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$30 USD / hour
Flag of UNITED KINGDOM
surbiton, united kingdom
$30 USD / hour
It's currently 2:19 AM here
Joined April 25, 2014
10 Recommendations

Basma F.

@basmafares

 5.0 (40 reviews)
6.1
6.1
77%
77%
$30 USD / hour
Flag of UNITED KINGDOM
surbiton, united kingdom
$30 USD / hour
100%
Jobs Completed
100%
On Budget
100%
On Time
38%
Repeat Hire Rate

Senior SharePoint / .NET Full Stack Developer

As a SharePoint .NET Full Stack Developer, I am a highly skilled and experienced professional with expertise in Microsoft SharePoint versions 2010, 2013, 2016, 2019, and Office 365. I have a deep understanding of SharePoint architecture and a proven track record of successfully developing, customizing, and administering SharePoint solutions. With a strong foundation in .NET development, I am proficient in utilizing C# programming language and ASP.NET framework to create robust and scalable applications on the SharePoint platform. I have hands-on experience in SharePoint Essentials, Administration, Branding, Development, and Workflow, allowing me to deliver comprehensive solutions tailored to business requirements. In front-end development, I am adept at HTML, CSS, JavaScript, XML, XSLT, Bootstrap, AngularJS, and AJAX. These skills enable me to design and develop visually appealing and user-friendly interfaces for SharePoint sites and web applications. Furthermore, I possess expertise in database management with SQL, ensuring efficient data storage and retrieval within SharePoint solutions. I have a strong understanding of networking principles, which is supplemented by my CCNA R&S certification, ensuring reliable and secure SharePoint deployments. I am proficient in project management methodologies such as Prince2 and AGILE, allowing me to lead and coordinate development projects effectively. My strong communication, presentation, and team-building skills enable me to collaborate seamlessly with cross-functional teams, delivering projects within defined timelines and budgets. In summary, I bring a comprehensive skill set encompassing SharePoint development, .NET programming, front-end design, database management, networking, and project management. I am dedicated to delivering high-quality solutions and leveraging my expertise to drive innovation and success as a SharePoint .NET Full Stack Developer within an organization.
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Portfolio

11390311
This project focuses on developing a Smart Waste Management System by harnessing cutting-edge Machine Learning Techniques, specifically utilizing Deep Learning concepts and YOLOv8 algorithm to create custom algorithms for waste management tasks. The integration of these algorithms with a web application aims to build an efficient and user-friendly website. The project encompasses four essential algorithms: Trash Detection and Classification, Garbage Detection, Trash Flow and Status Monitoring, and Recycle Code Finder. The Trash Detection and Classification algorithm employs Deep Learning techniques to effectively identify and categorize various types of trash objects.
SWMS: Smart Waste Management System
This project focuses on developing a Smart Waste Management System by harnessing cutting-edge Machine Learning Techniques, specifically utilizing Deep Learning concepts and YOLOv8 algorithm to create custom algorithms for waste management tasks. The integration of these algorithms with a web application aims to build an efficient and user-friendly website. The project encompasses four essential algorithms: Trash Detection and Classification, Garbage Detection, Trash Flow and Status Monitoring, and Recycle Code Finder. The Trash Detection and Classification algorithm employs Deep Learning techniques to effectively identify and categorize various types of trash objects.
SWMS: Smart Waste Management System
This project focuses on developing a Smart Waste Management System by harnessing cutting-edge Machine Learning Techniques, specifically utilizing Deep Learning concepts and YOLOv8 algorithm to create custom algorithms for waste management tasks. The integration of these algorithms with a web application aims to build an efficient and user-friendly website. The project encompasses four essential algorithms: Trash Detection and Classification, Garbage Detection, Trash Flow and Status Monitoring, and Recycle Code Finder. The Trash Detection and Classification algorithm employs Deep Learning techniques to effectively identify and categorize various types of trash objects.
SWMS: Smart Waste Management System
This project focuses on developing a Smart Waste Management System by harnessing cutting-edge Machine Learning Techniques, specifically utilizing Deep Learning concepts and YOLOv8 algorithm to create custom algorithms for waste management tasks. The integration of these algorithms with a web application aims to build an efficient and user-friendly website. The project encompasses four essential algorithms: Trash Detection and Classification, Garbage Detection, Trash Flow and Status Monitoring, and Recycle Code Finder. The Trash Detection and Classification algorithm employs Deep Learning techniques to effectively identify and categorize various types of trash objects.
SWMS: Smart Waste Management System
This project focuses on developing a Smart Waste Management System by harnessing cutting-edge Machine Learning Techniques, specifically utilizing Deep Learning concepts and YOLOv8 algorithm to create custom algorithms for waste management tasks. The integration of these algorithms with a web application aims to build an efficient and user-friendly website. The project encompasses four essential algorithms: Trash Detection and Classification, Garbage Detection, Trash Flow and Status Monitoring, and Recycle Code Finder. The Trash Detection and Classification algorithm employs Deep Learning techniques to effectively identify and categorize various types of trash objects.
SWMS: Smart Waste Management System
This project focuses on developing a Smart Waste Management System by harnessing cutting-edge Machine Learning Techniques, specifically utilizing Deep Learning concepts and YOLOv8 algorithm to create custom algorithms for waste management tasks. The integration of these algorithms with a web application aims to build an efficient and user-friendly website. The project encompasses four essential algorithms: Trash Detection and Classification, Garbage Detection, Trash Flow and Status Monitoring, and Recycle Code Finder. The Trash Detection and Classification algorithm employs Deep Learning techniques to effectively identify and categorize various types of trash objects.
SWMS: Smart Waste Management System
This project focuses on developing a Smart Waste Management System by harnessing cutting-edge Machine Learning Techniques, specifically utilizing Deep Learning concepts and YOLOv8 algorithm to create custom algorithms for waste management tasks. The integration of these algorithms with a web application aims to build an efficient and user-friendly website. The project encompasses four essential algorithms: Trash Detection and Classification, Garbage Detection, Trash Flow and Status Monitoring, and Recycle Code Finder. The Trash Detection and Classification algorithm employs Deep Learning techniques to effectively identify and categorize various types of trash objects.
SWMS: Smart Waste Management System
This project focuses on developing a Smart Waste Management System by harnessing cutting-edge Machine Learning Techniques, specifically utilizing Deep Learning concepts and YOLOv8 algorithm to create custom algorithms for waste management tasks. The integration of these algorithms with a web application aims to build an efficient and user-friendly website. The project encompasses four essential algorithms: Trash Detection and Classification, Garbage Detection, Trash Flow and Status Monitoring, and Recycle Code Finder. The Trash Detection and Classification algorithm employs Deep Learning techniques to effectively identify and categorize various types of trash objects.
SWMS: Smart Waste Management System
This project focuses on developing a Smart Waste Management System by harnessing cutting-edge Machine Learning Techniques, specifically utilizing Deep Learning concepts and YOLOv8 algorithm to create custom algorithms for waste management tasks. The integration of these algorithms with a web application aims to build an efficient and user-friendly website. The project encompasses four essential algorithms: Trash Detection and Classification, Garbage Detection, Trash Flow and Status Monitoring, and Recycle Code Finder. The Trash Detection and Classification algorithm employs Deep Learning techniques to effectively identify and categorize various types of trash objects.
SWMS: Smart Waste Management System
This project focuses on developing a Smart Waste Management System by harnessing cutting-edge Machine Learning Techniques, specifically utilizing Deep Learning concepts and YOLOv8 algorithm to create custom algorithms for waste management tasks. The integration of these algorithms with a web application aims to build an efficient and user-friendly website. The project encompasses four essential algorithms: Trash Detection and Classification, Garbage Detection, Trash Flow and Status Monitoring, and Recycle Code Finder. The Trash Detection and Classification algorithm employs Deep Learning techniques to effectively identify and categorize various types of trash objects.
SWMS: Smart Waste Management System
This project focuses on developing a Smart Waste Management System by harnessing cutting-edge Machine Learning Techniques, specifically utilizing Deep Learning concepts and YOLOv8 algorithm to create custom algorithms for waste management tasks. The integration of these algorithms with a web application aims to build an efficient and user-friendly website. The project encompasses four essential algorithms: Trash Detection and Classification, Garbage Detection, Trash Flow and Status Monitoring, and Recycle Code Finder. The Trash Detection and Classification algorithm employs Deep Learning techniques to effectively identify and categorize various types of trash objects.
SWMS: Smart Waste Management System
This project focuses on developing a Smart Waste Management System by harnessing cutting-edge Machine Learning Techniques, specifically utilizing Deep Learning concepts and YOLOv8 algorithm to create custom algorithms for waste management tasks. The integration of these algorithms with a web application aims to build an efficient and user-friendly website. The project encompasses four essential algorithms: Trash Detection and Classification, Garbage Detection, Trash Flow and Status Monitoring, and Recycle Code Finder. The Trash Detection and Classification algorithm employs Deep Learning techniques to effectively identify and categorize various types of trash objects.
SWMS: Smart Waste Management System
This project focuses on developing a Smart Waste Management System by harnessing cutting-edge Machine Learning Techniques, specifically utilizing Deep Learning concepts and YOLOv8 algorithm to create custom algorithms for waste management tasks. The integration of these algorithms with a web application aims to build an efficient and user-friendly website. The project encompasses four essential algorithms: Trash Detection and Classification, Garbage Detection, Trash Flow and Status Monitoring, and Recycle Code Finder. The Trash Detection and Classification algorithm employs Deep Learning techniques to effectively identify and categorize various types of trash objects.
SWMS: Smart Waste Management System
This project focuses on developing a Smart Waste Management System by harnessing cutting-edge Machine Learning Techniques, specifically utilizing Deep Learning concepts and YOLOv8 algorithm to create custom algorithms for waste management tasks. The integration of these algorithms with a web application aims to build an efficient and user-friendly website. The project encompasses four essential algorithms: Trash Detection and Classification, Garbage Detection, Trash Flow and Status Monitoring, and Recycle Code Finder. The Trash Detection and Classification algorithm employs Deep Learning techniques to effectively identify and categorize various types of trash objects.
SWMS: Smart Waste Management System
This project focuses on developing a Smart Waste Management System by harnessing cutting-edge Machine Learning Techniques, specifically utilizing Deep Learning concepts and YOLOv8 algorithm to create custom algorithms for waste management tasks. The integration of these algorithms with a web application aims to build an efficient and user-friendly website. The project encompasses four essential algorithms: Trash Detection and Classification, Garbage Detection, Trash Flow and Status Monitoring, and Recycle Code Finder. The Trash Detection and Classification algorithm employs Deep Learning techniques to effectively identify and categorize various types of trash objects.
SWMS: Smart Waste Management System
This project focuses on developing a Smart Waste Management System by harnessing cutting-edge Machine Learning Techniques, specifically utilizing Deep Learning concepts and YOLOv8 algorithm to create custom algorithms for waste management tasks. The integration of these algorithms with a web application aims to build an efficient and user-friendly website. The project encompasses four essential algorithms: Trash Detection and Classification, Garbage Detection, Trash Flow and Status Monitoring, and Recycle Code Finder. The Trash Detection and Classification algorithm employs Deep Learning techniques to effectively identify and categorize various types of trash objects.
SWMS: Smart Waste Management System
This project focuses on developing a Smart Waste Management System by harnessing cutting-edge Machine Learning Techniques, specifically utilizing Deep Learning concepts and YOLOv8 algorithm to create custom algorithms for waste management tasks. The integration of these algorithms with a web application aims to build an efficient and user-friendly website. The project encompasses four essential algorithms: Trash Detection and Classification, Garbage Detection, Trash Flow and Status Monitoring, and Recycle Code Finder. The Trash Detection and Classification algorithm employs Deep Learning techniques to effectively identify and categorize various types of trash objects.
SWMS: Smart Waste Management System
This project focuses on developing a Smart Waste Management System by harnessing cutting-edge Machine Learning Techniques, specifically utilizing Deep Learning concepts and YOLOv8 algorithm to create custom algorithms for waste management tasks. The integration of these algorithms with a web application aims to build an efficient and user-friendly website. The project encompasses four essential algorithms: Trash Detection and Classification, Garbage Detection, Trash Flow and Status Monitoring, and Recycle Code Finder. The Trash Detection and Classification algorithm employs Deep Learning techniques to effectively identify and categorize various types of trash objects.
SWMS: Smart Waste Management System
This project focuses on developing a Smart Waste Management System by harnessing cutting-edge Machine Learning Techniques, specifically utilizing Deep Learning concepts and YOLOv8 algorithm to create custom algorithms for waste management tasks. The integration of these algorithms with a web application aims to build an efficient and user-friendly website. The project encompasses four essential algorithms: Trash Detection and Classification, Garbage Detection, Trash Flow and Status Monitoring, and Recycle Code Finder. The Trash Detection and Classification algorithm employs Deep Learning techniques to effectively identify and categorize various types of trash objects.
SWMS: Smart Waste Management System
This project focuses on developing a Smart Waste Management System by harnessing cutting-edge Machine Learning Techniques, specifically utilizing Deep Learning concepts and YOLOv8 algorithm to create custom algorithms for waste management tasks. The integration of these algorithms with a web application aims to build an efficient and user-friendly website. The project encompasses four essential algorithms: Trash Detection and Classification, Garbage Detection, Trash Flow and Status Monitoring, and Recycle Code Finder. The Trash Detection and Classification algorithm employs Deep Learning techniques to effectively identify and categorize various types of trash objects.
SWMS: Smart Waste Management System
This project focuses on developing a Smart Waste Management System by harnessing cutting-edge Machine Learning Techniques, specifically utilizing Deep Learning concepts and YOLOv8 algorithm to create custom algorithms for waste management tasks. The integration of these algorithms with a web application aims to build an efficient and user-friendly website. The project encompasses four essential algorithms: Trash Detection and Classification, Garbage Detection, Trash Flow and Status Monitoring, and Recycle Code Finder. The Trash Detection and Classification algorithm employs Deep Learning techniques to effectively identify and categorize various types of trash objects.
SWMS: Smart Waste Management System
This project focuses on developing a Smart Waste Management System by harnessing cutting-edge Machine Learning Techniques, specifically utilizing Deep Learning concepts and YOLOv8 algorithm to create custom algorithms for waste management tasks. The integration of these algorithms with a web application aims to build an efficient and user-friendly website. The project encompasses four essential algorithms: Trash Detection and Classification, Garbage Detection, Trash Flow and Status Monitoring, and Recycle Code Finder. The Trash Detection and Classification algorithm employs Deep Learning techniques to effectively identify and categorize various types of trash objects.
SWMS: Smart Waste Management System
This project focuses on developing a Smart Waste Management System by harnessing cutting-edge Machine Learning Techniques, specifically utilizing Deep Learning concepts and YOLOv8 algorithm to create custom algorithms for waste management tasks. The integration of these algorithms with a web application aims to build an efficient and user-friendly website. The project encompasses four essential algorithms: Trash Detection and Classification, Garbage Detection, Trash Flow and Status Monitoring, and Recycle Code Finder. The Trash Detection and Classification algorithm employs Deep Learning techniques to effectively identify and categorize various types of trash objects.
SWMS: Smart Waste Management System
This project focuses on developing a Smart Waste Management System by harnessing cutting-edge Machine Learning Techniques, specifically utilizing Deep Learning concepts and YOLOv8 algorithm to create custom algorithms for waste management tasks. The integration of these algorithms with a web application aims to build an efficient and user-friendly website. The project encompasses four essential algorithms: Trash Detection and Classification, Garbage Detection, Trash Flow and Status Monitoring, and Recycle Code Finder. The Trash Detection and Classification algorithm employs Deep Learning techniques to effectively identify and categorize various types of trash objects.
SWMS: Smart Waste Management System
This project focuses on developing a Smart Waste Management System by harnessing cutting-edge Machine Learning Techniques, specifically utilizing Deep Learning concepts and YOLOv8 algorithm to create custom algorithms for waste management tasks. The integration of these algorithms with a web application aims to build an efficient and user-friendly website. The project encompasses four essential algorithms: Trash Detection and Classification, Garbage Detection, Trash Flow and Status Monitoring, and Recycle Code Finder. The Trash Detection and Classification algorithm employs Deep Learning techniques to effectively identify and categorize various types of trash objects.
SWMS: Smart Waste Management System
This project focuses on developing a Smart Waste Management System by harnessing cutting-edge Machine Learning Techniques, specifically utilizing Deep Learning concepts and YOLOv8 algorithm to create custom algorithms for waste management tasks. The integration of these algorithms with a web application aims to build an efficient and user-friendly website. The project encompasses four essential algorithms: Trash Detection and Classification, Garbage Detection, Trash Flow and Status Monitoring, and Recycle Code Finder. The Trash Detection and Classification algorithm employs Deep Learning techniques to effectively identify and categorize various types of trash objects.
SWMS: Smart Waste Management System
This project focuses on developing a Smart Waste Management System by harnessing cutting-edge Machine Learning Techniques, specifically utilizing Deep Learning concepts and YOLOv8 algorithm to create custom algorithms for waste management tasks. The integration of these algorithms with a web application aims to build an efficient and user-friendly website. The project encompasses four essential algorithms: Trash Detection and Classification, Garbage Detection, Trash Flow and Status Monitoring, and Recycle Code Finder. The Trash Detection and Classification algorithm employs Deep Learning techniques to effectively identify and categorize various types of trash objects.
SWMS: Smart Waste Management System
This project focuses on developing a Smart Waste Management System by harnessing cutting-edge Machine Learning Techniques, specifically utilizing Deep Learning concepts and YOLOv8 algorithm to create custom algorithms for waste management tasks. The integration of these algorithms with a web application aims to build an efficient and user-friendly website. The project encompasses four essential algorithms: Trash Detection and Classification, Garbage Detection, Trash Flow and Status Monitoring, and Recycle Code Finder. The Trash Detection and Classification algorithm employs Deep Learning techniques to effectively identify and categorize various types of trash objects.
SWMS: Smart Waste Management System
This project focuses on developing a Smart Waste Management System by harnessing cutting-edge Machine Learning Techniques, specifically utilizing Deep Learning concepts and YOLOv8 algorithm to create custom algorithms for waste management tasks. The integration of these algorithms with a web application aims to build an efficient and user-friendly website. The project encompasses four essential algorithms: Trash Detection and Classification, Garbage Detection, Trash Flow and Status Monitoring, and Recycle Code Finder. The Trash Detection and Classification algorithm employs Deep Learning techniques to effectively identify and categorize various types of trash objects.
SWMS: Smart Waste Management System
This project focuses on developing a Smart Waste Management System by harnessing cutting-edge Machine Learning Techniques, specifically utilizing Deep Learning concepts and YOLOv8 algorithm to create custom algorithms for waste management tasks. The integration of these algorithms with a web application aims to build an efficient and user-friendly website. The project encompasses four essential algorithms: Trash Detection and Classification, Garbage Detection, Trash Flow and Status Monitoring, and Recycle Code Finder. The Trash Detection and Classification algorithm employs Deep Learning techniques to effectively identify and categorize various types of trash objects.
SWMS: Smart Waste Management System
This project focuses on developing a Smart Waste Management System by harnessing cutting-edge Machine Learning Techniques, specifically utilizing Deep Learning concepts and YOLOv8 algorithm to create custom algorithms for waste management tasks. The integration of these algorithms with a web application aims to build an efficient and user-friendly website. The project encompasses four essential algorithms: Trash Detection and Classification, Garbage Detection, Trash Flow and Status Monitoring, and Recycle Code Finder. The Trash Detection and Classification algorithm employs Deep Learning techniques to effectively identify and categorize various types of trash objects.
SWMS: Smart Waste Management System
11095745
The front end (Website View) is designed using HTML, CSS, JavaScript, jQuery, and Bootstrap 5 (fully responsive and user friendly) The backend (Website View) is developed using Microsoft Technologies ASP.NET (MVC) and .NET 6.0 The desktop Applications are developed using Microsoft Windows form Entity Framework (Database first) The code editor used is Microsoft’s Visual Studio 2022 Public View contains general information about ABC Bank Login View includes three Roles Customer, Employee, and Administrator Each role has a different view with different authorization
ABC Banking Web Application
The front end (Website View) is designed using HTML, CSS, JavaScript, jQuery, and Bootstrap 5 (fully responsive and user friendly) The backend (Website View) is developed using Microsoft Technologies ASP.NET (MVC) and .NET 6.0 The desktop Applications are developed using Microsoft Windows form Entity Framework (Database first) The code editor used is Microsoft’s Visual Studio 2022 Public View contains general information about ABC Bank Login View includes three Roles Customer, Employee, and Administrator Each role has a different view with different authorization
ABC Banking Web Application
The front end (Website View) is designed using HTML, CSS, JavaScript, jQuery, and Bootstrap 5 (fully responsive and user friendly) The backend (Website View) is developed using Microsoft Technologies ASP.NET (MVC) and .NET 6.0 The desktop Applications are developed using Microsoft Windows form Entity Framework (Database first) The code editor used is Microsoft’s Visual Studio 2022 Public View contains general information about ABC Bank Login View includes three Roles Customer, Employee, and Administrator Each role has a different view with different authorization
ABC Banking Web Application
The front end (Website View) is designed using HTML, CSS, JavaScript, jQuery, and Bootstrap 5 (fully responsive and user friendly) The backend (Website View) is developed using Microsoft Technologies ASP.NET (MVC) and .NET 6.0 The desktop Applications are developed using Microsoft Windows form Entity Framework (Database first) The code editor used is Microsoft’s Visual Studio 2022 Public View contains general information about ABC Bank Login View includes three Roles Customer, Employee, and Administrator Each role has a different view with different authorization
ABC Banking Web Application
The front end (Website View) is designed using HTML, CSS, JavaScript, jQuery, and Bootstrap 5 (fully responsive and user friendly) The backend (Website View) is developed using Microsoft Technologies ASP.NET (MVC) and .NET 6.0 The desktop Applications are developed using Microsoft Windows form Entity Framework (Database first) The code editor used is Microsoft’s Visual Studio 2022 Public View contains general information about ABC Bank Login View includes three Roles Customer, Employee, and Administrator Each role has a different view with different authorization
ABC Banking Web Application
The front end (Website View) is designed using HTML, CSS, JavaScript, jQuery, and Bootstrap 5 (fully responsive and user friendly) The backend (Website View) is developed using Microsoft Technologies ASP.NET (MVC) and .NET 6.0 The desktop Applications are developed using Microsoft Windows form Entity Framework (Database first) The code editor used is Microsoft’s Visual Studio 2022 Public View contains general information about ABC Bank Login View includes three Roles Customer, Employee, and Administrator Each role has a different view with different authorization
ABC Banking Web Application
The front end (Website View) is designed using HTML, CSS, JavaScript, jQuery, and Bootstrap 5 (fully responsive and user friendly) The backend (Website View) is developed using Microsoft Technologies ASP.NET (MVC) and .NET 6.0 The desktop Applications are developed using Microsoft Windows form Entity Framework (Database first) The code editor used is Microsoft’s Visual Studio 2022 Public View contains general information about ABC Bank Login View includes three Roles Customer, Employee, and Administrator Each role has a different view with different authorization
ABC Banking Web Application
The front end (Website View) is designed using HTML, CSS, JavaScript, jQuery, and Bootstrap 5 (fully responsive and user friendly) The backend (Website View) is developed using Microsoft Technologies ASP.NET (MVC) and .NET 6.0 The desktop Applications are developed using Microsoft Windows form Entity Framework (Database first) The code editor used is Microsoft’s Visual Studio 2022 Public View contains general information about ABC Bank Login View includes three Roles Customer, Employee, and Administrator Each role has a different view with different authorization
ABC Banking Web Application
11095741
ABC ATM is a desktop application designed using Microsoft windows form C#, SQL server database, and entity framework design pattern. The application helps the customer to do the following using a Card linked to a bank account:- 1- Balance Enquire 2- Fast Cash/ withdrawal from the bank account 3- Credit the bank account 4- Withdraw money with a limit of 8,000 GBP per transaction 5- Change the PIN of the card 6- Create a mini bank statement with the latest transactions 7- Print the mini bank statement as a PDF
ATM Cash Machine Desktop Application
ABC ATM is a desktop application designed using Microsoft windows form C#, SQL server database, and entity framework design pattern. The application helps the customer to do the following using a Card linked to a bank account:- 1- Balance Enquire 2- Fast Cash/ withdrawal from the bank account 3- Credit the bank account 4- Withdraw money with a limit of 8,000 GBP per transaction 5- Change the PIN of the card 6- Create a mini bank statement with the latest transactions 7- Print the mini bank statement as a PDF
ATM Cash Machine Desktop Application
ABC ATM is a desktop application designed using Microsoft windows form C#, SQL server database, and entity framework design pattern. The application helps the customer to do the following using a Card linked to a bank account:- 1- Balance Enquire 2- Fast Cash/ withdrawal from the bank account 3- Credit the bank account 4- Withdraw money with a limit of 8,000 GBP per transaction 5- Change the PIN of the card 6- Create a mini bank statement with the latest transactions 7- Print the mini bank statement as a PDF
ATM Cash Machine Desktop Application
ABC ATM is a desktop application designed using Microsoft windows form C#, SQL server database, and entity framework design pattern. The application helps the customer to do the following using a Card linked to a bank account:- 1- Balance Enquire 2- Fast Cash/ withdrawal from the bank account 3- Credit the bank account 4- Withdraw money with a limit of 8,000 GBP per transaction 5- Change the PIN of the card 6- Create a mini bank statement with the latest transactions 7- Print the mini bank statement as a PDF
ATM Cash Machine Desktop Application
ABC ATM is a desktop application designed using Microsoft windows form C#, SQL server database, and entity framework design pattern. The application helps the customer to do the following using a Card linked to a bank account:- 1- Balance Enquire 2- Fast Cash/ withdrawal from the bank account 3- Credit the bank account 4- Withdraw money with a limit of 8,000 GBP per transaction 5- Change the PIN of the card 6- Create a mini bank statement with the latest transactions 7- Print the mini bank statement as a PDF
ATM Cash Machine Desktop Application
ABC ATM is a desktop application designed using Microsoft windows form C#, SQL server database, and entity framework design pattern. The application helps the customer to do the following using a Card linked to a bank account:- 1- Balance Enquire 2- Fast Cash/ withdrawal from the bank account 3- Credit the bank account 4- Withdraw money with a limit of 8,000 GBP per transaction 5- Change the PIN of the card 6- Create a mini bank statement with the latest transactions 7- Print the mini bank statement as a PDF
ATM Cash Machine Desktop Application
ABC ATM is a desktop application designed using Microsoft windows form C#, SQL server database, and entity framework design pattern. The application helps the customer to do the following using a Card linked to a bank account:- 1- Balance Enquire 2- Fast Cash/ withdrawal from the bank account 3- Credit the bank account 4- Withdraw money with a limit of 8,000 GBP per transaction 5- Change the PIN of the card 6- Create a mini bank statement with the latest transactions 7- Print the mini bank statement as a PDF
ATM Cash Machine Desktop Application
ABC ATM is a desktop application designed using Microsoft windows form C#, SQL server database, and entity framework design pattern. The application helps the customer to do the following using a Card linked to a bank account:- 1- Balance Enquire 2- Fast Cash/ withdrawal from the bank account 3- Credit the bank account 4- Withdraw money with a limit of 8,000 GBP per transaction 5- Change the PIN of the card 6- Create a mini bank statement with the latest transactions 7- Print the mini bank statement as a PDF
ATM Cash Machine Desktop Application
11095719
The website Application is designed using MVC Architectural Style. It is like the 3-Tier Architecture, which contains a Model (Data), View (UI), and Controller (Logic). The UI sends updates to the Logic, the Logic updates the Data, and then the UI directly gets updates from the Data (triangular style) The front end (Website View) is designed using HTML, CSS, JavaScript, jQuery, and Bootstrap 5 (fully responsive and user-friendly) The backend (Website View) is developed using Microsoft Technologies ASP.NET (MVC) and .NET 6.0 Entity Framework (Database first) The code editor used is Microsoft’s Visual Studio 2022 Public View contains public information about TalentTrack Login View includes two Roles, Jobseeker and Employer; Each role has a different view with different authorization.
TalentTrack Job Platform
The website Application is designed using MVC Architectural Style. It is like the 3-Tier Architecture, which contains a Model (Data), View (UI), and Controller (Logic). The UI sends updates to the Logic, the Logic updates the Data, and then the UI directly gets updates from the Data (triangular style) The front end (Website View) is designed using HTML, CSS, JavaScript, jQuery, and Bootstrap 5 (fully responsive and user-friendly) The backend (Website View) is developed using Microsoft Technologies ASP.NET (MVC) and .NET 6.0 Entity Framework (Database first) The code editor used is Microsoft’s Visual Studio 2022 Public View contains public information about TalentTrack Login View includes two Roles, Jobseeker and Employer; Each role has a different view with different authorization.
TalentTrack Job Platform
The website Application is designed using MVC Architectural Style. It is like the 3-Tier Architecture, which contains a Model (Data), View (UI), and Controller (Logic). The UI sends updates to the Logic, the Logic updates the Data, and then the UI directly gets updates from the Data (triangular style) The front end (Website View) is designed using HTML, CSS, JavaScript, jQuery, and Bootstrap 5 (fully responsive and user-friendly) The backend (Website View) is developed using Microsoft Technologies ASP.NET (MVC) and .NET 6.0 Entity Framework (Database first) The code editor used is Microsoft’s Visual Studio 2022 Public View contains public information about TalentTrack Login View includes two Roles, Jobseeker and Employer; Each role has a different view with different authorization.
TalentTrack Job Platform
The website Application is designed using MVC Architectural Style. It is like the 3-Tier Architecture, which contains a Model (Data), View (UI), and Controller (Logic). The UI sends updates to the Logic, the Logic updates the Data, and then the UI directly gets updates from the Data (triangular style) The front end (Website View) is designed using HTML, CSS, JavaScript, jQuery, and Bootstrap 5 (fully responsive and user-friendly) The backend (Website View) is developed using Microsoft Technologies ASP.NET (MVC) and .NET 6.0 Entity Framework (Database first) The code editor used is Microsoft’s Visual Studio 2022 Public View contains public information about TalentTrack Login View includes two Roles, Jobseeker and Employer; Each role has a different view with different authorization.
TalentTrack Job Platform
The website Application is designed using MVC Architectural Style. It is like the 3-Tier Architecture, which contains a Model (Data), View (UI), and Controller (Logic). The UI sends updates to the Logic, the Logic updates the Data, and then the UI directly gets updates from the Data (triangular style) The front end (Website View) is designed using HTML, CSS, JavaScript, jQuery, and Bootstrap 5 (fully responsive and user-friendly) The backend (Website View) is developed using Microsoft Technologies ASP.NET (MVC) and .NET 6.0 Entity Framework (Database first) The code editor used is Microsoft’s Visual Studio 2022 Public View contains public information about TalentTrack Login View includes two Roles, Jobseeker and Employer; Each role has a different view with different authorization.
TalentTrack Job Platform
The website Application is designed using MVC Architectural Style. It is like the 3-Tier Architecture, which contains a Model (Data), View (UI), and Controller (Logic). The UI sends updates to the Logic, the Logic updates the Data, and then the UI directly gets updates from the Data (triangular style) The front end (Website View) is designed using HTML, CSS, JavaScript, jQuery, and Bootstrap 5 (fully responsive and user-friendly) The backend (Website View) is developed using Microsoft Technologies ASP.NET (MVC) and .NET 6.0 Entity Framework (Database first) The code editor used is Microsoft’s Visual Studio 2022 Public View contains public information about TalentTrack Login View includes two Roles, Jobseeker and Employer; Each role has a different view with different authorization.
TalentTrack Job Platform
The website Application is designed using MVC Architectural Style. It is like the 3-Tier Architecture, which contains a Model (Data), View (UI), and Controller (Logic). The UI sends updates to the Logic, the Logic updates the Data, and then the UI directly gets updates from the Data (triangular style) The front end (Website View) is designed using HTML, CSS, JavaScript, jQuery, and Bootstrap 5 (fully responsive and user-friendly) The backend (Website View) is developed using Microsoft Technologies ASP.NET (MVC) and .NET 6.0 Entity Framework (Database first) The code editor used is Microsoft’s Visual Studio 2022 Public View contains public information about TalentTrack Login View includes two Roles, Jobseeker and Employer; Each role has a different view with different authorization.
TalentTrack Job Platform
The website Application is designed using MVC Architectural Style. It is like the 3-Tier Architecture, which contains a Model (Data), View (UI), and Controller (Logic). The UI sends updates to the Logic, the Logic updates the Data, and then the UI directly gets updates from the Data (triangular style) The front end (Website View) is designed using HTML, CSS, JavaScript, jQuery, and Bootstrap 5 (fully responsive and user-friendly) The backend (Website View) is developed using Microsoft Technologies ASP.NET (MVC) and .NET 6.0 Entity Framework (Database first) The code editor used is Microsoft’s Visual Studio 2022 Public View contains public information about TalentTrack Login View includes two Roles, Jobseeker and Employer; Each role has a different view with different authorization.
TalentTrack Job Platform
The website Application is designed using MVC Architectural Style. It is like the 3-Tier Architecture, which contains a Model (Data), View (UI), and Controller (Logic). The UI sends updates to the Logic, the Logic updates the Data, and then the UI directly gets updates from the Data (triangular style) The front end (Website View) is designed using HTML, CSS, JavaScript, jQuery, and Bootstrap 5 (fully responsive and user-friendly) The backend (Website View) is developed using Microsoft Technologies ASP.NET (MVC) and .NET 6.0 Entity Framework (Database first) The code editor used is Microsoft’s Visual Studio 2022 Public View contains public information about TalentTrack Login View includes two Roles, Jobseeker and Employer; Each role has a different view with different authorization.
TalentTrack Job Platform
The website Application is designed using MVC Architectural Style. It is like the 3-Tier Architecture, which contains a Model (Data), View (UI), and Controller (Logic). The UI sends updates to the Logic, the Logic updates the Data, and then the UI directly gets updates from the Data (triangular style) The front end (Website View) is designed using HTML, CSS, JavaScript, jQuery, and Bootstrap 5 (fully responsive and user-friendly) The backend (Website View) is developed using Microsoft Technologies ASP.NET (MVC) and .NET 6.0 Entity Framework (Database first) The code editor used is Microsoft’s Visual Studio 2022 Public View contains public information about TalentTrack Login View includes two Roles, Jobseeker and Employer; Each role has a different view with different authorization.
TalentTrack Job Platform
The website Application is designed using MVC Architectural Style. It is like the 3-Tier Architecture, which contains a Model (Data), View (UI), and Controller (Logic). The UI sends updates to the Logic, the Logic updates the Data, and then the UI directly gets updates from the Data (triangular style) The front end (Website View) is designed using HTML, CSS, JavaScript, jQuery, and Bootstrap 5 (fully responsive and user-friendly) The backend (Website View) is developed using Microsoft Technologies ASP.NET (MVC) and .NET 6.0 Entity Framework (Database first) The code editor used is Microsoft’s Visual Studio 2022 Public View contains public information about TalentTrack Login View includes two Roles, Jobseeker and Employer; Each role has a different view with different authorization.
TalentTrack Job Platform
The website Application is designed using MVC Architectural Style. It is like the 3-Tier Architecture, which contains a Model (Data), View (UI), and Controller (Logic). The UI sends updates to the Logic, the Logic updates the Data, and then the UI directly gets updates from the Data (triangular style) The front end (Website View) is designed using HTML, CSS, JavaScript, jQuery, and Bootstrap 5 (fully responsive and user-friendly) The backend (Website View) is developed using Microsoft Technologies ASP.NET (MVC) and .NET 6.0 Entity Framework (Database first) The code editor used is Microsoft’s Visual Studio 2022 Public View contains public information about TalentTrack Login View includes two Roles, Jobseeker and Employer; Each role has a different view with different authorization.
TalentTrack Job Platform
The website Application is designed using MVC Architectural Style. It is like the 3-Tier Architecture, which contains a Model (Data), View (UI), and Controller (Logic). The UI sends updates to the Logic, the Logic updates the Data, and then the UI directly gets updates from the Data (triangular style) The front end (Website View) is designed using HTML, CSS, JavaScript, jQuery, and Bootstrap 5 (fully responsive and user-friendly) The backend (Website View) is developed using Microsoft Technologies ASP.NET (MVC) and .NET 6.0 Entity Framework (Database first) The code editor used is Microsoft’s Visual Studio 2022 Public View contains public information about TalentTrack Login View includes two Roles, Jobseeker and Employer; Each role has a different view with different authorization.
TalentTrack Job Platform
The website Application is designed using MVC Architectural Style. It is like the 3-Tier Architecture, which contains a Model (Data), View (UI), and Controller (Logic). The UI sends updates to the Logic, the Logic updates the Data, and then the UI directly gets updates from the Data (triangular style) The front end (Website View) is designed using HTML, CSS, JavaScript, jQuery, and Bootstrap 5 (fully responsive and user-friendly) The backend (Website View) is developed using Microsoft Technologies ASP.NET (MVC) and .NET 6.0 Entity Framework (Database first) The code editor used is Microsoft’s Visual Studio 2022 Public View contains public information about TalentTrack Login View includes two Roles, Jobseeker and Employer; Each role has a different view with different authorization.
TalentTrack Job Platform
The website Application is designed using MVC Architectural Style. It is like the 3-Tier Architecture, which contains a Model (Data), View (UI), and Controller (Logic). The UI sends updates to the Logic, the Logic updates the Data, and then the UI directly gets updates from the Data (triangular style) The front end (Website View) is designed using HTML, CSS, JavaScript, jQuery, and Bootstrap 5 (fully responsive and user-friendly) The backend (Website View) is developed using Microsoft Technologies ASP.NET (MVC) and .NET 6.0 Entity Framework (Database first) The code editor used is Microsoft’s Visual Studio 2022 Public View contains public information about TalentTrack Login View includes two Roles, Jobseeker and Employer; Each role has a different view with different authorization.
TalentTrack Job Platform
The website Application is designed using MVC Architectural Style. It is like the 3-Tier Architecture, which contains a Model (Data), View (UI), and Controller (Logic). The UI sends updates to the Logic, the Logic updates the Data, and then the UI directly gets updates from the Data (triangular style) The front end (Website View) is designed using HTML, CSS, JavaScript, jQuery, and Bootstrap 5 (fully responsive and user-friendly) The backend (Website View) is developed using Microsoft Technologies ASP.NET (MVC) and .NET 6.0 Entity Framework (Database first) The code editor used is Microsoft’s Visual Studio 2022 Public View contains public information about TalentTrack Login View includes two Roles, Jobseeker and Employer; Each role has a different view with different authorization.
TalentTrack Job Platform
The website Application is designed using MVC Architectural Style. It is like the 3-Tier Architecture, which contains a Model (Data), View (UI), and Controller (Logic). The UI sends updates to the Logic, the Logic updates the Data, and then the UI directly gets updates from the Data (triangular style) The front end (Website View) is designed using HTML, CSS, JavaScript, jQuery, and Bootstrap 5 (fully responsive and user-friendly) The backend (Website View) is developed using Microsoft Technologies ASP.NET (MVC) and .NET 6.0 Entity Framework (Database first) The code editor used is Microsoft’s Visual Studio 2022 Public View contains public information about TalentTrack Login View includes two Roles, Jobseeker and Employer; Each role has a different view with different authorization.
TalentTrack Job Platform
9419879
Value Architecture (VArch) is an integrated approach for finding the right balance between Physical and non-physical Functions of architectural components “5S” and Architectural resources raising its Values by an appropriate technology to meet today’s requirements, un-ignoring the future needs towards sustainable architectural buildings . Architectural Components “5S”: (Site – Space- Skin – Structure- Systems) Architectural : (Area- Time – Cost - Energy –Water– Machines – Manpower – Materials- Wastes – ventilation- lightening) Architectural Values: (Functional, Formational, Structural and technology Values). The main object of the proposed program “Value Architecture <VArch>” is : providing an integrated methodology and the mechanism of analyzing the project’s architectural values. Linking the physical and non-physical functions of the users and linking them with the consumed resources towards sustainable architectural buildings all over the world and especially in Egypt.
“Value Architecture ” Program
4462392
Contest Details ------------------------------ We have a Microsoft Word document that we need to have converted to a SharePoint form where our users can go and fill in the requested fields. This work will be done on our SharePoint Intranet site. Attached is the form we need to have created on our SharePoint site. NOTE: This job should be a quick turn around job for you, a total of 24 hours, if you can't do it that quick, please don't bid for the job.
Data Request Form (SharePoint 2016)
Contest Details ------------------------------ We have a Microsoft Word document that we need to have converted to a SharePoint form where our users can go and fill in the requested fields. This work will be done on our SharePoint Intranet site. Attached is the form we need to have created on our SharePoint site. NOTE: This job should be a quick turn around job for you, a total of 24 hours, if you can't do it that quick, please don't bid for the job.
Data Request Form (SharePoint 2016)
Contest Details ------------------------------ We have a Microsoft Word document that we need to have converted to a SharePoint form where our users can go and fill in the requested fields. This work will be done on our SharePoint Intranet site. Attached is the form we need to have created on our SharePoint site. NOTE: This job should be a quick turn around job for you, a total of 24 hours, if you can't do it that quick, please don't bid for the job.
Data Request Form (SharePoint 2016)
Contest Details ------------------------------ We have a Microsoft Word document that we need to have converted to a SharePoint form where our users can go and fill in the requested fields. This work will be done on our SharePoint Intranet site. Attached is the form we need to have created on our SharePoint site. NOTE: This job should be a quick turn around job for you, a total of 24 hours, if you can't do it that quick, please don't bid for the job.
Data Request Form (SharePoint 2016)
Contest Details ------------------------------ We have a Microsoft Word document that we need to have converted to a SharePoint form where our users can go and fill in the requested fields. This work will be done on our SharePoint Intranet site. Attached is the form we need to have created on our SharePoint site. NOTE: This job should be a quick turn around job for you, a total of 24 hours, if you can't do it that quick, please don't bid for the job.
Data Request Form (SharePoint 2016)
11390311
This project focuses on developing a Smart Waste Management System by harnessing cutting-edge Machine Learning Techniques, specifically utilizing Deep Learning concepts and YOLOv8 algorithm to create custom algorithms for waste management tasks. The integration of these algorithms with a web application aims to build an efficient and user-friendly website. The project encompasses four essential algorithms: Trash Detection and Classification, Garbage Detection, Trash Flow and Status Monitoring, and Recycle Code Finder. The Trash Detection and Classification algorithm employs Deep Learning techniques to effectively identify and categorize various types of trash objects.
SWMS: Smart Waste Management System
This project focuses on developing a Smart Waste Management System by harnessing cutting-edge Machine Learning Techniques, specifically utilizing Deep Learning concepts and YOLOv8 algorithm to create custom algorithms for waste management tasks. The integration of these algorithms with a web application aims to build an efficient and user-friendly website. The project encompasses four essential algorithms: Trash Detection and Classification, Garbage Detection, Trash Flow and Status Monitoring, and Recycle Code Finder. The Trash Detection and Classification algorithm employs Deep Learning techniques to effectively identify and categorize various types of trash objects.
SWMS: Smart Waste Management System
This project focuses on developing a Smart Waste Management System by harnessing cutting-edge Machine Learning Techniques, specifically utilizing Deep Learning concepts and YOLOv8 algorithm to create custom algorithms for waste management tasks. The integration of these algorithms with a web application aims to build an efficient and user-friendly website. The project encompasses four essential algorithms: Trash Detection and Classification, Garbage Detection, Trash Flow and Status Monitoring, and Recycle Code Finder. The Trash Detection and Classification algorithm employs Deep Learning techniques to effectively identify and categorize various types of trash objects.
SWMS: Smart Waste Management System
This project focuses on developing a Smart Waste Management System by harnessing cutting-edge Machine Learning Techniques, specifically utilizing Deep Learning concepts and YOLOv8 algorithm to create custom algorithms for waste management tasks. The integration of these algorithms with a web application aims to build an efficient and user-friendly website. The project encompasses four essential algorithms: Trash Detection and Classification, Garbage Detection, Trash Flow and Status Monitoring, and Recycle Code Finder. The Trash Detection and Classification algorithm employs Deep Learning techniques to effectively identify and categorize various types of trash objects.
SWMS: Smart Waste Management System
This project focuses on developing a Smart Waste Management System by harnessing cutting-edge Machine Learning Techniques, specifically utilizing Deep Learning concepts and YOLOv8 algorithm to create custom algorithms for waste management tasks. The integration of these algorithms with a web application aims to build an efficient and user-friendly website. The project encompasses four essential algorithms: Trash Detection and Classification, Garbage Detection, Trash Flow and Status Monitoring, and Recycle Code Finder. The Trash Detection and Classification algorithm employs Deep Learning techniques to effectively identify and categorize various types of trash objects.
SWMS: Smart Waste Management System
This project focuses on developing a Smart Waste Management System by harnessing cutting-edge Machine Learning Techniques, specifically utilizing Deep Learning concepts and YOLOv8 algorithm to create custom algorithms for waste management tasks. The integration of these algorithms with a web application aims to build an efficient and user-friendly website. The project encompasses four essential algorithms: Trash Detection and Classification, Garbage Detection, Trash Flow and Status Monitoring, and Recycle Code Finder. The Trash Detection and Classification algorithm employs Deep Learning techniques to effectively identify and categorize various types of trash objects.
SWMS: Smart Waste Management System
This project focuses on developing a Smart Waste Management System by harnessing cutting-edge Machine Learning Techniques, specifically utilizing Deep Learning concepts and YOLOv8 algorithm to create custom algorithms for waste management tasks. The integration of these algorithms with a web application aims to build an efficient and user-friendly website. The project encompasses four essential algorithms: Trash Detection and Classification, Garbage Detection, Trash Flow and Status Monitoring, and Recycle Code Finder. The Trash Detection and Classification algorithm employs Deep Learning techniques to effectively identify and categorize various types of trash objects.
SWMS: Smart Waste Management System
This project focuses on developing a Smart Waste Management System by harnessing cutting-edge Machine Learning Techniques, specifically utilizing Deep Learning concepts and YOLOv8 algorithm to create custom algorithms for waste management tasks. The integration of these algorithms with a web application aims to build an efficient and user-friendly website. The project encompasses four essential algorithms: Trash Detection and Classification, Garbage Detection, Trash Flow and Status Monitoring, and Recycle Code Finder. The Trash Detection and Classification algorithm employs Deep Learning techniques to effectively identify and categorize various types of trash objects.
SWMS: Smart Waste Management System
This project focuses on developing a Smart Waste Management System by harnessing cutting-edge Machine Learning Techniques, specifically utilizing Deep Learning concepts and YOLOv8 algorithm to create custom algorithms for waste management tasks. The integration of these algorithms with a web application aims to build an efficient and user-friendly website. The project encompasses four essential algorithms: Trash Detection and Classification, Garbage Detection, Trash Flow and Status Monitoring, and Recycle Code Finder. The Trash Detection and Classification algorithm employs Deep Learning techniques to effectively identify and categorize various types of trash objects.
SWMS: Smart Waste Management System
This project focuses on developing a Smart Waste Management System by harnessing cutting-edge Machine Learning Techniques, specifically utilizing Deep Learning concepts and YOLOv8 algorithm to create custom algorithms for waste management tasks. The integration of these algorithms with a web application aims to build an efficient and user-friendly website. The project encompasses four essential algorithms: Trash Detection and Classification, Garbage Detection, Trash Flow and Status Monitoring, and Recycle Code Finder. The Trash Detection and Classification algorithm employs Deep Learning techniques to effectively identify and categorize various types of trash objects.
SWMS: Smart Waste Management System
This project focuses on developing a Smart Waste Management System by harnessing cutting-edge Machine Learning Techniques, specifically utilizing Deep Learning concepts and YOLOv8 algorithm to create custom algorithms for waste management tasks. The integration of these algorithms with a web application aims to build an efficient and user-friendly website. The project encompasses four essential algorithms: Trash Detection and Classification, Garbage Detection, Trash Flow and Status Monitoring, and Recycle Code Finder. The Trash Detection and Classification algorithm employs Deep Learning techniques to effectively identify and categorize various types of trash objects.
SWMS: Smart Waste Management System
This project focuses on developing a Smart Waste Management System by harnessing cutting-edge Machine Learning Techniques, specifically utilizing Deep Learning concepts and YOLOv8 algorithm to create custom algorithms for waste management tasks. The integration of these algorithms with a web application aims to build an efficient and user-friendly website. The project encompasses four essential algorithms: Trash Detection and Classification, Garbage Detection, Trash Flow and Status Monitoring, and Recycle Code Finder. The Trash Detection and Classification algorithm employs Deep Learning techniques to effectively identify and categorize various types of trash objects.
SWMS: Smart Waste Management System
This project focuses on developing a Smart Waste Management System by harnessing cutting-edge Machine Learning Techniques, specifically utilizing Deep Learning concepts and YOLOv8 algorithm to create custom algorithms for waste management tasks. The integration of these algorithms with a web application aims to build an efficient and user-friendly website. The project encompasses four essential algorithms: Trash Detection and Classification, Garbage Detection, Trash Flow and Status Monitoring, and Recycle Code Finder. The Trash Detection and Classification algorithm employs Deep Learning techniques to effectively identify and categorize various types of trash objects.
SWMS: Smart Waste Management System
This project focuses on developing a Smart Waste Management System by harnessing cutting-edge Machine Learning Techniques, specifically utilizing Deep Learning concepts and YOLOv8 algorithm to create custom algorithms for waste management tasks. The integration of these algorithms with a web application aims to build an efficient and user-friendly website. The project encompasses four essential algorithms: Trash Detection and Classification, Garbage Detection, Trash Flow and Status Monitoring, and Recycle Code Finder. The Trash Detection and Classification algorithm employs Deep Learning techniques to effectively identify and categorize various types of trash objects.
SWMS: Smart Waste Management System
This project focuses on developing a Smart Waste Management System by harnessing cutting-edge Machine Learning Techniques, specifically utilizing Deep Learning concepts and YOLOv8 algorithm to create custom algorithms for waste management tasks. The integration of these algorithms with a web application aims to build an efficient and user-friendly website. The project encompasses four essential algorithms: Trash Detection and Classification, Garbage Detection, Trash Flow and Status Monitoring, and Recycle Code Finder. The Trash Detection and Classification algorithm employs Deep Learning techniques to effectively identify and categorize various types of trash objects.
SWMS: Smart Waste Management System
This project focuses on developing a Smart Waste Management System by harnessing cutting-edge Machine Learning Techniques, specifically utilizing Deep Learning concepts and YOLOv8 algorithm to create custom algorithms for waste management tasks. The integration of these algorithms with a web application aims to build an efficient and user-friendly website. The project encompasses four essential algorithms: Trash Detection and Classification, Garbage Detection, Trash Flow and Status Monitoring, and Recycle Code Finder. The Trash Detection and Classification algorithm employs Deep Learning techniques to effectively identify and categorize various types of trash objects.
SWMS: Smart Waste Management System
This project focuses on developing a Smart Waste Management System by harnessing cutting-edge Machine Learning Techniques, specifically utilizing Deep Learning concepts and YOLOv8 algorithm to create custom algorithms for waste management tasks. The integration of these algorithms with a web application aims to build an efficient and user-friendly website. The project encompasses four essential algorithms: Trash Detection and Classification, Garbage Detection, Trash Flow and Status Monitoring, and Recycle Code Finder. The Trash Detection and Classification algorithm employs Deep Learning techniques to effectively identify and categorize various types of trash objects.
SWMS: Smart Waste Management System
This project focuses on developing a Smart Waste Management System by harnessing cutting-edge Machine Learning Techniques, specifically utilizing Deep Learning concepts and YOLOv8 algorithm to create custom algorithms for waste management tasks. The integration of these algorithms with a web application aims to build an efficient and user-friendly website. The project encompasses four essential algorithms: Trash Detection and Classification, Garbage Detection, Trash Flow and Status Monitoring, and Recycle Code Finder. The Trash Detection and Classification algorithm employs Deep Learning techniques to effectively identify and categorize various types of trash objects.
SWMS: Smart Waste Management System
This project focuses on developing a Smart Waste Management System by harnessing cutting-edge Machine Learning Techniques, specifically utilizing Deep Learning concepts and YOLOv8 algorithm to create custom algorithms for waste management tasks. The integration of these algorithms with a web application aims to build an efficient and user-friendly website. The project encompasses four essential algorithms: Trash Detection and Classification, Garbage Detection, Trash Flow and Status Monitoring, and Recycle Code Finder. The Trash Detection and Classification algorithm employs Deep Learning techniques to effectively identify and categorize various types of trash objects.
SWMS: Smart Waste Management System
This project focuses on developing a Smart Waste Management System by harnessing cutting-edge Machine Learning Techniques, specifically utilizing Deep Learning concepts and YOLOv8 algorithm to create custom algorithms for waste management tasks. The integration of these algorithms with a web application aims to build an efficient and user-friendly website. The project encompasses four essential algorithms: Trash Detection and Classification, Garbage Detection, Trash Flow and Status Monitoring, and Recycle Code Finder. The Trash Detection and Classification algorithm employs Deep Learning techniques to effectively identify and categorize various types of trash objects.
SWMS: Smart Waste Management System
This project focuses on developing a Smart Waste Management System by harnessing cutting-edge Machine Learning Techniques, specifically utilizing Deep Learning concepts and YOLOv8 algorithm to create custom algorithms for waste management tasks. The integration of these algorithms with a web application aims to build an efficient and user-friendly website. The project encompasses four essential algorithms: Trash Detection and Classification, Garbage Detection, Trash Flow and Status Monitoring, and Recycle Code Finder. The Trash Detection and Classification algorithm employs Deep Learning techniques to effectively identify and categorize various types of trash objects.
SWMS: Smart Waste Management System
This project focuses on developing a Smart Waste Management System by harnessing cutting-edge Machine Learning Techniques, specifically utilizing Deep Learning concepts and YOLOv8 algorithm to create custom algorithms for waste management tasks. The integration of these algorithms with a web application aims to build an efficient and user-friendly website. The project encompasses four essential algorithms: Trash Detection and Classification, Garbage Detection, Trash Flow and Status Monitoring, and Recycle Code Finder. The Trash Detection and Classification algorithm employs Deep Learning techniques to effectively identify and categorize various types of trash objects.
SWMS: Smart Waste Management System
This project focuses on developing a Smart Waste Management System by harnessing cutting-edge Machine Learning Techniques, specifically utilizing Deep Learning concepts and YOLOv8 algorithm to create custom algorithms for waste management tasks. The integration of these algorithms with a web application aims to build an efficient and user-friendly website. The project encompasses four essential algorithms: Trash Detection and Classification, Garbage Detection, Trash Flow and Status Monitoring, and Recycle Code Finder. The Trash Detection and Classification algorithm employs Deep Learning techniques to effectively identify and categorize various types of trash objects.
SWMS: Smart Waste Management System
This project focuses on developing a Smart Waste Management System by harnessing cutting-edge Machine Learning Techniques, specifically utilizing Deep Learning concepts and YOLOv8 algorithm to create custom algorithms for waste management tasks. The integration of these algorithms with a web application aims to build an efficient and user-friendly website. The project encompasses four essential algorithms: Trash Detection and Classification, Garbage Detection, Trash Flow and Status Monitoring, and Recycle Code Finder. The Trash Detection and Classification algorithm employs Deep Learning techniques to effectively identify and categorize various types of trash objects.
SWMS: Smart Waste Management System
This project focuses on developing a Smart Waste Management System by harnessing cutting-edge Machine Learning Techniques, specifically utilizing Deep Learning concepts and YOLOv8 algorithm to create custom algorithms for waste management tasks. The integration of these algorithms with a web application aims to build an efficient and user-friendly website. The project encompasses four essential algorithms: Trash Detection and Classification, Garbage Detection, Trash Flow and Status Monitoring, and Recycle Code Finder. The Trash Detection and Classification algorithm employs Deep Learning techniques to effectively identify and categorize various types of trash objects.
SWMS: Smart Waste Management System
This project focuses on developing a Smart Waste Management System by harnessing cutting-edge Machine Learning Techniques, specifically utilizing Deep Learning concepts and YOLOv8 algorithm to create custom algorithms for waste management tasks. The integration of these algorithms with a web application aims to build an efficient and user-friendly website. The project encompasses four essential algorithms: Trash Detection and Classification, Garbage Detection, Trash Flow and Status Monitoring, and Recycle Code Finder. The Trash Detection and Classification algorithm employs Deep Learning techniques to effectively identify and categorize various types of trash objects.
SWMS: Smart Waste Management System
This project focuses on developing a Smart Waste Management System by harnessing cutting-edge Machine Learning Techniques, specifically utilizing Deep Learning concepts and YOLOv8 algorithm to create custom algorithms for waste management tasks. The integration of these algorithms with a web application aims to build an efficient and user-friendly website. The project encompasses four essential algorithms: Trash Detection and Classification, Garbage Detection, Trash Flow and Status Monitoring, and Recycle Code Finder. The Trash Detection and Classification algorithm employs Deep Learning techniques to effectively identify and categorize various types of trash objects.
SWMS: Smart Waste Management System
This project focuses on developing a Smart Waste Management System by harnessing cutting-edge Machine Learning Techniques, specifically utilizing Deep Learning concepts and YOLOv8 algorithm to create custom algorithms for waste management tasks. The integration of these algorithms with a web application aims to build an efficient and user-friendly website. The project encompasses four essential algorithms: Trash Detection and Classification, Garbage Detection, Trash Flow and Status Monitoring, and Recycle Code Finder. The Trash Detection and Classification algorithm employs Deep Learning techniques to effectively identify and categorize various types of trash objects.
SWMS: Smart Waste Management System
This project focuses on developing a Smart Waste Management System by harnessing cutting-edge Machine Learning Techniques, specifically utilizing Deep Learning concepts and YOLOv8 algorithm to create custom algorithms for waste management tasks. The integration of these algorithms with a web application aims to build an efficient and user-friendly website. The project encompasses four essential algorithms: Trash Detection and Classification, Garbage Detection, Trash Flow and Status Monitoring, and Recycle Code Finder. The Trash Detection and Classification algorithm employs Deep Learning techniques to effectively identify and categorize various types of trash objects.
SWMS: Smart Waste Management System
This project focuses on developing a Smart Waste Management System by harnessing cutting-edge Machine Learning Techniques, specifically utilizing Deep Learning concepts and YOLOv8 algorithm to create custom algorithms for waste management tasks. The integration of these algorithms with a web application aims to build an efficient and user-friendly website. The project encompasses four essential algorithms: Trash Detection and Classification, Garbage Detection, Trash Flow and Status Monitoring, and Recycle Code Finder. The Trash Detection and Classification algorithm employs Deep Learning techniques to effectively identify and categorize various types of trash objects.
SWMS: Smart Waste Management System
This project focuses on developing a Smart Waste Management System by harnessing cutting-edge Machine Learning Techniques, specifically utilizing Deep Learning concepts and YOLOv8 algorithm to create custom algorithms for waste management tasks. The integration of these algorithms with a web application aims to build an efficient and user-friendly website. The project encompasses four essential algorithms: Trash Detection and Classification, Garbage Detection, Trash Flow and Status Monitoring, and Recycle Code Finder. The Trash Detection and Classification algorithm employs Deep Learning techniques to effectively identify and categorize various types of trash objects.
SWMS: Smart Waste Management System
11095745
The front end (Website View) is designed using HTML, CSS, JavaScript, jQuery, and Bootstrap 5 (fully responsive and user friendly) The backend (Website View) is developed using Microsoft Technologies ASP.NET (MVC) and .NET 6.0 The desktop Applications are developed using Microsoft Windows form Entity Framework (Database first) The code editor used is Microsoft’s Visual Studio 2022 Public View contains general information about ABC Bank Login View includes three Roles Customer, Employee, and Administrator Each role has a different view with different authorization
ABC Banking Web Application
The front end (Website View) is designed using HTML, CSS, JavaScript, jQuery, and Bootstrap 5 (fully responsive and user friendly) The backend (Website View) is developed using Microsoft Technologies ASP.NET (MVC) and .NET 6.0 The desktop Applications are developed using Microsoft Windows form Entity Framework (Database first) The code editor used is Microsoft’s Visual Studio 2022 Public View contains general information about ABC Bank Login View includes three Roles Customer, Employee, and Administrator Each role has a different view with different authorization
ABC Banking Web Application
The front end (Website View) is designed using HTML, CSS, JavaScript, jQuery, and Bootstrap 5 (fully responsive and user friendly) The backend (Website View) is developed using Microsoft Technologies ASP.NET (MVC) and .NET 6.0 The desktop Applications are developed using Microsoft Windows form Entity Framework (Database first) The code editor used is Microsoft’s Visual Studio 2022 Public View contains general information about ABC Bank Login View includes three Roles Customer, Employee, and Administrator Each role has a different view with different authorization
ABC Banking Web Application
The front end (Website View) is designed using HTML, CSS, JavaScript, jQuery, and Bootstrap 5 (fully responsive and user friendly) The backend (Website View) is developed using Microsoft Technologies ASP.NET (MVC) and .NET 6.0 The desktop Applications are developed using Microsoft Windows form Entity Framework (Database first) The code editor used is Microsoft’s Visual Studio 2022 Public View contains general information about ABC Bank Login View includes three Roles Customer, Employee, and Administrator Each role has a different view with different authorization
ABC Banking Web Application
The front end (Website View) is designed using HTML, CSS, JavaScript, jQuery, and Bootstrap 5 (fully responsive and user friendly) The backend (Website View) is developed using Microsoft Technologies ASP.NET (MVC) and .NET 6.0 The desktop Applications are developed using Microsoft Windows form Entity Framework (Database first) The code editor used is Microsoft’s Visual Studio 2022 Public View contains general information about ABC Bank Login View includes three Roles Customer, Employee, and Administrator Each role has a different view with different authorization
ABC Banking Web Application
The front end (Website View) is designed using HTML, CSS, JavaScript, jQuery, and Bootstrap 5 (fully responsive and user friendly) The backend (Website View) is developed using Microsoft Technologies ASP.NET (MVC) and .NET 6.0 The desktop Applications are developed using Microsoft Windows form Entity Framework (Database first) The code editor used is Microsoft’s Visual Studio 2022 Public View contains general information about ABC Bank Login View includes three Roles Customer, Employee, and Administrator Each role has a different view with different authorization
ABC Banking Web Application
The front end (Website View) is designed using HTML, CSS, JavaScript, jQuery, and Bootstrap 5 (fully responsive and user friendly) The backend (Website View) is developed using Microsoft Technologies ASP.NET (MVC) and .NET 6.0 The desktop Applications are developed using Microsoft Windows form Entity Framework (Database first) The code editor used is Microsoft’s Visual Studio 2022 Public View contains general information about ABC Bank Login View includes three Roles Customer, Employee, and Administrator Each role has a different view with different authorization
ABC Banking Web Application
The front end (Website View) is designed using HTML, CSS, JavaScript, jQuery, and Bootstrap 5 (fully responsive and user friendly) The backend (Website View) is developed using Microsoft Technologies ASP.NET (MVC) and .NET 6.0 The desktop Applications are developed using Microsoft Windows form Entity Framework (Database first) The code editor used is Microsoft’s Visual Studio 2022 Public View contains general information about ABC Bank Login View includes three Roles Customer, Employee, and Administrator Each role has a different view with different authorization
ABC Banking Web Application
11095741
ABC ATM is a desktop application designed using Microsoft windows form C#, SQL server database, and entity framework design pattern. The application helps the customer to do the following using a Card linked to a bank account:- 1- Balance Enquire 2- Fast Cash/ withdrawal from the bank account 3- Credit the bank account 4- Withdraw money with a limit of 8,000 GBP per transaction 5- Change the PIN of the card 6- Create a mini bank statement with the latest transactions 7- Print the mini bank statement as a PDF
ATM Cash Machine Desktop Application
ABC ATM is a desktop application designed using Microsoft windows form C#, SQL server database, and entity framework design pattern. The application helps the customer to do the following using a Card linked to a bank account:- 1- Balance Enquire 2- Fast Cash/ withdrawal from the bank account 3- Credit the bank account 4- Withdraw money with a limit of 8,000 GBP per transaction 5- Change the PIN of the card 6- Create a mini bank statement with the latest transactions 7- Print the mini bank statement as a PDF
ATM Cash Machine Desktop Application
ABC ATM is a desktop application designed using Microsoft windows form C#, SQL server database, and entity framework design pattern. The application helps the customer to do the following using a Card linked to a bank account:- 1- Balance Enquire 2- Fast Cash/ withdrawal from the bank account 3- Credit the bank account 4- Withdraw money with a limit of 8,000 GBP per transaction 5- Change the PIN of the card 6- Create a mini bank statement with the latest transactions 7- Print the mini bank statement as a PDF
ATM Cash Machine Desktop Application
ABC ATM is a desktop application designed using Microsoft windows form C#, SQL server database, and entity framework design pattern. The application helps the customer to do the following using a Card linked to a bank account:- 1- Balance Enquire 2- Fast Cash/ withdrawal from the bank account 3- Credit the bank account 4- Withdraw money with a limit of 8,000 GBP per transaction 5- Change the PIN of the card 6- Create a mini bank statement with the latest transactions 7- Print the mini bank statement as a PDF
ATM Cash Machine Desktop Application
ABC ATM is a desktop application designed using Microsoft windows form C#, SQL server database, and entity framework design pattern. The application helps the customer to do the following using a Card linked to a bank account:- 1- Balance Enquire 2- Fast Cash/ withdrawal from the bank account 3- Credit the bank account 4- Withdraw money with a limit of 8,000 GBP per transaction 5- Change the PIN of the card 6- Create a mini bank statement with the latest transactions 7- Print the mini bank statement as a PDF
ATM Cash Machine Desktop Application
ABC ATM is a desktop application designed using Microsoft windows form C#, SQL server database, and entity framework design pattern. The application helps the customer to do the following using a Card linked to a bank account:- 1- Balance Enquire 2- Fast Cash/ withdrawal from the bank account 3- Credit the bank account 4- Withdraw money with a limit of 8,000 GBP per transaction 5- Change the PIN of the card 6- Create a mini bank statement with the latest transactions 7- Print the mini bank statement as a PDF
ATM Cash Machine Desktop Application
ABC ATM is a desktop application designed using Microsoft windows form C#, SQL server database, and entity framework design pattern. The application helps the customer to do the following using a Card linked to a bank account:- 1- Balance Enquire 2- Fast Cash/ withdrawal from the bank account 3- Credit the bank account 4- Withdraw money with a limit of 8,000 GBP per transaction 5- Change the PIN of the card 6- Create a mini bank statement with the latest transactions 7- Print the mini bank statement as a PDF
ATM Cash Machine Desktop Application
ABC ATM is a desktop application designed using Microsoft windows form C#, SQL server database, and entity framework design pattern. The application helps the customer to do the following using a Card linked to a bank account:- 1- Balance Enquire 2- Fast Cash/ withdrawal from the bank account 3- Credit the bank account 4- Withdraw money with a limit of 8,000 GBP per transaction 5- Change the PIN of the card 6- Create a mini bank statement with the latest transactions 7- Print the mini bank statement as a PDF
ATM Cash Machine Desktop Application
11095719
The website Application is designed using MVC Architectural Style. It is like the 3-Tier Architecture, which contains a Model (Data), View (UI), and Controller (Logic). The UI sends updates to the Logic, the Logic updates the Data, and then the UI directly gets updates from the Data (triangular style) The front end (Website View) is designed using HTML, CSS, JavaScript, jQuery, and Bootstrap 5 (fully responsive and user-friendly) The backend (Website View) is developed using Microsoft Technologies ASP.NET (MVC) and .NET 6.0 Entity Framework (Database first) The code editor used is Microsoft’s Visual Studio 2022 Public View contains public information about TalentTrack Login View includes two Roles, Jobseeker and Employer; Each role has a different view with different authorization.
TalentTrack Job Platform
The website Application is designed using MVC Architectural Style. It is like the 3-Tier Architecture, which contains a Model (Data), View (UI), and Controller (Logic). The UI sends updates to the Logic, the Logic updates the Data, and then the UI directly gets updates from the Data (triangular style) The front end (Website View) is designed using HTML, CSS, JavaScript, jQuery, and Bootstrap 5 (fully responsive and user-friendly) The backend (Website View) is developed using Microsoft Technologies ASP.NET (MVC) and .NET 6.0 Entity Framework (Database first) The code editor used is Microsoft’s Visual Studio 2022 Public View contains public information about TalentTrack Login View includes two Roles, Jobseeker and Employer; Each role has a different view with different authorization.
TalentTrack Job Platform
The website Application is designed using MVC Architectural Style. It is like the 3-Tier Architecture, which contains a Model (Data), View (UI), and Controller (Logic). The UI sends updates to the Logic, the Logic updates the Data, and then the UI directly gets updates from the Data (triangular style) The front end (Website View) is designed using HTML, CSS, JavaScript, jQuery, and Bootstrap 5 (fully responsive and user-friendly) The backend (Website View) is developed using Microsoft Technologies ASP.NET (MVC) and .NET 6.0 Entity Framework (Database first) The code editor used is Microsoft’s Visual Studio 2022 Public View contains public information about TalentTrack Login View includes two Roles, Jobseeker and Employer; Each role has a different view with different authorization.
TalentTrack Job Platform
The website Application is designed using MVC Architectural Style. It is like the 3-Tier Architecture, which contains a Model (Data), View (UI), and Controller (Logic). The UI sends updates to the Logic, the Logic updates the Data, and then the UI directly gets updates from the Data (triangular style) The front end (Website View) is designed using HTML, CSS, JavaScript, jQuery, and Bootstrap 5 (fully responsive and user-friendly) The backend (Website View) is developed using Microsoft Technologies ASP.NET (MVC) and .NET 6.0 Entity Framework (Database first) The code editor used is Microsoft’s Visual Studio 2022 Public View contains public information about TalentTrack Login View includes two Roles, Jobseeker and Employer; Each role has a different view with different authorization.
TalentTrack Job Platform
The website Application is designed using MVC Architectural Style. It is like the 3-Tier Architecture, which contains a Model (Data), View (UI), and Controller (Logic). The UI sends updates to the Logic, the Logic updates the Data, and then the UI directly gets updates from the Data (triangular style) The front end (Website View) is designed using HTML, CSS, JavaScript, jQuery, and Bootstrap 5 (fully responsive and user-friendly) The backend (Website View) is developed using Microsoft Technologies ASP.NET (MVC) and .NET 6.0 Entity Framework (Database first) The code editor used is Microsoft’s Visual Studio 2022 Public View contains public information about TalentTrack Login View includes two Roles, Jobseeker and Employer; Each role has a different view with different authorization.
TalentTrack Job Platform
The website Application is designed using MVC Architectural Style. It is like the 3-Tier Architecture, which contains a Model (Data), View (UI), and Controller (Logic). The UI sends updates to the Logic, the Logic updates the Data, and then the UI directly gets updates from the Data (triangular style) The front end (Website View) is designed using HTML, CSS, JavaScript, jQuery, and Bootstrap 5 (fully responsive and user-friendly) The backend (Website View) is developed using Microsoft Technologies ASP.NET (MVC) and .NET 6.0 Entity Framework (Database first) The code editor used is Microsoft’s Visual Studio 2022 Public View contains public information about TalentTrack Login View includes two Roles, Jobseeker and Employer; Each role has a different view with different authorization.
TalentTrack Job Platform
The website Application is designed using MVC Architectural Style. It is like the 3-Tier Architecture, which contains a Model (Data), View (UI), and Controller (Logic). The UI sends updates to the Logic, the Logic updates the Data, and then the UI directly gets updates from the Data (triangular style) The front end (Website View) is designed using HTML, CSS, JavaScript, jQuery, and Bootstrap 5 (fully responsive and user-friendly) The backend (Website View) is developed using Microsoft Technologies ASP.NET (MVC) and .NET 6.0 Entity Framework (Database first) The code editor used is Microsoft’s Visual Studio 2022 Public View contains public information about TalentTrack Login View includes two Roles, Jobseeker and Employer; Each role has a different view with different authorization.
TalentTrack Job Platform
The website Application is designed using MVC Architectural Style. It is like the 3-Tier Architecture, which contains a Model (Data), View (UI), and Controller (Logic). The UI sends updates to the Logic, the Logic updates the Data, and then the UI directly gets updates from the Data (triangular style) The front end (Website View) is designed using HTML, CSS, JavaScript, jQuery, and Bootstrap 5 (fully responsive and user-friendly) The backend (Website View) is developed using Microsoft Technologies ASP.NET (MVC) and .NET 6.0 Entity Framework (Database first) The code editor used is Microsoft’s Visual Studio 2022 Public View contains public information about TalentTrack Login View includes two Roles, Jobseeker and Employer; Each role has a different view with different authorization.
TalentTrack Job Platform
The website Application is designed using MVC Architectural Style. It is like the 3-Tier Architecture, which contains a Model (Data), View (UI), and Controller (Logic). The UI sends updates to the Logic, the Logic updates the Data, and then the UI directly gets updates from the Data (triangular style) The front end (Website View) is designed using HTML, CSS, JavaScript, jQuery, and Bootstrap 5 (fully responsive and user-friendly) The backend (Website View) is developed using Microsoft Technologies ASP.NET (MVC) and .NET 6.0 Entity Framework (Database first) The code editor used is Microsoft’s Visual Studio 2022 Public View contains public information about TalentTrack Login View includes two Roles, Jobseeker and Employer; Each role has a different view with different authorization.
TalentTrack Job Platform
The website Application is designed using MVC Architectural Style. It is like the 3-Tier Architecture, which contains a Model (Data), View (UI), and Controller (Logic). The UI sends updates to the Logic, the Logic updates the Data, and then the UI directly gets updates from the Data (triangular style) The front end (Website View) is designed using HTML, CSS, JavaScript, jQuery, and Bootstrap 5 (fully responsive and user-friendly) The backend (Website View) is developed using Microsoft Technologies ASP.NET (MVC) and .NET 6.0 Entity Framework (Database first) The code editor used is Microsoft’s Visual Studio 2022 Public View contains public information about TalentTrack Login View includes two Roles, Jobseeker and Employer; Each role has a different view with different authorization.
TalentTrack Job Platform
The website Application is designed using MVC Architectural Style. It is like the 3-Tier Architecture, which contains a Model (Data), View (UI), and Controller (Logic). The UI sends updates to the Logic, the Logic updates the Data, and then the UI directly gets updates from the Data (triangular style) The front end (Website View) is designed using HTML, CSS, JavaScript, jQuery, and Bootstrap 5 (fully responsive and user-friendly) The backend (Website View) is developed using Microsoft Technologies ASP.NET (MVC) and .NET 6.0 Entity Framework (Database first) The code editor used is Microsoft’s Visual Studio 2022 Public View contains public information about TalentTrack Login View includes two Roles, Jobseeker and Employer; Each role has a different view with different authorization.
TalentTrack Job Platform
The website Application is designed using MVC Architectural Style. It is like the 3-Tier Architecture, which contains a Model (Data), View (UI), and Controller (Logic). The UI sends updates to the Logic, the Logic updates the Data, and then the UI directly gets updates from the Data (triangular style) The front end (Website View) is designed using HTML, CSS, JavaScript, jQuery, and Bootstrap 5 (fully responsive and user-friendly) The backend (Website View) is developed using Microsoft Technologies ASP.NET (MVC) and .NET 6.0 Entity Framework (Database first) The code editor used is Microsoft’s Visual Studio 2022 Public View contains public information about TalentTrack Login View includes two Roles, Jobseeker and Employer; Each role has a different view with different authorization.
TalentTrack Job Platform
The website Application is designed using MVC Architectural Style. It is like the 3-Tier Architecture, which contains a Model (Data), View (UI), and Controller (Logic). The UI sends updates to the Logic, the Logic updates the Data, and then the UI directly gets updates from the Data (triangular style) The front end (Website View) is designed using HTML, CSS, JavaScript, jQuery, and Bootstrap 5 (fully responsive and user-friendly) The backend (Website View) is developed using Microsoft Technologies ASP.NET (MVC) and .NET 6.0 Entity Framework (Database first) The code editor used is Microsoft’s Visual Studio 2022 Public View contains public information about TalentTrack Login View includes two Roles, Jobseeker and Employer; Each role has a different view with different authorization.
TalentTrack Job Platform
The website Application is designed using MVC Architectural Style. It is like the 3-Tier Architecture, which contains a Model (Data), View (UI), and Controller (Logic). The UI sends updates to the Logic, the Logic updates the Data, and then the UI directly gets updates from the Data (triangular style) The front end (Website View) is designed using HTML, CSS, JavaScript, jQuery, and Bootstrap 5 (fully responsive and user-friendly) The backend (Website View) is developed using Microsoft Technologies ASP.NET (MVC) and .NET 6.0 Entity Framework (Database first) The code editor used is Microsoft’s Visual Studio 2022 Public View contains public information about TalentTrack Login View includes two Roles, Jobseeker and Employer; Each role has a different view with different authorization.
TalentTrack Job Platform
The website Application is designed using MVC Architectural Style. It is like the 3-Tier Architecture, which contains a Model (Data), View (UI), and Controller (Logic). The UI sends updates to the Logic, the Logic updates the Data, and then the UI directly gets updates from the Data (triangular style) The front end (Website View) is designed using HTML, CSS, JavaScript, jQuery, and Bootstrap 5 (fully responsive and user-friendly) The backend (Website View) is developed using Microsoft Technologies ASP.NET (MVC) and .NET 6.0 Entity Framework (Database first) The code editor used is Microsoft’s Visual Studio 2022 Public View contains public information about TalentTrack Login View includes two Roles, Jobseeker and Employer; Each role has a different view with different authorization.
TalentTrack Job Platform
The website Application is designed using MVC Architectural Style. It is like the 3-Tier Architecture, which contains a Model (Data), View (UI), and Controller (Logic). The UI sends updates to the Logic, the Logic updates the Data, and then the UI directly gets updates from the Data (triangular style) The front end (Website View) is designed using HTML, CSS, JavaScript, jQuery, and Bootstrap 5 (fully responsive and user-friendly) The backend (Website View) is developed using Microsoft Technologies ASP.NET (MVC) and .NET 6.0 Entity Framework (Database first) The code editor used is Microsoft’s Visual Studio 2022 Public View contains public information about TalentTrack Login View includes two Roles, Jobseeker and Employer; Each role has a different view with different authorization.
TalentTrack Job Platform
The website Application is designed using MVC Architectural Style. It is like the 3-Tier Architecture, which contains a Model (Data), View (UI), and Controller (Logic). The UI sends updates to the Logic, the Logic updates the Data, and then the UI directly gets updates from the Data (triangular style) The front end (Website View) is designed using HTML, CSS, JavaScript, jQuery, and Bootstrap 5 (fully responsive and user-friendly) The backend (Website View) is developed using Microsoft Technologies ASP.NET (MVC) and .NET 6.0 Entity Framework (Database first) The code editor used is Microsoft’s Visual Studio 2022 Public View contains public information about TalentTrack Login View includes two Roles, Jobseeker and Employer; Each role has a different view with different authorization.
TalentTrack Job Platform
9419879
Value Architecture (VArch) is an integrated approach for finding the right balance between Physical and non-physical Functions of architectural components “5S” and Architectural resources raising its Values by an appropriate technology to meet today’s requirements, un-ignoring the future needs towards sustainable architectural buildings . Architectural Components “5S”: (Site – Space- Skin – Structure- Systems) Architectural : (Area- Time – Cost - Energy –Water– Machines – Manpower – Materials- Wastes – ventilation- lightening) Architectural Values: (Functional, Formational, Structural and technology Values). The main object of the proposed program “Value Architecture <VArch>” is : providing an integrated methodology and the mechanism of analyzing the project’s architectural values. Linking the physical and non-physical functions of the users and linking them with the consumed resources towards sustainable architectural buildings all over the world and especially in Egypt.
“Value Architecture ” Program
4462392
Contest Details ------------------------------ We have a Microsoft Word document that we need to have converted to a SharePoint form where our users can go and fill in the requested fields. This work will be done on our SharePoint Intranet site. Attached is the form we need to have created on our SharePoint site. NOTE: This job should be a quick turn around job for you, a total of 24 hours, if you can't do it that quick, please don't bid for the job.
Data Request Form (SharePoint 2016)
Contest Details ------------------------------ We have a Microsoft Word document that we need to have converted to a SharePoint form where our users can go and fill in the requested fields. This work will be done on our SharePoint Intranet site. Attached is the form we need to have created on our SharePoint site. NOTE: This job should be a quick turn around job for you, a total of 24 hours, if you can't do it that quick, please don't bid for the job.
Data Request Form (SharePoint 2016)
Contest Details ------------------------------ We have a Microsoft Word document that we need to have converted to a SharePoint form where our users can go and fill in the requested fields. This work will be done on our SharePoint Intranet site. Attached is the form we need to have created on our SharePoint site. NOTE: This job should be a quick turn around job for you, a total of 24 hours, if you can't do it that quick, please don't bid for the job.
Data Request Form (SharePoint 2016)
Contest Details ------------------------------ We have a Microsoft Word document that we need to have converted to a SharePoint form where our users can go and fill in the requested fields. This work will be done on our SharePoint Intranet site. Attached is the form we need to have created on our SharePoint site. NOTE: This job should be a quick turn around job for you, a total of 24 hours, if you can't do it that quick, please don't bid for the job.
Data Request Form (SharePoint 2016)
Contest Details ------------------------------ We have a Microsoft Word document that we need to have converted to a SharePoint form where our users can go and fill in the requested fields. This work will be done on our SharePoint Intranet site. Attached is the form we need to have created on our SharePoint site. NOTE: This job should be a quick turn around job for you, a total of 24 hours, if you can't do it that quick, please don't bid for the job.
Data Request Form (SharePoint 2016)

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Project for Basma F. -- 2
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@NaniCharles
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Need help to create custom page layout for SharePoint
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@NaniCharles
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Project for Basma F. -- 2
Again, outstanding work, I am so pleased to have found someone that understand my needs and able to provide a high level of service. Bravo.
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@dajames4
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6 years ago
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Experience

ITI

information technology institute
Oct 2013 - Jun 2014 (8 months, 1 day)
information technology institute

Education

Faculty Of Engineering Good with owner degree(81.3%).

Suez Canal University, Egypt 2008 - 2013
(5 years)

Qualifications

International ESOL Communicator B2 (Listening, Reading, Writing)

LanguageCert
2022
https://badges.peoplecert.org/Badge/en/12730515-9C68-48F1-B065-2F3F5FD6C91F

International ESOL Communicator B2 (Speaking)

LanguageCert
2022
Certification URL: https://badges.peoplecert.org/Badge/en/6AD0B540-545E-4BDD-B41F-E2EA68CA2AC4

Fundamentals of Network Communication

Coursera
2018
Certification name: Fundamentals of Network Communication License number: NWH2YKARF73Q Certification URL: https://www.coursera.org/account/accomplishments/verify/NWH2YKARF73Q

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