Rayana K. Antar
IT Specialist | rayanah@asi.sa
ABOUT
I’m IT Specialist & front-end developer with 15 years of professional experience.
I’m interested in all kinds of visual communication, but my major focus & interests lie at the intersection of mathematics,
computer science, optimization, machine learning, deep structured learning & federated learning . I also have skills in other
fields like Cloud computing or IoT.
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EDUCATION
Master of Computer Science (Artificial Intelligence)
UMM Al-Qura University
2 Years Course | Current GPA 3.94 out of 4
GRADUATING IN JUNE 2022
IN PROGRESS
Bachelors of Science in (Information Technology & computing)
Arab Open University
4 Years Course | GPA 3.68 out of 4 | with First Class Honours
FEBRUARY 2018
Bachelors of Science in (Management Information System)
Nixon University
3 Years Course | GPA 3.18 out of 4
JULY 2013
Secondary School Certificate (Scientific Field)
Government School
3 Years Course | GPA 91.71%.
NOVEMBER 2004
LANGUAGES
International English Language Testing System
British Council
Academic IELTS | Score 6.5
NOVEMBER 2019
Diploma in French language DELF A1
Alliance française d’Arabie Saoudite | Republic of France–ministry of education
6 Months Course
MARCH 2013
COURSES
AI Ready Program
By Zaka | Microsoft
March 22nd to June 26th, 2021
Azure AI Fundamentals
Microsoft Certified
Basics of Cybersecurity
UMM Al-Qura University
NOVEMBER 2020
Google Apps Script – المخصصة الوظائف
Google Technologies
JUNE 2020
Introduction to Koltlin Coroutines
Google Technologies
JUNE 2020
Google Analytics
Google Technologies
JUNE 2020
Fighting toxic comments in social media with deep learning
Google Technologies
JUNE 2020
How to build multi-platform AR applications for both Android and iOS
Google Technologies
JUNE 2020
Intoduction to open banking & Artificial Intelligence applications
Google Technologies
JUNE 2020
Understand & interpret machine learning model decisions
Google Technologies
JUNE 2020
Introduction to Natural Language Processing
Google Technologies
JUNE 2020
WORK
IT Specialist
Advanced Smart Integration Co.
Manage information technology and computer systems.
Plan, organize, control, and evaluate IT and electronic data operations.
Manage IT staff by recruiting, training, and coaching employees, communicating job expectations, and appraising their performance.
Design, develop, implement, and coordinate systems, policies, and procedures.
Act in alignment with user needs and system functionality to contribute the policy.
Identify problematic areas and implement strategic solutions in time.
Assessed employee performance and issued disciplinary notices.
Audit systems and assess their outcomes.
Preserve assets, information security and control structures.
Handle annual budget and ensure cost effectiveness.
FEB 2018 – CURRENT
Personal & HR Officer
Advanced Smart Integration Co.
Development of internal policies to Employees and Explained human resources policies and procedures to all employees.
Responsible for the transfer of sponsorship, Renewal of residences and medical insurance.
Preparing all the legal documents for custom clearance for importing goods.
Follow-up to the Petty Cash of staff and audited and check the presence and the departure of employees and their absence.
Scheduling employees and annual vacations and end of service.
Assessed employee performance and issued disciplinary notices.
Managed over [50] personnel files according to policy and federal and state law and regulations.
Generated employee tracking reports each month & completed payroll processing for more than 50 employees.
Issuing bank guarantee, LC to local and international suppliers.
Keep firm track of all incoming & outgoing letters & maintain correspondence files.
JULY 2011 – JAN 2018
Executive Secretary
Dr.Abdulrahman Bakhsh Hospital – Finance Dept
Responsible for maintaining inward & outward communication.
Processing letter to banks concerning letters of Guarantees, Letters of Credit & other confidential correspondence with various financial institutions.
Receiving vendor’s invoices & employee’s benefits papers & present these to finance manager & have these processed by concerned departmen.
Checks processed for employees & vendors to be logged in and sent to the management for different approval levels & to be signed.
Follow-up on receiving back processed checks & provide these to chief cashier for distribution/disposal.
Process letters to bank for salary transfers of loaners staff & Hospital Trainees staff.
Answer vendors & employees enquiries on their payment, keep firm track of all incoming & outgoing letters & maintain correspondence files Preparing all the legal documents for custom clearance for importing goods.
MAY 2007 – MAY 2011
Administrative Coordinator
Dr.Abdulrahman Bakhsh Hospital
Coordinator for staff nurses, Doctors, Ambulances, physiotherapy.
Accountant for the Patients.
Making claims (internal/external) for the Home Care Department.
MAY 2006 – April 2007
AWARDS
First class Honours
The Open University – UK
FEBRUARY 2018
High performance computing
Docker Inc. & Saudi Aramco & King Abdulaziz University
Containers: Portable, repeatable user-oriented application delivery. Build, deploy, run any app anywhere.
during thr Eighth International High Performance Computing Conference in Saudi Arabia (HPC Saudi 2018)
MARCH 2018
SKILLS
Javascript
HTML/CSS
Bootstrap
PHP
Python
Machine learning
RESEARCH & PUBLICATION
Paper Title: Heart Attack Prediction using Neural Network and Different Online Leraning Methods
Heart Failure represents a critical pathological case that is challenging to predict and discover at an early age, with a notable
increase in morbidity and mortality. Machine Learning and Neural Network techniques play a crucial role in predicting heart attacks,
diseases and more. These techniques give valuable perspectives for clinicians who may then adjust their diagnosis for eachindividual
patient. This paper evaluated neural network models for heart attacks predictions. Several online learning methods were investigated to
automatically and accurately predict heart attacks.The UCI dataset was used in this work to train and evaluate First Order and Second Order
Online Learning methods; namely Backpropagation, Delta bar Delta, Levenberg Marquardt and QuickProp learning methods. An optimizer technique
was also used to minimize the random noise in the database. A regularization concept was employed to further improve the generalization of the
model. Results show that a three layers’ NN model with a Backpropagation algorithm and Nadam optimizer achieved a promising accuracy for the
heart attach prediction tasks
Paper Title: Employees Attrition Analysis Based on Different Factors using Neural Networks and Various Machine Learning Techniques
Machine Learning (ML) has the ability to explore the algorithms that can learn from and make predictions on a given data. Organizations, due to their strategic demands, invest much time and effort in workforce hiring. When employee’s attrition, the companies not only lose a productive staff member but also the resources and funds, in particular, the efforts of HR staff by hiring and selecting certain staff members and training them for their related tasks is invested in them. This paper considers the prediction and analysis of employee attrition using Machine Learning models. Applying on IBM dataset, five main tests were conducted to predict employee attrition using classifiers such as k Neighbor (KNN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF) classifiers, and Multilayer Perceptron (MLP). The dataset was tested on random examples for unknown cases. Then, increasing the size of the minority class while decreasing the size of the majority class. For the MLP model, providing the details of the Neural Network (NN) model, mentioning the number of inputs, hidden, output layers, types of activation function and the optimizer technique. In terms of performance evaluation metrics, the proposed approach outperforms the aforementioned classifiers, according to the results of the experiment
Survey Paper: Skin Image Processing in Convolutional Neural Network Approach
Skin cancer is one of the significant contributors to the cause of death over the world. Melanoma is a well-known kind of skin cancer, which usually is the most malignant lesion compared to other skin lesion types. Classification and diagnosis of skin cancer in medical science field are challenging task due to the amount of data. Skin cancer images or dataset are usually coming in different format. Identification of data involves incredible efforts for preprocessing before the auto-diagnostic work. In this report, Deep Learning (Convolutional Neural Network) is represented to build a model for predicting new cases of skin cancer.
In this paper, reviewing several attempts to diagnose skin cancer cases using Deep Learning techniques, such as Convolutional Neural Network CNN for diagnosing melanoma lesions.
Basic clinical images were preprocessed to reduce image illumination, then images fed to Convolutional Neural Network models.In the end, the result of each study to differentiate between malignant and benign images in CNN models.
RESEARCH & PUBLICATION
Paper Title: Heart Attack Prediction using Neural Network and Different Online Leraning Methods
Heart Failure represents a critical pathological case that is challenging to predict and discover at an early age, with a notable
increase in morbidity and mortality. Machine Learning and Neural Network techniques play a crucial role in predicting heart attacks,
diseases and more. These techniques give valuable perspectives for clinicians who may then adjust their diagnosis for eachindividual
patient. This paper evaluated neural network models for heart attacks predictions. Several online learning methods were investigated to
automatically and accurately predict heart attacks.The UCI dataset was used in this work to train and evaluate First Order and Second Order
Online Learning methods; namely Backpropagation, Delta bar Delta, Levenberg Marquardt and QuickProp learning methods. An optimizer technique
was also used to minimize the random noise in the database. A regularization concept was employed to further improve the generalization of the
model. Results show that a three layers’ NN model with a Backpropagation algorithm and Nadam optimizer achieved a promising accuracy for the
heart attach prediction tasks
Paper Title: Employees Attrition Analysis Based on Different Factors using Neural Networks and Various Machine Learning Techniques
Machine Learning (ML) has the ability to explore the algorithms that can learn from and make predictions on a given data. Organizations, due to their strategic demands, invest much time and effort in workforce hiring. When employee’s attrition, the companies not only lose a productive staff member but also the resources and funds, in particular, the efforts of HR staff by hiring and selecting certain staff members and training them for their related tasks is invested in them. This paper considers the prediction and analysis of employee attrition using Machine Learning models. Applying on IBM dataset, five main tests were conducted to predict employee attrition using classifiers such as k Neighbor (KNN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF) classifiers, and Multilayer Perceptron (MLP). The dataset was tested on random examples for unknown cases. Then, increasing the size of the minority class while decreasing the size of the majority class. For the MLP model, providing the details of the Neural Network (NN) model, mentioning the number of inputs, hidden, output layers, types of activation function and the optimizer technique. In terms of performance evaluation metrics, the proposed approach outperforms the aforementioned classifiers, according to the results of the experiment
Survey Paper: Skin Image Processing in Convolutional Neural Network Approach
Skin cancer is one of the significant contributors to the cause of death over the world. Melanoma is a well-known kind of skin cancer, which usually is the most malignant lesion compared to other skin lesion types. Classification and diagnosis of skin cancer in medical science field are challenging task due to the amount of data. Skin cancer images or dataset are usually coming in different format. Identification of data involves incredible efforts for preprocessing before the auto-diagnostic work. In this report, Deep Learning (Convolutional Neural Network) is represented to build a model for predicting new cases of skin cancer.
In this paper, reviewing several attempts to diagnose skin cancer cases using Deep Learning techniques, such as Convolutional Neural Network CNN for diagnosing melanoma lesions.
Basic clinical images were preprocessed to reduce image illumination, then images fed to Convolutional Neural Network models.In the end, the result of each study to differentiate between malignant and benign images in CNN models.
CONTACT
Email
rayanah@asi.sa
Adress
7743, As Safa Dist
Jeddah 23454
Jeddah, Saudi Arabia.
Phone
+966 561307707
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