| 1. |
- Abba, S.I., et al.
(författare)
-
Integrating feature extraction approaches with hybrid emotional neural networks for water quality index modeling
- 2022
-
Ingår i: Applied Soft Computing. - : Elsevier. - 1568-4946 .- 1872-9681. ; 114
-
Tidskriftsartikel (refereegranskat)abstract
- The establishment of water quality prediction models is vital for aquatic ecosystems analysis. The traditional methods of water quality index (WQI) analysis are time-consuming and associated with a high degree of errors. These days, the application of artificial intelligence (AI) based models are trending for capturing nonlinear and complex processes. Therefore, the present study was conducted to predict the WQI in the Kinta River, Malaysia by employing the hybrid AI model i.e., GA-EANN (genetic algorithm-emotional artificial neural network). The extreme gradient boosting (XGB) and neuro-sensitivity analysis (NSA) approaches were utilized for feature extraction, and six different model combinations were derived to examine the relationship among the WQI with water quality (WQ) variables. The efficacy of the proposed hybrid GA-EANN model was evaluated against the backpropagation neural network (BPNN) and multilinear regression (MLR) models during calibration, and validation periods based on Nash–Sutcliffeefficiency (NSE), mean square error (MSE), root mean square error (RMSE), mean absolute percentage error (MAPE), and correlation coefficient (CC) indicators. According to results of appraisal the hybrid GA-EANN model produced better outcomes (NSE = 0.9233/ 0.9018, MSE = 10.5195/ 9.7889 mg/L, RMSE = 3.2434/ 3.1287 mg/L, MAPE = 3.8032/ 3.0348 mg/L, CC = 0.9609/ 0.9496) in calibration/ validation phases than BPNN and MLR models. In addition, the results indicate the better performance and suitability of the hybrid GA-EANN model with five input parameters in predicting the WQI for the study site.
|
|
| 2. |
- Abbasi, Shirin, et al.
(författare)
-
A fault-tolerant adaptive genetic algorithm for service scheduling in internet of vehicles
- 2023
-
Ingår i: Applied Soft Computing. - : Elsevier Ltd. - 1568-4946 .- 1872-9681. ; 143
-
Tidskriftsartikel (refereegranskat)abstract
- Over the years, a range of Internet of Vehicles services has emerged, along with improved quality parameters. However, the field still faces several limitations, including resource constraints and the time response requirement. This paper extracts cost, energy, processing power, service management, and resource allocation parameters. Mathematical equations are then defined based on these parameters. To simplify the process complexity and ensure scalability, we propose an algorithm that uses the genetic algorithm for fault and cost management during resource allocation to services. The main concept is to pick resources for services using a genetic algorithm. We discuss the processing and energy costs associated with this function, which is the algorithm's objective function and is created to optimize cost. Our approach goes beyond the conventional genetic algorithm in two stages. In the first step, services are prioritized, and resources are allocated in accordance with those priorities; in the second step, load balancing in message transmission paths is ensured, and message failures are avoided. The algorithm's performance is evaluated using various parameters, and it was shown to outperform other metaheuristic algorithms like the classic genetic algorithm, particle swarm, and mathematical models. Different scenarios with various nodes and service variables are defined in various system states, including fault occurrences to various percentages of 10, 20, and 30. To compare methods, we consider different parameters, the most significant being performance success rate. Moreover, the cost optimization has a good convergence after iterations, and the rate of improvement in the big scenario has slowed down after 150 iterations. Besides, it provides acceptable performance in response time for services.
|
|
| 3. |
- Aslani, Mohammad, et al.
(författare)
-
A fast instance selection method for support vector machines in building extraction
- 2020
-
Ingår i: Applied Soft Computing. - : Elsevier BV. - 1568-4946 .- 1872-9681. ; 97
-
Tidskriftsartikel (refereegranskat)abstract
- Training support vector machines (SVMs) for pixel-based feature extraction purposes from aerial images requires selecting representative pixels (instances) as a training dataset. In this research, locality-sensitive hashing (LSH) is adopted for developing a new instance selection method which is referred to as DR.LSH. The intuition of DR.LSH rests on rapidly finding similar and redundant training samples and excluding them from the original dataset. The simple idea of this method alongside its linear computational complexity make it expeditious in coping with massive training data (millions of pixels). DR.LSH is benchmarked against two recently proposed methods on a dataset for building extraction with 23,750,000 samples obtained from the fusion of aerial images and point clouds. The results reveal that DR.LSH outperforms them in terms of both preservation rate and maintaining the generalization ability (classification loss). The source code of DR.LSH can be found in https://github.com/mohaslani/DR.LSH.
|
|
| 4. |
- Bandaru, Sunith, et al.
(författare)
-
Development, analysis and applications of a quantitative methodology for assessing customer satisfaction using evolutionary optimization
- 2015
-
Ingår i: Applied Soft Computing. - : Elsevier. - 1568-4946 .- 1872-9681. ; 30, s. 265-278
-
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.
|
|
| 5. |
- Bashir, Shariq, et al.
(författare)
-
Opinion-based entity ranking using learning to rank
- 2016
-
Ingår i: Applied Soft Computing. - : Elsevier BV. - 1568-4946 .- 1872-9681. ; 38:1, s. 151-163
-
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.
|
|
| 6. |
- Deb, Kalyanmoy, et al.
(författare)
-
An integrated approach to automated innovization for discovering useful design principles : Case studies from engineering
- 2014
-
Ingår i: Applied Soft Computing. - : Elsevier. - 1568-4946 .- 1872-9681. ; 15:2, s. 42-56
-
Tidskriftsartikel (refereegranskat)abstract
- Computational optimization methods are most often used to find a single or multiple optimal or near-optimal solutions to the underlying optimization problem describing the problem at hand. In this paper, we elevate the use of optimization to a higher level in arriving at useful problem knowledge associated with the optimal or near-optimal solutions to a problem. In the proposed innovization process, first a set of trade-off optimal or near-optimal solutions are found using an evolutionary algorithm. Thereafter, the trade-off solutions are analyzed to decipher useful relationships among problem entities automatically so as to provide a better understanding of the problem to a designer or a practitioner. We provide an integrated algorithm for the innovization process and demonstrate the usefulness of the procedure to three real-world engineering design problems. New and innovative design principles obtained in each case should clearly motivate engineers and practitioners for its further application to more complex problems and its further development as a more efficient data analysis procedure.
|
|
| 7. |
- Deng, Jifei, et al.
(författare)
-
Mass customization with reinforcement learning : Automatic reconfiguration of a production line
- 2023
-
Ingår i: Applied Soft Computing. - : Elsevier. - 1568-4946 .- 1872-9681. ; 145
-
Tidskriftsartikel (refereegranskat)abstract
- This paper addresses the problem of efficient automation system configuration for mass customization in industrial manufacturing. Due to the various demands from customers, production lines need to adjust the process parameters of the machines based on specific quality parameters. Reinforcement learning, which learns from samples, can tackle the problem more efficiently than the currently used methods. Based on the proximal policy optimization and centralized training with decentralized execution, a multi-agent reinforcement learning method (MARL) is proposed to reconfigure process parameters of machines based on the changed specifications. The proposed method has the actor of each agent observing only its own state, the agents are made to collaborate by a centralized critic which observes all the states. To evaluate the method, a steel strip rolling line with six collaborating mills is studied. Simulation results show that the proposed method outperforms the existing methods and state-of-the-art multi-agent reinforcement learning methods in terms of accuracy and computing costs.
|
|
| 8. |
- dos Santos Neto, Pedro de Alcântara, et al.
(författare)
-
A hybrid approach to suggest software product line portfolios
- 2016
-
Ingår i: Applied Soft Computing. - : Elsevier. - 1568-4946 .- 1872-9681. ; 49, s. 1243-1255
-
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.
|
|
| 9. |
- Feng, Hailin, et al.
(författare)
-
Innovative soft computing-enabled cloud optimization for next-generation IoT in digital twins
- 2023
-
Ingår i: Applied Soft Computing. - : Elsevier. - 1568-4946 .- 1872-9681. ; 136
-
Tidskriftsartikel (refereegranskat)abstract
- The research aims to reduce the network resource pressure on cloud centers (CC) and edge nodes, to improve the service quality and to optimize the network performance. In addition, it studies and designs a kind of edge–cloud collaboration framework based on the Internet of Things (IoT). First, raspberry pi (RP) card working machines are utilized as the working nodes, and a kind of edge–cloud collaboration framework is designed for edge computing. The framework consists mainly of three layers, including edge RP (ERP), monitoring & scheduling RP (MSRP), and CC. Among the three layers, collaborative communication can be realized between RPs and between RPs and CCs. Second, a kind of edge–cloud matching algorithm is proposed in the time delay constraint scenario. The research results obtained by actual task assignments demonstrate that the task time delay in face recognition on edge–cloud collaboration mode is the least among the three working modes, including edge only, CC only, and edge–CC collaboration modes, reaching only 12 s. Compared with that of CC running alone, the identification results of the framework rates on edge–cloud collaboration and CC modes are both more fluent than those on edge mode only, and real-time object detection can be realized. The total energy consumption of the unloading execution by system users continuously decreases with the increase in the number of users. It is assumed that the number of pieces of equipment in systems is 150, and the energy-saving rate of systems is affected by the frequency of task generation. The frequency of task generation increases with the corresponding reduction in the energy-saving rate of systems. Based on object detection as an example, the system energy consumption is decreased from 18 W to 16 W after the assignment of algorithms. The included framework improves the resource utility rate and reduces system energy consumption. In addition, it provides theoretical and practical references for the implementation of the edge–cloud collaboration framework.
|
|
| 10. |
- Gharehbaghi, Arash, et al.
(författare)
-
An artificial intelligent-based model for detecting systolic pathological patterns of phonocardiogram based on time-growing neural network
- 2019
-
Ingår i: Applied Soft Computing. - : ELSEVIER. - 1568-4946 .- 1872-9681. ; 83
-
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.
|
|
