Farshad Farahnakian
Project Researcher / Data Analytics / Doctoral Researcher
farshad.farahnakian@utu.fi : Forth Floor You can usually find me between 09:00AM to 18:00PM. |
Machine Learning, Deep Learning, Computer Vision, Sensor Fusion, Data Analysis, Autonomous Systems, Human-Computer Interaction, Interface Design, and Cloud Computing.
Department of Computing
Farshad is a doctoral researcher at the University of Turku with a strong background in computer science and electrical engineering. His passion for applying machine learning to real-world problems led him to his current research focus on developing AI solutions for the maritime industry. As part of the EU-funded AI-ARC project, Farshad leverages his expertise in computer vision and big data analysis to tackle intricate challenges in ship navigation. Farshad holds a Bachelor's degree in Electrical and Electronics Engineering (2016) and a Master of Science in Engineering from Tallinn University. He enjoys staying up-to-date on the latest advancements in technology through podcasts.
Currenctly, I am working on a project name "AI-ARC" founded by European Commission. The main objective of the AI-ARC proposal is to create an innovative, robust, efficient, and user-friendly artificial intelligence (AI) based- platform for coast and border guards, which allows traditional and VR-based interfaces to adapt to users’ preferences in terms of information management, anomaly detection, risk analyses and interoperability; in order to realize a comprehensive surveillance system that delivers powerful sensor fusion based situational awareness for decision making, and safety for all maritime actors.
In spring semester (2024), I had privilege of serving as a Teaching Assistant for the TKO_796 Computer Vision and Sensor Fusion course. My responsibilities encompassed a range of educational support tasks, including:
1. Designing and evaluating course assignments.
2. Assisting students with installation of necessary libraries for running python scripts.
3. Delivering lectures on sensor fusion topics.
- Maritime vessel movement prediction: A temporal convolutional network model with optimal look-back window size determination (2025)
- Multimodal transportation
- Wide-Area Ship Movement Prediction Using Random Forests (2025)
- Communications in Computer and Information Science
- A comparative study of state-of-the-art deep learning architectures for rice grain classification (2024)
- Journal of agriculture and food research
- AI-ARC Baltic Demo: Detecting Illegal Activities at Sea (2024) 2024 27th International Conference on Information Fusion (FUSION) Svenson, Pontus; Holst, Anders; Wallberg, Anders; Nevalainen, Paavo; Farahnakian, Farshad; Álamo, Alfonso; Germinara, Vincenzo; Schweizer, Daniel; Leicht, Matthis; Anneken, Mathias; Hoppe, Adrian H.; Karalis, Aristeidis; Ashraf, Labib; Beltrán, Maria Eugenia; Hernandez, Liss; Partanen, Petteri; Markkanen, Minna
- Enhancing Peatland Classification using Sentinel-1 and Sentinel-2 Fusion with Encoder-Decoder Architecture (2024) 2024 27th International Conference on Information Fusion (FUSION) Zelioli, Luca; Farahnakian, Fahimeh; Farahnakian, Farshad; Middleton, Maarit; Heikkonen, Jukka
- Pose estimation of sow and piglets during free farrowing using deep learning (2024)
- Journal of agriculture and food research
- SEDA: Similarity-Enhanced Data Augmentation for Imbalanced Learning (2024)
- Lecture Notes in Computer Science
- Short and Long Term Vessel Movement Prediction for Maritime Traffic (2024)
- Lecture Notes in Computer Science
- A Comprehensive Study of Clustering-Based Techniques for Detecting Abnormal Vessel Behavior (2023)
- Remote Sensing
- Ice-Water Segmentation Using Deep Convolutional Neural Network-Based Fusion Approach (2023) The 28th International Conference on Automation and Computing: Digitalisation for Smart Manufacturing and Systems Sheikh Javad, Farahnakian Fahimeh, Farahnakian Farshad, Heikkonen Jukka
- Sea Ice Concentration Estimation Via Fusion of Sentinel-1 and AMSR2 Based on Encoder-Decoder Architecture (2023)
- Proceedings of the IEEE international conference on intelligent transportation systems
- Abnormal Behaviour Detection by Using Machine Learning-Based Approaches in the Marine Environment: A Literature Survey (2022) 2022 International Conference on Electrical, Computer and Energy Technologies (ICECET) Farahnakian Farshad, Heikkonen Jukka, Nevalainen Paavo
- Driver Drowsiness Detection Using Deep Convolutional Neural Network (2021) 2021 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME) Farahnakian Farshad, Leoste Janika, Farahnakian Fahimeh
