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1
Zhu, Guangyu, Fuquan Zhao, Haokun Song, and Zongwei Liu. "Cost Analysis of Vehicle-Road Cooperative Intelligence Solutions for High-Level Autonomous Driving: A Beijing Case Study." Journal of Advanced Transportation 2024 (January 23, 2024): 1–22. http://dx.doi.org/10.1155/2024/6170743.
Повний текст джерелаАнотація:
The development of the vehicle-road cooperative intelligence can effectively resolve the current technical impediment and cost quandary associated with high-level autonomous driving. Nevertheless, the intelligent infrastructure entails initial deployment costs and ongoing energy consumption and maintenance costs, necessitating a comprehensive and quantitative analysis of the costs of intelligent infrastructure and the corresponding changes in comprehensive costs. The cost evaluation model for the cooperative intelligent system is designed in this paper, considering the corresponding intelligent infrastructure layout scheme for different road types within the technical framework. The intelligent configuration and corresponding cost transfer from roadside to vehicle side under the synergy effect is also analyzed. Using Beijing as a case study, the results indicate that the deployment of intelligent infrastructure will effectively reduce acquisition and usage costs of high-level intelligent vehicles and achieve a greater “reuse” effect by serving more intelligent connected vehicles (ICVs). Compared to the vehicle intelligence, collaborative intelligence will reduce cumulative total costs by more than ¥200 billion from 2023 to 2050, even with the inclusion of intelligent infrastructure’s costs.
2
Rahman, Mir Mehedi, Bishwo Prakash Pokharel, Sayed Abu Sayeed, Sujan Kumar Bhowmik, Naresh Kshetri, and Nafiz Eashrak. "riskAIchain: AI-Driven IT Infrastructure—Blockchain-Backed Approach for Enhanced Risk Management." Risks 12, no. 12 (December 19, 2024): 206. https://doi.org/10.3390/risks12120206.
Повний текст джерелаАнотація:
In the evolving landscape of cybersecurity, traditional information technology (IT) infrastructures often struggle to meet the demands of modern risk management frameworks, which require enhanced security, scalability, and analytical capabilities. This paper proposes a novel artificial intelligence (AI)–driven IT infrastructure backed by blockchain technology, specifically designed to optimize risk management processes in diverse organizational environments. By leveraging artificial intelligence for predictive analytics, anomaly detection, and data-driven decision-making, combined with blockchain’s secure and immutable ledger for data integrity and transparency, the proposed infrastructure offers a robust solution to existing challenges in risk management. The infrastructure is adaptable and scalable to support a variety of risk management methodologies, providing a more secure, efficient, and intelligent system. The findings highlight significant improvements in the accuracy, speed, and reliability of risk management, underscoring the infrastructure’s capability to proactively address emerging cyber threats. To ensure the proposed model effectively addresses the most critical issues, the Decision-Making Trial and Evaluation Laboratory (DEMATEL) technique will be used to analyze and evaluate the interrelationships among the existing critical factors. This approach evaluates the interrelationships and impacts of these factors, verifying the model’s comprehensiveness in managing organizational risk. This study lays the foundation for future research aimed at refining AI-driven infrastructures and exploring their broader applications in enhancing organizational cybersecurity.
3
Venkatesh Kata. "Intelligent cloud automation: Leveraging AI and machine learning for enhanced cloud management." World Journal of Advanced Research and Reviews 26, no. 1 (April 30, 2025): 295–301. https://doi.org/10.30574/wjarr.2025.26.1.1094.
Повний текст джерелаАнотація:
Artificial Intelligence and Machine Learning are fundamentally transforming cloud computing infrastructure, introducing unprecedented capabilities for intelligent, adaptive, and autonomous system management. This article delves into the profound technological metamorphosis occurring in computational ecosystems, highlighting the transition from static, predetermined infrastructure models to dynamic, self-optimizing environments. Intelligent cloud automation represents a revolutionary paradigm that enables predictive maintenance, sophisticated resource allocation, and autonomous problem resolution. By integrating advanced machine learning algorithms, quantum computing principles, and probabilistic computational models, cloud infrastructures can now anticipate, respond, and optimize themselves with minimal human intervention. The article illuminates the complex technological landscape where neural network architectures, reinforcement learning, and anomaly detection systems converge to create intelligent computational platforms that transcend traditional management approaches.
4
Zhang, Jiayi. "Digital Infrastructure and Corporate Intelligence." Advances in Economics, Management and Political Sciences 175, no. 1 (April 10, 2025): 142–51. https://doi.org/10.54254/2754-1169/2025.21987.
Повний текст джерелаАнотація:
With continuous progress in information technology, the construction of digital infrastructure has become a key force in promoting corporate intelligence. This study in-depth explores and analyzes the mechanism by which digital infrastructure promotes the intelligent transformation of enterprises. The research finds that digital infrastructure significantly promotes the intelligence of enterprises, and technology spillover, talent cultivation, and market orientation significantly enhance the positive effect of digital infrastructure on corporate intelligence. The research conclusion points out that digital infrastructure is the cornerstone for enterprises to explore the potential of intelligence. Enterprises should actively embrace digital transformation and inject strong momentum into the intelligent transformation of enterprises through formulating forward-looking technical strategies and upgrading plans. Looking to the future, the deep integration of digital infrastructure and corporate intelligence will drive the innovation of the entire economic ecosystem and have a profound impact on the sustainable development of enterprises and the competitive pattern of the industry.
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Sherrill, John T., and Michael J. Salvo. "Automated infrastructures." Communication Design Quarterly 10, no. 2 (July 2022): 22–31. http://dx.doi.org/10.1145/3507857.3507860.
Повний текст джерелаАнотація:
As artificial intelligence (AI) automates technical and dialogic processes, technical communicators produce value through articulating complex problems, facilitating new forms of participation, and managing user-generated content via experience architecture. Automated and intelligent agents are least able to grasp the context of experiences, requiring human input/feedback for maximum performance. The examples we trace both prepare communities to embrace AI as part of the available information infrastructure and create an automated infrastructure of intelligent augmented action. Following Star's anthropological investigation of infrastructure, we analyze organizational examples where rhetoric entangles AI, automation, generative design, additive manufacturing, gift labor, and assembly lines.
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Spragins, J. "Converging Infrastructures: Intelligent Transportation and the National Information Infrastructure [New Books]." IEEE Network 10, no. 6 (November 1996): 3. http://dx.doi.org/10.1109/mnet.1996.546348.
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Liu, Zongwei, Haokun Song, Hong Tan, Han Hao, and Fuquan Zhao. "Evaluation of the Cost of Intelligent Upgrades of Transportation Infrastructure for Intelligent Connected Vehicles." Journal of Advanced Transportation 2022 (January 18, 2022): 1–15. http://dx.doi.org/10.1155/2022/5841373.
Повний текст джерелаАнотація:
Intelligent connected vehicles (ICVs) have become the focus and development direction of the automobile industry. As a flexible intelligent terminal, ICVs will become a necessary part of the intelligent transportation system. The routes of developing ICVs based on “vehicle to X” (V2X) can effectively alleviate the demands of vehicles for intelligent functions and cut related research costs, accelerating commercialization of ICVs and leading to many social benefits. At present, China has made it clear to develop ICVs based on V2X, which requires simultaneous intelligent upgrades of vehicles and transportation infrastructure. Therefore, intelligent upgrades of transportation infrastructure must match the functional requirements of ICVs. In addition, the investment in intelligent upgrades of transportation infrastructure is mainly from the government, so the costs must be controlled reasonably to find the most cost-effective upgrade route. In this paper, the types of intelligent transportation infrastructures were determined by sorting out the demands of ICVs for transportation infrastructure, and the deployment methods and upgrade routes of intelligent transportation infrastructures were designed. Then, the cost evaluation model for intelligent upgrade of transportation infrastructures was established, based on which, the cost evaluation of different intelligent upgrade routes of transportation infrastructure was carried out in closed highway and open urban road scenarios to determine the optimal route. Besides, the key elements affecting the cost of transportation infrastructure upgrades were identified, and their impact degrees on transportation infrastructure upgraded were analyzed by scenario analysis. The results show that the intelligent transportation infrastructure for advanced ICVs mainly includes communication base stations, roadside units (RSUs), vision sensors, millimeter-wave radars, laser radars (LiDARs), meteorological sensors, intelligent signal machines, edge computing servers, and cloud computing centers. The route of deploying primary intelligent transportation infrastructure at first and then directly upgrading them to advanced level can well match the functional requirements of ICVs on the basis of lower costs. The costs of RSUs, LIDARS, and edge computing servers as well as data transmission rate of 5G are key elements affecting the costs of intelligent upgrades of transportation infrastructure.
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VASYLKIVSKYI, Mikola, Ganna VARGATYUK, and Olga BOLDYREVA. "INTELLIGENT RADIO INTERFACE WITH THE SUPPORT OF ARTIFICIAL INTELLIGENCE." Herald of Khmelnytskyi National University. Technical sciences 217, no. 1 (February 23, 2023): 26–32. http://dx.doi.org/10.31891/2307-5732-2023-317-1-26-32.
Повний текст джерелаАнотація:
The peculiarities of the implementation of the 6G intelligent radio interface infrastructure, which will use an individual configuration for each individual subscriber application and flexible services with lower overhead costs, have been studied. A personalized infrastructure consisting of an AI-enabled intelligent physical layer, an intelligent MAC controller, and an intelligent protocol is considered, followed by a potentially novel AI-based end-to-end (E2E) device. The intelligent controller is investigated, in particular the intelligent functions at the MAC level, which may become key components of the intelligent controller in the future. The joint optimization of these components, which will provide better system performance, is considered. It was determined that instead of using a complex mathematical method of optimization, it is possible to use machine learning, which has less complexity and can adapt to network conditions. A 6G radio interface design based on a combination of model-driven and data-driven artificial intelligence is investigated and is expected to provide customized radio interface optimization from pre-configuration to self-learning. The specifics of configuring the network scheme and transmission parameters at the level of subscriber equipment and services using a personalized radio interface to maximize the individual user experience without compromising the throughput of the system as a whole are determined. Artificial intelligence is considered, which will be a built-in function of the radio interface that creates an intelligent physical layer and is responsible for MAC access control, network management optimization (such as load balancing and power saving), replacing some non-linear or non-convex algorithms in receiver modules or compensation of shortcomings in non-linear models. Built-in intelligence has been studied, which will make the 6G physical layer more advanced and efficient, facilitate the optimization of structural elements of the physical layer and procedural design, including the possible change of the receiver architecture, will help implement new detection and positioning capabilities, which, in turn, will significantly affect the design of radio interface components. The requirements for the 6G network are defined, which provide for the creation of a single network with scanning and communication functions, which must be integrated into a single structure at the stage of radio interface design. The specifics of carefully designing a communication and scanning network that will offer full scanning capabilities and more fully meet all key performance indicators in the communications industry are explored.
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Hu, Limingyue, Jingjing Wu, Xiang Li, Bingyi Li, Huiyu Shan, and Ranran Zhang. "Intelligent transport construction solutions in the form of new infrastructure." Highlights in Business, Economics and Management 47 (February 8, 2025): 244–50. https://doi.org/10.54097/gqa4g367.
Повний текст джерелаАнотація:
With the advent of the new infrastructure era, new infrastructures such as 5G, AI, BeiDou Navigation and IOT will bring great changes to the transport industry, driving it towards intelligence and digitalization. As an important part of the development of new infrastructure, the transport industry has become a key factor in driving the country towards ecology, digitalization, intelligence, high speed, transformation of old and new dynamics and symmetrical economic structure. Smart light poles are an important part of smart city construction, combining multiple functions in one, including small base stations, video surveillance, LED information screens, environmental sensors, IP broadcasting sound poles and charging posts. This new type of information infrastructure can provide accurate data services for urban governance through edge-cloud collaboration, and provide low-latency support for multi-scene linkage. In the new situation, the study of how to plan and build intelligent traffic light poles is an important part of the transport industry's contribution to the national information development strategy, and is a new topic of great significance for the transport industry's research.
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Braga, Vasili. "DECENTRALISED AUTONOMOUS SOCIETY THROUGH LARGE LANGUAGE MODELS’ BASED AGENTS: A PATHWAY TO EMPOWER SMALL COMMUNITIES." Journal of Engineering Science 30, no. 3 (November 2, 2023): 99–120. http://dx.doi.org/10.52326/jes.utm.2023.30(3).07.
Повний текст джерелаАнотація:
This paper explores the concept of Decentralized Autonomous Society through the lens of Large Language Models focusing on the transformative potential of integrating these technologies. The paper on the role of Large Language Models based agents in providing a versatile, responsive, and contextually intelligent resource within a Decentralized Autonomous Society, fostering intellectual exploration, assisting in complex tasks, and aiding real-time problem solving. One delves into their integration with Decentralized Autonomous Society infrastructures, including robotic and automated systems. While promising, the integration of Large Language Models and their agents into a Decentralized Autonomous Society poses several challenges, including infrastructure and connectivity limitations, information accuracy, artificial intelligence bias, privacy and data security, and ethical concerns. This paper critically discusses these issues and proposes potential solutions. Through the lens of the Decentralized Autonomous Society construct, the paper considers the future possibilities and implications of artificial intelligence, where self-sustaining, digitally-empowered communities leverage artificial intelligence as a cornerstone of their collective intelligence.
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Onyelowe, Kennedy C. "Sustainable Intelligent Infrastructure, Inaugural Editorial." Sustainable Intelligent Infrastructure 1, no. 1 (January 28, 2025): 1–3. https://doi.org/10.62762/sii.2025.187975.
Повний текст джерелаАнотація:
The inaugural editorial of Sustainable Intelligent Infrastructure introduces the journal's mission to address global challenges such as urbanization, climate change, and resource depletion by integrating sustainability principles with advanced technologies. It aims to provide a multidisciplinary platform for high-quality research that explores the synergy between smart technologies, artificial intelligence, machine learning, and data analytics in sustainable infrastructure. The journal aspires to become a leading voice in global discourse, bridging the gap between traditional infrastructure and intelligent systems while driving innovation to meet the United Nations' Sustainable Development Goals. Highlighting current trends like AI-driven decision-making, climate-resilient infrastructure, and renewable energy integration, the editorial acknowledges both the opportunities and challenges in the field, including data standardization and ethical considerations. This inaugural issue features pioneering studies exemplifying the journal's vision, inviting global collaboration to shape a smarter, more sustainable, and resilient future for infrastructure development.
12
Zhu, Guangyu, Fuquan Zhao, Haokun Song, Wang Zhang, and Zongwei Liu. "Research on the Social Values of Vehicle–Road Collaborative Intelligence Systems: A Case Study in Beijing." Sustainability 17, no. 4 (February 13, 2025): 1565. https://doi.org/10.3390/su17041565.
Повний текст джерелаАнотація:
Intelligent vehicles are expected to yield significant benefits in traffic safety, traffic efficiency, energy conservation, and carbon emission reduction. As the collaborative intelligence technology route becomes an industry consensus, intelligent vehicles will generate greater social benefits under the empowerment of roadside intelligence infrastructure. At the same time, the introduction of roadside intelligence infrastructure also adds corresponding deployment costs and operation and maintenance costs. Currently, assessments of the comprehensive social benefits and cost inputs associated with the application of vehicle–road collaborative intelligence systems remain unclear, making it difficult to provide effective references for industry development. Therefore, it is necessary to conduct a comprehensive assessment of the multi-dimensional benefits generated by collaborative intelligence systems and the incremental costs. This study constructs a social value assessment model for vehicle–road collaborative intelligence systems, which includes three benefit sub-models for safety, efficiency, and carbon emission reduction, as well as two cost sub-models for vehicle-side networking and roadside intelligence infrastructure. Beijing is selected for case analysis. The social benefits and social incremental cost inputs of different intelligence deployment scenarios are scientifically evaluated and analyzed. The study indicates that by deploying roadside intelligence infrastructure and in-vehicle networking terminals as planned in Beijing, an accumulated safety benefit of 925.6 billion RMB, a traffic efficiency benefit of 628.9 billion RMB, and a carbon emission reduction benefit of 2.66 billion RMB are expected to be generated from 2024 to 2050. The cumulative cost investment of 28.8 billion RMB in roadside intelligence infrastructure and vehicle networking terminals is projected to yield approximately 20.8 times the increment in social comprehensive benefits. The deployment progress of roadside intelligence infrastructure and the loading progress of fleet networking terminals should be fully coordinated to maximize the social value of the system. The corresponding research findings can provide references for city managers in decision-making on intelligent road deployment, and for the coordination of vehicle manufacturers in equipping vehicle networking terminals.
13
Li, Zhaoyang, Hao Duan, Jiarui Lei, Zijiang Yang, Feng Lin, Yumei Li, and Zhiwei Zhang. "An Integrated Assessment Scheme of Network Infrastructure Following Security Standards and Specifications." Journal of Networking and Network Applications 2, no. 3 (2022): 95–106. http://dx.doi.org/10.33969/j-nana.2022.020301.
Повний текст джерелаАнотація:
In this intelligent era, with deeper research, faster development, and wider application of information technologies, the network infrastructure plays one more and more important role in data communication and processing that affects almost every field worldwide. Correspondingly, cyberspace security, especially the security of network infrastructure has become elementary for countries and companies. Then, various security standards and specifications have been proposed to guide network infrastructure’s design, development, and operation. Consequently, it is the key to assess whether a network infrastructure is compliant with the related standards and specifications. However, most of the existing security assessment schemes are manual, that is, testers should check all issues depending on their understanding of the network infrastructures and related documents. That results in the lack of accuracy, continuity as well as comprehensiveness. Therefore, in this paper, we propose an Al-based network infrastructure security assessment (ISA-CN) scheme, which concerns China’s current fundamental network infrastructure security assessment related standards and specifications and evaluates the object’s security states with multi-dimensional automatically monitored network traffic data continuously and comprehensively. The analytical and experimental results show that our ISA-CN scheme is suitable for the assessment of real-world network infrastructure systems.
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Gupta, Richa. "Decision Support Systems for IoT Based Infrastructures." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 9, no. 3 (December 17, 2018): 1074–81. http://dx.doi.org/10.17762/turcomat.v9i3.13896.
Повний текст джерелаАнотація:
Computation offloading divides enormous computational tasks across numerous computer resources, circumventing hardware limits. Edge computing can employ vast quantities of data, individual preferences, and clever algorithms by offloading smart models to high-performance cloud servers. We propose a getaway-centric IoT system to enable intelligent and autonomous IoT devices at the computer infrastructure's edge. Edge computing manages IoT devices by selecting and applying the best control factor from a pool of intelligent services. The cloud-based intelligent service engine provides intelligent services by offloading intelligence and optimisation algorithms. Thus, the gateway's decision-making model may choose the best alternative. Resource virtualization-based gateway-based device management facilitates user monitoring and visualisation in the proposed IoT system. The gateway evaluates context-based profiles to enable real-time connection with intelligent services and dynamic application of the appropriate control factor to the physical device using the virtual resource. We propose two smart models to learn characteristics of a user's home environment using deep learning and build inference models for the intelligent service engine to optimise energy usage with the recommended IoT system. Inference methods forecast heater energy usage. Heaters set the environment. The decision-making algorithm also lowers the heater setting based on the two use numbers, reducing energy consumption and producing a user-desired environment.
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Dovzhenko, Nadiia, Nataliia Mazur, Yuliia Kostiuk, and Svitlana Rzaieva. "INTEGRATION OF IOT AND ARTIFICIAL INTELLIGENCE INTO INTELLIGENT TRANSPORTATION SYSTEMS." Cybersecurity: Education, Science, Technique 2, no. 26 (2024): 430–44. https://doi.org/10.28925/2663-4023.2024.26.708.
Повний текст джерелаАнотація:
The article provides a detailed analysis of the conceptual and practical features of integrating Intelligent Transportation Systems (ITS) into urban environments, with a focus on the use of Internet of Things (IoT), Artificial Intelligence (AI), and edge computing technologies. A conceptual model of ITS has been developed, enabling not only real-time collection and processing of sensor data but also dynamic decision-making based on big data analytics. The multi-level architecture of ITS is examined, employing modern optimization, prediction, and clustering algorithms to enhance traffic management adaptability, minimize congestion, and reduce CO₂ emissions. Examples of successful ITS implementations in leading global cities are presented, showcasing their positive impact on increasing traffic throughput, reducing accident rates, and improving environmental conditions. Particular attention is paid to cybersecurity issues, which are critical for the stable and reliable operation of ITS. Potential threats associated with unauthorized access to system resources are analyzed, and the implementation of advanced encryption mechanisms, multi-factor authentication, and blockchain technologies is proposed to ensure data integrity and confidentiality. The article also highlights the development of effective anomaly detection algorithms capable of promptly responding to non-standard situations, such as traffic accidents or sudden changes in traffic flows. This ensures system resilience and flexibility in dynamic urban environments. The prospects for further ITS development through deeper integration of edge computing, Big Data technologies, and AI are emphasized, contributing to enhanced overall efficiency, safety, and adaptability of urban transportation infrastructure. The proposed ITS model incorporates energy efficiency, enables highly accurate traffic flow prediction, and ensures environmental sustainability in urbanized spaces. Practical recommendations for implementing the developed ITS are provided, emphasizing its ability to evolve and adapt to changes in traffic intensity, infrastructural constraints, and environmental safety requirements.
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Qian, Xiuqi. "Digital Infrastructure, Opening-up to the Outside World and the Transformation of Digital Intelligence of Enterprises." Advances in Economics and Management Research 12, no. 1 (October 28, 2024): 725. https://doi.org/10.56028/aemr.12.1.725.2024.
Повний текст джерелаАнотація:
Nowadays, the trend of the digital economy is prevalent, enterprises actively carry out the transformation of digital intelligence to seek a favourable position, and the Chinese government also vigorously promotes digital infrastructure, but the relationship between them remains to be investigated. Based on the panel data of A-share listed companies from 2011 to 2022, this paper constructs a double fixed effect model and demonstrates that digital infrastructure has a positive effect on the digital intelligent transformation of enterprises. The results show that technology spillover effect, production scale and digital sales play an intermediary role in the promotion of digital infrastructure to the transformation of digital intelligence of enterprises. Digital infrastructure has a significant role in promoting the transformation of enterprises in the eastern and western regions and areas with weak business environments. The level of enterprises’ opening-up plays a moderating role in the promotion of digital infrastructure to the digital intelligent transformation of enterprises. Based on this, some suggestions are put forward to speed up the construction of digital infrastructure and build a digital infrastructure system according to local conditions.
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Klus, Holger, and Dirk Niebuhr. "Integrating Sensor Nodes into a Middleware for Ambient Intelligence." International Journal of Ambient Computing and Intelligence 1, no. 4 (October 2009): 1–11. http://dx.doi.org/10.4018/jaci.2009062201.
Повний текст джерелаАнотація:
The development of infrastructures enabling dynamic and automated composition of IT systems is a big challenge. This paper addresses a new idea of allowing component-based systems to reconfigure themselves. Therefore, the authors propose DAiSI - a Dynamic Adaptive System Infrastructure for dynamic integration of components as well as their reconfiguration during runtime. Thereby, one of the features of the infrastructure is that it is capable of binding components based on their availability. In this paper the authors concentrate on presenting how resource constrained sensor nodes can be integrated into a system using this infrastructure.
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Robbins, Scott, and Aimee van Wynsberghe. "Our New Artificial Intelligence Infrastructure: Becoming Locked into an Unsustainable Future." Sustainability 14, no. 8 (April 18, 2022): 4829. http://dx.doi.org/10.3390/su14084829.
Повний текст джерелаАнотація:
Artificial intelligence (AI) is becoming increasingly important for the infrastructures that support many of society’s functions. Transportation, security, energy, education, the workplace, the government have all incorporated AI into their infrastructures for enhancement and/or protection. In this paper, we argue that not only is AI seen as a tool for augmenting existing infrastructures, but AI itself is becoming an infrastructure that many services of today and tomorrow will depend upon. Considering the vast environmental consequences associated with the development and use of AI, of which the world is only starting to learn, the necessity of addressing AI alongside the concept of infrastructure points toward the phenomenon of carbon lock-in. Carbon lock-in refers to society’s constrained ability to reduce carbon emissions technologically, economically, politically, and socially. These constraints are due to the inherent inertia created by entrenched technological, institutional, and behavioral norms. That is, the drive for AI adoption in virtually every sector of society will create dependencies and interdependencies from which it will be hard to escape. The crux of this paper boils down to this: in conceptualizing AI as infrastructure we can recognize the risk of lock-in, not just carbon lock-in but lock-in as it relates to all the physical needs to achieve the infrastructure of AI. This does not exclude the possibility of solutions arising with the rise of these technologies; however, given these points, it is of the utmost importance that we ask inconvenient questions regarding these environmental costs before becoming locked into this new AI infrastructure.
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Alqasi, Mohammed Ali Younus, Youssif Ahmed Mohamed Alkelanie, and Ahmed Jamah Ahmed Alnagrat. "Intelligent Infrastructure for Urban Transportation: The Role of Artificial Intelligence in Predictive Maintenance." Brilliance: Research of Artificial Intelligence 4, no. 2 (November 11, 2024): 625–37. https://doi.org/10.47709/brilliance.v4i2.4889.
Повний текст джерелаАнотація:
Urban transportation infrastructure, encompassing roads, bridges, and tunnels, is vital for city mobility but remains vulnerable to wear and damage over time. Traditional maintenance methods, which rely on reactive repairs and scheduled inspections, often fall short in preventing sudden failures, resulting in costly disruptions and safety risks. This study examines how artificial intelligence (AI) is revolutionizing infrastructure management through predictive maintenance. By deploying smart sensors and utilizing predictive analytics, AI enables the continuous monitoring of structural health and the proactive identification of potential issues before they escalate into serious failures. The research develops and tests an AI-based predictive maintenance model, which analyzes real-time data from embedded sensors in urban infrastructure to detect anomalies and predict failure patterns. Results indicate that the predictive maintenance model can enhance response times, reduce maintenance costs by 30%, and prevent approximately 92% of unexpected failures. These findings underscore the potential of AI-driven approaches to reduce unplanned disruptions, optimize resource allocation, and extend infrastructure lifespan, ultimately creating safer and more sustainable urban transportation systems. However, challenges in data variability and environmental interference are noted, suggesting areas for future refinement. This study provides a framework for integrating AI in urban infrastructure maintenance, highlighting its potential to transform how cities approach long-term infrastructure health and reliability.
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Choi, Wonsang. "A Study on the Intelligent Disaster Management System Based on Artificial Intelligence." Journal of the Korean Society of Hazard Mitigation 20, no. 1 (February 29, 2020): 127–40. http://dx.doi.org/10.9798/kosham.2020.20.1.127.
Повний текст джерелаАнотація:
This study was conducted with the help of information and communications technology in the government's disaster safety management, which is promoting an intelligent government. We would like to suggest policy measures for the application of artificial intelligence (AI), also referred to as determinants. To this end, we review the government's disaster safety management and review the development and operation of AI. AI-based government disaster safety management is examined, showing databases according to disaster area, the infrastructure of information systems that are operated by institutions, and that the existing infrastructure operates on one disaster platform. Furthermore, this paper proposes the fostering of manpower and organizations for the operation of AI on the deployed infrastructure and platforms.
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Zhu, Guangyu, Fuquan Zhao, Haokun Song, Wang Zhang, and Zongwei Liu. "Research on Vehicle-Road Intelligent Capacity Redistribution and Cost Sharing in the Context of Collaborative Intelligence." Applied Sciences 14, no. 16 (August 19, 2024): 7286. http://dx.doi.org/10.3390/app14167286.
Повний текст джерелаАнотація:
The vehicle-road collaborative intelligence approach has become an industry consensus. It can efficiently tackle the technical hurdles and reduce the performance requirements and costs of on-board perception and computing devices. There is a need for in-depth quantitative studies to optimize the allocation of vehicle-road intelligent capabilities for collaborative intelligence. However, current research tends to focus more on qualitative analysis, and there is little research on the redistribution of vehicle and roadside intelligent capabilities. In this paper, we present a model for distributing perception and computing capabilities between vehicle-side and roadside, ensuring to meet the needs of various autonomous driving levels. Meanwhile, the collaborative intelligence approach will also introduce the costs of intelligent infrastructure deployment, energy, and maintenance. Different roads have varying scene characteristics and usage intensities. It is necessary to conduct a cost-effectiveness analysis of the intelligent deployment of different road types. A vehicle-road cost allocation model is developed based on the lifecycle traveled distance of vehicles and the lifecycle traffic flow of various roads to evaluate the function-cost effectiveness. Our study presents several vehicle-road intelligent schemes that meet the needs of various autonomous driving levels and selects Beijing for case analysis. The results indicate that primary intelligent infrastructure can reduce the lifecycle cost of the vehicle-side intelligent scheme for intermediate autonomous driving from ¥65,301 to ¥37,703, and advanced intelligent infrastructure can reduce the lifecycle cost for advanced autonomous driving from ¥126,938 to ¥42,180. Considering the distributed cost of vehicle-side and roadside, urban roads in Beijing have higher function-cost effectiveness compared to highways, especially urban expressways, which are expected to generate 43.3 times the vehicle-function-cost benefits after the advanced intelligent upgrades. The corresponding research findings can serve as a reference for city managers to make decisions on intelligent road deployment.
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Nikitin, Petr Vladimirovich, and Rimma Ivanovna Gorokhova. "Analysis of modern intelligent methods for protecting critical information infrastructure." Вопросы безопасности, no. 3 (March 2024): 14–38. http://dx.doi.org/10.25136/2409-7543.2024.3.69980.
Повний текст джерелаАнотація:
Critical information infrastructure (CII), including the financial sector, plays a key role in ensuring the sustainable functioning of economic systems and the financial stability of States. However, the growing digitalization of the financial industry and the introduction of innovative technologies are opening up new attack vectors for attackers. Modern cyber attacks are becoming more sophisticated, and traditional defenses are proving ineffective against new, previously unknown threats. There is an urgent need for more flexible and intelligent cybersecurity systems. Thus, the subject of the study is modern intelligent methods and technologies for protecting critical information infrastructure (CII) from cyber attacks. The object of the research is methods and means of ensuring the protection of critical information infrastructure using artificial intelligence and machine learning technologies. The methodological basis of this study is a comprehensive analysis of the scientific literature on the use of intelligent methods and technologies to protect critical information infrastructure. During the review and critical analysis of relevant scientific publications, key problems and unresolved tasks requiring further scientific research and practical developments in this subject area were identified. This methodological approach allowed us to form a holistic view of the current state and prospects for the development of intelligent cybersecurity tools for critical financial systems, as well as to identify priority areas for further research. The main directions of scientific novelty of this research are: 1. A detailed review of promising approaches based on artificial intelligence and machine learning technologies to ensure effective protection of CII organizations from modern complex cyber attacks. 2. Identification and analysis of a number of key scientific and technical problems that need to be solved to increase reliability, interpretability and trust in intelligent cybersecurity systems, including issues of robustness to attacks, active online learning, federated and differential private data processing. 3. Identification of promising areas for further research and development in the field of application of specialized methods of secure and trusted AI to protect critical financial infrastructure. Thus, this research makes a significant contribution to the development of scientific and methodological apparatus and practical solutions for the use of intelligent methods to ensure cybersecurity.
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Mustyala, Anirudh. "Artificial Intelligence and Machine Learning in Infrastructure Security Whitepaper." International Journal of Science and Research (IJSR) 12, no. 10 (October 5, 2023): 868–70. http://dx.doi.org/10.21275/sr231005100307.
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Rani, Dr Geeta, Ananya Singh, and Siddharth Singh. "IntelliHome: The Automated Household Infrastructure." International Journal for Research in Applied Science and Engineering Technology 12, no. 1 (January 31, 2024): 414–22. http://dx.doi.org/10.22214/ijraset.2024.57192.
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Abstract: The rapid advancement of technology in recent years has ushered in an era of unprecedented connectivity and automation in our daily lives. One of the most promising developments in this domain is the concept of IntelliHome – an integrated and intelligent household infrastructure that seamlessly combines smart devices, artificial intelligence, and data analytics to enhance convenience, efficiency, and sustainability within the home environment. This research paper delves into the multifaceted realm of IntelliHome, exploring its technological underpinnings, potential benefits, challenges, and societal implications. The paper begins by elucidating the fundamental components of an IntelliHome, encompassing a spectrum of smart devices such as thermostats, lighting systems, security systems, and appliances, all interconnected through the Internet of Things (IoT). It discusses the pivotal role of artificial intelligence in orchestrating these devices, enabling autonomous decision-making, predictive analytics, and adaptive customization to cater to the unique needs and preferences of each household. Furthermore, the research paper delves into the manifold benefits of an IntelliHome, including energy conservation, increased security, enhanced convenience, and improved quality of life. It examines how IntelliHome systems can reduce carbon footprints, optimize resource utilization, and contribute to the development of sustainable smart cities. Finally, the research paper contemplates the broader societal implications of IntelliHome technology. It explores how these systems may redefine the boundaries of work, leisure, and domesticity, and how they may influence family dynamics, social interactions, and community engagement. It also investigates the economic aspects and economic impacts.
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Gopinath S., P. Vasuki, V. Arun, Purushottama TL, Billa Pardhasaradhi, C. Shilaja,. "Intelligent Integration: Harnessing Artificial Intelligence for Enhanced Performance and Efficiency in Electric Vehicles." Journal of Electrical Systems 20, no. 5s (April 13, 2024): 376–85. http://dx.doi.org/10.52783/jes.2042.
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The transition towards electric vehicles (EVs) necessitates the development of efficient and reliable charging infrastructure. This paper presents an AI-driven approach to optimize EV infrastructure, focusing on five key aspects: profiling, augmentation, forecasting, explainability, and charging efficiency. Profiling involves understanding EV drivers' behaviors and preferences, facilitating targeted infrastructure development. Augmentation utilizes AI algorithms to identify optimal locations for new charging stations or upgrades based on usage patterns and demand forecasts. Forecasting models leverage machine learning techniques to predict future EV adoption rates and charging demands, aiding in infrastructure planning. These datasets can be used to generate insights and decisions through the use of artificial intelligence (AI) algorithms. A thorough analysis of the usefulness of AI in charge-demand profiling, data augmentation, demand forecasting, demand explainability, and charge optimization of the EVI has not yet been conducted, despite a number of recent studies in this area. This study's goal was to create, develop, and assess a thorough AI framework that fills in this EVI gap. The findings of an empirical assessment of this AI framework on an actual EVI case study validate its usefulness in tackling the new issues surrounding dispersed energy resources in the deployment of EVs.
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Moosavi, Sajad, Maryam Farajzadeh-Zanjani, Roozbeh Razavi-Far, Vasile Palade, and Mehrdad Saif. "Explainable AI in Manufacturing and Industrial Cyber–Physical Systems: A Survey." Electronics 13, no. 17 (September 3, 2024): 3497. http://dx.doi.org/10.3390/electronics13173497.
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This survey explores applications of explainable artificial intelligence in manufacturing and industrial cyber–physical systems. As technological advancements continue to integrate artificial intelligence into critical infrastructure and industrial processes, the necessity for clear and understandable intelligent models becomes crucial. Explainable artificial intelligence techniques play a pivotal role in enhancing the trustworthiness and reliability of intelligent systems applied to industrial systems, ensuring human operators can comprehend and validate the decisions made by these intelligent systems. This review paper begins by highlighting the imperative need for explainable artificial intelligence, and, subsequently, classifies explainable artificial intelligence techniques systematically. The paper then investigates diverse explainable artificial-intelligence-related works within a wide range of industrial applications, such as predictive maintenance, cyber-security, fault detection and diagnosis, process control, product development, inventory management, and product quality. The study contributes to a comprehensive understanding of the diverse strategies and methodologies employed in integrating explainable artificial intelligence within industrial contexts.
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Fang, Shimin. "The characteristics of artificial intelligence model description language and its research direction." Advances in Engineering Technology Research 7, no. 1 (September 7, 2023): 432. http://dx.doi.org/10.56028/aetr.7.1.432.2023.
Повний текст джерелаАнотація:
As an important branch of the development of computer science, artificial intelligence not only belongs to the basic component of computer science and technology research, but also has been widely used in computer engineering. As the basic environment for the research of artificial intelligence system, it is the focus of Chinese researchers to deeply explore the intelligent model and describe the characteristics and future development direction of language. Therefore, after understanding the development trend of artificial intelligence and intelligent language, this paper mainly studies how to use modeling elements in UML to describe infrastructure, cooperation model, information model and behavior model in business models, and describes language and algorithm generation according to intelligent models oriented to multi-objective problems, providing an effective basis for artificial intelligence language research in the new era.
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Jiang, Lin. "Directions for the development of the digital logistics infrastructure of China's smart cities." Vestnik of Astrakhan State Technical University. Series: Economics 2024, no. 2 (June 28, 2024): 104–9. http://dx.doi.org/10.24143/2073-5537-2024-2-104-109.
Повний текст джерелаАнотація:
A study of the directions of development of the digital logistics infrastructure of the “smart” cities of China has been conducted. The origin of the terms “infrastructure” and “logistics infrastructure” has been studied. The role of the logistics infrastructure is revealed, its functions and objects are defined. Using the example of the development of the digital logistics infrastructure of smart cities in China, it is proved that the functioning and development of urban agglomerations is inextricably linked with the formation of a logistics infrastructure based on the introduction of information and communication technologies. The scheme of the logistics infrastructure of the “smart” city of China is illustrated. The software system City Brain (“City brain”), developed in 2016 by Alibaba on the basis of artificial intelligence, is investigated. A diagram of the intelligent transport logistics market of China's “smart” cities with a forecast for 2024 is presented. It is noted that the transport management system is one of the key aspects of the development of digital logistics infrastructure. The contribution of China's largest companies forming the logistics infrastructure of smart cities is analyzed. It is revealed that in order to implement the smart city system, it is necessary to prepare such a logistical infrastructure that will be able to ensure the development of urban agglomerations in the following areas: intelligent public safety; intelligent transport management; intelligent construction and real estate; intelligent utilities; intelligent healthcare; intelligent education, etc. The forecast for the development of the global smart cities market until 2029 is outlined. Conclusions are drawn about the impact of the degree of quality of management of available resources, assets and services on the level of life in smart cities.
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Qiang Wu. "Application of BIM+GIS Integration Technology in the Construction of Urban Intelligent Water Informatization." Journal of Electrical Systems 20, no. 6s (May 2, 2024): 2352–62. http://dx.doi.org/10.52783/jes.3215.
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Urban intelligent water informatization is the process of managing and operating urban water systems with the use of contemporary information and communication technology in order to increase productivity, security, and sustainability. Establishing the appropriate infrastructure is the initial stage in building an intelligent water system so that management can choose wisely how to manage and operate the water system. Building information modeling (BIM) is a digital depiction of a building's or infrastructure's structural and functional details. In most metropolitan regions, a fast development phase is predicted to cause several water issues, including water resource shortages, floods, poor drainage, and water resource contamination. As a result, a practical approach is required for urban intelligent water information technology with the help of artificial intelligence techniques. Intelligent water systems may be built using Building Information Modeling (BIM) software to increase the system's effectiveness, stability, and durability. By offering a potent tool for storing and analyzing geographical data connected to the water distribution system, a geographic information system (GIS) can play a crucial role in the development of urban intelligent water informatization using ArcGIS mapping Software. The Internet of Things (IoT) system is used to implement intelligent water informatization, hardware implementation using ESP32, and software development using MQTT Broker and Node-Red. As a result, we recommended utilizing BIM+GIS integration technology for building urban intelligent water informatization. The suggested system is evaluated using management efficiency, urban flood control, innovative Construction, intensive management, and computation cost. In this research, the main objective is to identify where there are water leaks broken pipes and early warning system and monitoring system for flash flood disasters in urban areas. The result findings show that the integration of BIM and GIS provides a comprehensive platform for managing complex water informatization infrastructure projects and maximizes efficiency and sustainability.
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Shen, Yi. "Emerging scenarios of data infrastructure and novel concepts of digital libraries in intelligent infrastructure for human-centred communities: A qualitative research." Journal of Information Science 45, no. 5 (November 22, 2018): 691–704. http://dx.doi.org/10.1177/0165551518811459.
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This research investigated the strategic development of a large-scale transdisciplinary area, named intelligent infrastructure for human-centred communities, at Virginia Tech. Within such development, this study explored the future vision and anticipated scenarios of data infrastructure and digital libraries for smart community development. It draws upon the mixed-methods approach combining ethnographic participant observation, document analysis and semi-structured interviews. Grounded in socio-technical framework and rooted in empirical methods, this research produces results that augment design thinking and visioning practice for digital data libraries beyond traditional boundaries. The findings reveal the emerging scenarios around complex adaptive systems, intelligent data infrastructure and future digital libraries all in the context of building infrastructure for human-centred communities. Situated in this advancing reality, the results further discuss the next-generation data and information user experience, smart infrastructure data environment and future library capabilities. The article concludes that a smart library system, whether in its conceptual form of a ‘digital octopus’ or a ‘smart village data hub’ or an ‘intelligent virtual assistant’, will provide intelligence in data gathering, processing, summarising, communication and recommendations. By delivering unified and personalised data solutions, it will offer an end-to-end seamless experience for users throughout their journey of knowledge pursuit.
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Marchuk, Sviatoslav, Oleksandr Babich, and Tymur Makukha. "Creating favorable conditions for attracting private capital in the post-war reconstruction of Ukraine." Ukrainian Journal of Applied Economics and Technology 9, no. 2 (April 24, 2024): 358–62. http://dx.doi.org/10.36887/2415-8453-2024-2-61.
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The article examines the formation of favorable conditions for attracting private capital in the post-war reconstruction of Ukraine's economy. Special attention is given to integrating modern technologies to enhance the efficiency of recovery efforts. The study emphasizes the importance of creating a favorable investment environment, which includes a transparent legal system, stable macroeconomic policy, and adequate infrastructure. The possibilities of public-private partnerships and special economic zones that can stimulate investments in various sectors, including manufacturing, infrastructure, and technology, are considered. An essential component is the implementation of effective anti-corruption mechanisms that ensure transparency and accountability of government bodies, significantly reducing risks for investors. Digital technologies, such as Big Data and artificial intelligence, are proposed to ensure transparency and optimize recovery processes. These technologies can assist in making informed decisions regarding priority areas of recovery, resource allocation, and assessing the effectiveness of implemented measures. The use of smart infrastructure, which includes intelligent systems for managing energy consumption, transportation, and water supply, can significantly enhance the efficiency and resilience of critical infrastructures. Special attention is paid to the need for international coordination and the involvement of experts to develop effective strategies. Attracting private capital to the recovery process is one of the critical aspects of ensuring sustainable economic growth. Private investments can become an important funding source, contributing to faster infrastructure recovery, job creation, and economic growth. Additionally, the private sector can introduce innovative solutions and technologies that will improve the efficiency of recovery efforts and contribute to the long-term sustainability of Ukraine's economy. The article also explores the possibilities of creating special funds to support recovery projects that can attract resources from various sources, including international aid. Keywords: post-war reconstruction, private capital, investments, public-private partnership, digital technologies, artificial intelligence, smart infrastructure.
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Anagnostopoulos, Christos-Mikolaos. "Intelligent Infrastructure [Book Review]." IEEE Intelligent Transportation Systems Magazine 2, no. 4 (2010): 50–51. http://dx.doi.org/10.1109/mits.2010.939928.
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Sharma, Ankita, and Prateek Arora. "Resilience of Networks in Intelligent Urban Environments." International Research Journal of Computer Science 10, no. 11 (October 30, 2023): 488–94. http://dx.doi.org/10.26562/irjcs.2023.v1011.01.
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As urban areas develop to integrate new technology, smart city infrastructures depend significantly on seamless and resilient networks for operational stability, safety, and service continuity. This article examines the essential significance of robust network infrastructure in smart cities and emphasizes techniques aimed at maintaining uninterrupted connectivity during disasters, network failures, or peak demand periods. This article seeks to deliver a thorough analysis of resilient network practices that protect vital infrastructure, sustain data flows, and facilitate key services by investigating real-world case studies, contemporary best practices, and available technology. Principal findings underscore the incorporation of decentralized networking models, adaptive routing protocols, and hybrid connectivity systems as vital components in enhancing smart city resilience.
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Ramamohan Kummara. "AI-Driven Cloud Optimization : Transforming Modern Infrastructure Management." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 2 (March 15, 2025): 1152–69. https://doi.org/10.32628/cseit25112447.
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This article explores how AI-driven cloud optimization is transforming modern infrastructure management by enabling organizations to maximize their cloud investments while maintaining optimal performance. The convergence of artificial intelligence, machine learning, and cloud computing technologies has created systems capable of analyzing operational patterns, predicting resource requirements, and automatically adjusting cloud configurations without human intervention. It examines five key benefits of AI-driven optimization: cost reduction through intelligent resource allocation, performance enhancement via dynamic resource management, intelligent scalability through predictive capacity planning, operational automation that reduces IT burden, and environmental sustainability through efficient resource utilization. The article further analyzes three implementation approaches—cloud provider native tools, third-party optimization platforms, and custom AI solutions—while discussing critical technical considerations including data collection infrastructure, AI/ML model selection, integration requirements, and governance frameworks. The article concludes by examining emerging trends such as autonomous operations, cross-layer optimization, and quantum-enhanced optimization that will shape the future of cloud resource management and deliver even greater efficiency, performance, and business value.
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Alferov, O. L. "THE CONCEPT OF "SMART CITY" - THE PROJECT OF INTELLECTUAL INFRASTRUCTURE OF THE HUMAN HABITAT." Pravovedenie IAZH, no. 1 (2021): 140–50. http://dx.doi.org/10.31249/rgpravo/2021.01.15.
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The article discusses the introduction of new digital technologies, including artificial intelligence, in the intelligent infrastructure of cities, scientific, economic and information technology views on the implementation of the «Smart city» concept, and the experience of implementing this project in Moscow and other cities around the world.
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Hoschek, Miloslav. "Quantum security and 6G critical infrastructure." Serbian Journal of Engineering Management 6, no. 1 (2021): 1–8. http://dx.doi.org/10.5937/sjem2101001h.
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In the mid 2030-s in the field of defense and national security communications the quantum computers and 6G artificial intelligence will have domination. 6G communication is accepted in a variety of mobile data comparts transmitted through spectral technologies. The human body becomes a part of the 6G network architecture. A set of network nodes or wearable devices, embedded sensors or nanodes collect confidential information that is exchanged for multiple purposes, such as health, statistics, and safety. An important part of the 6G new paradigm will be intelligent reflective surfaces, quantum teleportation, quantum encrypted messaging, 6G holography, distributed ledger, 6G layer security threats. The 6G wireless standards will allow real-time time zone high-speed internet communication with 1TB data per second. The radio frequency networks, THZ communications, molecular communications, and quantum communications will dramatically improve data rates.
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Ahmad Walela. "Multiple Intelligence in the Teaching and Learning Process: A Study of Howard Gardner's Thought, Challenges and Opportunities." International Journal of Education, Language, Literature, Arts, Culture, and Social Humanities 2, no. 4 (September 20, 2024): 133–55. http://dx.doi.org/10.59024/ijellacush.v2i4.1006.
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This study explores how the theory of Multiple Intelligence proposed by Howard Gardner can be effectively integrated into the teaching and learning process and the challenges faced in its implementation in various educational contexts. This study analyzes existing literature through a literature review approach to find common patterns, research gaps, and best practices in applying Multiple intelligence theory in the classroom. The literature analyzed includes journal articles, academic books, and research reports focusing on teachers' understanding of multiple intelligences, learning methods based on dual Intelligence, student responses to applying these methods, and varied and inclusive assessment systems. The study results show that although the theory of multiple intelligences has been widely recognized in education, its application still faces various challenges. Teachers' understanding of this theory is often limited, and many have been unable to adapt teaching methods to accommodate students' diverse intelligences optimally. In addition, more infrastructure and resources in schools are also needed to support kinesthetic, musical, and visual-spatial Intelligence. Nonetheless, previous research has shown that students respond positively when the learning methods used match their dominant Intelligence, which can improve their motivation, engagement, and learning outcomes. This literature review concludes that a more varied and inclusive learning approach must accommodate all student intelligences. In addition, Assessment and evaluation in education must be more flexible, considering non-cognitive intelligences that have been ignored in standardized tests. This study recommends increasing teacher training, providing supporting infrastructure, and developing a curriculum more adaptive to the diversity of students' Intelligence. The results of this study make an essential contribution to enriching the understanding of applying the theory of multiple intelligences and offering solutions to the challenges faced in modern education.
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Sousa, V., J. P. Matos, N. Almeida, and J. Saldanha Matos. "Risk assessment of sewer condition using artificial intelligence tools: application to the SANEST sewer system." Water Science and Technology 69, no. 3 (November 23, 2013): 622–27. http://dx.doi.org/10.2166/wst.2013.758.
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Operation, maintenance and rehabilitation comprise the main concerns of wastewater infrastructure asset management. Given the nature of the service provided by a wastewater system and the characteristics of the supporting infrastructure, technical issues are relevant to support asset management decisions. In particular, in densely urbanized areas served by large, complex and aging sewer networks, the sustainability of the infrastructures largely depends on the implementation of an efficient asset management system. The efficiency of such a system may be enhanced with technical decision support tools. This paper describes the role of artificial intelligence tools such as artificial neural networks and support vector machines for assisting the planning of operation and maintenance activities of wastewater infrastructures. A case study of the application of this type of tool to the wastewater infrastructures of Sistema de Saneamento da Costa do Estoril is presented.
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Muhammad Ibrahim khan, Bilal Ahmad, Muhammad Shoaib, Sana Anwar, and Zeshan Ali. "Artificial Intelligence and Machine Learning Applications in Smart Infrastructure and Electrical Systems." Critical Review of Social Sciences Studies 3, no. 1 (February 14, 2025): 2005–16. https://doi.org/10.59075/v664jq68.
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AI and ML integration within smart infrastructure and electrical systems transforms the way classical engineering works due to improvements brought about in terms of efficiency, accuracy, and sustainability. It discusses the uses of AI-based approaches for predictive maintenance, optimization of energy resources, fault detection, and smart traffic management applications. Infrastructure monitoring and the efficiency of the power system significantly improve through the application of deep learning, reinforcement learning, and neural networks AI models. The data collection involved IoT sensors, AI-based predictive modeling, and real-time performance analysis. The results show that the use of AI-controlled systems outperforms traditional methods in structural monitoring, load forecasting, and grid management. These result in lower costs, increased safety, and efficient use of energy. Artificial intelligence also affects traffic systems, making them better at reducing congestion in the city and lowering emissions. Despite these strengths, challenges continue to be numerous due to the high computation requirements for implementation, high risks of cyber threats, and dependence on available data. These, however, may be overcome with hybrid AI models, blockchain-based security, and XAI explainability. The key integration of AI and sustainable energy is resiliency, as it can make the infrastructure of upgrading the grid structure, turn self-learning over time. The paper presents the opportunity of AI and its ability to revolutionize the practices of civil and electrical engineering for smarter, adaptive, and more sustainable infrastructures in the city. This will lead AI to constantly make its way toward the forefront through intelligent future infrastructures and global energy systems.
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Muhammad Ibrahim Khan, Bilal Ahmad, Muhammad Shoaib, Sana Anwar, and Zeshan Ali. "Artificial Intelligence and Machine Learning Applications in Smart Infrastructure and Electrical Systems." Critical Review of Social Sciences Studies 3, no. 1 (February 21, 2025): 2005–16. https://doi.org/10.59075/z5e95467.
Повний текст джерелаАнотація:
AI and ML integration within smart infrastructure and electrical systems transforms the way classical engineering works due to improvements brought about in terms of efficiency, accuracy, and sustainability. It discusses the uses of AI-based approaches for predictive maintenance, optimization of energy resources, fault detection, and smart traffic management applications. Infrastructure monitoring and the efficiency of the power system significantly improve through the application of deep learning, reinforcement learning, and neural networks AI models. The data collection involved IoT sensors, AI-based predictive modeling, and real-time performance analysis. The results show that the use of AI-controlled systems outperforms traditional methods in structural monitoring, load forecasting, and grid management. These result in lower costs, increased safety, and efficient use of energy. Artificial intelligence also affects traffic systems, making them better at reducing congestion in the city and lowering emissions. Despite these strengths, challenges continue to be numerous due to the high computation requirements for implementation, high risks of cyber threats, and dependence on available data. These, however, may be overcome with hybrid AI models, blockchain-based security, and XAI explainability. The key integration of AI and sustainable energy is resiliency, as it can make the infrastructure of upgrading the grid structure, turn self-learning over time. The paper presents the opportunity of AI and its ability to revolutionize the practices of civil and electrical engineering for smarter, adaptive, and more sustainable infrastructures in the city. This will lead AI to constantly make its way toward the forefront through intelligent future infrastructures and global energy systems.
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Taleghani, Mohammad, and Shahram Gilaninia. "The Relationship between Business Intelligence and Organizational Agility with Emphasis on the Mediating Role of IT Infrastructure Flexibility (Case Study: Active Companies in Rasht Industrial City)." Frontiers in Education Technology 6, no. 3 (May 23, 2023): p14. http://dx.doi.org/10.22158/fet.v6n3p14.
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The purpose of this research was to investigate the relationship between business intelligence and organizational agility, emphasizing the mediating role of IT infrastructure flexibility in companies operating in Rasht Industrial City. The current research is a cross-sectional and correlational research, and its statistical population consists of all managers of active companies in Rasht Industrial City, and 255 samples that were selected based on Morgan’s table and by random sampling were used for analysis. Descriptive analyses were performed using SPSS 22 statistical software and final analysis was performed using Smart PLS 2 statistical software. The results of the research showed that business intelligence has a significant relationship with organization agility and IT infrastructure flexibility, and IT infrastructure flexibility has a significant relationship with organization agility. Also, the analysis results confirmed the mediating role of IT infrastructure flexibility in the relationship between business intelligence and organization agility, but the moderating role of IT infrastructure flexibility in the relationship between business intelligence and organization agility was not confirmed. Based on the results of the research, in addition to analysing key information and optimizing strategic decisions, company managers can use business intelligence as a tool to master new technologies for the development and prosperity of IT infrastructures.
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Ganesh Vanam. "AI-Enhanced Cloud Automation: A Framework for Next-Generation Infrastructure Management." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 1 (January 3, 2025): 12–19. https://doi.org/10.32628/cseit25111204.
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The integration of artificial intelligence with cloud automation represents a paradigm shift in infrastructure management, offering organizations unprecedented capabilities to optimize and maintain complex IT environments. This article examines the transformative impact of AI-driven cloud automation, focusing on three key innovations: predictive scaling mechanisms, autonomous remediation systems, and intelligent container orchestration. Through analysis of current implementations across major cloud platforms and industry-leading AIOps tools, the article explores how these technologies are revolutionizing resource management, incident response, and operational efficiency. The article draws insights from implementations in healthcare and financial services sectors, demonstrating tangible improvements in system reliability, cost optimization, and innovation acceleration. The article findings suggest that AI-driven cloud automation not only enhances traditional infrastructure management practices but also enables organizations to build more resilient, scalable, and intelligent systems that can adapt to dynamic workload requirements while minimizing human intervention. This article contributes to the growing body of knowledge on intelligent infrastructure management and provides practical insights for organizations seeking to leverage AI capabilities in their cloud environments.
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Gagan Deep. "The impact of technology on Urban infrastructure." International Journal of Science and Research Archive 10, no. 2 (December 30, 2023): 664–68. http://dx.doi.org/10.30574/ijsra.2023.10.2.0995.
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The rapid advancement of technology has brought transformative changes to urban infrastructure, reshaping the way cities function and interact with their inhabitants. Smart technologies, including IoT devices, data analytics, and artificial intelligence, have revolutionized traditional urban systems. Intelligent transportation systems optimize traffic flow, reducing congestion and emissions. Smart grids enhance energy efficiency and sustainability, while digital sensors monitor and manage critical infrastructure, ensuring resilience and quick response to emergencies. The integration of technology into urban planning enhances resource allocation, promotes sustainability, and improves overall quality of life. However, challenges such as data privacy, digital inequality, and the potential for cyber threats necessitate careful consideration in the adoption of these technologies. Balancing innovation with ethical and equitable implementation is crucial to harness the full potential of technology for sustainable and resilient urban infrastructure.
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SCHERMERHORN, PAUL, and MATTHIAS SCHEUTZ. "NATURAL LANGUAGE INTERACTIONS IN DISTRIBUTED NETWORKS OF SMART DEVICES." International Journal of Semantic Computing 02, no. 04 (December 2008): 503–24. http://dx.doi.org/10.1142/s1793351x08000579.
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Advances in sensing and networking hardware have made the prospect of ambient intelligence more realistic, but the challenge of creating a software framework suitable for ambient intelligence systems remains. We present ADE, the Agent Development Environment, a distributed agent infrastructure with built-in natural language processing capabilities connected to a sophisticated goal manager that controls access to the world via multiple server interfaces for sensing and actuating devices. Unlike other ambient intelligence infrastructures, ADE includes support for multiple autonomous robots integrated into the system. ADE allows developers of ambient intelligence environments to implement agents of varying complexity to meet the varying requirements of each scenario, and it provides facilities to ensure security and fault tolerance for distributed computing. Natural language processing is conducted incrementally, as utterances are acquired, allowing fast, accurate responses from system agents. We describe ADE and a sample of the many experiments and demonstrations conducted using the infrastructure, then an example architecture for a "smart home" is proposed to demonstrate ADE's utility as an infrastructure for ambient intelligence.
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Moreno-Vozmediano, Rafael, Rubén S. Montero, Eduardo Huedo, and Ignacio M. Llorente. "Intelligent Resource Orchestration for 5G Edge Infrastructures." Future Internet 16, no. 3 (March 19, 2024): 103. http://dx.doi.org/10.3390/fi16030103.
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The adoption of edge infrastructure in 5G environments stands out as a transformative technology aimed at meeting the increasing demands of latency-sensitive and data-intensive applications. This research paper presents a comprehensive study on the intelligent orchestration of 5G edge computing infrastructures. The proposed Smart 5G Edge-Cloud Management Architecture, built upon an OpenNebula foundation, incorporates a ONEedge5G experimental component, which offers intelligent workload forecasting and infrastructure orchestration and automation capabilities, for optimal allocation of virtual resources across diverse edge locations. The research evaluated different forecasting models, based both on traditional statistical techniques and machine learning techniques, comparing their accuracy in CPU usage prediction for a dataset of virtual machines (VMs). Additionally, an integer linear programming formulation was proposed to solve the optimization problem of mapping VMs to physical servers in distributed edge infrastructure. Different optimization criteria such as minimizing server usage, load balancing, and reducing latency violations were considered, along with mapping constraints. Comprehensive tests and experiments were conducted to evaluate the efficacy of the proposed architecture.
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Leitão, Paulo, Jonas Queiroz, and Lucas Sakurada. "Collective Intelligence in Self-Organized Industrial Cyber-Physical Systems." Electronics 11, no. 19 (October 7, 2022): 3213. http://dx.doi.org/10.3390/electronics11193213.
Повний текст джерелаАнотація:
Cyber-physical systems (CPS) play an important role in the implementation of new Industry 4.0 solutions, acting as the backbone infrastructure to host distributed intelligence capabilities and promote the collective intelligence that emerges from the interactions among individuals. This collective intelligence concept provides an alternative way to design complex systems with several benefits, such as modularity, flexibility, robustness, and reconfigurability to condition changes, but it also presents several challenges to be managed (e.g., non-linearity, self-organization, and myopia). With this in mind, this paper discusses the factors that characterize collective intelligence, particularly that associated with industrial CPS, analyzing the enabling concepts, technologies, and application sectors, and providing an illustrative example of its application in an automotive assembly line. The main contribution of the paper focuses on a comprehensive review and analysis of the main aspects, challenges, and research opportunities to be considered for implementing collective intelligence in industrial CPS. The identified challenges are clustered according to five different categories, namely decentralization, emergency, intelligent machines and products, infrastructures and methods, and human integration and ethics. Although the research indicates some potential benefits of using collective intelligence to achieve the desired levels of autonomy and dynamic adaptation of industrial CPS, such approaches are still in the early stages, with perspectives to increase in the coming years. Based on that, they need to be further developed considering some main aspects, for example, related to balancing the distribution of intelligence by the vertical and horizontal dimensions and controlling the nervousness in self-organized systems.
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Ponjavic, Mirza, and Almir Karabegovic. "Location Intelligence Systems and Data Integration for Airport Capacities Planning." Computers 8, no. 1 (February 7, 2019): 13. http://dx.doi.org/10.3390/computers8010013.
Повний текст джерелаАнотація:
This paper describes an approach introducing location intelligence using open-source software components as the solution for planning and construction of the airport infrastructure. As a case study, the spatial information system of the International Airport in Sarajevo is selected. Due to the frequent construction work on new terminals and the increase of existing airport capacities, as one of the measures for more efficient management of airport infrastructures, the development team has suggested to airport management to introduce location intelligence, meaning to upgrade the existing information system with a functional WebGIS solution. This solution is based on OpenGeo architecture that includes a set of spatial data management technologies used to create an online internet map and build a location intelligence infrastructure.
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Wadnere, Prof Dhanashree G., Prof Gopal A. Wadnere, Prof Suvarana Somvanshi, and Prof Pranali Bhusare. "Recent Progress on the Convergence of the Internet of Things and Artificial Intelligence." International Journal for Research in Applied Science and Engineering Technology 11, no. 12 (December 31, 2023): 1286–89. http://dx.doi.org/10.22214/ijraset.2023.57576.
Повний текст джерелаАнотація:
Abstract: Artificial Intelligence of Things (AIoT) is the natural growth for both Artificial Intelligence (AI) and Internet of Things (IoT) as they are mutually gainful.. AI raise the value of the IoT through Machine Learning by transforming the data into useful information, although the IoT increases the value of AI through connectivity and data exchange. Hence, InSecTT – Intelligent Secure Trustable Things, a pan-European effort with 52 key partners from 12 countries (EU and Turkey), gives intelligent, secure and trustworthy systems for industrial purposes. This results in global cost-efficient solutions of intelligent, end-to-end secure, authentic connectivity and interoperability to bring the Internet of Things and Artificial Intelligence in sync. InSecTT targets at creating trust in AI-based intelligent systems and solutions as a major part of the AIoT. This paper provides an overview regarding the concept and ideas behind InSecTT and introduces the InSecTT Reference Architecture for infrastructure organization of AIoT use cases.
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Gerasimenko, O. M. "Problems and prospects of the application of artificial intelligence in the combat of criminal offenses by The Security Service of Ukraine at critical infrastructure facilities." Analytical and Comparative Jurisprudence, no. 5 (October 12, 2024): 680–87. http://dx.doi.org/10.24144/2788-6018.2024.05.105.
Повний текст джерелаАнотація:
The article is devoted to the study of the problems and prospects of the use of artificial intelligence in countering criminal offenses by the Security Service of Ukraine at critical infrastructure facilities. The peculiarities of state policy, the current state of legal regulation of the use of innovative technology in Ukraine are considered, and problematic issues of the use of artificial intelligence in the course of the tasks of prevention, detection, termination, and pretrial investigation of criminal proceedings in the relevant field are analyzed. The main areas of application of artificial intelligence in combating criminal offenses at critical infrastructure facilities have been worked out, such as: intelligent detection of threats in the process of countermeasures; automation of algorithms for combating illegal encroachments; information and analytical support. The key methods and technologies of artificial intelligence that can be used in the process of combating criminal offenses at critical infrastructure facilities by the Security Service of Ukraine are highlighted, in particular: deep learning, machine learning, natural language processing and computer vision. The potential risks and problematic issues of introducing artificial intelligence into the automated process of combating criminal offenses at critical infrastructure facilities are outlined. Attention is drawn to compliance with the principles of transparency and accountability of activity, legality, responsibility for erroneous decisions, ensuring human rights and freedoms, prevention of risks of discrimination, excessive reliance on artificial intelligence. The need to improve the legal basis for the use of artificial intelligence in combating criminal offenses at critical infrastructure facilities, as well as introducing changes and additions to the legislation of Ukraine concerning the criminal-procedural activity of the Security Service of Ukraine, was noted. The introduction of artificial intelligence technologies into the system of combating criminal offenses at critical infrastructure facilities is identified as a promising direction for further research.
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Samad Umarella, Mahatir Afandi Attamimi,. "IMPLEMENTATION OF THE THEORY MULTIPLE INTELLIGENCES IN IMPROVE COMPETENCE OF LEARNERS ON THE SUBJECTS OF ISLAMIC RELIGIOUS EDUCATION IN SMP NEGERI 14 AMBON." al-Iltizam: Jurnal Pendidikan Agama Islam 4, no. 1 (June 24, 2019): 73. http://dx.doi.org/10.33477/alt.v4i1.817.
Повний текст джерелаАнотація:
The purpose is to discover about Implementation Of Multiple
Intelligences Theory to increase the ability of student in Islamic education’s
subject of SMP Negeri 14 Ambon and to discover about supporting and
obstacle factors of Implementation Of Multiple Intelligences Theory to
increase the ability of student in Islamic education’s subject of SMP Negeri
14 Ambon. This research is using mixed method (qualitative facilitated by
quantitative). The writer using observation, interview, documentation, and
questionnaire to collect data. The results of this research has shown that
Implementation Of Multiple Intelligences Theory to increase the ability of
student in Islamic education’s subject of SMP Negeri 14 Ambon is greatly
achieved. Proven with all activities like learning process and extracurricular
programs in schools is already supported and developed the ninth
intelligences. Although in developing process not all of that intelligences can
perfectly achieved and not all that intelligences has achieved at the same
time. The ninth intelligencesis linguistic-verbal intelligence, mathematiclogic
intelligence, visual-spacial intelligence, kinesthetic intelligence,
musical intelligence, interpersonal intelligence, intrapersonal intelligence,
and existencial spiritual intelligence. The supporting factors of Multiple
Intelligences Theory is suficient infrastructure facilities and teacher, good
interaction within teacher and student, and great cooperation with public.
Otherwise the obstacle factors of Multiple Intelligences Theory is less
participation from parents, busy teacher, less information about intraschool
competition’s events and the variance levels of student intelligences.