Soft Computing - A Fusion of Foundations, Methodologies and Applications

With the widespread popularity of cloud storage, a growing quantity of tenants prefer to upload their massive data to remote cloud data center for saving local cost. Due to the great market prospect, a large quantity of enterprises provide cloud ...

In this paper, we introduce a concept of L-E-fuzzy skew lattices as a particular fuzzified version of skew lattices relative to lattice-valued algebraic structures, with fuzzy equality and fuzzy identities. In particular, L-E-fuzzy version of ...

Binary equivalence relations have been investigated in hyperstructures and specially hypermodules theory. By regular and strongly regular relation, we can construct a hypermodule structure on the quotient set. The motivation for such an ...

Information quantification in numerical form for any given data is very useful in decision-making problems. In Atanassov intuitionistic fuzzy sets (A-IFSs), such quantification becomes more important due to uncertainties such as intuitionism and ...

We study the impact of fuzziness on the behavior of Fuzzy Rule-Based Classifiers (FRBCs) defined by trapezoidal fuzzy sets forming Strong Fuzzy Partitions. In particular, if an FRBC selects the class related to the rule with the highest activation ...

Predicting energy demand plays an important role in devising energy development plans for cities and countries. Available data on energy demand usually consist of a nonlinear real-valued sequence, but the samples are often derived from uncertain ...

Representation learning has attracted growing attention in recommendation system. In addition, deep learning has been adopted to build a representation generator based on content data (e.g., reviews, descriptions), and has been verified to be an ...

Traditional quantum system control methods often face different constraints, and are easy to cause both leakage and stochastic control errors under the condition of limited resources. Reinforcement learning has been proved as an efficient way to ...

This paper demonstrates ML techniques and regression modeling to predict the distribution of optical performance-dependent parameters (PDPs) and performance monitoring factors (PMFs) of the unestablished lightpaths. Specifically, we discuss and ...

The K-nearest neighbor-weighted multi-class twin support vector machine (KWMTSVM) is an effective multi-classification algorithm which utilizes the local information of all training samples. However, it is easily affected by the noises and ...

This paper proposes a quantitative method for the selection of strategic objectives and causal relationships to be included in a strategy map of a Balanced Scorecard. A strategy map usually contains the strategic objectives of an organization, ...

A number of problems from specifiers for Boolean networks to programs for quantum computers can be encoded as matrices. The paper presents a novel family of linear, generative representations for evolving matrices. The matrices can be general or ...

In the present paper, a physics-inspired metaheuristic algorithm is presented to solve multi-objective optimization problems. The algorithm is developed based on the concept of Newtonian cooling law that recently has been employed by the thermal ...

This paper deals with the distributed economic dispatch problem (EDP) using incremental cost (IC) consensus approach for multiple distributed generators (DGs) integrated in a smart grid under supply-demand constraint. The communication topology ...

The purpose of this study is to introduce a stochastic computing solver for the multi-pantograph delay differential equation (MP-DDE). The MP-DDE is not easy to solve due to the singularities and pantograph terms. An advance computational ...

Within the area of regional port clusters, this paper establishes a multi-period mixed integer programming model to optimize the empty container repositioning between public hinterlands and ports, comprehensively considering the quantitative and ...

This paper proposes an improved genetic algorithm (IGA) to provide feasible solutions to the technician routing and scheduling problem (TRSP). The TRSP covers the feasible team generation, the assignment of feasible teams to suitable tasks, the ...

Several population-based techniques have subsequently been proposed. Despite their broad use in a variety of applications, we are still investigating the use of proposed methods to tackle real-world challenges. As a result, researchers must ...

For identifying nonlinear discrete systems, a new Hammerstein model consisting of Volterra neural network (VNN) and infinite impulse response (IIR) digital system is developed in this paper. The nonlinear static part of Hammerstein model is the ...

Casing steel is prone to reduced mechanical properties and occurred environmentally assisted cracking finally in sour well environment containing H₂S and CO₂. The paper mainly researched on the relationship between mechanical properties and ...

To explore the optimal government subsidy strategy for a green supply chain (GSC) under behaviour-based pricing (BBP), three types of GSC subsidy models under BBP are explored by using game theory, and the influence of different subsidy ratios and ...

Supervisory control and data acquisition (SCADA) stands as a control system consisting of computers and networked data communications. At present, many industries use SCADA to monitor as well as control the processes. In recent days, numerous ...

The cross-efficiency method is an effective way to rank decision-making units (DMUs) in data envelopment analysis. The traditional approach for cross-efficiency aggregation relies on an equally weighted average that ignores their relative ...

Settlement estimation of a footing located over a buried conduit in a sloping terrain is a challenging task for practicing civil/geotechnical engineers. In the recent past, the advent of machine learning technology has made many traditional ...

Measurement and recognition of partial discharge (PD) in power apparatus are considered a protuberant tool for condition monitoring and assessing the state of a dielectric system. Several machine learning (ML) approaches are used for recognizing ...

In this paper Enhanced Heat Transfer Search algorithm and Enriched Replicated Coronary Circulation System Optimization algorithm are applied to solve the real power loss reduction problem. Key objectives of the paper are Voltage stability ...

The current experimental teaching quality evaluation methods have been difficult to meet the quantitative and accurate requirements of evaluation indicators. For that, in this paper, we propose an ICNNs-DS (improved convolution neural networks-...

Conditional nonlinear optimal perturbation (CNOP) defines an optimization problem to study predictability and sensitivity of the oceanic and climatic events in the nonlinear system. One effective method to solve the corresponding problem is ...

Nowadays, due to the constant increase in size and complexity of the software systems imposed by their evolution, developing qualitative software systems becomes a highly important task. To achieve this goal, early detection of software defects is ...

Transfer learning involves transferring prior knowledge of solving similar problems in order to achieve quick and efficient solution. The aim of fuzzy transfer learning is to transfer prior knowledge in an imprecise environment. Time series like ...