Oral Squamous Cell Carcinoma (OSCC) is a type of Head and Neck Squamous Cell Carcinoma (HNSCC) and it should be diagnosed at early stages to accomplish efficient treatment, increase the survival rate, and reduce death rate. Histopathological imaging is a wide-spread standard used for OSCC detection. However, it is a cumbersome process and demands expert's knowledge. So, there is a need exists for automated detection of OSCC using Artificial Intelligence (AI) and Computer Vision (CV) technologies. In this background, the current research article introduces Improved Slime Mould Algorithm with Artificial Intelligence Driven Oral Cancer Classification (ISMA-AIOCC) model on Histopathological images (HIs). The presented ISMA-AIOCC model is aimed at identification and categorization of oral cancer using HIs. At the initial stage, linear smoothing filter is applied to eradicate the noise from images. Besides, MobileNet model is employed to generate a useful set of feature vectors. Then, Bidirectional Gated Recurrent Unit (BGRU) model is exploited for classification process. At the end, ISMA algorithm is utilized to fine tune the parameters involved in BGRU model. Moreover, ISMA algorithm is created by integrating traditional SMA and Chaotic Oppositional Based Learning (COBL). The proposed ISMA-AIOCC model was validated for performance using benchmark dataset and the results pointed out the supremacy of ISMA-AIOCC model over other recent approaches.

Digital data has been accelerating day by day with a bulk of dimensions. Analysis of such an immense quantity of data popularly termed as big data, which requires tremendous data analysis scalable techniques. Clustering is an appropriate tool for data analysis to observe hidden similar groups inside the data. Clustering distinct datasets involve both Linear Separable and Non-Linear Separable clustering algorithms by defining and measuring their inter-point similarities as well as non-linear similarity measures. Problem Statement: Yet there are many productive clustering algorithms to cluster linearly; they do not maintain quality clusters.Kernel-based algorithms make use of non-linear similarity measures to define similarity while forming clusters specifically with arbitrary shapes and frequencies. Existing System:Current Kernel-based clustering algorithms have few restraints concerning complexity, memory, and performance. Time and Memory will increase equally when the size of the dataset increase. It is challenging to elect kernel similarity function for different datasets. We have classical random sampling and low-rank matrix approximation linear clustering algorithms with high cluster quality and low memory essentials. Proposed work: in our research, we have introduced a parallel computation performing Kernel-based clustering algorithm using Particle Swarm Optimization approach. This methodology can cluster large datasets having maximum dimensional values accurately and overcomes the issues of high dimensional datasets.