AU2021102301A4 - Decision support system based on machine learning and deep learning for secure data management - Google Patents

Abstract

DECISION SUPPORT SYSTEM BASED ON MACHINE LEARNING AND DEEP LEARNING FOR SECURE DATA MANAGEMENT ABSTRACT The present invention relates to Decision support system based on machine learning and deep learning for secure data management. The objective of the present invention is to solve the problems in the prior art related to adequacies in technologies of secure data management using decision support system based on machine learning. 23 DRAWINGS Applicants: Dr. Parmod Kumar & Other I'=*N1. I M)Yia FIGURE 1 24

Description

DRAWINGS

Applicants: Dr. Parmod Kumar & Other

I'=*N1. I M)Yia

FIGURE 1

DECISION SUPPORT SYSTEM BASED ON MACHINE LEARNING AND DEEP LEARNING FOR SECURE DATA MANAGEMENT FIELD OF INVENTION

[001]. The present invention relates to the technical field of decision

support systems (DSSs) and electronic Data records (EDRs). More

specifically, the present invention relates to methods of delivering decision

support systems to data resource.

[002]. The present invention relates to the technical field to data

management systems and, more particularly to decision support response

systems and methods for managing data content using decision support

systems of the content management systems.

[003]. More particularly, the present invention is related to decision

support system based on machine learning and deep learning for secure data

management.

BACKGROUND & PRIOR ART

[004]. The subject matter discussed in the background section should not

be assumed to be prior art merely as a result of its mention in the background

section. Similarly, a problem mentioned in the background section or

associated with the subject matter of the background section should not be

assumed to have been previously recognized in the prior art. The subject

matter in the background section merely represents different approaches,

which in-and-of-themselves may also be inventions.

[005]. Today a growing amount of tasks for which in earlier times human

interaction was needed are today fulfilled by automatized system, thanks to

so-called artificial intelligence methods, mainly subsumed under "machine

learning" systems. Such machine learning system comprise at least a

machine learning model which is trained with many input data. Such data

may come from various sources, and are supposed to build a link between a

subject and a qualification of the subject. E.g. for face recognition hundreds

of thousands of pictures of a person are loaded, and the qualification assigns

names to such pictures. This assignment is typically done by humans.

[006]. But more and more such tasks relate to decisions where people can

be really severely impacted depending upon the decision. Such situations

may in particular occur in automated systems relating to traffic, e.g. break

assistance, autonomous speedometer with sign detection, up to autonomous

driving. In other areas like health and financial analysis this applies as well,

as it is known for credibility scoring, access to health care etc. This already

indicates the wide range of scenarios where machine learning based

decision methods may heavily impact the affected individuals.

[007]. It is apparent that such systems may lead to outcomes, where a

human decision would most likely come to less impacting decisions. This is

in particular true in case of car accidents, that a conscious human driver

would be able to avoid.

[008]. Furthermore, such systems are also due to the heavy impact prone to

hacking activities. Should hackers may make themselves empowered to full

brake a good share of cars produced by a certain brand at one time, this

could costs lives, ruin large enterprises or make them open to blackmailing.

[009]. Some of the work listed herewith:

[0010]. IN202011054366A - A DECISION SUPPORT SYSTEM FOR

SECURED MANAGEMENT OF A WATER DISTRIBUTION

NETWORK AND METHOD THEREOF presents "a decision support

system (100, 200) for secured management of a water distribution network

and method (300) thereof. The decision support system (100) comprises a

plurality of first computing devices (20), where each first computing device

(20) is associated with each water resource (10) and is configured to transmit

a first status of water of each water resource (10); a plurality of second

computing devices (40), where each second computing device (40) is in

electronic communication with at least one sensor associated with each

branching point (50) of the water distribution network, and is configured to

transmit a second status of water detected by at least one sensor associated

with each branching point (50); a distributed ledger (60) for secured

information exchange between plurality of first computing devices (20),

plurality of second computing devices (40) and a plurality of third

computing devices (80); plurality of third computing devices (80), where

each third computing device (80) is enabled to authorize commencement or

decline of extraction of water from each water resource (10) based on the

first status of water, commencement or decline of distribution of water from

each branching point (50) based on the second status of water; and a data center (90) in communication with the distributed ledger (60) to store data related to each transaction."

[0011]. IN201911001689A - SMART DECISION SUPPORT SYSTEM

ARCHITECTURE FOR IOT BASED AGRICULTURE APPLICATIONS

presents "A smart decision support system (DSS) for IOT based agriculture

applications comprises soil MC prediction model and DSS scheme for

irrigation control; and all sensor data (1.16) are collected and arranged in

proper structure for soil MC prediction; and the aggregated real-time

agriculture data is given input to the partial least square regression model

(1.4). The weather data (1.14) is also considered as input to the prediction

model."

[0012]. US10817545B2 - Cognitive decision system for security and log

analysis using associative memory mapping in graph database Presents

"Methods, systems, and apparatus, including computer programs encoded

on a computer storage medium, for a system to create and employ

associative memory maps for analysis of security file and/or logs are

disclosed. In one aspect, a method includes the actions of receiving, from

an external application, a request for a recommended action; extracting

information regarding the entities and relationships between the entities

from a data source; constructing an associative memory map from the

extracted information; selecting a subgraph from the associative memory

map based on a result of employing a vector to search nodes in the

associative memory map; identifying the nodes most relevant to the

requested recommend action base on a shortest paths of traversal in the

selected subgraph of nodes; determining the requested recommended action

based on an event identified in the relationships between the identified most

relevant nodes; and transmitting the recommended action to the external

application."

[0013]. IN201841038453A - REAL-TIME AGRARIAN DECISION

SUPPORT SYSTEM Presents "Real-time Agrarian Decision Support

System (100) comprises a first module (101) that facilitates the collection

of a plurality of data (106) from a plurality of sources, which is then

forwarded to a third module (103); a second module (102) that comprises a

plurality of sensors (107) and a base station (108), said plurality of sensors

(107) facilitating the collection of a plurality of real-time data and said base

station (108) facilitating the collection of the real-time data transmitted by

the plurality of sensors (107), which is then forwarded to the third module

(103), said third module (103) facilitating the maintenance of the data

received from the first module (101) and the second module (102); a fourth

module (104) that facilitates the real-time analysis of the various data in the

third module (103); and a fifth module (105) that facilitates the

communication of the one or more users with the system (100) and also

facilitates the viewing of the results of the system (100) by the one or more

users. Figure to be included"

[0014]. W02018002953A1 - INTEGRATED DECISION SUPPORT

SYSTEM AND METHOD FOR DERIVING INFERENCES FROM

DATA SETS presents "an integrated decision support system for deriving

inferences from data sets. The integrated decision support system includes

a reasoning mechanism to quantify the uncertainty from the incomplete or

inaccurate data, a learning mechanism to evaluate rules and an inference

mechanism to make inferences from the learned rules. The integrated

decision support system and method provides mechanism to reason and

learn by obtaining a plurality of data sets. Each data set includes a set of

attributes. The method includes determining sub-attributes for each of the

attribute in each data set. The method includes constructing a neural based

on a relation between the set of attributes and the sub-attributes in each data

set. The method includes creating a training set for each constructed neural.

The method includes deriving the inferences from each constructed neural

based on set of attributes and the sub-attributes in each data set."

[0015]. W02008144662A1 - SUSTAINABLE DESIGN DECISION

SUPPORT SYSTEM Presents "system and methods for using Web Services

to connect an Analysis calculator, a Recommendations engine, Social

Networking, and Knowledge Management technologies in a platform for

operationalizing sustainability into Product Life Cycle Management (e.g.,

conception, design, manufacture, service, end-of-life disposition) and

Enterprise Resource Planning (ERP) (including enterprise-wide activities of

manufacturing, supply change management, financials, human resources,

customer relationship management, and external stakeholder engagement).

A Web Service Framework integrates Life Cycle Assessment (LCA)

software technology with product design, manufacturing, and distribution

process design tools. A logic layer can perform sustainability estimates

within a Knowledge Management System. A Web Service Framework is

utilized for constructing or entering LCA models, methodologies and source

data. A social software-based participation environment is integrated with

sustainable product design and LCA tools and processes'

[0016]. IN232CHE2014A - METHOD AND SYSTEM FOR

EVALUATING CLINICAL DECISION SUPPORT SYSTEMS presents

"system to evaluating a clinical decision support system (CDSS). In one

embodiment, the method include feeding a plurality of input data to the

CDSS by a data feeder module based on an evaluation scenario selected by

a user and configurator ion of the CDSS under evaluation, generating a

plurality of output data corresponding to the fed plurality of input data by

the CDSS under evaluation, evaluating the generated plurality of output data

by a validation engine based on a plurality of evaluation parameters having

a weightage and a range assigned by the user, wherein the plurality of

evaluation parameters comprises one or more mandatory parameters and

one or more optional parameters and determining a rating for the CDSS

under evaluation based on the evaluation performed by the validation

engine."

[0017]. IN202041054739A - SYSTEM AND METHOD FOR THEMATIC

CONTEXT-BASED DECISION SUPPORT USING MULTI-SENSORY MULTI-DIMENSIONAL INPUT DATA FUSION AND ASSOCIATION

[0018]. Presents "a system and method for thematic context-based decision

support using multi-sensory multi-dimensional input data fusion and

association, comprising Iota enabled devices configured to capture sensor

data/content through sensors, the Iota enabled devices configured to deliver

the sensor data/content captured by the sensors to a cloud server over a

network. Computing device configured to receive the sensor data/content

from the cloud server by a thematic context-based decision module, the

thematic context-based decision module configured to perform data fusion

and association for the sensor data/content to provide one or more thematic

decisions."

[0019]. Groupings of alternative elements or embodiments of the invention

disclosed herein are not to be construed as limitations. Each group member

can be referred to and claimed individually or in any combination with other

members of the group or other elements found herein. One or more members

of a group can be included in, or deleted from, a group for reasons of

convenience and/or patentability. When any such inclusion or deletion

occurs, the specification is herein deemed to contain the group as modified,

thus fulfilling the written description of all Markus groups used in the

appended claims.

[0020]. As used in the description herein and throughout the claims that

follow, the meaning of "a," "an," and"the" includes plural reference unless

the context clearly dictates otherwise. Also, as used in the description

herein, the meaning of"in" includes "in" and "on"unless the context clearly

dictates otherwise.

[0021]. The recitation of ranges of values herein is merely intended to serve

as a shorthand method of referring individually to each separate value

falling within the range. Unless otherwise indicated herein, each individual

value is incorporated into the specification as if it were individually recited

herein. All methods described herein can be performed in any suitable order

unless otherwise indicated herein or otherwise clearly contradicted by

context.

[0022]. The use of any and all examples, or exemplary language (e.g. "Such

as") provided with respect to certain embodiments herein is intended merely

to better illuminate the invention and does not pose a limitation on the scope

of the invention otherwise claimed. No language in the specification should

be construed as indicating any non-claimed element essential to the practice

of the invention.

[0023]. The above information disclosed in this Background section is only

for the enhancement of understanding of the background of the invention

and therefore it may contain information that does not form the prior art that

is already known in this country to a person of ordinary skill in the art.

SUMMARY

[0024]. Before the present systems and methods, are described, it is to be

understood that this application is not limited to the particular systems, and

methodologies described, as there can be multiple possible embodiments

which are not expressly illustrated in the present disclosure. It is also to be

understood that the terminology used in the description is for the purpose of

describing the particular versions or embodiments only and is not intended

to limit the scope of the present application.

[0025]. The present invention mainly cures and solves the technical

problems existing in the prior art. In response to these problems, the present

invention discloses a Decision support system based on machine learning

and deep learning for secure data management.

[0026]. As an aspect of the present invention, it presents a decision support

system based on machine learning and deep learning for secure data

management, wherein the system comprises: A memory unit, comprising: a

data collection module configured to receive data from the plurality of

electronic devices; A risk factor identification module configured to

construct a decision tree, build decision rules and identify one or more risk

factors thereof, wherein the risk factors are identified using a probability

function that captures the probability of features and thereby building one

or more decision rules; and A computing device, associated with a data

resource and is configured to transmit a first status of data of each data

resource, wherein the computing devices, where each second computing

device is in wireless communication with at least one sensor associated with

each branching point of the data distribution network; and a data source

, with communication with the computing device to store data related to each

transaction, wherein computing device is configured to receive a private key

from each data source.

OBJECTIVE OF THE INVENTION

[0027]. The principle objective of the present invention is to provide a

Decision support system based on machine learning and deep learning for

secure data management.

BRIEF DESCRIPTION OF DRAWINGS

[0028]. To clarify various aspects of some example embodiments of the

present invention, a more particular description of the invention will be

rendered by reference to specific embodiments thereof which are illustrated

in the appended drawings. It is appreciated that these drawings depict only

illustrated embodiments of the invention and are therefore not to be

considered limiting of its scope. The invention will be described and

explained with additional specificity and detail through the use of the

accompanying drawings.

[0029]. In order that the advantages of the present invention will be easily

understood, a detailed description of the invention is discussed below in

conjunction with the appended drawings, which, however, should not be considered to limit the scope of the invention to the accompanying drawings, in which:

[0030]. Figure 1 shows a block -diagram representation of method for

decision support system based on machine learning and deep learning for

secure data management., according to one of the embodiment of the present

invention.

DETAIL DESCRIPTION

[0031]. The present invention is related to Decision support system based on

machine learning and deep learning for secure data management

[0032]. Figure 1 shows a flow -diagram representation of method for

decision support system based on machine learning and deep learning for

secure data management., according to one of the embodiment of the present

invention.

[0033]. Although the present disclosure has been described with the purpose

of Decision support system based on machine learning and deep learning

for secure data management, it should be appreciated that the same has been

done merely to illustrate the invention in an exemplary manner and to

highlight any other purpose or function for which explained structures or configurations could be used and is covered within the scope of the present disclosure.

[0034]. Some embodiments of this disclosure, illustrating all its features,

will now be discussed in detail. The words and other forms thereof, are

intended to be open ended in that an item or items following any one of

these words are not meant to be an exhaustive listing of such item or items,

or meant to be limited to only the listed item or items. It must also be noted

that as used herein and in the appended claims, the singular forms "a," "an,"

and "the" include plural references unless the context clearly dictates

otherwise. Although any systems and methods similar or equivalent to those

described herein can be used in the practice or testing of embodiments of

the present disclosure, the exemplary systems and methods are now

described. The disclosed embodiments are merely exemplary of the

disclosure, which may be embodied in various forms.

[0035]. The decision support system based on machine learning and deep

learning for secure data management, comprises a memory unit, a risk factor

identification module and a computing device.

[0036]. The memory unit comprises a data collection module configured to

receive data from the plurality of electronic devices.

[0037]. The risk factor identification module is configured to construct a

decision tree.

[0038]. The risk factor identification module is used to build decision rules

and identify one or more risk factors thereof.

[0039]. The risk factors are identified using a probability function that

captures the probability of features and thereby building one or more

decision rules

[0040]. A computing device is associated with a data resource and is

configured to transmit a first status of data of each data resource.

[0041]. The computing device is in wireless communication with at least one

sensor associated with each branching point of the data distribution

network; and

[0042]. A data source is in communication with the computing device, used

to store data related to each transaction. The computing device is configured

to receive a private key from each data source.

[0043]. The computing device is used for prediction comprises three stages,

collecting data from the different sources, preprocessing of the data

collected from the data sources; and Preprocessing of a model to construct

used to predict and secure the data.

[0044]. Although implementations of the invention have been described in

a language specific to structural features and/or methods, it is to be

understood that the appended claims are not necessarily limited to the specific features or methods described. Rather, the specific features and methods are disclosed as examples of implementations of the invention.

Claims (2)

CLAIMS We claim:

1. A decision support system based on machine learning and deep

learning for secure data management, wherein the system comprises:

A memory unit, comprising: a data collection module configured to receive

data from the plurality of electronic devices;

A risk factor identification module configured to construct a decision tree,

build decision rules and identify one or more risk factors thereof, wherein the

risk factors are identified using a probability function that captures the

probability of features and thereby building one or more decision rules; and

A computing device, associated with a data resource and is configured to

transmit a first status of data of each data resource, wherein the computing

devices, where each second computing device is in wireless communication with at least one sensor associated with each branching point of the data distribution network; and

A data source, with communication with the computing device to store data

related to each transaction, wherein computing device is configured to receive

a private key from each data source.

2. The decision support system based on machine learning and deep learning

for secure data management as claimed in claim 1, wherein the computing

device is used for prediction comprises three stages,

collecting data from the different sources;

Preprocessing of the data collected from the data sources; and

Preprocessing of a model used to predict and secure the data.