The security and integrity of computer systems and networks highly depend on malware detection. In the realm of malware detection, the K-Nearest Neighbors (KNN) algorithm is a well-liked and successful machine learning algorithm. However, the choice of an acceptable distance metric parameter has a significant impact on the KNN algorithm's performance. This study tries to improve malware detection by adjusting the KNN algorithm's distance metric parameter. The distance metric greatly influences the similarity or dissimilarity between instances in the feature space. The KNN algorithm for malware detection can be more accurate and effective by carefully choosing or modifying the distance metric. This paper analyzes multiple distance metrics, including Minkowski distance, Manhattan distance, and Euclidean distance. These metrics account for the traits of malware samples while capturing various aspects of similarity. The effectiveness of the KNN algorithm is evaluated using the MalMem-2022 malware dataset, and the results are broken down into these three-distance metrics. The experimental findings show that, among the three distance metric parameters, the Euclidean and Minkowski distance metric parameters considerably produced the best outcomes with binary classification. While with multiclass classification, the KNN algorithm has achieved the highest outcomes using Manhattan distance.

Given the widespread adoption of social media as a primary information source, organizations can no longer do without it. The importance of social media in creating sustainable company strategies has increased in tandem with the public's growing knowledge of sustainability issues. This study examines how marketing and social media can create brand sustainability plans. To create a practical framework for creating sustainability strategies, the relevant literature was evaluated. Market managers and digital marketers were surveyed using an online questionnaire. To extract conclusions from the data and model variables, a structural equation model was used. Findings from the survey indicate that businesses are increasingly turning to social media to share information with stakeholders, solicit opinions, and collect information for use in developing sustainability plans. Market managers play a vital role in ensuring the sustainability of their businesses. The research has theoretical and managerial implications.

Digitization technology has developed in the preservation of cultural heritage, especially ancient manuscripts. In this context, image retrieval methods allow for efficient access to the information contained in ancient manuscripts. Optimization techniques play a role in building an effective image retrieval method. This paper presents a systematic literature review that focuses on the role of optimization in digital image retrieval methods for preserving ancient manuscripts as part of cultural heritage preservation. This paper is organized based on the following research questions: (1) What are the research objectives regarding optimization in digital image retrieval of ancient manuscripts?; (2) What is the role of optimization in image retrieval methods for the preservation of ancient manuscripts?; and (3) How have existing studies determined the formulation of novel and new research? This study involved searching articles through Scopus, Dimensions, Science Direct, and Google Scholar databases and using bibliometric analysis to visualize research themes and trends. The results of the literature review show that research related to the role of optimization in the digital image retrieval method of ancient manuscripts is still open and this paper can be used as a reference for further research on this topic.

In the realm of financial security, the detection and prevention of credit card fraud has become paramount. With the ever-increasing reliance on digital transactions, the risk of fraudulent activities targeting credit card systems has grown significantly. To combat this, sophisticated techniques are required to swiftly identify and mitigate potential threats. Machine learning, a cornerstone of modern data analysis, has emerged as a powerful tool in this pursuit. By leveraging vast datasets and employing advanced algorithms, machine learning enables the automated scrutiny of transactions, distinguishing between legitimate and fraudulent activities with remarkable precision. This paper introduces an intelligent method for credit card fraud detection that relies on Competitive Swarm Optimization (CSO) and Random Weight Network (RWN). Additionally, the system includes an automated hybrid feature selection capability to identify the most pertinent features during the detection process. The experimental outcomes validate that this system can attain outstanding results in G-Mean, RUC, and Recall values.

The Indonesian real estate market has shown promising development in recent years, attracting investors seeking lucrative opportunities. This research aims to increase the success rate of real estate investments in Indonesia by examining the connection between macroeconomic factors and consumer behavior. This study employs vector autoregression (VAR) models to investigate the influence of macroeconomic data on the Indonesian house price index (HPI). We intend to determine the primary determinants of the HPI by analyzing various macroeconomic indicators, including GDP growth, CPI, crude oil, interest rates, and the unemployment rate. In addition, the research investigates the role of consumer behavior in Indonesian real estate searches. Real estate developers and investors can gain valuable insight into the effect of consumer preferences, motivations, and decision-making processes on demand functions by understanding consumer preferences, incentives, and decision-making processes. A comprehensive analysis of historical data and econometric modeling techniques are employed to accomplish research objectives. The research is conducted using pertinent macroeconomic indicators, real estate market data, and consumer surveys.

This study delves into the intricate landscape of metaverse technology adoption within higher education institutions in the UAE, investigating the multifaceted interplay of accessibility, technology adaptability, and policies and regulations. Using a cross-sectional research design, data was meticulously collected through a multistage sampling approach, combining probability and non-probability methods. A pretested questionnaire underwent rigorous evaluation, ensuring unbiased item formulation and adherence to best practices. The investigation challenges and extends the Technology Acceptance Model (TAM) by revealing unexpected findings. The absence of a significant relationship between accessibility and metaverse adoption prompts a call for an expanded TAM framework. Surprisingly, a negative correlation between technology adaptability and adoption is highlighted, emphasizing the need for a cautious assimilation approach. Moreover, the research underscores the influential role of policies and regulations in metaverse adoption, advocating for a comprehensive TAM framework that encompasses regulatory dynamics. Findings offer practical implications for stakeholders, policymakers, and institutions, emphasizing diverse adoption facets beyond accessibility. The study contributes to the discourse on metaverse adoption and advances theoretical frameworks for technology integration within educational contexts. The methodology's meticulous design underscores the study's rigor, ensuring the robustness of the insights gleaned from the investigation.

This research aims to explore the impact of AI-induced anxiety on the adoption of mobile payment services in supply chain firms, viewed through the lens of Mental Accounting Theory. In an era driven by technological advancement, supply chain companies' use of mobile payment services has arisen as a crucial problem. This study is the first to investigate the complicated links between AI-induced anxiety, perceived utility, and the adoption rate of mobile payment systems using the Mental Accounting Theory as a theoretical framework. The study employs a quantitative research approach, using Smart PLS for regression analysis, and gathers its data from major supply chain business players. Our analysis offered important insights into the many aspects influencing the adoption of mobile payment services in supply chain companies. The acceptance rate was shown to be adversely connected with AI-induced anxiety and integration expenses, posing obstacles for businesses seeking to embrace mobile payment systems. In contrast, characteristics such as perceived utility, usability, confidence in security, and backing from upper management were positively connected with adoption rates. These findings provide not only theoretical contributions to the current research, but also concrete advice for supply chain practitioners seeking to exploit mobile payment systems for operational and strategic advantage.

Pandemic moments have generated mental and emotional problems in students at all levels. These have been affected by the format of virtual classes, the mandatory confinement and the little physical relationship due to the existing restrictions, generating academic burnout and anxiety in university students. In this context, the objective was to know the existing relationship between burnout and anxiety in students of the FIS-UNCP, the 15-question Maslach Burnout Inventory Student Questionnaire (MBI-SS) was used with the dimensions: Emotional Exhaustion, Cynicism and Loss of Academic Efficacy and 5 questions to know the level of technological anxiety or technostress, with a population of 328 university students of 10 semesters, through the questionnaire in Office Forms. The research design was non-experimental, transectional, with a qualitative-quantitative approach and descriptive-explanatory levels. The descriptive data analysis was made based on the scale, allowing the identification of students with burnout and the structural equation modeling facilitated the establishment of the relationship between the variables. The study showed that 26 students (7.93%) suffer from academic burnout. At the same time, it has been demonstrated that there is a positive and significant relationship between emotional exhaustion and lack of academic efficacy, with technological anxiety with path values of 0.701 and 0.345 respectively, the p-values allowed demonstrating hypotheses 1 and 3 formulated. At the level of the structural model, it allows anticipating future results, since the coefficient of determination (R2) calculated was 0.838.

One time series model developed to predict economic data is Spatial Vector Autoregressive with Exogenous Variables (SpVARX). This model can accommodate simultaneously the interrelationships between variables, the impact of exogenous variables, and Inter-regional linkages. However, this model has not adjusted the nonlinearity relationships between variables. The relationship between economic variables is usually not linear. Therefore, we introduce the Threshold Spatial Vector Autoregressive with exogenous variables (TSpVARX). This paper assesses the forecasting performance of TSpVARX and compares it with SpVARX models. We conducted a simulation study by generating 100 times the simulation data with twelve scenarios. We found that the forecasting performance of the TSpVARX model is better than SpVARX when there is a nonlinear relationship between variables. In addition, we find that the forecasting performance of TSpVARX models will improve as the sample size increases.

Research on human-computer interaction currently focuses on enhancing system usability by establishing an appropriate user interface (UI) that depends on users’ features. Online users typically have different perceptions of their favored interface design depending on their preferences. Thus, those interface preferences could be utilized to recognize online users’ identities. User authentication is another critical issue that should be considered to improve online security mechanisms without compromising usability. This study investigates the feasibility of using UI preferences as evidence of user identity. The proposed method applies to the design preferences of users dealing with online systems (e.g., e-exam and e-banking). These preferences are closely associated with individual characteristics, whether physical, cognitive, psychological, psychomotor, demographic, or experience based. Many design characteristics could be used in online systems; for example, the e-exam interface design may use features such as the font (size, color, and face), the number of questions per page, background color, questions group, timer type, and sound alert. The feasibility evaluation of this study indicated that 96.8% of research participants have variations in their preferences, and each participant kept 94.5% of their design preferences throughout different sessions.