Deep transfer modeling for classification of Maize Plant Leaf Disease

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...     

A privacy-preserving model based on differential approach for sensitive data in cloud environment

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.

     

Neo Arithmetic and Ranking Techniques for Trapezoidal Generalized Interval Valued Fuzzy Numbers: Their Applications in Decision Making for Medical Investigation

Neural Processing Letters - The uncertainty caused mainly by the deficiency of precision and data, artificial/human-made errors, information accessed from expert opinions or very miniature size of...     

Towards finding the best-fit distribution for OSN data

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...     

Performance Investigations and PAPR Reduction Analysis Using Very Efficient and Optimized Amended SLM Algorithm for Wireless Communication OFDM System

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...     

Modeling of diffusional limitations in styrene acrylonitrile random bulk copolymerization

Diffusional limitations (the gel, glass, and cage effects) are manifested in several bulk free radical homopolymerizations as well as in random copolymerizations. These are associated with decreases of several orders of magnitude of the rate constants of termination, propagation, and initiation (the initiator efficiency), respectively. These phenomena have been modeled earlier using the free volume theory for the diffusivities of primary radicals, macro‐radicals, and monomer molecules, and have been applied to homopolymerizations. In this study, a similar model is developed for random bulk copolymerizations. The parameters of the model are fitted using isothermal data on styrene acrylonitrile random copolymerization carried out in small ampoules. Thereafter, best‐fit global correlations have been developed for this system. This enables the model to be used for studying non‐isothermal copolymerizations, as well as for carrying out optimization of industrial reactors, where non‐isothermal conditions are a norm. POLYM. ENG. SCI., 55:2098–2110, 2015. © 2014 Society of Plastics Engineers     

A QoS Aware Resource Placement Approach Inspired on the Behavior of the Social Spider Mating Strategy in the Cloud Environment

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...     

Electrospun microporous gelatin–polycaprolactone blend tubular scaffold as a potential vascular biomaterial

The work reported involved the fabrication of an electrospun tubular conduit of a gelatin and polycaprolactone (PCL) blend as an adventitia‐equivalent construct. Gelatin was included as the matrix for increased biocompatibility with the addition of PCL for durability. This is contrary to most of the literature available for biomaterials based on blends of gelatin and PCL where PCL is the major matrix. The work includes the assiduous selection of key electrospinning parameters to obtain smooth bead‐free fibres with a narrow distribution of pore size and fibre diameter. Few reports elucidate the optimization of all electrospinning parameters to fabricate tubular conduits with a focus on obtaining homogeneous pores and fibres. This stepwise investigation would be unique for the fabrication of gelatin–PCL electrospun tubular constructs. The fabricated microfibrous gelatin–PCL constructs had pores of size ca 50–100 μm reportedly conducive for cell infiltration. The measured value of surface roughness of 57.99 ± 17.4 nm is reported to be favourable for protein adhesion and cell adhesion. The elastic modulus was observed to be similar to that of the tunica adventitia of the native artery. Preliminary in vitro and in vivo biocompatibility tests suggest safe applicability as a biomaterial. Minimal cytotoxicity was observed using MTT assay. Subcutaneous implantation of the scaffold demonstrated acute inflammation which decreased by day 15. The findings of this study could enable the fabrication of smooth bead‐free microfibrous gelatin–PCL tubular construct as viable biomaterial which can be included in a bilayer or a trilayer scaffold for vascular tissue engineering. © 2019 Society of Chemical Industry