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The Journal of Applied Soft Computing (ASOC) is a highly reputed journal in the field of engineering and computer science. This study reviews the ASOC publications during the period 2004-2016 which are indexed in the Web of Scienc...
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The Journal of Applied Soft Computing (ASOC) is a highly reputed journal in the field of engineering and computer science. This study reviews the ASOC publications during the period 2004-2016 which are indexed in the Web of Science (WoS). The motive behind this study is to reveal the main influencing aspects that govern the ASOC publications and its citation structure using scientometric methods The citation structure of the journal is analyzed first, which includes the distribution of citations over the years, citing sources and an aerial view of the citation structure. Then, the ASOC authorship is analyzed and the author co-citation network is displayed. Further, a country-wise temporal and quantitative analysis of the publications is given along with the highly cited documents among the ASOC publications. Document co-citation analysis is also performed to reveal the intellectual base of the ASOC publications. (C) 2018 Elsevier B.V. All rights reserved.
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The vendor managed inventory (VMI) is the most commonly known marketing and distribution methodology among the service provider and the retailer in a supply chain environment. The VMI framework's critical feature is to satisfy the...
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The vendor managed inventory (VMI) is the most commonly known marketing and distribution methodology among the service provider and the retailer in a supply chain environment. The VMI framework's critical feature is to satisfy the demand of the products or items by the supplier rapidly generated from the retailers with the number of orders (order quantities). Due to the business's changing conditions, necessities of the particular product, production cycle, and manufacturing expenses, the VMI system's demand and order quantity are highly uncertain. Therefore, the total cost of a VMI system possesses higher-order uncertainties for a real-world scenario. This paper proposes a novel interval type-2 fuzzy vendor managed inventory (IT2FVMI) system, in which demand and order quantity are represented by the interval type-2 fuzzy numbers. The proposed IT2FVMI model aims to minimize the overall cost for the single vendor-retailer business, merchandise of multi-product, and a centralized warehouse of retailers' inventory managed by the vendor. Since the proposed model is an NP-hard, a particle swarm optimization (PSO) based solution approach is developed for solving it appropriately. Moreover, we have also formulated the classical/crisp VMI model and type-1 fuzzy vendor managed inventory (T1FVMI) model via considering demand and order quantity as the deterministic and the type-1 fuzzy numbers, respectively. The proposed solution technique is capable of solving both crisp VMI and T1FVMI problems. An appropriate real-world application is considered to conduct experimental simulations for the five test problems that displayed the various situations, and the minimum costs of the models are obtained. All the three models are thoroughly analyzed, and from comparison, it is demonstrated that the proposed IT2FVMI model outperforms both T1FVMI and crisp VMI models by providing a more trustworthy solution in terms of minimum cost, statistical analysis, and significantly faster convergence.
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Industrial systems usually draw huge energy to run various machines. The amount of energy requirement has again increased due to the automation of the industrial plants to make them Industry 4.0 compliant. As a result, demand of e...
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Industrial systems usually draw huge energy to run various machines. The amount of energy requirement has again increased due to the automation of the industrial plants to make them Industry 4.0 compliant. As a result, demand of energy is on the rise in almost all manufacturing and industrial plants. The necessity of critical and smart manufacturing processes in Industry 4.0 and its increased energy requirements force us to look for energy efficient techniques for running the deployed computing systems, which are often embedded and integrated within larger machines and have to function under time constraints. Computational efficiency of these real-time embedded systems (RTESs) depends solely on the timely completion of tasks. Task execution with less energy consumption within critical timing constraints is a challenging issue for the designers of RTESs. Thus, task scheduling in these systems require sophisticated energy efficient mechanisms. However, energy efficiency and timeliness are two mutually contradictory objectives, since the former is achieved only with a significant compromise of the later. In this paper, we propose a novel approach, based on the popular multi-objective evolutionary algorithm, Non-dominated sorting genetic algorithm-II, to solve this problem. Moreover, in RTESs, precise prediction of timing constraints is difficult before runtime which causes a form of imprecision or uncertainty in the system. Therefore, we use type-2 fuzzy sets (T2 FSs) to model the timing constraints in RTESs and introduce novel algorithms for membership function generation and calculation of fuzzy earliness. Numerical as well as real-life examples are included to demonstrate our proposed technique.
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This paper models the problem of precedence based real-time task scheduling as a dynamic constraint problem. It presents a new scheduling approach, termed as 'Dynamic Genetic Algorithm for Real-Time Scheduling' ((d)GA-RTS). The si...
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This paper models the problem of precedence based real-time task scheduling as a dynamic constraint problem. It presents a new scheduling approach, termed as 'Dynamic Genetic Algorithm for Real-Time Scheduling' ((d)GA-RTS). The significant feature of (d)GA-RTS is that it can handle dynamic as well as static scheduling of inter-dependent tasks for real-time systems. In parallel or distributed systems, the main aim of dynamic task scheduling is to allocate processors to a given set of tasks to execute them within optimized completion times without violating the task dependencies, if any. The (d)GA-RTS schedules the tasks in the waiting list when any new task arrives in the scheduler and minimizes the overall schedule length of the task set ensuring deadline compliance. We also illustrate an implementation technique to deal with synchronization problems in multi-processor systems. In order to exhibit the applicability of our approach, we perform experiments with suitable benchmark as well as synthetic test cases. Further, we conduct extensive simulations and compare the results with different performance metrics. The comparative study of the results with existing approaches indicates that our proposed approach is more efficient in generating feasible solutions. (C) 2018 Elsevier B.V. All rights reserved.
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During the designing phase of real-time embedded systems (RTESs), available information on task characteristics is either incomplete or imprecise. So, task timing constraints are mostly approximated estimations by designers. This ...
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During the designing phase of real-time embedded systems (RTESs), available information on task characteristics is either incomplete or imprecise. So, task timing constraints are mostly approximated estimations by designers. This indicates that there are underlying uncertainties in these timing constraints, which can be appropriately modeled using fuzzy numbers. Moreover, feasible scheduling of tasks and energy efficiency are two essential requirements for better utilization and durability of RTESs. Thus, justifiable performance of this kind of systems warrants energy savings amid timely production of the computational outputs, although these two issues are mutually contradictory. This article reports a novel formulation of the energy efficient real-time scheduling problem in a fuzzy uncertain environment and proposes a novel solution approach called "epsilon-constraint coupled energy efficient genetic algorithm (epsilon-EEGA)." The working of the proposed approach is demonstrated taking a real-life example. Also, a thorough comparative analysis is provided considering well-known existing approaches including multiobjective evolutionary algorithms. Results are compared using popular performance metrics, which suggests that the proposed epsilon-EEGA is efficient in giving better energy savings with faster computations than its existing counterparts. Standard statistical tests such as analysis of variance and Kruskal-Wallis are performed.
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The rapid increase in transportation has led to alarming levels of pollution globally, which has, in turn adverse effects on both the environment and the health of people. This has motivated researchers to develop efficient soluti...
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The rapid increase in transportation has led to alarming levels of pollution globally, which has, in turn adverse effects on both the environment and the health of people. This has motivated researchers to develop efficient solutions for limiting the fuel consumption of vehicles so that greenhouse gas emission can be reduced. The pollution emitted by a vehicle depends primarily on two controllable factors viz. load and distance traveled. This paper considers a Pollution-Routing Problem (PRP) formulation with two objectives, minimization of fuel consumption (CO2 emissions), and minimization of total distance to be traversed, and proposes a novel solution based on the well-known Non-Dominated Sorting Genetic Algorithm-II (NSGA-II). Since the problem requires optimization of several routes formed at the same time, traditional NSGA-II frameworks are incapable of handling it efficiently. Thus, we incorporate a new paradigm of evolutionary algorithm, called multi-factorial optimization into NSGA-II to solve the problem of several routes generated simultaneously. The results of our experiments with benchmark datasets confirm the feasibility of the proposed approach with better solutions and faster convergence.
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Multi-objective linguistic optimization problems (MOLOPs) are quite useful in modelinga number of real life problems. The decision variables used to define the problem constraints and the objective functions take linguistic values...
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Multi-objective linguistic optimization problems (MOLOPs) are quite useful in modelinga number of real life problems. The decision variables used to define the problem constraints and the objective functions take linguistic values in MOLOPs. In those MOLOPs, where the values of objective functions are unknown at all the points of the variable space, the link between the values of decision variables and objective functions is defined using if-then rules. MOLOPs formulated in this way were often solved using the Tsukamoto's inference method. However, the Tsukamoto's method can solve MOLOPs if the consequents in the if-then rules are defined as monotonic functions. Also, the solution obtained from Tsukamoto's method is a precise number. So in the present work, we propose a novel extension of Tsukamoto's inference method. The extended Tsukamoto's inference method can overcome both the above limitations. That is, it can solve MOLOPs where the consequents are defined as non-monotonic functions as well as monotonic functions. Also the extended Tsukamoto's method generates intervals instead of unique numbers, in the solution. From these intervals, we then generate type-1 fuzzy set membership functions and compare them using two of their fuzzy set uncertainty measures viz. centroid and fuzziness. Furthermore, we generate linguistic recommendations for these generated type-1 fuzzy set membership functions, as humans understand linguistic information naturally. We have also demonstrated the applicability of the proposed extended Tsukamoto's inference method to the case study for student's performance evaluation. We strongly believe that our proposed method is a novel one and will provide directions to future research. (C) 2019 Elsevier B.V. All rights reserved.
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In online social networks (OSNs), socialbots are responsible for various malicious activities, and they are mainly programmed to imitate human-behavior to bypass the existing detection systems. The socialbots are generally success...
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In online social networks (OSNs), socialbots are responsible for various malicious activities, and they are mainly programmed to imitate human-behavior to bypass the existing detection systems. The socialbots are generally successful in their malicious intent due to the existence of OSN users who follow them and thereby increase their reputation in the network. Analysis of the socialbot networks and their users is vital to comprehend the socialbot problem from target users' perspective. In this paper, we present a machine learning-based approach for characterizing and detecting socialbot targets, i.e., users who are susceptible to be trapped by the socialbots. We model OSN users based on their identity and behavior information, representing the static and dynamic components of their personality. The proposed approach classifies socialbot targets into three categories viz. active, reactive, and inactive users. We evaluate the proposed approach using three classifiers over a dataset collected from a live socialbot injection experiment conducted on Twitter. We also present a comparative evaluation of the proposed approach with a state-of-the-art method and show that it performs significantly better. On feature ablation analysis, we found that network structure and user intention and personality related dynamic features are most discriminative, whereas static features show the least impact on the classification. Additionally, following rate, multimedia ratio, and follower rate are most relevant to segregate different categories of the socialbot targets. We also perform a detailed topical and behavioral analysis of socialbot targets and found active users to be suspicious. Further, joy and agreeableness are the most dominating personality traits among the three categories of the users.
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The Decision making has been a major research topic in the computing literature for so long due to its vast significance in many real-world applications. Traditional fuzzy decision making (FDM) approaches have limitations due to t...
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The Decision making has been a major research topic in the computing literature for so long due to its vast significance in many real-world applications. Traditional fuzzy decision making (FDM) approaches have limitations due to the inability of the type-1 fuzzy sets (T1 FSs) in modeling higher order uncertainties. Since, the membership function (MF) of an interval type-2 fuzzy set (IT2 FS) is also fuzzy, as superior to T1 FSs, researchers considered IT2 FSs to model higher level of uncertainties in FDM and proposed a number of IT2 FDM methods. However, unlike IT2 FSs, general type-2 fuzzy sets (GT2 FSs) do not consider equal secondary membership values for all its primary membership functions. Hence, GT2 FSs offer more suitability in modelling uncertainties that exist in real-world scenarios. Thus, this paper proposes a more efficient decision making method called the "GT2 Fuzzy Decision Making (GT2 FDM)", which considers GT2 FSs to model the fuzzy goals and fuzzy constraints in a problem. The working of the proposed approach is demonstrated using an example of room temperature selection. Then we have applied it to the problem of convenient travel time selection using a real-time traffic data set. It is observed that the proposed GT2 FDM approach offers more flexibility to the decision makers in choosing an optimal solution from a much wider solution space and hence is found to be more efficient than the IT2 FDM and classical FDM approaches.
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Twin support vector machine (TWSVM) is an efficient supervised learning algorithm, proposed for the classification problems. Motivated by its success, we propose Tree-based localized fuzzy twin support vector clustering (Tree-TWSV...
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Twin support vector machine (TWSVM) is an efficient supervised learning algorithm, proposed for the classification problems. Motivated by its success, we propose Tree-based localized fuzzy twin support vector clustering (Tree-TWSVC). Tree-TWSVC is a novel clustering algorithm that builds the cluster model as a binary tree, where each node comprises of proposed TWSVM-based classifier, termed as localized fuzzy TWSVM (LF-TWSVM). The proposed clustering algorithm Tree-TWSVC has efficient learning time, achieved due to the tree structure and the formulation that leads to solving a series of systems of linear equations. Tree-TWSVC delivers good clustering accuracy because of the square loss function and it uses nearest neighbour graph based initialization method. The proposed algorithm restricts the cluster hyperplane from extending indefinitely by using cluster prototype, which further improves its accuracy. It can efficiently handle large datasets and outperforms other TWSVM-based clustering methods. In this work, we propose two implementations of Tree-TWSVC: Binary Tree-TWSVC and One-against-all Tree-TWSVC. To prove the efficacy of the proposed method, experiments are performed on a number of benchmark UCI datasets. We have also given the application of Tree-TWSVC as an image segmentation tool.
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