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JournalISSN: 1828-6003

International Review on Computers and Software 

Praise Worthy Prize, s.r.l.
About: International Review on Computers and Software is an academic journal published by Praise Worthy Prize, s.r.l.. The journal publishes majorly in the area(s): Cluster analysis & Wireless sensor network. It has an ISSN identifier of 1828-6003. Over the lifetime, 561 publications have been published receiving 1876 citations. The journal is also known as: IRECOS & I.RE.CO.S..


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Journal ArticleDOI
TL;DR: This paper proposes algorithms to perform load balancing and admission control using multipath routing in Wireless Mesh Networks (WMNs) and describes how this process is triggered during the arrival of new traffic request.
Abstract: In wireless mesh networks, admission control and congestion mitigation are the essential techniques along with routing issues. Multi path routing can be used for efficiently allocating the resources and balancing the load. In this paper we propose algorithms to perform load balancing and admission control using multipath routing in Wireless Mesh Networks (WMNs). Admission control process is triggered during the arrival of new traffic request which is accepted or rejected based on the value of requested bandwidth in comparison with the available bandwidth. When we observe congestion in any of the links along the primary path, load balancing is performed in which the traffic is distributed along the least loaded alternate paths.

38 citations

Journal ArticleDOI
TL;DR: The paper demonstrates that the fitness of process models obtained by the proposed algorithm are relatively higher respect to those obtained by Heuristics Miner and Time-based Heuristic Miner algorithms.
Abstract: One of all the works on process mining is the process discovery which produces a representation of a parallel business process. This representation is called process model and it consists of sequence and parallel control-flow patterns. The parallel control-flow patterns contain XOR, AND, and OR relations. Hidden Markov Model is rarely used to represent a process model since XOR, AND and OR relations are not visible. In Hidden Markov Model, the control-flow patterns are represented by probabilities of state transitions. This research proposes an algorithm consisting in a process discovery based on Hidden Markov Model. This algorithm contains equations and rules: the equations are used to differentiate XOR, AND, and OR relations, while the rules are used to establish the process model utilizing detected control-flow patterns. The experiment results show that the proposed algorithm obtain the right control-flow patterns in the process model. The paper demonstrates that the fitness of process models obtained by the proposed algorithm are relatively higher respect to those obtained by Heuristics Miner and Time-based Heuristics Miner algorithms. This paper also shows that the validity of process models obtained by the proposed algorithm are better than those obtained by other algorithms.

30 citations

Journal ArticleDOI
TL;DR: The robust sensor array optimization method based on Wavelet Transform to handle the noise and the modified Fast Correlation-Based Filter to find the best combination of sensor array with minimizing feature redundancy to improve the quality of input to classifier.
Abstract: Mobile Electronic Nose (MoLen) is a prospective concept for Sensing as a Service (S2aaS) development. Furthermore, gas sensor array is a substantial part of MoLen. This work treats about two issues related with gas sensor array. First, commonly used resistive sensor e.g. MOS (Metal-Oxide Semiconductor) gas sensor is highly contaminated with noise. Second, a poor combination of sensor array leads to features redundancy issue. These problems cause significant performance degradation on classifier. It will get worse if the classifier runs on S2aaS environment. To deal with these issues, this study proposes the robust sensor array optimization method based on Wavelet Transform to handle the noise and the modified Fast Correlation-Based Filter (FCBF) to find the best combination of sensor array with minimizing feature redundancy. This study has the following contribution: i) reducing the noise from gas sensor array that generated irrelevant data; ii) finding the best sensor array for beef quality classification to improve the quality of input to classifier. The experimental results show that the proposed method successfully reduces the noise power at maximum 14.41% and it is able to determine the best combination of sensors in sensor array with the 16% of improvement of General Resolution Factor (GRF)that is associated with larger classification rate.

27 citations

Journal ArticleDOI
TL;DR: The proposed algorithm contains rules and equations utilizing probability of state transition of Coupled Hidden Markov and double time-stamped in event logs and decreases usage of the invisible prime task in A# algorithm without reducing the quality of discovered process models.
Abstract: Process mining provides process improvement in a variety of application domains. A primary focus of process mining is transferring information from event logs into process model. One of the issues of process mining is dealing with invisible prime tasks. An invisible prime task is an additional task in the process model to assist in showing real processes. However, a few of algorithm solves the issue. This research proposes an algorithm for dealing with invisible prime tasks. The proposed algorithm contains rules and equations utilizing probability of state transition of Coupled Hidden Markov and double time-stamped in event logs. The rules and equations are used for determining invisible prime tasks and parallel control-flows patterns. In addition to dealing with invisible prime tasks, the experiment results also show that the proposed algorithm obtains right parallel control-flow patterns from non-complete event logs. This proposed algorithm also decreases usage of the invisible prime task in A# algorithm without reducing the quality of discovered process models. It has proven with the fitness of process models obtained by the proposed algorithm are relatively high as those obtained by A# algorithm.

25 citations

Journal ArticleDOI
TL;DR: The proposed algorithm is called Modified Time-based Heuristics Miner because it considers not only the sequence of activities but also the time-based information from the event log, and can effectively distinguish parallel (AND), single choice (XOR) and conditional (OR) patterns.
Abstract: Process Mining, or Process Discovery, is a method for modeling the workflow of a business process from event logs. Business process models contain sequential and parallel traces. In this paper, a modification of the frequently used process-mining algorithm Heuristics Miner is proposed. The proposed algorithm is called Modified Time-based Heuristics Miner because it considers not only the sequence of activities but also the time-based information from the event log. It can effectively distinguish parallel (AND), single choice (XOR) and conditional (OR) patterns; the latter cannot be discovered by the original Heuristics Miner. The threshold intervals are determined on the basis of the average dependency measure in the dependency graph. The experimental results show that the proposed algorithm is able to discover concurrent business processes formed by parallel (AND) and conditional (OR) patterns, whereas the existing Heuristics Miner algorithm can only discover concurrent business processes formed by parallel (AND) patterns. This paper also provides an evaluation of validity an fitness of the discovered process model.

24 citations

Performance
Metrics
No. of papers from the Journal in previous years
YearPapers
20236
20229
20201
20191
20181
20177