HUMAN RESOURCES PERFORMANCE EVALUATION USING ENHANCED ARTIFICIAL NEURON NETWORK AND SIGMOID LOGISTICS
In some examples, a computing device may gather data associated with activities performed by individuals from multiple locations (e.g., code repositories). The computing device may determine data gathered by a data monitor application at individual locations of the multiple locations over a predetermined amount of time. The computing device may filter, based on criteria, the gathered data and perform an analysis of the filtered data using a machine learning algorithm (e.g., an artificial neural network and a logistic sigmoid). The criteria may be selected based at least in part on a job function associated with the particular individual. The machine learning algorithm may create a human resource evaluation of a particular individual of the plurality of individuals recommending an increase in salary, a bonus, or a promotion. The human resources evaluation may include a probability that the particular individual will leave a current job in the organization.
1 . A method comprising:
determining, by one or more processors, activities to be monitored at multiple locations comprising at least a first location and a second location, the activities performed by a plurality of individuals in an organization;
sending, by the one or more processors, a first instruction to a first data monitor application to monitor the activities at the first location;
sending, by the one or more processors, a second instruction to a second data monitor application to monitor the activities at the second location;
receiving, by the one or more processors, first data gathered by the first data monitor application over a predetermined amount of time, the first data including first activities performed by a first subset of the plurality of individuals;
receiving, by the one or more processors, second data gathered by the second data monitor application over the predetermined amount of time, the second data including second activities performed by a second subset of the plurality of individuals;
determining, by the one or more processors, one or more questions to be answered;
determining, by the one or more processors using natural language processing, one or more criteria based on the one or more questions;
filtering, by the one or more processors and based on the one or more criteria, the first data and the second data to create filtered data;
performing, by the one or more processors, an analysis of the filtered data using a machine learning algorithm; and
creating, by the one or more processors and based on the analysis, a human resource evaluation of a particular individual of the plurality of individuals, the human resources evaluation comprising a compensation recommendation.
2 . The method of claim 1 , wherein:
the machine learning algorithm comprises an artificial neural network and a logistic sigmoid function.
3 . The method of claim 1 , wherein:
the one or more criteria are selected based at least in part on a job function associated with the particular individual.
4 . The method of claim 1 , wherein individual locations of the multiple locations host a code repository that provides version control, the code repository comprising at least one of:
GitLab,
GitHub,
Azure DevOps Server,
Apache Subversion (SVN),
Jenkins,
TeamCity,
Octopus,
Pivotal Tracker,
Jira, or
ServiceNow.
5 . The method of claim 1 , wherein the activities comprise:
how many lines of code are committed;
a difference between a deadline associated with the code and a time the code was committed;
how many issues are caused by the code;
an amount time taken to resolve each issue caused by the code;
how frequently the code is committed to a master branch;
how many builds are completed;
a success rate of completed builds;
a percentage of code the particular individual contributed to a master branch; and
a success rate of each commit.
6 . The method of claim 1 , wherein the compensation recommendation comprises one of:
providing an increase in salary;
providing a bonus; or
providing a promotion.
7 . The method of claim 1 , wherein the human resources evaluation further comprises:
a prediction that the particular individual will leave a current job in the organization.
8 . A computing device comprising:
one or more processors; and
one or more non-transitory computer-readable media storing instructions that are executable by the one or more processors to perform operations comprising:
determining activities to be monitored at multiple locations comprising at least a first location and a second location, the activities performed by a plurality of individuals in an organization;
sending a first instruction to a first data monitor application to monitor the activities at the first location;
sending a second instruction to a second data monitor application to monitor the activities at the second location;
receiving first data gathered by the first data monitor application over a predetermined amount of time, the first data including first activities performed by a first subset of the plurality of individuals;
receiving second data gathered by the second data monitor application over the predetermined amount of time, the second data including second activities performed by a second subset of the plurality of individuals;
filtering, based on one or more criteria, the first data and the second data to create filtered data;
performing an analysis of the filtered data using a machine learning algorithm; and
creating, based on the analysis, a human resource evaluation of a particular individual of the plurality of individuals, the human resources evaluation comprising a compensation recommendation.
9 . The computing device of claim 8 , wherein:
the machine learning algorithm comprises an artificial neural network and a logistic sigmoid function.
10 . The computing device of claim 8 , wherein:
the one or more criteria are selected based at least in part on a job function associated with the particular individual.
11 . The computing device of claim 8 , wherein individual locations of the multiple locations host a code repository that provides version control, the code repository comprising at least one of:
GitLab,
GitHub,
Azure DevOps Server,
Apache Subversion (SVN),
Jenkins,
TeamCity,
Octopus,
Pivotal Tracker,
Jira, or
ServiceNow.
12 . The computing device of claim 8 , wherein the criteria comprise, for the particular individual within the predetermined amount of time:
how many lines of code are committed;
a difference between a deadline associated with the code and a time the code was committed;
how many issues are caused by the code;
an amount time taken to resolve each issue caused by the code;
how frequently the code is committed to a master branch;
how many builds are completed;
a success rate of completed builds;
a percentage of code the particular individual contributed to a master branch; and
a success rate of each commit.
13 . The computing device of claim 8 , wherein the compensation recommendation comprises one of:
providing an increase in salary;
providing a bonus; or
providing a promotion.
14 . The computing device of claim 8 , wherein the human resources evaluation further comprises:
a probability that the particular individual will leave a current job in the organization.
15 . One or more non-transitory computer-readable media storing instructions that are executable by one or more processors to perform operations comprising:
determining activities to be monitored at multiple locations comprising at least a first location and a second location, the activities performed by a plurality of individuals in an organization;
sending a first instruction to a first data monitor application to monitor the activities at the first location;
sending a second instruction to a second data monitor application to monitor the activities at the second location;
receiving first data gathered by the first data monitor application over a predetermined amount of time, the first data including first activities performed by a first subset of the plurality of individuals;
receiving second data gathered by the second data monitor application over the predetermined amount of time, the second data including second activities performed by a second subset of the plurality of individuals;
filtering, based on one or more criteria, the first data and the second data to create filtered data;
performing an analysis of the filtered data using a machine learning algorithm; and
creating, based on the analysis, a human resource evaluation of a particular individual of the plurality of individuals, the human resources evaluation comprising a compensation recommendation.
16 . The one or more non-transitory computer-readable media of claim 15 , wherein:
the machine learning algorithm comprises an artificial neural network and a logistic sigmoid function.
17 . The one or more non-transitory computer-readable media of claim 15 , wherein:
the one or more criteria are selected based at least in part on a job function associated with the particular individual.
18 . The one or more non-transitory computer-readable media of claim 15 , wherein individual locations of the multiple locations host a code repository that provides version control, the code repository comprising at least one of:
GitLab,
GitHub,
Azure DevOps Server,
Apache Subversion (SVN),
Jenkins,
TeamCity,
Octopus,
Pivotal Tracker,
Jira, or
ServiceNow.
19 . The one or more non-transitory computer-readable media of claim 15 , wherein the activities comprise:
how many lines of code are committed;
a difference between a deadline associated with the code and a time the code was committed;
how many issues are caused by the code;
an amount time taken to resolve each issue caused by the code;
how frequently the code is committed to a master branch;
how many builds are completed;
a success rate of completed builds;
a percentage of code the particular individual contributed to a master branch; and
a success rate of each commit.
20 . The one or more non-transitory computer-readable media of claim 15 , wherein:
the compensation recommendation comprises one of:
providing an increase in salary;
providing a bonus; or
providing a promotion; and
the human resources evaluation further comprises:
a probability that the particular individual will leave a current job in the organization.