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Your search keyword '"Nejatian, Samad"' showing total 154 results
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154 results on '"Nejatian, Samad"'

15. Application of Learning Methods for QoS Provisioning of Multimedia Traffic in IEEE802.11e

Author

Ghazanfar, Hajar, Taheri, Razieh, Nejatian, Samad, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Ruediger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, and Montaser Kouhsari, Shahram, editor

Published
2019
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17. An Innovative and Improved Mamdani Inference (IMI) Method

Author

Jamalinia, Hamid, Alizadeh, Zahra, Nejatian, Samad, Parvin, Hamid, Rezaie, Vahideh, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Batyrshin, Ildar, editor, Martínez-Villaseñor, María de Lourdes, editor, and Ponce Espinosa, Hiram Eredín, editor

Published
2018
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18. Introducing a Classification Model Based on SVM for Network Intrusion Detection

Author

Dastfal, Ghodratolah, Nejatian, Samad, Parvin, Hamid, Rezaie, Vahideh, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Castro, Félix, editor, Miranda-Jiménez, Sabino, editor, and González-Mendoza, Miguel, editor

Published
2018
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19. Trend Detection in Gold Worth Using Regression

Author

Rashidi, Seyedeh Foroozan, Parvin, Hamid, Nejatian, Samad, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Sidorov, Grigori, editor, and Herrera-Alcántara, Oscar, editor

Published
2017
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20. Integrating Information of Films by a Multi-source Combining Framework

Author

Dasturian, Elias, Parvin, Hamid, Nejatian, Samad, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Pichardo-Lagunas, Obdulia, editor, and Miranda-Jiménez, Sabino, editor

Published
2017
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43. A novel missing value imputation relying on K-means clustering and kernel-based weighting using grey relation (KWGI).

Author

Dehghani, Alireza, Bagherifard, Karamolah, Nejatian, Samad, and Parvin, Hamid

Subjects
MISSING data (Statistics), K-means clustering, STANDARD deviations, KERNEL functions, GENERATING functions
Abstract

Data pre-processing is one of the crucial phases of data mining that enhances the efficiency of data mining techniques. One of the most important operations performed on data pre-processing is missing values imputation in incomplete datasets. This research presents a new imputation technique using K-means and samples weighting mechanism based on Grey relation (KWGI). The Grey-based K-means algorithm applicable to all samples of incomplete datasets clusters the similar samples, then an appropriate kernel function generates appropriate weights based on the Grey relation. The missing values estimation of the incomplete samples is done based on the weighted mean to reduce the impact of outlier and vague samples. In both clustering and imputation steps, a penalty mechanism has been considered to reduce the similarity of ambiguous samples with a high number of missing values, and consequently, increase the accuracy of clustering and imputation. The KWGI method has been applied on nine natural datasets with eight state-of-the-art and commonly used methods, namely CMIWD, KNNI, HotDeck, MeanI, KmeanI, RKmeanI, ICKmeanI, and FKMI. The imputation results are evaluated by the Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) criteria. In this study, the missing values are generated at two levels, namely sample and value, and the results are discussed in a wide range of missingness from low rate to high rate. Experimental results of the t-test show that the proposed method performs significantly better than all the other compared methods. [ABSTRACT FROM AUTHOR]

Published
2023
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50. An Energy and Load Aware Multipath Routing Protocol in the Internet of Things

Author

Khaleghnasab, Rogayye, Bagherifard, Karamolah, Ravaei, Bahman, Parvin, Hamid, and Nejatian, Samad

Subjects
ComputerSystemsOrganization_COMPUTER-COMMUNICATIONNETWORKS, algebra_number_theory
Abstract

Internet of things (IoT) is a network of smart things. This indicates the ability of these physical things to transfer information with other physical things. The characteristics of these networks, such as topology dynamicity and energy constraint, challenges the routing problem in these networks. Previous routing methods could not achieve the required performance in this type of network. Therefore, developers of this network designed and developed specific methods in order to satisfy the requirements of these networks. One of the routing methods is utilization of multipath protocols which send data to its destination using routes with separate links. One of such protocols is RPL routing protocol. In this paper, this method is improved using composite metrics which chooses the best paths used for separate routes to send packets. We propose Energy and Load aware RPL (ELaM-IoT) protocol, which is an enhancement of RPL protocol. It uses a composite metric, calculated based on remaining energy, hop count, Link Expiration Time (LET), load and battery depletion index (BDI) for the route selection. In order to evaluate and report the results, the proposed ELaM-IoT method is compared to the ERGID and ADRM-IoT approaches with regard to average remaining energy, and network lifetime. The results demonstrate the superior performance of the proposed ELaM-IoT compared to the ERGID and ADRM-IoT approaches.

Published
2020

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