Phishing is an act of cybersecurity attack that tricks people into sharing sensitive data. Due to the inefficiency of the current security technologies, researchers have been paying much attention to employing machine learning methods for phishing detection lately. In our proposed solution, the effectiveness of machine learning techniques with feature selection techniques for phishing detection is investigated. To be specific, Random Forest (RF) and Artificial Neural Network (ANN) are integrated with feature selection techniques, Principal Component Analysis (PCA) and Recursive Feature Elimination (RFE). The goal was to identify and classify the model with the highest accuracy. The experiments were evaluated using a dataset of 4,898 phishing sites and 6,157 legitimate sites, with the phishing data sourced from Kaggle.com. Our experiments demonstrate that the combination of RF model with PCA achieved 95.83% accuracy, while the ANN model with PCA reached 95.07% accuracy. The incorporation of PCA and RFE not only optimised the models' predictive performance but also improved computational efficiency. Overfitting can also be reduced. The experimental results also demonstrate that the proposed ANN with PCA method outperforms the state-of-the-art methods. Consequently, this research highlights the potential of combining advanced feature selection techniques with machine learning algorithms to develop robust solutions for phishing detection. Yet, this undoubtedly contributes to a safer internet environment.

Long-Term Evolution (LTE) was implemented to fulfill and satisfy users’ needs as well as their demands for an improvised, fast and efficient Quality of service (QoS). A minimal aggregate of waiting time in return would give users a better Quality of experience (QoE). Real-time service packet scheduling is an important process in allocating resources to users. An efficient packet scheduling scheme will be able to cater fairly and efficiently to its users in the LTE network. Hence, studies are performed focusing on real-time traffic which includes video as well as Voice over Internet Protocol (VoIP) transmissions. In this work, the existing exponential rule (EXP rule) is utilized to benchmark our proposed packet scheduling techniques so that we are able to further evaluate the scheduling performance. In response to the increasing likelihood of losing packets in the EXP rule’s algorithm and maximizing the throughput rate, several schemes have been experimented with. The proposed schemes include 1) simplified EXP rule (sEXP Rule), 2) modified EXP rule (mEXP Rule), 3) EXP rule with maximum throughput (MT) (EXP_MT Rule), and 4) enhanced EXP rule with MT (E2M). By adding MT as a weight to the EXP rule, the throughput is maximized, thus providing higher throughput rates for real-time and non-real-time traffic. The simulation results show that the sEXP rule has a better performance in throughput, packet loss rate (PLR), and spectral efficiency for video traffic. Aside from this, our proposed E2M rule performs better than the benchmark EXP rule and outperforms the other proposed schemes, as well. It is observed that the E2M rule has better QoS support for real-time transmission in terms of delay, packet loss, throughput and spectral efficiency, within the LTE network. Hence, our proposed E2M rule is an enhancement of the benchmark EXP rule, which is commonly used in LTE packet scheduling.

IEEE 802.16e is an advanced wireless access technology that provides high-speed data transmission in long distance and offers quality of service (QoS) to subscribers. The provisioning of QoS is one of the great features by IEEE 802.16 to support both real-time and non-real-time applications. In IEEE 802.16, the common part sublayer in the MAC layer is responsible for maintaining the QoS services. There are many functions in the common part sublayer; the most popular topics discussed by researchers are the uplink and downlink scheduling algorithms. Many discussions had been made and focused on these two classes of schedulers. Another equally important component but overlooked so far is the bandwidth request and grant module. Bandwidth request and grant module arbitrates the amount of bandwidth to be granted, besides handling the bandwidth requests. This bandwidth request and grant process has always been developed in a conventional way, and its importance has been underestimated. In addition, the bandwidth distribution within a same service class or category also attracted little attention thus far. Many algorithms for interclass scheduling have been studied and proposed but not as much for intraclass scheduling. However, in bandwidth request and grant process, constraints on the required knowledge by the schedulers limit the intraclass scheduling algorithms to be applied onto them. We view the bandwidth request and grant process as an important part of the QoS architecture. In this paper, we proposed a new bandwidth granting scheme for the bandwidth request and granting process, which enables bandwidth to be fairly granted based on the necessity to all the requests from the same service class or category. By applying our scheme, significant improvements have been observed and recorded. Experiment results have proven and confirmed the effectiveness of our proposed scheme as compared to the conventional scheme.

Malaysia, a country with a tropical climate characterized by consistent warmth and year-long high humidity, houses the perfect conditions for mushroom growth. Recently, there has been a surge in back-to-nature activities in Malaysia. However, many participants lack prior knowledge about the local flora and fungi, leading to a rise in mushroom poisoning cases, some of which have been fatal. Despite thorough research, there is a notable lack of identification studies specifically focused on mushroom species in Malaysia. Identifying these species is crucial for medical providers to effectively counteract the toxins from ingested mushrooms and also serves as an important educational tool. This study aims to determine the most suitable architecture for mushroom identification, focusing specifically on mushroom species found in Malaysia. A dataset of these mushrooms was curated, augmented, and processed through multiple variants of Vision Transformers (ViTs) and ResNet models, with uniform hyperparameters to ensure fairness. The results indicate that the ViT-L/16 model achieved the highest accuracy at 90.47%.

In the Malaysian concurrent tyre retailing industry, most of the business activities are carried out in the traditional labour-intensive ways. This manual process is prone to inefficiencies, increases operating costs and increases the likelihood of error. The current paper outlines Tracktive, a unified and cloud-based solution that was conceptualized and built to automate the industry by centralizing the ordering of tyres, inventory and order management to a single cloud-based platform. Tracktive is built on microservice architecture and event-driven architecture and deployed with the help of a simulated cloud environment. All the core functional modules are independent, scalable modules, each having its own management. The event-driven architecture is used in inter-module communication via Apache Kafka, which allows asynchronous communication between each microservices. A specific Application Programming Interface (API) Gateway is deployed to avoid unauthorized access and uses JSON Web Token (JWT) authentication and Bcrypt hashing. The system is finally tested with the help of LocalStack, which emulates Amazon Web Service (AWS) services, thus, proving the cloud-native design of the system cost-efficiently and effectively. Tracktive have the potential to improve the accuracy of operations, streamline the processes, and grant the business more agility by replacing the outdated manual processes with a centralized digital system, which could also be a driving force of digital transformation in the tyre retailing industry.