Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Conference Papers Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Innovative Method for Detecting Liver Cancer using Auto Encoder and Single Feed Forward Neural Network Subject The topic of the resource Auto Encoder (AE); Convolutional Neural Network (CNN); Extreme Learning Machine; Support Vector Machine (SVM) Description An account of the resource Liver cancer ranks sixth among all cancers in frequency of incidence. A CT scan is the gold standard for diagnosis. These days, CT scan images of the liver and its tumor can be segmented using deep learning and Neural Network techniques. In this proposed approach to identifying cancer cells, it's focus on four important areas: To enhance a photo by taking out imperfections and unwanted details. An ostu method is used for this purpose. Specifically, this proposed approach to use the watershed segmentation technique for image segmentation, followed by feature extraction, in an effort to isolate the offending cancer cell. After finishing the model training with AE-ELM. To do this, Extreme Learning Machine incorporates an auto encoder. To achieve effective and supervised recognition, the network's strengths of Extreme Learning Machine (ELM) are thoroughly leveraged, including its few training parameters, quick learning speed, and robust generalization ability. The auto encoder-extreme learning machine (AE-ELM) network has been shown to have a respectable recognition impact when the sigmoid activation function is used and the number of hidden layer neurons is set to 1200. According to the results of this investigation, a method based on AE-ELM can be utilized to detect the liver tumor. As compared to the CNN and ELM models, this technique achieves superior accuracy (around 99.23%). 2023 IEEE. Creator An entity primarily responsible for making the resource Sowparnika B.; Yamini K.; Walid M.A.A.; Prasad J.; Aparna N.; Chauhan A. Source A related resource from which the described resource is derived Proceedings of the 2nd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2023, pp. 156-161. Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Date A point or period of time associated with an event in the lifecycle of the resource 2023-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICAAIC56838.2023.10140207" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICAAIC56838.2023.10140207</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85163700872&amp;doi=10.1109%2FICAAIC56838.2023.10140207&amp;partnerID=40&amp;md5=997f62c923c1d9e75d0626636f431fe0" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85163700872&amp;doi=10.1109%2fICAAIC56838.2023.10140207&amp;partnerID=40&amp;md5=997f62c923c1d9e75d0626636f431fe0</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 978-166545630-2 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Conference paper Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Sowparnika B., Nandha Engineering College, Department of Biomedical Engineering, Tamilnadu, Erode, India; Yamini K., Sriher University, Cyber Security in Computer Science and Engineering, Sri Ramachandra Faculty of Engineering, Tamilnadu, Chennai, India; Walid M.A.A., Khulna University of Engineering &amp; Technology (KUET), Department of Computer Science and Engineering, Bangladesh; Prasad J., Shri Shankaracharya College of Pharmaceutical Sciences, Chhattisgarh, Junwani, Bhilai, India; Aparna N., Dhaanish Ahmed Institute of Technology, Department of Bio Medical Engineering, Tamilnadu, Coimbatore, India; Chauhan A., Christ (Deemed to Be University), Department of Life Sciences, Karnataka, Bengaluru, India