Ezhilarasie Rajapackiyam

The accurate and reliable derivation of the pap smear cell, which contains cytoplasm and nucleus regions, depends on the segmentation process employed in the cervical cancer detection mechanism. In this paper, an Improved Boykov’s Graph Cut-based Conditional Random Fields and Superpixel imposed Semantic Segmentation Technique (IBGC-CRF-SPSST) is proposed for efficient cervical cancer detection. This proposed IBGC-CRF-SPSST embeds the complete benefits of constraint association among pixels and superpixel edge data for accurate determination of the nuclei and cytoplasmic boundaries so as to ensure efficient differentiation of the healthy and unhealthy cancer cells. Finally, the pixel-level forecasting potential of Conditional Random Fields is included for enhancing the degree of semantic-based segmentation accuracy to a predominant level. The experimental evaluated results of the proposed IBGC-CRF-SPSST aim to produce an accuracy of 99.78%, a mean processing time of 2.18sec, a precision of 96%, a sensitivity of 98.92%, and a specificity of 99.32% value which is determined to be excellent and on par with the existing detection techniques used for investigating cervical cancer.

Edge computing offers potential benefits to applications working in IoT (Internet of Things) and CPS (Cyber Physical Systems) environments by bringing the power of computing proximate to the devices, which demand high computational resources. As computational capabilities are currently untapped in edge devices like the IoT gateway, the computational intensive part of an application like a thread, a module or a task can be offloaded to the edge devices rather than to the cloud by the end devices. In this paper, an approach that employs Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) is used to determine the near optimal solution for scheduling offloadable components in an application, with the intent of significantly reducing the execution time of an application and energy consumption of the smart devices. With a new inertial weight equation, an Adaptive Genetic Algorithm – Particle Swarm Optimization (AGA-PSO) algorithm is proposed which uses GA’s ability in exploration and PSO’s ability in exploitation to make the offloading optimized without violating the deadline constraint of an application.

Generally, several IoT (Internet of Things) applications employ cloud data centre for processing the data generated by edge devices like smartphones and tablets. Due to the increasing use of the IoT devices, the demand for higher computational and c