Despite substantial investments in business intelligence and analytics (BIA), limited research examines its impact on firm risk and how BIA architectures should be configured to maximize value. Drawing on resource orchestration theory, this study investigates whether BIA mitigates performance risk (return on asset risk) and stock market risk (idiosyncratic risk), and how resource configurations—presence, scope, and breadth—shape these effects. Using panel data from 1,015 firms, the results show that BIA significantly reduces both types of risk, with stronger risk-mitigating effects emerging as resource scope and breadth increase. The findings also reveal meaningful differences across configurations, suggesting that not all BIA deployments yield equal benefits. Moreover, BIA's impact on risk reduction persists over the intermediate term. Overall, this research highlights the importance of considering firm risk and resource configuration when evaluating the business value of innovative technologies, offering implications for both scholarship and managerial practice.
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.
Designing a system for analytics of high-frequency data (Big data) is a very challenging and crucial task in data science. Big data analytics involves the development of an efficient machine learning algorithm and big data processing techniques or frameworks. Today, the development of the data processing system is in high demand for processing high-frequency data in a very efficient manner. This paper proposes the processing and analytics of stochastic high-frequency stock market data using a modified version of suitable Gradient Boosting Machine (GBM). The experimental results obtained are compared with deep learning and Auto-Regressive Integrated Moving Average (ARIMA) methods. The results obtained using modified GBM achieves the highest accuracy (R2 = 0.98) and minimum error (RMSE = 0.85) as compared to the other two approaches.
Prediction of the stock price is a crucial task as predicting it may lead to profits. Stock price prediction is a challenge owing to non-stationary and chaotic data. Thus, the projection becomes challenging among the investors and shareholders to invest the money to make profits. This paper is a review of stock price prediction, focusing on metrics, models, and datasets. It presents a detailed review of 30 research papers suggesting the methodologies, such as support vector machine, random forest, linear regression, recursive neural network, and long short-term movement based on the stock price prediction. Aside from predictions, the limitations and future works are discussed in the papers reviewed. The commonly used technique for achieving effective stock price prediction are the RF, LSTM, and SVM techniques. Despite the research efforts, the current stock price prediction technique has many limits. From this survey, it is observed that the stock market prediction is a complicated task, and other factors should be considered to accurately and efficiently predict the future.
Indian Govt has taken broad step and declared lock down to reduce the community-transmission of the novel “Coronavirus”.Many people tried to utilize this period by doing online work and household work simulateneouly. Many small scale industries,shops ,agencies,school colleges shut their door following Govt rules and regulations to avoid spreading of virus.People working or engaged in these activities or duties became unemployed .As man is a social animal and feels safe and secured in society due to increase in distance from society from office space and due to financial crises , day by day negative thought impacts their mind and they are mental in stability or pressure . In this study, an attempt was made to prioritize the cause of mental pressure faced by common people. Such that precautionary measures can be taken for the public-health such that appropriate steps can be taken to protect their health from the transmission of this virus. By using the “Grey-technique for order of preference by similarity to ideal solution (Grey-TOPSIS)”method .
Detection of abnormal crowd behavior is one of the important tasks in real-time video surveillance systems for public safety in public places such as subway, shopping malls, sport complexes and various other public gatherings. Due to high density crowded scenes, the detection of crowd behavior becomes a tedious task. Hence, crowd behavior analysis becomes a hot topic of research and requires an approach with higher rate of detection. In this work, the focus is on the crowd management and present an end-to-end model for crowd behavior analysis. A feature extraction-based model using contrast, entropy, homogeneity, and uniformity features to determine the threshold on normal and abnormal activity has been proposed in this paper. The crowd behavior analysis is measured in terms of receiver operating characteristic curve (ROC) & area under curve (AUC) for UMN dataset for the proposed model and compared with other crowd analysis methods in literature to prove its worthiness. YouTube video sequences also used for anomaly detection.
Stochastic time series analysis of high-frequency stock market data is a very challenging task for the analysts due to the lack availability of efficient tool and techniques for big data analytics. This has opened the door of opportunities for the developer and researcher to develop intelligent and machine learning based tools and techniques for data analytics. This paper proposed an ensemble for stock market data prediction using three most prominent machine learning based techniques. The stock market dataset with raw data size of 39364 KB with all attributes and processed data size of 11826 KB having 872435 instances. The proposed work implements an ensemble model comprises of Deep Learning, Gradient Boosting Machine (GBM) and distributed Random Forest techniques of data analytics. The performance results of the ensemble model are compared with each of the individual methods i.e. deep learning, Gradient Boosting Machine (GBM) and Random Forest. The ensemble model performs better and achieves the highest accuracy of 0.99 and lowest error (RMSE) of 0.1.
This paper proposes a new training algorithm for artificial neural networks based on an enhanced version of the grey wolf optimizer (GWO) algorithm. The proposed model is used for classifying the patients of diabetes disease. The results showed that the proposed training algorithm enhanced the performance of ANNs with a better classification accuracy as compared to the other state of art training algorithms for the classification of diabetes on publicly available Pima Indian Diabetes (PID) dataset. Several experiments have been executed on this dataset with variation in size of the population, techniques to handle missing data, and their impact on classification accuracy has been discussed. Finally, the results are compared with other nature-inspired algorithms-trained ANN. EGWO attained better results in terms of classification accuracy than the other algorithms. The convergence curve proved that EGWO had balanced the local and global search abilities because it was faster to reach better positions than the original GWO.
A novel method for integrating multi-omics data, including gene expression, copy number variation, DNA methylation, and miRNA data, is proposed to identify biomarkers of cancer prognosis. First, survival analysis was performed for these four types of omics data to obtain survival-related genes. Next, survival-related genes detected in at least two types of omics data were selected as candidate genes. The four types of omics data only composed of candidate genes were subjected to dimension reduction using an autoencoder to obtain a one-dimensional data representation. The mRMR algorithm was used to screen for key genes. This method was applied to lung squamous cell carcinoma and 20 cancer-related genes were identified. Gene function analysis revealed that the genes were related to cancer. Using survival analysis, the genes were verified to distinguish between high- and low-risk groups. These results indicate that the genes can be used as biomarkers for cancer.
Cloud computing has risen as a new computing paradigm providing computing, resources for networking, and storage as a service across the network. Data replication is a phenomenon which brings the available and reliable data (e.g., maybe the databases) nearer to the consumers (e.g., cloud applications) to overcome the bottleneck and is becoming a suitable solution. In this paper, the authors study the performance characteristics of a replicated database in cloud computing data centres which improves QoS by reducing communication delays. They formulate a theoretical queueing model of the replicated system by considering the arrival process as Poisson distribution for both types of client request, such as read and write applications. They solve the proposed model with the help of the recursive method, and the relevant performance matrices are derived. The evaluated results from both the mathematical model and extensive simulations help to study the unveil performance and guide the cloud providers for modelling future data replication solutions.
User Authentication plays a crucial role in smart card based systems. Multi-application smart cards are easy to use as a single smart card supports more than one application. These cards are broadly divided into single identity cards and Multi-identity cards. In this paper we have tried to provide a secure Multi-identity Multi-application Smart Card Authentication Scheme. Security is provided to user’s data by using dynamic tokens as verifiers and nested cryptography. A new token is generated after every successful authentication for next iteration. Anonymity is also provided to data servers which provides security against availability attacks. An alternate approach to store data on servers is explored which further enhances the security of the underlying system.
The paper presents an approach to generate and optimize test sequences from the input UML activity diagram. For this, an algorithm is proposed called Unified Modelling Language for Test Sequence Generation (UMLTSG) that uses a search-based algorithm, named Test Sequence Prioritization using Ant Colony Optimization (TSP ACO) to generate and optimize test sequences. The algorithms overcome the existing limitations of handling complex decision-making activity such as conditional activity, fork activity, and join the activity. The optimization process helps to reduce the number of processing nodes that leads to minimizing the time and cost. The proposed approach experiments on a well-known application Railway Ticket Reservation System (RTRS). APFD metric measures the effectiveness of our approach and found that the prioritized order of test sequences achieved 20% higher APFD score. Apart from this, the authors have also experimented on six real life case studies and obtained an average of 52.16% reduction in redundant test paths.
With the technological advancements and its reach Social media has become an essential part of our daily lives. Using social media platforms allows propagandist to spread the propaganda more effortlessly and faster than ever before. Machine learning and Natural language processing applications to solve the problem of propaganda in social media has invited researchers attention in recent years. Several techniques and tools have been proposed to counter propagation of propaganda over social media. This work pursues to analyse the trends in research studies in the recent past which address this issue. Our purpose is to conduct a comprehensive literature review of studies focusing on this area. We perform meta-analysis, categorization, and classification of several existing scholarly articles to increase the understanding of the state-of-the-art in the mentioned field.
Traditional agriculture is facing numerous serious issues such as climate variation, population rise, water scarcity, soil degradation, and food security and many more. Though, Aquaponics is a promising solution, research on building an economically feasible smart Aquaponics system is still a challenge. In this paper, a sustainable smart Aquaponics system using Internet of Things (IOT) and Data Analytics is proposed. The acquired data from sensors such as Ph sensor, and temperature sensor, is analyzed using machine learning techniques to interpret the health of the system. Further, the proposed system includes automated fish feeder which is controlled by Raspberry Pi to automate and reduce the maintenance issues. The android application helps the user to remotely control and monitor the health of the system and also track the critical system parameters. Further the system is driven by the solar power to make it sustainable. A comprehensive survey on the key aspects of Aquaponics including comparison of the proposed model with the traditional aquaponics model is also presented.
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.
Internet users are increasing day by day due to its support for many applications and creation of innovative services. Along with this, energy consumption is also becoming an important concern in networking. Several researchers have investigated energy saving schemes for networks. Software-defined networking (SDN) is an excellent choice that improves network functionalities with flexible management aided by centralized control. Recent studies designed efficient algorithms for advancing SDN with overall energy savings. Shutting down idle links and switches are among numerous solutions available for SDN. In this paper, the authors proposed a novel algorithm named AttentiveSDN for reducing the energy consumption in SDNs. Here the controller collects the traffic and the link status from the switches involved in network operation and takes the decision to put which idle links and switches into sleep state. The authors evaluated the performance of the AttentiveSDN algorithm using Mininet. The result has shown that the proposed approach saves more power than existing solutions.
Intelligent manufacturing is an important method for transforming and upgrading enterprise intelligence. Studying the influencing factors of enterprises, intelligent manufacturing can help enterprises formulate more targeted intelligent manufacturing development strategies according to their own stage characteristics to accelerate the intelligent development. The concept of intelligent manufacturing ecosystem is proposed. By exploring the evolution process of intelligent manufacturing ecosystems, a three-stage theoretical model of influencing factors of intelligent manufacturing of enterprises is constructed. The theoretical model and related assumptions are verified using the empirical data of manufacturing enterprises of many provinces and cities in China. The results show that most factors in the digital stage, network stage, and intelligent stage significantly affect the development of enterprise intelligent manufacturing systems. This study provides theoretical reference and suggestions for manufacturing enterprises to develop intelligent manufacturing.
