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Journal : Indonesian Journal of Electrical Engineering and Computer Science

AI-based targeted advertising system Tew Jia Yu; Chin Poo Lee; Kian Ming Lim; Siti Fatimah Abdul Razak
Indonesian Journal of Electrical Engineering and Computer Science Vol 13, No 2: February 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v13.i2.pp787-793

Abstract

The most common technology used in targeted advertising is facial recognition and vehicle recognition. Even though there are existing systems serving for the targeting purposes, most propose limited functionalities and the system performance is normally unknown. This paper presents an intelligent targeted advertising system with multiple functionalities, namely facial recognition for gender and age, vehicle recognition, and multiple object detection. The main purpose is to improve the effectiveness of outdoor advertising through biometrics approaches and machine learning technology. Machine learning algorithms are implemented for higher recognition accuracy and hence achieved better targeted advertising effect.
Surveillance system with motion and face detection using histograms of oriented gradients Ri Cerd Ng; Kian Ming Lim; Chin Poo Lee; Siti Fatimah Abdul Razak
Indonesian Journal of Electrical Engineering and Computer Science Vol 14, No 2: May 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v14.i2.pp869-876

Abstract

With the rapidly increasing crime rate in recent years, community safety issues aroused a wide concern among public community. Various security technologies had been invented and carried out, for example password door lock, alarm system, and closed-circuit televisions (CCTVs). Although the installation of CCTVs is common in most premises, they require extensive man power to manually monitor the videos. Moreover, the reliability of human operator greatly deteriorates when they are in fatigue condition. In view of this, our project aims to develop an automated computer vision based surveillance system. Unlike ordinary CCTV system that requires human operator to manually observe and detect intruder, a computer vision based surveillance system automatically monitor the security of premises and trigger actions once an intrusion is detected. Basically, it is a simple surveillance camera system that will be setup at the entrance of the house. The reliability is being enhanced by applying the motion detection and face recognition algorithm, using histogram of oriented gradients that could detect the existence of people at the main entrance and try to validate the user. Apart from recognizing the user, the propose system also support mobile interaction whereby user can monitor the camera, activate alarm, and even received notification when a stranger was being detected at the entrance of the house. By including such functionalities, proposed system had highly surpassed the existing surveillance system by not only support monitoring, but also try to recognize the people and inform the user at the exact moment when stranger detected, so that user could take immediate action about it, for example activating the alarm or report to police. The project was executed with expected outcome and objectives had been accomplished.
Smart halal recognizer for muslim consumers Siti Fatimah Abdul Razak; Chin Poo Lee; Kian Ming Lim; Pei Xin Tee
Indonesian Journal of Electrical Engineering and Computer Science Vol 14, No 1: April 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v14.i1.pp193-200

Abstract

Halal is the term used for permissible food according to Islamic dietary law. Indicators such as Halal logo have been used to facilitate Muslims in identifying Halal food. In Malaysia, the Department of Islamic Development (JAKIM) has introduced a standard Halal logo for locally manufactured products and currently recognizes 67 Islamic bodies in 41 countries around the world as certification bodies for products imported into Malaysia. Therefore, a more practical way is required to assist Muslims in recognizing various forms of Halal logos on food packaging. A neural network (NN) approach is proposed to recognize authentic and recognized Halal logo on imported products. A dataset of available and recognized Halal logo images worldwide will be created for this purpose. The dataset will be used to train and test the performance of the learning algorithm to recognize logo of recognized foreign bodies by JAKIM. The approach is expected to complement current facilities for verification using Short Messaging Services (SMS) and web portal. The approach is assumed to be more efficient and accurate for Halal logo verification which eventually could win the trust of Halal product consumers and support the Halal industry in Malaysia.