BEGIN:VCALENDAR VERSION:2.0 PRODID:-//Date iCal//NONSGML kigkonsult.se iCalcreator 2.20.4// METHOD:PUBLISH X-WR-CALNAME;VALUE=TEXT:ԭ BEGIN:VTIMEZONE TZID:America/New_York BEGIN:STANDARD DTSTART:20171105T020000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 RDATE:20181104T020000 TZNAME:EST END:STANDARD BEGIN:DAYLIGHT DTSTART:20180311T020000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:EDT END:DAYLIGHT END:VTIMEZONE BEGIN:VEVENT UID:calendar.319286.field_event_date.0@www.wright.edu DTSTAMP:20260219T215157Z CREATED:20180419T203907Z DESCRIPTION:Ph.D. Committee:  Drs. Nikolaos Bourbakis\, Advisor\, Mateen Ri zki\, Soon Chung\, and Georgios Tsihrintzis (University of Piraeus\, Greec e)ABSTRACT:Human Activity Recognition is an actively researched area for t he past few decades\, and is one of the most eminent applications of today . It is already part of our life\, but due to high level of uncertainty an d challenges of human detection\, we have only application specific soluti ons. Thus\, the problem being very demanding and still remains unsolved.Wi thin this PhD we delve into the problem\, and approach it from a variety o f view-points. Initially\, we present and evaluate different architectures and frameworks for activity recognition. Henceforward\, the focal point o f our attention is automatic human activity recognition.We have conducted and presented a survey that compares\, categorizes\, and evaluates researc h surveys and reviews into four categories. Then a novel fully automatic v iew-independent multi-formal languages collaborative scheme is presented f or complex activity and emotion recognition\, which is the main contributi on of this dissertation. In particular\, we propose a collaborative scheme of three formal-languages\, that is responsible for parsing manipulating\ , and understanding all the data needed. Artificial Neural Networks\, as l earning mechanism\, are used to classify an action primitive (simple activ ity)\, as well as to define change of activity. Finally\, we capitalize th e advantages of Fuzzy Cognitive Maps\, and Rule-Based Colored Petri-Nets t o be able to classify a sequence of activities as normal or abnormal. DTSTART;TZID=America/New_York:20180423T113000 DTEND;TZID=America/New_York:20180423T133000 LAST-MODIFIED:20180420T133215Z LOCATION:499 Joshi SUMMARY:Ph.D. Dissertation Defense “A Multi-Formal Languages Collaborative Scheme for Complex Human Activity Recognition and Behavioral Patterns Extr action” By Anargyros Angeleas URL;TYPE=URI:/events/phd-dissertation-defense-%E2%80% 9C-multi-formal-languages-collaborative-scheme-complex-human-activity END:VEVENT END:VCALENDAR