List of Articles شبکه عصبي

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  1 - یک مدل پیش بینی برای شناسایی مشتریان اعتباری بد حساب در بانک سامان
  siamak noori    
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  2 - Petrophysical Modeling of Lower Zone of Ratawi Formation, using Neural Network Method in Assimilating Seismic and Geological Well Log Data
  Javid Hanachi Alireza Bashari
  Esfandiar field is located at the northern part of the Persian . This field is a single large anticline with Lulu field of Saudi Arabia, with , 20 KM length and 7 KM width. The field was discovered in 1966 by drilling of well E1, on the northern culmination of t More

  Esfandiar field is located at the northern part of the Persian . This field is a single large anticline with Lulu field of Saudi Arabia, with , 20 KM length and 7 KM width. The field was discovered in 1966 by drilling of well E1, on the northern culmination of the field. wells E3 and E2 were drilled at the top of structure in the southern part of the field. DSTs tests results of E1 proved that the top of Lower Ratawi formation contain 15 m oil column. E3 well test result regards as a dry hole DSTs test results of E2 were not conclusive due to inadequate testing plans . E4 Appraisal well contained, 14 m oil column at the Lower Ratawi. Log interpretations results indicated, E2 and E3 wells contains oil in Yamama formation in the southern part of the field which has not been tested properly. Lower Ratawi (Top oil-bearing zone ), Zone 'B' of Lower Ratawi (Oil bearing zone at bottom), Yamama were constructed based on the existing data. Petrophysical and geophysical data has been used for the Lower Ratawi reservoir, as a result the geological models (structural and porosity models), with applying, related software’s and neural network geophysical method are generated . At the conclusion, the recommended plan consists of horizontal drilling wells for oil production in Lower Ratawi in the north of the field has been proposed. Manuscript profile
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  3 - Predicting the workload of virtual machines in order to reduce energy consumption in cloud data centers using the combination of deep learning models
  Zeinab Khodaverdian Hossein Sadr Mojdeh Nazari Soleimandarabi Seyed Ahmad Edalatpanah
  Cloud computing service models are growing rapidly, and inefficient use of resources in cloud data centers leads to high energy consumption and increased costs. Plans of resource allocation aiming to reduce energy consumption in cloud data centers has been conducted usi More

  Cloud computing service models are growing rapidly, and inefficient use of resources in cloud data centers leads to high energy consumption and increased costs. Plans of resource allocation aiming to reduce energy consumption in cloud data centers has been conducted using live migration of Virtual Machines (VMs) and their consolidation into the small number of Physical Machines (PMs). However, the selection of the appropriate VM for migration is an important challenge. To solve this issue, VMs can be classified according to the pattern of user requests into Delay-sensitive (Interactive) or Delay-Insensitive classes, and thereafter suitable VMs can be selected for migration. This is possible by virtual machine workload prediction .In fact, workload predicting and predicting analysis is a pre-migration process of a virtual machine. In this paper, In order to classification of VMs in the Microsoft Azure cloud service, a hybrid model based on Convolution Neural Network (CNN) and Gated Recurrent Unit (GRU) is proposed. Microsoft Azure Dataset is a labeled dataset and the workload of virtual machines in this dataset are in two labeled Delay-sensitive (Interactive) or Delay-Insensitive. But the distribution of samples in this dataset is unbalanced. In fact, many samples are in the Delay-Insensitive class. Therefore, Random Over-Sampling (ROS) method is used in this paper to overcome this challenge. Based on the empirical results, the proposed model obtained an accuracy of 94.42 which clearly demonstrates the superiority of our proposed model compared to other existing models. Manuscript profile
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  4 - Design and implementation of a survival model for patients with melanoma based on data mining algorithms
  farinaz sanaei Seyed Abdollah  Amin Mousavi Abbas Toloie Eshlaghy ali rajabzadeh ghotri
  10.61186/jict.44093.15.57.1
  Background/Purpose: Among the most commonly diagnosed cancers, melanoma is the second leading cause of cancer-related death. A growing number of people are becoming victims of melanoma. Melanoma is also the most malignant and rare form of skin cancer. Advanced cases of More

  Background/Purpose: Among the most commonly diagnosed cancers, melanoma is the second leading cause of cancer-related death. A growing number of people are becoming victims of melanoma. Melanoma is also the most malignant and rare form of skin cancer. Advanced cases of the disease may cause death due to the spread of the disease to internal organs. The National Cancer Institute reported that approximately 99,780 people were diagnosed with melanoma in 2022, and approximately 7,650 died. Therefore, this study aims to develop an optimization algorithm for predicting melanoma patients' survival. Methodology: This applied research was a descriptive-analytical and retrospective study. The study population included patients with melanoma cancer identified from the National Cancer Research Center at Shahid Beheshti University between 2008 and 2013, with a follow-up period of five years. An optimization model was selected for melanoma survival prognosis based on the evaluation metrics of data mining algorithms. Findings: A neural network algorithm, a Naïve Bayes network, a Bayesian network, a combination of decision tree and Naïve Bayes network, logistic regression, J48, and ID3 were selected as the models used in the national database. Statistically, the studied neural network outperformed other selected algorithms in all evaluation metrics. Conclusion: The results of the present study showed that the neural network with a value of 0.97 has optimal performance in terms of reliability. Therefore, the predictive model of melanoma survival showed a better performance both in terms of discrimination power and reliability. Therefore, this algorithm was proposed as a melanoma survival prediction model. Manuscript profile