Intrusion detection systems (IDS) are used in analyzing huge data and diagnose anomaly traffic such as DDoS attack; thus, an efficient traffic classification method is necessary for the IDS. The IDS models attempt to decrease false alarm and increase true alarm rates in order to improve the performance accuracy of the system. To resolve this concern, three machine learning algorithms have been tested and evaluated in this research which are decision jungle (DJ), random forest (RF) and support vector machine (SVM). The main objective is to propose a ML-based network intrusion detection system (ML-based NIDS) model that compares the performance of the three algorithms based on their accuracy and precision of anomaly traffics. The knowledge discovery in databases (KDD) methodology and intrusion detection evaluation dataset (CIC-IDS2017) are used in the testing which both are considered as a benchmark in the evaluation of IDS. The average accuracy results of the SVM is 98.18%, RF is 96.76% and DJ is 96.50% in which the highest accuracy is achieved by the SVM. The average precision results of the SVM is 98.74, RF is 97.96 and DJ is 97.82 in which the SVM got a higher average precision compared with the other two algorithms. The average recall results of the SVM is 95.63, RF is 97.62 and DJ is 95.77 in which the RF achieves the highest average of recall than SVM and DJ. In overall, the SVM algorithm is found to be the best algorithm that can be used to detect an intrusion in the system.

Nowadays, the trend is clear that the use of pervasive computing technology has taken place to improve waste management by providing electronic system which utilizing radio frequency identification at bin level. In this paper, we proposed a smart recycle bin application based on information in the smart card to automatically calculate the weight of waste and convert the weight into point then store it into the card. The wastes are tracked by smart bins using a RFID-based system integrating the web-based information system at the host server. Two crucial features of the selective sorting process can be improved using this approach. First, the user is assisted in the application of material waste classification. Second, the smart bin knows its content and can report back to the rest of the recycling chain.

Studies have shown that many Malaysian non-technical students have low motivation in learning ICT course due to a number of reasons, such as a lack of learning practice and effective learning applications. In view of such a problem, the researchers carried out a quasi-experimental study to examine the impact of a novel mobile augmented reality learning application (MARLA) on students' motivation in learning a topic of a university ICT course. The research was based on the pretest-posttest control group design, and the study sample consisted of 120 non-technical undergraduates majoring in social science, with a mean age of 19.5 years. They were divided into an experimental group and a control group. The dependent variable was students' motivation in learning, and the independent variables were learning method and gender. The experimental group used MARLA on their mobile devices to learn one of the topics of the ICT Competency course, namely Computer System; whereas the control group used a similar application on their desktop computers. The Intrinsic Motivation Inventory (IMI) was the research instrument used to measure students' motivation before and after learning sessions, which spanned 6 hours. Utilizing the SPSS (version 21), an analysis of covariance was performed, showing there was a main effect attributed to gender only, with male and female students attaining mean scores of 4.24 and 3.90 respectively for the motivation construct. This finding showed male students were more motivated than their opposite counterparts. In contrast, no such main effect attributed to learning method was observed, as evidenced from the mean scores of 4.08 and 4.07 of the experimental group and control group respectively for the measured construct, suggesting both methods were both equally effective. Additionally, there was an interaction effect between gender and learning method, with male students attaining different levels of motivation based on learning method. Arguably, such a mobile learning tool can be used to help non-technical undergraduates learn with greater motivation, but its success will rely on proper planning and implementation by considering students' demographic background.

According to World Health Organization, cervical cancer is the fourth most frequent cancer that have high mortality rate which affected women all around the world especially in low and middle-income countries. As the computer science and information technology field growth, researches on analysing medical datasets such as diabetes, cervical cancer and liver disease and etc also growth. This paper is set to studies classification techniques in data mining on risk factor of cervical cancer datasets. The clssification techniques such as Naive Bayes (NB), C4.5 Decision Tree (C4.5), k-Nearest Neighbors (kNN), Sequential Minimal Optimization (SMO), Random Forest Decision Tree (RF), Multilayer Perceptron (MLP) Neural Network and Simple Logistic Regression (SLR) have been used to classify the dataset whether healthy or cancer result for cervical cancer diagnostic. The dataset is needed to be undergoing intense data pre-processing phase due to imbalance and have a lot of missing value. The performance of classification were evaluated using 10-folds cross validation where accuracy, precision and recall as evaluation metric were measured using confusion matrix to determine the performance power for all classification techniques.

Nowadays with the increasing amount of waste generated and limited landfill space for waste disposal, recycling is one of the important approaches to manage the waste effectively. The current manual recycling practice in which the user need to bring the waste in bulk to the recycling center might be hassle and hence become a discouraging factor for them to recycle. To overcome such an issue, in this project an automated recycle bin with a reward feature is proposed that derived from a reverse vending machine (RVM) concept. Basically, the system is implemented in a standard recycle bin provided by local municipal that equipped with microcontroller and collection of sensors. Throughout the process, the sensors responsible to identifying user information, weight the scale and eventually convert the weight to the corresponding points automatically. Once the process completed, the user can claim their points by using RFID point card. All the mentioned process will be controlled by a microcontroller. The system has been implemented in a small scale user testing and the framework shows its effectiveness for handling the whole process. The prototype is expected to aid in accelerating the motivation among Malaysian to recycle their waste, and can be one of the frameworks to overcome urban poverty issue by using waste to wealth concept.