No abstract

Colorectal cancer is associated with a high mortality rate and significant patient risk. Images obtained during a colonoscopy are used to make a diagnosis, highlighting the importance of timely diagnosis and treatment. Using techniques of deep learning could enhance the diagnostic accuracy of existing systems. Using the most advanced deep learning techniques, a brand-new EnsemDeepCADx system for accurate colorectal cancer diagnosis has been developed. The optimal accuracy is achieved by combining Convolutional Neural Networks (CNNs) with transfer learning via bidirectional long short-term memory (BILSTM) and support vector machines (SVM). Four pre-trained CNN models comprise the ADaDR-22, ADaR-22, and DaRD-22 ensemble CNNs: AlexNet, DarkNet-19, DenseNet-201, and ResNet-50. In each of its stages, the CADx system is thoroughly evaluated. From the CKHK-22 mixed dataset, colour, greyscale, and local binary pattern (LBP) image datasets and features are utilised. In the second stage, the returned features are compared to a new feature fusion dataset using three distinct CNN ensembles. Next, they incorporate ensemble CNNs with SVM-based transfer learning by comparing raw features to feature fusion datasets. In the final stage of transfer learning, BILSTM and SVM are combined with a CNN ensemble. The testing accuracy for the ensemble fusion CNN DarD-22 using BILSTM and SVM on the original, grey, LBP, and feature fusion datasets was optimal (95.96%, 88.79%, 73.54%, and 97.89%). Comparing the outputs of all four feature datasets with those of the three ensemble CNNs at each stage enables the EnsemDeepCADx system to attain its highest level of accuracy.

No abstract

The phrase "Web Services (WSs)" are emerging as a creative scheme for furnishing the services to various immanent devices over the World Wide Web. The hasty intensification of the WSs applications and the availability of the vast count of the Service Providers create the certainty of selecting the "efficient" Service Provider by the consumers. The scenario Deduplication and Quality-of-Service (QoS) swears out as an objective to distinguish various Service Providers (SPs). The process of selecting proficient WSs / SPs, positioning and optimization of WSs Compositions are exigent dimensions of research with momentous entailments for the fruition of the "Web of Services" revelation. The term "Semantic WSs" follows appropriate semantic descriptions of WS functionality and a medium to facilitate programmed cogitating over WS Compositions (WSCs). The persisting model of the Semantic Web Services (SWSs) deals with the intriguing emerges like wretched forecast of best WSs and gemination of services with effective SPs, which heads to Quality level degradation on the Semantic Web. To deal the above identified issues, the anticipated research is planned to construct a model to manipulative the content similarities (semantic), consumption of a mixture of WSs and its corresponding SPs. After assessing these params, all the WSs are stratified on the basis of its consumption. Ultimately, the nominated scheme, selects the best and non duplicated copy of the WSs on the basis of its rating and placed it in the WSC. The process of detecting the duplicate copy would be performed by the Cryptographic Hash value of the Services. From the experimental annotations, it is recognized that our anticipated design amends the functionality of the SWSs in terms of Processor Utilization, Accessing Time, and its Space optimizations.

No abstract