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168 results on '"Lotfi, Shahriar"'

4. GAP2WSS: A Genetic Algorithm based on the Pareto Principle for Web Service Selection

Author

Khatoonabadi, SayedHassan, Lotfi, Shahriar, and Isazadeh, Ayaz

Subjects
Computer Science - Neural and Evolutionary Computing, Computer Science - Artificial Intelligence, Computer Science - Distributed, Parallel, and Cluster Computing
Abstract

Despite all the progress in Web service selection, the need for an approach with a better optimality and performance still remains. This paper presents a genetic algorithm by adopting the Pareto principle that is called GAP2WSS for selecting a Web service for each task of a composite Web service from a pool of candidate Web services. In contrast to the existing approaches, all global QoS constraints, interservice constraints, and transactional constraints are considered simultaneously. At first, all candidate Web services are scored and ranked per each task using the proposed mechanism. Then, the top 20 percent of the candidate Web services of each task are considered as the candidate Web services of the corresponding task to reduce the problem search space. Finally, the Web service selection problem is solved by focusing only on these 20 percent candidate Web services of each task using a genetic algorithm. Empirical studies demonstrate this approach leads to a higher efficiency and efficacy as compared with the case that all the candidate Web services are considered in solving the problem.

Published
2021

15. Graph Colouring Problem Based on Discrete Imperialist Competitive Algorithm

Author

Emami, Hojjat and Lotfi, Shahriar

Subjects
Computer Science - Artificial Intelligence, Computer Science - Neural and Evolutionary Computing
Abstract

In graph theory, Graph Colouring Problem (GCP) is an assignment of colours to vertices of any given graph such that the colours on adjacent vertices are different. The GCP is known to be an optimization and NP-hard problem. Imperialist Competitive Algorithm (ICA) is a meta-heuristic optimization and stochastic search strategy which is inspired from socio-political phenomenon of imperialistic competition. The ICA contains two main operators: the assimilation and the imperialistic competition. The ICA has excellent capabilities such as high convergence rate and better global optimum achievement. In this research, a discrete version of ICA is proposed to deal with the solution of GCP. We call this algorithm as the DICA. The performance of the proposed method is compared with Genetic Algorithm (GA) on seven well-known graph colouring benchmarks. Experimental results demonstrate the superiority of the DICA for the benchmarks. This means DICA can produce optimal and valid solutions for different GCP instances., Comment: 12 pages

Published
2013
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19. A Novel Swarm Intelligence Algorithm Based on Cuckoo Search Algorithm (NSICS)

Author

Fouladgar, Nazanin, Lotfi, Shahriar, 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, Huang, De-Shuang, editor, Bevilacqua, Vitoantonio, editor, and Premaratne, Prashan, editor

Published
2015
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21. An Introduction to Yoyo Blind Man Algorithm (YOYO-BMA)

Author

Soltani-Sarvestani, Mohammad Amin, Lotfi, Shahriar, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Selamat, Ali, editor, Nguyen, Ngoc Thanh, editor, and Haron, Habibollah, editor

Published
2013
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23. The Modified Differential Evolution Algorithm (MDEA)

Author

Ramezani, Fatemeh, Lotfi, Shahriar, 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, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Pan, Jeng-Shyang, editor, Chen, Shyi-Ming, editor, and Nguyen, Ngoc Thanh, editor

Published
2012
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24. Quad Countries Algorithm (QCA)

Author

Soltani-Sarvestani, M. A., Lotfi, Shahriar, Ramezani, Fatemeh, 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, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Pan, Jeng-Shyang, editor, Chen, Shyi-Ming, editor, and Nguyen, Ngoc Thanh, editor

Published
2012
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25. A Hybrid Evolutionary Imperialist Competitive Algorithm (HEICA)

Author

Ramezani, Fatemeh, Lotfi, Shahriar, Soltani-Sarvestani, M. A., 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, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Pan, Jeng-Shyang, editor, Chen, Shyi-Ming, editor, and Nguyen, Ngoc Thanh, editor

Published
2012
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28. Register Allocation via Graph Coloring Using an Evolutionary Algorithm

Author

Shamizi, Sevin, Lotfi, Shahriar, 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, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Panigrahi, Bijaya Ketan, editor, Suganthan, Ponnuthurai Nagaratnam, editor, Das, Swagatam, editor, and Satapathy, Suresh Chandra, editor

Published
2011
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29. IAMGA: Intimate-Based Assortative Mating Genetic Algorithm

Author

Ramezani, Fatemeh, Lotfi, Shahriar, 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, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Panigrahi, Bijaya Ketan, editor, Suganthan, Ponnuthurai Nagaratnam, editor, Das, Swagatam, editor, and Satapathy, Suresh Chandra, editor

Published
2011
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30. An Evolutionary Approach to Task Graph Scheduling

Author

Parsa, Saeed, Lotfi, Shahriar, Lotfi, Naser, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Rangan, C. Pandu, editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Beliczynski, Bartlomiej, editor, Dzielinski, Andrzej, editor, Iwanowski, Marcin, editor, and Ribeiro, Bernardete, editor

Published
2007
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31. Loop Parallelization in Multi-dimensional Cartesian Space

Author

Parsa, Saeed, Lotfi, Shahriar, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Virbitskaite, Irina, editor, and Voronkov, Andrei, editor

Published
2007
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40. One-Dimensional Probabilistic Convolutional Neural Network for Prediction of Breast Cancer Survivability.

Author

Salehi, Mohsen, Razmara, Jafar, Lotfi, Shahriar, and Mahan, Farnaz

Subjects
CONVOLUTIONAL neural networks, ARTIFICIAL neural networks, BREAST cancer, PROGNOSIS, ERROR probability, MACHINE learning, DEEP learning
Abstract

Today, machine learning plays a major role in different branches of the healthcare industry, from prognosis and diagnosis to drug development providing a significant perspective on the medical landscape for disease prevention or treatment and the improvement of human life. Recently, the use of deep neural networks in different machine learning applications has shown a great contribution to the improvement of the accuracy of predictions. In this paper, a novel application of convolutional neural networks on medical prognosis is presented. The proposed method employs a one-dimensional convolutional neural network (1D-CNN) to predict the survivability of breast cancer patients. After further examining the network architecture, a number of 8, 14 and 24 convolutional filters were considered within three layers, respectively, followed by a max-pooling layer after the second and third layers. In addition, regarding the probabilistic nature of the survivability prediction problem, an extra layer was added to the network in order to calculate the probability of the patient survivability. To train the developed 1D-CNN machine, the SEER database as the most reliable repository of cancer survivability was used to retrieve the required training set. After a pre-processing to remove unusable records, a set of 50 000 breast cancer cases including 35 features was prepared for training the machine. Based on the results obtained in this study, the developed machine could reach an accuracy of 85.84%. This accuracy is the highest level of accuracy compared to the previous prediction methods. Furthermore, the mean squared error of the calculated probability was 0.112, which is an acceptable value of error for a probability calculation machine. The output of the developed machine can be used reliably by physicians to make decision about the most appropriate treatment strategy. [ABSTRACT FROM AUTHOR]

Published
2022
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