Applied Soft Computing
About: Applied Soft Computing is an academic journal published by Elsevier BV. The journal publishes majorly in the area(s): Computer science & Artificial intelligence. It has an ISSN identifier of 1568-4946. Over the lifetime, 5184 publications have been published receiving 121382 citations.
Topics: Computer science, Artificial intelligence, Fuzzy logic, Particle swarm optimization, Artificial neural network
Papers published on a yearly basis
TL;DR: A review on deep learning methods for semantic segmentation applied to various application areas and points out a set of promising future works to help researchers decide which are the ones that best suit their needs and goals.
Abstract: Image semantic segmentation is more and more being of interest for computer vision and machine learning researchers. Many applications on the rise need accurate and efficient segmentation mechanisms: autonomous driving, indoor navigation, and even virtual or augmented reality systems to name a few. This demand coincides with the rise of deep learning approaches in almost every field or application target related to computer vision, including semantic segmentation or scene understanding. This paper provides a review on deep learning methods for semantic segmentation applied to various application areas. Firstly, we formulate the semantic segmentation problem and define the terminology of this field as well as interesting background concepts. Next, the main datasets and challenges are exposed to help researchers decide which are the ones that best suit their needs and goals. Then, existing methods are reviewed, highlighting their contributions and their significance in the field. We also devote a part of the paper to review common loss functions and error metrics for this problem. Finally, quantitative results are given for the described methods and the datasets in which they were evaluated, following up with a discussion of the results. At last, we point out a set of promising future works and draw our own conclusions about the state of the art of semantic segmentation using deep learning techniques.
TL;DR: The comparative analysis has shown that the Fuzzy TOPSIS method is better suited to the problem of supplier selection in regard to changes of alternatives and criteria, agility and number of criteria and alternative suppliers.
Abstract: Supplier selection has become a very critical activity to the performance of organizations and supply chains. Studies presented in the literature propose the use of the methods Fuzzy TOPSIS (Fuzzy Technique for Order of Preference by Similarity to Ideal Solution) and Fuzzy AHP (Fuzzy Analytic Hierarchy Process) to aid the supplier selection decision process. However, there are no comparative studies of these two methods when applied to the problem of supplier selection. Thus, this paper presents a comparative analysis of these two methods in the context of supplier selection decision making. The comparison was made based on the factors: adequacy to changes of alternatives or criteria; agility in the decision process; computational complexity; adequacy to support group decision making; the number of alternative suppliers and criteria; and modeling of uncertainty. As an illustrative example, both methods were applied to the selection of suppliers of a company in the automotive production chain. In addition, computational tests were performed considering several scenarios of supplier selection. The results have shown that both methods are suitable for the problem of supplier selection, particularly to supporting group decision making and modeling of uncertainty. However, the comparative analysis has shown that the Fuzzy TOPSIS method is better suited to the problem of supplier selection in regard to changes of alternatives and criteria, agility and number of criteria and alternative suppliers. Thus, this comparative study contributes to helping researchers and practitioners to choose more effective approaches for supplier selection. Suggestions of further work are also proposed so as to make these methods more adequate to the problem of supplier selection.
TL;DR: A new wrapper feature selection approach is proposed based on Whale Optimization Algorithm based on the influence of using the Tournament and Roulette Wheel selection mechanisms instead of using a random operator in the searching process to search the optimal feature subsets for classification purposes.
Abstract: Classification accuracy highly dependents on the nature of the features in a dataset which may contain irrelevant or redundant data. The main aim of feature selection is to eliminate these types of features to enhance the classification accuracy. The wrapper feature selection model works on the feature set to reduce the number of features and improve the classification accuracy simultaneously. In this work, a new wrapper feature selection approach is proposed based on Whale Optimization Algorithm (WOA). WOA is a newly proposed algorithm that has not been systematically applied to feature selection problems yet. Two binary variants of the WOA algorithm are proposed to search the optimal feature subsets for classification purposes. In the first one, we aim to study the influence of using the Tournament and Roulette Wheel selection mechanisms instead of using a random operator in the searching process. In the second approach, crossover and mutation operators are used to enhance the exploitation of the WOA algorithm. The proposed methods are tested on standard benchmark datasets and then compared to three algorithms such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), the Ant Lion Optimizer (ALO), and five standard filter feature selection methods. The paper also considers an extensive study of the parameter setting for the proposed technique. The results show the efficiency of the proposed approaches in searching for the optimal feature subsets.
TL;DR: This paper reviews the state-of-the-art and focuses over a wide range of applications of SVMs in the field of hydrology, providing a brief synopsis of the techniques of SVM and other emerging ones (hybrid models), which have proven useful in the analysis of the various hydrological parameters.
Abstract: In the recent few decades there has been very significant developments in the theoretical understanding of Support vector machines (SVMs) as well as algorithmic strategies for implementing them, and applications of the approach to practical problems. SVMs introduced by Vapnik and others in the early 1990s are machine learning systems that utilize a hypothesis space of linear functions in a high dimensional feature space, trained with optimization algorithms that implements a learning bias derived from statistical learning theory. This paper reviews the state-of-the-art and focuses over a wide range of applications of SVMs in the field of hydrology. To use SVM aided hydrological models, which have increasingly extended during the last years; comprehensive knowledge about their theory and modelling approaches seems to be necessary. Furthermore, this review provides a brief synopsis of the techniques of SVMs and other emerging ones (hybrid models), which have proven useful in the analysis of the various hydrological parameters. Moreover, various examples of successful applications of SVMs for modelling different hydrological processes are also provided.
TL;DR: A comprehensive literature review on DL studies for financial time series forecasting implementations and grouped them based on their DL model choices, such as Convolutional Neural Networks (CNNs), Deep Belief Networks (DBNs), Long-Short Term Memory (LSTM).
Abstract: Financial time series forecasting is undoubtedly the top choice of computational intelligence for finance researchers in both academia and the finance industry due to its broad implementation areas and substantial impact. Machine Learning (ML) researchers have created various models, and a vast number of studies have been published accordingly. As such, a significant number of surveys exist covering ML studies on financial time series forecasting. Lately, Deep Learning (DL) models have appeared within the field, with results that significantly outperform their traditional ML counterparts. Even though there is a growing interest in developing models for financial time series forecasting, there is a lack of review papers that solely focus on DL for finance. Hence, the motivation of this paper is to provide a comprehensive literature review of DL studies on financial time series forecasting implementation. We not only categorized the studies according to their intended forecasting implementation areas, such as index, forex, and commodity forecasting, but we also grouped them based on their DL model choices, such as Convolutional Neural Networks (CNNs), Deep Belief Networks (DBNs), and Long-Short Term Memory (LSTM). We also tried to envision the future of the field by highlighting its possible setbacks and opportunities for the benefit of interested researchers.