Forthcoming and Online First Articles

International Journal of Critical Infrastructures

International Journal of Critical Infrastructures (IJCIS)

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International Journal of Critical Infrastructures (41 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
  • 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
  • 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
  • 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
  • A State of the art Prefix Based Frequent Pattern Mining Without Candidate Generation and Compact FP Tree Generation   Order a copy of this article
    by Sudarsan Biswas, Diganta Saha, Rajat Pandit 
    Abstract: Without the candidate generation approach, it is still dominating and gaining a good research impact to find the desired association rules The FP tree is a memory resident that sometimes memory overfitts for high-volume datasets The issue with the FP growth deals with numerous pointers It generates a massive number of conditional pattern base and conditional FP trees that pursue notable performance degradation with specific datasets FP Growth needs to maintain many pointers operations for large datasets during the rule mining process We present an efficient frequent patterns approach known as prefix-searched Based Frequent Pattern Mining (PBFPM) A straightforward novel array-based key-value pair approach for finding frequent patterns efficiently from large-volume datasets We induce an array structure table (AST) rather than an FP tree structure for storing the dataset’s pattern The proposed method does not generate duplicate frequent patterns and avoids numerous pointer dealings, which saves time in the rule-generation process. We compared the performance concerning time and memory complexity with the FP tree and state-of-the-art boss tree.
    Keywords: Association Rule Mining; Frequent Pattern Mining; Array Structure Table; Key value pair; Hash map.
    DOI: 10.1504/IJCIS.2025.10064031
  • Ideal Planning of Power Grid Integrating Various Small-Scale Powers Generating With Biogeography-Based Optimisation   Order a copy of this article
    by Jianying G.U.O.  
    Abstract: Gasoline cars are being replaced by electric vehicles (EVs), which adds to the strain on the power grid due to their charging needs. Uncontrolled EVs can disrupt the grid; therefore, reliable planning is necessary. Increased distributed generation (DG) resources, especially renewable energy, may disrupt the electrical system. Effective mitigation requires demand-side planning and wise utilisation of emerging technologies, including energy storage. This study recommends optimising EV and DG charging and discharging schedules to fulfil regulated planning needs. Power company schedules depend on parking lot traffic to meet grid goals. The primary objectives are to maximise vehicle holders' and companies' earnings, minimise losses, and reduce parking lot travel time. Investigating critical load sensitivity improves charge and discharge control. The proposed approach utilises a hybrid biogeographic harmony search (BHS). BHS models island species movement, speciation, and extinction using biogeographical mathematics. A sample test system illustrates the method and concept in various settings. Optimal distribution resource management increases network profitability by 8.4% and dependability by 6.63% in outage indices. This holistic strategy highlights flexible models facing greater EV integration and DG resource usage, with numerical figures demonstrating over an 8% network performance gain.
    Keywords: electric vehicles; parking zone; renewable energy sources; distributed generation; DG; harmony search algorithm; HS; biogeography-based optimisation algorithm; BBO.
    DOI: 10.1504/IJCIS.2024.10064353
  • Investigating and Validating the Critical Risk Factors in PPP: Confirmatory Factor Analysis of the Indian Road Sector   Order a copy of this article
    by Mohhammedshakil Malek, Rupesh Vasani, Viral Bhatt 
    Abstract: Critical risk factors (CRFs) may considerably impact PPP project success, hence they must be recognised and analysed. This study examines how private and public sectors affect PPP road project performance at different stages of development and throughout the construction life cycle. The literature review and survey of private and public professionals to identify and verify CRFs may provide insights from industry experts. CFA may disclose PPP road project dynamics by comparing the six phases and private and public sectors. The study’s findings that building project phases positively affect public and private sectors’ CRFs may help professionals focus on essential aspects to increase PPP road project efficiency. A mitigation handbook for avoiding and correcting issues may result from the study. Risk allocation, project management, and PPP success increase with this study. The study discusses Indian PPP road projects and the need of locating and assessing CRFs.
    Keywords: public-private partnership; PPP; confirmatory factor analysis; CFA; critical risk factors; CRFs; roads; AMOS.
    DOI: 10.1504/IJCIS.2025.10064480
  • Signalling Solution for Railway Diamond Crossing using Weight Sensor for Passenger Safety   Order a copy of this article
    by Sharad Nigam 
    Abstract: Railway double diamond crossing is a complex junction where four trains can approach the junction at the same time, but only two parallel opposite trains can cross the junction at the same time and non-parallel trains must wait for clear junction. The concurrent access of diamond crossing by multiple trains, caused accidents from last decades due to signalling conflicts. This article is proposing a wireless sensor network model with LoRa communication technique and weight sensor to automate all signals related to double diamond crossing. Weight sensor is used as a train detection method to measure the threshold weight of the incoming train, then all diamond crossing signals change their aspect according to input data. Reliability and accuracy of weight sensor in any atmospheric and flood condition is shown. A novel weight sensor-based algorithm is proposed in the presented manuscript to automate all related signal aspects for the safe movement of a train with minimum time delay through double diamond crossing.
    Keywords: double diamond crossing; weight sensor/load cell; LoRa; Arduino; WSN.
    DOI: 10.1504/IJCIS.2025.10064783
  • IoT-Aided Smart City Architecture For Anomaly Detection   Order a copy of this article
    by Jiaojie Yuan, Jiewen Zhao 
    Abstract: Anomaly detection in smart cities is critical for mitigating human fall-related injuries and fatalities, particularly within IoT devices. Despite numerous vision-based fall detection methods, challenges persist regarding accuracy and computation costs, especially in resource-constrained IoT environments. This paper proposes a novel fall detection approach leveraging the Yolo algorithm, known for its efficiency in minimising computation costs while maintaining high accuracy. By utilising a diverse fall image dataset, the method undergoes rigorous training and evaluation, employing standard performance metrics. The results reveal impressive precision, recall, and mean average precision (mAP) values of 93%, 89%, and 95%, respectively. Notably, the Yolo algorithm's computational efficiency ensures minimal resource utilisation, making it suitable for real-time deployment in IoT devices within smart city infrastructures. Consequently, this method presents a promising solution for enhancing fall detection accuracy while optimising computational resources, thus advancing safety measures in urban environments.
    Keywords: anomaly detection; fall detection; vision system; Yolo; smart city; internet of things; IoT; mean average precision; mAP; algorithm's computational efficiency.
    DOI: 10.1504/IJCIS.2025.10064830
  • Environmental and Social Governance Issues in AI-Era Electric Power Management and Information Disclosure   Order a copy of this article
    by Thirukumararan S. S, Priyanka Mathur, Sohail Khan, P. Suganya, Sukhwinder Sharma, Sunita Dhotre 
    Abstract: Artificial intelligence (AI) has dramatically transformed the electric power management sector, ushering in higher levels of efficiency, sustainability, and intelligent energy distribution. This shift has enabled more optimised consumption patterns and significantly reduced waste. However, AI complicates power management, particularly environmental and social governance (ESG). This study analyses the pros and cons of AI-powered electric power sector ESG issues. While AI improves power management through predictive maintenance and demand-response optimisation, it also presents transparency issues related to its decision-making algorithms, complicating ESG adherence. To address these concerns, we introduce a novel architectural framework designed to enhance transparency and directly confront ESG challenges associated with AI in power management. Our thorough trials validate the concept, presenting a potential strategy to harmonising technical advancement with ESG principles. The findings demonstrate the need for a balanced approach, embracing AI’s potential to transform power management and ESG challenges. A sustainable and equitable future for power management technology requires this balance. Our research shows the importance of proactive ESG engagement in the AI era and the framework’s ability to create a more open, accountable, and sustainable power management paradigm.
    Keywords: artificial intelligence; electric power management; environmental and social governance; ESG; transparency and information disclosure; technological advancements.
    DOI: 10.1504/IJCIS.2025.10064903
  • Investigation on cost effective smart construction techniques for quality monitoring and risk management in small scale construction sites in India   Order a copy of this article
    by Ganeshprabhu Parvathikumar, Brintha Sahadevan, Deepa Sree Pandiaraj, Marshal Raj 
    Abstract: The challenges and risks involved in construction sites varies depending upon the building size, economy, materials used, tools or equipments availability for safety measures, height, and geographical location. In this work, smart construction techniques are implemented and investigated for risk management and quality monitoring in a cost-effective manner in a small-scale construction site in India. The proposed work focuses on the general hazards and the risks faced by engineers in such sites. To mitigate the challenges, cost effective and reusable smart solutions set up is implemented and validated in a real-time small construction site. The smart solution setup provided support to the construction site engineers to predict the damages in the Scaffolds and Formwork, and testing the quality of concrete, verticality check, surface levelling and formwork deflection. The proposed solutions can be used to improve building critical infrastructures in a cost-effective manner especially in middle- and lower-income economies.
    Keywords: formwork; labour safety; quality monitoring; risk management; scaffoldings; smart construction; India.
    DOI: 10.1504/IJCIS.2025.10065076
  • Efficient Marine Debris Infrastructures on Optimising SVM with LoG Segmentation for Enhanced IoR, DC and Hausdorff Distance Performances   Order a copy of this article
    by S. Belina V.J. Sara, A. Jayanthiladevi 
    Abstract: In the face of escalating threats to aquatic ecosystems posed by marine debris, the demand for precise and efficient classification techniques becomes paramount. This study employs image segmentation methods Canny edge detection, Sobel operator, and Laplacian of Gaussian (LoG) to partition photographs of maritime trash. A notable addition is the integration of SVM-based classification, offering promising avenues for environmental surveillance and disaster management. By incorporating the LoG process, the identification of blob-like structures enhances the accuracy of debris segmentation. Comparative analysis utilising metrics like intersection over union (IoU), dice coefficient, and Hausdorff distance underscores the efficacy of the combined LoG and SVM approach. This synergistic method adeptly detects edges via the LoG operator and ensures accurate debris classification through SVM modelling. The results demonstrate significant improvements, yielding higher IoU (0.993), dice coefficient (0.996), and minimal Hausdorff distance (0.0000977). Executed in Python, this research propels marine debris analysis forward by furnishing a robust framework for automatic image categorisation, which is vital for initiatives aimed at environmental preservation.
    Keywords: marine debris infrastructures; image classification; SVM method; segmentation techniques; canny edge; Sobel operator; SO; Laplacian of Gaussian; LoG; IoR evaluation.
    DOI: 10.1504/IJCIS.2025.10065138
  • Detecting Malware in Linguistic Data Using Malware Detection Deep Belief Neural Network Method   Order a copy of this article
    by Gomathy M, A. Vidhya 
    Abstract: The widespread usage of high-end digital technologies has greatly increased cyber risks. To fight cybercrimes, a smart model should categorise and learn from data autonomously. Internet connectivity has made people’s lifestyles more intertwined, and virtual collaboration is happening across regions. Pop-up messages also entice users and enable fraud. We use a neural network to predict unexpected pop-up message content in this paper. Modern malware and its powerful obfuscation algorithms have made traditional malware detection methods ineffective. However, deep belief neural networks (DBNNs) have garnered attention from researchers for malware detection to fight conventional cybercrime prevention methods in the long run. MDDBNN (malware detection deep belief neural network), based on file properties and contents, is proposed in this research for malware classification. The CLaMP Integrated dataset provided 5210 instances for training and testing. MDDBNN beats GaussianNB, LDA, logistic regression, and support vector machine (SVM). This study found that MDDBNN has the highest accuracy of 97.8%.
    Keywords: deep belief networks; cyber security; cybercrime; spam and deep learning; DL; support vector machine; SVM.
    DOI: 10.1504/IJCIS.2026.10065352
  • Navigating the Next Wave with Innovations in Distributed Ledger Frameworks   Order a copy of this article
    by Venkata S.K. Settibathini, Sukhwinder Sharma, Sudha Kiran Kumar Gatala, Tirupathi Rao Bammidi, Ravi Kumar Batchu, Anil Kumar Vadlamudi 
    Abstract: The latest study sheds light on distributed ledger technologies (DLTs) outside blockchain systems. The first section of this article introduces DLTs, focusing on blockchain as the main paradigm. It highlights three critical characteristics of blockchain: decentralisation, transparency, and security, and emphasises how blockchain is transforming various industries, including supply chain management and finance. Subsequently, the discussion shifts to new developments and approaches in the DLT space. It introduces next-generation ledgers designed to address traditional blockchains' scalability, energy efficiency, and interoperability challenges. The study delves into modern innovations that achieve higher transaction speeds and greater flexibility, such as hybrid models and directed acyclic graphs (DAGs). A significant portion is dedicated to how these advanced DLTs are used to transform sectors like healthcare government, secure patient data management, and enhance transparency and citizen participation. The article also addresses the challenges and ethical considerations of using these technologies. Finally, the paper predicts that DLTs will improve efficiency and innovation in industries outside blockchain technology. To maximise these new technologies' potential, research and interdisciplinary collaboration are essential.
    Keywords: blockchain; decentralisation; cryptocurrency; smart contracts; ledger security; distributed computing; digital identity; interoperability; scalability; tokenisation.
    DOI: 10.1504/IJCIS.2026.10065512