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Michelle Alisha Rivers, 361311 Summit Creek Dr, Stone Mountain, GA 30083

Michelle Rivers Phones & Addresses

Stone Mountain, GA   

Decatur, GA   

Monroe, GA   

Fayetteville, GA   

Tallahassee, FL   

Albany, GA   

Work

Company: Wolverton & assoc inc Address: 6745 Sugar Loaf Pkwy, Norcross, GA 30093 Phones: 770-4478999 Position: Human resources executive Industries: Engineering Services

Mentions for Michelle Alisha Rivers

Michelle Rivers resumes & CV records

Resumes

Michelle Rivers Photo 45

Michelle Rivers

Location:
Greater Atlanta Area
Industry:
Computer & Network Security
Certifications:
PMP - Project Management Professional, PMI - Project Management Institute
Michelle Rivers Photo 46

Owner, Baskets By M

Location:
Greater Atlanta Area
Industry:
Retail
Michelle Rivers Photo 47

Michelle Rivers - Monroe, GA

Work:
ENTERPRISE RENT-A-CAR Jul 2014 to 2000
Assistant Manager
DEKALB COUNTY PARKS AND RECREAION - Lithonia, GA May 2007 to Aug 2014
Recreation Assistant
MACY'S FINE DEPARTMENT STORES - Atlanta, GA Mar 2012 to Oct 2013
Customer Service Specialist
COMCAST - Alpharetta, GA Feb 2011 to Aug 2012
Telesales Representative
Education:
GEORGIA STATE UNIVERSITY - Atlanta, GA Aug 2011 to May 2014
Bachelors of Arts in History
Stone Mountain High School May 2012 to May 2013 FLORIDA A&M UNIVERSITY - Tallahassee, FL Aug 2007 to May 2011
Bachelors of Science in Biology
Florida A&M University Aug 2008 to Apr 2011 STONE MOUNTAIN HIGH SCHOOL - Stone Mountain, GA May 2007
College Prep
Michelle Rivers Photo 48

Michelle Rivers - Fayetteville, GA

Work:
TGI FRIDAY'S Oct 2013 to 2000
Server
MACY'S FINE DEPARTMENT STORES - Atlanta, GA Mar 2012 to Oct 2013
Customer Service Specialist
DEKALB COUNTY PARKS AND RECREAION - Lithonia, GA May 2007 to Aug 2013
Recreation Assistant
COMCAST - Alpharetta, GA Feb 2011 to Aug 2012
Telesales Representative
Education:
GEORGIA STATE UNIVERSITY - Atlanta, GA Aug 2011 to 2000
Bachelors of Arts in History
Stone Mountain High School May 2012 to May 2013 FLORIDA A&M UNIVERSITY - Tallahassee, FL Aug 2007 to May 2011
Bachelors of Science in Forensic Science
Florida A&M University Aug 2008 to Apr 2011 STONE MOUNTAIN HIGH SCHOOL - Stone Mountain, GA May 2007
College Prep

Publications & IP owners

Us Patents

Deriving Optimal Actions From A Random Forest Model

US Patent:
2018026, Sep 13, 2018
Filed:
Mar 9, 2017
Appl. No.:
15/453991
Inventors:
- Armonk NY, US
Michael E. Nidd - Zurich, CH
Michelle Rivers - Marietta GA, US
George E. Stark - Lakeway TX, US
Srinivas B. Tummalapenta - Broomfield CO, US
Dorothea Wiesmann - Oberrieden, CH
International Classification:
H04L 29/06
G06N 99/00
Abstract:
Training a random forest model to relate settings of a network security device to undesirable behavior of the network security device is provided. A determination of a corresponding set of settings associated with each region of lowest incident probability is made using a random forest. The plurality of identified desired settings are presented as options for changing the network security device from the as-is settings to the identified desired settings. A choice is received from the plurality of options. The choice informs the random forest model. The random forest model ranks for a new problematic network security device the plurality of options for changing the new problematic network security device from as-is settings to desired settings by aggregating an identified cost of individual configuration changes, thereby identifying a most cost-effective setting for the network security device to achieve a desired output of the network security device.

System And Method For Improving Problematic Information Technology Device Prediction Using Outliers

US Patent:
2018017, Jun 21, 2018
Filed:
Dec 15, 2016
Appl. No.:
15/381096
Inventors:
- Armonk NY, US
Michael E. Nidd - Zurich, CH
Michelle Rivers - Marietta GA, US
George E. Stark - Lakeway TX, US
Srinivas B. Tummalapenta - Broomfield CO, US
Dorothea Wiesmann - Oberrieden, CH
International Classification:
G06N 99/00
H04L 12/751
Abstract:
A computer-implemented method of increasing reliability of an information technology environment comprising a plurality of hardware devices. Training data is received and a random forest is built from the training data using machine learning. A particular hardware device in the plurality of hardware devices is determined to be strange. Strange is defined as the particular hardware device having a proximity value lower than a predetermined threshold value for the random forest. A preventative action is determined to lower a risk of failure of the particular hardware device. The preventative action is reported. Reporting includes at least one of displaying a report on a display device, printing the report onto paper, and storing the report in a non-transitory computer recordable storage medium.

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