Page 79 - eBook_Proceedings of the International Conference on Digital Manufacturing V2
P. 79

Fabrication and Characterization of a Low-Cost Piezoelectric using Rochelle
                         Salt for Energy Harvesting and Sensor Applications

               Chitra,  B.,  & Kumar,  S. Senthil. (2021).  An optimised deep
                    learning model using  mutation-based atom search
                    optimisation algorithm for cervical cancer detection. Soft
                    Computing, 25(24), 15363–15376.
               Ding, Dong, Wang, Hanyan, Jiang, Xu, Li,  Hongjiang, Ma,
                    Liying, & Guo, Fang.  (2021). Machine  learning-based
                    prediction  of  survival  prognosis  in  cervical  cancer.  BMC
                    Bioinformatics, 22(1), Article 408.
               Fang, Min, Lei, Xin, Liao, Bin, & Wu, Felix X. (2022). A deep
                    neural network for cervical cell  classification based on
                    cytology images. IEEE Access, 10, 130968–130980.
               Fan, Zhen, Zhan, Yue, Wang, Ming, Chen, Yue, Xu, Jiayi, &
                    Zhang, Wei. (2023). CAM-VT: A weakly supervised cervical
                    cancer nest image identification approach using conjugated
                    attention mechanism and visual transformer. Computers in
                    Biology and Medicine, 162, Article 107127.
               Ghantasala, Gopi Sai Prakash, Hung, Binh Thanh, Chakrabarti,
                    Prasun, R, Shriram, & Pellakuri, Venkata. (2024). Artificial
                    intelligence based machine learning algorithm for prediction
                    of  cancer in female anatomy. Multimedia  Tools and
                    Applications.
               Ghoneim, Ahmed, Muhammad, Ghulam, & Hossain, M. Shamim.
                    (2020). Cervical  cancer  classification using convolutional
                    neural  networks and extreme learning machines.  Future
                    Generation Computer Systems, 102, 643–649.
               Glučina, Matea, Lorencin, Antun, Anđelić, Nikola, & Lorencin,
                    Ivan.  (2023).  Cervical  cancer  diagnostics  using  machine
                    learning algorithms and class balancing techniques. Applied
                    Sciences, 13(2), 913.
               Hong, Zhen, Xiong, Jian, Yang, Hao, & Mo, Yu Kang. (2024).
                    Lightweight low-rank adaptation vision  transformer
                    framework for cervical cancer detection  and cervix type
                    classification. Bioengineering, 11(5), 469.
               Hou, Xiaohui, Shen, Guibin, Zhou, Lei, Li, Yuting, Wang, Ting,
                    & Ma, Xiaosong. (2022). Artificial intelligence in cervical
                    cancer screening and diagnosis. Frontiers in Oncology, 12,
                    Article 977110.







                                              63
   74   75   76   77   78   79   80   81   82   83   84