IoT devices with weak security designs are a serious threat to organizations. They are the building blocks of Botnets, the platforms that launch organized attacks that are capable of shutting down an entire infrastructure. Researchers have been developing IDS solutions that can counter such threats, often by employing innovation from other disciplines like artificial intelligence and machine learning. One of the issues that may be encountered when machine learning is used is dataset purity. Since they are not captured from perfect environments, datasets may contain data that could affect the machine learning process, negatively. Algorithms already exist for such problems. Repeated Edited Nearest Neighbor (RENN), Encoding Length (Explore), and Decremental Reduction Optimization Procedure 5 (DROP5) algorithm can filter noises out of datasets. They also provide other benefits such as instance reduction which could help reduce larger Botnet datasets, without sacrificing their quality. Three datasets were chosen in this study to construct an IDS: IoTID20, N-BaIoT and MedBIoT. The filtering algorithms, RENN, Explore, and DROP5 were used on them to filter noise and reduce instances. Noise was also injected and filtered again to assess the resilience of these filters. Then feature optimizations were used to shrink the dataset features. Finally, machine learning was applied on the processed dataset and the resulting IDS was evaluated with the standard supervised learning metrics: Accuracy, Precision, Recall, Specificity, F-Score and G-Mean. Results showed that RENN and DROP5 filtering delivered excellent results. DROP5, in particular, managed to reduce the dataset substantially without sacrificing accuracy. However, when noise got injected, the DROP5 accuracy went down and could not keep up. Of the three dataset, N-BaIoT delivers the best accuracy overall across the learning techniques.

The pharmaceutical supply chain (PSC) plays a crucial role in ensuring the timely and reliable availability of essential drugs while maintaining high-quality standards. Balancing the triad of cost, time, and quality is paramount in optimizing the complexities of this supply chain. In this research, a multi-objective PSC optimization model is developed to maximize the key business factors. The dynamic nature of the PSC can significantly compromise the effectiveness of the decision making process. To deal with this challenge, a robust possibilistic flexible programming approach (RPFPA) solution methodology is proposed. This methodology provides a robust and flexible framework to tackle the uncertainties within the supply chain. To validate the proposed model and methodology, a computational analysis of a case study is conducted. The results of the analysis demonstrate the effectiveness of the model and methodology in addressing the uncertainties and complexities of the PSC. Specifically, the findings reveal that by accepting a 23.8% increase in costs, decision-makers can achieve a desirable level of robustness in their decisions. Moreover, the study identifies that the assignment of higher priority to cost objectives leads to more centralized decisions within the supply chain, while a greater emphasis on quality objectives results in a more decentralized approach. By employing the proposed approach, decision-makers can efficiently deal with the complexities and uncertainties inherent in the PSC, making well-informed choices that balance cost, time, and quality.

This research explores the influence of data mining methods on the managerial decision-making in Saudi Arabia's industrial sector, emphasizing the moderating function of cooperation. A total of 500 questionnaires were distributed to information technology managers, with 265 responses selected for data analysis. Smart PLS 4 software was used for the data analysis, and statistical measures were used to analyze the correlations between variables. The findings show that data mining approaches have a substantial positive relationship with improving decision-making and information exchange within external and internal contexts. The study also demonstrates that cooperation plays an important moderating role in these interactions, emphasizing the significance of building a cooperative atmosphere to improve the influence of data mining methods on decision-making and information sharing. The study's conclusions have practical relevance for organizations in the industrial sector. Organizations may improve their decision-making processes and information sharing by adopting data mining tools and boosting collaboration, enhancing performance and competitiveness.

This study examined the relationship between employee engagement, organizational performance, and electronic human resource management (e-HRM) in small- and medium-sized Saudi firms. In total, 180 participants were randomly selected using convenience sampling. SEM-PLS, or structural equation model-partial least squares, was used to analyze the data. The findings of this study show no statistically significant correlation between organizational success and e-HRM. E-HRM and employee engagement, as well as employee engagement and organizational performance, have a strong beneficial association. The implications of the results highlight the need to establish good E-HRM practices to increase employee engagement and boost organizational performance.

Designing sustainable and scalable educational systems is a challenge. Artificial Intelligence (AI) offers promising solutions to enhance the effectiveness and sustainability of blended learning systems. This research paper focuses on the integration of the Chat Generative Pre-trained Transformer (ChatGPT), with a blended learning system. The objectives of this study are to investigate the potential of AI techniques in enhancing the sustainability of educational systems, explore the use of ChatGPT to personalize the learning experience and improve engagement, and propose a model for sustainable learning that incorporates AI. The study aims to contribute to the body of knowledge on AI applications for sustainable education, identify best practices for integrating AI in education, and provide insights for policymakers and educators on the benefits of AI in education delivery. The study emphasizes the significance of AI in sustainable education by addressing personalized learning and educational accessibility. By automating administrative tasks and optimizing content delivery, AI can enhance educational accessibility and promote inclusive and equitable education. The study’s findings highlight the potential benefits of integrating AI chatbots like ChatGPT into education. Such benefits include promoting student engagement, motivation, and self-directed learning through immediate feedback and assistance. The research provides valuable guidance for educators, policymakers, and instructional designers who seek to effectively leverage AI technology in education. In conclusion, the study recommends directions for future research in order to maximize the benefits of integrating ChatGPT into learning systems. Positive results have been observed, including improved learning outcomes, enhanced student engagement, and personalized learning experiences. Through advancing the utilization of AI tools like ChatGPT, blended learning systems can be made more sustainable, efficient, and accessible for learners worldwide.

The Android operating system is the most used mobile operating system in the world, and it is one of the most popular operating systems for different kinds of devices from smartwatches, IoT, and TVs to mobiles and cockpits in cars. Security is the main challenge to any operating system. Android malware attacks and vulnerabilities are known as emerging risks for mobile devices. The development of Android malware has been observed to be at an accelerated speed. Most Android security breaches permitted by permission misuse are amongst the most critical and prevalent issues threatening Android OS security. This research performs several studies on malware and non-malware applications to provide a recently updated dataset. The goal of proposed models is to find a combination of noise-cleaning algorithms, features selection techniques, and classification algorithms that are noise-tolerant and can achieve high accuracy results in detecting new Android malware. The results from the empirical experiments show that the proposed models are able to detect Android malware with an accuracy that reaches 87%, despite the noise in the dataset. We also find that the best classification results are achieved using the RF algorithm. This work can be extended in many ways by applying higher noise ratios and running more classifiers and optimizers.

With the hope of improving students' social interactions who have been diagnosed with Autism Spectrum Disorder (ASD) in Saudi Arabia, the current study set out to examine the effectiveness of peer-mediated therapies delivered through the ubiquitous social media platform Snapchat. Thirty kids with ASD and 15 generally developing peers participated in the study. Alterations in social communication difficulties and observable social interactions were evaluated before and after the intervention to determine its efficacy. The results of the research revealed significant improvements in both self-reported difficulties with social communication and the quality of participants' interpersonal relationships. These findings demonstrate the groundbreaking methodology's deep applicability and widespread cross-cultural significance. Individuals with ASD have shown to benefit greatly from individualized, technology-based therapy to improve their social interactions, as shown by this study.

The profitability of the business organization is the relative measurement and explores the profit earning capacity. There are two concepts of measuring profitability, which are profitability based on revenue, and investments. Gross and net profitability are the means of expression of the profitability based on revenue while investment profitability can be measured based on owners’ investment and total investment or total assets. Secondary data from the websites of the energy sector companies are taken for the study and ratio analysis, rank correlation is applied to get the similarity or differences in the profitability and relational relationship of the energy sector companies of Saudi Arabia. The study reveals that there was a significant difference in the profitability of the energy sector companies. Possibly, internal and external factors of the business organizations govern the profitability. There is a perfect and positive relational correlation between revenue and profitability while a highly negative correlation exists between profitability and investments. This may be due to overcapitalization or underutilization of the resources. Enhancement of velocity of operational activity is necessary to enhance the operational level of energy sector companies of Saudi Arabia. There is a need to control the indirect manufacturing and administrative expenses in smaller organizations and further investment in the energy sector companies is not advisable.

This study aims to investigate the impact of human resource accounting (HRA) on the overall performance of the organization. By presenting the details of HRA the study identifies various dimensions of organizations’ financial aspects viz., human capital efficiency, organization profitability, return on asset, and return on equity. To understand the impact of various measurements, the study collected required data from 268 responses of human resource and finance departments of SME firms and analyzed the data using linear regression and the result of ANOVA and coefficient values illustrated there is a positive significant effect of HRA on human capital efficiency, organization profitability and return on equity. This is evident that the SME firms in Saudi Arabia are aware of the benefits on HRA of the organization and the only concern is it needs rapid implementation initiatives from the management which is possible with wide awareness across the nation. However, there is no significant effect of HRA on return on assets. This study contributes to the SME firms, human resource departments, and managerial decision-makers to understand the HRA concept and its usefulness to a make positive difference in their financial statements.

The objective of this study is to examine the role of external factors including MP, CP, and NP on the intention to adopt Cloud Based Accounting Information System (CB-AIS). The study proposes a theoretical framework based on institutional theory (INT). The data were collected from small and medium sized enterprise (SMEs) operating in Jordan. A total of 600 questionnaires were distributed to selected SMEs and only 142 were returned and used for the analysis. The empirical data were analyzed using the PLS-SEM modelling. The findings showed that MP, CP, and NP had significant direct associations with the CB-AIS intention to adopt. The results provide important insights to managers, researchers and policymakers to help them understand the importance of CB-AIS adopting to enhance firm performance.