References
- Affes, N., Ktari, J., Ben Amor, N., Frikha, T. and Hamam, H. (2022). ‘Real time detection and tracking in multi speakers video conferencing’, in Abraham, A., Pllana, S., Casalino, G., Ma, K. and Bajaj, A. (eds) Intelligent systems design and applications. ISDA 2022. Lecture notes in networks and systems, vol 716. Springer, Cham. DOI: 10.1007/978-3-031-35501-1_11, ISBN 978-3-031-35501-1.
- Affes, N., Ktari, J., Ben Amor, N., Frikha, T. and Hamam, H. (2023). ‘Comparison of YOLOV5, YOLOV6, YOLOV7 and YOLOV8 for intelligent video surveillance’, Journal of Information Assurance and Security, 18, pp. 147-161.
- Sliti, O. et al. (2019). ‘Efficient visual tracking via sparse representation and back-projection histogram’, Journal of Multimedia Tools and Applications, 78, pp. 21759–21783.
- Sliti, O. et al. (2018). ‘CLBP for scale and orientation adaptive mean shift tracking’, JKSU, 30, pp. 416-429. doi: 10.1016/j.jksuci.2017.05.003.
- Patel, S., Patel, N., Deshpande, S. and Siddiqui, A. (2021). ‘Ship intrusion detection using custom object detection system with YOLO algorithm’, International Research Journal of Engineering and Technology (IRJET), 8(1), pp. 669-677.
- Jamil, F., Ibrahim, M., Ullah, I., Kim, S., Kahng, H. K., & Kim, D. H. (2022). Optimal smart contract for autonomous greenhouse environment based on IoT blockchain network in agriculture. Computers and Electronics in Agriculture, 192, 106573.
- Ezzeddini, L., Ktari, J., Zouaoui, I., Talha, A., Jarray, N. and Frikha, T. (2022, November). ‘Blockchain for the electronic voting system: case study: student representative vote in Tunisian institute’, in 2022 15th International Conference on Security of Information and Networks (SIN) (pp. 01-07). IEEE.
- Malik, P. K., Singh, R., Gehlot, A., Akram, S. V., & Das, P. K. (2022). Village 4.0: Digitalization of village with smart internet of things technologies. Computers & Industrial Engineering, 165, 107938.
- Arena, A., Bianchini, A., Perazzo, P., Vallati, C., & Dini, G. (2019, June). BRUSCHETTA: An IoT blockchain-based framework for certifying extra virgin olive oil supply chain. In 2019 IEEE international conference on smart computing (SMARTCOMP) (pp. 173-179). IEEE.
- Ghadi, Y.Y. et al. (2023). ‘Integration of federated learning with IoT for smart cities applications, challenges, and solutions’, PeerJ Computer Science, 9.1657. doi: 10.7717/peerj-cs.1657.
- Shah, S.F.A. et al. (2024). ‘Applications, challenges, and solutions of unmanned aerial vehicles in smart city using blockchain’, PeerJ Computer Science, 10. doi: 10.7717/peerj-cs.1776.
- Mazhar, T. et al. (2023). ‘Analysis of cyber security attacks and its solutions for the smart grid using machine learning and blockchain methods’, Future Internet, 15, 38 pages. doi: 10.3390/fi15020083.
- Khan, A.A. et al. (2022). ‘Healthcare ledger management: A blockchain and machine learning-enabled novel and secure architecture for medical industry’, Human-centric Computing and Information Sciences, 12.
- Spraul, R., Sommer, L., Schumann, A. (2020). “A comprehensive analysis of modern object detection methods for maritime vessel detection.” In: Artificial Intelligence and Machine Learning in Defense Applications II, SPIE, pp. 13-24.
- Ophoff, T., Puttemans, S., Kalogirou, V., Robin, J.P., Goedemé, T. (2020). “Vehicle and vessel detection on satellite imagery: A comparative study on single-shot detectors.” Remote Sensing, 12(7), pp. 1217.
- Sirisha, U., Praveen, S.P., Srinivasu, P.N., Barsocchi, P., Bhoi, A.K. (2023). “Statistical analysis of design aspects of various YOLO-based deep learning models for object detection.” International Journal of Computational Intelligence Systems, 16(1), pp. 126.
- Rasi, D., AntoBennet, M., Renjith, P.N., Arun, M.R., Vanathi, D. (n.d.). “YOLO Based Deep Learning Model for Segmenting the Color Images.” IJEER, 11(2), pp. 359-370.
- Luan, S., Li, C., Xu, P., Huang, Y., Wang, X. (2023). “MI-YOLO: more information based YOLO for insulator defect detection.” Journal of Electronic Imaging, 32(4), 043014-043014.
- Real-time object detection using YOLO, iMerit. (n.d.). [Online]. Available: https://imerit.net/blog/real-time-object-detection-using-yolo/.
- Li, J., Qu, C., Shao, J. (2017). “Ship detection in SAR images based on an improved faster R-CNN.” In: Proceedings of the SAR Big Data Era, Models, Methods and Applications (BIGSARDATA), pp. 1–6. doi: 10.1109/BIGSARDATA.2017.8124934.
- Wang, Y., Wang, C., Zhang, H., Dong, Y., Wei, S. (2019). “A SAR dataset of ship detection for deep learning under complex backgrounds.” Remote Sensing, 11(7), 765. doi: 10.3390/rs11070765.
- Sun, X., Wang, Z.R., Sun, Y.R., Diao, W.H., Zhang, Y., Fu, K. (2019). “AIR-SARShip-1.0: High-resolution SAR ship detection dataset.” Journal of Radars, 8(6), 852–862. doi: 10.12000/JR19097.
- Wei, S., Zeng, X., Qu, Q., Wang, M., Su, H., Shi, J. (2020). “HRSID: A high-resolution SAR images dataset for ship detection and instance segmentation.” IEEE Access, 8, pp. 120234–120254. doi: 10.1109/ACCESS.2020.3005861.
- Lei, S., Lu, D., Qiu, X., Ding, C. (2021). “SRSDD-v1.0: A high-resolution SAR rotation ship detection dataset.” Remote Sensing, 13(24), 5104. doi: 10.3390/rs13245104.
- Hu, J., Shen, L., Sun, G. (2018). “Squeeze-and-excitation networks.” In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7132–7141. doi: 10.1109/CVPR.2018.00745.
- Zhang, T., Zhang, X., Shi, J., Wei, S. (2019). “High-speed ship detection in SAR images by improved YOLOv3.” In: Proceedings of the 16th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP), pp. 149–152.
- Ktari, J., Frikha, T., Hamdi, M., Hamam, H. (2024). “Enhancing Blockchain Consensus with FPGA: Accelerating Implementation for Efficiency.” IEEE Access. doi: 10.1109/ACCESS.2024.3379374.
- Kumari, P., Jain, A.K. (2023). “A comprehensive study of DDoS attacks over IoT network and their countermeasures.” Computers & Security, 103096.
- Chen, M.Y., Wu, H.T. (2022). «An automatic identification system-based vessel security system.» IEEE Transactions on Industrial Informatics, 19(1), pp. 870-879.
- Zhao, M., Yao, X., Sun, J., Zhang, S., Bai, J. (2019). «GIS-based simulation methodology for evaluating ship encounters probability to improve maritime traffic safety.» IEEE Transactions on Intelligent Transportation Systems, 20(1), pp. 323–337.
- Liu, Y., Yao, L., Xiong, W., Zhou, Z. (2019). «GF-4 satellite and automatic identification system data fusion for ship tracking.» IEEE Geosci. Remote Sens. Lett., 16(2), pp. 281–285.
- Awan, K.A., Din, I.U., Almogren, A., Guizani, M., Altameem, A., Jadoon, A.U. (2019). «Robusttrust— A pro-privacy robust distributed trust management mechanism for Internet of Things.» IEEE Access, 7, pp. 62095–62106.
- Xue, B., Tong, N. (2019). «DIOD: Fast and Efficient Weakly Semi-Supervised Deep Complex ISAR Object Detection.» IEEE Transactions on Cybernetics, 49, pp. 3991-4003.
- Ciocarlan, A., Stoian, A. (2021). «Ship Detection in Sentinel 2 Multi-Spectral Images with Self-Supervised Learning.» Remote Sensing, 13, 425.
- Rahimi, P., Khan, N.D., Chrysostomou, C., Vassiliou, V., Nazir, B. (2020). «A secure communication for maritime IoT applications using blockchain technology.» In 2020 16th International Conference on Distributed Computing in Sensor Systems (DCOSS) (pp. 244-251). IEEE.
- Howson, P. (2020). «Building trust and equity in marine conservation and fisheries supply chain management with blockchain.» Marine Policy, 115, pp. 103873.
- Iancu, B., Soloviev, V., Zelioli, L., Lilius, J. (2021). «Aboships—an in-shore and offshore maritime vessel detection dataset with precise annotations.» Remote Sensing, 13(5), pp. 988.
- Ganache | Overview-Truffle Suite. (n.d.). [Online]. Available: https://trufflesuite.com/docs/ganache/.