Multimedia Tools and Applications - Currently, Deep Learning is playing an influential role for Image analysis and object classification. Maize’s diseases reduce production that subsequently... 相似文献
A large amount of data and applications need to be shared with various parties and stakeholders in the cloud environment for storage, computation, and data utilization. Since a third party operates the cloud platform, owners cannot fully trust this environment. However, it has become a challenge to ensure privacy preservation when sharing data effectively among different parties. This paper proposes a novel model that partitions data into sensitive and non-sensitive parts, injects the noise into sensitive data, and performs classification tasks using k-anonymization, differential privacy, and machine learning approaches. It allows multiple owners to share their data in the cloud environment for various purposes. The model specifies communication protocol among involved multiple untrusted parties to process owners’ data. The proposed model preserves actual data by providing a robust mechanism. The experiments are performed over Heart Disease, Arrhythmia, Hepatitis, Indian-liver-patient, and Framingham datasets for Support Vector Machine, K-Nearest Neighbor, Random Forest, Naive Bayes, and Artificial Neural Network classifiers to compute the efficiency in terms of accuracy, precision, recall, and F1-score of the proposed model. The achieved results provide high accuracy, precision, recall, and F1-score up to 93.75%, 94.11%, 100%, and 87.99% and improvement up to 16%, 29%, 12%, and 11%, respectively, compared to previous works.
Surface integrity characterization of manufactured component is very important as it significantly affects the in-service performance of the component. Till now, surface integrity was evaluated using conventional measurement technique like microhardness tester, X-ray diffraction, optical microscopy and surface roughness tester. But, this technique being laboratory based cannot be used for in-service monitoring of the surface integrity. The present study focuses on the characterization of surface integrity upon electric discharge machined sample using non-destructive magnetic Barkhausen noise technique. Electric discharge machining was performed in die-sinking mode on die steel using copper–tungsten electrode (negative polarity). Experiment was performed by selecting different levels of peak current, gap voltage and pulse on time. Surface integrity characteristics like microhardness change, residual stress, microstructural alteration and surface roughness were analysed using microhardness tester, X-ray diffraction, optical microscopy and surface roughness tester, respectively, and were then correlated with magnetic parameter like root mean square value and peak value obtained from Barkhausen noise signal. The results show a good correlation between magnetic parameter (RMS and Peak value) of Barkhausen noise with the microhardness and surface roughness of the machined sample.
The Journal of Supercomputing - Currently, all online social networks (OSNs) are considered to follow a power-law distribution. In this paper, the degree distribution for multiple OSNs has been... 相似文献
Wireless Personal Communications - The orthogonal frequency division multiplexing (OFDM) is the most encouraging multi-carrier modulation system chosen for the high data rates but the objective is... 相似文献
Wireless Personal Communications - The efficient management of resource sharing plays a crucial role in the cloud execution environment. The constraints such as heterogeneity and dynamic nature of... 相似文献
Silicon - In this paper, we propose and develop an analytical model of a Triple material double gate Tunnel Field Effect Transistor (TM-DG TFET) with hetero-dielectric gate oxide stack comprising... 相似文献