I'm Shallom

Software Engineer & Security Researcher

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About

Shallom Ezekiel

I am a software engineer and security researcher with a passion for building innovative solutions that bridge technology and real-world challenges.
My goal is to drive technological progress in Ghana and across Africa by applying my expertise in software engineering and cybersecurity.
Above all, I aim to inspire others and point them to God through technology and innovation.

Skills

I have worked on a variety of projects, each showcasing my passion for innovation and high standards. From designing scalable applications to adopting the latest technologies, I strive to deliver impactful solutions. Below are some of the key areas I specialize in:

App Development Icon
Cross-Platform App Dev.
Web Development Icon
Web Development
Backend Development Icon
Backend Development
Database Icon
Relational Database Management Systems
Full-stack Icon
Full-Stack System Design Implementation
Cybersecurity Icon
Cybersecurity Research and Development

Tools:

HTML 5 & CSS 3

Vanilla JavaScript

Python Programming

Dart Programming

Flutter

Firebase

Fast API

Git

C Programming

MySQL

VB .NET

Relational Databases

Projects

Project 1

FarmNETS

An inclusive platform for smallholder farmers and extension officers featuring an AI-powered chatbot, voice mode, and local language support for sustainable farming.

Project 1

Spotify Clone

A visually stunning and fully responsive Spotify UI clone built using Flutter. This web-based application replicates the sleek design and intuitive user experience of the official Spotify app.

Project 1

Radregpay

A student registration and fee payment system designed for Radford University College to streamline processes and improve accessibility.

Project 3

RansomShield

A ransomware detection and mitigation tool that uses machine learning and dynamic malware analysis to identify and counter crypto-ransomware threats. It integrates with Cuckoo Sandbox for deep behavioral analysis and employs a Support Vector Machine (SVM) model for accurate ransomware classification.

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