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Publications

Book:

  1. “Deep Learning and Computer Vision in Remote Sensing II”, ISBN 978-3-0365-9364-7, 2023, here.
  2. Application of Multi-Sensor Fusion Technology in Target Detection and Recognition,  ISBN 978-3-0365-1352-2 , 2022 Application of Multi-Sensor Fusion Technology in Target Detection and Recognition
  3. “Deep Learning and Computer Vision in Remote Sensing” is published here. ISBN 978-3-0365-6368-8, 2023.Book cover: Deep Learning and Computer Vision in Remote Sensing
  4. C Programming, amokhte, ISBN: 978-600-91014-6-7, 2010.

Book Chapters:

  1. P. Jafarzadeh, F. Farahnakian, J-P. Paalassal, and O. Eerola, “IoT-Based Household Energy Consumption Prediction Using Machine Learning”,EAI/Springer Innovations in Communication and Computing.  https://doi.org/10.1007/978-3-030-69705-1_8, 2021.
  2. F. Farahnakian, J.Heikkonen, “RGB and Depth Image Fusion for Object Detection using Deep Learning”, Deep Learning Applications, Springer, vol 3, 2021.

Journal Papers:

  1. Farshad Farahnakian, Luca Zelioli, Fahimeh Farahnakian,Maarit Middleton, Timo P.Pitkänen, Sakari Tuominen,  Jonne Pohjankukka, Jukka Heikkonen, “Classifying Boreal Peatlands with Remote Sensing and Machine Learning: Methodological Comparison for Country-Wide Mapping”, IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2024(submitted).
  2. Fahimeh Farahnakian, Luca Zelioli, Farshad Farahnakian, Maarit Middleton, Javad Sheikh, Jukka Heikkonen, “GeoFusion: Transformer-Based Fusion for Boreal Peatland Classification”,

    Elsevier Computers and Electronics in Agriculture, 2024 (submitted).

  3. Fahimeh Farahnakian , Nike Luodes and Teemu Karlsson,”Machine learning algorithms for Acid Mine Drainage Mapping Using Sentinel-2 and Worldview-3″, Journal of Remote Sensing , 2024 (submitted).
  4. Pouya Jafarzadeh, Luca Zelioli, Petra Virjonen, Fahimeh Farahnakian, Paavo Nevalainen, Jukka Heikkonen, “Enhancing Hurdles Athletes’ Performance Analysis:A Comparative Study of CNN-Based Pose Estimation Frameworks”, Springer Multimedia Tools and Applications  , 2024 (under revision).
  5. Farshad Farahnakian, Paavo Nevalainen, Fahimeh Farahnakian, Jukka Heikkonena, “Maritime Vessel Movement Prediction: A Temporal Convolutional Network Model with Optimal Look-back Window Size Determination”, Elsevier Multi-modal Transportation, 2024.
  6. Luca Zelioli, Fahimeh Farahnakian, Maarit Middleton, Timo P.Pitkänen, Sakari Tuominen, Paavo Nevalainen, Jonne Pohjankukka, Jukka Heikkonen, “Peatland Pixel-level Classification via Multispectral, Multiresolution and Multisensor data using Convolutional Neural Network”, Elsevier Ecological Informatics, 2024 (under revision).

  7. Fahimeh Farahnakian,Javad Sheikh,Luca Zelioli, Dipak Nidhi, Iiro Seppä,Rami Ilo, Paavo Nevalainen, Jukka Heikkonen, “Addressing Imbalanced Data for Machine Learning Based Mineral Prospectivity Mapping”, Elsevier Ore Geology Journal, 2024.

  8. Fahimeh Farahnakian,  Farshad Farahnakian, S. Björkman, V.Bloch, M.Pastel, Jukka Heikkonen,Pose Estimation of Sow and Piglets During Free Farrowing Using Deep Learning , Journal of Agriculture and Food Research ( Elsevier), 2024.
  9. Farshad Farahnakian, Javad Sheikh, Fahimeh Farahnakian, Jukka Heikkonen, “Comparative study of state-of-the-art deep learning architectures for rice grain classification”, Journal of Agriculture and Food Research ( Elsevier),2023.
  10. Farshad. Farahnakian, F. Nicolas Florent, Fahimeh. Farahnakian , P. Nevalainen, J. Sheikh, J. Heikkonen, C. Raduly-Baka, “A Comprehensive Study of Clustering-Based Techniques for Detecting Abnormal Vessel Behavior”, Journal of Remote Sensing , 2023.
  11. F. Farahnakian, J.Heikkonen, “Deep Learning based Multi-modal Fusion Architectures for Maritime Vessel Detection”, Journal of Remote Sensing, 2020. Link
  12. F. Farahnakian, J.Heikkonen, “Anomaly-based Intrusion Detection Using Deep Neural Networks”, International Journal of Digital Content Technology and its Applications, 2018.
  13. F. Farahnakian,T. Pahikkala,  P. Liljeberg,  J. Plosila, N. T. Hieu, and  H.Tenhunen,  “Energy-aware VM Consolidation in Cloud Data Centers Using Utilization Prediction Model”, IEEE Transactions on Cloud Computing, 2016.
  14. F. Farahnakian, M. Ebrahimi, M. Daneshtalab, P. Liljeberg, and J. Plosila, “Bi-LCQ: A Low-Weight Clustering-based Q-Learning Approach for NoCs”, Journal of Microprocessors and Microsystems (MICPRO-Elsevier), 2014.
  15. F. Farahnakian, M. Ebrahimi, M. Daneshtalab, P. Liljeberg, and J. Plosila, “Adaptive Load Balancing in Learning-based Approaches for Many-core Embedded Systems”, Journal of Supercomputing (Springer), 2014.
  16. F. Hosseinpour, P. Vahdani Amoli, F. Farahnakian, J. Plosila, T. Hämäläinen, “Artificial Immune System Based Intrusion Detection: Innate Immunity using an Unsupervised Learning Approach”, International Journal of Digital Content Technology and its Applications, 2014.
  17. F. Farahnakian,A. Ashraf, P. Liljeberg, T. Pahikkala, J. Plosila and H.Tenhunen, “Using Ant Colony System to Consolidate VMs for Green Cloud Computing”, IEEE Transactions on Services Computing, 2015.

Technical reports:

1- Maarit Middleton, Matti Laatikainen, Janne Kivilompolo, Asta Harju, Jouni, Lerssi, Markus Valkama , Timo Pitkänen, Jonne Pohjankukka, Andras Balazs, Sakari Tuominen, Luca Zelioli, Fahimeh Farahnakian, Paavo Nevalainen, Jukka Heikkonen, “Technical description for the peatland site type data of Finland”, 2023. here 

Conference Papers:

 

  1. Fahimeh Farahnakian , Farshad Farahnakian , Javad Sheikh2 , Steven Downey, Vaughan Williams , and Jukka Heikkonen, “Multi-Modal Fusion of LiDAR and PRISMA Data for Cobalt Mapping: A Case Study from the Áramo Mine, Spain”,International Conference on Pattern Recognition (ICPR) , 2024, India. (Best paper research Award)
  2. Javad Sheikh, Farshad Farahnakian,Fahimeh Farahnakian , Luca Zelioli,and Jukka Heikkonen, “SEDA: Similarity-Enhanced Data Augmentation for Imbalanced Learning”, International Conference on Pattern Recognition (ICPR) , 2024, India.
  3. Fahimeh Farahnakian, Johanna Torppa, Nike Luodes, Hannu Panttila, Teemu Karlsson, “A Comparative Study of Machine Learning Models for Pixel-wise Acid Mine Drainage Classification Using Sentinel-2”, International Geoscience and Remote Sensing Symposium – IGARSS 2024 2024, Greece.
  4. Fahimeh Farahnakian, Luca Zelioli, Farshad Farahnakian, Maarit Middleton and Jukka Heikkonen,”Enhancing Peatland Classification using Sentinel-1 and Sentinel-2 Fusion with Encoder-Decoder Architecture”, The 27th edition of the IEEE International conference on information fusion (Fusion), 2024.
  5. Fahimeh Farahnakian, Luca Zelioli, Timo Pitkänen, Jonne Pohjankukka, Maarit Middleton, Sakari Tuominen, Paavo Nevalainen and Jukka Heikkonen, “CNN-based Boreal Peatland Fertility Classification from   Sentinel-1 and Sentinel-2 Imagery”, IEEE ROSE symposium series, 2023, Japan.
  6. D. K. Nidhi, I. Seppä, F. Farahnakian, L. Zelioli, J. Heikkonen and R. Kanth, “Enhancing Minerals Prospects Mapping with Machine Learning: Addressing Imbalanced Geophysical Datasets and Data Visualization Approaches,” 2023 34th Conference of Open Innovations Association (FRUCT), Riga, Latvia, 2023, pp. 125-135, doi: 10.23919/FRUCT60429.2023.10328164.
  7. Fahimeh Farahnakian, Luca Zelioli, Timo Pitkänen, Jonne Pohjankukka, Maarit Middleton, Sakari Tuominen, Paavo Nevalainen and Jukka Heikkonen, “Multistream Convolutional Neural Network Fusion for Pixel-wise Classification of Peatland”, The 26th edition of the IEEE International conference on information fusion (Fusion), 2023, USA.
  8. Farshad. Farahnakian,  Fahimeh. Farahnakian , P. Nevalainen, J. Sheikh, J. Heikkonen, “Short and Long term Vessel Movement Prediction for Maritime Traffic”, International Conference on Critical Information Infrastructures Security (CRITIS 2023), 2023,Finland.
  9. Javad Sheikh, Fahimeh Farahnakian, Farshad Farahnakian, J. Heikkonen, “Sea Ice Concentration Estimation Via Fusion Sentinel-1 and AMSR2 based on Encoder-Decoder Architecture”, 26th IEEE International Conference on Intelligent Transportation Systems (ITSC2023), 2023, Spain.
  10. Pouya Jafarzadeh, Luca Zelioli, Fahimeh Farahnakian, Paavo Nevalainen ,Petteri Hemminki, Christian Andersson, Jukka Heikkonen, “Real-Time Military Tank Detection Using YOLOv5 Implemented on Raspberry Pi”, 4th International Conference on Artificial Intelligence, Robotics and Control (AIRC 2023), 2023, Egypt.
  11. Javad Sheikh, Fahimeh Farahnakian, Farshad Farahnakian, J. Heikkonen, “Ice-Water Segmentation Using Deep Convolutional Neural Network-based Fusion Approach”,The 28th International Conference on Automation and Computing (ICAC2023), 2023, UK.
  12. F. Farahnakian, J. Heikkonen, S. Björkman, “Multi-pig Pose Estimation Using DeepLabCut”, 11th International Conference on Intelligent Control and Information Processing (ICICIP2021), China.
  13. P. Jafarzadeh, P. Virjonen, P. Nevalainen, F. Farahnakian, J. Heikkonen, “Pose Estimation of Hurdles Athletes using OpenPose”, International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), 2021, Mauritius.
  14. Farshad. Farahnakian, J. Leoste, F. Farahnakian, “Driver Drowsiness Detection Using Deep Convolutional Neural Network”, International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), 2021, Mauritius.
  15. F. Farahnakian, L. Zelioli, and J. Heikkonen, “Transfer Learning for Maritime Vessel Detection using Deep Neural Networks”, The 24th IEEE International Conference on Intelligent Transportation Systems (ITSC), 2021, USA.
  16. F. Farahnakian, L. Koivunen, T. Mäkilä, and J. Heikkonen, “Towards Autonomous Industrial Warehouse Inspection”, The 26th IEEE International Conference on Automation and Computing (ICAC’21) , 2021, UK.
  17. F. Farahnakian, and J. Heikkonen, “RGB-depth Fusion Framework for Object Detection in Autonomous Vehicles”, The 14th International Conference on Signal Processing and Communication Systems (ICSPCS), 2020, Australia.
  18. F. Farahnakian, and J. Heikkonen, “ A Comparative Study of Deep Learning-based RGB-depth Fusion Methods for Object Detection”, The 19th IEEE International Conference on Machine Learning and Applications (ICMLA), 2020, USA.
  19. F. Farahnakian, and J. Heikkonen, “Fusing LiDAR and Color Imagery for Object Detection using Convolutional Neural Networks”, The 23th edition of the IEEE International conference on information fusion (Fusion), 2020, South Africa. Summary link
  20. Valentin Soloviev, Fahimeh Farahnakian, Luca Zelioli, Bogdan Iancu, Johan Lilius, Jukka Heikkonen, “Comparing CNN-based Object Detectors on Two Novel Maritime Datasets”, The IEEE International Conference on Multimedia & Expo (ICME), 2020, UK. Summary link
  21. F. Farahnakian, J. Poikonen, M. Laurinen, and J. Heikkonen, “Deep Convolutional Neural Network-based Fusion of RGB and IR Images in Marine Environment”, The 22th IEEE International Conference on Intelligent Transportation Systems (ITSC), 2019, New Zealand.
  22. F. Farahnakian, J. Poikonen, M. Laurinen, D. Makris and J. Heikkonen, “Visible and Infrared Image Fusion Framework based on RetinaNet for Marine Environment”, The 22th edition of the IEEE International conference on information fusion (Fusion), 2019, Canada. Summary link 
  23. F. Farahnakian, P. Movahedi , J. Poikonen, E. Lehtonen, D. Makris and J. Heikkonen, “Comparative Analysis of Image Fusion Methods in Marine Environment”, Proceedings of the 13th edition of the IEEE International Symposium on RObotic and Sensors Environments (ROSE), 2019, Canada. Summary link
  24. F. Farahnakian, M.Haghbayan, J. Poikonen, M. Laurinen, P. Nevalainen and J. Heikkonen, “Object Detection based on Multi-sensor Proposal Fusion in Maritime Environment”, The 17th IEEE International Conference on Machine Learning and Applications (ICMLA), 2018, USA. Summary link
  25. M.Haghbayan, F. Farahnakian, J. Poikonen, M. Laurinen, P. Nevalainen and J. Heikkonen, “An Efficient Multi-sensor Fusion Approach for Object Detection in Maritime Environment”, The 21th IEEE International Conference on Intelligent Transportation Systems (ITSC), 2018, USA. Summary Link
  26. F. Farahnakian,J.Heikkonen, “A Deep Auto-Encoder based Approach for Intrusion Detection System”, The 20th IEEE International Conference on Advanced Communications Technology (IEEE ICACT), 2018, South Korea.
  27. F. Farahnakian, R. Bahsoon, P. Liljeberg, and T. Pahikkala, “Self-adaptive Resource Management System in IaaS Clouds”, The 8th IEEE International Conference on Cloud Computing (IEEE CLOUD), 2016, USA.
  28. F. Farahnakian, P. Liljeberg, T. Pahikkala,J. Plosila and H.Tenhunen, “Utilization Prediction Aware VM Consolidation Approach for Green Cloud Computing”, The 7th IEEE International Conference on Cloud Computing (IEEE CLOUD), 2015, USA.
  29. F. Farahnakian, T. Pahikkala, P. Liljeberg J. Plosila and H.Tenhunen, “Hierarchical VM Management Architecture for Cloud Data Centers”, The 6th IEEE International Conference on Cloud Computng Technology and Science (IEEE CloudCom), 2014, Singapore.
  30. F. Farahnakian, T. Pahikkala, P. Liljeberg,J. Plosila and H.Tenhunen, “Multi-Agent based Architecture for Dynamic VM Consolidation in Cloud Data Centers”, The 40th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), 2014, Italy.
  31. F. Farahnakian, A. Ashraf, P. Liljeberg, T. Pahikkala, J. Plosila and H.Tenhunen, “Energy-aware Dynamic VM Consolidation in Cloud Data Centers using Ant Colony System”, The 7th IEEE International Conference on Cloud Computing (IEEE CLOUD), 2014, USA.
  32. F. Farahnakian, P. Liljeberg, and J. Plosila, “Energy-Efficient Virtual Machines Consolidation in Cloud Data Centers using Reinforcement Learning”, The 22th IEEE Euromicro Conference on Parallel, Distributed and Network-Based Computing (PDP), 2014, Italy.
  33. F. Farahnakian, T. Pahikkala, P. Liljeberg,J. Plosila and H.Tenhunen, “Hierarchical Agent-based Architecture for Resource Management in Cloud Data Centers”, The 7th IEEE International Conference on Cloud Computing (IEEE CLOUD),  2014, USA
  34. F. Farahnakian T. Pahikkala, P. Liljeberg, and J. Plosila, “Energy Aware Consolidation Algorithm based on K-nearest Neighbor Regression for Data Centers”, The 6th IEEE/ACM International Conference on Utility and Cloud Computing (UCC), 2013, Germany.
  35. F. Farahnakian, P. Liljeberg, and J. Plosila, “LiRCUP: Linear Regression based CPU Usage Prediction Algorithm for Live Migration of Virtual Machines in Data Center”, The 39th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), 2013, Spain.
  36. F. Farahnakian, M. Ebrahimi, M. Daneshtalab, P. Liljeberg, and J. Plosila, “ Optimized Q-learning Model for Distributing Traffic in On-Chip Networks”, The international Conference on Networked Embedded Systems for Enterprise Applications (NESEA), 2012, UK.
  37. M. Ebrahimi, M. Daneshtalab, F. Farahnakian, J. Plosila, P. Liljeberg, M. Palesi, H. Tenhunen, “HARAQ: Congestion-Aware Learning Model for Highly Adaptive Routing Algorithm in On-Chip Networks”, The 6th ACM/IEEE International Symposium on Networks-on-Chip (NOCS), 2012, Denmark.
  38. F. Farahnakian, M. Ebrahimi, M. Daneshtalab, P. Liljeberg, and J. Plosila, “Adaptive Reinforcement Learning Method for Networks-on-Chip”, The IEEE of International Conference on Embedded Computer Systems (SAMOS), 2012, Greece.
  39. F. Farahnakian, M. Ebrahimi, M. Daneshtalab, P. Liljeberg, and J. Plosila, “Q-learning based Congestion-aware Routing Algorithm for On-Chip Network”, The 2th IEEE International Conference on Networked Embedded Systems for Enterprise Applications (NESEA), 2011, Australia.
  40. F. Farahnakian, M. Daneshtalab, P. Liljeberg, J. Plosila, “Efficient On-Chip Network architecture Using Reinforcement Learning”, The International conference on Digital System Design (WiP-DSD), 2011, Finland.
  41. F. Farahnakian, N. Mozayani, “Evaluating Feature Selection Techniques in Simulated Soccer Multi Agents System”, The IEEE International Conference on Advanced Computer Control (ICACC), 2009, Singapore.
  42. F. Farahnakian, N. Mozayani, “Reinforcement Learning for Soccer Multi-agents System”, The IEEE International Conference on Computational Intelligence and Security (CIS), 2009, China.
  43. F. Farahnakian, N. Mozayani, “Improvement of agent’s performance by Fuzzy Reinforcement Learning in Multi Agents System”, The 14th computer society of Iran conference (CSICC), 2009, Iran.
  44. F. Farahnakian, N. Mozayani, “Learning through Decision Tree in Simulated Soccer Environment”, The IEEE International Conference on Computational Intelligence and Security (CIS), 2008, China.

Magazine Papers:

  1. F. Farahnakian, “Usage Fuzzy Reinforcement Learning in Soccer Robots”, Journal of the Artificial intelligence, ISSN: 1735-3939, 2010, Iran.
  2. F. Farahnakian, “Manual for Rescue Simulation”, Journal of the Artificial intelligence, ISSN: 1735-3939, 2010, Iran.

Theses:

  1. F. Farahnakian, “Energy and Performance: Management of Virtual Machines: Provisioning, Placement, and Consolidation”, TUCS Dissertations 215, ISBN 978-952-12-3436-1, University of Turku, 2016.
  2. F. Farahnakian, “Fuzzy Reinforcement Learning Approach for Multi-agent Systems”, School of Computer Engineering, Iran University of Science and Technology (IUST), 2008.