• م.م سرور فائق محمد
  • suroor faiq mohmmed
  • تدريسي : قسم هندسة تقنيات الاجهزة الطبية
  • Teaching : Department of Medical Instrumentation
  • ماجستير رياضيات
  • mathmetic
  • surour@esraa.edu.iq
  • serorfaeqmohammed@gmail.com
  • السيرة الذاتية
  • ORCId
  • FaceBook
  • 66

    • نشاطات التدريسي

    البحوث المنشورة

    المؤلفات

    المقررات المكلف بها

    المحاضرات

    مشاريع التخرج

    المقالات داخل المجلة

    المقررات المكلف بها

    المقررات المكلف بها

    المقررات المكلف بها - 2
    القسم المرحلة الفصل رمز المقرر الوحدات توصيف المقرر
    قسم هندسة تقنيات الاجهزة الطبية المرحلة الثانية فصل اول MAT1322 6 الرياضيات/II
    قسم هندسة تقنيات الاجهزة الطبية المرحلة الاولى فصل اول 4 الرياضيات
    المحاضرات الالكترونية

    المحاضرات الالكترونية

    المحاضرات الالكترونية - 3
    العام المقرر القسم المرحلة المحاضرة
    2024-2025 الرياضيات قسم هندسة تقنيات الاجهزة الطبية المرحلة الاولى Integrative mathematics
    2024-2025 الرياضيات قسم هندسة تقنيات الاجهزة الطبية المرحلة الاولى Differential mathematics
    2024-2025 الرياضيات/II قسم هندسة تقنيات الاجهزة الطبية المرحلة الثانية Fundamentals of mathematics in engineering
    البحوث

    البحوث

    2023 Ibn Al-Haitham Journal for Pure and Applied Sciences
    Abstract: Support vector machine (SVM) is a supervised learning model, it can be used for classification or regression depending on datasets. SVM is used to classify data points by determining the best hyperplane between two or more groups. Working with enormous datasets, on the other hand, might result in a variety of issues, including inefficient accuracy and time-consuming. In this regard, the present study is concerned with support vector machine (SVM) and its application. It is mainly comprised of two parts. In the first part, SVM is updated by applying some non-linear kernel transformations, which are: linear, polynomial, radial basis, and multi-layer kernels. The non-linear SVM classification model is illustrated and summarized in an algorithm using kernel tricks. The proposed method is examined using three simulation datasets with different sample sizes (50, 100, 200). A comparison between non-linear SVM and two standard classification methods is illustrated using various compared features. Our study has shown that the non-linear SVM method gives better results by checking: sensitivity, specificity, accuracy, and time-consuming. In the second part, the SVM is considered employment a stochastic gradient descent procedure. Using two collections of simulation datasets, the new approach, enhanced stochastic gradient descent SVM (ESGD-SVM), is tested. The proposed method is compared to other classification approaches like as logistic regression, naive model and k nearest neighbor. The results show that ESGD-SVM has very strong accuracy and is quite resilient. ESGD-SVM is used to analyze heart disease dataset which was downloaded from Harvard data verse.

    2023 AIP Conference Proceedings
    Abstract Support vector machine (SVM) is a supervised learning model, it can be used for classification or regression depending on datasets. SVM is used to classify data points by determining the best hyperplane between two or more groups. Working with enormous datasets, on the other hand, might result in a variety of issues, including inefficient accuracy and timeconsuming. In this regard, the present study is concerned with support vector machine (SVM) and its application. It is mainly comprised of two parts. In the first part, SVM is updated by applying some non-linear kernel transformations, which are: linear, polynomial, radial basis, and multilayer kernels. The non-linear SVM classification model is illustrated and summarized in an algorithm using kernel tricks. The proposed method is examined using three simulation datasets with different sample sizes (50, 100, 200). A comparison between non-linear SVM and two standard classification methods is illustrated using various compared features. Our study has shown that the non-linear SVM method gives better results by checking: sensitivity, specificity, accuracy, and time-consuming. In the second part, the SVM is considered employment a stochastic gradient descent procedure. Using two collections of simulation datasets, the new approach, enhanced stochastic gradient descent SVM (ESGD-SVM), is tested. The proposed method is compared to other classification approaches like as logistic regression, naive model and k nearest neighbor. The results show that ESGD-SVM has very strong accuracy and is quite resilient. ESGD-SVM is used to analyze heart disease dataset which was downloaded from Harvard data verse.