Volume 18, Issue 2, Summer and Autumn 2021


Using Neural Network For Control Of Fuzzy Storage

Noor Sabah; Zena Modhur

IRAQI JOURNAL OF STATISTICAL SCIENCES, 2021, Volume 18, Issue 2, Pages 131-146
DOI: 10.33899/iqjoss.2021.169989

In this research, an optimal model will be created to control the storage in the blood bank in Nineveh Governorate by studying the continuous review system for storage in light of the ambiguity of random demand. Data were withdrawn from the blood bank and on three inputs (order quantity, damaged quantity and number of donors), where the data distribution was verified and the normal distribution was also linearly followed. At first, we fogged the data using the ready-made tool in the program ((matlab) and after obtaining the results we entered it onthe neural network (RNN).The best result obtained is the use of the fuzzy neural network as compared to the classical method.

Comparison Prediction of Transfer Function Models and State Space Models Using Fuzzy Method

Fahad Samar Subhy; Heyam Abd Al-majeed Hayawi

IRAQI JOURNAL OF STATISTICAL SCIENCES, 2021, Volume 18, Issue 2, Pages 116-130
DOI: 10.33899/iqjoss.2021.169968

The research aims to build dynamic models represented by the transfer function and State Space Models of a single input variable and a single output variable, The input and output variables are represented by the temperatures of the water before the filtration process and after the filtration process to convert it into potable water
.As a transfer function model will be built for a single input variable and a single output variable for real data and Fuzzy data, building transfer function models and state space models, finding predictive values and comparing results.

Bayesian Inference of a Non normal Multivariate Partial Linear Regression Model

Sarmad Abdulkhaleq Salih; Emad Aboudi

IRAQI JOURNAL OF STATISTICAL SCIENCES, 2021, Volume 18, Issue 2, Pages 91-115
DOI: 10.33899/iqjoss.2021.169967

This research includes the Bayesian estimation of the parameters of the multivariate partial linear regression model when the random error follows the matrix-variate generalized modified Bessel distribution and found the statistical test of the model represented by finding the Bayes factor criterion, the predictive distribution under assumption that the shape parameters are known. The prior distribution about the model parameters is represented by non-informative information, as well as the simulate on the generated data from the model by a suggested way based on different values ​​of the shape parameters, the kernel function used in the generation was a Gaussian kernel function, the bandwidth (Smoothing) parameter was according to the rule of thumb. It found that the posterior marginal probability distribution of the location matrix  and the predictive probability distribution is a matrix-t distribution with different parameters, the posterior marginal probability distribution of the scale matrix  is proper distribution but it does not belong to the conjugate family, Through the Bayes factor criterion, it was found that the sample that was used in the generation process was drawn from a population that does not belong to the generalized modified Bessel population.

Comparison of Two Methods for Estimating Parameters of the Model Binary Logistic Regression

Farah Fathi; Safaa Alsaffawi

IRAQI JOURNAL OF STATISTICAL SCIENCES, 2021, Volume 18, Issue 2, Pages 77-90
DOI: 10.33899/iqjoss.2021.169971

 
This paper we deal with one of the most important nonlinear regression models widely used in modeling statistical applications, which is the binary logistic regression model, and then estimating the parameters of this model using statistical estimation methods. However, while using this model we face a problem in estimating its parameters as the number of parameters is (p+1), and finding the estimation of parameters using numerical methods sometimes does not provide the best solution because it depends on primitive estimations. In this paper, some ordinary estimation methods are employed to fit the estimation of the parameters of this type of non-linear regression model, and then we compare these estimation methods. Further, the comparison includes some of the important estimation methods, which are the ordinary estimation methods that included the Weighted Least Squares Method (WLS), and the Bayes Method (BM). In order to choose the best method for estimating, by taking a number of models and different sample sizes and using the statistical standard mean error squares (MSE) for the logistic model estimations for the purpose of comparison. Among the preferred methods for estimating model parameters, and it was generally concluded that the WLS method provides the MSE of estimators compared to the other methods. On the practical side, this model was also used to model data for people with diabetes and to estimate parameters using the best methods, and it was reached by comparing patients with diabetes. A census of diabetes with those who did not have diabetes with the appropriateness of the model in modeling this type of data and extracting the main cause of diabetes incidence, which is insulin, as well as the accuracy of the methods in estimating the model parameters.

مشکلة حقیبة المستثمر متعدد الأبعاد باستعمال الخوارزمیات المستوحاة من الطبیعة – مراجعة مقال

Niam Al-Thanoon

IRAQI JOURNAL OF STATISTICAL SCIENCES, 2021, Volume 18, Issue 2, Pages 56-76
DOI: 10.33899/iqjoss.2021.169970

تعتبر مسألة حقیبة الظهر أو المستثمر متعدد الأبعاد من مسائل الأمثلیة التوافقیة الصعبة (المتقطعة ) المقیدة المهمة والمعروفة جدا فی بحوث العملیات والأمثلیة. فی الوقت الحاضر اصبحت الخوارزمیات المستوحاة من الطبیعة ذات اهمیة بالغی فی حل العدید من المشاکل الریاضیاتیة ومنها مشکلة حقیبة المستثمر. ولغرض الوصول الى افضل الحلول تم فی هذا البحث استعراض ثلاثة خوارزمیات استخدمت فی حل هذه المسائلة. اذ تفوقت خوارزمیة المفترسات البحریة وهی خوارزمیة حدیثة جدا على خوارزمیة الاعشاب الضارة وخوارزمیة الثقب الاسود فی الحصول على افضل احل وباقل وقت ممکن. فی حین جاءت خوارزمیة الثقب الاسود فی المرتبة الثالثة على الرغم من انها لا تحتاج الى تحدید ای معلمة بالخوارزمیة قبل عملها.

التنبؤ لبیانات تلوث الهواء باستخدام الطریقة الهجینة RNN-Wavelet بالاعتماد على نموذج MLR

Khetam Alzubaidy; Osama Hannon

IRAQI JOURNAL OF STATISTICAL SCIENCES, 2021, Volume 18, Issue 2, Pages 40-55
DOI: 10.33899/iqjoss.2021.169969

ان دراسة الجسیمات المعلقة (Particular matter (PM10)) والتکهن بها ضروری للتقلیل والسیطرة على الأضرار البیئیة وصحة الانسان. هنالک العدید من مصادر التلوث او ما یسمى بالملوثات والتی ربما تؤثر على متغیر PM10. کل هذه المتغیرات تصنف بیاناتها کغیر خطیة. اخذ بیانات الدراسة من محطة مناخیة فی مالیزیا. وتم استخدام نماذج الانحدار  الخطی المتعدد Multiple linear regression (MLR) کطریقة إحصائیة خطیة للتنبؤ بمتغیر PM10 من خلال تأثره  بمتغیرات الأرصاد الجویة المقابلة، لذلک فقد یعکس نتائج غیر دقیقة عند استخدامه مع مجموعات البیانات غیر الخطیة. ولتحسین نتائج التنبؤ تم استخدام الشبکات العصبیة المعاودةRecurrent neural network (RNN)  من  خلال توفیقه مع نموذج MLR ضمن الطریقة الهجینة MLR-RNN. وکذلک تم استخدام تحلیل المویجات Wavelet فی ترشیح النتائج المستحصلة من طریقة MLR-RNN للحصول على تحسین اکثر للنتائج التنبؤ من خلال ما یسمى الطریقة الهجینة  RNN-Wavelet واعتماداً على نموذج MLR. ان افضل نتائج التنبؤ بشکل عام کانت باستخدام الطریقة الهجینة  RNN-Wavelet.

Message Passing Applications: A Review

Haleema Solayman Essa Solayman; Ashraf Abdulmunim AL thanoon; Ghada Mohammad Aldabagh

IRAQI JOURNAL OF STATISTICAL SCIENCES, 2021, Volume 18, Issue 2, Pages 22-39
DOI: 10.33899/iqjoss.2021.169963

Message Passing Applications: A Re
 
It is known that message passing has become one of the most popular parallel programming paradigms because of its ease of use, so it was necessary to know or study the applications that were adopt message passing in their work.
Programming models are for the most part classified by how memory is utilized. In the shared memory model, each cycle gets to a shared location space, yet in the message passing model, an application runs as an assortment of self-ruling cycles, each with its own local memory. The principle preferences of setting up a message-passing standard are convey ability and convenience. In a circulated memory correspondence climate in which the more significant level schedules as well as reflections are based upon lower level message-passing schedules the benefits of normalization are especially evident. Moreover, the usage of a message passing, for example, that proposed here, gives sellers a plainly dined base arrangement of schedules that they can actualize in days of yore, or at times for which they can give equipment uphold, consequently improving adaptability.
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Air Pollution Forecasting using Hybrid MLR-RNN Method with Time-Stratified Method

Khetam Alzubaidy; Osama Hannon

IRAQI JOURNAL OF STATISTICAL SCIENCES, 2021, Volume 18, Issue 2, Pages 1-21
DOI: 10.33899/iqjoss.2021.169962

 
ان دراسة الجسیمات المعلقة (Particular matter (PM10)) والتکهن بها ضروری للتقلیل والسیطرة على الأضرار البیئیة وصحة الانسان. هنالک العدید من مصادر التلوث او ما یسمى بالملوثات والتی ربما تؤثر على متغیر PM10. کل هذه المتغیرات تصنف بیاناتها کغیر خطیة. تم اخذ بیانات الدراسة من محطة مناخیة فی مالیزیا. تم استخدام نماذج الانحدار  الخطی المتعدد Multiple linear regression (MLR) کطریقة إحصائیة خطیة للتنبؤ بمتغیر PM10 من خلال تأثره  بمتغیرات الأرصاد الجویة المقابلة، لذلک فقد یعکس نتائج غیر دقیقة عند استخدامه مع مجموعات البیانات غیر الخطیة. طریقة التراصف الزمنی فی أنماط مختلفة تم استخدامها لتحسین تلک النتائج وتحقیق التجانس ویتضمن مراصفة المواسم المتشابهة فی السنوات المختلفة سویة لتکوین متغیر جدید مختلف عن الاصلی. لتحسین نتائج التنبؤ تم اقتراح الشبکات العصبیة المعاودةRecurrent neural network (RNN)  لتستخدم بعد التوفیق مع نموذج MLR ضمن الطریقة الهجینة MLR-RNN. ان نتائج التنبؤ بشکل عام کانت الافضل باستخدام أسلوب التراصف الزمنی. وکذلک عکست النتائج افضلیة تنبؤات الطریقة الهجینة مقارنة مع نموذج MLR. وکاستنتاج فی هذه الدراسة فمن الممکن استخدام الشبکات العصبیة المعاودة وأسلوب التراصف الزمنی کاسالیب فعالة للحصول على افضل نتائج التنبؤ مع البیانات غیر الخطیة متعددة المتغیرات.