International Journal of Critical Infrastructures (44 papers in press)

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• Open Access

  Managing Technological Security of Smart Environment Monitoring Systems: Study Of a coastal province in Vietnam ( Free Full-text Access )

  by Anh Tuan Hoang, Xuan Ky Nguyen 
  Abstract: With the machinery and systemic interconnections of the current Industry 4.0 world that we live in today, water critical infrastructures (CI)are crucial, interdisciplinary with major urban entities such as medical and natural ecosystems. Environment monitoring technology (EMT), bounded to water CIs, providing the necessary real-time information for control and operation of urban water facilities, can become potential targets for physical or online attacks, disruption or destruction, imposing discomforts, outages and damages to affected water CI systems and related stakeholders, affecting the non-traditional security of affected region/cities. This is increasingly crucial as many cities in Vietnam are planning to implement digital transformation to become smart cities, creating multitudes of digital, internet connections, and numerous technological risks. In summary, this article attempts to analyse and draw conclusions about various aspects of technological security of environment monitoring systems (EMSs) of Quang Ninh, Vietnam and propose solutions that the city can apply to mitigate such non-traditional threats and prevent probable future incidents that can cause serious disruptions to such critical structures
  Keywords: critical infrastructure; technological security; monitoring systems; nontraditional security; smart city; Vietnam.
  DOI: 10.1504/IJCIS.2023.10056422
   
  
• 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
   
  
• Enabling Active Safety System along with Realtime monitoring and audio alerts in Two Wheelers through Smart Helmet System  
  by SUNIL GUPTA, Nikita Agarwala, Nikita Arora, Goldie Gabrani 
  Abstract: A report by the Indian Ministry of Road Transport and Highways, estimated that over 1.5 lakh lives were claimed in various accidents on Indian Roads in 2018. Over-speeding has been found as a major violation accounting for nearly 67% of the road accidents. To address this concern, authors in this paper, have proposed a methodology to make the helmet smarter using Internet of Things (IoT). This modification in the existing smart helmet adds on to the safety of the bikers. Drivers for two wheeled automobiles are cautioned about their speed through audio alerts. These alerts are in accordance with one’s speed and location and updated in real time. To implement this technology in the existing helmets, Bluetooth technology was deployed, and the communication channel was realised through various IoT protocols such as XMPP and MQTT. The suggested protocols have been analysed in the deployed communication channel of the proposed smart helmet for their performance. Further, ubiquitous connectivity is also an important feature of the proposed smart helmet.
  Keywords: accident avoidance; two wheelers; smart helmet; sensors; active safety system; IoT.
  DOI: 10.1504/IJCIS.2023.10046181
   
  
• Fog Based Smart Building IoT Model: Development and Energy Cost Estimation  
  by Sunil Gupta 
  Abstract: Due to the expansion and accessibility of the internet of things (IoTs), the user gets interacted to use advanced technology. The development and improvement of fog computing and cloud computing services allow developing the IoT application like smart building. In this paper, an IoT-fog-based prototype is proposed and implemented to check the cost estimation. Fog computing reduces the consumption of energy and latency during communication with the smart building. This prototype aims to develop the system based on real-time energy cost estimation, used further for a real-time billing system. In the proposed prototype, the information (current, voltage, power, and energy) is being collected in two real-time scenarios. One is based on fog-IoT-based system, and the other is a conventional system with the help of two circuits with the same equipment attached to find the energy consumed. In this paper, the analysis of energy consumption shows the advantages in terms of optimisation and cost-saving using the proposed prototype.
  Keywords: fog computing; internet of thing; IoT; NodeMCU; smart building; Arduino; energy efficient.
  DOI: 10.1504/IJCIS.2024.10046182
   
  
• Application of Deep Learning Approach for detecting Brain Tumor in MR Images  
  by Jyoti Agarwal, Manoj Kumar, Anuj Rani, Sunil Gupta 
  Abstract: A tumour is an abnormal mass of tissue which consume normal body cells, kill them, and continue to increase in size. For detection of infected tumour area and lesions, magnetic resonance imaging has been used widely in medical field. Image processing and machine learning is also used widely for brain tumour detection and segmentation, but they are not the most appropriate ones, therefore methods involving deep learning are also proposed for the same. In this paper, six traditional machine learning classification algorithms are compared. Afterwards, convolutional neural network is implemented using Keras and TensorFlow in python. Two different CNN based models VGG16 and DenseNet available in Keras trained on imagenet dataset is also used. The dataset contains in total 253 images which were later augmented to train the model better. From results, it was analysed that deep learning algorithms yield better results than the traditional ML classification algorithms.
  Keywords: brain tumour; CNN; deep learning; model; pooling; DenseNet; TensorFlow.
  DOI: 10.1504/IJCIS.2024.10046183
   
  
• Campus Smart Street Lamps Using Internet of Things Technology  
  by Mengxia Liu 
  Abstract: Based on the Internet of Things (IoT), this paper analysed the intelligent control of campus street lamps, designed an intelligent system combined with fuzzy control, and tested the system. The test found that the system had good networking and communication capabilities. When the distance was 100 m, the packet loss rate was 6.05%. Compared with the time control and light control systems, the designed system had smaller parameters and better energy-saving performance (63.59%). The results verify the effectiveness of the designed system in intelligent control. And the designed system can be further promoted and applied in the actual campus construction.
  Keywords: Internet of Things; IoT; intelligent street lamp system; campus construction; fuzzy control; ZigBee.
  DOI: 10.1504/IJCIS.2024.10046184
   
  
• Techniques to safeguard the Underground Tunnels against Surface Blast load  
  by SENTHIL KASILINGAM, Muskaan Sethi, Loizos Pelecanos 
  Abstract: Due to the growth of underground tunnels, the safety of structures under blast loading is a major threat. Therefore, this paper focused on various techniques such as tunnel burial depth, tunnel shape, tunnel lining materials and varying the location of the blast source to safeguard underground tunnels against blast load using numerical analysis. The behaviour of concrete, reinforcement steel and the soil were incorporated by using the different constitutive model available in ABAQUS v. 2020. The predicted results were compared with the experimental results available in literature and found in close agreement. It is concluded that the layering of soil filling and depth of the burial of the tunnel found to be most important in case of external blast, whereas the stress bearing capacity of the concrete found to be important in case of internal blast. It is also concluded that the circular shape tunnel is one of the best performing tunnels.
  Keywords: tunnels; blast load; burial depth; lining materials; tunnel shape; blast location.
  DOI: 10.1504/IJCIS.2024.10046185
   
  
• The Vibration Control of Magnetorheological Elastomer Damper for Eccentric Workshop  
  by Changsheng Wang 
  Abstract: Aiming at the problem of translational and torsional vibration of the eccentric structure under earthquake, the control effect of magnetorheological elastomer damper on translational and torsional vibration of the eccentric workshop structure is studied. Firstly, the mechanical model of magnetorheological elastomer damper and a finite element model of the structure are established. Then, the finite element simulation is utilised to analyse the damping effect of the damper on the structure. The analysis results show that the damping effect of the damper on the translational vibration of the eighth layer of the structure is 43.27%, and the damping effect on the torsional response of the fifth layer is 37.37%. Finally, do a shaking table test. The test results show that when the damper acts, the translational displacement of the top layer of the structure decreases by 34.97%, and the torsional response of the fifth layer of the structure reduces by 41.58%.
  Keywords: eccentric workshop; magnetorheological elastomer damper; mechanics model; shaking table test; damping effect.
  DOI: 10.1504/IJCIS.2024.10046208
   
  
• Critical Infrastructures: A Comparison of Definitions  
  by Kim Smith, Ian Wilson 
  Abstract: The aim of this paper is to identify common characteristics of critical infrastructure that is informed by national, standards and academic sources. A fundamental understanding of the interconnectivity and dependencies and interdependencies that exist within critical infrastructure is essential to ensure international protection. In working towards an international definition of critical infrastructure there will need to be cooperation and collaboration between nations. The paper determines, through analysis, the foundation of critical infrastructure and its complexity given national and international links to support collective understanding. The paper highlights the potential harm that can be caused if critical infrastructure links are not fully understood. A definition of critical infrastructure is proposed, and future work suggested.
  Keywords: critical infrastructure; dependencies; interdependencies; complexity; vulnerabilities; infrastructure characteristics.
  DOI: 10.1504/IJCIS.2024.10046186
   
  
• A Comparative Framework for Cyber Threat Modelling: Case of Healthcare and Industrial Control Systems  
  by Mobolarinwa Balogun, Hayretdin Bahsi, Omer F. Keskin, Unal Tatar 
  Abstract: Cyberattacks target organisations and cause property loss, disruption of operation, and for healthcare facilities, even loss of life. With the advent of the internet of things (IoT) devices, the attack surface has extended significantly. Organisations need a cyber threat modelling approach to assess their network from the attackers’ perspective to safeguard their assets better. In this study, a framework was developed to compare cyber threat modelling of various IoT networks by focusing on the capabilities of the threat actors in the light of various factors, such as accessibility, stealth, technical ability, and time. The developed framework is applied to two different networks: SCADA and healthcare IoT infrastructure for demonstration. The results suggest that it is possible to cause a physical impact in IoT-based healthcare systems by using less sophisticated cyberattacks.
  Keywords: cyberattack; SCADA; internet of things; IoT; threat modelling; attack trees; attack sophistication; healthcare.
  DOI: 10.1504/IJCIS.2024.10046187
   
  
• A Systematic Mapping on Cascading Effects in Critical Infrastructures  
  by Beatriz Toscano, André Fernandes, Miguel Mira Da Silva, Flávia Santoro 
  Abstract: Critical infrastructures (CIs) are crucial assets for society and the economy, since they are responsible for supplying essential goods and services. Currently, and increasingly, CIs depend on each other. This interdependency among CIs makes them vulnerable to cascading disasters on a large scale, which can have a huge impact. The interconnection among CIs makes that a failure in a CI can affect another CI(s), generating so-called cascading effects. Cascading effects on CIs make a research area worthy of study and investigation and have considerable interest in terms of resilience and risk management. This paper aims to provide, through a systematic mapping study, an overview of the existing literature on cascading effects in critical infrastructures, detailing the work performed in this domain. In addition, the paper also identifies the approaches that are being taken in the context of cascade effects in critical infrastructures. We also discuss the relationship between cascade effects and infrastructures interdependencies.
  Keywords: systematic mapping; cascading effects; interdependency; critical infrastructures.
  DOI: 10.1504/IJCIS.2024.10046189
   
  
• CPW-Fed Printed Patch Antenna for 5G-IoT Infrastructure Development  
  by KAUSHAL MUKHERJEE, Subhadeep Mukhopadhyay, Sahadev Roy 
  Abstract: Antennas with a high gain, good radiation efficiency, and an omnidirectional radiation pattern over a wide bandwidth will be able to solve some of the issues of existing communication networks. In this work a comparative analysis of different types of 5G antenna along with their performance improvements techniques are discussed. In this study we also proposed a simple planar printed antenna which is also offered high gain and desired radiation efficiencies. The proposed work is different in that, despite being a planar printed antenna, the antenna achieves a wider bandwidth of 4.5 GHz, peak gain of 11.6 dB, radiation efficiency of 92%, VSWR of 1.2, and SAR of 1.42 W/Kg for 1 g of tissue by utilising a U-shape of slot over the radiating patch structure and also useful for 5G-IoT-based Infrastructure developments as a replacement for multi-layer antennas.
  Keywords: 5G network; CPW-fed; single layer; printed patch antenna; data infrastructure development; internet of things; IoT; millimetre wave band; mmW.
  DOI: 10.1504/IJCIS.2024.10046124
   
  
• Success Factors and Lessons Learned during the Implementation of a Cooperative Space for Critical Infrastructures  
  by Luciano Morabito, Benoit Robert 
  Abstract: It is largely documented that the exchange of information among critical infrastructures (CIs) is crucial to strategies involving the identification of their interdependencies and increasing their resilience. Based on the experience of the Centre Risque & Performance, Polytechnique Montr
  Keywords: collaboration; cooperation; critical infrastructures; domino effects; information sharing; integrated risk management; interdependencies; large organisations; knowledge; social constructivism.
  DOI: 10.1504/IJCIS.2024.10046126
   
  
• Fault location of high voltage overhead transmission line based on ACO-ENN algorithm.  
  by Guangxin Zhang, Qi Zhang, Jun Ma, Gang Liu, Dong Sun, Zimeng Zhang 
  Abstract: Aiming at solving the problems of poor positioning accuracy and long time in traditional methods, a fault location of high voltage overhead transmission line based on ACO-ENN algorithm is proposed in this paper. Firstly, self-coding neural network is used to reduce dimension of transmission line signal data. Secondly, the relationship between fault distance and natural frequency is obtained by main frequency extraction method. Finally, ACO-ENN algorithm is used to construct the hidden interlayer weight matrix to obtain the fault location error function of the transmission line, and the fault location result of the high voltage overhead transmission line is obtained under the condition of the minimum error function. The results show that the mean square error of the proposed method is less than 187 m, and when the fault distance of the high voltage overhead transmission line is 300 km, the fault location time of the proposed method is only 0.43 s.
  Keywords: ACO-ENN algorithm; high voltage overhead; transmission line; fault location; main frequency extraction; self-coding neural network; error function.
  DOI: 10.1504/IJCIS.2024.10046155
   
  
• 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
   
  
• The dynamic and secure storage of enterprise financial data based on cloud platform  
  by Li Zhang  
  Abstract: n order to overcome the problems of poor data security, low integrity and long storage time of traditional methods, this paper proposes a dynamic and secure storage method of enterprise financial data based on cloud platform. Firstly, the absolute correlation degree of the data in the financial database is calculated by using the grey correlation analysis method, and the data mining and clustering are carried out according to the results of the absolute correlation degree calculation. Then, the DES algorithm is used to encrypt the clustering results of financial data and remove redundant data. Finally, according to the cloud platform access time length, access frequency and the relationship between enterprise financial datasets to achieve data dynamic security storage. Experimental results show that this method can encrypt all data, the average integrity of the storage results is 9.7, the average storage time is 0.51 s, and the practical application effect is good.
  Keywords: cloud platform; corporate financial data; dynamic security storage; absolute correlation degree; DES algorithm.
  DOI: 10.1504/IJCIS.2024.10046613
   
  
• CLOSING THE GAP: FINANCIAL CLOSURE IN PUBLIC PRIVATE PARTNERSHIP MARKETS  
  by Alex Nduhura, Thekiso Molokwane, Muhiya Tshombe Lukamba, Innocent Nuwagaba, Francis Can 
  Abstract: The paper analyses the context of financial closure in public private partnerships markets. Existing studies indicate that the success rate of projects that move to the financial closure is limited and stands at less than 50%. To improve the financial closure rates, this study aimed at establishing process for, factors and challenges associated with financial closure for public private partnerships. Based on a review of existing literature, this paper identifies the factors responsible for the high failure rate of financial closure and provides recommendations, necessary for improving the success rate of financial closure. The outcome of this study is important since it makes an invaluable and original contribution to the PPP body of knowledge, since the paper provides a range of issues and activities that must be considered to improve the success rate for financial closure is historically rated as too long and with low chance of success.
  Keywords: financial closure; public private partnerships; PPPs; infrastructure.
  DOI: 10.1504/IJCIS.2024.10046958
   
  
• Utilizing the Fuzzy Analytic Network Process Technique to Prioritize Safety Challenges in Construction Projects  
  by Pouyakian Mostafa, Ali Akbar Shafikhani, Amir Abbas Najafi, Behrouz Afshar-Nadjafi, Amir Kavousi 
  Abstract: This study aims to identify and rank the obstacles to implementing a safety program in the Iranian construction industry. The obstacles were identified through literature review and interviews with experts in the Iranian construction industry. Because of the complex structure of the relationships between the obstacles and their mutual effects, the fuzzy analysis network Process method was used to model them. Obstacles to safety implementation were identified and ranked using the proposed model. Fourteen obstacles were identified in the three organisational, contractors, and systems dimensions. The most critical obstacles include tight project schedules, resource constraints, fierce competition between contractors to reduce time and cost. This study showed that the Iranian construction industry, despite its advantages, faces obstacles in the successful implementation of safety programs. It seems that the identified obstacles can be removed by modelling the safety program in project scheduling. However, more studies are needed in this area.
  Keywords: safety; accidents; construction; analytic network process; ANP; fuzzy evaluation.
  DOI: 10.1504/IJCIS.2024.10047707
   
  
• Simulation-based seismic risk and robustness assessment of aging bridge networks  
  by Tian Lu, Luca Capacci, Mattia Anghileri, Silvia Bianchi, Luo Dong, Fabio Biondini 
  Abstract: Civil infrastructure systems are prone to environmental deterioration processes that gradually reduce their mechanical properties and their capability to sustain severe natural hazards, such as earthquakes. The rate of occurrence of major seismic events, the time-variant seismic vulnerability of single bridges, and the indirect economic losses due to traffic flow redistribution related to the closure of key routes within the highway network are affected by uncertainties to be addressed by modelling their constitutive parameters as random variables and processes. This paper presents an integrated simulation-based methodology to estimate the user cost-based risk and system robustness of spatially-distributed bridge networks subjected to prescribed deterioration and regional seismic hazard scenarios. Life-cycle seismic risk metrics are evaluated in terms of bridges failure rates and exceedance rates of indirect monetary losses based on free-flow traffic analysis and user cost assessment. The lifetime system robustness is also evaluated based on appropriate probabilistic performance indicators.
  Keywords: multi-hazard risk; structural robustness; Monte Carlo simulation; MCS; aging bridge networks; seismic hazard; fragility curves; user cost.
  DOI: 10.1504/IJCIS.2024.10048368
   
  
• Land Value Capture as Breakthrough of Financing Scheme in Urban Railway Development in Indonesia  
  by Fery Safaria, Najid Najid, Carunia Firdausy 
  Abstract: This study aims at examining whether or not the LVC can be used as an alternative financing instrument in urban railway infrastructure in Indonesia. The results confirmed that the LVC can be a breakthrough scheme in financing the development of urban railway infrastructure in Indonesia. The most urgent aspect to be prepared by the government to implement the LVC scheme in financing the urban railway infrastructure is the availability of regulations. Also, the approach to developing and executing the plan of LVC needs to be based on an assessment of the cost of the benefit analysis, the land value, the market condition of the location, sustainability, and people’s participation in urban railway development. Further detailed research to examine the full potential and the benefits of applying the LVC in financing urban railway infrastructure development is needed as Indonesia faces budget constraints and as we move into a post-pandemic recovery.
  Keywords: land value capture; LVC; financial scheme; budget limit; urban railway infrastructures; Indonesia.
  DOI: 10.1504/IJCIS.2024.10049301
   
  
• Investigating Safety Development Methodologies in the Construction Industry and Identifying Gaps in the Studies: A Review Article  
  by Mostafa Pouyakian, Ali Akbar Shafikhani, Amir Abbas Najafi, Behrouz Afshar Nadjafi, Amir Kavousi 
  Abstract: Identifying the appropriate safety methodology is essential to improving construction safety performance. This study aims to investigate safety development methodologies in the construction industry and identify gaps in the studies. Articles published from 2000 to 2022 were reviewed. Seventy-seven eligible articles were selected based on comprehensive and exclusive criteria. After obtaining selected literature, gaps in using these methodologies were discussed. Twelve criteria were used to compare safety methodologies. The selected literature focused more on the construction phase and did not provide an effective strategy in the project planning phase. Although the studies had specific benefits, none examined the safety program based on actual project conditions (resource, time, and cost constraints). There is a need for a model that examines safety in terms of actual project conditions (time, cost, and resource constraints). In addition, the model must optimise not only safety but also other vital components of the project (cost, time, and quality) while considering resource constraints (especially equipment constraints). If such a model is designed, the project team will not resist safety changes, which benefits all the construction stakeholders.
  Keywords: construction industry; safety management; project schedule; occupational health.
  DOI: 10.1504/IJCIS.2024.10049397
   
  
• Social and Economic Risk Analysis of Natural Gas Distribution Networks
  by Fabrizio Zuena, Marco Dell'Isola, Giorgio Ficco, Luisa Lavalle, Alberto Tofani 
  Abstract: The continuity of service as well as with its safety and security represent a crucial issue for natural gas transmission and distribution networks and a detailed analysis of the associated risks is essential to increase their reliability. In particular, natural gas distribution networks are characterised by a high number of users and present a very complex structure (with nodes and stretches and presenting mixed typologies, e.g., point to point, star, meshed) which make often difficult to forecast the effects of localised failure events, especially by a social and economic point of view. In this work, the authors develop a methodology for the analysis of the economic and social risk associated with natural gas distribution network failures and for the quantification of the related consequences on residential, commercial and/or industrial users. To this aim, the authors present and discuss the case study represented by a city distribution network located in southern Italy. The results demonstrate the developed method is effective in identifying the structural criticalities of the network, allowing the quick detection of the most critical areas affected by significant risk of service disruption.
  Keywords: failure analysis; risk analysis; distribution network; natural gas.
  
  
• Argentina’s critical infrastructures: topics for their regulation
  by Gonzalo CACERES 
  Abstract: Argentina’s last National Defence Policy Directive (2021) explicitly mentions the protection of critical infrastructures, making it necessary to define them, establish priorities and responsibilities. However, Argentina published a first critical infrastructure standard in 2019 that lists vaguely those sectors to be included. The resolution does not allow to program actions and establish responsibilities since there is no identification of actors and, therefore, of their duties. In this article, we will discuss the relevant aspects that could be considered for future legislative work in Argentina and the role of the Ministry of Defence. Main topics are: 1) the genesis of the notion of critical infrastructures and the aspects that gave rise to their identification as the object of security policy and that of National Defence in particular; 2) those cases that are of interest in thinking about the national case in comparative perspective; 3) a synthesis of the elements under discussion that we understand structure the treatment of critical infrastructures.
  Keywords: critical infrastructure legislation; Argentina; national defence; security.
  
  
• Efficient Indian Sign Language Recognition and Classification Using Enhanced Machine Learning Approach
  by Edwin Shalom Soji, T. Kamalakannan 
  Abstract: Deafness and voice impairment are two significant disabilities that make it difficult for people to communicate in verbal languages with others in a verbally communicating population. To solve this problem, the sign language recognition (SLR) system was constructed by combining machine learning and deep learning. The SLR employs hand gestures to convey messages. Earlier research aims to develop vision-based recognisers by extracting feature descriptors from gesture photos. When dealing with a large sign vocabulary recorded under chaotic and complex backgrounds, these strategies are ineffective. Hence, an improved convolution neural network is proposed in this paper to predict the most frequently used gestures in the Indian population with improved efficiency. The presented system is compared to SVM and CNN. The suggested approach is tested on 2,565 UCI instances and 22 training attributes. It showed both-handed ISL movements against various backgrounds. The augmented CNN has a precision of 89% and 90.1% accuracy, which is higher than most other approaches. According to this survey, we had an 83% recall and a 0.4 F score. Python evaluates our work.
  Keywords: sign language recognition; SLR; machine learning; convolution neural network; CNN; Indian sign languages; ISLs; accuracy; precision.
  DOI: 10.1504/IJCIS.2025.10054997
   
  
• Non-Invasive Prediction Mechanism for COVID Using Machine Learning Algorithms
  by Arnav Bhardwaj, Hitesh Agarwal, Anuj Rani, Prakash Srivastava, Manoj Kumar, Sunil Gupta 
  Abstract: This paper has focused on developing a model to detect non-diagnostically whether the person is infected with the COVID-19 disease using all relevant symptoms and details mentioned by the person and then comparing it with a pre-defined dataset of positive cases using machine learning. Different models have been developed to predict the same but none of them focused on the detection of COVID-19 based on symptoms. In a developing nation with huge population, where the diagnostic availability is scarce so, just scanning the body temperature will not help in detection of COVID-19 of a particular individual. This paper presents a model that can predict COVID-19 cases without any testing kit to an accuracy of 99.30%, performing better than other similar approaches with objective to put forward a method that can reduce the need of producing testing kits and also the need to wait for hours before we get the results.
  Keywords: COVID-19; non-invasive; symptoms; machine learning.
  DOI: 10.1504/IJCIS.2025.10054998
   
  
• A Structured Model for Identification and Classification of Critical Information Infrastructure
  by Alaba O. Adejimi, Adesina S. Sodiya, Olusegun Ojesanmi, Olusola J. Adeniran 
  Abstract: The growing incidence of attacks on critical information infrastructures has necessitated the development of a model for properly identifying and designating information infrastructure whose destruction or interruption could jeopardise the well-being of a state and ensure its preservation. Some of the previous methodologies and models were prejudiced, lacked scientific support, and the criticality criteria were not categorised to encompass global demands. To universally determine the criticality of information infrastructure, this work proposes a factor impact sensitivity approach (FISA), which identifies, structures, and models relevant state criteria into mathematical concepts. An application was developed to implement the structured mathematical model and designate an infrastructure as
  Keywords: criticality strength; information infrastructure; categorisation; multi-criteria; impact factor; risk assessment; alternative scope; likelihood; disruption.
  
  
• Stacking-based Multi-objective Approach for Detection of Smart Power Grid Attacks using Evolutionary Ensemble Learning
  by Manikant Panthi, Tanmoy Kanti Das 
  Abstract: Smart power grid (SPG) has gained a reputation as the advanced paradigm of the power grid. It provides a medium for exchanging real-time data between the company and users through the advanced metering infrastructure delivering transparent and resilient service to electricity consumers. The widespread deployment of remotely accessible networked equipment for grid monitoring and control has vastly increased the surface of SPG for attackers to locate vulnerable points. The early and accurate identification of the above counteracts is paramount to ensure stable and efficient power distribution. This paper proposes a stacking-based multi-objective evolutionary ensemble scheme to identify various attacks in the SPG. The proposed method used a non-dominated sorting genetic algorithm to learn the non-linear, overlapping, and complex electrical grid features to predict the type of malicious attacks. The experimental results and comparison using multiclass dataset validate the presented
  Keywords: non-dominated sorting genetic algorithm; cyber-attack; power grid; machine learning.
  
  
• Real-world application of face mask detection system using YOLOv6  
  by Jonathan Atrey, Rajeshkannan Regunathan, Rajasekaran Rajkumar 
  Abstract: The COVID-19 pandemic has drastically reshaped the human lifestyle and has placed immense importance on our health, safety, and sanitation practices. Among the various safety protocols assigned by the World Health Organisation (WHO) for the same, the usage of face masks to prevent the spread of the virus from an infected person to a healthy person has been of prime significance. To enable efficient execution of the WHO protocol, this case study proposes creating a real-time detection model built explicitly for capturing an audience to alert people who are not following COVID prevention protocols. The proposed case study utilises the state-of-the-art (SOTA) YOLOv6 algorithm along with different iterations of the YOLO algorithm, such as YOLOv4, and YOLOv5, for representing the variation in training performance among various iterations of YOLO. Further, it discusses and analyses the effectiveness of using a real-time detector for face mask detection. This study aims to decrease the risk of a healthy person being affected by the COVID-19 virus by keeping a check on a designated crowd and contributes towards the prevention of the further spread of the virus by crowd monitoring and control methods. The real-time implementation of the proposed case study reports a positive impact, with a 36% increment in people following the standard COVID-19 protocol of wearing masks in public places.
  Keywords: YOLOv6; DarkNetCSP; CNN; COVID-19; case study; computer vision; ecological studies; healthcare-related research; real-time monitoring system.
  DOI: 10.1504/IJCIS.2024.10052165
   
  
• IoT Cloud-based Telecare Medical Healthcare System with Lightweight Authentication Scheme  
  by Sunil Gupta, Goldie Gabrani 
  Abstract: TMIS has huge potential for enhancing health care delivery, it also creates certain issues with regards to accessibility, privacy, security and confidentiality of sensitive patient information. Son et al.’s (2020) proposal for a secure authentication mechanism for cloud-based TMIS differs from Amin and Biswas’ (2015a) proposal for a TMIS architecture based on numerous physical servers. For gaining access to the medical servers, they have devised an authentication system. They have argued that their scheme removes all the shortcomings of earlier schemes. We, in this paper, will show that their architecture based on dedicated multiple physical servers has certain limitations. Such systems are usually either under-provisioned or over-provisioned and are quite expensive. In addition, Amin and Biswas (2015a) Mutual authentication and user anonymity are not provided by the scheme, and therefore is vulnerable to malicious assaults. In order to get over these restrictions, we suggest I a unique design for the TMIS based on cloud based on OTP. We show that the performance of our suggested system is enhanced while simultaneously overcoming the drawbacks of Amin and Biswas (2015a) approach.
  Keywords: smart card; IoT-cloud computing; mutual authentication; telecare medical information systems; TMIS; one-time password; OTP; anonymity; AVISPA.
  DOI: 10.1504/IJCIS.2024.10052846
   
  
• Non-Linear Control Based Class-D Amplifier for Audio Intelligent Infrastructure Applications
  by Sridhar Joshi, S.Silvia Priscila, Suman Rajest George, Kriti Srivastava, Prasath Alias Surendhar S, Rajasekaran Rajkumar 
  Abstract: A nonlinear control-based class-D amplifier using a dc power source for medium-power audio applications is proposed in this paper. The amplifier utilises switches in a half-bridge configuration to realise the class-D power stage. A passive second-order bandpass filter is cascaded with the power stage to render a highly linear audio amplifier for high-quality audio reproduction. A nonlinear technique-based controller is used for a closed-loop amplifier system which offers high immunity to power supply noise, robustness, and fault tolerance without using a triangular carrier generator. An 80 W, 20 Hz to 20 kHz amplifier model is developed considering the nonlinearity present in the power electronic switches of the power stage. Simulation results of the proposed amplifier with a full range 4 ? loudspeaker load are presented. The amplifier’s response at different frequencies in the audio spectrum is presented, which confirms the amplifier’s linearity and command following property. To confirm the high linearity of the amplifier, the THD versus frequency plot is depicted, which ensures the suitability of the proposed amplifier for high-fidelity audio amplification.
  Keywords: nonlinear control; class-D; amplifier; audio applications; intelligent infrastructures; power supply noise; robustness; half-bridge audio amplifier; HBAA; PWM wave.
  
  
• Study on the influence of deep foundation pit excavation on the deformation of adjacent viaduct pile foundation  
  by Haitao Wang, Mingyang Xu, Tao Guo, Minghua Cui, Jiangtao Tian 
  Abstract: In order to study the influence of deep foundation pit excavation on the deformation of adjacent viaduct pile foundation in water-rich karst environment, a three-dimensional finite element analysis model was established, and the dewatering process of deep foundation pit was simulated. The results show that with the increasing of pile depth, the horizontal displacement of pile foundation is larger at the top and smaller at the bottom. The dewatering of deep foundation pit has influence on the bending moment and axial force of adjacent viaduct pile foundation, but they are in a relatively safe range. The horizontal displacement of bridge pile is negatively correlated with the distance between bridge pile and deep foundation pit, the supporting stiffness of deep foundation pit and the elastic modulus of the first layer soil.
  Keywords: Deep foundation pit; Pile foundation; Bridge pier; Numerical calculation; Influence parameters; Karst cave.
  DOI: 10.1504/IJCIS.2024.10053029
   
  
• Critical success factors of Composite LPG Cylinders in India
  by Binod Kumar Singh, Tamajeet Chatterjee, Nistha Srivastava Srivastava, Mahesh Amarjit S.V 
  Abstract: Metal cylinders are being phased out in favour of composite LPG cylinders. These cylinders are light, have a pleasing colour and shape, are rust and corrosion resistant, UV resistant, and also 100% explosion proof. The aim of this study is to understand consumer’s awareness level and likeliness to shift to composite LPG cylinders. The paper studies the relative strength and weaknesses of composite LPG cylinders in comparison with traditional metal cylinders. This paper provides the opportunity to identify the consumer’s needs and to bridge by suggesting technological applications, thus giving an idea to streamline the value chain of LPG cylinder distribution. This study also provides a holistic study of both B2B and B2C segments in the LPG cylinder value chain and thus provides a scope of improving the existing system. The paper will help the business leaders in composite LPG cylinder manufacturing along with cost saving opportunities to the distributors in the long run.
  Keywords: type IV composite cylinders; metal LPG cylinder; composite LPG cylinder; composite cylinder pricing; market feasibility; LPG cylinder safety; India.
  
  
• Intelligent Infrastructures Using Deep Learning Based Applications for Energy Optimization  
  by Monica Purushotham, Kriti Srivastava, Chitra A, Malathi S, D. Kerana Hanirex, S.Silvia Priscila 
  Abstract: Renewable energy could boost electricity and wave power. Increased electricity consumption necessitates hydropower integration. Wind energy is cost-effective and promising. This study examines wind farm viability in windy areas. This study summarises deep learning models, methods, and wind and wave energy conditions. Comparing approaches for similar applications. A computation technique can substitute a comprehensive computer model, with a 94% accuracy rate compared to model simulations and 84% compared to other data. The study found great promise in deep learning-based energy optimisation, storage, monitoring, forecasting, and behaviour inquiry and detection. Energy regulators and utility management could evaluate sustainable electricity diversification using the study’s findings. This study summarises deep learning models, methods, and wind and wave energy conditions. Comparing equivalent application approaches. A computing technique can replace a complex computer model with 94% accuracy compared to model simulations and 84% to other data. Deep learning applications for energy optimisation, storage, monitoring, forecasting, and behaviour identification and investigation were promising. The project would give energy regulators and utility management impartial advice on sustainable electricity diversification.
  Keywords: renewable energy; deep learning; wind turbine blade; electricity generation; wind energy; power management; wave energy; extended short-term memory.
  DOI: 10.1504/IJCIS.2024.10054806
   
  
• Linear Kernel Pattern Matched Discriminative Deep Convolutive Neural Network for Dynamic Web Page Ranking with Big Data  
  by Sujai P, Sangeetha V 
  Abstract: Websites and information are plentiful. Search engines return many pages based on user requests. Thus, unstructured web content compromises information retrieval. A new gestalt pattern matched linear kernel discriminant maxpooled deep convolutive neural network (GPMLKDMDCNN) is to rank web pages by query. At first, Szymkiewicz-Simpson coefficient and Gestalt pattern matching Paice-Husk method are to remove stop words and stem words during preparation. Fisher kernelised linear discriminant analysis then selects keywords from preprocessed data. Bivariate Rosenthal correlation is utilised for page rank-based correlation outcomes and saving time, and online sites are ranked by user query with higher accuracy. The experiment uses parameters such as accuracy, false-positive rate, ranking time, and memory consumption. The evaluation shows that the GPMLKDMDCNN method is superior in using the CACM dataset with maximum ranking accuracy of 5%, minimum false positive rate and memory consumption of 39% and 13%, and quicker ranking time by 20% than the existing methods, respectively.
  Keywords: web pages ranking; maxpooled deep convolutive neural network; Szymkiewicz–Simpson coefficient; gestalt pattern matched Paice-Husk algorithm.
  DOI: 10.1504/IJCIS.2024.10054915
   
  
• Adoption of Cloud Accounting for critical infrastructure with in Small Medium Enterprises in Odisha through Prioritization of its Sustainable Benefits  
  by Sarita Mishra, Suresh Sahoo, Srinivas Subbarao Pasumarti 
  Abstract: This study has attempted to use “Relative to an identified distribution” (RIDIT) algorithm based modeling for analyzing real time empirical data related to benefits realized by an enterprise through adoption of critical infrastructure of cloud accounting in context of Small Medium enterprises of Odisha. The study focuses on demand side aspect of cloud accounting aspects by considering its realized benefits in context of SMEs in Odisha. Reduction of Cost, reduction of wastage and gaining more sustainability, Security of financial information comes on the top positions in the priority list of benefits. The finding of the study is significant with respect to its practical orientation as the responses collected from real user of the system. Modeling of realized benefits of cloud accounting by enterprises with RIDIT analysis could contribute towards demand creation of cloud accounting; its adoption and improvement of services to the clients’ The finding of the study could be informative to such enterprises for taking proper decision towards adoption of cloud accounting in critical infrastructure.
  Keywords: Cloud accounting; Sustainable benefits; Critical infrastructure; Prioritization; RIDIT analysis.
  DOI: 10.1504/IJCIS.2024.10055083
   
  
• Mining Closed High Utility Itemsets Using Sliding Window Infrastructure Model Over Data Stream  
  by Mahesh Kumar Ponna, Srinivasa Rao P 
  Abstract: A group of products that has utility values and that are sold together greater than a preset lowest utility cut-off is produced by mining high-utility itemsets. These itemsets’ profit units have external and internal usefulness values. In each transaction, the quantity of each item sold, respectively, is considered to determine the utilities of these itemsets. As a result, assessing an itemset’s high utility is symmetrically dependent on all of its internal and external utilities. Both utilities contributed equally, and there are two key deciding considerations. First, selling groupings of low-external utility commodities generally meets the minimal utility requirement. Regular itemset mining can help find such itemsets. Second, numerous high-utility itemsets are created; thus, some interesting or significant ones may be omitted. This study applies an asymmetric technique that overlooks interior utility counts to discover those with considerable external utility counts. Two genuine datasets showed that external utility values strongly affect high utility itemsets. This study also shows that high minimal utility threshold values and a faster method increase this influence.
  Keywords: high utility itemset; sliding window; information extraction; high-utility itemset mining; HUIM; itemset mining.
  DOI: 10.1504/IJCIS.2024.10055102
   
  
• Cross domain and Adversarial Learning based Deep Learning approach for Web Recommendation  
  by Asha K. N, Rajasekaran Rajkumar 
  Abstract: The web has become a massive source of knowledge in the internet age. This extra information makes it hard to choose items based on individual needs. Today, choosing suitable products takes time and effort. Daily uploads and downloads from YouTube, Instagram, Facebook, and others generate massive volumes of data. Keep up with the internet’s wealth of information. Recommender systems can help users find useful data in vast datasets. User-interested recommender systems provide personalised and non-personalised recommendations. Real-time applications need recommender systems, but conventional methods have problems. In this work, we identified the issues and developed a cross-domain web recommendation system using a deep learning-based scheme. A joint reconstruction loss model reduces learning error with an autoencoder and adversarial learning technique. An open-source cross-domain dataset tests the proposed approach. For the Movie dataset, average HR, NDCG, and MRR are 0.8951, 0.5911, and 0.6121. The book dataset averages 0.8358, 0.6824, and 0.5575.
  Keywords: cross domain; adversarial learning; deep learning; web recommendation; cross-domain recommender system; demographic information; internet age.
  DOI: 10.1504/IJCIS.2024.10055283
   
  
• Thermal Performance Analysis of PCM Incorporated Roof Slab Infrastructures Using Deep Learning Algorithms  
  by Jaspal Singh, R.K. Tomar, Narandra Dutta Kaushika, Gopal Nandan 
  Abstract: PCM technology uses thermal energy storage (TES) to lessen the effects of changes in the outside temperature. PCM thermal energy storage may reduce ambient temperature changes (TES). Latent heat storage (LHS) reduces HVAC needs and enhances indoor comfort in conventional buildings. In buildings without HVAC, latent heat storage (LHS) enhances indoor thermal comfort by reducing the demand for HVAC. This study examines and measures the advantages of using PCM for building envelopes. In order to generalize the findings, a 1 m * 1 m * 1 m reference model is employed with four Indian towns located in various climate zones. Decision tree monitors temperature over time. Root mean square transforms actual and anticipated values, while mRMR selects features. Thermal testing equipment, a PCM wallboard heat storage experiment, and investigations on 5 mm, 10 mm, and 20 mm PCM plasterboard with a 220 C melting temperature are constructed to validate the results. PCM thickness reduced energy use logarithmically in all climatic zones, with temperate office buildings benefiting most.
  Keywords: phase change material; adaptive envelopes; PCM thickness and energy thermal energy storage; infrastructures energy efficiency; passive strategies.
  DOI: 10.1504/IJCIS.2024.10055409
   
  
• Multiple Criteria Decision Making for Determining the Optimal Wind Farm Site under Uncertainty  
  by Abdulaziz Almaktoom, Mawadda Samkari 
  Abstract: Optimal wind turbine location plays a major role in power generation and turbine life cycle. Advances have been made in the subject of multiple criteria decision-making (MCDM), resulting in new methods for improving and analysing the decision of wind farm location while considering various uncertainty resources. Sources of uncertainty, such as wind availability, demand variability, and the costs of maintenance and wind turbines on wind farm allocation, can reduce energy and operations costs. In this research, a novel robust MCDM model for wind farm allocation has been developed. A case study involving mathematical simulation for three wind farm locations in Saudi Arabia has been employed to demonstrate the developed research and tools. The research contributions proposed the developed robust MCDM approach using the analytic hierarchy process (AHP), technique for order of preference by similarity to ideal solution (TOPSIS), and robust design (RD) could empower wind farm designers to have a better grasp of the weaknesses and strengths of their decision on wind farm allocation. Also, the paper advances a new approach that is practical and flexible for decision-makers. In addition, the research gives a valuable guideline for selecting the optimal site for a wind farm in other countries.
  Keywords: multiple criteria decision methods; MCDM; multiple criteria decision analysis; MCDA; analytic hierarchy process; AHP; TOPSIS; robust design methodology; RDM.
  DOI: 10.1504/IJCIS.2024.10055554
   
  
• LSTM-CNN: A Deep Learning Model for Network Intrusion Detection in Cloud Infrastructures  
  by Srilatha Doddi, Thillaiarasu N 
  Abstract: In cloud computing, resources are shared and accessed over the internet to perform intended computations remotely to minimise infrastructure costs. The usage and dependency on the cloud network have increased, and the chances of invasion and loss of data and challenges to develop a reliable intrusion detection and prevention system (IDPS). The existing machine learning-based approaches require the manual extraction of features, which produces low accuracy and high computational time. Providing a secure network involves a framework based on multi-fold validation and privacy in information transmission. The deep learning-based network IDPS model has been proposed to handle the large volume of network traffic in the cloud. This paper proposes a tailored long short-term memory and convolution neural network (LSTM-CNN)-based approach to design a new IDS. The proposed model productively examines intrusions and generates alerts proficiently by incorporating users'; information and conducting examinations to detect intrusions. The model's performance is assessed using accuracy, precision, F1-score and recall measures. The proposed model achieves outstanding performance with a test accuracy of 99.27%.
  Keywords: cloud intelligent infrastructures; convolution neural network; intrusion detection and prevention; long short-term memory; random forest; neural network; security.
  DOI: 10.1504/IJCIS.2024.10055712
   
  
• COVID-19 and the drinking water consumption pattern in Bogot  
  by Alfonso Duran Chico, Juan Sebastian De Plaza Solorzano 
  Abstract: Covid-19 caused a high impact on the human life sectors, and water distribution services were no exception, given that a change in water consumption behaviour has been registered in several planet regions to the isolation and hygiene measures imposed to contain the new Coronavirus. This study analysed the water consumption pattern in Bogot
  Keywords: Colombia; Coronavirus; Covid-19; critical infrastructure; Bogotá; emergency; lockdown; post-COVID; resilient water supply systems; SARS-CoV-2; water consumption pattern.
  DOI: 10.1504/IJCIS.2023.10055789
   
  
• Role of E-Adoption of Emerging Technology in 4P Organizational Framework During Covid-19  
  by Pushpa Singh, Narendra Singh, Rajnesh Singh, Nishu Panwar, Sunil Gupta 
  Abstract: During the COVID-19, micro, small, and medium-scale business organisations have suffered economic fragility. Apart from lockdown, social distancing and the traditional style of the business process are the factors that affect the business organisation. A business organisation utilising e-adoption of emerging technologies such as artificial intelligence (AI), blockchain, internet of things (IoT), cloud computing, etc., have survived well in the market and achieved high-profit gain. In this paper, we explore the challenges of traditional business organisations. Traditional business organisation frameworks based on 4P: people, process, product and profit are based on manual processes and away from emerging technologies. The proposed organisational framework revolutionised traditional business practices and enhanced productivity, efficiency, and customer retention. People can connect and access business organisations with end-user devices such as smartphones, desktops, laptops, and other hand-held devices.
  Keywords: e-adoption; organisation; COVID-19; AI; blockchain.
  DOI: 10.1504/IJCIS.2024.10056042
   
  
• Unsupervised Strategies In Detecting Log Anomalies using AIOps Monitoring to Amplify Performance by PCA and ANN Systems  
  by Vivek Basavegowda Ramu, Ajay Reddy Yeruva 
  Abstract: A fundamental task that artificial intelligent operations (AIOps) perform is to mitigate the risk of abnormal system behaviours and identify and demystify the alerts when encountering the presence of log anomalies and assess the reasons for the different system failures and run smoothly, system flaws must be fixed and to empower this functionality, the infusion of related artificial intelligence needs to be integrated, there have been several innovative strategies that have been incorporated with systems utilising AIOps platforms. However, the study has been limited, and some grey areas remain. Suppressing incorrect logs in system performance analysis is unsupervised in this paper. PCA and ANN produce a feed input for detailed analysis. System performance improves. Pseudo positives false alerts in log anomaly detection theories are introduced in the study. The proposed strategy reduces aberrant logs by 72%, outperforming most other experiments. It is unique in log analysis since it reduces false positives, making it easier to find true anomalies and improving system efficiency. This approach has promising research possibilities.
  Keywords: artificial intelligent operations; AIOps; anomaly log detection; log data analysis; performance; pseudo positives; recurring anomalies; monitoring; observability.
  DOI: 10.1504/IJCIS.2024.10056177