{"id":969,"date":"2020-04-07T09:52:32","date_gmt":"2020-04-07T07:52:32","guid":{"rendered":"http:\/\/users.utu.fi\/fahfar\/?page_id=969"},"modified":"2026-01-28T12:12:36","modified_gmt":"2026-01-28T10:12:36","slug":"publications","status":"publish","type":"page","link":"https:\/\/users.utu.fi\/fahfar\/publications\/","title":{"rendered":"Publications"},"content":{"rendered":"

Book:<\/h2>\n
    \n
  1. \u201cDeep Learning and Computer Vision in Remote Sensing II\u201d, ISBN\u00a0978-3-0365-9364-7, <\/span>2023, here.<\/a><\/li>\n
  2. Application of Multi-Sensor Fusion Technology in Target Detection and Recognition,\u00a0\u00a0ISBN 978-3-0365-1352-2 , 2022<\/span>\u00a0<\/a><\/li>\n
  3. \u201cDeep Learning and Computer Vision in Remote Sensing\u201d\u00a0is published here.<\/a> ISBN 978-3-0365-6368-8, 2023.<\/span><\/li>\n
  4. C Programming, amokhte, ISBN: 978-600-91014-6-7, 2010.<\/li>\n<\/ol>\n

    Book Chapters:<\/h2>\n
      \n
    1. P. Jafarzadeh,\u00a0<\/span>F. Farahnakian,\u00a0<\/span>J-P.\u00a0Paalassal, and\u00a0<\/span>O. Eerola,\u00a0<\/span>\u201cIoT-Based Household Energy Consumption Prediction Using Machine Learning\u201d,EAI\/Springer Innovations in Communication and Computing. \u00a0https:\/\/doi.org\/10.1007\/978-3-030-69705-1_8, 2021.<\/li>\n
    2. F. Farahnakian, J.Heikkonen, \u201cRGB and Depth Image Fusion for Object Detection using Deep Learning\u201d, Deep Learning Applications, Springer, vol 3, 2021.<\/li>\n<\/ol>\n

      Journal Papers:<\/h2>\n
        \n
      1. Farshad Farahnakian, Luca Zelioli, Fahimeh Farahnakian,Maarit Middleton, Timo P.Pitk\u00e4nen, Sakari Tuominen,\u00a0 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, (Under revision).<\/li>\n
      2. Morgana Carvalho, Joana Cardoso-Fernandes, Fahimeh Farahnakian,
        \nVaughan Williams, Diana Guimar\u00e3es, Diana Capela, Ana Claudia Teodoro, “Satellite data augmentation and ensemble predictive learning for cobalt-nickel-copper exploration based on hydrothermal alteration patterns”, Remote Sensing, (Under revision).<\/li>\n
      3. Luca Zelioli, Fahimeh Farahnakian, Maarit Middleton, Timo P.Pitk\u00e4nen, 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, 2025.<\/span><\/li>\n
      4. Fahimeh Farahnakian, Luca Zelioli, Farshad Farahnakian, Maarit Middleton, Javad Sheikh, Jukka Heikkonen, “GeoFusion: Transformer-Based Fusion for Boreal Peatland Classification”, Elsevier Engineering Applications of Artificial Intelligence (Under revision).<\/span><\/li>\n
      5. 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.<\/span><\/span><\/li>\n
      6. Jonne <\/span>Pohjankukka<\/span><\/span>a<\/span><\/span>, Sakari <\/span>Tuominen<\/span><\/span>a<\/span><\/span>, <\/span>Fahimeh<\/span> Farahnakian<\/span><\/span>,<\/span> Timo <\/span>P. <\/span>Pitk\u00e4nen<\/span><\/span>a<\/span><\/span>, <\/span>Andras<\/span> Bala<\/span>zs<\/span><\/span>, <\/span>Jukka <\/span>Heikkonen<\/span><\/span>, <\/span>Maarit <\/span>Middleton,<\/span><\/span>“Machine learning algorithms in predicting <\/span>boreal <\/span>peatland site type<\/span>s<\/span> from remotely sensed multi-source data”,<\/span><\/span>\u00a0<\/span>International Journal of Applied Earth Observation and Geoinformation (under revision).<\/li>\n
      7. Pouya Jafarzadeh, Luca Zelioli, Petra Virjonen, Fahimeh Farahnakian, Paavo Nevalainen, Jukka Heikkonen, “Enhancing Hurdles Athletes\u2019 Performance Analysis:<\/span>A Comparative Study of CNN-Based Pose <\/span>Estimation Frameworks”, Springer <\/span>Multimed Tools Appl<\/i> (2025). https:\/\/doi.org\/10.1007\/s11042-024-20587-z<\/li>\n
      8. 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 Multimodal Transportation, 2024,https:\/\/doi.org\/10.1016\/j.multra.2025.100191<\/li>\n
      9. \n

        Fahimeh Farahnakian,Javad Sheikh,Luca Zelioli, Dipak Nidhi, Iiro Sepp\u00e4,Rami Ilo, Paavo Nevalainen, Jukka Heikkonen, “Addressing Imbalanced Data for Machine Learning Based Mineral Prospectivity Mapping”, Elsevier Ore Geology Journal, 2024.<\/h1>\n<\/li>\n
      10. Fahimeh Farahnakian,\u00a0 Farshad Farahnakian, S. Bj\u00f6rkman, 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.<\/span><\/li>\n
      11. Farshad Farahnakian, Javad Sheikh, Fahimeh Farahnakian, Jukka Heikkonen, “Comparative study of state-of-the-art deep learning architectures for rice<\/span>\u00a0grain\u00a0classification”, Journal of Agriculture and Food Research ( Elsevier),2023.<\/span><\/li>\n
      12. 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.<\/span><\/li>\n
      13. F. Farahnakian, J.Heikkonen, \u201cDeep Learning based Multi-modal Fusion Architectures for Maritime Vessel Detection\u201d, Journal of Remote Sensing, 2020. Link<\/a><\/li>\n
      14. F. Farahnakian, J.Heikkonen, \u201cAnomaly-based Intrusion Detection Using Deep Neural Networks\u201d, International Journal of Digital Content Technology and its Applications, 2018.<\/li>\n
      15. F. Farahnakian,T. Pahikkala,\u00a0 P. Liljeberg,\u00a0 J. Plosila, N. T. Hieu, and\u00a0 H.Tenhunen,\u00a0 \u201cEnergy-aware VM Consolidation in Cloud Data Centers Using Utilization Prediction Model\u201d, IEEE Transactions on Cloud Computing, 2016.<\/li>\n
      16. F. Farahnakian, M. Ebrahimi, M. Daneshtalab, P. Liljeberg, and J. Plosila, \u201cBi-LCQ: A Low-Weight Clustering-based Q-Learning Approach for NoCs\u201d, Journal of Microprocessors and Microsystems (MICPRO-Elsevier), 2014.<\/li>\n
      17. F. Farahnakian, M. Ebrahimi, M. Daneshtalab, P. Liljeberg, and J. Plosila, \u201cAdaptive Load Balancing in Learning-based Approaches for Many-core Embedded Systems\u201d, Journal of Supercomputing (Springer), 2014.<\/li>\n
      18. F. Hosseinpour, P. Vahdani Amoli, F. Farahnakian, J. Plosila, T. H\u00e4m\u00e4l\u00e4inen, \u201cArtificial Immune System Based Intrusion Detection: Innate Immunity using an Unsupervised Learning Approach\u201d, International Journal of Digital Content Technology and its Applications, 2014.<\/li>\n
      19. F. Farahnakian,A. Ashraf, P. Liljeberg, T. Pahikkala, J. Plosila and H.Tenhunen, \u201cUsing Ant Colony System to Consolidate VMs for Green Cloud Computing\u201d, IEEE Transactions on Services Computing, 2015.<\/li>\n<\/ol>\n

        Technical reports:<\/h2>\n

        1- Maarit Middleton, Matti Laatikainen, Janne Kivilompolo, Asta Harju, Jouni, Lerssi, Markus Valkama , Timo Pitk\u00e4nen, 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\u00a0<\/a><\/p>\n

        Conference Papers:<\/h2>\n
          \n
        1. Farahnakian, F. and Luodes, N.\u00a0 “Acid Mine Drainage Assessment Using Multispectral Drone Imagery and Machine Learning: A Case Study from Outokumpu, Finland”,37th Nordic Geological Winter meeting in Turku, Finland on 13-15 January 2026.<\/li>\n
        2. Farahnakian, F. and Luodes, N.: Predicting Acid Mine Drainage Indicators Using Drone Data and Machine Learning Techniques, EGU General Assembly 2025, Vienna, Austria, 27 Apr\u20132 May 2025, EGU25-15740, https:\/\/doi.org\/10.5194\/egusphere-egu25-15740, 2025.<\/li>\n
        3. \n
          Fahimeh Farahnakian , Mahyar Yousefi, Ana Cl\u00e1udia Teodor, “An Ensemble Modeling Approach for Mapping Critical Mineral Distribution with LiDAR and PRISMA Data”, 11th \u00a0International Conference on Geographical Information Systems Theory (GISTAM), 2025.<\/div>\n<\/li>\n
        4. \u00a0 Ana Cl\u00e1udia Teodoro1a, Joana Cardoso-Fernandes, Mihaela Gheorghe, Francesco Falabella, Fabiana Cal\u00f2, Antonio Pepe, Delira Hanelli, Andreas Knobloch, Roberto De La Rosa, Fahimeh Farahnakian, Georgios Georgalas, Enoc Sanz-Ablanedo, Kri\u0161tof O\u0161ti,” S34I Project – Secure and Sustainable Supply of Raw
          \nMaterials for EU Industry”, GISTAM2025.<\/li>\n
        5. Pouya Jafarzadeh, Luca Zelioli, Fahimeh Farahnakian, Paavo Nevalainen,. Heikkonen, “Real-Time multi-perdon tracking system using YOLO11”, The 7th International confernce on Activity and Behaviour computing, 2025.<\/li>\n
        6. Fahimeh Farahnakian , Farshad Farahnakian , Javad Sheikh , Steven Downey, Vaughan Williams , and Jukka Heikkonen, “Multi-Modal Fusion of LiDAR and PRISMA Data for Cobalt Mapping: A Case Study from the \u00c1ramo Mine, Spain”,International Conference on Pattern Recognition (ICPR) , 2024, India. (Best paper research Award)<\/li>\n
        7. 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.<\/li>\n
        8. 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.<\/li>\n
        9. 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.<\/li>\n
        10. Fahimeh Farahnakian, Luca Zelioli, Timo Pitk\u00e4nen, Jonne Pohjankukka, Maarit Middleton, Sakari Tuominen, Paavo Nevalainen and Jukka Heikkonen, “CNN-based Boreal Peatland Fertility Classification from\u00a0\u00a0\u00a0Sentinel-1 and Sentinel-2 Imagery”, IEEE ROSE symposium series, 2023, Japan.<\/li>\n
        11. D. K. Nidhi, I. Sepp\u00e4, F. Farahnakian, L. Zelioli, J. Heikkonen and R. Kanth, “Enhancing Minerals Prospects Mapping with Machine Learning: Addressing Imbalanced Geophysical Datasets and Data Visualization Approaches,”\u00a02023 34th Conference of Open Innovations Association (FRUCT)<\/em>, Riga, Latvia, 2023, pp. 125-135, doi: 10.23919\/FRUCT60429.2023.10328164.<\/li>\n
        12. Fahimeh Farahnakian, Luca Zelioli, Timo Pitk\u00e4nen, Jonne Pohjankukka, Maarit Middleton, Sakari Tuominen, Paavo Nevalainen and Jukka Heikkonen, “Multistream Convolutional Neural Network Fusion for Pixel-wise Classification of Peatland”,\u00a0The 26th edition of the IEEE International conference on information fusion (Fusion), 2023, USA.<\/li>\n
        13. Farshad. Farahnakian, \u00a0Fahimeh. 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.<\/li>\n
        14. Javad Sheikh, Fahimeh Farahnakian, Farshad Farahnakian, J. Heikkonen, “<\/span><\/span>Sea Ice Concentration Estimation Via Fusion Sentinel-1 and AMSR2 based on Encoder-Decoder Architecture”,\u00a026th IEEE International Conference on Intelligent Transportation Systems (ITSC2023),\u00a02023, Spain.<\/li>\n
        15. 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.<\/li>\n
        16. Javad Sheikh, Fahimeh Farahnakian, Farshad Farahnakian, J. Heikkonen,\u00a0“Ice<\/span>-Water Segmentation Using Deep Convolutional Neural Network-based Fusion Approach”,The 28th International Conference on Automation and Computing (ICAC2023),\u00a02023, UK.<\/li>\n
        17. F. Farahnakian, J. Heikkonen, S. Bj\u00f6rkman, “Multi-pig Pose Estimation Using DeepLabCut”, 11th International Conference on Intelligent Control and Information Processing (ICICIP2021), China.<\/li>\n
        18. P. Jafarzadeh, P. Virjonen, P. Nevalainen, F. Farahnakian, J. Heikkonen, \u201cPose Estimation of Hurdles Athletes using OpenPose\u201d, International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME),\u00a0<\/b>2021, Mauritius.<\/li>\n
        19. Farshad. Farahnakian, J. Leoste, F. Farahnakian,\u00a0\u201cDriver Drowsiness Detection Using Deep Convolutional Neural Network\u201d, International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME),\u00a0<\/b>2021, Mauritius.<\/li>\n
        20. F. Farahnakian, L. Zelioli, and J. Heikkonen, \u201cTransfer Learning for Maritime Vessel Detection using Deep Neural Networks\u201d, The 24th IEEE International Conference on Intelligent Transportation Systems (ITSC), 2021, USA.<\/li>\n
        21. F. Farahnakian, L. Koivunen, T. M\u00e4kil\u00e4, and J. Heikkonen, \u201cTowards Autonomous Industrial Warehouse Inspection\u201d, The 26th IEEE International Conference on Automation and Computing\u00a0(ICAC’21) , 2021, UK.<\/li>\n
        22. F. Farahnakian, and J. Heikkonen, \u201cRGB-depth Fusion Framework for Object Detection in Autonomous Vehicles\u201d, The 14th International Conference on Signal Processing and Communication Systems <\/span>(ICSPCS), 2020, Australia.<\/li>\n
        23. F. Farahnakian, and J. Heikkonen, \u201c A Comparative Study of Deep Learning-based RGB-depth Fusion Methods for Object Detection\u201d, The 19th IEEE International Conference on Machine Learning and Applications\u00a0<\/span>(ICMLA), 2020, USA.<\/li>\n
        24. F. Farahnakian, and J. Heikkonen, \u201cFusing LiDAR and Color Imagery for Object Detection using Convolutional Neural Networks\u201d, The 23th edition of the IEEE International conference on information fusion (Fusion), 2020, South Africa. Summary link<\/a><\/li>\n
        25. Valentin Soloviev, Fahimeh Farahnakian, Luca Zelioli, Bogdan Iancu, Johan Lilius, Jukka Heikkonen, \u201cComparing CNN-based Object Detectors on Two Novel Maritime Datasets\u201d, The IEEE International Conference on Multimedia & Expo (ICME), 2020, UK. Summary link<\/a><\/li>\n
        26. F. Farahnakian, J. Poikonen, M. Laurinen, and J. Heikkonen, \u201cDeep Convolutional Neural Network-based Fusion of RGB and IR Images in Marine Environment\u201d, The 22th IEEE International Conference on Intelligent Transportation Systems (ITSC), 2019, New Zealand.<\/li>\n
        27. F. Farahnakian, J. Poikonen, M. Laurinen, D. Makris and J. Heikkonen, \u201cVisible and Infrared Image Fusion Framework based on RetinaNet for Marine Environment\u201d, The 22th edition of the IEEE International conference on information fusion (Fusion), 2019, Canada. Summary link\u00a0<\/a><\/li>\n
        28. F. Farahnakian, P. Movahedi , J. Poikonen, E. Lehtonen, D. Makris and J. Heikkonen, \u201cComparative Analysis of Image Fusion Methods in Marine Environment\u201d, Proceedings of the 13th edition of the IEEE International Symposium on RObotic and Sensors Environments (ROSE), 2019, Canada. Summary link<\/a><\/li>\n
        29. F. Farahnakian, M.Haghbayan, J. Poikonen, M. Laurinen, P. Nevalainen and J. Heikkonen, \u201cObject Detection based on Multi-sensor Proposal Fusion in Maritime Environment\u201d, The 17th IEEE International Conference on Machine Learning and Applications (ICMLA), 2018, USA. Summary link<\/a><\/li>\n
        30. M.Haghbayan, F. Farahnakian, J. Poikonen, M. Laurinen, P. Nevalainen and J. Heikkonen, \u201cAn Efficient Multi-sensor Fusion Approach for Object Detection in Maritime Environment\u201d, The 21th IEEE International Conference on Intelligent Transportation Systems (ITSC), 2018, USA. Summary Link<\/a><\/li>\n
        31. F. Farahnakian,J.Heikkonen, \u201cA Deep Auto-Encoder based Approach for Intrusion Detection System\u201d, The 20th IEEE International Conference on Advanced Communications Technology (IEEE ICACT), 2018, South Korea.<\/li>\n
        32. F. Farahnakian, R. Bahsoon, P. Liljeberg, and T. Pahikkala, \u201cSelf-adaptive Resource Management System in IaaS Clouds\u201d, The 8th IEEE International Conference on Cloud Computing (IEEE CLOUD), 2016, USA.<\/li>\n
        33. F. Farahnakian, P. Liljeberg, T. Pahikkala,J. Plosila and H.Tenhunen, \u201cUtilization Prediction Aware VM Consolidation Approach for Green Cloud Computing\u201d, The 7th IEEE International Conference on Cloud Computing (IEEE CLOUD), 2015, USA.<\/li>\n
        34. F. Farahnakian, T. Pahikkala, P. Liljeberg J. Plosila and H.Tenhunen, \u201cHierarchical VM Management Architecture for Cloud Data Centers\u201d, The 6th IEEE International Conference on Cloud Computng Technology and Science (IEEE CloudCom), 2014, Singapore.<\/li>\n
        35. F. Farahnakian, T. Pahikkala, P. Liljeberg,J. Plosila and H.Tenhunen, \u201cMulti-Agent based Architecture for Dynamic VM Consolidation in Cloud Data Centers\u201d, The 40th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), 2014, Italy.<\/li>\n
        36. F. Farahnakian, A. Ashraf, P. Liljeberg, T. Pahikkala, J. Plosila and H.Tenhunen, \u201cEnergy-aware Dynamic VM Consolidation in Cloud Data Centers using Ant Colony System\u201d, The 7th IEEE International Conference on Cloud Computing (IEEE CLOUD), 2014, USA.<\/li>\n
        37. F. Farahnakian, P. Liljeberg, and J. Plosila, \u201cEnergy-Efficient Virtual Machines Consolidation in Cloud Data Centers using Reinforcement Learning\u201d, The 22th IEEE Euromicro Conference on Parallel, Distributed and Network-Based Computing (PDP), 2014, Italy.<\/li>\n
        38. F. Farahnakian, T. Pahikkala, P. Liljeberg,J. Plosila and H.Tenhunen, \u201cHierarchical Agent-based Architecture for Resource Management in Cloud Data Centers\u201d, The 7th IEEE International Conference on Cloud Computing (IEEE CLOUD),\u00a0 2014, USA<\/li>\n
        39. F. Farahnakian T. Pahikkala, P. Liljeberg, and J. Plosila, \u201cEnergy Aware Consolidation Algorithm based on K-nearest Neighbor Regression for Data Centers\u201d, The 6th IEEE\/ACM International Conference on Utility and Cloud Computing (UCC), 2013, Germany.<\/li>\n
        40. F. Farahnakian, P. Liljeberg, and J. Plosila, \u201cLiRCUP: Linear Regression based CPU Usage Prediction Algorithm for Live Migration of Virtual Machines in Data Center\u201d, The 39th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), 2013, Spain.<\/li>\n
        41. F. Farahnakian, M. Ebrahimi, M. Daneshtalab, P. Liljeberg, and J. Plosila, \u201c Optimized Q-learning Model for Distributing Traffic in On-Chip Networks\u201d, The international Conference on Networked Embedded Systems for Enterprise Applications (NESEA), 2012, UK.<\/li>\n
        42. M. Ebrahimi, M. Daneshtalab, F. Farahnakian, J. Plosila, P. Liljeberg, M. Palesi, H. Tenhunen, \u201cHARAQ: Congestion-Aware Learning Model for Highly Adaptive Routing Algorithm in On-Chip Networks\u201d, The 6th ACM\/IEEE International Symposium on Networks-on-Chip (NOCS), 2012, Denmark.<\/li>\n
        43. F. Farahnakian, M. Ebrahimi, M. Daneshtalab, P. Liljeberg, and J. Plosila, \u201cAdaptive Reinforcement Learning Method for Networks-on-Chip\u201d, The IEEE of International Conference on Embedded Computer Systems (SAMOS), 2012, Greece.<\/li>\n
        44. F. Farahnakian, M. Ebrahimi, M. Daneshtalab, P. Liljeberg, and J. Plosila, \u201cQ-learning based Congestion-aware Routing Algorithm for On-Chip Network\u201d, The 2th IEEE International Conference on Networked Embedded Systems for Enterprise Applications (NESEA), 2011, Australia.<\/li>\n
        45. F. Farahnakian, M. Daneshtalab, P. Liljeberg, J. Plosila, \u201cEfficient On-Chip Network architecture Using Reinforcement Learning\u201d, The International conference on Digital System Design (WiP-DSD), 2011, Finland.<\/li>\n
        46. F. Farahnakian, N. Mozayani, \u201cEvaluating Feature Selection Techniques in Simulated Soccer Multi Agents System\u201d, The IEEE International Conference on Advanced Computer Control (ICACC), 2009, Singapore.<\/li>\n
        47. F. Farahnakian, N. Mozayani, \u201cReinforcement Learning for Soccer Multi-agents System\u201d, The IEEE International Conference on Computational Intelligence and Security (CIS), 2009, China.<\/li>\n
        48. F. Farahnakian, N. Mozayani, \u201cImprovement of agent\u2019s performance by Fuzzy Reinforcement Learning in Multi Agents System\u201d, The 14th computer society of Iran conference (CSICC), 2009, Iran.<\/li>\n
        49. F. Farahnakian, N. Mozayani, \u201cLearning through Decision Tree in Simulated Soccer Environment\u201d, The IEEE International Conference on Computational Intelligence and Security (CIS), 2008, China.<\/li>\n<\/ol>\n

          Magazine Papers:<\/h2>\n
            \n
          1. F. Farahnakian, \u201cUsage Fuzzy Reinforcement Learning in Soccer Robots\u201d, Journal of the Artificial intelligence, ISSN: 1735-3939, 2010, Iran.<\/li>\n
          2. F. Farahnakian, \u201cManual for Rescue Simulation\u201d, Journal of the Artificial intelligence, ISSN: 1735-3939, 2010, Iran.<\/li>\n<\/ol>\n

            Theses:<\/h2>\n
              \n
            1. F. Farahnakian, \u201cEnergy and Performance: Management of Virtual Machines: Provisioning, Placement, and Consolidation\u201d, TUCS Dissertations 215, ISBN 978-952-12-3436-1, University of Turku, 2016.<\/li>\n
            2. F. Farahnakian, \u201cFuzzy Reinforcement Learning Approach for Multi-agent Systems\u201d, School of Computer Engineering, Iran University of Science and Technology (IUST), 2008.<\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"

              Book: \u201cDeep Learning and Computer Vision in Remote Sensing II\u201d, ISBN\u00a0978-3-0365-9364-7, 2023, here. Application of Multi-Sensor Fusion Technology in Target Detection and Recognition,\u00a0\u00a0ISBN 978-3-0365-1352-2 , 2022\u00a0 \u201cDeep Learning and Computer Vision in Remote Sensing\u201d\u00a0is published here. ISBN 978-3-0365-6368-8, 2023. C Programming, amokhte, ISBN: 978-600-91014-6-7, 2010. Book Chapters: P. Jafarzadeh,\u00a0F. Farahnakian,\u00a0J-P.\u00a0Paalassal, and\u00a0O. Eerola,\u00a0\u201cIoT-Based Household Energy Consumption […]<\/p>\n","protected":false},"author":31,"featured_media":0,"parent":0,"menu_order":4,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-969","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/users.utu.fi\/fahfar\/wp-json\/wp\/v2\/pages\/969","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/users.utu.fi\/fahfar\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/users.utu.fi\/fahfar\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/users.utu.fi\/fahfar\/wp-json\/wp\/v2\/users\/31"}],"replies":[{"embeddable":true,"href":"https:\/\/users.utu.fi\/fahfar\/wp-json\/wp\/v2\/comments?post=969"}],"version-history":[{"count":58,"href":"https:\/\/users.utu.fi\/fahfar\/wp-json\/wp\/v2\/pages\/969\/revisions"}],"predecessor-version":[{"id":1878,"href":"https:\/\/users.utu.fi\/fahfar\/wp-json\/wp\/v2\/pages\/969\/revisions\/1878"}],"wp:attachment":[{"href":"https:\/\/users.utu.fi\/fahfar\/wp-json\/wp\/v2\/media?parent=969"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}