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Disability management in information systems refers to the process of ensuring that digital technologies and applications are designed to be accessible and usable by individuals with disabilities. Traditional methods face several challenges such as privacy concerns, high cost, and accessibility issues. To overcome these issues, this paper proposed a novel method named bidirectional federated learning-based Gradient Optimization (BFL-GO) for disability management in information systems. In this study, bidirectional long short-term memory (Bi-LSTM) was utilized to capture sequential disability data, and federated learning was employed to enable training in the BFL-GO method. Also, gradient-based optimization was used to adjust the proposed BFL-GO method's parameters during the process of hyperparameter tuning. In this work, the experiments were conducted on the Disability Statistics United States 2018 dataset. The performance evaluation of the BFL-GO method involves analyzing its effectiveness based on evaluation metrics, namely, specificity, F1-score, recall, precision, AUC-ROC, computational time, and accuracy and comparing its performance against existing methods to assess its effectiveness. The experimental results illustrate the effectiveness of the BFL-GO method for disability management in information systems. [ABSTRACT FROM AUTHOR]

Many countries are using digital transformation to increase their productivity and organizational performance. In Saudi Arabia, digital transformation is a crucial part of their Saudi Vision 2030 plan, but it is still in its early stages. To understand the factors that affect the adoption of digital transformation. The study used a qualitative interview to identify the critical success factors and challenges in adopting digital transformation at the Ministry of Education of Saudi Arabia. The main results of the study show, first, the seven main success factors include technology, employee engagement, vendor partnerships, budget, top management support, culture, and strategy. Second, the main seven challenges include organizational and strategic stakes, resistance to change, governance, data, cost, and IT infrastructure. The study developed a framework that shows the main success factors and challenges that affect adopting digital transformation in the Ministry of Education. These findings can benefit many individuals and groups, such as academics, business people, and the public, and can apply this research in other contexts. This research aimed to determine the primary factors contributing to the success of digital transformation in the Ministry of Education and the challenges that arise when implementing it, specifically within the Saudi Arabian Ministry of Education. [ABSTRACT FROM AUTHOR]

This research investigates the uncertainties in supply chains using symmetrical and asymmetrical modeling tools, focusing on the attitudes of millennials towards Facebook retail shopping. By exploring antecedents such as pleasure, credibility, and peer interaction, this study delves into the extent of E-commerce via Facebook among Generation Z in the Middle East. Built on an exhaustive literature review, a conceptual framework is designed targeting solely Generation Z members. Employing partial least squares structural equation modeling for data analysis, the findings indicate a strong correlation between attitude and the propensity of Generation Z to make Facebook retail purchases (R2 = 0.540), affecting enjoyment, credibility, and peer communication (R2 = 0.589). This study offers strategies for supply chain improvements and validates the potential of E-commerce on Facebook among Generation Z. [ABSTRACT FROM AUTHOR]

The rapid advancements in digital technologies have prompted organizations to embrace digital transformations (DTs) in order to enhance efficiency, gain a competitive advantage, and achieve long-term sustainability objectives. However, the successful adoption of innovative digital technologies necessitates the careful consideration of various factors, such as stakeholder engagement, resource allocation, risk mitigation, and the availability of resources and implementation support. This study examines the sustainable adoption of innovative digital technologies (DTs) within digital transformations. The data for this study were collected from 760 stakeholders through a questionnaire survey and analyzed using SPSS software (Version 27). This study's results underscore the significance of considering the efficiency of the transformation process and the long-term sustainability outcomes for organizations. The findings of the analysis clarify that integrating sustainability principles and DT has a positive impact on the effectiveness of the transformation, as indicated by environmental, social, and economic performance indicators. This study's novelty lies in its focus on incorporating sustainability principles into the digital transformation process. The results of this study demonstrate that organizations' long-term sustainability outcomes are enhanced when their digital transformation goals align with the Sustainable Development Goals (SDGs). The purpose of this study emphasizes the importance of arranging digital transformations with sustainable objectives to ensure the overall success and longevity of transformation efforts. [ABSTRACT FROM AUTHOR]

The proposed way of estimating the associated macroeconomic index based on a supply chain network is a novel strategy that might give useful insights for firms and organizations trying to enhance their supply chain logistics operations. To aid in this process, the suggested technique utilizes multiple regression analysis and adaptive extreme learning machine models to determine the relative importance of each indicator in the supply chain logistics decision-making process. Firms may be able to save expenses and boost economic value by employing enhanced genetic algorithms and mathematical modeling to develop a logistics distribution model for urban supply chains based on reconstructive genetic algorithms. It is possible that the paper's study of the issues plaguing the current distribution of logistics in metropolitan areas would prove valuable to organizations that are seeking to improve their own supply chain logistics procedures. This paper appears to take a thorough strategy that might assist assure the accuracy of the forecasting method by preprocessing macroeconomic indicators using imputation, classification, and other approaches to generate a time series consistency model. Some novel ways that may shed light on logistics in the supply chain include using a two-dimensional discrete mesh structure built on wireless sensors to depict the national economic development scenario and using a coding matrix to convey the extent of economic growth. A thorough and all-encompassing way that might aid organizations in making better judgments about their supply chain logistics strategies is to employ multiple regression analysis, an adaptive extreme learning machine model, and other approaches to examine the effect degree of each key indicator. Experiments demonstrating excellent and steady prediction accuracy of the algorithm model are encouraging and point to the possible usefulness of the suggested forecasting approach. The paper's contributions, including its analysis of supply chain logistics cost accounting, determination of the basic path optimization, improvement of the genetic algorithm, and design of the mathematical model of supply chain logistics distribution path decision, all look promising in terms of their potential to help businesses cut costs and boost economic value. The positive outcome of the successful design of the mathematical model demonstrates the possible efficacy of the suggested technique. [ABSTRACT FROM AUTHOR]

This paper introduces an informed decision support framework (IDSF) from a strategic perspective in the health sector, focusing on Saudi Arabia. The study addresses the existing challenges and gaps in decision-making processes within Saudi organizations, highlighting the need for proper systems and identifying the loopholes that hinder informed decision making. The research aims to answer two key research questions: (1) how do decision makers ensure the accuracy of their decisions? and (2) what is the proper process to govern and control decision outcomes? To achieve these objectives, the research adopts a qualitative research approach, including an intensive literature review and interviews with decision makers in the Saudi health sector. The proposed IDSF fills the gap in the existing literature by providing a comprehensive and adaptable framework for decision making in Saudi organizations. The framework encompasses structured, semi-structured, and unstructured decisions, ensuring a thorough approach to informed decision making. It emphasizes the importance of integrating non-digital sources of information into the decision-making process, as well as considering factors that impact decision quality and accuracy. The study's methodology involves data collection through interviews with decision makers, as well as the use of visualization tools to present and evaluate the results. The analysis of the collected data highlights the deficiencies in current decision-making practices and supports the development of the IDSF. The research findings demonstrate that the proposed framework outperforms existing approaches, offering improved accuracy and efficiency in decision making. Overall, this research paper contributes to the state of the art by introducing a novel IDSF specifically designed for the Saudi health sector. [ABSTRACT FROM AUTHOR]

Ubiquitous mobile edge computing (MEC) using the internet of things (IoT) is a promising technology for providing low-latency and high-throughput services to end-users. Resource allocation and quality of service (QoS) optimization are critical challenges in MEC systems due to the large number of devices and applications involved. This results in poor latency with minimum throughput and energy consumption as well as a high delay rate. Therefore, this paper proposes a novel approach for resource allocation and QoS optimization in MEC using IoT by combining the hybrid kernel random Forest (HKRF) and ensemble support vector machine (ESVM) algorithms with crossover-based hunter–prey optimization (CHPO). The HKRF algorithm uses decision trees and kernel functions to capture the complex relationships between input features and output labels. The ESVM algorithm combines multiple SVM classifiers to improve the classification accuracy and robustness. The CHPO algorithm is a metaheuristic optimization algorithm that mimics the hunting behavior of predators and prey in nature. The proposed approach aims to optimize the parameters of the HKRF and ESVM algorithms and allocate resources to different applications running on the MEC network to improve the QoS metrics such as latency, throughput, and energy efficiency. The experimental results show that the proposed approach outperforms other algorithms in terms of QoS metrics and resource allocation efficiency. The throughput and the energy consumption attained by our proposed approach are 595 mbit/s and 9.4 mJ, respectively. [ABSTRACT FROM AUTHOR]

Logistics in the healthcare industry involves coordinating the distribution of medical supplies and equipment across various departments and organizations. Supply Chain Management can help healthcare facilities identify weaknesses and devise strategies to address them. Using the Unified Theory of Acceptance and Use of Technology (UTAUT), the study investigates the motivations behind the individuals' desire to use Internet of Things (IoT) solutions in healthcare. In order to better understand the factors that influence the use of IoT for e-HMS, a survey was administered to 210 healthcare IoT users. The study focuses on the potential medicinal applications of IoT technologies and incorporates the concepts of performance expectations, healthcare hazard, and trust (PHT) and perceived enabling circumstances (PFC) to complement past findings in the field. Overall, the study appears to be focused on contributing to the existing knowledge about the factors that influence the adoption of IoT technologies in healthcare, and it emphasizes the importance of considering theoretical constructs such as PHT and PFC in this context. The findings of the study can be used by IoT creators, medical experts, and vendors to optimize e-HMS and provide insight into the potential and limitations of UTAUT simulation to improve the logistic of Supply Chain Management in healthcare 4.0. The results have been analyzed by applying machine learning classifiers and have been visualized using different metrics. [ABSTRACT FROM AUTHOR]

A number of recent studies have examined the impact of advanced technologies on organizations. However, many (particularly those in developing countries) still face challenges when it comes to the adoption of mature technologies and have also continued to repeat many of the mistakes of early adopters, primarily in relation to automated workflow systems. The current paper analyses a case study of a public organization in the developing country of Saudi Arabia, with the aim of understanding its resistance to change brought about by the implementation of a mature technology, i.e., automated workflow systems. The study undertook semi-structured interviews with employees to establish the nature of this resistance, identifying their preference for familiar processes and systems, alongside their unwillingness to embrace the new system. Furthermore, the study highlighted a number of issues experienced during the implementation of automated workflow systems, including job security; changes in laws and rules; an inability to understand, and/or trust, the technology; the perceived risks and costs associated with change; and the transformation of business processes. It also cited factors related to organizational structure and power, and the discomfort involved in making difficult decisions. This study, therefore, aims to assist organizations to create a sound foundation for change prior to the adoption of more advanced technologies. [ABSTRACT FROM AUTHOR]

Opinion summarization recapitulates the opinions about a common topic automatically. The primary motive of summarization is to preserve the properties of the text and is shortened in a way with no loss in the semantics of the text. The need of automatic summarization efficiently resulted in increased interest among communities of Natural Language Processing and Text Mining. This paper emphasis on building an extractive summarization system combining the features of principal component analysis for dimensionality reduction and bidirectional Recurrent Neural Networks and Long Short-Term Memory (RNN-LSTM) deep learning model for short and exact synopsis using seq2seq model. It presents a paradigm shift with regard to the way extractive summaries are generated. Novel algorithms for word extraction using assertions are proposed. The semantic framework is well-grounded in this research facilitating the correct decision making process after reviewing huge amount of online reviews, considering all its important features into account. The advantages of the proposed solution provides greater computational efficiency, better inferences from social media, data understanding, robustness and handling sparse data. Experiments on the different datasets also outperforms the previous researches and the accuracy is claimed to achieve more than the baselines, showing the efficiency and the novelty in the research paper. The comparisons are done by calculating accuracy with different baselines using Rouge tool. [ABSTRACT FROM AUTHOR]

The last decade has seen a rapid increase in big data, which has led to a need for more tools that can help organizations in their data management and decision making. Business intelligence tools have removed many of the obstacles to data visibility, and numerous data mining technologies are playing an essential role in this visibility. However, the increase in big data has also led to an increase in 'dark data', data that does not have any predefined structure and is not generated intentionally. In this paper, we show how dark data can be mined for practical purposes and utilized to gain business insight. The most common type of dark data is a log file generated on a web server. Using the example of log files generated by e-commerce transactions, this paper shows how residual data and data trails can prove to be valuable when an actual dataset is inaccessible, and explains the usage of residual data for modeling purposes. The work uses a system identification approach, based on natural language processing for log file tokenization and feature extraction. The features are then embedded into the next step, which uses a deep neural network to identify customers for targeted advertising. The results achieve a significant accuracy and show how dark data has the potential to deliver value for business. Locating, organizing, and understanding dark data can unlock its relevance, usefulness, and potential monetization, but it is important to act when the benefits of use outweigh the costs of access and analysis. [ABSTRACT FROM AUTHOR]

IoT emergence has given rise to a new digital experience of payment transactions where physical objects like refrigerators, cars, and wearables will make payments. These physical objects will be storing the cardholder credentials and will directly make payments with the vendors over insecure public networks. For such payment transactions, government regulations and standards organizations require to implement PCI DSS for adapting similar set of security measures at the global level. The current version of PCI DSS is not suitable for IoT-based payment systems due to characteristics of IoT such as resource-constrained nature of devices and updating software/firmware of so many physical devices. Also, there arises an emergent need of implementing PCI DSS requirements and assessments for security of all stakeholders that store or process the user credentials in a payment. This paper is an initial effort to bring the researcher's attention to make upcoming versions of PCI DSS suitable for IoT and thus securing the new ways of IoT-based payment systems. The paper has reviewed the traditional payment process along with considerations for IoT-based payment systems to make recommendations to modify the PCI DSS in a suitable way for IoT. [ABSTRACT FROM AUTHOR]