Data Preprocessing
S. Mahmoudikhah; S. H. Zahiri; I. Behravan
Abstract
Background and Objectives: Sonar data processing is used to identify and track targets whose echoes are unsteady. So that they aren’t trusty identified in typical tracking methods. Recently, RLA have effectively cured the accuracy of undersea objective detection compared to conventional sonar objective ...
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Background and Objectives: Sonar data processing is used to identify and track targets whose echoes are unsteady. So that they aren’t trusty identified in typical tracking methods. Recently, RLA have effectively cured the accuracy of undersea objective detection compared to conventional sonar objective cognition procedures, which have robustness and low accuracy. Methods: In this research, a combination of classifiers has been used to improve the accuracy of sonar data classification in complex problems such as identifying marine targets. These classifiers each form their pattern on the data and store a model. Finally, a weighted vote is performed by the LA algorithm among these classifiers, and the classifier that gets the most votes is the classifier that has had the greatest impact on improving performance parameters.Results: The results of SVM, RF, DT, XGboost, ensemble method, R-EFMD, T-EFMD, R-LFMD, T-LFMD, ANN, CNN, TIFR-DCNN+SA, and joint models have been compared with the proposed model. Considering that the objectives and databases are different, we benchmarked the average detection rate. In this comparison, Precision, Recall, F1_Score, and Accuracy parameters have been considered and investigated in order to show the superior performance of the proposed method with other methods.Conclusion: The results obtained with the analytical parameters of Precision, Recall, F1_Score, and Accuracy compared to the latest similar research have been examined and compared, and the values are 87.71%, 88.53%, 87.8%, and 87.4% respectively for each of These parameters are obtained in the proposed method.
Artificial Intelligence
M. Khazaei
Abstract
Background and Objectives: IP multimedia subsystems (IMS) have been introduced as the Next Generation Network (NGN) platform while considering Session Initiation Protocol (SIP) as the signaling protocol. SIP lacks a proper overload mechanism. Hence, this challenge causes decline in the multimedia QoS. ...
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Background and Objectives: IP multimedia subsystems (IMS) have been introduced as the Next Generation Network (NGN) platform while considering Session Initiation Protocol (SIP) as the signaling protocol. SIP lacks a proper overload mechanism. Hence, this challenge causes decline in the multimedia QoS. The main propose of overload control mechanism is to keep the network throughput at the same network capacity with overload.Methods: NGN distributed with IMS is a complex innovative network consisting of interacting subsystems. Hence, multi-agent systems (MAS) receiving further attention for solving complex problems can solve the problem of overload in these networks. To this end, each IMS server is considered as an intelligent agent that can learn and negotiate with other agents while maintaining autonomy, thus eliminating the overload by communication and knowledge transfer between the agents. In the present research, using MAS and their properties, the intelligent hop by hop method is provided based on Q-learning and negotiation capability for the first time.Results: In the proposed method, parameters of overload controller are obtained by reinforcement learning. In order to check the validity of controller performance, a comparison is made with the similar method in which the optimal parameters are achieved based on trial and error. The result of the comparison confirms the validity of the proposed method. In order to evaluate the efficiency of the learner method, it is compared with similar and standard methods, for which the results are compared to show performance. The results show, the proposed method has approximately improved the throughput by 13%, the delay by 49% and the number of rejected sessions by 17% compare with methods, passing control messages through the network such as CPU occupancy methods. While compare with external controller methods like holonic, throughput is improved by 1% and the number of rejected requests is decreased by 10%, but delay is increased by 6% due to the convergence time of the learning and negotiation process.Conclusion: To overcome overload, complex IMS servers are considered as learner and negotiator agents. This is a new method to achieve the required parameters without relying on expert knowledge or person as well as, heterogeneous IMS entities can be inserted into the problem to complete study in future.
Computational Intelligence
N. Sayyadi Shahraki; S.H. Zahiri
Abstract
Background and Objectives: Today, the use of methods derived from Reinforcement learning-based approaches, due to their powerful in learning and extracting optimal/desirable solutions to various problems, shows a significant wideness and success. This paper presents the application of reinforcement learning ...
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Background and Objectives: Today, the use of methods derived from Reinforcement learning-based approaches, due to their powerful in learning and extracting optimal/desirable solutions to various problems, shows a significant wideness and success. This paper presents the application of reinforcement learning in automatic analog integrated circuit design. Methods: In this work, the multi-objective approach by learning automata is evaluated for accommodating required functionalities and performance specifications considering optimal minimizing the MOSFETs area and power consumption for two famous CMOS op-amps. Results: The performance of the circuits is evaluated through HSPICE and the approach is implemented in MATLAB, so a combination of MATLAB and HSPICE is performed. The two-stage and single-ended folded-cascode op-amps are designed in 0.25μm and 0.18μm CMOS technologies, respectively. According to the simulation results, a power of 560.42 and an area of 72.825 are obtained for a two-stage CMOS op-amp, and also a power of 214.15 and an area of 13.76 are obtained for a single-ended folded-cascode op-amp. In addition, in terms of total optimality index, MOLA for both cases has the best performance between the applied methods, and other research works with values of -25.683 and -34.162 dB, respectively. Conclusion: The results shown the ability of the proposed method to optimize aforementioned objectives, compared with three multi-objective well-known algorithms.======================================================================================================Copyrights©2018 The author(s). This is an open access article distributed under the terms of the Creative Commons Attribution (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, as long as the original authors and source are cited. No permission is required from the authors or the publishers.======================================================================================================
