The digital transformation has made information technology (IT) architecture a key factor in firm risk. This study examined how the strategic use of enterprise systems (ESs), particularly the balance between cloud-based software-as-a-service (SaaS) and on-premises solutions, affects financial stability. Introducing the SaaS ES configuration ratio (SaaS_ES_Balance), the research analyzed its impact on idiosyncratic risk—the firm-specific stock volatility—using data from 674 public companies over eight years. Findings indicate that a higher SaaS ratio significantly reduces risk, likely due to operational standardization, better governance, and increased financial flexibility from shifting expenses from capital expenditures to operational expenditures. The risk-reduction benefits are stronger for companies with low information risk, highlighting the importance of organizational readiness. This work contributes to the IT value literature by positioning ES configuration as a strategic risk management tool and provides guidance for managers on deploying SaaS solutions to bolster financial resilience.

Despite their potential, no significant scientific work has yet conceptually integrated AR and VR into digital twins, which presents a critical gap. A conceptual integration through the development of a meta-model would offer several key advantages. First, it would provide a unified framework that standardizes the interaction between physical systems, digital twins, and immersive technologies, ensuring consistency across various applications. Second, the meta-model would enable cross-domain scalability, allowing AR and VR-enhanced digital twins to be adapted more easily across different fields like manufacturing, healthcare, and mobility. Finally, a comprehensive meta-model would facilitate the systematic development and extension of digital twins, ensuring that AR/VR capabilities are seamlessly incorporated into existing and future DT systems. This foundational conceptual structure would drive innovation by offering a clear, modular approach to expanding DT functionalities without needing to reinvent frameworks for each new implementation.

Investigated in this study is the influence of information communication technology (ICT) on the exchange rate in Zambia from 2010 to 2021. The research methods employed involve using descriptive, exploratory, and experimental designs. The results are reported focusing on ICT acquisition, usage, and production in relation to the performance of the exchange rate: imports of ICTs from the Zambia Revenue Authority (ZRA) represented the uptake while the performance of the exchange rate from the Bank of Zambia represented actual volatilities. It was further discovered that the ICTs influence movements in financial transactions by increasing the quantum of transactions. The impact on the movements of currencies advertently affects the exchange rate causing volatility. The role of ICTs in the amalgamation of markets, trade linkages, and openness, is key. Conclusively, ICTs influence the exchange rate: inherent in productive means and permeate consumptive aspects of ICTs and financial transactions. The main limitation arose from data collection on ICT software and services due to lack of a consolidated data capturing information system.

Semantic segmentation was traditionally performed using primitive methods; however, in recent times, a significant growth in the advancement of deep learning techniques for the same is observed. In this paper, an extensive study and review of the existing deep learning (DL)-based techniques used for the purpose of semantic segmentation is carried out along with a summary of the datasets and evaluation metrics used for the same. The paper begins with a general and broader focus on semantic segmentation as a problem and further narrows its focus on existing DL-based approaches for this task. In addition to this, a summary of the traditional methods used for semantic segmentation is also presented towards the beginning. Since the problem of scene understanding is being vastly explored in the computer vision community, especially with the help of semantic segmentation, the authors believe that this paper will benefit active researchers in reviewing and studying the existing state-of-the-art as well as advanced methods for the same.

The stock market volume and price are active areas of research. Behind every dollar of investment, the customer will be hoping for profit in one or the other way. There is a positive correlation between investor sentiment and stock volume. Predicting the stock market is the most difficult task due to the dynamic fluctuation of volume and price. The traditional analysis methods carried out lead to satisfactory results. In this paper, the proposed system uses real-time data from Twitter to detect the user opinion about the product along with the stock volume for prediction. The stock volume data and the Twitter data are collected first, and then the classification of the polarity is carried out using the SentiWordnet dictionary. The algorithm for the prediction of the stock prices uses long short-term memory, a neural network, as the prices are sequentially evolving in nature. The results of the proposed system are correlated between the stock market and Twitter data to obtain better insights that are positive.

Within the advanced computation time, real-time application pulled in much more attention. Implementing a better high-quality real-time system requires to improve the responsiveness of the tasks set. This research work aims to achieve the best quality of service (QoS) in terms of improving the responsiveness of aperiodic tasks and also improved acceptability domain, by accepting to execute multiple aperiodic functions while maintaining the feasibility of periodic tasks in a real-time system.The functional analysis with simulation shows that the proposed algorithm is highly effective in terms of task sets deemed schedulable and also by allowing aperiodic tasks that were rejected by existing approaches. The simulation results indicate that it reduces overall average response time of aperiodic tasks approximately 13% at lowest periodic load (35%), 7% at 60% periodic load, and 4% at 80% periodic load, and in all observed circumstances, the proposed novel algorithm received 7%-10% improvement over the existing one.

Big data refers to the enormous heterogeneous data being produced at a brisk pace by a large number of diverse data generating sources. Since traditional data processing technologies are unable to process big data efficiently, big data is processed using newer distributed storage and processing frameworks. Big data view materialization is a technique to process big data queries efficiently on these distributed frameworks. It generates valuable information, which can be used to take timely decisions, especially in cases of disasters. As there are a very large number of big data views, it is not possible to materialize all of them. Therefore, a subset of big data views needs to be selected for materialization, which optimizes the query response time for a given set of workload queries with minimum overheads. This big data view materialization problem, having objectives minimization of the query evaluation cost of a set of workload queries, while simultaneously minimizing the update processing costs of the materialized views, has been addressed using improved strength pareto evolutionary algorithm (SPEA-2) in this paper. The proposed big data view selection algorithm, which is able to compute a set of diverse non-dominated big data views, is shown to perform better that existing big data view selection algorithms..

The increasing prevalence of business cases utilizing internet of things (IoT) analytics, coupled with the diversity of IoT analytics platforms and their capabilities, poses an immense challenge for organizations seeking to make the best choice of IoT analytics platform for their specific use cases. Aiming to characterize the capabilities of IoT analytics, this article presents a reference architecture for IoT analytics platforms created through a qualitative content analysis of online reviews and published implementation architectures of IoT analytics platforms. A further contribution is a taxonomy of the functional and cross-functional capabilities of IoT analytics platforms derived from the analysis of published use cases and related business surveys. Both the reference architecture and the associated taxonomy provide a theoretical basis for further research into IoT analytics capabilities and should therefore facilitate the evaluation, selection, and adoption of IoT analytics solutions through a unified description of their capabilities and functional requirements.