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Showing 3 results for Alizadeh
Learning FCM by Data Mining in a Purchase System
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Volume 20, Issue 1 (IJIEPR 2009)
Abstract
Fuzzy Cognitive Maps (FCMs) have successfully been applied in numerous domains to show the relations between essential components in complex systems. In this paper, a novel learning method is proposed to construct FCMs based on historical data and by using meta-heuristic: Genetic Algorithm (GA), Simulated Annealing (SA), and Tabu Search (TS). Implementation of the proposed method has demonstrated via real data of a purchase system in order to simulate the system’s behavior.
A fixed and flexible maintenance operations planning optimization in a parallel batch machines manufacturing system
Amir Noroozi, Saber Molla-Alizadeh-Zavardehi, Hadi Mokhtari,
Volume 27, Issue 2 (IJIEPR 2016)
Abstract

Scheduling has become an attractive area for artificial intelligence researchers. On other hand, in today's real-world manufacturing systems, the importance of an efficient maintenance schedule program cannot be ignored because it plays an important role in the success of manufacturing facilities. A maintenance program may be considered as the heath care of manufacturing machines and equipments. It is required to effectively reduce wastes and have an efficient, continuous manufacturing operation. The cost of preventive maintenance is very small when it is compared to the cost of a major breakdown. However, most of manufacturers suffer from lack of a total maintenance plan for their crucial manufacturing systems. Hence, in this paper, we study a maintenance operations planning optimization on a realistic variant of parallel batch machines manufacturing system which considers non-identical parallel processing machines with non-identical job sizes and fixed/flexible maintenance operations. To reach an appropriate maintenance schedule, we propose solution frameworks based on an Artificial Immune Algorithm (AIA), as an intelligent decision making technique. We then introduce a new method to calculate the affinity value by using an adjustment rate. Finally, the performance of proposed methods are investigated. Computational experiments, for a wide range of test problems, are carried out in order to evaluate the performance of methods.


Determining the relationship between cognitive performance and LED lamp color temperature and personality type in students
Hamed Alizadeh, Ali Khavanin, Farahnaz Khajehnasiri, Niloofar Valizadeh, Ali Salehi Sahlabadi,
Volume 34, Issue 4 (IJIEPR 2023)
Abstract
Background: The lighting of the work environment and its quantitative and qualitative characteristics, such as the intensity of the light and the color temperature, as a physical characteristic, have a great impact on the mental health, behavior and performance of people. The physical factors of the work environment, the personality type and behavioral characteristics of people are effective in their efficiency and productivity. Methods: The current research is an interventional and laboratory research which was done in 2022, 35 male students of Tarbiat Modares University were studied. This study was designed in 3 locations with different lighting systems of LED lamps with color temperature of 3000, 4000 and 5000 degrees Kelvin. Stroop test software was used to check cognitive activities and Neo questionnaire was used to determine personality type. Results: The results showed that the average reaction time when facing the LED lamp with a color temperature of 4000 degrees Kelvin in the group of consonant words was the lowest (average response time 601.22 milliseconds) and at a color temperature of 3000 degrees Kelvin in the group of dissonant words the highest value (average 88. 645 milliseconds). The average number of errors in the group of dissonant words was the highest when faced with a color temperature of 3000 degrees Kelvin (the average number of errors was 10.8), the lowest amount of errors was observed in the group of consonant words at a color temperature of 5000 degrees Kelvin (the average number of errors was 2.71 ). Also, according to the obtained results and checking the interference score of the people, which shows the level of their selective attention, it was found that the average interference score at the color temperature of 3000 degrees Kelvin is the highest (average 6.05) and when faced with the color temperature of 4000 degrees Kelvin The lowest value was (average 4.14). The results of investigating the relationship between cognitive activities and the personality type of the subjects studied at different color temperatures showed that there was a negative and significant correlation between the interference score of the personality type of the subjects at a temperature of 3000 degrees Kelvin (P value = 0.33). Also, by examining this relationship at a color temperature of 5000 degrees Kelvin, it was found that there is a negative and significant correlation between the interference score and the interference time (another parameter affecting selective attention) with the personality type of people (P value = 0.42 and 0.38, respectively). = P value) Conclusions: The results of this study showed that the LED lighting system with high color temperature can be effective on people's cognitive performance by reducing errors and increasing attention and reaction time. In order to improve people's cognitive performance, it is suggested to use lighting system with high color temperature in sensitive places. 

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