Modular Platforms based on Clouded Web Technology and Distributed Deep Learning Systems
References
- Salim, B. W., & Zeebaree, S. R. (2023). Kurdish Sign Language Recognition Based on Transfer Learning. International Journal of Intelligent Systems and Applications in Engineering, 11(6s), 232-245.
- Sadeeq, M. M., Abdulkareem, N. M., Zeebaree, S. R., Ahmed, D. M., Sami, A. S., & Zebari, R. R. (2021). IoT and Cloud computing issues, challenges and opportunities: A review. Qubahan Academic Journal, 1(2), 1-7.
- Zangana, H. M., & Zeebaree, S. R. (2024). Distributed Systems for Artificial Intelligence in Cloud Computing: A Review of AI-Powered Applications and Services. International Journal of Informatics, Information System and Computer Engineering (INJIISCOM), 5(1), 1-20.
- Jacksi, K., Dimililer, N., & Zeebaree, S. (2016). State of the art exploration systems for linked data: a review. Int. J. Adv. Comput. Sci. Appl. IJACSA, 7(11), 155-164.
- Zebari, S., & Yaseen, N. O. (2011). Effects of parallel processing implementation on balanced load-division depending on distributed memory systems. J. Univ. Anbar Pure Sci, 5(3), 50-56.
- Ibrahim, R. K., et al. (2022). Clustering Document based on Semantic Similarity Using Graph Base Spectral Algorithm. In 2022 5th International Conference on Engineering Technology and its Applications (IICETA) (pp. 254-259). IEEE.
- Mohsin, S., Salim, B. W., Mohamedsaeed, A. K., Ibrahim, B. F., & Zeebaree, S. R. (2024). American Sign Language Recognition Based on Transfer Learning Algorithms. International Journal of Intelligent Systems and Applications in Engineering, 12(5s), 390-399.
- Omer, M. A., Yazdeen, A. A., Malallah, H. S., & Abdulrahman, L. M. (2022). A Survey on Cloud Security: Concepts, Types, Limitations, and Challenges. Journal of Applied Science and Technology Trends, 3(02), 47-57.
- Abdulrahman, L. M., Ahmed, S. H., Rashid, Z. N., Jghef, Y. S., Ghazi, T. M., & Jader, U. H. (2023). Web Phishing Detection Using Web Crawling, Cloud Infrastructure and Deep Learning Framework. Journal of Applied Science and Technology Trends, 4(01), 54-71.
- Zeebaree, S. R., Shukur, H. M., Haji, L. M., Zebari, R. R., Jacksi, K., & Abas, S. M. (2020). Characteristics and analysis of hadoop distributed systems. Technology Reports of Kansai University, 62(4), 1555-1564.
- Yazdeen, A. A., Qashi, R., Malallah, H. S., Abdulrahman, L. M., & Omer, M. A. (2023). Internet of Things Impact on Web Technology and Enterprise Systems. Journal of Applied Science and Technology Trends, 4(01), 19-33.
- Malallah, H. S., Qashi, R., Abdulrahman, L. M., Omer, M. A., & Yazdeen, A. A. (2023). Performance Analysis of Enterprise Cloud Computing: A Review. Journal of Applied Science and Technology Trends, 4(01), 01-12.
- Abdullah, P. Y., Zeebaree, S., Jacksi, K., & Zeabri, R. R. (2020). An HRM system for small and medium enterprises (SME) based on cloud computing technology. International Journal of Research-GRANTHAALAYAH, 8(8), 56-64.
- Saeed, J., & Zeebaree, S. (2021). Skin lesion classification based on deep convolutional neural networks architectures. Journal of Applied Science and Technology Trends, 2(01), 41-51.
- Zeebaree, S. R., Zebari, R. R., Jacksi, K., & Hasan, D. A. (2019). Security approaches for integrated enterprise systems performance: A Review. Int. J. Sci. Technol. Res, 8(12), 2485-2489.
- Abdullah, P. Y., Zeebaree, S., Shukur, H. M., & Jacksi, K. (2020). HRM system using cloud computing for Small and Medium Enterprises (SMEs). Technology Reports of Kansai University, 62(04), 04.
- Salim, N. O., Zeebaree, S. R., Sadeeq, M. A., Radie, A., Shukur, H. M., & Rashid, Z. N. (2021). Study for food recognition system using deep learning. Journal of Physics: Conference Series, 1963(1), 012014.
- Majety, V. D., et al. (2022). Ensemble of Handcrafted and Deep Learning Model for Histopathological Image Classification. Computers, Materials & Continua, 73(2).
- Mostafa, S. A., et al. (2019). Applying Trajectory Tracking and Positioning Techniques for Real-time Autonomous Flight Performance Assessment of UAV Systems. Journal of Southwest Jiaotong University, 54(3).
- ABDULKAREEM, N. M., & ZEEBAREE, S. R. (2022). OPTIMIZATION OF LOAD BALANCING ALGORITHMS TO DEAL WITH DDOS ATTACKS USING WHALE OPTIMIZATION ALGORITHM. Journal of Duhok University, 25(2), 65-85.
- Hammed, Z. S., Ameen, S. Y., & Zeebaree, S. R. (2023). Investigation of 5G wireless communication with dust and sand storms. Journal of Communications, 18(1).
- Abdulrahman, L. M., Zeebaree, S. R., & Omar, N. (2022). State of Art Survey for Designing and Implementing Regional Tourism Web based Systems. Academic Journal of Nawroz University, 11(3), 100-112.
- Zhou, Q., Wang, K., Lu, H., Xu, W., Sun, Y., & Guo, S. (2020). Canary: Decentralized distributed deep learning via gradient sketch and partition in multi-interface networks. IEEE Transactions on Parallel and Distributed Systems, 32(4), 900-917.
- Tanaka, K., et al. (2020). Communication-efficient distributed deep learning with GPU-FPGA heterogeneous computing. In 2020 IEEE Symposium on High-Performance Interconnects (HOTI) (pp. 43-46). IEEE.
- Soltani, M., Pourahmadi, V., & Sheikhzadeh, H. (2020). Pilot pattern design for deep learning-based channel estimation in OFDM systems. IEEE Wireless Communications Letters, 9(12), 2173-2176.
- Shu, J., Zhou, L., Zhang, W., Du, X., & Guizani, M. (2020). Collaborative intrusion detection for VANETs: A deep learning-based distributed SDN approach. IEEE Transactions on Intelligent Transportation Systems, 22(7), 4519-4530.
- Shi, S., et al. (2020). Communication-efficient distributed deep learning with merged gradient sparsification on GPUs. In IEEE INFOCOM 2020-IEEE Conference on Computer Communications (pp. 406-415).
- Qian, G., Li, Z., He, C., Li, X., & Ding, X. (2020). Power allocation schemes based on deep learning for distributed antenna systems. IEEE Access, 8, 31245-31253.
- Mohammed, S. A., & Shirmohammadi, S. (2020). A multimodal deep learning-based distributed network latency measurement system. IEEE Transactions on Instrumentation and Measurement, 69(5), 2487-2494.
- Han, R., Liu, C. H., Li, S., Wen, S., & Liu, X. (2020). Accelerating deep learning systems via critical set identification and model compression. IEEE Transactions on Computers, 69(7), 1059-1070.
- Cui, D., et al. (2020). Cloud workflow task and virtualized resource collaborative adaptive scheduling algorithm based on distributed deep learning. In 2020 IEEE International Conference on Advances in Electrical Engineering and Computer Applications (AEECA) (pp. 137-140).
- Dong, J., Wu, W., Gao, Y., Wang, X., & Si, P. (2020). Deep reinforcement learning based worker selection for distributed machine learning enhanced edge intelligence in the internet of vehicles. Intelligent and Converged Networks, 1(3), 234-242.
- Bui, V. -H., Nguyen, T. -T., & Kim, H. -M. (2020). Distributed operation of wind farm for maximizing output power: A multi-agent deep reinforcement learning approach. IEEE Access, 8, 173136-173146.
- Langer, M., He, Z., Rahayu, W., & Xue, Y. (2020). Distributed training of deep learning models: A taxonomic perspective. IEEE Transactions on Parallel and Distributed Systems, 31(12), 2802-2818.
- Qian, X. (2019). Wearable Computing Architecture over Distributed Deep Learning Hierarchy: Fall Detection Study. Case Western Reserve University.
- Wang, H., Chen, X., Xu, H., Liu, J., & Huang, L. (2019). Joint job offloading and resource allocation for distributed deep learning in edge computing. In 2019 IEEE 21st International Conference on High Performance Computing and Communications; IEEE 17th International Conference on Smart City; IEEE 5th International Conference on Data Science and Systems (HPCC/SmartCity/DSS) (pp. 734-741).
- Tian, Z., Luo, C., Qiu, J., Du, X., & Guizani, M. (2019). A distributed deep learning system for web attack detection on edge devices. IEEE Transactions on Industrial Informatics, 16(3), 1963-1971.
- Sattler, F., Wiedemann, S., Müller, K. -R., & Samek, W. (2019). Sparse binary compression: Towards distributed deep learning with minimal communication. In 2019 International Joint Conference on Neural Networks (IJCNN) (pp. 1-8).
- Lyu, Y. -H., Liu, C. -Y., Lee, C. -P., Tu, C. -H., & Hung, S. -H. (2019). Modeling Interprocessor Communication and Performance Scalability for Distributed Deep Learning Systems. In 2019 International Conference on High-Performance Computing & Simulation (HPCS) (pp. 169-176).
- Lee, H., Lee, S. H., & Quek, T. Q. (2019). Deep learning for distributed optimization: Applications to wireless resource management. IEEE Journal on Selected Areas in Communications, 37(10), 2251-2266).
- Kuang, D., Chen, M., Xiao, D., & Wu, W. (2019). Entropy-based gradient compression for distributed deep learning. In 2019 IEEE 21st International Conference on High Performance Computing and Communications; IEEE 17th International Conference on Smart City; IEEE 5th International Conference on Data Science and Systems (HPCC/SmartCity/DSS) (pp. 231-238).
DOI: https://doi.org/10.2478/jsiot-2023-0018 | Journal eISSN: 2956-8323
Language: English
Page range: 154 - 173
Submitted on: Aug 13, 2023
Accepted on: Sep 21, 2023
Published on: Dec 15, 2023
Published by: Future Sciences For Digital Publishing
In partnership with: Paradigm Publishing Services
Publication frequency: 2 issues per year
Keywords:
Related subjects:
© 2023 Rozin Majeed Abdullah, Lozan M. Abdulrahman, Nasiba M. Abdulkareem, Azar Abid Salih, published by Future Sciences For Digital Publishing
This work is licensed under the Creative Commons Attribution 4.0 License.