Forthcoming and Online First Articles

International Journal of Critical Infrastructures

International Journal of Critical Infrastructures (IJCIS)

Forthcoming articles have been peer-reviewed and accepted for publication but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.

Forthcoming articles must be purchased for the purposes of research, teaching and private study only. These articles can be cited using the expression "in press". For example: Smith, J. (in press). Article Title. Journal Title.

Articles marked with this shopping trolley icon are available for purchase - click on the icon to send an email request to purchase.

Online First articles are published online here, before they appear in a journal issue. Online First articles are fully citeable, complete with a DOI. They can be cited, read, and downloaded. Online First articles are published as Open Access (OA) articles to make the latest research available as early as possible.

Open AccessArticles marked with this Open Access icon are Online First articles. They are freely available and openly accessible to all without any restriction except the ones stated in their respective CC licenses.

Register for our alerting service, which notifies you by email when new issues are published online.

We also offer which provide timely updates of tables of contents, newly published articles and calls for papers.

International Journal of Critical Infrastructures (36 papers in press)

Regular Issues

  • Seismic Isolation of Data Centers for Business Continuity   Order a copy of this article
    by M.Fevzi Esen 
    Abstract: Economic losses of earthquakes raised many questions regarding the adequacy of the current seismic design criteria and seismic isolation in data centers. Some organizations have accommodated new explicit seismic isolation applications in their business continuity and disaster recovery plans. These applications aim acceptable damage levels that correspond acceptable business interruption for data centers in case of an earthquake. In this study, we aim to discuss the importance of seismic isolation technologies which can be implemented for data centers against seismic disasters within business continuity and disaster recovery planning context. We conduct a literature review to provide a clearer aspect on seismic isolation applications for data centers. We conclude that GSA, ASCE and Uptime Institute provide internationally recognized standards which make raised floors a good option for data centers. These standards provide technical documentation for service functioning with high levels of availability during an outage.
    Keywords: information technologies; data centers; seismic isolation; business continuity.
    DOI: 10.1504/IJCIS.2022.10034563
  • new A construction schedule management method of large-scale construction project based on BIM model   Order a copy of this article
    by Sheng Yin 
    Abstract: In order to overcome the problems of long response time and small number of manageable indicators existing in traditional construction project schedule management methods, a new construction schedule management method based on BIM model is designed in this paper. The construction progress data acquisition and decoding module circuit is set to complete the construction progress data acquisition, and the K-means algorithm is used to preprocess the construction progress data. Decompose the construction project progress, divide the large-scale construction project into different progress management levels by WBS analysis method, establish functional information module, import the construction project progress data into BIM model, and realise the BIM information function management of the method. The experimental results show that the proposed method has low response time and multiple schedule management indicators, and the shortest response time of the proposed method is only 1.1 s.
    Keywords: management pheromone; management rules; definition residue; BIM model.
    DOI: 10.1504/IJCIS.2023.10046163
  • new Maritime Cyber-Insurance: The Norwegian Case   Order a copy of this article
    by Ulrik Franke, Even Langfeldt Friberg, Hayretdin Bahsi 
    Abstract: Major cyber incidents such as the Maersk case have demonstrated that the lack of cyber security can induce huge operational losses in the maritime sector. Cyber-insurance is an instrument of risk transfer, enabling organisations to insure themselves against financial losses caused by cyber incidents and get access to incident management services. This paper provides an empirical study of the use of cyber-insurance in the Norwegian maritime sector, with a particular emphasis on the effects of the General Data Protection Regulation and the Directive on Security of Network and Information Systems. Norway constitutes a significant case as a country having a highly mature IT infrastructure and well-developed maritime industry. Interviews were conducted with supplier- and demand-side maritime actors. Findings point to a widespread lack of knowledge about cyber-insurance. Furthermore, neither GDPR nor NIS were found to be significant drivers of cyber-insurance uptake among maritime organisations.
    Keywords: security; risk; policy; regulation; cyber-insurance; information sharing.
    DOI: 10.1504/IJCIS.2022.10046164
  • Stacking-based Multi-objective Approach for Detection of Smart Power Grid Attacks using Evolutionary Ensemble Learning   Order a copy of this article
    by Manikant Panthi, Tanmoy Kanti Das 
    Abstract: Smart power grid (SPG) has gained a reputation as the advanced paradigm of the power grid. It provides a medium for exchanging real-time data between the company and users through the advanced metering infrastructure delivering transparent and resilient service to electricity consumers. The widespread deployment of remotely accessible networked equipment for grid monitoring and control has vastly increased the surface of SPG for attackers to locate vulnerable points. The early and accurate identification of the above counteracts is paramount to ensure stable and efficient power distribution. This paper proposes a stacking-based multi-objective evolutionary ensemble scheme to identify various attacks in the SPG. The proposed method used a non-dominated sorting genetic algorithm to learn the non-linear, overlapping, and complex electrical grid features to predict the type of malicious attacks. The experimental results and comparison using multiclass dataset validate the presented
    Keywords: non-dominated sorting genetic algorithm; cyber-attack; power grid; machine learning.
    DOI: 10.1504/IJCIS.2025.10061535
  • Real-world application of face mask detection system using YOLOv6   Order a copy of this article
    by Jonathan Atrey, Rajeshkannan Regunathan, Rajasekaran Rajkumar 
    Abstract: The COVID-19 pandemic has drastically reshaped the human lifestyle and has placed immense importance on our health, safety, and sanitation practices. Among the various safety protocols assigned by the World Health Organisation (WHO) for the same, the usage of face masks to prevent the spread of the virus from an infected person to a healthy person has been of prime significance. To enable efficient execution of the WHO protocol, this case study proposes creating a real-time detection model built explicitly for capturing an audience to alert people who are not following COVID prevention protocols. The proposed case study utilises the state-of-the-art (SOTA) YOLOv6 algorithm along with different iterations of the YOLO algorithm, such as YOLOv4, and YOLOv5, for representing the variation in training performance among various iterations of YOLO. Further, it discusses and analyses the effectiveness of using a real-time detector for face mask detection. This study aims to decrease the risk of a healthy person being affected by the COVID-19 virus by keeping a check on a designated crowd and contributes towards the prevention of the further spread of the virus by crowd monitoring and control methods. The real-time implementation of the proposed case study reports a positive impact, with a 36% increment in people following the standard COVID-19 protocol of wearing masks in public places.
    Keywords: YOLOv6; DarkNetCSP; CNN; COVID-19; case study; computer vision; ecological studies; healthcare-related research; real-time monitoring system.
    DOI: 10.1504/IJCIS.2024.10052165
  • IoT Cloud-based Telecare Medical Healthcare System with Lightweight Authentication Scheme   Order a copy of this article
    by Sunil Gupta, Goldie Gabrani 
    Abstract: TMIS has huge potential for enhancing health care delivery, it also creates certain issues with regards to accessibility, privacy, security and confidentiality of sensitive patient information. Son et al.’s (2020) proposal for a secure authentication mechanism for cloud-based TMIS differs from Amin and Biswas’ (2015a) proposal for a TMIS architecture based on numerous physical servers. For gaining access to the medical servers, they have devised an authentication system. They have argued that their scheme removes all the shortcomings of earlier schemes. We, in this paper, will show that their architecture based on dedicated multiple physical servers has certain limitations. Such systems are usually either under-provisioned or over-provisioned and are quite expensive. In addition, Amin and Biswas (2015a) Mutual authentication and user anonymity are not provided by the scheme, and therefore is vulnerable to malicious assaults. In order to get over these restrictions, we suggest I a unique design for the TMIS based on cloud based on OTP. We show that the performance of our suggested system is enhanced while simultaneously overcoming the drawbacks of Amin and Biswas (2015a) approach.
    Keywords: smart card; IoT-cloud computing; mutual authentication; telecare medical information systems; TMIS; one-time password; OTP; anonymity; AVISPA.
    DOI: 10.1504/IJCIS.2024.10052846
  • Non-Linear Control Based Class-D Amplifier for Audio Intelligent Infrastructure Applications   Order a copy of this article
    by Sridhar Joshi, S.Silvia Priscila, Suman Rajest George, Kriti Srivastava, Prasath Alias Surendhar S, Rajasekaran Rajkumar 
    Abstract: A nonlinear control-based class-D amplifier using a dc power source for medium-power audio applications is proposed in this paper. The amplifier utilises switches in a half-bridge configuration to realise the class-D power stage. A passive second-order bandpass filter is cascaded with the power stage to render a highly linear audio amplifier for high-quality audio reproduction. A nonlinear technique-based controller is used for a closed-loop amplifier system which offers high immunity to power supply noise, robustness, and fault tolerance without using a triangular carrier generator. An 80 W, 20 Hz to 20 kHz amplifier model is developed considering the nonlinearity present in the power electronic switches of the power stage. Simulation results of the proposed amplifier with a full range 4 ? loudspeaker load are presented. The amplifier’s response at different frequencies in the audio spectrum is presented, which confirms the amplifier’s linearity and command following property. To confirm the high linearity of the amplifier, the THD versus frequency plot is depicted, which ensures the suitability of the proposed amplifier for high-fidelity audio amplification.
    Keywords: nonlinear control; class-D; amplifier; audio applications; intelligent infrastructures; power supply noise; robustness; half-bridge audio amplifier; HBAA; PWM wave.
    DOI: 10.1504/IJCIS.2025.10061547
  • Study on the influence of deep foundation pit excavation on the deformation of adjacent viaduct pile foundation   Order a copy of this article
    by Haitao Wang, Mingyang Xu, Tao Guo, Minghua Cui, Jiangtao Tian 
    Abstract: In order to study the influence of deep foundation pit excavation on the deformation of adjacent viaduct pile foundation in water-rich karst environment, a three-dimensional finite element analysis model was established, and the dewatering process of deep foundation pit was simulated. The results show that with the increasing of pile depth, the horizontal displacement of pile foundation is larger at the top and smaller at the bottom. The dewatering of deep foundation pit has influence on the bending moment and axial force of adjacent viaduct pile foundation, but they are in a relatively safe range. The horizontal displacement of bridge pile is negatively correlated with the distance between bridge pile and deep foundation pit, the supporting stiffness of deep foundation pit and the elastic modulus of the first layer soil.
    Keywords: Deep foundation pit; Pile foundation; Bridge pier; Numerical calculation; Influence parameters; Karst cave.
    DOI: 10.1504/IJCIS.2024.10053029
  • Intelligent Infrastructures Using Deep Learning Based Applications for Energy Optimization   Order a copy of this article
    by Monica Purushotham, Kriti Srivastava, Chitra A, Malathi S, D. Kerana Hanirex, S.Silvia Priscila 
    Abstract: Renewable energy could boost electricity and wave power. Increased electricity consumption necessitates hydropower integration. Wind energy is cost-effective and promising. This study examines wind farm viability in windy areas. This study summarises deep learning models, methods, and wind and wave energy conditions. Comparing approaches for similar applications. A computation technique can substitute a comprehensive computer model, with a 94% accuracy rate compared to model simulations and 84% compared to other data. The study found great promise in deep learning-based energy optimisation, storage, monitoring, forecasting, and behaviour inquiry and detection. Energy regulators and utility management could evaluate sustainable electricity diversification using the study’s findings. This study summarises deep learning models, methods, and wind and wave energy conditions. Comparing equivalent application approaches. A computing technique can replace a complex computer model with 94% accuracy compared to model simulations and 84% to other data. Deep learning applications for energy optimisation, storage, monitoring, forecasting, and behaviour identification and investigation were promising. The project would give energy regulators and utility management impartial advice on sustainable electricity diversification.
    Keywords: renewable energy; deep learning; wind turbine blade; electricity generation; wind energy; power management; wave energy; extended short-term memory.
    DOI: 10.1504/IJCIS.2024.10054806
  • Linear Kernel Pattern Matched Discriminative Deep Convolutive Neural Network for Dynamic Web Page Ranking with Big Data   Order a copy of this article
    by Sujai P, Sangeetha V 
    Abstract: Websites and information are plentiful. Search engines return many pages based on user requests. Thus, unstructured web content compromises information retrieval. A new gestalt pattern matched linear kernel discriminant maxpooled deep convolutive neural network (GPMLKDMDCNN) is to rank web pages by query. At first, Szymkiewicz-Simpson coefficient and Gestalt pattern matching Paice-Husk method are to remove stop words and stem words during preparation. Fisher kernelised linear discriminant analysis then selects keywords from preprocessed data. Bivariate Rosenthal correlation is utilised for page rank-based correlation outcomes and saving time, and online sites are ranked by user query with higher accuracy. The experiment uses parameters such as accuracy, false-positive rate, ranking time, and memory consumption. The evaluation shows that the GPMLKDMDCNN method is superior in using the CACM dataset with maximum ranking accuracy of 5%, minimum false positive rate and memory consumption of 39% and 13%, and quicker ranking time by 20% than the existing methods, respectively.
    Keywords: web pages ranking; maxpooled deep convolutive neural network; Szymkiewicz–Simpson coefficient; gestalt pattern matched Paice-Husk algorithm.
    DOI: 10.1504/IJCIS.2024.10054915
  • Adoption of Cloud Accounting for critical infrastructure with in Small Medium Enterprises in Odisha through Prioritization of its Sustainable Benefits   Order a copy of this article
    by Sarita Mishra, Suresh Sahoo, Srinivas Subbarao Pasumarti 
    Abstract: This study has attempted to use “Relative to an identified distribution” (RIDIT) algorithm based modeling for analyzing real time empirical data related to benefits realized by an enterprise through adoption of critical infrastructure of cloud accounting in context of Small Medium enterprises of Odisha. The study focuses on demand side aspect of cloud accounting aspects by considering its realized benefits in context of SMEs in Odisha. Reduction of Cost, reduction of wastage and gaining more sustainability, Security of financial information comes on the top positions in the priority list of benefits. The finding of the study is significant with respect to its practical orientation as the responses collected from real user of the system. Modeling of realized benefits of cloud accounting by enterprises with RIDIT analysis could contribute towards demand creation of cloud accounting; its adoption and improvement of services to the clients’ The finding of the study could be informative to such enterprises for taking proper decision towards adoption of cloud accounting in critical infrastructure.
    Keywords: Cloud accounting; Sustainable benefits; Critical infrastructure; Prioritization; RIDIT analysis.
    DOI: 10.1504/IJCIS.2024.10055083
  • Mining Closed High Utility Itemsets Using Sliding Window Infrastructure Model Over Data Stream   Order a copy of this article
    by Mahesh Kumar Ponna, Srinivasa Rao P 
    Abstract: A group of products that has utility values and that are sold together greater than a preset lowest utility cut-off is produced by mining high-utility itemsets. These itemsets’ profit units have external and internal usefulness values. In each transaction, the quantity of each item sold, respectively, is considered to determine the utilities of these itemsets. As a result, assessing an itemset’s high utility is symmetrically dependent on all of its internal and external utilities. Both utilities contributed equally, and there are two key deciding considerations. First, selling groupings of low-external utility commodities generally meets the minimal utility requirement. Regular itemset mining can help find such itemsets. Second, numerous high-utility itemsets are created; thus, some interesting or significant ones may be omitted. This study applies an asymmetric technique that overlooks interior utility counts to discover those with considerable external utility counts. Two genuine datasets showed that external utility values strongly affect high utility itemsets. This study also shows that high minimal utility threshold values and a faster method increase this influence.
    Keywords: high utility itemset; sliding window; information extraction; high-utility itemset mining; HUIM; itemset mining.
    DOI: 10.1504/IJCIS.2024.10055102
  • Cross domain and Adversarial Learning based Deep Learning approach for Web Recommendation   Order a copy of this article
    by Asha K. N, Rajasekaran Rajkumar 
    Abstract: The web has become a massive source of knowledge in the internet age. This extra information makes it hard to choose items based on individual needs. Today, choosing suitable products takes time and effort. Daily uploads and downloads from YouTube, Instagram, Facebook, and others generate massive volumes of data. Keep up with the internet’s wealth of information. Recommender systems can help users find useful data in vast datasets. User-interested recommender systems provide personalised and non-personalised recommendations. Real-time applications need recommender systems, but conventional methods have problems. In this work, we identified the issues and developed a cross-domain web recommendation system using a deep learning-based scheme. A joint reconstruction loss model reduces learning error with an autoencoder and adversarial learning technique. An open-source cross-domain dataset tests the proposed approach. For the Movie dataset, average HR, NDCG, and MRR are 0.8951, 0.5911, and 0.6121. The book dataset averages 0.8358, 0.6824, and 0.5575.
    Keywords: cross domain; adversarial learning; deep learning; web recommendation; cross-domain recommender system; demographic information; internet age.
    DOI: 10.1504/IJCIS.2024.10055283
  • Thermal Performance Analysis of PCM Incorporated Roof Slab Infrastructures Using Deep Learning Algorithms   Order a copy of this article
    by Jaspal Singh, R.K. Tomar, Narandra Dutta Kaushika, Gopal Nandan 
    Abstract: PCM technology uses thermal energy storage (TES) to lessen the effects of changes in the outside temperature. PCM thermal energy storage may reduce ambient temperature changes (TES). Latent heat storage (LHS) reduces HVAC needs and enhances indoor comfort in conventional buildings. In buildings without HVAC, latent heat storage (LHS) enhances indoor thermal comfort by reducing the demand for HVAC. This study examines and measures the advantages of using PCM for building envelopes. In order to generalize the findings, a 1 m * 1 m * 1 m reference model is employed with four Indian towns located in various climate zones. Decision tree monitors temperature over time. Root mean square transforms actual and anticipated values, while mRMR selects features. Thermal testing equipment, a PCM wallboard heat storage experiment, and investigations on 5 mm, 10 mm, and 20 mm PCM plasterboard with a 220 C melting temperature are constructed to validate the results. PCM thickness reduced energy use logarithmically in all climatic zones, with temperate office buildings benefiting most.
    Keywords: phase change material; adaptive envelopes; PCM thickness and energy thermal energy storage; infrastructures energy efficiency; passive strategies.
    DOI: 10.1504/IJCIS.2024.10055409
  • Multiple Criteria Decision Making for Determining the Optimal Wind Farm Site under Uncertainty   Order a copy of this article
    by Abdulaziz Almaktoom, Mawadda Samkari 
    Abstract: Optimal wind turbine location plays a major role in power generation and turbine life cycle. Advances have been made in the subject of multiple criteria decision-making (MCDM), resulting in new methods for improving and analysing the decision of wind farm location while considering various uncertainty resources. Sources of uncertainty, such as wind availability, demand variability, and the costs of maintenance and wind turbines on wind farm allocation, can reduce energy and operations costs. In this research, a novel robust MCDM model for wind farm allocation has been developed. A case study involving mathematical simulation for three wind farm locations in Saudi Arabia has been employed to demonstrate the developed research and tools. The research contributions proposed the developed robust MCDM approach using the analytic hierarchy process (AHP), technique for order of preference by similarity to ideal solution (TOPSIS), and robust design (RD) could empower wind farm designers to have a better grasp of the weaknesses and strengths of their decision on wind farm allocation. Also, the paper advances a new approach that is practical and flexible for decision-makers. In addition, the research gives a valuable guideline for selecting the optimal site for a wind farm in other countries.
    Keywords: multiple criteria decision methods; MCDM; multiple criteria decision analysis; MCDA; analytic hierarchy process; AHP; TOPSIS; robust design methodology; RDM.
    DOI: 10.1504/IJCIS.2024.10055554
  • LSTM-CNN: A Deep Learning Model for Network Intrusion Detection in Cloud Infrastructures   Order a copy of this article
    by Srilatha Doddi, Thillaiarasu N 
    Abstract: In cloud computing, resources are shared and accessed over the internet to perform intended computations remotely to minimise infrastructure costs. The usage and dependency on the cloud network have increased, and the chances of invasion and loss of data and challenges to develop a reliable intrusion detection and prevention system (IDPS). The existing machine learning-based approaches require the manual extraction of features, which produces low accuracy and high computational time. Providing a secure network involves a framework based on multi-fold validation and privacy in information transmission. The deep learning-based network IDPS model has been proposed to handle the large volume of network traffic in the cloud. This paper proposes a tailored long short-term memory and convolution neural network (LSTM-CNN)-based approach to design a new IDS. The proposed model productively examines intrusions and generates alerts proficiently by incorporating users'; information and conducting examinations to detect intrusions. The model's performance is assessed using accuracy, precision, F1-score and recall measures. The proposed model achieves outstanding performance with a test accuracy of 99.27%.
    Keywords: cloud intelligent infrastructures; convolution neural network; intrusion detection and prevention; long short-term memory; random forest; neural network; security.
    DOI: 10.1504/IJCIS.2024.10055712
  • Role of E-Adoption of Emerging Technology in 4P Organizational Framework During Covid-19   Order a copy of this article
    by Pushpa Singh, Narendra Singh, Rajnesh Singh, Nishu Panwar, Sunil Gupta 
    Abstract: During the COVID-19, micro, small, and medium-scale business organisations have suffered economic fragility. Apart from lockdown, social distancing and the traditional style of the business process are the factors that affect the business organisation. A business organisation utilising e-adoption of emerging technologies such as artificial intelligence (AI), blockchain, internet of things (IoT), cloud computing, etc., have survived well in the market and achieved high-profit gain. In this paper, we explore the challenges of traditional business organisations. Traditional business organisation frameworks based on 4P: people, process, product and profit are based on manual processes and away from emerging technologies. The proposed organisational framework revolutionised traditional business practices and enhanced productivity, efficiency, and customer retention. People can connect and access business organisations with end-user devices such as smartphones, desktops, laptops, and other hand-held devices.
    Keywords: e-adoption; organisation; COVID-19; AI; blockchain.
    DOI: 10.1504/IJCIS.2024.10056042
  • Unsupervised Strategies In Detecting Log Anomalies using AIOps Monitoring to Amplify Performance by PCA and ANN Systems   Order a copy of this article
    by Vivek Basavegowda Ramu, Ajay Reddy Yeruva 
    Abstract: A fundamental task that artificial intelligent operations (AIOps) perform is to mitigate the risk of abnormal system behaviours and identify and demystify the alerts when encountering the presence of log anomalies and assess the reasons for the different system failures and run smoothly, system flaws must be fixed and to empower this functionality, the infusion of related artificial intelligence needs to be integrated, there have been several innovative strategies that have been incorporated with systems utilising AIOps platforms. However, the study has been limited, and some grey areas remain. Suppressing incorrect logs in system performance analysis is unsupervised in this paper. PCA and ANN produce a feed input for detailed analysis. System performance improves. Pseudo positives false alerts in log anomaly detection theories are introduced in the study. The proposed strategy reduces aberrant logs by 72%, outperforming most other experiments. It is unique in log analysis since it reduces false positives, making it easier to find true anomalies and improving system efficiency. This approach has promising research possibilities.
    Keywords: artificial intelligent operations; AIOps; anomaly log detection; log data analysis; performance; pseudo positives; recurring anomalies; monitoring; observability.
    DOI: 10.1504/IJCIS.2024.10056177
  • Lithium-ion batteries SoC estimation using an ANFIS-based adaptive Sliding Mode Observer For Electric Vehicle Applications Infrastructures   Order a copy of this article
    by Weize Liu, Zhiyi Huo, Xinwen Luo 
    Abstract: State of charge (SoC) estimation is a key function in battery management systems (BMSs) that is not directly measurable and should be estimated using estimation methods. Estimating the SoC requires addressing model uncertainty while determining battery model parameters. Robust battery SoC estimation approaches overcome this challenge. Sliding mode parameter estimation chatters in its original form. To solve this problem, this paper adapts the sliding gain switching estimator by an adaptive fuzzy system to solve the chattering problem. A neural network is used to optimise fuzzy systems, which demand optimisation strategies. The research proposes an adaptive neuro-fuzzy SMO for SoC estimation to improve robustness, accuracy, and response chattering. SoC estimation uses a lithium-ion battery cell equivalent circuit model (ECM). The open circuit voltage's nonlinear relationship with charge makes this model nonlinear. The recommended methodology has been tested using a set of software-in-the-loop experiments, which show that chattering has been abolished and accuracy can be decreased by 5% compared to the standard SMO.
    Keywords: fuzzy system; battery management systems; BMSs; sliding-mode; state of charge; SoC; lithium-ion battery; applications infrastructures.
    DOI: 10.1504/IJCIS.2024.10057805
  • Smart Technical Control Infrastructures in Electrical Automation Through Digital Application Systems   Order a copy of this article
    by S. SAKTHIVEL, Charu Virmani, S.Silvia Priscila, Ravindra Pathak, Prasath Alias Surendhar S, Bobur Sobirov 
    Abstract: Both technological and social systems combine to construct the infrastructure and processes of digital technologies, ensuring that an organisation's aims and objectives are achieved. The firm created and employed access controls and measures to protect its data and information systems. The exploitation of information systems and disregard for internet security protocols are the main causes of computer security breaches. Non-compliance with information security regulations is a serious risk for businesses. It is crucial to identify, investigate, and consider the elements that affect compliance and the deployment of computer security for successful conformity and human adoption of computer security technology and compliance with computer practices. Computer engineering is increasingly automated with high tech. Technology and engineering in technical control systems have improved. The study examines clever technical control in electrical automation and intelligent technologies. It also analyses this technology's potential applications and future development trends in electrical engineering. Reviewing machine learning methods for technical control issues, we concentrate on the deterministic situation to illustrate the numerically complex issues.
    Keywords: computer security abiding; stiffness adjusting; evaluating and monitoring; levelling; technical controls; controlling impedance.
    DOI: 10.1504/IJCIS.2025.10060620
  • The Adoption of eXtensible Business Reporting Language (XBRL): An Empirical Investigation of the Perceptions of Accounting Professionals   Order a copy of this article
    by Zakia Sanad, Abdalmuttaleb Al-Sartawi 
    Abstract: The current study aims to gain a better understanding of XBRL adoption awareness, benefits, drawbacks and suggests XBRL adoption strategies that could be implemented in the Kingdom of Bahrain. Additionally, the study also investigated the relationship between the perception of issues regarding XBRL adoption and demographic characteristics such as gender, age, and professional experience. A survey research instrument was developed and distributed to accountants and auditors working in listed companies in Bahrain Stock Exchange. The results revealed that, XBRL adoption could help in decreasing information asymmetry, while the lack of XBRL training is one of the biggest concerns. It further appears that the most suitable strategy to disseminate XBRL according to the respondents is a voluntary approach rather than a mandated policy. The empirical analysis conducted in this study shows that age, nationality, experience in XBRL and training impact the perceptions of accountants. The findings also have various practical and policy implications indicating that regulators, policy makers and firms should work together to sustain and improve the awareness, adoption, and reliability of XBRL.
    Keywords: extensible business reporting language; XBRL; XBRL adoption; XBRL implementation; accountants; accounting technology; financial reporting; digital transformation; digitalisation.
    DOI: 10.1504/IJCIS.2025.10060621
  • The economic effects of infrastructure investment on industrial sector growth in sub-Sahara Africa: A Disaggregated System-GMM Approach.   Order a copy of this article
    by Keji Sunday Anderu, Josue Mbonigaba, Akinola Gbenga 
    Abstract: Investment in economically inclined infrastructure is pertinent to industrial sector growth in sub-Sahara Africa (SSA), especially during this period of financial belt-tightening recovery due to the recent global pandemic. Findings suggest a dilapidated infrastructure spread across SSA, which has mired productivity growth, hence slow industrial sector growth. This study fills a vacuum in the literature by investigating the economic effects of infrastructure investment on industrial sector growth in SSA. The study aims to systematically unravel the short-run and long-run effects of infrastructural inputs on industrial sector growth, using disaggregated System-GMM approach. Findings disclosed that infrastructural investment significantly influence industrial sector growth in SSA. Overall outcomes revealed diverse significant effects from various types of infrastructural tech on industrial growth across sub-regional countries. Similarly, post estimations analysis via robust Arellano-Bond Autocorrelation and Hansen tests were adopted to establish the absence of first and second-order autocorrelation and over-identifying restrictions of instruments in the estimated models. The study uniquely disaggregated short-run and long-run effects of infrastructure investment on industrial sector growth via system GMM to provide valuable insights to policymakers. Hence, sub-regional countries should draft more policy support to prioritise economically motivated factor inputs such as information techs, access to energy, transport and water resources to expedite industrial sector growth.
    Keywords: Industrial Sector Growth; Infrastructural Investment; System Generalized Methods of Moments; GMM.
    DOI: 10.1504/IJCIS.2025.10060622
  • Game of Life based Critical Security Key Mechanism infrastructure in Internet-of-Things (IoT)   Order a copy of this article
    by A. Anandhavalli, A. Bhuvaneswari 
    Abstract: Modern technology's blessing, the internet of things (IoT), has made remote monitoring and automation a reality. IoT devices are now the most economical option for wireless sensor networks. These gadgets were created with a specific purpose; therefore, computing power and power sources are restricted to meet that need. Due to power limitations, providing security for this type of network is a real issue. The game of life-based security key mechanism (GLSKM) technique is designed to leverage more low-level hardware bitwise operations during the key generation and exchanging phase instead of more computationally integrated energy-starving activities. This work presents two modules: the game of life-based key exchange mechanism and the random seed and iteration limit selector. Both modules are built to use simpler bitwise hardware-targeted instructions to achieve minimal power consumption without sacrificing security. The GLSKM approach also recognises the network's overall performance.
    Keywords: energy efficient; internet of things; IoT; game of life; security key exchange; wireless sensor networks; WSNs.
    DOI: 10.1504/IJCIS.2025.10060623
  • Application of Silica Fume, Pumice and Nylon to Identify the Characteristics of LWC after Critical infrastructure Analysis   Order a copy of this article
    by Anish C, R.Venkata Krishnaiah, K.Vijaya Bhaskar Raju 
    Abstract: Finding lucrative building designs has been the major problem the construction industry has been experiencing lately. This issue can be fixed by dramatically lowering the structural part's self-weight and sizing it down. Lightweight concrete (LWC) is the sole material that can be used to achieve this. In earlier tests, various lightweight aggregates were utilised to lower the density. The primary benefits of LWC columns are that they do not require a reinforced cage or forms because their steel tubes can be used just as well as scaffolding and are fireproof. Based on the numerous research projects undertaken, it can be concluded that circular poles should be favoured over a square LWC to boost stability and satisfy various design needs. This study defines LWC while considering strength component development. Thus, this experiment examines silica fume and pumice stone as entire substitutions. After moulding samples with the desired mix ratio, compression, tensile, and bending capacities are assessed. This specially designed LWC mix of M30 grade concrete has 0.6 to 0.7 times the strength of regular concrete, according to tests. The strength measures dramatically increased by adding 20% silica fume and 1.5% nylon fibre.
    Keywords: critical infrastructure; lightweight concrete; LWC; pumice; silica fume; nylon fibre; waste rubber powder; mechanical properties; thermal properties.
    DOI: 10.1504/IJCIS.2025.10060624
  • GRA-based Study on The Vulnerability and Sustainable Development of Economic Systems in Tourist Cities   Order a copy of this article
    by Jie Kong 
    Abstract: The vulnerability of China's tourism city economies due to natural disasters, infectious diseases, and emergencies has become a hindrance to their sustainable development. To this end, the study takes Dali city as the research object and constructs a corresponding grey correlation degree model of the fragility of tourism city economic system based on the objective entropy value method and GRA. The study uses this model to systematically analyse the causes and mechanisms of action of the economic system fragility of tourism-oriented cities. The results show that Dali's economic subsystem has a relatively homogeneous industrial structure, and its coping capacity is growing flatly while its sensitivity is generally on the rise. The fragility of the social and economic subsystems correlates highly with the vulnerability of the city's economic system. This study provides targeted suggestions for sustainable development of tourism cities through a comprehensive analysis of their economic system fragility.
    Keywords: tourist cities; economic system vulnerability; sustainable development; entropy method; GRA.
    DOI: 10.1504/IJCIS.2025.10060625
  • Hyper Chaotic Chen Model-Based Medical Image Encryption and DNA Coding Framework for Secure Data Transfer Critical Infrastructures   Order a copy of this article
    by J. Helen Arockia Selvi, T. Rajendran 
    Abstract: Image encryption in the healthcare sector is used to protect sensitive medical images, such as X-rays, MRI scans, and CT scans, from unauthorised access and disclosure. This is important because medical images often contain personal and confidential information that can be used for malicious purposes if it falls into the wrong hands. The proposed research utilises a hyperchaotic system along with DNA coding for the secure data transfer of medical images. The closed hash table method was used to scramble the random chaotic sequences produced by the Chen system. The DNA substitution approach and DNA coding and decoding principles were used to perform the diffusion. The encryption approach breaks down the robust pixel correlation and allows safe data transfer for teleradiology applications. The two-stage scrambling followed by a single-stage diffusion ensures security in data transfer and robustness against attacks. The real-time medical images are used in this research and validated by the performance metrics.
    Keywords: encryption; chaotic function; teleradiology; decryption; data transfer critical infrastructures.
    DOI: 10.1504/IJCIS.2025.10060626
  • Study of Corporate Management Financial Early Warning Combining BP Algorithm and KLR   Order a copy of this article
    by Xiaoli Yu 
    Abstract: China has a large number of small and micro enterprises, which are an important part of our market economy. The study analyses the causes of enterprise financial crises from internal factors and external factors, and constructs an early warning system for enterprise management financial crises (FCWS) based on the analysis results. To address the shortcomings of traditional early warning methods in terms of low accuracy and efficiency, the study combines signal analysis model (KLR) and BP neural network (BPNN) to build a KLR-BP enterprise management financial crisis early warning model. The performance of the KLR-BP model was tested using the financial data of 50 micro and small enterprises over the years, and the accuracy of the model exceeded 95%. Thus, the KLR-BP model can be practically applied to enterprise management financial early warning and make a certain contribution to the development of China's market economy.
    Keywords: BPNN; KLR model; financial early warning; market economy.
    DOI: 10.1504/IJCIS.2025.10059504
  • A Blockchain based Solution for Efficient and Secure Healthcare Management   Order a copy of this article
    by Deepak Kumar Sharma, Adarsh Kumar 
    Abstract: Healthcare, being a vital and rapidly evolving field, necessitates robust systems for managing medical records and ensuring data security. The article proposes a blockchain based healthcare management system that addresses critical challenge of secure medical data sharing. The system incorporates zero trust principles and blockchain technology to verify compliance with patient records and facilitate secure data exchange among research institutions, patients, and servers. The proposed distributed zero trust based blockchain structure (DZTBS) effectively meets the privacy and security requirements of availability, integrity, and confidentiality. Notably, compared to traditional systems, DZTBS achieves a remarkable reduction of approximately 20% in both total execution and block-generation time. Furthermore, our system outperforms existing encryption algorithms, including the advanced encryption standard and elliptic curve digital signature algorithm with a mean encryption time of 0.001053 seconds and a decryption time of 0.00365 seconds. These results show improved security and efficiency offered by proposed healthcare management system.
    Keywords: blockchain technology; data sharing; electronic medical records; security; zero trust principle.
    DOI: 10.1504/IJCIS.2025.10060627
  • Prediction of the fracture energy properties of concrete using COOA-RBF neural network   Order a copy of this article
    by Yongcun Zhang, Zhe Bai 
    Abstract: Evaluating the energy requirements for crack propagation in concrete structures has been a subject of considerable interest since applying fracture mechanics principles to concrete. Concrete fracture energy is important for safe structural design and failure behaviour modelling because it is quasi-brittle. The complex nonlinear behaviour of concrete during fracture has led to ongoing debates regarding fracture energy prediction using existing estimation techniques. Using the previous dataset, prediction approaches were developed to measure the preliminary (Gf) and total (GF) fracture energies of concrete utilising mechanical properties and mixed design elements. Two hundred sixty-four experimental recordings were gathered from an earlier study to construct and analyse ideas. This study combines the radial basis function neural network (RBFNN) with the Coot optimisation algorithm (COOA) and whale optimisation algorithm (WOA). The computation and analysis of Gf and GF used five performance measures, which show that both optimised COOA-RBFNN and WOA-RBFNN evaluations could execute superbly during the estimation mechanism. Therefore, even though the WOA-RBFNN approach has unique characteristics for simulating, the COOA-RBFNN analysis seems quite dependable for calculating. Gf and GF given the rationale and model processing simplicity.
    Keywords: concrete; fracture energy; neural network; estimation; radial basis function; coot optimisation algorithm; whale optimisation algorithm; WOA.
    DOI: 10.1504/IJCIS.2025.10060630
  • From Shovels to Snowplows: The Evolution of Snow Clearance Infrastructure in Kashmir, India   Order a copy of this article
    by Nadeem Najar, D. Parthasarathy, Arnab Jana 
    Abstract: This research examines the evolution of snow clearance infrastructure in the Kashmir Valley and its direct link to critical infrastructure-transportation. The study analyses numerous data sources such as snow removal action plans, departmental letters, notes, presentations, requisition letters, and official communications using a qualitative research approach, specifically content analysis. The research demonstrates the severe influence of snow removal on critical infrastructure by applying the theoretical framework of punctuated equilibrium theory and analysing its components, including pluralism, conflict expansion, policy image, and venue shopping. The data show a major shift from manual snow removal practices to mechanised operations between 1987 and 2022, which was driven by significant punctuations. Furthermore, the study emphasises the continual evolution of snow removal practices in Kashmir, with a focus on the incorporation of cutting-edge technologies and globally popular methodologies to ensure the resilience and functionality of critical transportation networks. The study provides important insights for policymakers and winter road maintenance managers involved in managing essential infrastructure in snowy regions.
    Keywords: critical infrastructure; snow clearance; evolution; punctuations; policy; action plans; India.
    DOI: 10.1504/IJCIS.2025.10060878
  • Efficiency of the Framework for Industrial Information Security Management Utilizing Machine Learning Techniques   Order a copy of this article
    by Nisha Nandal, Naveen Negi, Aarushi Kataria, Rita S 
    Abstract: Discover the innovative integration of crowd sense technology and artificial intelligence in the industrial machine learning (ML) mining sphere. This fusion transcends data processing to encompass meticulous safety monitoring via collective knowledge management. Envision a harmonised framework where management of keys, tables, hardware, and ML mining supervision coalesce to shield enterprise data robustly. This approach, examined through various lenses, including security and big data capacity testing, assesses risk mitigation enthusiastically while crafting a business management platform that contemplates corporate leadership needs, offering an ML data security architecture blueprint. Although challenges like refining neural networks for optimal global efficiency persist, the study highlights its remarkable, unblemished performance across modules on the ML-based corporate data safety regulation platform. It proficiently meets daily organisational needs and assures AI's vital role in enterprise data security management, providing a scaffold for future research and marking a paradigm for upcoming explorations in the domain.
    Keywords: artificial intelligence; AI; industrial information; security management; machine learning techniques; crowd sense technology; information security management.
    DOI: 10.1504/IJCIS.2025.10062097
  • Assessment of critical success factors for PPP airports using the AHP   Order a copy of this article
    by Avin S. Chourasia, Narendra Dalei, Karunakar Jha 
    Abstract: Critical Success Factors (CSFs) for various infrastructure projects have been the subject of numerous research, but there are relatively few studies available for airports, and none have been done as yet to identify the CSFs for the development of PPP airports in India. To bridge this gap, this study was carried out using the Analytical Hierarchy Process (AHP) to identify the critical factors for the development of PPP airports in India. The success of the PPP airport was estimated using Ordinary Least Square (OLS) and Tobit Models. The result shows that Effective project management, Procurement process, Profit expectations, and Information disclosure have statistically, and significantly positive impacts on the success of PPP airport as perceived by professionals in India. The study recommends that the government and all other PPP airport stakeholders must properly take into account the identified CSFs for the development of PPP airports in India to be successful.
    Keywords: Critical success factors; CSFs ranking; Public-private partnership; Analytical hierarchy process; Airport development; PPP airport.
    DOI: 10.1504/IJCIS.2024.10062163
  • Systematic literature review and future research trends on Building Information Modelling (BIM) using bibliometric analysis   Order a copy of this article
    by Rajath B.S., Abhilash G, Kavya Shabu, Deepak MD, Shridev ., Rajesh Kalli 
    Abstract: The advent of building information modelling (BIM) has increased as a defined methodology for improving construction work processes. Despite the significance of its usage, there is dearth of studies that comprehend the applications of BIM and its potential benefits for construction work. The present work aims to understand the recent developments and applications of BIM research in the construction industry. In this regard, a systematic nine-step approach using bibliometric analysis is performed to scrutinise articles published in Scopus database. Based on the scrutinised articles, a detailed examination using thematic and cluster analysis was applied to explore the potential BIM areas. Findings indicated key clusters: 1) architectural design aspects; 2) sustainable development; 3) project management knowledge areas. The outcome of the study provides a holistic understanding of these clusters and suggests exploration of potentially challenging areas for future BIM applications.
    Keywords: building information modelling; BIM; construction industry; bibliometric analysis; thematic analysis; cluster analysis; sustainable development.
    DOI: 10.1504/IJCIS.2025.10062595
  • IoT-Based Intelligent Infrastructure Decision Support System with Correlation Filter and Wrapper Framework for Smart Farming   Order a copy of this article
    by Suresh M, Manju Priya 
    Abstract: Agriculture is the backbone of the Indian economy in a world where the market is battleground, and technology is constantly changing. More than 75% of the population relies on this ancient craft. Each farmer must produce high-quality harvests despite water shortages and plant illnesses. They must delicately balance soil nutrients, sustaining fertility like a nation's lifeline. From these trials emerged the modern Indian farmer's hero: an IoT-based decision support system, a smart agricultural beacon. This miracle anticipates agricultural yield and guards their livelihood like a sentinel. It monitors soil fertility, stops soil degradation, and considers excessive irrigation a crime against nature. Wireless sensor devices elegantly communicate data to a central server to arrange this technology symphony. In the digital world, a machine learning system does predictive irrigation. The weather, soil, rainfall, seed damage, drought, and alchemical pesticides and fertilisers are considered. Many pioneers in this growing industry have failed, resulting in incorrect estimates and low crop yields. CBF-SF, an artisanal hybrid correlation-based filter (CBF) and sequential forward wrapper architecture is the solution. This clever technique turns parched areas into bountiful goldmines by predicting crop yields with precision, making farmers contemporary alchemists.
    Keywords: correlation filter; sequential forward; prediction; IoT-based intelligent infrastructure; decision support system; correlation filter.
    DOI: 10.1504/IJCIS.2025.10062624
  • Ensemble Machine Learning Regression Technique to Select the Type of Concrete as Radiation Shielding Material   Order a copy of this article
    by Debabrata Datta, S. Seema, S. Suman Rajest, Biswaranjan Senapati, S.Silvia Priscila, Deepak K. Sinha 
    Abstract: The selection of exact material for shielding analysis is challenging in radiation protection. The primary objective of shielding analysis is to reduce radiation exposure to the occupational worker at their workplace. Generally, high-density concrete is selected as the shielding material to prevent accidental exposure to gamma and neutron radiation. Composite material or multilayer shielding materials are generally used to optimize the cost of concrete with maximum benefit to the society of occupational radiation workers. A surrogate model for concrete's overall strength using cement, fly ash, and coarse and fine aggregates is created using machine learning and ensemble learning. Ensemble learning in machine learning solves underfitting and overfitting problems when fitting a regression model for shielding analysis. As density increases, concrete overall strength decreases. Several samples of various types of concrete (different compositions) are collected as input data. Finally, a multi-attribute decision-making method is applied to select the appropriate type of concrete. The research presents the ensemble learning based regression technique coupled with multi attribute decision making method to recommend the exact variety of concrete for shielding gamma and neutron radiation.
    Keywords: Gamma and Neutron; Technique of Order Preference for Similarity Ideal Solution (TOPSIS); Type of Concrete; Radiation Shielding Material; Ensemble Machine Learning; Regression Technique; Mean Square.
    DOI: 10.1504/IJCIS.2025.10063154
  • Logic Realization of a Spatial Domain Image Watermarking with Single Electron Transistors- An Innovative Approach   Order a copy of this article
    by Abhishek Basu, Arpita Ghosh, Anirban Mukherjee 
    Abstract: Multimedia articles exchanged over the digital network are increasing day by day causing enhanced threats of losing authenticity or copyright of those contents. As a result, requirement for low power and high speed copyright protection system for multimedia objects is hovering. In this article, authors have projected one spatial domain-based image watermarking structure for multimedia copyright protection and its hardware level implementation based on field programmable gate array (FPGA). Moreover, single electron transistor (SET) implementation for the structure has also been presented. The technique uses least significant bit (LSB) plane-based information hiding and all the modules of embedding and extraction block are realised with SET. It has been observed that this scheme shows noteworthy imperceptibility along with robustness. The result of SET execution confirms significantly low power consumption.
    Keywords: image watermarking; multimedia copyright protection; field programmable gate array; FPGA; single electron transistor; SET; least significant bit; LSB; low power.
    DOI: 10.1504/IJCIS.2025.10063422