Registered Data

[CT121]

  • Session Time & Room
    • CT121 (1/1) : 2E @E803 [Chair: Walid Gomaa]
  • Classification
    • CT121 (1/1) : Artificial intelligence (68T)

[00310] Human Activity Recognition from Inertial Motion Data

  • Session Time & Room : 2E (Aug.22, 17:40-19:20) @E803
  • Type : Contributed Talk
  • Abstract : Human activity recognition (HAR) using inertial motion streaming has gained a lot of momentum in recent years. This has been driven by smart environments and the ubiquity of inertial-motion sensors in modern commodity devices. HAR applications span all aspects of human life such as healthcare, sports, manufacturing, etc. In this talk we give a brief description of the state-of-the-art work in HAR including action recognition, biometrics analysis (gender, age,..), sensor’s location determination, gait analysis, etc.
  • Classification : 68T01, 68T05, 92C47
  • Format : Talk at Waseda University
  • Author(s) :
    • Walid Gomaa (Egypt Japan University of Science and Technology)

[01035] Reinforcement learning-based routing strategy in IoT applications using MDC

  • Session Time & Room : 2E (Aug.22, 17:40-19:20) @E803
  • Type : Contributed Talk
  • Abstract : WSNs and IoT devices consume more power for data transmission. To reduce energy consumption, most of the traditional learning methodologies need enormous volumes of data and feature engineering, thus raising the learning complexity. A reliable reinforcement learning-based MDC model for effective routing is proposed to lower the learning complexity. Furthermore, the Q-Learning methodology is used to enhance learning along the shortest path. Combining these techniques can improve network stability while also enhancing routing performance significantly.
  • Classification : 68T01, 68T07, 68T35, Reinforcement learning, Machine Learning
  • Format : Talk at Waseda University
  • Author(s) :
    • Muralitharan Krishnan (Sungkyunkwan University)
    • Yongdo Lim (Sungkyunkwan University)

[01078] DNN-based hybrid ensemble learning strategy for XSS detection and defense

  • Session Time & Room : 2E (Aug.22, 17:40-19:20) @E803
  • Type : Contributed Talk
  • Abstract : Due to the high level of intelligence displayed by attackers, existing web-based security applications have failed. When attackers make changes to an organization's data, it is one of the most dangerous attacks (XSS). Combining ML and DL frameworks is proposed as a way to detect and defend against XSS assaults with high accuracy and efficiency. Using this representation, a new method is developed for integrating stacking ensembles into web-based software, which is called "hybrid stacking".
  • Classification : 68T01, 68T05, 68T07, Machine Learning, Deep Learning
  • Format : Online Talk on Zoom
  • Author(s) :
    • Seethalakshmi Perumal (MIT Campus, Anna University - Chennai)