Editorial review on Vol. 6 No. 1
Journal: Journal of Autonomous Intelligence DOI: 10.32629/jai.v6i1.1300
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1.Chandana S, Dhanyashree CJ, Ashwini KL, et al. Fuel automata: Smart fuel dispenser using RFID technology and IoT-based monitoring for automotive applications. Journal of Autonomous Intelligence 2023; 6(1): 682. doi: 10.32629/jai.v6i1.682.
2.Wang X, Chen G, Qian G, et al. Large-scale multi-modal pre-trained models: A comprehensive survey. Machine Intelligence Research 2023; 20(4): 447–482. doi: 10.1007/s11633-022-1410-8.
3.Li Z, Hou Z, Liu H, et al. Federated learning in big model era: Domain-specific multimodal large models. arXiv 2023; arXiv:2308.11217v3. doi: 10.48550/arXiv.2308.11217.
4.Kerley J, Anderson DT, Alvey B, Buck A. How should simulated data be collected for AI/ML and unmanned aerial vehicles?. In: Howell CL, Manser KE, Rao RM (editors). Synthetic data for artificial intelligence and machine learning: Tools, techniques, and applications, Proceedings of the International Society for Optics and Photonics; 2023 July 13; Orlando, FL, USA. The International Society for Optics and Photonics; 2023. Volume 12529, p. 153–173.
5.Al-Jumaily AF, Al-Jumaily A, Al-Jumaili SJ. Prediction method of business process remaining time based on attention bidirectional recurrent neural network. Journal of Autonomous Intelligence 2023; 6(1): 639. doi: 10.32629/jai.v6i1.639.
6.Meena G, Mohbey KK, Acharya M, Lokesh K. An improved convolutional neural network-based model for detecting brain tumors from augmented MRI images. Journal of Autonomous Intelligence 2023; 6(1): 561. doi: 10.32629/jai.v6i1.561.
7.Mustaqeem M, Siddiqui T. A hybrid software defects prediction model for imbalance datasets using machine learning techniques:(S-SVM model). Journal of Autonomous Intelligence 2023; 6(1): 559. doi: 10.32629/jai.v6i1.559.
8.Durairaj M, Suneetha Ch, Krishna Mohan BH. Financial time series prediction using deep computing approaches. Journal of Autonomous Intelligence 2023; 6(1): 558. doi:10.32629/jai.v6i1.
9.Pal P, Kumar A, Saini G. Contactless methods to acquire heart and respiratory signals—A review. Journal of Autonomous Intelligence 2023; 6(1): 715. doi: 10.32629/jai.v6i1.715.
10.Saluja R, Rai M, Saluja R. Designing new student performance prediction model using ensemble machine learning. Journal of Autonomous Intelligence 2023; 6(1): 583. doi:10.32629/jai.v6i1.583.
11.Waheed SA, Matheen MA, Hussain S, et al. Machine learning approach to analyze the impact of demographic and linguistic features of children on their stuttering. Journal of Autonomous Intelligence 2023; 6(1): doi: 10.32629/jai.v6i1.553.
12.Kondala M, Nudurupati SS, Riwayadi E, et al. Moving towards a sustainable world with the circular economy practices concerning the SMEs in Visakhapatnam’s ice-cream industry. Journal of Autonomous Intelligence 2023; 6(1): 676. doi: 10.32629/jai.v6i1.676.
2.Wang X, Chen G, Qian G, et al. Large-scale multi-modal pre-trained models: A comprehensive survey. Machine Intelligence Research 2023; 20(4): 447–482. doi: 10.1007/s11633-022-1410-8.
3.Li Z, Hou Z, Liu H, et al. Federated learning in big model era: Domain-specific multimodal large models. arXiv 2023; arXiv:2308.11217v3. doi: 10.48550/arXiv.2308.11217.
4.Kerley J, Anderson DT, Alvey B, Buck A. How should simulated data be collected for AI/ML and unmanned aerial vehicles?. In: Howell CL, Manser KE, Rao RM (editors). Synthetic data for artificial intelligence and machine learning: Tools, techniques, and applications, Proceedings of the International Society for Optics and Photonics; 2023 July 13; Orlando, FL, USA. The International Society for Optics and Photonics; 2023. Volume 12529, p. 153–173.
5.Al-Jumaily AF, Al-Jumaily A, Al-Jumaili SJ. Prediction method of business process remaining time based on attention bidirectional recurrent neural network. Journal of Autonomous Intelligence 2023; 6(1): 639. doi: 10.32629/jai.v6i1.639.
6.Meena G, Mohbey KK, Acharya M, Lokesh K. An improved convolutional neural network-based model for detecting brain tumors from augmented MRI images. Journal of Autonomous Intelligence 2023; 6(1): 561. doi: 10.32629/jai.v6i1.561.
7.Mustaqeem M, Siddiqui T. A hybrid software defects prediction model for imbalance datasets using machine learning techniques:(S-SVM model). Journal of Autonomous Intelligence 2023; 6(1): 559. doi: 10.32629/jai.v6i1.559.
8.Durairaj M, Suneetha Ch, Krishna Mohan BH. Financial time series prediction using deep computing approaches. Journal of Autonomous Intelligence 2023; 6(1): 558. doi:10.32629/jai.v6i1.
9.Pal P, Kumar A, Saini G. Contactless methods to acquire heart and respiratory signals—A review. Journal of Autonomous Intelligence 2023; 6(1): 715. doi: 10.32629/jai.v6i1.715.
10.Saluja R, Rai M, Saluja R. Designing new student performance prediction model using ensemble machine learning. Journal of Autonomous Intelligence 2023; 6(1): 583. doi:10.32629/jai.v6i1.583.
11.Waheed SA, Matheen MA, Hussain S, et al. Machine learning approach to analyze the impact of demographic and linguistic features of children on their stuttering. Journal of Autonomous Intelligence 2023; 6(1): doi: 10.32629/jai.v6i1.553.
12.Kondala M, Nudurupati SS, Riwayadi E, et al. Moving towards a sustainable world with the circular economy practices concerning the SMEs in Visakhapatnam’s ice-cream industry. Journal of Autonomous Intelligence 2023; 6(1): 676. doi: 10.32629/jai.v6i1.676.
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