Phishing detection is a momentous problem which can be deliberated by many
researchers with numerous advanced approaches. Current anti-phishing mechanisms
such as blacklist-base anti-phishing, Heuristic-based anti-phishing does suffer low
detection accuracy and high false alarm. There is need for efficient mechanism to
protect users from phishing websites. The purpose of this study is to investigate the
capability of 6 machine learning algorithms i.e. Multi-Layer Perceptron (MLP), Support
Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), Logistic Regression
(LR) and Naïve Bayes (NB) to classify phishing and non-phishing websites. These
algorithms were trained with two different groups of training in WEKA environment
and then were tested in terms of accuracy, precision, TP rate, and FP rate on a 3
different sets of dataset which contains dissimilar portion of phishing and non-phishing
instances. Results presented that Naïve Bayes classifier has better detection accuracy
between other classifiers for predicting phishing websites while Multi-Layer
Perceptron gave worst result in terms of detection accuracy. The result also showed
that Support Vector machine has better FP rate between other classifier. In addition,
Random Forest, Decision Tree, and Naïve Bayes can classify all phishing websites as
phishing correctly. It means that TP rate is 100% for these classifiers. In conclusion this
paper suggests using NB as the best classifier for predicting phishing and non-phishing
websites.
Computer vision is applied in many software and devices. The detection and
reconstruction of the human skeletal structure is one of area of interest, where the
camera will identify the human parts and construct the joints of the person standing in
front. Three-dimensional pose estimation is solved using various learning approaches,
such as Support Vector Machines and Gaussian processes. However, difficulties in
cluttered scenarios are encountered, and require additional input data, such as
silhouettes, or controlled camera settings. The paper focused on estimating the threedimensional
pose of a person without requiring background information, which is
robust to camera variations. Each of the joint has three-dimensional space position and
matrix orientation with respect to the sensor. Matlab Simulink was utilized to provide
communication tools with depth camera using Kinect device for skeletal detection.
Results on the skeletal detection using Kinect sensor is analysed in measuring the
abilities to detect skeletal structure accurately, and it is shown that the system is able
to detect human skeletal performing non-complex basic motions in daily life.
This paper aim is to design an education kit for wastewater system that can maintain
the standard parameters of neutralized wastewater by maintaining the suitable pH
(Potential Hydronium) level and temperature of the wastewater from industry by using
fuzzy controller. This study is capable to control the unwanted bacteria by automatic
regulatory and monitoring the temperature, pH and water level. Fuzzy logic method is
use to control and monitor pH level as well as the temperature during clarifying process
because pH control process is a complex physical-chemistry process of strong
individuality of time-varying and non-linearity properties. Pumps used in the prototype
need to be controlled precisely to enable either acid or base to be pumped into mix
tank of the wastewater treatment. The control and monitoring system, which has been
designed through LabVIEW front panel will ease end user in inspection of the
parameters involve in wastewater treatment. The entire system output could be
observed remotely in Data Dashboard application in smartphone or tablet. The GUI
was designed and interfaced with the prototype constructed to carry out the process
of controlling and monitoring the required parameters. Few tests were conducted
repetitively to analyse the performance of the system parameters. It was found that
the controlled set point fixed within the range of pH 7.6-8.4, temperature 25-29.44
Celsius and water level of 20cm in this research that was effectively achieved in the
entire test conducted. In addition, the wastewater system accuracy and performance
is 96.72% and 90.22% respectively.