Different techniques for hiding the text information using text steganography techniques: A survey

Narayana, V. Lakshman; Gopi, Arya; Kumar, Nitesh

doi:10.3166/isi.23.6.115-125

Cited by 13 publications

(10 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A data classifier requires a choice of features that must be custom fitted independently for different issues [19] [20]. Following component determination, classifier improvement requires detachment of the data into preparing and test data and experiences two noteworthy periods of data classifier development as shown in the Figure 2.…”

Section: Fig 1: Schematic Classification Representationmentioning

confidence: 99%

See 1 more Smart Citation

Machine Learning Methodology for Medical Data Analysis for Prediction of Risk

Sravanthi¹,

Harika²,

Archana³

et al. 2020

jcr

View full text Add to dashboard Cite

Mining data is a nontrivial procedure of finding information from a large volume of data. Such information can be helpful in settling on significant choices. Medical data show special features including noise coming about because of human just as methodical blunders, missing qualities and even meager conditions. The nature of data has huge ramifications for the nature of the mining results. Medical data classification is important to perform preprocessing steps so as to expel or at least lighten a portion of the issues related with medical data. Clustering is a descriptive-based data mining task. The clustering algorithm is also called as unsupervised learning algorithm that learns the unlabeled dataset and groups or clusters the instances based on their similarity and builds the clustering model. Clustering is same as classification in which data is grouped, but in this, groups are not predefined. In clustering, clusters are not predefined. Classification of different types of clustering is as follows: Hierarchical clustering, Partition clustering, Categorical clustering, Density based clustering and Grid based clustering. The main intension of the research is to classify the medical data with high accuracy value. In order to achieve promising results, a novel data classification methods have been designed that utilize a Improved Cluster Optimal Classifier (ICOC). The proposed method is compared with traditional methods and the results show that the proposed method performance is better and accurate.

show abstract

Section: Fig 1: Schematic Classification Representationmentioning

confidence: 99%

“…The Bayesian system is additionally seen as a procedure in medical expectation Particular it has been used for Brest malignancy guess and determination [20]. In future we mean to plan and actualize such framework for online applications [21].…”

Section: Literature Workmentioning

confidence: 99%

Machine Learning Methodology for Medical Data Analysis for Prediction of Risk

Sravanthi¹,

Harika²,

Archana³

et al. 2020

jcr

View full text Add to dashboard Cite

show abstract

“…The standard speck item can be invested to the exact portion kernel space by opening up the space [12].Direct component determination can be ideally performed in the limited dimensional experimental bit kernel space prompting ideal choice of relating unbounded dimensional kernel spaces. This is proportionate to ideal nonlinear choice of unique highlights in the info space that would have created those relating dimensional kernel spaces [13] [14].…”

Section: Fig 1: Kernel Function Flowmentioning

confidence: 99%

“…This oversampled informational index is then used by the classifier to take in a discriminative border between the two classes that isn't onesided because of class circulation in the informational collection. Cost-sensitive strategies in [15] and [16], then again, use an information subordinate cost framework either amid preparing or adjust a ultimate choice capacity [14].…”

Section: Nonlinear Separable Problemmentioning

confidence: 99%

Imbalanced Data in Sensible Kernel Space With Support Vector Machines Multiclass Classifier Design

Lakshmi¹,

Vasanthi²,

Suneetha³

et al. 2020

jcr

View full text Add to dashboard Cite

The utilization of various assessment measures for grouping different tasks have picked up a lot of consideration in previous decades extraordinarily for such issues through different and redundant classes. A classifier is proposed particularly to advance one of the conceivable measures, to be specific, the hypothetical G-mean method. In any case, the method is general, and it very well may be utilized to streamline bland assessment measures. The streamlining calculation to prepare the classifier is depicted, and the numerical plan is tried demonstrating its ease of use and power. The proposed oversampling calculation alongside a cost-reduction SVM classification is appeared to enhance execution when contrasted with other estimated strategies on numerous benchmark imbalanced informational indexes. What's more, a various leveled system is produced for multiclass imbalanced issues that have a dynamic class arrange. A novel structure for kernel space instruction in a limited space named Sensible Kernel Space (SKS) is introduced in this manuscript. The SKS can be expressly worked by utilizing any optimistic clear bit counting Gaussian BCG bit by means of an exact portion outlined. The proposed sensible Kernel space can ideally choose various subsets of recently mapped datasets in SKS keeping in mind the end goal to enhance the speculation execution of the classifier.

show abstract

“…We additionally consider the virtual machine screen programming and comprehend the extent of upgrade in the equivalent. Virtual machine the board utilizing programming segments are famous and exhibited utilizing numerous inquiries about [15]. The virtual machine screen or VMM are the product A Novel Hadoop Based Virtualization Automated Framework…”

Section: Introductionmentioning

confidence: 99%

A Novel Hadoop Based Virtualization Automated Framework

Deol*,

Nagaraju

2019

IJITEE

View full text Add to dashboard Cite

With the huge development in relocation to distributed computing for customary applications, the interest inHadoop VIRTUALIZATION is the new requirement for further research and improvements. The extreme interest for Hadoop VIRTUALIZATION in distributed computing is been tended to with the innovation improvement and regarding virtual machines. Significantly the equipment segments the executives of virtual machine is impressively achieving the pick of research with the ongoing headways by different organizations and open source explores like AlphaVM, Hyper-V, Integrity Virtual Machines, JPC (Virtual Machine), PowerVM, Sun xVM, VMware Workstation and z LPARs. Another most imperative segment of Hadoop VIRTUALIZATION and virtual machine picture stockpiling is been the bottleneck for the further headway. The impediments are being tended to by bunching and capacity territory organizing, anyway these current arrangements are no counterpart for the modern evaluation interest for the VM picture stockpiling necessities. Subsequently in this work we propose a cloud based virtual machine stockpiling structure intended for huge scale arrangement fulfilling alternate requirements for capacity highlights like replication and estimations of execution enhancements. This work additionally exhibits the headways in execution for Hadoop VIRTUALIZATION the board

show abstract

Different techniques for hiding the text information using text steganography techniques: A survey

Cited by 13 publications

References 0 publications

Machine Learning Methodology for Medical Data Analysis for Prediction of Risk

Machine Learning Methodology for Medical Data Analysis for Prediction of Risk

Imbalanced Data in Sensible Kernel Space With Support Vector Machines Multiclass Classifier Design

A Novel Hadoop Based Virtualization Automated Framework

Contact Info

Product

Resources

About