Abstract
Privacy and security in the medical field are major aspects to consider in the current era. This is due to the huge necessity for data among providers, payers and patients, respectively. ...Recently, more researchers are making their contributions in this field under different aspects. But, there need more enhancements concerning security. In this circumstance, this paper intends to propose a new IoT-dependent health care privacy preservation model with the impact of the machine learning algorithm. Here, the information or data from IoT devices is subjected to the proposed sanitization process via generating the optimal key. In this work, the utility of the machine learning model is the greatest gateway to generating optimal keys as it is already trained with the neural network. Moreover, identifying the optimal key is the greatest crisis, which is done by a new Improved Dragonfly Algorithm algorithm. Thereby, the sanitization process works, and finally, the sanitized data are uploaded to IoT. The data restoration is the inverse process of sanitization, which gives the original data. Finally, the performance of the proposed work is validated over state-of-the-art models in terms of sanitization and restoration analysis.
•The highlights of the article are given below for your kind perusal. Kindly, consider and forward my article for further processes.•In this research work, a novel viral host prediction model from ...genomic datasets was introduced with the following three major phases: (a) Pre-processing, (b) Feature extraction and (c) Prediction phase. Initially, the collected raw genomic datasets was subjected to pre-processing, where the data cleaning operations was undergone.•Then, the features like the statistical features, high order statistical features, weighted holoentropy, chi-squared features, relief based features, symmetric uncertainty based features was extracted from the pre-processed data. Then, the ensemble technique was used for the prediction, which includes the “SVM, NN, RF and optimized CNN”, respectively. Here, the extracted features were fed as input to the SVM, NN and RF classifiers.•The resultant from these classifiers was as the input to optimized CNN, which provides the final results Moreover, with the objective of enhancing the prediction accuracy of CNN, its weights will be fine-tuned using AFPA, which be an improved version of standard FPA. On observing the accuracy value, the AFPA+EC had exhibit the highest value, and this in turn clearly says that the AFPA+EC is sufficient for accurately predicting the host of the virus.•The proposed model is evaluated in terms of “specificity, sensitivity, accuracy, and precision, FPR, FNR, NPV, FDR, F1-Score and MCC”, respectively.
Viruses are common biological agents that are supposed to be the world's greatest repositories of undiscovered genetic diversity. One of the common problems in bioinformatics is gene-disease prediction. Techniques for taxonomic classification, host range, and biological properties of newly discovered viruses are needed for complete functional characterization and annotation. Understanding the behaviors as well as interactions of microbial populations needs research into virus-host infectious associations. The following three main steps of an unique viral host prediction method using genomic datasets are introduced in this research work: “(a) Pre-processing, (b) Feature extraction, and (c) Prediction phase”. In starting stage, raw genomic datasets are exposed to pre-processing, which would include data cleaning activities. The pre-processed data is then used to extract the statistical features, high order statistical features, weighted holoentropy, chi-squared features, relief-based features, and symmetric uncertainty-based features. The ensemble approach, which uses the Support Vector Machine (SVM), Neural Network (NN), Random Forest (RF), and Convolutional Neural Network (CNN), respectively, is then employed for the prediction. Here, SVM, NN, and RF classifiers are fed the retrieved features as input. These classifiers' outputs will be fed into an optimised CNN, which produces the final prediction outcome. Additionally, the Adaptive Flower Pollination Algorithm (AFPA), an upgraded variant of the conventional Flower Pollination Protocol, is used to fine-tune the weights of optimised CNN in order to increase prediction accuracy (FPA). The AFPA+EC's F1-Score is 0.75026, which is correspondingly 61%, 52.4%, 20.2%, and 26.5% better than the conventional methods SVM, KNN, RF, and CNN.
Indistinct soft tissue sarcomas are a form of tumor that can be difficult to diagnose in a tremendous population. For earlier prediction of distant metastasis, some traditional classifications are ...suffered by technological issues, lack of enhancement methods, reliability, and so on. To provide a better classification, this paper introduces a new deep learning-based soft tissue sarcoma classification framework. Initially, spatial features and LVP features are extracted.The main aim of this phase is to generate LVP using each pixel vector and provides the benefits of inherent structures in edge patches. The subsequent classification phase is utilized an optimized Convolutional Neural Network (CNN). Moreover, the weight and filter size of CNN will be optimally tuned by the new Self Adaptive Bat Algorithm (SA-BA). Finally, SA-BA method is compared over some existing classifiers in terms of various measures.
