aspg-18-1540-1792025642 20261015005402 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Journal of Intelligent Systems and Internet of Things JISIoT 2769-786X 2690-6791 2023 8 1 Ahmed Abdelhafeez Faculty of Information Systems and Computer Science, October 6th University, Cairo, 12585, Egypt Hoda K. Mohamed Faculty of Engineering, Ain shams University, Cairo, 11566, Egypt Melanoma is the kind of skin cancer that poses the greatest risk to one's life and has the maximum mortality rate within the group of skin cancer disorders. Even so, the automated placement and classification of skin lesions at initial phases remains a complicated task due to the lack of contrast melanoma molarity and skin fraction and a greater level of color similarity among melanoma-affected and -nonaffected areas. Contemporary technological improvements and research methods enabled it to recognize and distinguish this type of skin cancer more successfully. A clustering technique called neutrosophic c-means clustering (NCMC) is presented in this research to group ambiguous data in the detection of skin cancer. This algorithm takes its cues from both fuzzy c-means and the neutrosophic set structure. To arrive at such a structure, an appropriate objective function must first be created and then minimized. The clustering issue must then be stated as a restricted minimization problem, the solution of which is determined by the objective function. This paper made a comparison between NCMC and fuzzy c-means clustering (FCMC). The results show that the NCMC is more suitable than the FCMC. 2023 33 42 1540 https://creativecommons.org/licenses/by/4.0/ 10.54216/JISIoT.080103 https://americaspg.com/journal/18/article/1540 https://americaspg.com/storage/41683498700.pdf