FORGOT YOUR DETAILS?

Rohollah Ramezani

Instructor of Statistics

Education

  • M.Sc. 2004-2006

    Mathematical Statistics

    Amirkabir University of Technology, Tehran, Iran

  • B.Sc. 2000-2004

    Bachelor of Statistics

    Imam Khomeini International University, Qazvin, Iran

Teaching

  • Mathematical Statistic
  • Design Of Experiment
  • Data Mining
  • Quality Control
  • Reliability
  • Statistcal Method
  • Stochastic Process

Selected Publications

Ramezani, R., Maadi, M., Khatami, S.M. A novel hybrid intelligent system with missing value imputation for diabetes diagnosis (2018) Alexandria Engineering Journal, 57 (3), pp. 1883-1891.

DOI: 10.1016/j.aej.2017.03.043

Recently, diabetes becomes the widespread and major disease in the world. In this paper, we propose a novel hybrid classifier for diabetic diseases. The proposed hybrid classifier named Logistic Adaptive Network-based Fuzzy Inference System (LANFIS) is a combination of Logistic regression and Adaptive Network-based Fuzzy Inference System. Our proposed intelligent system does not use classifiers to continuous output, does not delete samples with missing values, and does not use insignificant attributes which reduces number of tests required during data acquisition. The diagnosis performance of the LANFIS intelligent system is calculated using sensitivity, specificity, accuracy and confusion matrix. Our findings show that the classification accuracy of LANFIS intelligent system is about 88.05%. Indeed, 3–5% increase in accuracy is obtained by the proposed intelligent system and it is better than fuzzy classifiers in the available literature by deleting all samples to missing values and applying traditional classifiers to different sets of features. © 2017 Faculty of Engineering, Alexandria University

AUTHOR KEYWORDS: ANFIS; Diabetes; Intelligent system; Logistic regression; Missing value
PUBLISHER: Elsevier B.V.

Maadi, M., Javidnia, M., Ramezani, R. Modified Cuckoo Optimization Algorithm (MCOA) to solve Precedence Constrained Sequencing Problem (PCSP) (2018) Applied Intelligence, 48 (6), pp. 1407-1422.

DOI: 10.1007/s10489-017-1022-0

In recent years, new meta-heuristic algorithms have been developed to solve optimization problems. Recently-introduced Cuckoo Optimization Algorithm (COA) has proven its excellent performance to solve different optimization problems. Precedence Constrained Sequencing Problem (PCSP) is related to locating the optimal sequence with the shortest traveling time among all feasible sequences. The problem is motivated by applications in networks, scheduling, project management, logistics, assembly flow and routing. Regarding numerous practical applications of PCSP, it can be asserted that PCSP is a useful tool for a variety of industrial planning and scheduling problems. However it can also be seen that the most approaches may not solve various types of PCSPs and in related papers considering definite conditions, a model is determined and solved. In this paper a new approach is presented for solving various types of PCSPs based on COA. Since COA at first was introduced to solve continuous optimization problems, in order to demonstrate the application of COA to find the optimal sequence of the PCSP, some proposed schemes have been applied in this paper with modifications in operators of the basic COA. In fact due to the discrete nature and characteristics of the PCSP, the basic COA should be modified to solve PSCPs. To evaluate the performance of the proposed algorithm, at first, an applied single machine scheduling problem from the literature that can be formulated as a PCSP and has optimal solution is described and solved. Then, several PCSP instances with different sizes from the literature that do not have optimal solutions are solved and results are compared to the algorithms of the literature. Computational results show that the proposed algorithm has better performance compared to presented well-known meta-heuristic algorithms presented to solve various types of PCSPs so far. © 2017, Springer Science+Business Media, LLC.

AUTHOR KEYWORDS: Modified cuckoo optimization algorithm; Nonlinear optimization; Precedence constrained sequencing problem
INDEX KEYWORDS: Constrained optimization; Heuristic algorithms; Nonlinear programming; Optimal systems; Problem solving; Project management; Scheduling; Scheduling algorithms, Computational results; Continuous optimization problems; Meta heuristic algorithm; Non-linear optimization; Optimization algorithms; Optimization problems; Sequencing problems; Single machine scheduling problems, Optimization
PUBLISHER: Springer New York LLC

Seyedkarimi, M.-S., Aramvash, A., Ramezani, R. High production of bacteriorhodopsin from wild type Halobacterium salinarum (2015) Extremophiles, 19 (5), pp. 1021-1028.

DOI: 10.1007/s00792-015-0778-6

Bacteriorhodopsin (bR) is a trans-membrane proton pump found in the purple membrane of Halobacterium salinarum. This protein has high photochemical and photoelectric conversion efficiency and thermal stability, allowing it to withstand high temperatures, high salinity, and nutritionally-limited environments. The ability of this protein to convert light energy into chemical energy has applications that are mainly therapeutic/diagnostic and research-oriented. There is increasing demand for bacteriorhodopsin production in different fields. The present study maximized bacteriorhodopsin production using H. salinarum. The physical parameters of illumination, agitation speed, temperature, and nitrogen source were studied using a fractional factorial design to determine the optimal levels of each. The most suitable nitrogen source was determined to be peptone from meat. The optimal temperature was 39 °C, agitation speed was 150 rpm, and light intensity was 6300 lux for bR production. Under these conditions, the maximum bR yield was 196 mg/l, which is about 4.23 fold greater than those obtained with basal medium. The proposed strategies could be used for bR production using this archaeobacterium; the results are the highest reported thus far from a batch culture of H. salinarum. © 2015, Springer Japan.

AUTHOR KEYWORDS: Bacteriorhodopsin; Fractional factorial design; Halobacterium; Proton pump; Purple membrane
INDEX KEYWORDS: Halobacterium; Halobacterium salinarum, bacteriorhodopsin; nitrogen, biomass; genetics; growth, development and aging; Halobacterium salinarum; metabolism; temperature, Bacteriorhodopsins; Biomass; Halobacterium salinarum; Nitrogen; Temperature
PUBLISHER: Springer Tokyo

TOP