Mohd Fairuz Muhamad Fadzil
Thumbnail Image
Preferred name
Mohd Fairuz Muhamad Fadzil
Official Name
Mohd Fairuz , Muhamad Fadzil
Alternative Name
Fadzil, M. Fairuz M.
Fadzil, Mohd
Fairuz, M. F.M.
Fadzil, Mohd Fairuz Muhamad
Main Affiliation
Scopus Author ID
57193198559
Researcher ID
CPD-8200-2022

Research Output

Filters

2
2
2
Reset filters

Settings
Now showing 1 - 2 of 2
  • Publication
    Cross-layer optimization for VANET city scenario using Taguchi mechanism
    (Institute of Electrical and Electronics Engineers Inc., 2022-02-14)
    Aznor Hanah Abdul Halim
    ;
    Rohani S Mohamed Farook
    ;
    Mohamad Rizal Abdul Rejab
    ;
    Mohd Fairuz Muhamad Fadzil
    ;
    Elias S.J.
    ;
    Dawam S.R.M.
    Because of the dynamic vehicle density, vehicle speed, and unexpected and harsh communication environment, the Vehicular Ad Hoc Network (VANET) differs from conventional ad hoc networks in terms of functioning. VANET has two primary applications: safety and non-safety. Network optimization is one method of preserving current protocols and other network characteristics rather than inventing and implementing new enhanced protocols, which is prohibitively expensive. As a result, this study proposes an optimization city scenario of VANET for maximizing throughput and packet delivery ratio (PDR) while reducing delay using the Taguchi method. For three target performances evaluated, packet size has the highest rank control factors for two conditions. Packet size plays the major control factor in contributing to lower delay and maximizing throughput. Therefore, packet size is a major control factor and is applicable for optimizing both safety and non-safety applications focusing on the city scenarios of VANET.
      3  3
  • Publication
    Motion detection system for recognition of early sign of depression
    (Institute of Electrical and Electronics Engineers Inc., 2022-02-14)
    Aliza Ya'Kob N.A.
    ;
    Rohani S Mohamed Farook
    ;
    Aznor Hanah Abdul Halim
    ;
    Mohd Fairuz Muhamad Fadzil
    ;
    Mohamad Rizal Abdul Rejab
    ;
    Elias S.J.
    Depression is one of the mental health disorders that affect many humans, especially campus students. In certain cases, people with depression-prone to commit suicide without any warning signs and symptoms observed by family and friends. There is a need to be able to identify and proceed for treatment from the professionals as soon as possible. There is a lack of tools to identify students' depression behavior through quantified motion characteristics. The advancement of algorithms could be used in detecting such behaviors. This research is motivated to classify depression among students using artificial intelligence. The motion characteristics are quantified using accelerometer and GPS data and trained using neural networks to enable human activity prediction. Once predicted the prone to depression behavior notification will be sent to alert the user on their mental health condition. The user should react and respond to the alert and meet their doctors for further treatment.
      2  2