems which are designing the optimal side quantizers and estimating optimal parameters of the denoising filter. Existing MD coding (MDC) generation methods are capable of solving only one problem which is to design side quantizers from the given noise-free image but they can fail reducing any type of noise on the descriptions if they applied to the given noisy image and this will cause bad quality of multimedia transmission in networks. Proposed method is used to overcome these difficulties to provide effective multimedia transmission in lossy networks. To achieve it, Dual Tree-Complex Wavelet Transform (DT-CWT) is first applied to the noisy image to obtain the subbands or set of coefficients which are used as a search space in the optimization problem. After that, two different objective functions are simultaneously employed in the MOEA to find pareto optimal solutions with the minimum costs by evolving the initial individuals through generations. Thus, optimal quantizers are created for MDCs generation and obtained optimum parameters are used in the image filter to remove the mixed Gaussian impulse noise on the descriptions effectively. The results demonstrate that proposed method is robust to the mixed Gaussian impulse noise, and offers a significant improvement of optimal side quantizers for balanced MDCs generation at different bitrates. © 2018 Elsevier B.V.
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| 7. |
- Marculescu, Bogdan, et al.
(författare)
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An initial industrial evaluation of interactive search-based testing for embedded software
- 2015
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Ingår i: Applied Soft Computing. - : Elsevier. - 1568-4946 .- 1872-9681. ; 29, s. 26-39
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Tidskriftsartikel (refereegranskat)abstract
- Search-based software testing promises the ability to generate and evaluate large numbers of test cases at minimal cost. From an industrial perspective, this could enable an increase in product quality without a matching increase in the time and effort required to do so. Search-based software testing, however, is a set of quite complex techniques and approaches that do not immediately translate into a process for use with most companies. For example, even if engineers receive the proper education and training in these new approaches, it can be hard to develop a general fitness function that covers all contingencies. Furthermore, in industrial practice, the knowledge and experience of domain specialists are often key for effective testing and thus for the overall quality of the final software system. But it is not clear how such domain expertise can be utilized in a search-based system. This paper presents an interactive search-based software testing (ISBST) system designed to operate in an industrial setting and with the explicit aim of requiring only limited expertise in software testing. It uses SBST to search for test cases for an industrial software module, while also allowing domain specialists to use their experience and intuition to interactively guide the search. In addition to presenting the system, this paper reports on an evaluation of the system in a company developing a framework for embedded software controllers. A sequence of workshops provided regular feedback and validation for the design and improvement of the ISBST system. Once developed, the ISBST system was evaluated by four electrical and system engineers from the company (the ’domain specialists’ in this context) used the system to develop test cases for a commonly used controller module. As well as evaluating the utility of the ISBST system, the study generated interaction data that were used in subsequent laboratory experimentation to validate the underlying search-based algorithm in the presence of realistic, but repeatable, interactions. The results validate the importance that automated software testing tools in general, and search-based tools, in particular, can leverage input from domain specialists while generating tests. Furthermore, the evaluation highlighted benefits of using such an approach to explore areas that the current testing practices do not cover or cover insufficiently. © 2014 Elsevier B.V. All rights reserved.
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| 8. |
- Marinakis, Yannis, et al.
(författare)
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An Adaptive Bumble Bees Mating Optimization algorithm
- 2017
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Ingår i: Applied Soft Computing. - : Elsevier. - 1568-4946 .- 1872-9681. ; 56, s. 13-30
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Tidskriftsartikel (refereegranskat)abstract
- The finding of the suitable parameters of an evolutionary algorithm, as the Bumble Bees Mating Optimization (BBMO) algorithm, is one of the most challenging tasks that a researcher has to deal with. One of the most common used ways to solve the problem is the trial and error procedure. In the recent few years, a number of adaptive versions of every evolutionary and nature inspired algorithm have been presented in order to avoid the use of a predefined set of parameters for all instances of the studied problem. In this paper1, an adaptive version of the BBMO algorithm is proposed, where initially random values are given to each one of the parameters and, then, these parameters are adapted during the optimization process. The proposed Adaptive BBMO algorithm is used for the solution of the Multicast Routing Problem (MRP). As we would like to prove that the proposed algorithm is suitable for solving different kinds of combinatorial optimization problems we test the algorithm, also, in the Probabilistic Traveling Salesman Problem (PTSP) and in the Hierarchical Permutation Flowshop Scheduling Problem (HPFSP). Finally, the algorithm is tested in four classic benchmark functions for global optimization problems (Rosenbrock, Sphere, Rastrigin and Griewank) in order to prove the generality of the procedure. A number of benchmark instances for all problems are tested using the proposed algorithm in order to prove its effectiveness.
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| 9. |
- Seyed Jalaleddin, Mousavirad, et al.
(författare)
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Effective image clustering based on human mental search
- 2019
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Ingår i: Applied Soft Computing. - : Elsevier BV. - 1568-4946 .- 1872-9681. ; 78, s. 209-220
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Tidskriftsartikel (refereegranskat)abstract
- Image segmentation is one of the fundamental techniques in image analysis. One group of segmentation techniques is based on clustering principles, where association of image pixels is based on a similarity criterion. Conventional clustering algorithms, such as -means, can be used for this purpose but have several drawbacks including dependence on initialisation conditions and a higher likelihood of converging to local rather than global optima.In this paper, we propose a clustering-based image segmentation method that is based on the human mental search (HMS) algorithm. HMS is a recent metaheuristic algorithm based on the manner of searching in the space of online auctions. In HMS, each candidate solution is called a bid, and the algorithm comprises three major stages: mental search, which explores the vicinity of a solution using Levy flight to find better solutions; grouping which places a set of candidate solutions into a group using a clustering algorithm; and moving bids toward promising solution areas. In our image clustering application, bids encode the cluster centres and we evaluate three different objective functions.In an extensive set of experiments, we compare the efficacy of our proposed approach with several state-of-the-art metaheuristic algorithms including a genetic algorithm, differential evolution, particle swarm optimisation, artificial bee colony algorithm, and harmony search. We assess the techniques based on a variety of metrics including the objective functions, a cluster validity index, as well as unsupervised and supervised image segmentation criteria. Moreover, we perform some tests in higher dimensions, and conduct a statistical analysis to compare our proposed method to its competitors. The obtained results clearly show that the proposed algorithm represents a highly effective approach to image clustering that outperforms other state-of-the-art techniques.
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| 10. |
- Syberfeldt, Anna, 1981-, et al.
(författare)
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A two-step multi-objectivization method for improved evolutionary optimization of industrial problems
- 2018
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Ingår i: Applied Soft Computing. - : Elsevier. - 1568-4946 .- 1872-9681. ; 64, s. 331-340
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Tidskriftsartikel (refereegranskat)abstract
- Multi-objectivization means that helper objectives are added to an optimization problem with the purpose of altering the search space in a way that improves the progress of the optimization algorithm. In this paper, a new method for multi-objectivization is proposed that is based on a two-step process. In the first step, a helper objective that conflicts with the main objective is added, and in the second step a helper objective that is in harmony with, but subservient to, the main objective is added. In contrast to existing methods for multi-objectivization, the proposed method aims at obtaining improved results in real-world optimizations by focusing on three aspects: (a) adding as little extra complexity to the problem as possible, (b) achieving an optimal balance between exploration and exploitation in order to promote an efficient search, and (c) ensuring that the main objective, which is of main interest to the user, is always prioritized. Results from evaluating the proposed method on a complex real-world scheduling problem and a theoretical benchmark problem show that the method outperforms both a traditional single-objective approach and the prevailing method for multi-objectivization. Besides describing the proposed method, the paper also outlines interesting aspects of multi-objectivization to investigate in the future.
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