Applied Soft Computing

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• New strategy or rail breakage detection for a double-track railway line.
• ...

This work describes a classifier designed to identify rail breakages in double-track railway lines, completing the electronic equipment carried out by authors. The main objective of this proposal is to guarantee the integrity of tracks ...

• A distributed evolutionary algorithm using a hybrid island master-slave model is proposed.

Buildings play a significant role in climate change mitigation. In North America, energy used to construct and operate buildings accounts for some 40% of total energy use, largely originating from fossil fuels. The strategic reduction ...

• Developed a fuzzy logic based methodology for multi criteria decision making by directly computing the big data driven graphical information.

Graphical information (visualized data, information, and knowledge generated from different investigations and experimentations) is a useful form of decision-relevant information in all fields of study. The usages of such information ...

• We propose a trapezoidal intuitionistic fuzzy prospect function based on multiple reference points.

Previous studies on decision analyses typically assume a single reference point, yet under a complex environment, decision behavior is often influenced by multiple reference points (mRPs). This paper develops a prospect value ...

• Regression models are proposed to diagnose short-circuit faults in induction motors.

Induction motors are robust machines that are often exposed to a variety of environmental and operating conditions that can result in a number of failures during their use. One such fault is a short-circuit that starts in a few turns, ...

• Using clustering coefficient for initial population results in high modularity.

A community structure is an integral part of a social network. Detecting such communities plays an important role in a wide range of applications, including but not limited to cluster analysis, recommendation systems and understanding ...

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• We investigated the combination schemes for evolving rule ensembles using genetic programming for dynamic job shop scheduling.

Genetic programming based hyper-heuristic (GP-HH) approaches that evolve ensembles of dispatching rules have been effectively applied to dynamic job shop scheduling (JSS) problems. Ensemble GP-HH approaches have been shown to be more ...

• A novel data partitioning scheme is presented.
• FLR for each time lag are ...

Fuzzy time series forecasting is an emergent research topic. In fuzzy time series model design, accuracy of forecast is dependent on two major issues: (1) Efficient data partitioning (2) Establishing Fuzzy logical relationships for ...

• Our Tabu Search based clustering algorithm produces more accurate and stable solutions compared to the widely-applied Spark MLlib K-means algorithm.

The well-known K-means clustering algorithm has been employed widely in different application domains ranging from data analytics to logistics applications. However, the K-means algorithm can be affected by factors such as the initial ...

• BPNN is applied to depict the nonlinear relationships between the features.
• OGA ...

Currently, forecasting and feature selection tasks are attracting considerable attention from various scientific fields including global solar radiation forecasting, signal processing, microarray data analysis, finance, medicine and ...

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• We propose a method of estimating the parameters of the PSF from the degraded image. This is achieved by training ELM using geometric moments as feature ...

Image moments such as zernike, tchebichef and geometric moments have been widely used in image processing applications. They have useful properties to detect edges. In this paper, we present how one of the moment families, in ...

• Reduction of the number of elements in time series.
• Coral Reef Optimization ...

This paper is focused on reducing the number of elements in time series with minimum information loss, with specific applications on time series segmentation. A modification of the coral reefs optimization metaheuristic (CRO) is ...

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• The method determines the threshold value in an image using interval type-2 fuzzy set.

In this paper, we propose a novel image thresholding method based on the interval type-2 fuzzy set (IT2FS). The interval type-2 fuzzy membership function (IT2FMF) is generated from a bag of type-1 fuzzy membership functions (T1FMFs) ...

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• An improved FMMOCS (i-FMMOCS) filter for salt and pepper noise in images is presented.

Noise reduction is a paramount preliminary step in many computer vision applications in which the presence of undesired noise can distort the results. Lately, the removal of high density salt and pepper noise, as a particular case of ...

• Individual lactate threshold estimation using recurrent neural networks.
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Lactate threshold is considered an essential parameter when assessing performance of elite and recreational runners and prescribing training intensities in endurance sports. However, the measurement of blood lactate concentration ...

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• Propose a generic scheme, using deep CNNs from classification domain to retrieval.

Learning and extracting representative features along with similarity measurements in high dimensional feature spaces is a critical task. Moreover, the problem of how to bridge the semantic gap, between the low-level information ...

• A parallel hurricane optimization algorithm (PHOA) is proposed.
• PHOA is to ...

This paper proposes a parallel hurricane optimization algorithm (PHOA) for solving economic emission load dispatch (EELD) problem in modern power systems. In PHOA, several sub-populations moving independently in the search space with ...

• This paper extends the ELECTRE II method to solve the MCDM problems under the uncertain environment in terms of hesitant fuzzy linguistic term set (HFLTS).

In real life, it is not always the case that experts are able to assess alternatives by precise values. With the incomplete and vague information obtained, the experts are inclined to provide their assessments by eliciting linguistic ...

• An algorithm fast mining product t-norm based fuzzy association rules is proposed.

In this study, we propose a fast algorithm to form product-based fuzzy association rules from large quantitative dataset, which reduces data size and ensures the quality of the obtained results. A method is designed to transform mining ...

• A novel bi-objective load balancing model is developed.
• This problem has been ...

High level architecture (HLA) is a software-architecture specification of a distributed simulation system which does not involve load balancing. As a result, problems of long simulation time and distortion caused by unequally ...

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• The paper develops dynamic network DEA with fuzzy data.
• The proposed model is a ...

Dynamic network data envelopment analysis (DNDEA) is a non-parametric model for evaluating the relative efficiency of decision-making units (DMUs) with network structure in several periods of time. The model uses accurate inputs/...

Fig. 4. The graphical comparison of the methods on standard benchmark problems

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• Tree-Seed Algorithm is modified to solve constrained optimization problems by using Deb’s rules.

This study focuses on the modification of Tree-Seed Algorithm (TSA) to solve constrained optimization problem. TSA, which is one of the population-based iterative search algorithms, has been developed by inspiration of the relations ...