With the increasing prevalence of network intrusions, the development of effective network intrusion detection systems (NIDS) has become crucial. In this study, we propose a novel NIDS approach that combines the power of long short-term memory (LSTM) and attention mechanisms to analyze the spatial and temporal features of network traffic data. We utilize the benchmark UNSW-NB15 dataset, which exhibits a diverse distribution of patterns, including a significant disparity in the size of the training and testing sets. Unlike traditional machine learning techniques like support vector machines (SVM) and k-nearest neighbors (KNN) that often struggle with limited feature sets and lower accuracy, our proposed model overcomes these limitations. Notably, existing models applied to this dataset typically require manual feature selection and extraction, which can be time-consuming and less precise. In contrast, our model achieves superior results in binary classification by leveraging the advantages of LSTM and attention mechanisms. Through extensive experiments and evaluations with state-of-the-art ML/DL models, we demonstrate the effectiveness and superiority of our proposed approach. Our findings highlight the potential of combining LSTM and attention mechanisms for enhanced network intrusion detection.

This research is to examine the role of management of foreign direct investment and control of corruption in economic growth. The research data were collected from the ASEAN-6 countries including Indonesia, Malaysia, Thailand, Singapore, Philippines, and Vietnam during the period of 2002-2021. The research utilizes the panel vector autoregressive (PVAR) method developed by Abrrigo and Love (2015) [Abrigo, M. R. M., & Love, I. (2016). Estimation of panel vector autoregression in Stata.] to estimate the research model. The estimation results show that foreign direct investment and corruption control play an important role in promoting economic growth in the ASEAN-6 countries. Furthermore, foreign direct investment and corruption control are closely related to each other, indicating that economic growth is not only directly affected by foreign direct investment and corruption control but also indirectly influenced by each of these factors. This is a new finding of this research compared to previous studies. These findings provide significant empirical evidence for the ASEAN-6 countries, particularly in managing foreign direct investment and controlling corruption to promote economic growth. The implication of these results is that these countries identify appropriate policies to manage FDI and corruption control in order to maximize the level of economic growth.

Ransomware has evolved into a pervasive and extremely disruptive cybersecurity threat, causing substantial operational and financial damage to individuals and businesses. This article explores the critical domain of Ransomware detection and employs Machine Learning (ML) classifiers, particularly Decision Tree (DT), for Ransomware detection. The article also delves into the usefulness of DT in identifying Ransomware attacks, leveraging the innate ability of DT to recognize complex patterns within datasets. Instead of merely introducing DT as a detection method, we adopt a comprehensive approach, emphasizing the importance of fine-tuning DT hyperparameters. The optimization of these parameters is essential for maximizing the DT capability to identify Ransomware threats accurately. The obfuscated-MalMem2022 dataset, which is well-known for its extensive and challenging Ransomware-related data, was utilized to evaluate the effectiveness of DT in detecting Ransomware. The implementation uses the versatile Python programming language, renowned for its efficiency and adaptability in data analysis and ML tasks. Notably, the DT classifier consistently outperforms other classifiers in Ransomware detection, including K-Nearest Neighbors, Gradient Boosting Tree, Naive Bayes, and Linear Support Vector Classifier. For instance, the DT demonstrated exceptional effectiveness in distinguishing between Ransomware and benign data, as evidenced by its remarkable accuracy of 99.97%.

Rising sea surface height is one of the world's vital issues in marine ecosystems because it greatly affects the ecosystems as well as the socio-economic life of the surrounding environment. Pekalongan is one area in Indonesia facing the effects of this phenomenon. This problem deserves to be explored further with complex approaches. One of them is a neural network to perform forecasting more accurately. In neural networks, the time series approach can be used with Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). By adding the bidirectional method to each of these two approaches, we will find the best method to use to perform the analysis. The best results were obtained by forecasting for 960 days using Vanilla BiGRU. The results can be interpreted from multiple perspectives. The forecasting results showed a fluctuating pattern as in previous periods, so it can be said that the pattern is still quite normal, which indicates that the terminal can continue to operate normally. However, the forecasting results from this study are expected to be a reference for information for the government to prevent future dangers.

While many small and medium enterprises (SMEs)recognize the benefits of Big Data Analytics (BDA) for digital transformation, they face challenges in implementing this technology, highlighting the need for more research on its adoption by SMEs. The objective of this study is to amalgamate the Technology Organization Environment (TOE) framework with the Diffusion of Innovation (DOI) theory, aiming to dissect the factors that sway BDA adoption in Jordanian SMEs. Additionally, the study delves into how perceived usefulness impacts this adoption process. Utilizing structural equation modeling, the study examined data from 388 managers in Jordan. The study validates all its hypotheses, revealing that variables like relative advantage, compatibility, complexity, top management support, competitive pressure, and security influence perceived usefulness, which subsequently has a positive impact on BDA adoption. This research presents a range of theoretical and practical insights.

Responsive, timely, and friendly service quality plays a central role in shaping trust between the government and citizens. With the improvement of service quality, the public feels valued and heard, reinforcing the mutual trust relationship between the government and citizens. In this regard, technology-enabled service processes can streamline time and cost, while automation reduces the risk of human errors. Through web platforms or applications, the government can provide easier access for citizens to various services without the need to physically visit government offices. Good and quality public services are not only aimed at meeting the practical needs of the public but also play a role in shaping the mutual trust relationship between the government and citizens. Therefore, the concept of e-service quality, which encompasses the quality of services provided through electronic platforms, becomes crucial. The objective of this research is to explore the extent to which e-service quality can influence the level of public satisfaction. The research method employs a quantitative approach using primary data sources, where random sampling is applied as the sampling technique. The research respondents are citizens using digital public service platforms organized by the local government of Jakarta. The sample size used in this study is 262. The variables tested in this research involve e-service quality, public trust, and public satisfaction. In analyzing the data, this research utilizes SmartPLS 4 software. The analysis results show that e-service quality has a significant influence on the formation of public trust. Furthermore, findings indicate that e-service quality also significantly affects public satisfaction. However, the analysis results do not support the idea that public trust mediates the relationship between e-service quality and public satisfaction. This signifies that while public trust directly contributes to public satisfaction with public services, other unmeasured factors also play a role in shaping public perceptions and satisfaction.

The space-time model combines spatial and temporal elements. One example is the Generalized Space-Time Autoregressive (GSTAR) Model, which improves the Space-Time Autoregressive (STAR) model. The GSTAR model assumes that each location has heterogeneity characteristics, and that the data is stationary. In this research, the moving average component is calculated by involving the relationship between variable values at a certain time and residual values at a previous time, and it is assumed that the data is not stationary, so the model used is the Generalized Space-Time Autoregressive Integrated Moving Average (GSTARIMA) Model. The model order for GSTARIMA is determined through the Space-Time Autocorrelation Function (STACF) and Space-Time Partial Autocorrelation Function (STPACF) to ensure accurate forecasting. Previous research only discussed the GSTARIMA(1,1,1) model, so in this research, the GSTARIMA(3,1,1) model will be addressed as a form of development of the GSTARIMA(1,1,1) model and applied to climate data. The climate data used in this research is sourced from NASA POWER and consists of rainfall variables with large data sizes, requiring the use of the data analytics lifecycle method to analyse Big Data. The lifecycle includes six phases: discovery, data preparation, model planning, model building, communicating results, and operationalization. Based on the data processing results with Python software, the GSTARIMA(3,1,1) model has a MAPE value of 9% for out-sample data and 11% for in-sample data. In contrast, the GSTARIMA(1,1,1) model has a MAPE value of 11% for out-sample data and 12% for in-sample data. So the GSTARIMA(3,1,1) model provides more accurate forecasting results. Therefore, selecting the correct model order is crucial for accurate forecasting.

This study conducts a quantitative inquiry into how components of gamification and customization are being used in Saudi Arabia's educational system. In our investigation, we zero in on how these factors could contribute to skill development. This investigation uses a thorough and rigorous quantitative research approach to probe students' preferences for gamification components and their thoughts on customization. The findings highlight the amazing congruence between people's preference for gamification components like points and badges and the need for adaptation and feedback in optimizing the effectiveness of the educational process. Through careful component analysis, the current investigation successfully separates two distinct constructs: one highlights the importance of flexibility and responsiveness, while the other emphasizes the significance of pace and cultural appropriateness. The results of this research have important policy and practice implications for Saudi Arabia, where educational reforms are now underway. The goal of these changes is to boost academic performance by introducing student-specific, interactive gaming into the classroom.

AI is tanking different functions of businesses, and marketing is no exception. Digital marketing is gaining pace with the advancement in technology and the internet. The research aims to find the answer to the research question that marketers can benefit from AI in content creation for the digital market. The study also finds the relevance and use of AI in content creation and develops an AI infrastructure adoption model for content creators in digital marketing. The findings of this study were compiled using a systematic literature review that adhered to the Preferred Reporting Items for Systematic Reviews (PRISMA) statement and the criteria established by Meta-Analyses. The findings revealed that using AI in content creation provides personalized data, which helps the creators make relevant, targeted, and specific content. The research also finds that AI alone is not mature enough to carry out the whole content creation procedure as there is some limitation attached, especially regarding ethical implications. That’s why human surveillance of AI systems involved in content creation for the digital market is needed.

Adolescents increasingly turn to online sources for health information, raising concerns about the credibility of information and its impact on their behaviors. This study explores the factors shaping adolescents' online health information seeking (OHIS) behaviors. The study surveyed 381 adolescents to assess trust in online health information, eHealth literacy, parental behaviors, and AI-generated credibility scores. Structural Equation Modeling (SEM) was used to analyze data and test hypotheses based on the Social Cognitive Theory. Trust in online health information positively influenced disease-related OHIS behaviors (β = 0.24, p < 0.05) and fitness-related OHIS behaviors (β = 0.18, p < 0.05). Higher eHealth literacy correlated with increased disease-related OHIS behaviors (β = 0.32, p < 0.05) and fitness-related OHIS behaviors (β = 0.28, p < 0.05). Parental OHIS behaviors influenced adolescents' disease-related OHIS behaviors (β = 0.16, p < 0.05) and fitness-related OHIS behaviors (β = 0.22, p < 0.05). Parental OHIS mediation positively mediated these relationships (H7, H8). Higher AI-generated credibility scores associated with more disease-related OHIS behaviors (β = 0.20, p < 0.05) and fitness-related OHIS behaviors (β = 0.15, p < 0.05). Adolescents' eHealth literacy mediated these relationships (H11, H12). Trust, eHealth literacy, parental influence, and AI-generated credibility play vital roles in shaping adolescents' OHIS behaviors. Educators should prioritize enhancing eHealth literacy and promoting credible online sources to improve adolescents' health information seeking practices. This study contributes insights into the factors influencing adolescents' OHIS behaviors, emphasizing the role of parental mediation and AI-generated credibility scores. The findings inform the development of targeted health education interventions to encourage responsible online health information seeking among adolescents.