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.

During Covid-19, organizations, particularly hospitals, encountered difficulties in providing services. Telemedicine has shown to be an alternative in service provision during these times. Based on this, acceptance of telemedicine in Jordanian public hospitals has become a very important issue to increase the attention of the health care organization toward it. A conceptual model was constructed based on previous literature. The model includes government policy, the capacity of external suppliers, and the capacity of the project team, top management support, as independent variables where their influence on the acceptance of telemedicine in Jordanian public hospitals as a dependent variable is examined. Respondents were chosen using a Purposive sampling technique. Questionnaires were delivered to 320 respondents using Google Forms. SEM was used for statistical analysis. The findings revealed that all the proposed factors including government policy, external supplier capacity, project team capacity, and top management support have a significant influence toward accepting telemedicine. The results of this study may aid Jordanian public hospitals in making the best use of the proposed factors to increase the acceptance of telemedicine in Jordanian public hospitals.

Technological advancements affect everyday life; they benefited our daily routines, habits, and activities. Underwater diving is one of the most interesting and attractive activities for tourists worldwide but could be risky and challenging. When paths are not clear, diving might take additional time and effort leading to some health problems. Thus, providing divers with proper direction information to surf underwater can be useful and helpful. Also, monitoring diverse health statuses and alerting them in case of any undesirable condition can increase their safety. Smart devices such as mobiles, watches, sensor devices, cellular networks along with the Internet of Things (IoT) can all provide location-based services. Such services can help in providing the best path for the divers and monitor their health status during diving. This paper proposes a new underwater routing approach, called Underwater Routing for Smart Diving “URSD”, which provides divers with routing information to visit underwater cultural or natural resources and monitors their health status during the diving period. The URSD approach was simulated and compared with the shortest path. Results showed that the URSD helped divers to route within paths that have a larger number of nodes, furthermore, it could enhance and improve divers experience and help them mitigate underwater risks.

This research aimed to study the opinion and perspectives of Commuter Line passengers in Indonesia by using 18 attributes of service quality. There still needed to be more understanding about which service attributes were less satisfying and which were more pleasing to the Commuter Line passengers in the area of Jakarta and its surroundings. This research used factor analysis and Principal Component Analysis to select among the 18 Commuter Line service quality variables with the Varimax and Ordered Logit model rotation method. The number of samples used was 384 respondents from Commuter Line passengers in Jakarta and its surroundings. The result of factor analysis stated that the 18 attributes of service quality with three factors were the main attributes of service quality being used, namely the factor of station facilities and passenger behavior, the factor of ticket and security system, and they had reasonably strong correlations. The key finding of this research was that some service quality attributes, such as the crowd or density of trains, station stair facility, station lift facility, station seat facility, and shelter, were perceived as the attributes of service that were less satisfying. This research provided valuable insights into important factors affecting the opinion and perspective of Commuter Line passengers in Jakarta and its surroundings.

This study integrates the Technology Acceptance Model (TAM) with the Theory of Planned Behavior (TPB) to analyze consumers' decisions to use a Shariah-based e-wallet in Indonesia. With convenience sampling, a hundred samples were calculated by using SEM-PLS analysis. It found that only perceived trust and intention influenced consumers' decisions to use a Shariah-based e-wallet, while perceived ease of use, usefulness, enjoyment, attitude, subjective norm, and perceived behavioral control did not. Perceived ease of use and usefulness influenced consumers' intentions but not the decision to use Sharia-based e-wallets. Thus, intention does not necessarily describe actual behavior. Consumers feel that the Sharia-based e-wallet is not as easy and useful as other e-wallets they mostly use, like Go-pay, Shopee-pay, and OVO. Other implications and some recommendations are discussed further in this study.

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.

The current manuscript aimed to explain the impact of big data on the financial reporting quality of the industrial sector in Jordan. To achieve the manuscript goals and validate hypotheses, a field study was conducted by distributing a questionnaire to 325 financial managers in industrial companies listed on the Amman Stock Exchange during a specific period. Gathered data were analyzed using structural equation modeling (SEM). The manuscript concluded that the big data dimensions, including variety, volume, and velocity, had a positive impact on financial reporting quality. Therefore, a set of recommendations were provided to managers of the industrial companies in Jordan to put in place an extensive data governance system to as-sure data quality, security, privacy, and compliance. To ensure the integrity and dependability of financial reporting, define data ownership, create data quality standards, and develop processes for data access, use, and preservation.

Energy efficiency is one of the critical aspects, because the hotel sector has significant energy consumption and has an impact on environmental information. Energy efficiency helps reduce operational costs. By reducing excessive energy consumption, hotels can set a good example of responsible and sustainable business practices. It is important to understand the factors that affect energy efficiency. Some of the relevant factors are knowledge sharing, risk perception, and environmental information. This study aims to analyze the effect of knowledge sharing, risk perception, and environmental information factors on energy efficiency in the hospitality industry. The research method used in this study is a quantitative method with a survey approach. The number of samples used in this study were 176 hotel managers in Indonesia. Questionnaires were distributed to respondents using a Likert scale of 1 to 7. Data were analyzed using the Partial Least Square (PLS) method using SmartPLS software. The research results conclude that knowledge sharing, perceived risk, and environmental information have a significant influence on energy efficiency in the hospitality industry. Well-informed consumers tend to be more aware of the impact of their decisions and actions on environmental information, so they are more likely to choose hotels that adopt sustainable and energy efficient practices. In addition, the perception of risk also affects energy efficiency in the hospitality industry. Consumers who perceive risks associated with low sustainability or negative environmental information impacts are less likely to choose the hotel. Environmental information factors also play an important role in energy efficiency. Hotels that have an environment that supports and facilitates sustainable practices have the potential to achieve higher energy efficiency.

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.

Digital transformation has become fundamental to improving the competitiveness of microenterprises worldwide, and Huancayo, Peru is no exception. By adopting digital technologies, microenterprises can improve their operational efficiency, expand their market reach and enhance their abilities to make informed decisions. They have therefore found innovative ways to adapt to changing market conditions, such as incorporating information technologies, online sales, use of social networks and home delivery. Despite this, many microenterprises struggle to survive due to lack of access to financing and adequate government support. This study aimed to analyze how individual, group and organizational factors influence the digital transformation of microenterprises and its impact on their competitiveness. The research was carried out in a sample of 80 multi-sector microenterprises, using a non-probabilistic and cross-sectional research design of a quali-quantitative and explanatory nature, using SEM-PLS. The results of the study indicate a positive relationship between individual, group and organizational factors and digital transformation, as well as with the competitiveness of microenterprises. The coefficients of determination (R2) obtained were 0.8897 and 0.7931 for digital transformation and competitiveness, respectively, indicating a predictive ability in both cases. These findings are of great use to policy makers, business owners and researchers interested in fostering the growth and development of microenterprises in emerging economies.