International Journal of Critical Infrastructures (74 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
   
  
• 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
   
  
• Cyber Security Risks Management for Critical Information Infrastructure Assets under Nontraditional Security Lens in Vietnam  
  by Anh Tuan Hoang, Anh Tuan Luong, Huy Anh Nguyen 
  Abstract: With the global spread of Industry Revolution 4.0, it is becoming more urgent for engineering asset managers around the world to embrace and adjust to this new paradigm. Given the increasing popularity of cyber physical systems, it is crucial for asset managers, particularly those responsible for critical information infrastructures, to adopt a new and more comprehensive framework for managing threats and risks. This is necessary to guarantee the safety, resilience, and value of the assets they oversee. Using mixed methodology, the paper also examines the perceptions of cyber threats and dangers among several prominent firms and organisations in Vietnam, as well as their views on the significance of cyber security management and suggests an updated framework for identifying and prioritising critical information infrastructure security assets for firms and organisations, with a focus on ensuring non-traditional security protection of such assets.
  Keywords: Cyber security; risk management; critical information infrastructure assets; nontraditional security; Vietnam.
  DOI: 10.1504/IJCIS.2026.10068076
   
  
• Extrapolation Against Categorisation for Neural Network-Based System Equalisation in a Narrowband Electric Grid  
  by Eronimus Jeslin Renjith, J. Vimala Roselin, R. Ramyadevi, R. Prema, S.Silvia Priscila 
  Abstract: This article analyses the main issues and design limits for neural networks and disruptive channel equalisers in synchronous optical networks. Our study aids field researchers and engineers. We first discuss the criteria used to evaluate the equalisers and how they relate to the kernel functions used to train neural equalisers. We analyse and quantify the relationships between equaliser efficacy and network transmission scheme correctness. The key optimal design alternatives for nonlinear activation functions are then discussed. Field-programmable gate arrays (FPGAs) may be used to build neural network-based equalisers in coordinated optical transmission systems. We found considerable performance increases and reduced computing cost, enabling real-time adaptive equalisation in high-speed optical networks. The main findings include a tracking system, a perceptron implementation research, and a hardware complication report. Next, we examine how capacity restrictions in digital to analogue converters affect performance in modified neural network algorithms spanning training and testing durations, as well as semi-bit timings utilised to generate empirical data and numerical values. Finally, we address overfitting limitations, categorising rather than extrapolating, and batch size peculiarities. Analytical expressions for equaliser complexity employing digital data analysis finish our paper.
  Keywords: recurring neuronal systems; neural nets technology; bidirectional equaliser; restriction classification; logical identification; photonic beams; high-speed optical networks.
  DOI: 10.1504/IJCIS.2026.10068080
   
  
• Novel Memristor Emulator based on Fin Field-Effect Transistor and its Radio Frequency Filter Applications  
  by Sivakani R, Swakantik Mishra, Thejo Lakshmi Gudipalli, Saikat Deb, S. Suman Rajest 
  Abstract: The paper presents a novel FinFET-based memristor emulator design with an investigation of its potential applications for RF-filter designs. Memristors, being a basic building block in memory and resistance, hold a lot of potential in neuromorphic computing, non-volatile memory, and the like. However, scalability issues and non-ideal behaviours limit their practical applicability. It presents, in this study, a FinFET-based memristor emulator that attempts to tackle these challenges for better scalability, reliability, and performance. An efficient memristor emulator will be a strong candidate because FinFET possesses superior electrical characteristics and enhanced control over short-channel effects. The simulations and experiments test the proposed emulator as stable enough to show performance in a variety of conditions, especially in RF filter applications, where the invented circuit emulator outperforms the usual versions of designs in efficiency and functionality. Other possible analogue circuits, with this development will permit even more reliable and sophisticated electronic devices. In a general set of findings, this study portrays how the possible impact of FinFET-based memristor emulators could drive future concepts for the RF filter design and thereby lead to technological advances in electronic components.
  Keywords: Memristor Emulator; FinFET Transistor; RF Filters; Neuromorphic Computing; Non-Volatile Memory; Analog Circuitry; Scalability; Resistance Properties.
  DOI: 10.1504/IJCIS.2026.10068086
   
  
• A Smart Rural Tourism Resources Recommendation based on Audience Preference  
  by Jin Lu 
  Abstract: How to provide users with more accurate smart rural tourism recommendation services has become a hot research topic at present. To address the short-term audience preference issue caused by data scarcity, firstly, graph convolutional networks (GCN) are applied to recommend smart rural tourism resources. For long-term tourism audiences with sufficient data, use long short-term memory (LSTM) to construct a recommendation model based on users long-term dynamic preferences. The results showed that in the case of data scarcity, the recall and accuracy of the GCN recommendation method increased by 17.9% and 11.8%, respectively. In long-term rural tourism applications, the hits ratio (HR)@10 and HR@20 of the dynamic preference recommendation model were as high as 42% and 50%, respectively. The results indicate that the proposed method provides more reliable technical support for intelligent rural tourism recommendation and can more effectively discover audience preferences.
  Keywords: audience preference; rural tourism; resource recommendation; long short-term memory; LSTM; graph convolutional network; GCN.
  DOI: 10.1504/IJCIS.2026.10068089
   
  
• Enhanced Cipher - RSA (E-RSA) Source Code Obfuscation: Scheme and Implementation  
  by Pallavi Ahire, Jibi Abraham, Anuradha Yenkikar, Jayshree Mahajan 
  Abstract: To protect source code security, most sophisticated programming languages save source codes in byte code representations on application servers. Some cloud-based source code repositories and storage servers require these source codes in their original form. The attacker will feast on the source code; thus, obfuscation is utilised. Cloud source code obfuscation is extensively used to protect against insider attacks. Many obfuscation tactics are needed to hide the written logic and make source code difficult to understand. There are many ways to hide source code, but none of them uses the cipher algorithm. Therefore, this work introduces a novel data obfuscation technique called Enhanced Rivest, Shamir, Adleman (E-RSA) with the RSA technique, which uses a set of keys known as public and private keys, and it is one of the most widely used encryption methods. That may open up new avenues for obfuscation by extending the existing RSA and applying it to the + arithmetic operator. We are unaware of any adoption of this method despite its benefits. The suggested method examines performance to achieve symmetrical results between original and obfuscated code and complexity to minimise execution time.
  Keywords: cloud computing; public key; private key; software security; source code obfuscation; enhanced Rivest-Shamir-Adleman; E-RSA.
  DOI: 10.1504/IJCIS.2026.10068330
   
  
• Harnessing the Potential of Edge Computing for Next-Generation IoT Solutions  
  by Sukhwinder Sharma, Selvamani K, Kanimozhi S, M.S. Bennet Praba, Rayappan Lotus 
  Abstract: Edge computing is one of the most innovative approaches within the internet of things (IoT) in that it shifts network-enabled computation close to data sources without depending on centralised cloud processing in order to enable real-time applications. Such a paradigm provides ultra-low latency, safe data processing, and efficient real-time analytics; hence, it will boost the power of cloud infrastructure and extend the capabilities of IoT across healthcare, smart cities, and industrial automation. This paper discusses edge computing on the architecture, algorithms, and applications across several real use cases, including intelligent transportation systems, and shows how it effectively solves IoT challenges. Edge computing enables higher efficiency and scalability in the system and a better user experience than running standalone IoT platforms. The following case studies from healthcare, smart cities and industrial automation show edge computings ability to deliver real-time performance metrics, system logs, and user feedback. Architectural study and performance benchmarking using statistical analysers, simulators, and monitoring tools prove these benefits beyond empirical data. This article examines future edge computing integration potential and challenges in IoT ecosystems, stressing sustainability and resilience for vigorous technological frontier expansion.
  Keywords: Edge Computing; Internet of Things (IoT); Real-time Analytics; Data Security; Latency Reduction; Smart Cities; Decentralised Processing; Cloud Infrastructure.
  DOI: 10.1504/IJCIS.2026.10068651
   
  
• Combining the DEMATEL and Delphi Approaches for Occupational Safety and Health Management of Workers in the Construction Industry  
  by Nguyen Ngoc Long, Tran D.N. Khoa, Le T.K. Hoa 
  Abstract: Occupational accidents pose a severe risk to the health and lives of construction workers. The management of occupational safety and health (OSH) is crucial in reducing hazards and safeguarding employees' health. The current study used the Delphi and DEMATEL approaches to identify and assess worker safety risks and provide effective responses. Previous research have frequently ignored the causal linkages between risks that are crucial for managers to comprehend in order to make informed risk management decisions. The present study identifies 19 OSH risks experienced by Vietnamese construction industry workers and establishes cause-and-effect relationships between these risks using a mix of the Delphi and DEMATEL techniques. According to research findings, the elements that have the most effects on OSHs for workers in Vietnam's construction sector include organisational commitment, safety environment, regulations and policies, safety culture and behaviour, managerial capacity, and training. The study offers academics a fresh perspective and assists policymakers and managers in efficiently deciding on OSH management strategies for workers in the construction sector.
  Keywords: Delphi-Dematel; OSH; Occupational safety and health; Construction risks; Risk response.
  DOI: 10.1504/IJCIS.2026.10068840
   
  
• Cybersecurity of Industrial Control Systems and the Benefits of AI Application  
  by Lyudmila Sukhostat 
  Abstract: The convergence of operational technology and information technology in modern industrial control systems (ICS) leads to a wide range of external and internal cyberthreats. It makes the ICS security problem quite acute in different areas. This paper describes several architectural solutions for ICS. Current ICS security issues, including various vulnerabilities and threats, are analysed. State-of-the-art methods based on AI allow for monitoring the ICS in case of abnormal situations, reporting possible failures, and the need for preventive maintenance, as well as improving such systems' cyber resilience and security. The benefits of using Big data technologies are explored. The shortcomings and viability of Big data for various systems are analysed. This paper identifies several key issues such as risk analysis, intrusion detection, predictive analytics, cyber defense and resilience, data leakage protection, and incident response and their solutions. Directions for future research are given with a focus on resilience, performance, and scalability.
  Keywords: industrial control system; IT/OT; cybersecurity threats; AI; cyberresilience.
  DOI: 10.1504/IJCIS.2026.10068927
   
  
• Applying Artificial Rabbit Optimisation-LSSVR Analysis for HPC's Compressive Strength Estimation  
  by Jianjian Wang, Zhigang Liu, Guanglei Zhao 
  Abstract: High-performance concrete (HPC) functions stronger because it contains more components than ordinary concrete. The compressive strength (CS) of HPC prepared with fly ash (FA) and blast furnace slag (BFS) was assessed using several artificially-based analytics. In this study, the artificial rabbit optimisation (ARO) technique, abbreviated as AROR and AROLS for the radial basis function (RBF) neural network and the least square support vector regression (LSSVR) analysis, accordingly, was employed to identify the optimal values for the parameters that could be adjusted to enhance performance. The CS was used as the predicting objective, and 1,030 experiments and eight input parameters were used to construct the suggested techniques. After that, the outcomes of the enhanced model were compared to those documented in the corpus of current scientific literature. The calculations suggest that combining AROLS with AROR research might be advantageous. The AROLS demonstrated much higher R2 2 Train (R = 0.9853 and 2 Test R = 0.9912) and lower error metrics when compared to the AROR and previous papers. Finally, the offered technique for computing the CS of HPC increased by BFS and FA may be created using the recommended LSSVR analysis enhanced by ARO.
  Keywords: High-performance concrete; Compressive strength; Artificial neural network; Least square support vector regression; Artificial rabbit optimization.
  DOI: 10.1504/IJCIS.2026.10068986
   
  
• An Overview of Blockchain Technology in Finance: A Jordanian Exploratory Study  
  by Suleiman Mohammad, Asem Tahtamouni, Yaser Jalghoum 
  Abstract: The transformational potential of blockchain technology within Jordan's banking system is examined in this research study. It looks at the benefits, challenges, and opportunities associated with integrating blockchain technology into several banking operations, such as payments, trade finance, and customer verification. Using a mixed-methods approach, the study combines a comprehensive examination of the literature with in-depth interviews with stakeholders and industry professionals. According to the findings, blockchain technology has the potential to significantly improve security, efficiency, and transparency in Jordan's banking industry. For implementation to be successful, nevertheless, issues including public awareness, technology infrastructure, and legal frameworks must be resolved. The study concludes with suggestions for how regulators, banks, and other interested parties may encourage innovation in the Jordanian banking industry and make it easier for blockchain technology to be used.
  Keywords: Financial technology; innovation; disruption; block chain; banking sector; Jordan.
  DOI: 10.1504/IJCIS.2026.10068995
   
  
• Research on the Visualisation Method of Building Surface Design based on 3D Modelling Technology  
  by Mei Qu 
  Abstract: In the design of a building surface, it is necessary to accurately express the characteristics of building materials and textures. To optimise the visualisation effect of building surface design and ensure the consistency between the building design and the actual effect after completion, the visualisation method of building surface design based on 3D modelling technology is studied. The surface subdivision modelling technology is utilised to build the 3D model of the building surface, and texture mapping and 3D rendering are used to boost the visualisation effect of the 3D model. Through the construction of the BIM+VR building surface visualisation design system, the visual display and interactive design of the building surface 3D model can be realised. The experiment reveals that this technique can greatly increase the precision and quality of 3D modelling of building surfaces; and the collaborative design of multi-disciplines and multi-angles can be realised.
  Keywords: 3D modeling; building surface design; visualization; surface subdivision modeling; building information model; virtual reality technology.
  DOI: 10.1504/IJCIS.2026.10069002
   
  
• A Comprehensive Naive Bayes Model with Meta-Heuristic Optimisation for Accurate Bearing Capacity Prediction  
  by Zibin Li 
  Abstract: Accurately predicting the bearing capacity of piles (Pu) plays a critical role in the design of pile foundations. Numerous soil characteristics and various parameters related to the soil and the foundation influence the Pu. In this extensive research endeavour, an innovative model has been developed for precise Pu estimation by leveraging the Naive Bayes (NB) algorithm. This approach is grounded in a comprehensive dataset comprising 200 case histories of static load tests conducted on driven piles. These case histories were meticulously analysed to create and validate these models. To ensure these predictions highest accuracy and reliability, the study seamlessly incorporated 2 powerful meta-heuristic algorithms: the Victoria Amazonica optimisation (VAO) and the Runge Kutta optimisation (RUK). Using Pu data gathered from a variety of soil types, these algorithms were crucial in validating these models. Furthermore, 3 separate models have been generated containing NBVA, NBRK, and a standalone NB model. With an astounding R2 score of 0.995 and an incredibly low RMSE of 35.217, the NBVA model has surprisingly emerged victorious. So, for precisely forecasting soil Pu, the NBVA model represents a novel and highly successful approach.
  Keywords: Pile Bearing Capacity; Naïve Bayes Regression; Victoria Amazonica Optimisation; Runge Kutta optimization.
  DOI: 10.1504/IJCIS.2027.10069165
   
  
• Strengthening Cyber Resilience with Groundbreaking Methods for Privacy and Security in the Digital Landscape  
  by Sukhwinder Sharma, Puneet Mittal, R. Deeptha, Pallavi Ahire, G. Meena Devi 
  Abstract: This paper explores the critical balance between cybersecurity measures and privacy protection in the evolving digital landscape. Through a mixed-method approach, the research collected both qualitative and quantitative data from a comprehensive literature review and real-world cybersecurity incidents. The data was visualized using tools such as Python and Matplotlib, enabling a clear depiction of trends in cyber threats, breaches, and public sentiment. Detailed charts and tables illustrate the effectiveness of cybersecurity practices and the growing concern over privacy. The findings reveal that while advanced cybersecurity technologies significantly reduce risks, they often come at the expense of personal privacy. This raises key ethical and practical challenges that require careful consideration by businesses and policymakers. The analysis highlighted gaps in current cybersecurity strategies, underscoring the need for improved privacy-preserving technologies and more robust regulatory frameworks. The study proposes greater public-private partnership to address these issues. The paper acknowledges its shortcomings and suggests future research to stay up with cybersecurity and privacys rapid changes. The report underlines the need to balance security and privacy to secure persons and businesses in the digital age.
  Keywords: Data Breaches; Digital World; Cybersecurity; Personal Information; Privacy Rights; Security Technologies; Surveillance.
  DOI: 10.1504/IJCIS.2027.10069554
   
  
• Real-Time Wheelchair Control Technique using Eye-ball Tracking and Machine Learning Algorithm  
  by Sudipta Chatterjee 
  Abstract: The normal lifestyle of older or quadriplegic people hinders their physical disability, which is the leading cause of their mental anxiety every moment; these people are deprived of standards in their life. This paper introduces an automatic eye monitoring interface (AEMI) wheelchair using a real-time machine learning framework to bring all these people back to their normal lives. This wheelchair is operated manually as well as users eyeball movement. To design this AEMI wheelchair, the system uses a Raspberry pi 4, a Pi camera, an ultrasonic sensor, and a BLDC hub motor. A novel algorithm is applied to develop the deep learning framework. For the image processing technique, Viola-Jones algorithm is used to track eyeball position. There is an emergency switch arrangement for security, which makes the system stand up in any circumstances. Experimentally the system has performed very well in the laboratory, and the user has enjoyed driving.
  Keywords: Wheelchair; Machine learning algorithm; Raspberry Pi; BLDC hub motor; Far-end control.
  DOI: 10.1504/IJCIS.2026.10069557
   
  
• Prediction of The Recycled Aggregate Concrete Compressive Strength Using Novel Machine Learning Model and Metaheuristic Algorithms  
  by Zhiyong Wang, Xiuyuan Li 
  Abstract: Recycling construction materials is pivotal in safeguarding precious natural resources in the swiftly industrializing world. Compared to natural aggregates, recycled aggregates show notable differences in composition and characteristics. As such, it is difficult to accurately predict how RAC would function and how to combine the ingredients properly. Concrete specimens utilising recycled aggregates featuring varying water-cement ratios and degrees of replacement for RCA were meticulously crafted. Evaluating concrete's strength at specific ages, such as 7 days, 28 days, and beyond, is essential for construction and quality control. The 28-day compressive strength is a well-recognised benchmark in concrete assessment, reflecting its strength for most design and construction purposes. The present study used GPR to forecast the compression strength of concrete, including recycled material, after 28 days. Regression challenges are handled by GPR, a machine learning and statistical modelling approach. The model is optimised by ARO and CHIO. Given that GPR's RMSE is 2.529, which is almost twice as high as GPARs, the findings show that the GPAR prediction model performs better than GPR regarding predictive accuracy. In addition, an ideal R2 value of 0.995 was reached by the GPAR models performance, especially during the training phase.
  Keywords: Recycled Aggregate Concrete (RAC); Gaussian Process Regression (GPR); Artificial Rabbits Optimization (ARO); Coronavirus Herd Immunity Optimization (CHIO); Recycled Coarse Aggregates (RCA).
  DOI: 10.1504/IJCIS.2027.10069677
   
  
• Weighting Asset Integrity Indicators Related to HSE to Determine Critical Equipment Using ANP Method  
  by Hadi Ahmadi Vafa, Saeid Mousavi, Sorena Heidarpour Tabrizi, Rasoul Ahmadpour-geshlagi, Seyed Shamseddin Alizadeh 
  Abstract: The objective of this study was to identify the indicators of asset integrity and to assign a weight to each indicator utilising the Analytic Network Process (ANP) methodology and the Super Decision software. The evaluation of the indicators was conducted by a panel of 15 specialists, and subsequently, the paired comparison questionnaire was fulfilled. The determination of the weights assigned to the indicators was accomplished through the utilisation of the ANP method and the Super decision software. The outcomes revealed that the "Safety," indicator holds the utmost significance, accounting for 26% of the overall weight. Following this, the "Environmental", "Hygiene", "Maintenance/Inspection", "Technical Specifications" and "Economic/Shopping" indicators were respectively positioned from second to sixth place. These findings offer valuable insights into assessing the criticality of equipment and assigning an appropriate rating to it.
  Keywords: Asset Integrity; HSE; ANP Method; Safety; Critical Equipment.
  DOI: 10.1504/IJCIS.2027.10069724
   
  
• Object and lane detection using lightweight deep convolution neural networks for advanced driver assistance application  
  by Divyalakshmi S. G, Adhithya S, Kathirvelan Jayaraman 
  Abstract: Road hazards such as stray animals, bumpy edges, pedestrians, and weak-lane markings pose significant risks, leading to frequent accidents. While collision avoidance technologies exist, their high-cost limits widespread adoption. This study proposes a low-cost vision and sensor-based approach to enhance road safety by detecting objects and lane boundaries for advanced driver assistance system (ADAS) applications. A deep convolutional neural network (DCNN) also known as you only look once (YOLO) was used for object detection, with YOLO v7 outperforming YOLO v2, v3, v5, v8, and faster R-CNN. A proprietary dataset was used for training, and Houghs transformation was implemented for lane detection. YOLO v7's integration capabilities with automotive systems influenced its selection. The customised YOLO v7 model achieved 94.21% accuracy, 88.7% precision, 77.5% recall, an F1-score of 82.7%, and a mAP of 56.8%, surpassing prior studies. These findings demonstrate that cost-effective computer vision technology can significantly enhance hazard detection and road safety.
  Keywords: ADAS; Hazard awarness; Computer vision; Deep learning; YOLO; Fast R-CNN; Hough’s transformation; Object detection and lane detection.
  DOI: 10.1504/IJCIS.2027.10069729
   
  
• Solar Photovoltaic System-Fed Battery Charging with Decision-Tree-Assisted Maximum Power Point Tracking  
  by Vaibhav Sharma, Ankur Kumar Gupta, Ranjan Walia, Akshay Raj 
  Abstract: Maximum power point tracking Technique (MPPT), a machine-learning (ML) algorithm for PV power output optimisation, is compared in this study. PV energy generation is non-linear and weather-dependent, hence power production must be optimised. This study fills the gap in non-linear data management by optimising photovoltaic (PV) systems at their maximum power points (MPP) using ML. DT regression accurately calculates MPP. Training and testing the DT model requires PV module-specific data. These strong algorithms predict maximum power and voltage from irradiance and temperature. After projections, we calculate boost converter duty cycle. To validate modelling results, tests were done on a 12-W solar panel under prescribed conditions (1000 W/m2 to 800 W/m2). The MPP PV panel performs well as predicted by DT algorithms, confirming this technology works. This study illuminates PV system nonlinear data handling and how ML could improve renewable energy applications. DT-MPPT for battery charging may optimise solar photovoltaic energy harvesting and storage. To demonstrate its advantages, compare the algorithm to current topologies like adaptive P&O. These findings suggest that ML-driven MPPT improves PV system performance and reliability in practice. The DT MPPT algorithm was compared to others under abrupt irradiance changes.
  Keywords: Decision-tree (DT); Machine Learning Regression; Photovoltaic System; Power Conversion Efficiency; Renewable Energy Systems; Solar Energy Optimization; Decision-tree-aided maximum power point tracking.
  DOI: 10.1504/IJCIS.2027.10069855
   
  
• Integrating Machine Learning and Meta-Heuristic Algorithms for Single Machine Scheduling Failure Management  
  by Mehdi Safaei, Rehab El Gamil, Nasir Mustafa, Hina Zahoor, K.M. Ashifa 
  Abstract: Forecasting unforeseen occurrences remains a pivotal challenge in contemporary industrial contexts. This study addresses the optimization of single-machine scheduling, integrating failure management protocols, and endeavours to refine delivery timelines by mitigating minimum throughput requisites. A mathematical model is introduced, encompassing diverse parameters, including processing durations, machine operational states, idle intervals, breakdown intervals, and post-repair operational durations. Utilising machine learning algorithms enables the anticipation of potential equipment failures. Empirical findings underscore the efficacy of the proposed model in managing modest-scale scenarios. However, for more expansive problem domains, meta-heuristic algorithms, notably the Firefly Algorithm, are adopted. The research is bifurcated into two key components: the initial phase focuses on failure prediction leveraging historical data, while the subsequent phase delves into single-machine scheduling. Machine breakdown forecasts inform the scheduling process and are executed through a numerical optimisation framework. This approach underscores the significance of proactive failure management in optimising scheduling strategies, ultimately enhancing operational efficiency and facilitating more precise delivery timelines.
  Keywords: Single Machine Scheduling; Meta-Heuristic Algorithms; Forecasting and Optimisation; Throughput Requisites; Mathematical Model; Processing Durations; Machine Operational States; Firefly Algorithm.
  DOI: 10.1504/IJCIS.2027.10069859
   
  
• Optimising 360 panoramic imaging: fisheye image stitching for drone-based aerial surveillance  
  by Jiang Xiaoyan, Khairul Hamimah Binti Abas, Abdul Rashid Husain 
  Abstract: This research suggests merging four fisheye cameras appropriately spaced to create seamless 360-degree panoramic images using a drone. Each camera covers 222 degrees of the environment. This research aims to remove lens distortion and stitching errors. The SURF algorithm extracts and matches features, homography matrices physically align images, and advanced image processing reduces distortions. Overlapping components are meticulously processed to create seamless panoramic photos. The four mosaiced pictures form a 360-degree panorama. A drone-mounted 360 panoramic picture system with four fisheye cameras is tested in this study. An accurate 98% SURF feature matching, 95% distortion correction, and less than 0.5-pixel picture alignment variation are noteworthy. The system stitches 4,096
  Keywords: Fisheye Images; Speeded Up Robust Features (SURF); Image Stitching; Distortion and Panoramic; Optimising 360° Panoramic Imaging; Fisheye Image Stitching.
  DOI: 10.1504/IJCIS.2027.10069915
   
  
• Meta-Heuristic Ensemble Feature Selection (MEFS) And Stacking Ensemble Model for Renewable Energy Demand (RED) Forecasting  
  by Lekshmi Mohan, R. Durga 
  Abstract: As projected demand has increased, renewable energy (RE) research and development has received focus. Renewable energy demand is forecast using ML. Their high calculation time, dimensionality, and inability to simplify for different datasets with class types are major limitations. Feature selection involves choosing a subset of dataset features and building a predictive model. To improve wind speed generalisability and weather forecasting accuracy, MEFS and stacking ensemble model are introduced in this study. Kaggle collected four years of Spanish electricity generation, pricing, use, and weather data. Consumption and generation data came from the public TSO website ENTSOE. Data are scaled within a range using min-max normalisation (0-1). MEFS combines EBDA, AWDBO, and IWWHO feature selection methods to get the best subset. MEFS increases feature space and reduces unstable subset selection. A single strategy may produce a local optimum subset, but an ensemble may yield more stable results. Ensemble rating combines individual technique feature subset findings. Finally, the stacking ensemble model (SEM) has two layers: LSTM and bi-LSTM models, and OHPNN and EBDA to modify HPNN parameters for better prediction accuracy. Nash Sutcliffe efficiency (NSE), Pearson correlation coefficient (r), mean absolute error (MAE), and root mean square error assessed forecasting methods.
  Keywords: Meta-Heuristic Ensemble Feature Selection (MEFS); Adaptive Weight Dung Beetle Optimization (AWDBO); Inertia Weight Wildhorse Optimizer (IWWHO); Entropy Binary Dragonfly Algorithm (EBDA); Optimized Her.
  DOI: 10.1504/IJCIS.2027.10070068
   
  
• Application Analysis of Optimised Risk Management Utilising a Deep Learning Long Short-Term Memory Model  
  by Jie Wu, Yuanyuan Tong 
  Abstract: The fusion of business and network information technology, known as the internet economy, has ushered in a new economic paradigm marked by a wide range of services and a large user base, resulting in the generation of significant data in the era of big data. This economy utilises social platform data to assess customer credit and offer electronic services like credit and consumption. Nevertheless, the virtual nature of the internet economy makes it vulnerable to substantial operational and business risks. This study examines and mitigates these risks using a deep learning long short-term memory (DL-LSTM) mathematical model, offering valuable insights for internet economy enterprises and regulatory bodies. Analysis based on empirical data indicates elevated levels of risk within Chinas internet economy, with legal and political risks standing out as the most critical. A composite corporate public opinion index system is developed, integrating emotional and general public opinion indices while employing vector autoregression (VAR) models to investigate the relationships between public opinion shifts and economic risks. The outcomes underscore the significance of enhancing economic risk management strategies to bolster resilience and stability in the internet economy.
  Keywords: Mathematical Modelling; Deep Learning; Long and Short Memory Model; Economic Risk; Application Analysis; Model Analysis.
  DOI: 10.1504/IJCIS.2027.10070069
   
  
• Innovative Cooling Load Prediction with Machine Learning and Artificial Intelligence for Energy-Efficient Building Operations  
  by Gengqiang Huang, Jie Gan, Ying Huang 
  Abstract: The paper examines the complexity of forecasting cooling demands for building operations and energy savings. Innovative machine learning (ML) and artificial intelligence (AI) methods improve cooling load estimates. The project aims to improve naive Bayes (NB) models, commonly used for cooling load prediction, through hybridisation. This new approach boosts accuracy and reliability. Hybrid models created by combining two powerful optimisation algorithms outperform conventional approaches in predictive modelling. The study analyses single and hybrid model configurations to evaluate performance objectively and thoroughly. The careful decision between dingo optimisation algorithm (DOA) and fox optimisation (FOX) shows how important it is to exploit each optimisers strengths. This study analysed NB, NBFO, and NBDO iterations of the NB model. The NBFO model, which includes the FOX optimiser, has an impressive R2 score of 0.982, indicating a close match to the data. The models 1.296 RMSE shows its good precision. The study shows how ML and AI improve NBFO cooling load forecasts. The hybrid approach provides more accurate insights for sustainable building operations and energy savings to improve the future.
  Keywords: Building energy; Cooling load; Machine Learning; Naïve Bayes; Fox optimization; Dingo Optimization Algorithm.
  DOI: 10.1504/IJCIS.2027.10070071
   
  
• Classifinder: A Novel Meta Search Engine Approach for Search Query Optimisation  
  by Anu Mittal, Sridaran Rajagopal, Vinothina Veerachamy 
  Abstract: A meta-search engine (MSE) is an application that retrieves results from a user’s search query and ranks the outcome for the easy accessibility of the required information. However, many currently available search engines are basic and based on keyword-based mechanisms, often resulting in nonrelevant outcomes. Classifinder is a novel model presented in this paper optimized to improve the quality of domain-specific searches. The six-phase Classifinder model uses Artificial Intelligence (AI) and Machine Learning (ML) to enhance the efficiency and accuracy of search. It has been tested with various queries, with a page ranking accuracy of 98.66% and a hit rate of 91.27%. These results prove that it outperforms all existing MSEs. Classifinder addresses the shortcomings of keyword-based search methods by focusing on domain-based search optimization. Its ability to return accurate and meaningful results is expected to greatly benefit end users, who will save time and effort in retrieving information. This proposed model presents a step in the advancement of search engine technology and the experience of the users.
  Keywords: Artificial Intelligence (AI); Machine Learning (ML); Meta Search Engine; Query Optimization; Optimization Model; Search Engine Technology; Classifinder Architecture.
  DOI: 10.1504/IJCIS.2027.10070253
   
  
• China's Financial Market Infrastructures: an Updated Analysis  
  by Kerry Liu 
  Abstract: Financial Market Infrastructures (FMIs) are crucial for the efficient operation of financial markets. However, studies focusing on Chinese FMIs are limited. Following the release of a draft regulation on Chinese FMIs in December 2022, this study, building on Zhang and Yin (2023), provides an updated analysis. It introduces non-central bank affiliated FMIs with the latest data, explores the evolution of regulatory reforms, and examines the newly-released regulations. In particular, it offers a critical review of specific regulations and their implications for China's recent policy shift towards prioritizing security and establishing financial dominance. Finally, the study outlines a research agenda for future investigations.
  Keywords: China; Financial Market Infrastructure; FMI; People’s Bank of China; Regulation; payment system; Central securities depositories; Securities settlement systems; Central counterparties.
  DOI: 10.1504/IJCIS.2026.10070263
   
  
• Optimised Nuts Classification Using VGG Integrated CAPSNET With Pre-processing and Segmentation Techniques  
  by P. Saranya, R. Durga 
  Abstract: Everybody's diet has different ingredients. Nuts are rich in proteins, vitamins, antioxidants, fibre, minerals, and healthy fats, making them necessary to the body. Nut categorisation is necessary for food safety, quality control, and market fragmentation in the food industry. Traditional nut categorisation is subjective, tedious, and error-prone. This study proposes a new nut classification system. The suggested approach improves sensitivity, specificity, and accuracy with enhanced methodologies. Feature extraction and classification are done using VGG Net with CapsNet, a capsule system that can capture spatial grading and relationships. ESMF preprocessing reduces noise and preserves important features. FFA segmentation is essential for image-derived object correctness. The recommended technique is evaluated using Dry Fruit Image Dataset and several performance measures. The integration of VGG Net with CapsNet enhanced using CSA yielded superior performance with FO for segmentation, ESMF for noise reduction, and ResNet and AlexNet results. The suggested model performed better than other traditional algorithms according to classification metrics, pinpointing its effectiveness in accurate nuts classification in numerous industrial applications.
  Keywords: Nuts Classification; Firefly Algorithm (FFA); Deep Learning; Optimization; Enhanced Selective Median Filter (ESMF); Cuckoo Search Algorithm (CSA),.
  DOI: 10.1504/IJCIS.2027.10070270
   
  
• Design and Implementation of Scalable Encryption Frameworks for Real-Time Data Transfer in Critical Infrastructures  
  by Mingzhen Zhang 
  Abstract: The Internet of Things (IoT) is being used to make cities better. An awful lot of IoT systems, on the other hand, have issues with privacy, speed, low latency, and growth. This article talks about the Blockchain and Federated Learning for the Internet of Things (BFL IoT) idea as a possible solution to the issues that have been discovered. To begin, it would get real-time data like how fast traffic is moving and what the weather is like. After that, it is fixed, kept safe, and put on a blockchain. There is no way to change the info. High-tech safety measures are then used by the Data Authorisation Centre (DAC) to make sure that only people who are allowed to can see and change the data. Making keys is how this is done. Computers look at data that is close to where it is saved at the edges.
  Keywords: Encryption Scheme and Security; Random Oracle Model; Security Analysis; Background; State of the Art.
  DOI: 10.1504/IJCIS.2027.10070351
   
  
• Robust Cyber Hacking Breach Classification Model using MLP With Progressive Optimisation Techniques  
  by J. Christina Deva Kirubai, S.Silvia Priscila 
  Abstract: In the cybersecurity domain, breach classification is important for increasing the efficiency of defence approaches. This research study suggests a vigorous classification model that uses an MLP model with inclusive pre-processing and feature abstraction methods, followed by optimization using innovative techniques. The pre-processing stage starts with Z-Score normalization, which assists in creating similar features. Outliers are extracted using the IQR method, which improves the dataset by eliminating extreme information that could tilt the outcomes. One-Hot Encoding concept is applied to convert categorical identifiers into a numerical form appropriate for handling by the MLP model. The mixture of SMOTE and ENN is applied to ensure that minority classes are adequately represented and noise is minimized. For feature extraction, the Chi-Square test recognises the most statistically important attributes. At the same time, PCA decreases the dimensionality of the given dataset, holding the most important data. The MLP model is then optimised using metaheuristic approaches, including GA, ACO, FA, and SMA. The projected system is tested on a cybersecurity dataset from Kaggle, which illustrates superior accuracy of 92.52%, precision of 0.90, and recall of 0.88 compared to traditional approaches.
  Keywords: Interquartile Range (IQR); Edited Nearest Neighbors (ENN); Multilayer Perceptron (MLP); Dimensionality Reduction; Principal Component Analysis (PCA); Genetic Algorithm (GA); Ant Colony Optimization.
  DOI: 10.1504/IJCIS.2027.10070354
   
  
• An Outline of Critical Infrastructure Protection Strategy against Hydraulic Warfare for Republic of Croatia  
  by Mladen Viher, Blaž Beretin, Marica Mamut 
  Abstract: Prompted by the recent example of hydraulic warfare; the demolition of the Nova Kahovka dam, in 6th June 2023, as well as the experience from the Homeland War in Croatia (1991-1995), the authors investigated the advantages of a national civil-military organisation for the permanent protection of critical infrastructure against hydraulic warfare. Despite the favourable outcome of the Peru?a operation in 1993, there are still no continual organised forces in the Republic of Croatia that can fully utilise the potential of civil-military cooperation. Using multi-criteria analysis four concepts for the development and sustainability of the capability to protect national critical infrastructure, primarily hydrotechnical facilities, were compared and ranked. The findings of the multi-criteria research point to much improved interdepartmental and international cooperation as the best option for significantly developing the required capabilities without the need for significant investments and the establishment of new organisations in related ministries.
  Keywords: hydraulic warfare; hydrotechnical facilities; multi criteria decision analysis; civil-military cooperation; national civil infrastructure protection concept.
  DOI: 10.1504/IJCIS.2026.10070370
   
  
• Design of Manufacturing Enterprise Financial Early Warning System based on Machine Learning from the Perspective of Circular Economy  
  by Yang Huang, Xinyu Li, Dan Li 
  Abstract: This study designs a financial early warning system for manufacturing enterprises, focusing on machine learning and the circular economy. The random forest model is used as the base model, optimised by the artificial jellyfish algorithm to enhance prediction accuracy. Financial and non-financial indicators are selected through significance testing and feature screening methods. The results show that the optimised model achieves the highest accuracy of 88.42% and AUC of 0.918. Key warning indicators include inventory turnover rate, accounts receivable turnover rate, Herfindahl index, and liquidity ratio. The study highlights the importance of timely warnings for maintaining financial stability in manufacturing enterprises, helping them manage financial crises and supporting sustainable growth. The proposed system provides valuable support for policymakers and industry leaders in managing financial risks and advancing circular economy goals.
  Keywords: Circular economy; Manufacturing enterprises; Financial early warning; Random forest; Artificial jellyfish algorithm.
  DOI: 10.1504/IJCIS.2027.10070396
   
  
• Optimizing Computational Efficiency through Strategic Management of Machine Learning Techniques  
  by Swakantik Mishra, Vaidianathan Balasubramanian, N.Rajesh Kumar, E. Hymavathi, M. Lavanya 
  Abstract: Across various applications, including data analysis, image processing, creativity, gaming, and artificial intelligence, computing performance is a vital consideration. The performance of computing engines can be boosted by machine learning, which, in turn, optimizes parameters, architectures, and strategies. Due to trade-offs between accuracy, efficiency, scalability, and robustness, different algorithms may be best suited for different tasks. It is imperative to sweep the machine learning algorithm spectrum for better computational power. This paper compares and concludes on seven machine learning algorithms (Linear Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), K-means Clustering (KM), Reinforcement Learning (RL), Proposed method). We study three problems of enhancing computing performance: CPU scheduling, memory allocation, and cache replacement, and evaluate their performance using synthetic and real-world datasets. Performance is assessed quantitatively through rigorous experimental evaluations measuring speed, precision, and resource utilisation. The specific implementation of the proposed method is more effective on all metrics than other algorithms. These results are supported by statistical tests (ANOVA and Tukeys HSD). We assess their weaknesses and strengths and provide insights for further research and performance optimisation in computing.
  Keywords: Machine Learning; Computing Performance Enhancement; Strategic Management; Resource Optimization; hardware specifications; CPU scheduling.
  DOI: 10.1504/IJCIS.2027.10070410
   
  
• Measuring Network interdependency using Mutual Information Technique: a Case study of Power-Water Network  
  by Paras Garg, Gunjan Soni, O.P. Yadav, Ajay Rathore 
  Abstract: This paper presents a novel approach to the dependent network interdependencies analysis, based on the mutual information technique. This is a statistical measure that quantifies the amount of information shared between two variables. In the context of networks, we can calculate the mutual information between the nodes in each network and use it as a measure of the strength of their interdependency. This technique is useful in capturing the functional and service level interdependencies between the networks. The Mutual Information based approach is a useful analytical tool for determining the degree of mutual effect and reliance between variables in linked systems. We want to shed light on how mutual information might help us comprehend and analyse the interplay of critical infrastructure networks by applying this technique to the example of a hydroelectric power plant depending on a river for water supplies a case study of simulated power network used in this paper for illustration and validation of the technique.
  Keywords: Dependent Networks; Interdependencies measure; Mutual Information; Coupled Networks.
  DOI: 10.1504/IJCIS.2027.10070530
   
  
• Advancing Imaging Technology: Intelligent Design and Optimisation of Barcode Applications for Enhanced Data Encoding and Retrieval  
  by K.R. Radhika, Sheela S. V 
  Abstract: Imaging technologies offer more accurate data encoding and retrieval, which has substantially impacted barcode application design and optimisation. Standard barcode systems are beneficial in retail, healthcare, and logistics, but low-resolution, distorted, or disordered images limit them elsewhere. A recent LEADTOOLS Barcode Recognition SDK development helps address these difficulties. Strong preprocessing approaches including adaptive contrast enhancement, automated binarization, and noise reduction give the suggested systems unsurpassed barcode identification accuracy. Additionally, 2D barcode formats like Data Matrix and QR Codes have several industrial applications due to their higher data density and error robustness. Real-time testing methods and performance objectives like detection speed and error correction success rates enable operational flexibility. The proposed barcode decoding method always exceeds 99% accuracy. Most high-resolution pictures decode in 2225 milliseconds. It supports over 100 symbologies, including QR code, DataMatrix, UPC, and PDF417, demonstrating tremendous scalability. During high-throughput procedures, 70% of GPU and 85% of CPU resources are distributed for concurrent activities. The technology generates high-density barcodes efficiently at 3.54.2 bytes/mm
  Keywords: Barcode Technology; Imaging Optimization; Error Correction; 2D Barcodes; Leadtools SDK; Artificial Intelligence; Inventory Control; Machine Learning.
  DOI: 10.1504/IJCIS.2027.10070750
   
  
• Data Optimisation Privacy Protection Model Drawing on ElGamal Encryption Framework  
  by Hua-Yong Wei, Ling Zhou, Ling Wang, Qian Chen 
  Abstract: Data privacy protection is constantly challenged by low encryption success rates and heavy loss of information. In response to this, this paper presents a privacy protection model using the ElGamal encryption system. The model uses query functions and Laplace mechanisms to estimate local and global sensitivity, process privacy data structures, and estimate data correlation. The model examines private data patterns in machine learning, reduces dimensionality using local preserving projection, and introduces complete file detection to detect duplicate data after reduction. Sliding average filtering removes noise. The model generates public keys, shared secret values, and private key polynomials using random selection and pairing, assuming attacks. Multiple public-private key pairs are created for sequential message signing and verification. The concept provides robust privacy protection and user access to encrypted data by combining these strategies. Experimental results demonstrate 99% encryption success at 2% information loss, better than conventional approaches. Large databases and IoT networks confront scalability and computational overhead issues despite their efficiency. To improve data privacy and security, future work must improve scalability, compute efficiency, and support edge computing, 5G, and quantum computing.
  Keywords: Elgamal Encryption Framework; Privacy Data; Protect Model; Public Key; Private Key; Complete File Detection.
  DOI: 10.1504/IJCIS.2027.10070753
   
  
• Distributed Optimization in Blockchain Data Security: A Comparative Study of Hyperledger and Ethereum Networks  
  by Renuka P, Booba Rajashekar 
  Abstract: Neoteric trends in data management demand augmented security and efficient handling. The transparency of data and the need for a decentralised environment have become indispensable. Distributed optimisation blockchain technology has revolutionised the management and protection of data in various ways, making it essential to comprehend the scalability and performance of blockchain architectures for their effective implementation across diverse domains. This study explores the use of the Hyperledger framework for securing and managing data in different industries. However, the potential applications of the Ethereum framework in various industries remain understudied. This paper focuses on utilising the Hyperledger and Ethereum environments to secure and transmit medical records while effectively analysing the security performance in both environments. The blockchain-based smart agreement proof of authentication (BSAPA) and consensus smart contract (CMSC), using the Sepolia Testnet, are employed by the Hyperledger and Ethereum frameworks, respectively. The security performance is examined by analysing the strength of the generated hash keys, latency, authentication performance, and block generation. The results indicate that the encrypted hash generated by the Ethereum-Sepolia network provides higher security compared to the hash key generated by the Hyperledger framework.
  Keywords: Distributed Optimization Blockchain; Ethereum and Hash Key; Sepolia Testnet; Smart Agreement Proof of Authentication; Consensus – Smart Contract; Digital Transactions.
  DOI: 10.1504/IJCIS.2027.10070882
   
  
• Data Driven Approaches for Enhancing Critical Infrastructure Protection and Resilience  
  by Dhina Suresh, Vaidianathan Balasubramanian, J.Salomi Backia Jothi, Rayappan Lotus, Prasanta Parida, Manikandan S 
  Abstract: Critical infrastructure forms the backbone of modern societies, providing essential services such as electricity, transportation, and telecommunications. However, these infrastructures face growing vulnerabilities due to threats like cyberattacks, natural disasters, and system failures. This paper explores strategies for enhancing protection and resilience in critical infrastructures through the integration of advanced technologies, specifically artificial intelligence (AI) and the internet of things (IoT). The study utilises real-time monitoring data, case studies from various sectors, and performance metrics gathered from IoT systems to evaluate the effectiveness of these strategies. AI-driven analytics software analysed data, and mesh plots, multiline graphs, and heatmaps visualised resilience patterns and vulnerability distributions. Three tables show performance comparisons, cost-benefit evaluations, and resilience methods using seven criteria. System resilience, operational efficiency, and cost-effectiveness improve significantly when AI and IoT are integrated. Predictive analytics, real-time monitoring, and automatic response systems help infrastructures handle predicted and unexpected interruptions. The findings suggest that governments and businesses should integrate AI and IoT to protect their infrastructures against changing threats. Future research should improve these systems scalability and affordability for wider deployment.
  Keywords: Critical Infrastructure; Resilience and Risk Management; Cybersecurity and Cyberattacks; Disaster Recovery; Artificial Intelligence; Internet of Things; System Design.
  DOI: 10.1504/IJCIS.2027.10071068
   
  
• Accurate Prediction of Settlement during Deep Excavation Construction of High Rise Residential Buildings  
  by Qing Chen, Yi Zhang 
  Abstract: In order to solve the problem of low accuracy and poor prediction effect of settlement prediction during foundation pit construction, this paper conducts research on accurate prediction methods of settlement based on Kalman filtering. Firstly, set up settlement monitoring points to collect data and perform preprocessing; Secondly, analyze and calculate the instantaneous settlement, consolidation settlement, and stress settlement deformation processes that occur under load; Again, the grey correlation algorithm is used to conduct correlation analysis on the influencing factors of foundation pit settlement; Finally, a precise prediction model for settlement is constructed based on Kalman filtering to achieve accurate prediction of settlement during the construction process of deep foundation pits in high-rise residential buildings. Through experiments, it has been proven that the proposed method is relatively accurate in predicting surface subsidence, with a prediction error of no more than 0.4m and high prediction accuracy.
  Keywords: Deep foundation pit for construction; Settlement amount; Settlement data; Grey correlation; Kalman filter.
  DOI: 10.1504/IJCIS.2027.10071213
   
  
• Disaster Tweet Classification Using Transfer Critical Learning for Multimodal Data  
  by Tarun Jain, Dinesh Gopalani, Yogesh Kumar Meena 
  Abstract: Disasters challenge our resilience and response mechanisms, testing our civilization's progress. We'll summarise and propose some major crisis informatics advances using the Crisis Multimodal Data dataset, which captures the intricacies of disaster-related conversations across social media platforms. We describe a unique method for recognizing and classifying disaster-like conditions using textual and visual clues to aid rescue and humanitarian efforts. Our model uses advanced transfer learning methods, pre-trained embeddings, and deep neural architectures. Our framework captures crisis-related content's subtleties by combining contextual embeddings from textual data with transformer-based models. This multimodal fusion improves classification accuracy and reliability by providing a more complete data picture. Our strategy uses social media data's collective intelligence to enable prompt and targeted responses, potentially saving lives and reducing crisis impacts on affected communities. In CrisisMMD experiments, our technique achieved a state-of-the-art accuracy of 93.22%. Multimodal learning in crisis informatics can enable more responsive and data-driven disaster
  Keywords: Transfer Critical Learning; Crisismmd Dataset; Classification Accuracy; Multimodal Approach; Imagenet; Transformer Based Embeddings; Technological Advancement.
  DOI: 10.1504/IJCIS.2027.10071282