International Journal of Critical Infrastructures (40 papers in press)

Regular Issues

• Seismic Isolation of Data Centers for Business Continuity  
  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  
  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  
  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
   
  
• Utilizing the Fuzzy Analytic Network Process Technique to Prioritize Safety Challenges in Construction Projects  
  by Pouyakian Mostafa, Ali Akbar Shafikhani, Amir Abbas Najafi, Behrouz Afshar-Nadjafi, Amir Kavousi 
  Abstract: This study aims to identify and rank the obstacles to implementing a safety program in the Iranian construction industry. The obstacles were identified through literature review and interviews with experts in the Iranian construction industry. Because of the complex structure of the relationships between the obstacles and their mutual effects, the fuzzy analysis network Process method was used to model them. Obstacles to safety implementation were identified and ranked using the proposed model. Fourteen obstacles were identified in the three organisational, contractors, and systems dimensions. The most critical obstacles include tight project schedules, resource constraints, fierce competition between contractors to reduce time and cost. This study showed that the Iranian construction industry, despite its advantages, faces obstacles in the successful implementation of safety programs. It seems that the identified obstacles can be removed by modelling the safety program in project scheduling. However, more studies are needed in this area.
  Keywords: safety; accidents; construction; analytic network process; ANP; fuzzy evaluation.
  DOI: 10.1504/IJCIS.2024.10047707
   
  
• Land Value Capture as Breakthrough of Financing Scheme in Urban Railway Development in Indonesia  
  by Fery Safaria, Najid Najid, Carunia Firdausy 
  Abstract: This study aims at examining whether or not the LVC can be used as an alternative financing instrument in urban railway infrastructure in Indonesia. The results confirmed that the LVC can be a breakthrough scheme in financing the development of urban railway infrastructure in Indonesia. The most urgent aspect to be prepared by the government to implement the LVC scheme in financing the urban railway infrastructure is the availability of regulations. Also, the approach to developing and executing the plan of LVC needs to be based on an assessment of the cost of the benefit analysis, the land value, the market condition of the location, sustainability, and people’s participation in urban railway development. Further detailed research to examine the full potential and the benefits of applying the LVC in financing urban railway infrastructure development is needed as Indonesia faces budget constraints and as we move into a post-pandemic recovery.
  Keywords: land value capture; LVC; financial scheme; budget limit; urban railway infrastructures; Indonesia.
  DOI: 10.1504/IJCIS.2024.10049301
   
  
• Investigating Safety Development Methodologies in the Construction Industry and Identifying Gaps in the Studies: A Review Article  
  by Mostafa Pouyakian, Ali Akbar Shafikhani, Amir Abbas Najafi, Behrouz Afshar Nadjafi, Amir Kavousi 
  Abstract: Identifying the appropriate safety methodology is essential to improving construction safety performance. This study aims to investigate safety development methodologies in the construction industry and identify gaps in the studies. Articles published from 2000 to 2022 were reviewed. Seventy-seven eligible articles were selected based on comprehensive and exclusive criteria. After obtaining selected literature, gaps in using these methodologies were discussed. Twelve criteria were used to compare safety methodologies. The selected literature focused more on the construction phase and did not provide an effective strategy in the project planning phase. Although the studies had specific benefits, none examined the safety program based on actual project conditions (resource, time, and cost constraints). There is a need for a model that examines safety in terms of actual project conditions (time, cost, and resource constraints). In addition, the model must optimise not only safety but also other vital components of the project (cost, time, and quality) while considering resource constraints (especially equipment constraints). If such a model is designed, the project team will not resist safety changes, which benefits all the construction stakeholders.
  Keywords: construction industry; safety management; project schedule; occupational health.
  DOI: 10.1504/IJCIS.2024.10049397
   
  
• Social and Economic Risk Analysis of Natural Gas Distribution Networks
  by Fabrizio Zuena, Marco Dell'Isola, Giorgio Ficco, Luisa Lavalle, Alberto Tofani 
  Abstract: The continuity of service as well as with its safety and security represent a crucial issue for natural gas transmission and distribution networks and a detailed analysis of the associated risks is essential to increase their reliability. In particular, natural gas distribution networks are characterised by a high number of users and present a very complex structure (with nodes and stretches and presenting mixed typologies, e.g., point to point, star, meshed) which make often difficult to forecast the effects of localised failure events, especially by a social and economic point of view. In this work, the authors develop a methodology for the analysis of the economic and social risk associated with natural gas distribution network failures and for the quantification of the related consequences on residential, commercial and/or industrial users. To this aim, the authors present and discuss the case study represented by a city distribution network located in southern Italy. The results demonstrate the developed method is effective in identifying the structural criticalities of the network, allowing the quick detection of the most critical areas affected by significant risk of service disruption.
  Keywords: failure analysis; risk analysis; distribution network; natural gas.
  
  
• Argentina’s critical infrastructures: topics for their regulation
  by Gonzalo CACERES 
  Abstract: Argentina’s last National Defence Policy Directive (2021) explicitly mentions the protection of critical infrastructures, making it necessary to define them, establish priorities and responsibilities. However, Argentina published a first critical infrastructure standard in 2019 that lists vaguely those sectors to be included. The resolution does not allow to program actions and establish responsibilities since there is no identification of actors and, therefore, of their duties. In this article, we will discuss the relevant aspects that could be considered for future legislative work in Argentina and the role of the Ministry of Defence. Main topics are: 1) the genesis of the notion of critical infrastructures and the aspects that gave rise to their identification as the object of security policy and that of National Defence in particular; 2) those cases that are of interest in thinking about the national case in comparative perspective; 3) a synthesis of the elements under discussion that we understand structure the treatment of critical infrastructures.
  Keywords: critical infrastructure legislation; Argentina; national defence; security.
  
  
• Efficient Indian Sign Language Recognition and Classification Using Enhanced Machine Learning Approach
  by Edwin Shalom Soji, T. Kamalakannan 
  Abstract: Deafness and voice impairment are two significant disabilities that make it difficult for people to communicate in verbal languages with others in a verbally communicating population. To solve this problem, the sign language recognition (SLR) system was constructed by combining machine learning and deep learning. The SLR employs hand gestures to convey messages. Earlier research aims to develop vision-based recognisers by extracting feature descriptors from gesture photos. When dealing with a large sign vocabulary recorded under chaotic and complex backgrounds, these strategies are ineffective. Hence, an improved convolution neural network is proposed in this paper to predict the most frequently used gestures in the Indian population with improved efficiency. The presented system is compared to SVM and CNN. The suggested approach is tested on 2,565 UCI instances and 22 training attributes. It showed both-handed ISL movements against various backgrounds. The augmented CNN has a precision of 89% and 90.1% accuracy, which is higher than most other approaches. According to this survey, we had an 83% recall and a 0.4 F score. Python evaluates our work.
  Keywords: sign language recognition; SLR; machine learning; convolution neural network; CNN; Indian sign languages; ISLs; accuracy; precision.
  DOI: 10.1504/IJCIS.2025.10054997
   
  
• Non-Invasive Prediction Mechanism for COVID Using Machine Learning Algorithms
  by Arnav Bhardwaj, Hitesh Agarwal, Anuj Rani, Prakash Srivastava, Manoj Kumar, Sunil Gupta 
  Abstract: This paper has focused on developing a model to detect non-diagnostically whether the person is infected with the COVID-19 disease using all relevant symptoms and details mentioned by the person and then comparing it with a pre-defined dataset of positive cases using machine learning. Different models have been developed to predict the same but none of them focused on the detection of COVID-19 based on symptoms. In a developing nation with huge population, where the diagnostic availability is scarce so, just scanning the body temperature will not help in detection of COVID-19 of a particular individual. This paper presents a model that can predict COVID-19 cases without any testing kit to an accuracy of 99.30%, performing better than other similar approaches with objective to put forward a method that can reduce the need of producing testing kits and also the need to wait for hours before we get the results.
  Keywords: COVID-19; non-invasive; symptoms; machine learning.
  DOI: 10.1504/IJCIS.2025.10054998
   
  
• A Structured Model for Identification and Classification of Critical Information Infrastructure
  by Alaba O. Adejimi, Adesina S. Sodiya, Olusegun Ojesanmi, Olusola J. Adeniran 
  Abstract: The growing incidence of attacks on critical information infrastructures has necessitated the development of a model for properly identifying and designating information infrastructure whose destruction or interruption could jeopardise the well-being of a state and ensure its preservation. Some of the previous methodologies and models were prejudiced, lacked scientific support, and the criticality criteria were not categorised to encompass global demands. To universally determine the criticality of information infrastructure, this work proposes a factor impact sensitivity approach (FISA), which identifies, structures, and models relevant state criteria into mathematical concepts. An application was developed to implement the structured mathematical model and designate an infrastructure as
  Keywords: criticality strength; information infrastructure; categorisation; multi-criteria; impact factor; risk assessment; alternative scope; likelihood; disruption.
  
  
• Stacking-based Multi-objective Approach for Detection of Smart Power Grid Attacks using Evolutionary Ensemble Learning
  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.
  
  
• Real-world application of face mask detection system using YOLOv6  
  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  
  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
  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.
  
  
• Study on the influence of deep foundation pit excavation on the deformation of adjacent viaduct pile foundation  
  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
   
  
• Critical success factors of Composite LPG Cylinders in India
  by Binod Kumar Singh, Tamajeet Chatterjee, Nistha Srivastava Srivastava, Mahesh Amarjit S.V 
  Abstract: Metal cylinders are being phased out in favour of composite LPG cylinders. These cylinders are light, have a pleasing colour and shape, are rust and corrosion resistant, UV resistant, and also 100% explosion proof. The aim of this study is to understand consumer’s awareness level and likeliness to shift to composite LPG cylinders. The paper studies the relative strength and weaknesses of composite LPG cylinders in comparison with traditional metal cylinders. This paper provides the opportunity to identify the consumer’s needs and to bridge by suggesting technological applications, thus giving an idea to streamline the value chain of LPG cylinder distribution. This study also provides a holistic study of both B2B and B2C segments in the LPG cylinder value chain and thus provides a scope of improving the existing system. The paper will help the business leaders in composite LPG cylinder manufacturing along with cost saving opportunities to the distributors in the long run.
  Keywords: type IV composite cylinders; metal LPG cylinder; composite LPG cylinder; composite cylinder pricing; market feasibility; LPG cylinder safety; India.
  
  
• Intelligent Infrastructures Using Deep Learning Based Applications for Energy Optimization  
  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  
  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  
  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  
  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  
  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  
  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  
  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  
  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  
  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  
  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
   
  
• Seismic Economic Loss Assessment of Highway Girder Bridges Using Wenchuan Earthquake as a Sample  
  by Fan Yanyan, Haiyan Zhang, Li Ziqi 
  Abstract: To study the seismic economic loss of highway girder bridges, taking 596 highway girder bridges in the Wenchuan earthquake as examples, the seismic damage phenomenon of highway girder bridges was statistically analysed, and the vulnerability of highway girder bridges was studied and analysed, the vulnerability matrix and vulnerability curve of highway girder bridges was obtained. And two seismic economic loss calculation models for highway girder bridges are proposed the probability-based seismic economic loss assessment model and the loss rate-based seismic economic loss assessment model, and then the seismic loss of highway girder bridges is predicted. The seismic loss prediction results can not only provide a reference range for the bridge seismic design level, but also the evaluation results can be used as a reference for the seismic capacity of highway girder bridges and the basis for measures for earthquake prevention and disaster reduction.
  Keywords: historical earthquake damage data; highway girder bridge; seismic vulnerability matrix; seismic vulnerability curve; seismic economic loss assessment.
  DOI: 10.1504/IJCIS.2024.10056761
   
  
• Lithium-ion batteries SoC estimation using an ANFIS-based adaptive Sliding Mode Observer For Electric Vehicle Applications Infrastructures  
  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  
  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  
  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.  
  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)  
  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  
  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  
  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  
  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  
  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  
  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  
  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  
  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