IoT has facilitated predominant advancements in cancer research in incorporating Artificial intelligence (AI) that enables the human decision makers to achieve better decision. Cervical cancer being a significant cause of mortalities among women across the world. Recently, Least Absolute Shrinkage and Selection Operator (LASSO) classifier has launched in predicting recurrence cancer genes in the cervix. However, the optimal selection of genes or recurrence genes in the prediction becomes a challenging task. Hilbert-Schmidt independence criterion with Diversity based Artificial Fish Swarm (H.S.D.A.F.S.) is phased for gene selection in the recurrence prediction to solve this paradigm. At the initial phase, the recurrence gene expression of lncRNA is collected from Geo Datasets. Secondly, data imputation, accomplished with Mode and Mean Missing method (MMM-DI). Thirdly, feature selection is compassed using H.S.D.A.F.S. In the H.S.D.A.F.S. algorithm, the diversity parameter is added based on the gene value and their risk score of the lncRNAs is computed using Artificial intelligence (AI) technique. Finally, recurrence prediction, an ENSemble Classification Framework (E.N.S.C.F.), is proposed by integrating the Internet of Things (IoT) based recurrent neural networks. The results are then combined via weighted majority voting-the prognostic factor computed with a risk score of nine lncRNA signatures for 300 samples taken from GSE44001. The Chi-Square method has pursued to obtain statistical results. The survival of the patient with recurrence cervical cancer is also portrayed in the proposed model.
Abstract. Swarm robotics is a number of small robots that are synchronically works together to accomplish a given task. Swarm robotics faces many problems in performing a given task. The problems are pattern formation, aggregation, Chain formation, self-assembly, coordinated movement, hole avoidance, foraging and self-deployment. Foraging is most essential part in swarm robotics. Foraging is the task to discover the item and get back into the shell. The researchers conducted foraging experiments with random-movement of robots and they have end up with unique solutions. Most of the researchers have conducted experiments using the circular arena. The shell is placed at the centre of the arena and environment boundary is well known. In this study, an attempt is made to different strategic movements like straight line approach, parallel line approach, divider approach, expanding square approach, and parallel sweep approach. All these approaches are to be simulated by using player/stage open-source simulation software based on C and C++ programming language in Linux operating system. Finally statistical comparison will be done with task completion time of all these strategies using ANOVA to identify the significant searching strategy.
Coronary artery diseases are one of the high-risk diseases, which occur due to the insufficient blood supply to the heart. The different types of plaques formed inside the artery leads to the blockage of the blood stream. Understanding the type of plaques along with the detection and classification of plaques supports in reducing the mortality of patients. The objective of this study is to present a novel clustering method of plaque segmentation followed by wavelet transform based feature extraction. The extracted features of all different kinds of calcified and sub calcified plaques are applied to first train and test three machine learning classifiers including support vector machine, random forest and decision tree classifiers. The bootstrap ensemble classifier then decides the best classification result through a voting method of three classifiers. A training dataset including 64 normal CTA images and 73 abnormal CTA images is used, while a testing dataset consists of 111 normal CTA images and 103 abnormal CTA images. The evaluation metrics shows better classification rate and accuracy of 97.7%. The Sensitivity and Specificity rates are 97.8% and 97.5%, respectively. As a result, our study results demonstrate the feasibility and advantages of developing and applying this new image processing and machine learning scheme to assist coronary artery plaque detection and classification.
Evaluation is a critical issue in any information systems. This problem has become more and more important with the rapid development of multimedia systems. Feature measures and similarity measures play a central role in content-based retrieval. Evaluation of their effectiveness and efficiency then become a key issue in assessing the performance of a content-based multimedia system.A learning a1gorithm has been studied to find a suitable and hopefully the best similarity function for a given set of feature measure and a given set of training data set.
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