KEMAL KILIÇASLAN

KEMAL KILIÇASLAN

AI Developer

I am Kemal. I graduated from Kastamonu University, Department of Mathematics in 2022. After graduating, I set myself the goal of applying the knowledge from my mathematics background to areas such as artificial intelligence, machine learning, deep learning, and image processing. In line with this goal, I explore AI algorithms through personal projects, online courses, and practical applications specific to image processing, developing projects that involve tasks such as image detection, recognition, and interpretation. Through this hands-on work, I have deepened my understanding of AI concepts and gained experience in turning my technical skills into concrete projects. My goal is to continue developing these skills and contribute to real-world AI solutions.



Education

Kastamonu Üniversitesi

KASTAMONU ÜNİVERSİTESİ FEN EDEBİYAT FAKÜLTESİ MATEMATİK BÖLÜMÜ

2022 Graduate - Bachelor's Degree

Personal Initiatives

Kastamonu Üniversitesi Matematik ve Bilim Toplulugu

KASTAMONU ÜNİVERSİTESİ MATEMATİK & BİLİM TOPLULUĞU


(Founding President)

[December 2021 - June 2022]

 Kemal Kılıçaslan - My speech on the meaning and importance of the day

'14 March π Day'



Türkiye Matematik Kulübü

Türkiye Matematik Kulübü


(Member)

[December 2020 - ]

 I took an active role in the organisation team of 6 events, 4 of which were academic and 2 of which were popular, where we addressed more than 1500 people in total.

Talks & Workshops

Niğde Ömer Halisdemir Üniversitesi Bilgisayar Mühendisliği Kulübü

Niğde Ömer Halisdemir Üniversitesi Bilgisayar Mühendisliği Kulübü
(Speaker)

[15 May 2025]

At the TeknoKonferans NÖHÜ'25 event held within the scope of the "Training, Cooperation and Competition Event for the Software Industry" organized at Niğde Ömer Halisdemir University, I gave a speech titled "From Mathematics to Artificial Intelligence: An Interdisciplinary Career Journey".

Experiences

BİLGİ TEKNOLOJİLERİ VE İLETİŞİM KURUMU

BİLGİ TEKNOLOJİLERİ VE İLETİŞİM KURUMU


Artificial Intelligence Instructor

[July 2024 - ]

Delivered face-to-face AI training (Python, ML, deep learning, computer vision) to 20+ participants using a hands-on curriculum built on TensorFlow, OpenCV and GPU-accelerated model training demos, guiding students through end-to-end model development from data preparation to deployment with an 85% project completion rate.



Calorin

Calorin


Computer Vision Developer

[August 2024 - September 2024]

Developed a real-time food volume estimation system using YOLOv8 and OpenCV for pixel-level segmentation of fruits and vegetables, applying geometric calculations to convert segmented areas into calorie estimates with an optimised low-latency inference pipeline validated across diverse lighting and scale conditions.



Ultralytics

Ultralytics


AI Content Creator (Brand Partnership)

[March 2026 - April 2026]

Produced a sponsored LinkedIn post and a YouTube tutorial for the Ultralytics Platform launch, demonstrating the full computer vision pipeline (annotation → training → export → deployment) using YOLO on two original real-world datasets.

🔗 Try the Ultralytics Platform




Tech Skills



Python C++ MATLAB Wolfram Mathematica PostgresSQL HTML5 CSS JavaScript Swift LaTeX

scikit-learn TensorFlow PyTorch OpenCV BootStrap FFmpeg






SERVICES



I help businesses and individuals turn AI ideas into working systems. Here is how we can work together.


AI & Computer Vision Development

Custom object detection, recognition, tracking and analysis systems for your business, from data preparation to deployment. Like the projects below, built for your real-world use case.

Get a project quote

AI Training for Teams & Individuals

Hands-on training in Python, machine learning, deep learning and computer vision, tailored to your team's level. Backed by my experience as an AI instructor at BTK Akademi.

Request a training program

Consulting & Mentorship

One-on-one guidance on AI project feasibility, model selection and career transition from mathematics or other fields into AI.

Book a session






PROJECTS



Algorithms & Programming

Kaprekar Constant with Python Programming

This project implements the fascinating Kaprekar Constant (6174) discovered by Indian mathematician D.R. Kaprekar in 1949. Features iterative arithmetic operations that converge any four-digit number to 6174 within 7 steps, plus three-digit variant (495). Built with Python for educational exploration of number theory.
Data Analysis & Visualization

Data Visualization of Turkey Population with Plotly

This project implements comprehensive data visualization of Turkey's population from 1927 to 2023 using Plotly and Folium. Features interactive charts, geographic maps, and demographic analysis across 81 provinces with gender distribution trends and historical comparisons.
Deep Learning

Garbage Classification with Convolutional Neural Network (CNN)

Garbage Classification Class
Garbage classification class

Garbage Classification Predicted Class
Garbage classification predicted class

This project implements a Garbage Classification System using Convolutional Neural Networks (CNN) with TensorFlow/Keras. Classifies waste into 6 categories (cardboard, glass, metal, paper, plastic, trash) using the TrashNet dataset. Achieves 62.2% accuracy with real-time image prediction capabilities for automated recycling applications.

Facial Expression Recognition System

This project implements a Facial Expression Recognition System using deep learning with VGG19 architecture. Classifies 7 emotions (angry, disgust, fear, happy, neutral, sad, surprise) from grayscale facial images. Achieves 66.33% accuracy on FER-2013 dataset with transfer learning and data augmentation techniques.
Computer Vision

Face Detection and Person Recognition System

This project implements a versatile Face Detection and Person Recognition System using computer vision and deep learning. It provides a modular toolkit for detecting faces and identifying specific individuals in both static media (images, videos) and real-time webcam streams.

Pose Detection with YOLOv8 using Wolfram Mathematica

This project implements Pose Detection using YOLOv8 within Wolfram Mathematica. Features real-time human pose estimation with 17 keypoint detection, skeleton visualization, and comprehensive analysis tools. Leverages pre-trained models from Ultralytics for accurate body pose recognition and movement tracking.

Vehicle Speed Estimation System

This project implements a real-time Vehicle Speed Estimation System using YOLOv8 object detection and perspective transformation. Tracks multiple vehicles within a defined ROI, calculates speeds in km/h using geometric principles, and provides annotated video output for traffic monitoring and analysis applications.

Safety Lane Violation Detection System

This project implements a real-time Safety Lane Violation Detection System using YOLOv12 object detection and tracking. Monitors emergency lane usage, detects vehicles crossing safety boundaries, and provides live violation statistics with color-coded visualization for traffic enforcement applications.

Road Lane Lines Detection System

This project implements a real-time Road Lane Detection System using computer vision techniques. Features Canny edge detection, ROI masking, and Hough Transform to identify road lane markings in video streams. Processes visual data for accurate lane boundary detection with OpenCV and Python for ADAS and autonomous driving applications.

Traffic Signs Recognition, Vehicle Plate and Person Blurring System

This project implements a comprehensive computer vision system for real-time traffic sign recognition (24 classes) with automatic privacy protection through vehicle plate and person blurring. Built with YOLOv11 models, it's designed for autonomous vehicles and GDPR-compliant surveillance applications.

Vehicle Distance Measurement System

This project implements a real-time Vehicle Distance Measurement System using YOLOv12 and perspective geometry. Calculates distances to surrounding vehicles in three ROI lanes with adaptive warning thresholds, automatic license plate blurring for privacy, and color-coded safety indicators for dashcam applications.

Road Surface Obstacle and Traffic Warning Object Segmentation System

This project implements a real-time Road Surface Obstacle and Traffic Warning Object Segmentation System using a custom-trained YOLO segmentation model. Detects and segments 5 classes: pothole, crack, patch (road surface obstacles) and traffic-cone, road-barrier (traffic warning objects), with per-class colored masks, anti-aliased contour outlines, and instance-level confidence labels for ADAS and autonomous driving applications.

Industrial Safety Gear Detection System

This project implements a real-time Industrial Safety Gear Detection System using a custom-trained YOLO model. Detects workers and classifies PPE compliance status (helmet, safety vest, gloves, face mask) with color-coded visualization and warning banners for non-compliant workers in industrial environments.

Vehicle and Pedestrian Traffic Analysis System

This project implements a real-time, zone-aware Vehicle and Pedestrian Traffic Analysis System in native C++, combining a VisDrone-trained YOLO26x detector with a from-scratch BoT-SORT multi-object tracker. Tracks and classifies road users across 10 crosswalk/road/sidewalk zones, producing per-zone occupancy and dwell-time statistics, crosswalk safety masking, a live dashboard, annotated video, CSV time series, and JSON summary output.
SOFTWARE APPLICATION

Video to Audio Converter

This project implements a professional video-to-audio conversion application for macOS using SwiftUI and FFmpeg. Features support for 19 audio formats, real-time video preview, drag-and-drop interface, customizable metadata editing, and 10-language localization for multimedia processing applications.





CERTIFICATES