Intrusion detection system (IDS) is a combination of software application and hardware devices that monitors the network and filters activities for malicious or unauthorized access attempts. IDS are deployed for generating large volumes of stream data, which can be challenging to identify relevant features and reduce false positives. Feature selection is a candidate solution for preserving only relevant features and filtering out non-relevant features in IDS. The feature selection is performed using multi-objective optimization based meta-heuristic searching algorithms (MOO-MHS), minimizing two objective functions: error rate and memory usage. Traditional FS has limited suitability due to the stream nature of IDS data and the occurrence of concept drift. To solve this challenge, the MOO-MHS is promising to be updated to support dynamic feature selection (DFS) for serving IDS. Therefore, this paper proposes a novel framework for DFS in IDS using MOO-MHS. The proposed framework is an Enhanced Dynamic Filter-Based Feature Selection (E-DFBFS) that enables periodic call of non-dominated sorting genetic algorithm 2 (NSGA-II) that provides a novel solution selection algorithm using two selection types, namely, mean and median. The performance of the proposed E-DFBFS framework is compared with existing state-of-the-art benchmarking algorithms considering both synthetic and real-world data, providing superiority over DFBFS in terms of accuracy and F-measure and most classification metrics.

The concept of cloud computing has completely changed how computational resources are delivered and used. By enabling on-demand access to collective computing resources through the internet. While this technological shift offers unparalleled flexibility, it also brings considerable challenges, especially in scheduling and resource allocation, particularly when optimizing multiple objectives in a dynamic environment. Efficient allocation and scheduling of resources are critical in cloud computing, as they directly impact system performance, resource utilization, and cost efficiency in dynamic and heterogeneous conditions. Existing approaches often face difficulties in balancing conflicting objectives, such as reducing task completion time while staying within budget constraints or minimizing energy consumption while maximizing resource utilization. As a result, many solutions fall short of optimal performance, leading to increased costs and degraded performance. This systematic literature review (SLR) focuses on research conducted between 2019 and 2023 on scheduling and resource allocation in cloud environment. Following preferred reporting items for systematic reviews and meta-analyses guidelines, the review ensures a transparent and replicable process by employing systematic inclusion criteria and minimizing bias. The review explores key concepts in resource management and classifies existing strategies into mathematical, heuristic, and hyper-heuristic approaches. It evaluates popular algorithms designed to optimize key metrics such as energy consumption, resource utilization, cost reduction, makespan minimization, and performance satisfaction. Through a comparative analysis, the SLR discusses the strengths and limitations of various resource management schemes and identifies emerging trends. It underscores a steady growth in research within this field, emphasizing the importance of developing efficient allocation strategies to address the complexities of modern cloud systems. The findings provide a comprehensive overview of current methodologies and pave the way for future research aimed at tackling unresolved challenges in cloud computing resource management. This work serves as a valuable resource for practitioners and academics seeking to optimize scheduling and allocation in dynamic cloud environments, contributing to advancements in resource management strategies of cloud computing.

Selecting the most miniature possible set of genes from microarray datasets for clinical diagnosis and prediction is one of the most challenging machine learning tasks. A robust gene selection technique is required to identify the most significant subset of genes by removing spurious or non-predictive genes from the original dataset without sacrificing or reducing classification accuracy. Numerous studies have attempted to address this issue by implementing either a filter or a wrapper. Although the filter approaches are computationally efficient, they are completely independent of the induction algorithm. On the other hand, wrapper approaches outperform filter approaches but are computationally more expensive. Therefore, this study proposes an enhanced gene selection method that uses a hybrid technique that combines the Symmetrical Uncertainty (SU) filter and Reference Set Harmony Search Algorithm (RSHSA) wrapper method, known as SU-RSHSA. The framework to develop the proposed SU-RSHSA includes numerous stages: (1) investigate a novel gene selection method based on the HSA and will then determine appropriate values for the HSA’s parameters, (2) enhance the construction process of the initial harmony memory while satisfying the diversity of the solution by embedding a reference set within the HSA (RSHSA), and (3) investigates the effect of integrating Symmetrical Uncertainty (SU) as a filter and RSHSA as a wrapper (SU-RSHSA) to maximize classification accuracy by leveraging their respective advantages. The results demonstrate that the SU-RSHSA outperforms the original HSA and SU-HSA in terms of classification accuracy, a small number of selected relevant genes, and reduced computational time. More importantly, the proposed SU-RSHSA gene selection method effectively generates a small subset of salient genes with high classification accuracy.

The overall aim of the research was to identify influential factors that best predict career decisions or job choice among graduates of the Department of Tafseer and Hadith at the Universitas Islam Negeri Sultan Syarif Kasim Riau. Instead of using a longitudinal, cohort study using statistical analysis, machine learning techniques such as Decision Tree and Naïve Bayes was applied to search for unknown patterns or rules. This study compares the performance of the machine learning methods in discovering hidden patterns or factors that influenced the alumni career decisions. One of the primary concern of the university is whether their career choice after graduation are relevant or match to their field of studies. Our studies show that CGPA, cohort, additional expertise, and gender are the main factors that influenced alumni career success. We found that a cohort of graduate students was unable to find relevant professions in their field of studies. The experimental result shows that Naïve Bayes outperforms Decision Tree with the best accuracy score of 97.1 % and 92.6% subsequently. Thus, it can be concluded that the prediction model and analysis using Naïve Bayes have the potential to be used effectively. Despite its low performance, Decision Tree able to extract the main factors that influenced an alumnus career efficiently. These findings are valuable and useful both for the institution to better understand and improve the quality of its program and graduates, and also the community of machine learning in understanding the techniques behaviors with small datasets.

Requirement prioritization (RP) is a crucial task in managing requirements as it determines the order of implementation and, thus, the delivery of a software system. Improper RP may cause software project failures due to over budget and schedule as well as a low-quality product. Several factors influence RP. One of which is requirements dependency. Handling inappropriate handling of requirements dependencies can lead to software development failures. If a requirement that serves as a prerequisite for other requirements is given low priority, it affects the overall project completion time. Despite its importance, little is known about requirements dependency in RP, particularly its impacts, types, and techniques. This study, therefore, aims to understand the phenomenon by analyzing the existing literature. It addresses three objectives, namely, to investigate the impacts of requirements dependency on RP, to identify different types of requirements dependency, and to discover the techniques used for requirements dependency problems in RP. To fulfill the objectives, this study adopts the Systematic Literature Review (SLR) method. Applying the SLR protocol, this study selected forty primary articles, which comprise 58% journal papers, 32% conference proceedings, and 10% book sections. The results of data synthesis indicate that requirements dependency has significant impacts on RP, and there are a number of requirements dependency types as well as techniques for addressing requirements dependency problems in RP. This research discovered various techniques employed, including the use of Graphs for RD visualization, Machine Learning for handling large-scale RP, decision making for multi-criteria handling, and optimization techniques utilizing evolutionary algorithms. The study also reveals that the existing techniques have encountered serious limitations in terms of scalability, time consumption, interdependencies of requirements, and limited types of requirement dependencies.