International Journal of Critical Infrastructures (45 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
   
  
• 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 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
   
  
• Efficiency of the Framework for Industrial Information Security Management Utilizing Machine Learning Techniques  
  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  
  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  
  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  
  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  
  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  
  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  
  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  
  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  
  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  
  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  
  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  
  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  
  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  
  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  
  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
   
  
• Critical Infrastructures Challenges and Requirements Meet Blockchain Features and Benefits: A Literature Review  
  by Hosny Abbas, Ibrahim E. Ibrahim, Hamada Esmaiel, Bassem Abd-El-Atty 
  Abstract: Since its invention by Satoshi Nakamoto in 2008 (Nakamoto, 2008) as the backbone of the first successful Bitcoin digital cryptocurrency, blockchain technology has evolved and experienced several innovative breakthroughs. It has become a disruptive solution for developing distributed and decentralised applications in many domains beyond cryptocurrencies. One example of these domains is the contemporary, riskily interdependent ICT-based critical infrastructure. This multi-domain literature review explores the literature of blockchain and critical infrastructure domains, attempting to match the features and benefits provided by the former to the challenges and requirements encountered in the latter. The review concludes that despite the known limitations of blockchain technology regarding scalability, interoperability, implementation complexity, and real-time requirements, it represents a promising enabling technology for addressing several challenges and requirements in the design and development of contemporary integrated and highly interdependent CIs. Future research directions are also highlighted.
  Keywords: critical infrastructures; critical infrastructures requirements and challenges; interdependency; risk assessment; complexity; blockchain technology; consortium blockchains; blockchain applications.
  DOI: 10.1504/IJCIS.2025.10065683
   
  
• Criticality Assessment Model for Water Distribution Networks  
  by Ahmed Moursi, Samer El-Zahab, Tarek Zayed 
  Abstract: The Canadian Infrastructure Report Card of 2016 rates the water system as good, but with 29% of pipelines in fair to poor condition, demanding urgent repairs costing $60 billion. Municipalities struggle to prioritise asset rehabilitation due to financial constraints. This study aims to develop a criticality model for water pipeline prediction, integrating expert insights. Three dimensions economic, environmental/operational, and social are assessed using the paprika technique. Sensitivity analysis identifies key factors influencing criticality. The model combines criticality and performance indexes to form a priority index, aiding municipalities in strategic capital planning. By pinpointing critical areas requiring immediate attention, this model enhances infrastructure management decision making.
  Keywords: assent management; risk management; paprika; criticality index.
  DOI: 10.1504/IJCIS.2026.10065717
   
  
• Application of Machine Learning And Neural Network Technology in Art Design  
  by Yu Wang  
  Abstract: In the digital art domain, the integration of intelligent design and analytical capabilities necessitates effective methods for automatically discerning and evaluating artworks. This research suggests a machine learning-based neural network method to the challenge. To investigate emotional resonance in numerous art forms across disciplines, a deep recurrent neural network is built. A new cross-domain edge cloud model uses cloud computing advances. This architecture offloads streaming media services to edge network sub-clouds, revolutionary storage and compute. Edge networks make cross-media data collecting easy, enabling analysis. Deep neural networks analyse visual and linguistic input to classify viewer emotions via multimodal classification. Experimental results show that the model can accurately identify unlabelled cross-media data. The technique also mitigates the possibility of erroneous emotion representation in AI systems by addressing artificial emotion simulation. The MMBT model outperformed others with 66.33% accuracy and 62.24% F1 value. This research provides a complete framework for discovering emotional nuances in cross-media art and intelligent art design and analysis.
  Keywords: convolutional neural network; CNN; cross-media; emotion analysis; art design; machine-learning; neural network technology; streaming media services; artificial neural networks; ANNs.
  DOI: 10.1504/IJCIS.2025.10065858
   
  
• A Conceptual Framework for Adoption of Digitalization in Construction Organizations  
  by Vandana Bhavsar, Pradeepta Samanta, Sagar Malsane, DEEPAK MD 
  Abstract: Organisations worldwide are grappling with substantial difficulties following the current technological developments, environment related issues, and socioeconomic disruptions. Consequently, organisations have embraced Industry 4.0 to overcome these challenges and devise digital integration. Numerous frameworks, models, and tools have been developed to gauge the digital adoption or digital readiness of various sectors/organisations. However, though the adoption rates of various digital tools in construction firms have increased significantly since 2020, there is a paucity of systematic frameworks with construction-specific digitalisation dimensions and indicators required for successful technology adoption and readiness in the construction organisation. The study therefore proposes a holistic framework comprising dimensions and indicators specific to digitalisation readiness for construction organisations. The developed framework of the study will help construction organisations develop a concrete strategic graduation that sets up the roadmap for digital transformation and also ensures the identification of appropriate digital measures and investments.
  Keywords: Industry 4.0; Construction 4.0; construction sector; digitalisation; digital transformation; digital adoption; digital readiness; maturity models; digitalisation adoption frameworks.
  DOI: 10.1504/IJCIS.2025.10065932
   
  
• Impact of Market Incentive-Based Environmental Regulations on Corporate Financial Performance in a Circular Economy  
  by Linhui Yang 
  Abstract: In the Chinese capital market, environmental regulations based on market incentives will have a significant impact on the economic activities of enterprises. To understand the impact of market incentive based environmental regulations on corporate financial performance, this study proposes a financial performance calculation model based on an improved long short-term memory network to evaluate corporate financial performance. On the basis of making assumptions, impact analysis is conducted through regression analysis and other methods. The experimental results indicate that the difference between output and expected financial performance is only 0.023. Technological innovation (TI) was significantly negatively correlated with market-based environmental regulation (p < 0.05), and significantly positively correlated with corporate financial performance (p < 0.01). The research method can effectively analyse the impact of environmental regulations on corporate financial performance based on market incentives. Most existing research analyses national or regional data, with less emphasis on the perspective of individual enterprises.
  Keywords: circular economy; market incentives; environmental regulation; financial performance; FP; LSTM.
  DOI: 10.1504/IJCIS.2025.10066076
   
  
• Optimising Inventory Management in Commercial Construction through IoT for Enhanced Cost Efficiency  
  by Deepak Tulsiram Patil, Amiya Bhaumik, Ashutosh Kolte 
  Abstract: The internet of things (IoT) may be integrated into stock management in the industrial creation business to improve task delivery timeliness and cost effectiveness. The study analyses how IoT technology may reduce manual errors, automate inventory monitoring, and give real-time data to improve decision-making. A radical literature review reveals construction inventory management issues include theft, material waste, and inefficient supply networks. We combine qualitative and quantitative studies to focus on managed production IoT device deployment. This observation analyses stock stages before and after IoT generation implementation using 458 samples, showing that inventory management performance and stability have improved. The results demonstrate how the internet of things may transform operational optimisation. Material waste reduction, on-web page productivity, and inventory accuracy improved significantly. We offer an internet of things (IoT)-based inventory management architecture with analytical tables and graphs illustrating performance advantages and fee savings. The speech discusses multinational IoT integration efforts, including operational issues and acceptance challenges. The final paragraph shows how the internet of things can change building stock management. This article also covers future research goals and limits, focusing on IoT technology conversion and production management software growth.
  Keywords: internet of things; IoT; inventory management; commercial construction; cost efficiency; real-time tracking; supply chain; automation; and productivity.
  DOI: 10.1504/IJCIS.2026.10066078
   
  
• Verification and Analysis of Solution Based on Mobile PKI for Signing and User Identity  
  by Kapil Kant Kamal, Sunil Gupta, Padmaja Joshi, Monit Kapoor 
  Abstract: As mobile devices become more popular, there is an increasing need for user identification and digital identity verification for online and offline transactions. In certain countries, mobile phones are widely available at affordable prices, offering identity solutions based on either SIM (Subscriber Identity Module) or Hardware Security Module (HSM) that operate on Public Key Infrastructure (PKI). This paper proposes a novel solution for a mobile identity framework based on Elliptic Curve Cryptography (ECC) encompassing user authentication and signature. Our proposed approach is hardware-agnostic and does not rely on a SIM card. Additionally, it is cost-efficient without any third-party dependency. We perform informal security analysis to prove that our framework is secure from various attacks. Furthermore, we conduct formal security evaluations utilizing the Scyther Security Protocols tool and Burrows-Abadi-Needham (BAN) logic. We also evaluate the performance of our system and compare it with other protocols.
  Keywords: Encryption; Signing; Authentication; Cryptographic; ECC; Mobile Services.
  DOI: 10.1504/IJCIS.2026.10066286
   
  
• Safety Plan Modeling for Resource-Constrained Construction Projects to Optimize Cost, Time and Safety Risk  
  by Ali Akbar Shafikhani, Mostafa Pouyakian, Amir Abbas Najafi, Behrouz Afshar-Nadjafi, Amir Kavousi 
  Abstract: The intense competition to achieve project goals has increased due to limited resources in construction projects. No studies have compared the trade-off between time, cost, and safety while considering resource and equipment constraints. Equipment constraints may affect project scheduling and increase safety risks. Therefore, a project scheduling model that considers equipment constraints, time, cost, and safety risks is needed. This study aims to optimise cost, time, and safety risk by modelling safety plans in project scheduling problems with resource constraints. By solving this model, feasible solutions for time, cost, and safety risk trade-offs are provided. In addition, the model could also evaluate the risks of project activity, the risk of equipment and overtime, and minimise the overall safety risk of the project.
  Keywords: safety risks; equipment planning; project-scheduling; construction; RCPSP; NSGA-II.
  DOI: 10.1504/IJCIS.2026.10066349
   
  
• A Comprehensive Survey on the Role of Explanation in Artificial Intelligence: a Case Study on Prediction of Gross Calorific Value of Coal  
  by Sindhu Menon 
  Abstract: The study presented here could act as a basis for researchers interested in learning about essential components of the nascent and quickly developing field of research on XAI (explainable artificial intelligence). (SHAP-Xgboost) is applied to show the working principle of XAI. This is archived by analysing the coal content in the coal reserves. SHapley Additive explanations will be proposed as a revolutionary XAI for this aim. SHAP allows users to understand the extent of relationships between each unique input data along with its corresponding output, as well as rank input variables in order of efficacy. SHAP was combined with extreme gradient boosting (xgboost) (SHAP-Xgboost) which is one of the latest technological developments. SHAPXgboost was able to model GCV accurately (R2 = 0.99) using proximate and ultimate analysis(chemical content in coal) from the coal samples These significant discoveries pave the way for the development of high-interpretability algorithms to learn coal properties and point out crucial variables.
  Keywords: explainable artificial intelligence; XAI; artificial intelligence; gross calorific value; explainability.
  DOI: 10.1504/IJCIS.2026.10066359
   
  
• Vertical Integration for Stakeholder Management of Hydroelectric Power Megaproject Construction in The Lao People's Democratic Republic (Lao PDR)  
  by Sombat Trivisvavet, Winai Wongsurawat 
  Abstract: Lao national policy of becoming the “Battery of Asia” has driven the construction of numerous Hydroelectric Power Projects (HPPs). The purpose of this research is to analyze the critical roles of internal and external stakeholders. Data is gathered through in-depth interviews with internal and external stakeholders of two mega-HPPs. We found that deep collaboration and trust among internal stakeholders are critical for success. Such collaboration and trust can be achieved by not only solid communications and strictly following the contract agreement, but also through strategic choices that can limit excessive transaction costs and foster credible commitments of future benefit sharing among internal stakeholders. The critical requirements for a successful management of external stakeholders are the mitigation of environmental impacts. These factors have a performance-enhancing effect upon mega-HPP construction. The results speak to the following critical infrastructure problem domains: long term investment, stakeholder engagement, and environmental management in critical infrastructure construction.
  Keywords: Internal stakeholders; External stakeholders; Megaproject; Stakeholder Management; Hydroelectric Power Project.
  DOI: 10.1504/IJCIS.2026.10066393
   
  
• Understanding the Complexities of Tunnel-Pile-Soil Interaction: A Comprehensive Investigation of Vibratory Effects and Seismic Dynamics  
  by Musabur Rehman, Syed Mohd Abbas, Altaf Usmani 
  Abstract: Excavating tunnels can significantly affect pre-existing structures like raft or pile foundations and the surrounding substructures. Additionally, tunnelling combined with seismic waves can lead to severe consequences. This study aims to thoroughly examine the interactions among tunnels, piles, and adjacent soil (TPS), focusing on vibrational effects and mathematical modelling. A framework is proposed to predict pile behaviour before tunnelling and validate results from computational simulations. Using PLAXIS3D finite element software, the study investigates the complexities of tunnel-pile-soil interaction (TPSI) during seismic events, particularly evaluating the impact of tunnel excavation and seismic activity on 2
  Keywords: tunnelling; seismic waves; structural interaction; pile foundations; PLAXIS3D software; deformation patterns.
  DOI: 10.1504/IJCIS.2026.10066545
   
  
• IoT Based-Malware-Detection using Artificial Intelligence in the Cyber Security Field  
  by Keerthi Vardhan K. L. S. D. T, V. S. R. K. Sarma 
  Abstract: The field of study for this work centers on enhancing security within the expanding domain of the Internet of Things (IoT), where the need for reliable detection of malicious activities is critical. As IoT integrates a wide array of applications and hardware, the inherent online nature of these technologies makes vital infrastructure susceptible to cyberattacks. Despite the involvement of a significant community in critical applications like CPSs, traditional computational methodologies in anomaly-based programs often prove insufficient. This study aims to identify and classify issues at both the network and host levels using advanced ML and DL models, which offer promising solutions. Specifically, the research employs the IoT-23 dataset to conduct a comprehensive analysis using algorithms such as DT, SVM, and ECLDNN. By evaluating the precision and energy efficiency of these classifiers, the study seeks to determine the most accurate and time-efficient solution for defect detection in IoT systems. This work advances the field by proposing and validating sophisticated ML and DL techniques that significantly improve the detection and classification of cyber threats, thereby enhancing the security of IoT infrastructure.
  Keywords: Decision Trees ; Support Vector Machines ; Enhanced Convolutional Long Short-Term Memory Deep Neural Network ,Intrusion Detection; IoT-23; Machine Learning; Malware; Deep Learning.
  DOI: 10.1504/IJCIS.2026.10066755
   
  
• Assessing the Applicability of Game Theory and Generative Adversarial Networks (GANs) in Forensics Threat Detection  
  by Jiji Mol D.R.  
  Abstract: The implementation of forensic techniques for password detection has garnered substantial scientific attention recently. Prior studies have explored the detection of forensic attacks on passwords but did not optimise interactions between attackers and defenders. They also failed to accurately detect fake passwords. Addressing these issues, this approach uses appropriate datasets and a novel generative adversarial network (GAN) technique for detecting digital forensic attacks. Integrating game theory and GANs for forensic threat detection enhances robustness and adaptability, enabling proactive defence plans and dynamic threat modelling. This fusion improves the interaction between attackers and defenders and increases the accuracy of false password detection. Utilising the RockYou dataset, the research trains a GAN model to detect forensic attacks. The generator produces new training instances, while the discriminator classifies them. Game theory significantly optimises the generated samples through accurate decision-making, enhancing interaction comfort between attackers and defenders. The proposed framework achieves a prediction accuracy of 97.89%, surpassing existing methods. Consistently enhancing GAN structures could further improve the creation of realistic password patterns, benefiting applications like system security and password authentication.
  Keywords: Digital Forensic; Game Theory; Generative Adversarial Network; Password Detection; Multimodal forensics; Decision-Making Skills; Detect Digital Forensic Attack.
  DOI: 10.1504/IJCIS.2026.10066758
   
  
• Federated Learning for the Detection of Malware in IoT Devices  
  by K. Hazeena, Gnaneswari G, Lalitha Guna, S.Silvia Priscila 
  Abstract: The increasing expansion of IoT devices in smart homes has created new security issues, including malware detection. Traditional malware detection approaches often fail on IoT devices due to resource constraints and heterogeneity. Novel malware detection in smart home IoT devices is proposed using deep federated learning. Methods: We employ deep learning models while protecting data privacy by training them jointly across numerous devices. Our solution uses smart homes' dispersed nature to provide a shared malware detection model without compromising device privacy. The study quantifies encrypted communication, differential privacy, and local aggregation success rates across 10 IoT devices, averaging 95%. The proposed solution is compared to encrypted communication, privacy, and local aggregations. Novelty: The proposed method may improve smart home security against changing malware threats. We demonstrate the architecture and methods of our deep federated learning-based smart home malware detection system. We test our technique on the dataset and show that it can detect new malware. Our revolutionary malware detection solution for smart home IoT devices improves security and privacy.
  Keywords: Internet of Things; smart homes; malware detection; deep learning; federated learning; privacy-preserving; resource-constrained devices; malware detection; cybersecurity; machine learning algorithms,.
  DOI: 10.1504/IJCIS.2026.10067414
   
  
• Enhancing Risk Assessment of Bridge Construction: applying an Integrated Failure Mode and Effect Analysis and Analytic Hierarchy Process Methods  
  by Souad Dakel, Munive Eduardo, Amir Khan 
  Abstract: Bridge construction projects demand a rigorous risk assessment process to ensure safety and structural integrity. Traditional risk assessment methods often operate in isolation, failing to manage risks objectively and effectively. This paper addresses this gap by integrating the Analytic Hierarchy Process (AHP) and Failure Mode and Effect Analysis (FMEA) to assess risks in bridge construction projects. The proposed methodology identifies potential hazards and uses FMEA to calculate the Risk Priority Number (RPN) for identified risks. These risks are further assessed through AHP, combining quantitative and qualitative evaluations. This approach leads to a more objective and effective risk evaluation, resulting in more reliable ranking and prioritization of risks. By applying this novel approach, decision-making and risk mitigation strategies can be enhanced, improving the overall safety of bridge construction practices. This approach is validated with its application to the Jofra footbridge project in Libya.
  Keywords: Failure Mode and Effect Analysis; Analytic Hierarchy Process; Risk Priority Number; Construction Projects; Risk Management; Risk Assessment; Jofra footbridge.
  DOI: 10.1504/IJCIS.2026.10067445
   
  
• The Impact of Cybersecurity on the Financial Institutions-the Case of the Jordanian Financial Institutions  
  by Asem Tahtamouni 
  Abstract: As the contemporary world's technological advancements continue to accelerate, protecting the protection of personal and sensitive data against cybersecurity breaches has become more essential. The purpose of this study is to investigate the present state of cyber security risk management systems in Jordanian financial institutions. In contrast to other existing frameworks, the cybersecurity framework in Jordanian financial institutions is extremely inadequate, according to the results of this study. Institutions often lack a database of previous breaches, are unable to properly assess risk probabilities, and suffer a slew of additional problems that jeopardise their systems' security. As a consequence, based on the category factors examined, the system received a level 2 out of 4, indicating a need for development in the near future. This study provides scientific results on the impact of the quality of the cybersecurity framework in Jordanian financial institutions which need to be developed in the near future.
  Keywords: Cyber Security; Risk Management; Financial Institutions; Jordan.
  DOI: 10.1504/IJCIS.2026.10067584
   
  
• Road Traffic Crashes in Thailand 2017  
  by Alessandro Stasi, Alfonso Pellegrino 
  Abstract: Thailand continues to have one of the highest road traffic fatality rates globally, creating significant public health and economic challenges. This study examines road traffic crash trends in Thailand from 2017 to 2023, with a focus on the high involvement of motorcycles in fatal crashes, the demographics of victims, and the timing of incidents. The analysis highlights critical areas for intervention, including stricter enforcement of traffic laws, targeted measures for young motorcycle riders, and improvements in road infrastructure. Additionally, the study identifies shortcomings in current data collection methods that limit the effectiveness of safety policies. The findings underscore the need for a comprehensive approach that integrates technological advancements, legislative reforms, and public education to reduce crashes and enhance road safety in Thailand.
  Keywords: Keywords: Road traffic crashes; Thailand; Motorcycle safety; Traffic law enforcement; Helmet regulations; Road safety policies.
  DOI: 10.1504/IJCIS.2026.10067902