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Journal articles on the topic 'AI-Driven Resilience'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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1

Venkadesh, Dr P. "Aegis AI - Intelligent Cyber Resilience." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem42978.

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As cyber threats continue to evolve in complexity and scale, traditional security measures have become insufficient. Aegis AI (AAI): Intelligent Cyber Resilience presents a cutting-edge approach that integrates artificial intelligence (AI) and machine learning (ML) to strengthen cybersecurity defenses. This study explores the role of AI-driven threat intelligence, automated incident response, and adaptive learning in combating cyberattacks. The proposed AAI framework utilizes deep learning, anomaly detection, and reinforcement learning techniques to predict and mitigate threats in real time. B

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Singh, Deepank Kumar, and Vedhus Hoskere. "Climate Resilience through AI-Driven Hurricane Damage Assessments." Proceedings of the AAAI Symposium Series 2, no. 1 (2024): 140–47. http://dx.doi.org/10.1609/aaaiss.v2i1.27661.

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Evolving hurricane patterns intensified by climate change are expected to exacerbate economic hardships on coastal communities. Climate resilience for these communities requires both the capability to recover rapidly from devastating storms, and the ability to develop an accurate and actionable understanding of vulnerabilities to reduce the impact of future storms. Available data from past storms can provide invaluable insight in addressing both these requirements. Post-disaster preliminary damage assessments (PDAs) are a crucial initial step toward a rapid recovery. They also provide the most

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Dakshaja Prakash Vaidya. "AI-Driven Predictive Resilience in Multi-Cloud Environments." Journal of Computer Science and Technology Studies 7, no. 4 (2025): 1097–108. https://doi.org/10.32996/jcsts.2025.7.4.124.

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This article introduces a novel AI-driven framework designed to enhance resilience in multi-cloud environments by predicting infrastructure failures and resource constraints before they impact service availability. The article leverages advanced machine learning techniques, including anomaly detection and time-series forecasting, to analyze telemetry data across heterogeneous cloud providers and identify emerging failure patterns with sufficient lead time for preventive intervention. Through a graduated remediation approach that automatically triggers appropriate response actions via integrati

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Satya Sai Ram Alla. "Demystifying AI-driven cloud resiliency: How machine learning enhances fault tolerance in hybrid cloud infrastructure." World Journal of Advanced Engineering Technology and Sciences 15, no. 2 (2025): 1203–15. https://doi.org/10.30574/wjaets.2025.15.2.0591.

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The evolution of cloud infrastructure resilience has transitioned from traditional redundancy-based approaches to sophisticated AI-driven frameworks that enhance fault tolerance in hybrid and multi-cloud environments. This article examines how machine learning models improve cloud-native resiliency through predictive analytics, automated remediation, and intelligent resource allocation. Through systematic literature review and case studies across streaming media, container orchestration, and retail platforms, the effectiveness of various AI techniques is evaluated against traditional methods.

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Al-Hourani, Shireen, and Dua Weraikat. "A Systematic Review of Artificial Intelligence (AI) and Machine Learning (ML) in Pharmaceutical Supply Chain (PSC) Resilience: Current Trends and Future Directions." Sustainability 17, no. 14 (2025): 6591. https://doi.org/10.3390/su17146591.

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The resilience of the pharmaceutical supply chain (PSC) is crucial to ensuring the availability of medical products. However, increasing complexity and logistical bottlenecks have exposed weaknesses within PSC frameworks. These challenges underscore the urgent need for more resilient and intelligent supply chain solutions. Recently, Artificial Intelligence and machine learning (AI/ML) have emerged as transformative technologies to enhance PSC resilience. This study presents a systematic review evaluating the role of AI/ML in advancing PSC resilience and their applications across PSC functions.

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Calengor, Sally, Sai Prathyush Katragadda, and Joshua Steier. "Adversarial Threats in Climate AI: Navigating Challenges and Crafting Resilience." Proceedings of the AAAI Symposium Series 2, no. 1 (2024): 46–53. http://dx.doi.org/10.1609/aaaiss.v2i1.27648.

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The convergence of Artificial Intelligence (AI) with climate science is a double-edged sword. AI-enhanced modeling has transformative potential for the field, but it comes with new vulnerabilities, especially from adversarial machine learning. Such adversarial tactics can distort AI-driven climate models, producing misleading projections on phenomena like sea-level changes and temperature predictions. Beyond just mod-eling, AI-enhanced systems in resource management, conserva-tion, and agriculture are at risk. Tampering with climate da-tasets can undermine decades of global research and erode

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Uzozie, Ogechi Thelma, Osazee Onaghinor, Oluwafunmilayo Janet Esan, Grace Omotunde Osho, and Julius Olatunde Omisola. "AI-Driven Supply Chain Resilience: A Framework for Predictive Analytics and Risk Mitigation in Emerging Markets." International Journal of Multidisciplinary Research and Growth Evaluation 4, no. 1 (2023): 1141–50. https://doi.org/10.54660/.ijmrge.2023.4.1.1141-1150.

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The increasing complexity and volatility of global supply chains, particularly in emerging markets, necessitate the adoption of advanced technologies to enhance resilience and mitigate risks. This paper presents a robust AI-driven supply chain resilience framework, leveraging predictive analytics and risk mitigation strategies to address disruptions. It explores the role of AI-powered models, including machine learning and deep learning, in forecasting potential risks and improving adaptive decision-making. Additionally, the study examines AI applications in proactive risk mitigation, such as

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Sunil Jorepalli. "AI-Driven Incident Response in Enterprise Networks: Enhancing Security and Resilience." Journal of Information Systems Engineering and Management 10, no. 42s (2025): 692–97. https://doi.org/10.52783/jisem.v10i42s.8109.

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This study explores the role of AI-driven incident response mechanisms in enhancing the security and resilience of enterprise networks. By employing a quantitative and descriptive research design, the study analyzes the frequency and effectiveness of AI responses across various types of security incidents. Data was collected through a simulated enterprise network environment, where a total of 150 security incidents and corresponding AI response actions were recorded. The results indicate that AI significantly accelerates threat detection and mitigation, with automated threat containment and ma

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Zhang, Daren. "AI integration in supply chain and operations management: Enhancing efficiency and resilience." Applied and Computational Engineering 90, no. 1 (2024): 8–13. http://dx.doi.org/10.54254/2755-2721/90/2024melb0060.

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Artificial intelligence (AI) has become a transformative force in supply chain and operations management, offering significant enhancements in efficiency and resilience. This paper examines the integration of AI technologies such as machine learning, predictive analytics, and real-time data processing in demand forecasting, inventory management, logistics, and risk mitigation. By analyzing diverse data sources, AI improves demand forecasting accuracy, reduces inventory costs, optimizes logistics routes, and enhances supply chain visibility. Case studies and data-driven insights demonstrate how

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Bhanu, Raju Nida. "AI-Powered Procurement: Transforming Efficiency, Agility, and Resilience in Supply Chains." INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH AND CREATIVE TECHNOLOGY 11, no. 1 (2025): 1–8. https://doi.org/10.5281/zenodo.14945011.

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Effective procurement methods are crucial, for boosting business efficiency; however traditional approaches often fall short due to their reliance on processes and disconnected systems that require intervention for decision making purposes. This research delves into the impact of Artificial Intelligence (AI) in revolutionizing procurement practices. Modern AI driven tools such as machine learning algorithms and natural language processing (NLP) along, with predictive analytics capabilities are evaluated for their ability to streamline tasks promote decision making and offer instantaneous valua

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Alnaser, Aljawharah A., Mina Maxi, and Haytham Elmousalami. "AI-Powered Digital Twins and Internet of Things for Smart Cities and Sustainable Building Environment." Applied Sciences 14, no. 24 (2024): 12056. https://doi.org/10.3390/app142412056.

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This systematic literature review explores the intersection of AI-driven digital twins and IoT in creating a sustainable building environment. A comprehensive analysis of 125 papers focuses on four major themes. First, digital twins are examined in construction, facility management, and their role in fostering sustainability and smart cities. The integration of IoT and AI with digital twins and energy optimization for zero-energy buildings is discussed. Second, the application of AI and automation in manufacturing, particularly in Industry 4.0 and cyber-physical systems, is evaluated. Third, e

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Vamsi Krishna Vemulapalli. "AI-driven autonomous cloud monitoring and resilience in AWS Environments." World Journal of Advanced Engineering Technology and Sciences 15, no. 2 (2025): 3043–49. https://doi.org/10.30574/wjaets.2025.15.2.0770.

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Modern cloud infrastructures face escalating challenges from service disruptions that cause substantial business impact, with complexity growing exponentially as organizations embrace microservice architectures. This article explores a comprehensive AI-driven autonomous monitoring and resilience system designed specifically for AWS environments. The framework integrates multi-agent monitoring, intelligent anomaly detection, and automated failover orchestration to address the limitations of traditional monitoring approaches. By establishing dynamic baselines across monitored components, the sys

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Chioma Susan Nwaimo, Adetumi Adewumi, and Daniel Ajiga. "Advanced data analytics and business intelligence: Building resilience in risk management." International Journal of Science and Research Archive 6, no. 2 (2022): 336–44. https://doi.org/10.30574/ijsra.2022.6.2.0121.

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This paper explores the transformative role of advanced data analytics, business intelligence (BI), and artificial intelligence (AI) in enhancing risk management strategies for organizations navigating digital transformation and cybersecurity challenges. It examines how predictive analytics enables the early identification and mitigation of risks, empowering businesses to adopt proactive measures. The integration of BI tools is highlighted for their ability to support strategic decision-making under uncertainty through data visualization, scenario planning, and real-time insights. Additionally

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Uday B. Acharya. "Butterfly Effect in Fintech Cyberspace: The System Dynamics of AI Orchestrated Attacks." Journal of Information Systems Engineering and Management 10, no. 43s (2025): 511–23. https://doi.org/10.52783/jisem.v10i43s.8411.

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JISEM. Please publish. VenuReceived date:06-01-2025Revised:20-02-2025Accepted:09-05-2025Volume 2025Artificial intelligence (AI) has radically changed the cybersecurity environment in fintech, bringing sophisticated, adaptive threats that can pressure spilling disruptions. This paper examines how malicious AI agents can trigger disproportionately large effects through subtle, targeted disturbances—mirroring the "butterfly effect" of chaos theory. Employing a system dynamics approach, we simulate how AI-powered cyber-attacks use feedback loops, time delays, and interdependencies to spread across

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Rane, Nitin, Saurabh Choudhary, and Jayesh Rane. "Artificial intelligence for enhancing resilience." Journal of Applied Artificial Intelligence 5, no. 2 (2024): 1–33. http://dx.doi.org/10.48185/jaai.v5i2.1053.

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In an increasingly complex and unpredictable world, resilience-the ability to withstand and recover from adverse conditions is essential across various sectors. This research paper investigates the transformative potential of artificial intelligence (AI) in enhancing resilience across multiple domains. We explore how AI technology can be utilized to develop resilient infrastructure, providing advanced predictive maintenance and real-time monitoring capabilities that ensure robustness and longevity. The study examines the role of AI in improving disaster response, offering rapid data analysis a

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Kulkarni, Tanay. "Leveraging digital infrastructure and data management systems for water infrastructure emergency response planning." International Journal of Multidisciplinary Research and Growth Evaluation 1, no. 3 (2020): 56–62. https://doi.org/10.54660/.ijmrge.2020.1.3-56-62.

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Water infrastructure resilience is critical for ensuring the safety, reliability, and sustainability of water supply systems, particularly in the face of natural disasters, climate change, and human-induced disruptions. Traditional resilience strategies have relied on structural reinforcements and reactive interventions, often lacking the adaptability required for modern risk management. This paper explores smart resilience strategies, incorporating digital infrastructure, real-time monitoring, predictive analytics, and automation to enhance emergency preparedness. IoT-enabled sensors, AI-driv

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Sasilatha, T., Adolf Asih Suprianto, and Hamdani Hamdani. "AI-Driven Approaches to Power Grid Management: Achieving Efficiency and Reliability." International Journal of Advances in Artificial Intelligence and Machine Learning 2, no. 1 (2025): 27–37. https://doi.org/10.58723/ijaaiml.v2i1.380.

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The main objective of this research is to improve the efficiency, reliability, and security of the power grid through the integration of artificial intelligence (AI) techniques. The research method involves developing an integrated AI-SGMS framework, including: (1) AI-based Load Forecasting using LSTM and transformer models; (2) Reinforcement Learning for Network Optimization with deep reinforcement learning (DRL) agents; (3) AI-enabled Fault Detection using CNN and autoencoder; (4) AI-driven Intrusion Detection System (IDS) for cybersecurity; and (5) Edge Computing for Decentralized Decision

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Kumawat, Priyank. "Impact of Artificial Intelligence in Building Supply Chain Resiliency." International Journal of Supply Chain Management 13, no. 6 (2024): 10–20. https://doi.org/10.59160/ijscm.v13i6.6283.

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Supply chains are frequently exposed to disruptions, which can be either positive, driven by technological advancements, or negative, caused by natural and man-made disasters. This study aims to explore the possibilities and implications of building supply chain resilience through AI-driven AR/VR simulations. In light of the disruptions experienced during the COVID-19 pandemic, there has been a growing interest among both researchers and practitioners in the role of digital technologies in enhancing end-to-end visibility within supply chains and their potential for boosting resilience.The stud

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Wei Gee*, Rachel Ooi. "Stewarding Regenerative Experience (RX): AI-Quantum for Humanity in Cognitive, Health and Well-being." Journal of Biomedical Research & Environmental Sciences 6, no. 3 (2025): 266–81. https://doi.org/10.37871/jbres2081.

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As Artificial Intelligence (AI), quantum intelligence, and deep technologies advance rapidly, human cognitive flexibility, emotional resilience, and adaptability are experiencing a significant decline. AI excels in optimization, automation, and efficiency, yet has not fostered human cognitive regeneration, worse hinders well-being restorations. Mental health problems account for over $1 trillion in global productivity losses annually [1] while neurodegenerative diseases, such as dementia, are projected to cost $2.8 trillion by 2025 [2]. The World Bank estimates that AI-driven automation will d

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Liem, Andrew Tanny, I.-Shyan Hwang, Razat Kharga, and Chin-Hung Teng. "Enhancing Tactile Internet Reliability: AI-Driven Resilience in NG-EPON Networks." Photonics 11, no. 10 (2024): 903. http://dx.doi.org/10.3390/photonics11100903.

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To guarantee the reliability of Tactile Internet (TI) applications such as telesurgery, which demand extremely high reliability and are experiencing rapid expansion, we propose a novel smart resilience mechanism for Next-Generation Ethernet Passive Optical Networks (NG-EPONs). Our architecture integrates Artificial Intelligence (AI) and Software-Defined Networking (SDN)-Enabled Broadband Access (SEBA) platform to proactively enhance network reliability and performance. By harnessing the AI’s capabilities, our system automatically detects and localizes fiber faults, establishing backup communic

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Siva, Krishna Jampani. "AI-Driven Threat Intelligence: Revolutionizing Proactive Cyber Defense." Journal of Scientific and Engineering Research 8, no. 6 (2021): 220–27. https://doi.org/10.5281/zenodo.14637382.

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Artificial Intelligence is revolutionizing cybersecurity, changing defensive strategies from reactive to proactive. This article has focused on AI-driven threat intelligence as the transformative force, detailing machine learning models that enable real-time data analysis, pattern recognition, and predictive analytics. AI's ability to detect anomalies and predict cyber threats before they materialize has surpassed traditional reactive measures. This proactive approach significantly enhances organizational resilience against constantly evolving threats. Innovations in anomaly detection and pred

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Researcher. "AI-DRIVEN TRANSFORMATION OF MAINFRAME ENVIRONMENTS: A COMPREHENSIVE FRAMEWORK FOR OPERATIONAL RESILIENCE." International Journal of Engineering and Technology Research (IJETR) 9, no. 2 (2024): 420–33. https://doi.org/10.5281/zenodo.13852336.

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This article explores the transformative potential of integrating artificial intelligence (AI), machine learning (ML), and generative AI technologies to enhance operational resilience in mainframe environments. As mainframes play a critical role in enterprise computing, ensuring their robustness, scalability, and adaptability becomes paramount. We present a comprehensive framework that leverages AI for process automation, intelligent resource allocation, and predictive maintenance; ML for anomaly detection, capacity planning, and data-driven decision-making; and generative AI for advanced cont

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Gunawat, Chhaya, Rohit Gupta, and Jay Sunil Nankani. "AI-Driven Fault Injection Testing: Enhancing System Resilience with Automated Chaos Engineering." Asian Journal of Research in Computer Science 18, no. 6 (2025): 1–8. https://doi.org/10.9734/ajrcos/2025/v18i6675.

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This paper presents a novel approach to enhancing system resilience through AI-driven fault injection testing, leveraging automated chaos engineering. As modern distributed systems grow in complexity, traditional resilience testing techniques—often limited by static fault models and insufficient adaptability—struggle to expose hidden vulnerabilities under dynamic real-world scenarios. To address these challenges, we propose an intelligent framework that integrates artificial intelligence, specifically reinforcement learning, with automated chaos tools to dynamically generate and execute contex

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Rangaraju, Sakthiswaran. "SECURE BY INTELLIGENCE: ENHANCING PRODUCTS WITH AI-DRIVEN SECURITY MEASURES." EPH - International Journal of Science And Engineering 9, no. 3 (2023): 36–41. http://dx.doi.org/10.53555/ephijse.v9i3.212.

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In an increasingly interconnected digital landscape, the proliferation of sophisticated cyber threats poses significant challenges to the security and integrity of products and services. As traditional security measures struggle to keep pace with evolving threats, there exists a pressing need for innovative and adaptive approaches to safeguarding digital assets. This abstract introduces the concept of "Secure by Intelligence," a paradigm shift in product security that leverages the power of Artificial Intelligence (AI) to fortify defenses and proactively mitigate risks. This paper explores the

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K., Sruthy Suresh. "Enhancing Supply Chain Resilience through Artificial Intelligence: A Strategic Framework for Executives." Emerging Science Journal 8, no. 4 (2024): 1462–73. http://dx.doi.org/10.28991/esj-2024-08-04-013.

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In today's contemporary turbulent business environment, marked by disruptions ranging from natural disasters to global pandemic, supply chain resilience is crucial. This research addresses the pressing need to understand challenges faced by Indian supply chain executives by adopting AI-driven solutions for enhancing resilience. Analyzing data from 300 executives using ANOVA and t-tests reveals critical patterns in encountered barriers. Simultaneously, the study aims to fill gaps in existing literature by developing a strategic framework for executives. Using Structured Equation Modeling (SEM),

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Arome Salifu. "AI-driven risk identification and mitigation strategies for government projects." International Journal of Science and Research Archive 15, no. 3 (2025): 303–6. https://doi.org/10.30574/ijsra.2025.15.3.1454.

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This review is focused on AI-Driven Risk Identification and Mitigation Strategies for Government Projects. The review addresses Risk Mitigation Strategies and AI-Related Vulnerabilities putting into consideration accuracy and bias. The study also highlights NIST framework application in Nigeria, Impacts of AI-Driven risk mitigation and implementing AI for risk mitigation and challenges for government projects. Conclusively, the study confirms that AI holds transformative potential for government projects through its innovative solutions to mitigate risks and enhance operational efficiency. By

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MAHIPAL REDDY YALLA. "Zero-trust security architecture in the ai era: a novel framework for enterprise cyber resilience." International Journal of Science and Research Archive 13, no. 2 (2024): 4341–56. https://doi.org/10.30574/ijsra.2024.13.2.0172.

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The dependency of enterprises on artificial intelligence (AI) for operational efficiency creates new and emerging problems for cybersecurity because of threats generated by AI systems. ZTSA has replaced perimeter-based security models as a necessary means to defend against modern innovative attacks. This analysis traces the development of Zero-Trust Security systems backed by artificial intelligence for defending against contemporary malware threats and discusses their implementation practices. The analysis presents Zero-Trust's core concepts including "never trust, always verify" with micro-s

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Sheikh, Nuruddin. "AI-Driven Observability: Enhancing System Reliability and Performance." Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023 7, no. 01 (2025): 229–39. https://doi.org/10.60087/jaigs.v7i01.322.

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AI-powered observability will revolutionize how modern systems are monitored, analyzed, and optimized for performance and resilience. With traditional observability, it requires manual analysis of logs, metrics, and traces, which can often make it too late to respond to system anomalies. With the integration of AI and machine learning in observability platforms, they can use the data collected to find out patterns, identify anomalies, bad actors, late trends, and offer insights based on alert patterns and defined ratios. It assesses how the tools of tomorrow will build on observability for inn

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Li, Qi, and Lin Zou. "A Study on the Mechanisms and Processes of Artificial Intelligence's Impact on Economic Resilience." Journal of Computers 36, no. 2 (2025): 297–313. https://doi.org/10.63367/199115992025043602020.

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The widespread adoption of artificial intelligence (AI), driven by rapid computer technology and data processing developments, has accelerated economic transformation. AI systems, powered by sophisticated algorithms and significant computational resources, have substantially contributed to improvements in food security, ecological sustainability, and overall economic development. This paper presents an empirical analysis examining how AI influences economic resilience using data from 30 provincial-level regions in China from 2011 to 2022. Results indicate that rapid AI advancements have reinfo

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Pakalapati, Naveen, Jawaharbabu Jeyaraman, and Sai Mani Krishna Sistla. "Building Resilient Systems: Leveraging AI/ML within DevSecOps Frameworks." Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online) 2, no. 2 (2023): 213–30. http://dx.doi.org/10.60087/jklst.vol2.n2.p230.

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This paper explores the integration of Artificial Intelligence (AI) and Machine Learning (ML) techniques within DevSecOps frameworks to enhance system resilience. In today's dynamic and rapidly evolving technological landscape, resilience has become a critical aspect of software development and operations. DevSecOps, an evolution of the DevOps methodology, emphasizes the importance of integrating security practices throughout the software development lifecycle. By leveraging AI/ML capabilities within DevSecOps frameworks, organizations can proactively identify and mitigate security threats, op

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Bin Muhammad, Mohd Hilal, Zulhazlin Bin Abas, Anas Suzastri Bin Ahmad, and Mohd Sufyan Bin Sulaiman. "AI-Driven Security: Redefining Security Informations Systems within Digital Governance." International Journal of Research and Innovation in Social Science VIII, no. IX (2024): 2923–36. http://dx.doi.org/10.47772/ijriss.2024.8090245.

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The increasing integration of Artificial Intelligence (AI) within Security Information Systems (SIS) presents a significant shift in digital governance, where governments rely heavily on secure digital infrastructures to manage public services. The escalating threat landscape has necessitated a proactive approach to cybersecurity, and AI is proving crucial in enhancing threat detection, automating responses, and minimizing human error. However, many governments, particularly in developing nations, are struggling to bridge the gap between their current security measures and the complex challeng

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Shen, Qi. "AI-driven financial risk management systems: Enhancing predictive capabilities and operational efficiency." Applied and Computational Engineering 69, no. 1 (2024): 141–46. http://dx.doi.org/10.54254/2755-2721/69/20241494.

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The integration of artificial intelligence (AI) in financial risk management systems has revolutionized traditional approaches, providing enhanced predictive capabilities and operational efficiency. This paper explores the various applications of AI in credit risk assessment, market risk analysis, operational risk management, and regulatory compliance. AI-driven systems leverage advanced machine learning algorithms to analyze vast datasets, including real-time market data and non-traditional sources, improving risk predictions and enabling proactive risk management. Scenario simulations, predi

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Temitope Ayemoro and Toheeb Ekundayo. "AI-driven risk stratification for targeted public health interventions." World Journal of Biology Pharmacy and Health Sciences 20, no. 2 (2024): 988–94. https://doi.org/10.30574/wjbphs.2024.20.2.0901.

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The increasing complexity of public health challenges, particularly during infectious disease outbreaks, necessitates innovative approaches to identify and protect vulnerable populations. This study explores the use of supervised machine learning algorithms to stratify populations based on risk factors and predict severe outcomes during outbreaks. By leveraging demographic, clinical, and socioeconomic data, the proposed AI-driven models aim to enable healthcare systems to prioritize vulnerable groups, allocate resources effectively, and implement preventive measures. The results demonstrate th

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babu, P. Hari. "AI-Driven Network Anomaly Detection System." International Journal for Research in Applied Science and Engineering Technology 13, no. 3 (2025): 1590–95. https://doi.org/10.22214/ijraset.2025.67599.

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In today's digital landscape, the increasing sophistication of cyber threats necessitates more advanced security solutions. Traditional Network Intrusion Detection Systems (NIDS) often rely on signature-based methods, which can struggle to detect novel or evolving attacks. To address this limitation, this project focuses on enhancing anomaly detection by incorporating advanced AI techniques, specifically behavioral analysis. By continuously profiling normal network behavior, the system can identify deviations that may indicate potential threats. This proactive approach allows for real-time ano

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Mugalakhod, Miss Sneha, and Miss Shweta M. Nirmanik. "AI Driven Smart Homes Energy Efficiency and Model." International Journal for Research in Applied Science and Engineering Technology 10, no. 7 (2022): 461–65. http://dx.doi.org/10.22214/ijraset.2022.45266.

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Abstract: Artificial intelligence (AI) has received immense attention from the research community and the industry, leading to AI being adopted in many real-world applications. The growing trend of deploying AI has dramatically changed the ergonomics of modern-day practices in many realms, including smart homes, healthcare, insurance, investment and banking, social services, infrastructure, and marketing. A smart home, often referred to as an intelligent home, comprises smart technologies supported by AI. Smart home has its applications in household appliances, home safety and security, lighti

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DEMURIN, Deborah Anuoluwapo, and Donald Abidemi ODELEYE. "Resilience in the Age of Artificial Intelligence (AI): The Role of Spirituality in Fostering a Balanced Future." Pastoral Counsellors: Journal of Nigerian Association of Pastoral Counsellors 4 (January 7, 2025): 132–37. https://doi.org/10.5281/zenodo.14611335.

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This research investigates the relationship among resilience, spirituality, and the challenges posed by artificial intelligence in modern society. As technological innovations continuously transform multiple dimensions of existence, the capacity to adjust and flourish is rendered imperative. This research investigates the role of spirituality in bolstering resilience, facilitating the capacity of individuals and communities to manage the challenges posed by AI. In this study, resilience is conceptualized through individual and communal experiences in an AI-centric environment. The psychologica

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Gündüzyeli, Bora. "The Role of Social Media and Artificial Intelligence (AI) in Enhancing Digital Marketing Resilience During Crises." Sustainability 17, no. 7 (2025): 3134. https://doi.org/10.3390/su17073134.

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In an increasingly interconnected world, businesses may face the challenge of managing crises, whether they are economic downturns, natural disasters, or global pandemics. During such times, building strong and sustainable marketing resilience becomes crucial for businesses aiming to survive and thrive. Digital technologies—particularly social media platforms and artificial intelligence (AI)—can play a vital role in enhancing marketing resilience. This research seeks to answer the core question: “How can social media and AI technologies help businesses build marketing resilience during crises”

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Yadav, Dr Neha. "AI in Cybersecurity: A Literature review." International Scientific Journal of Engineering and Management 04, no. 05 (2025): 1–7. https://doi.org/10.55041/isjem03378.

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Abstract - In cybersecurity, artificial intelligence is revolutionizing incident response, risk management, and threat identification in a progressively hostile cyber threat landscape. This research presents a thorough literature review on AI in cybersecurity, focusing on both aspects of the balance sheet. This paper discusses how AI-driven technologies like machine learning, deep learning, natural language processing, and expert systems improve security frameworks through predictive analytics, real-time threat intelligence, and anomaly detection. The research explores various uses of AI such

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Somanathan, Sureshkumar. "AI-Powered Decision-Making in Cloud Transformation: Enhancing Scalability and Resilience Through Predictive Analytics." Nanotechnology Perceptions 20, S1 (2024): 1223–32. https://doi.org/10.5281/zenodo.15263934.

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Research Background: The fast acceptance of cloud technologies has transformed IT infrastructure management; nonetheless, especially in multi-hybrid cloud transformation projects, enterprises find great difficulty guaranteeing scalability and reliability. In dynamic cloud systems, conventional decision-making methods can find it difficult to foresee the unknowns that impacts negatively the digital transformation initiatives and maximize resource allocation. Predictive analytics driven by artificial intelligence (AI) offers a transforming solution since it lets companies aggressively manage thi

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Somanathan, Sureshkumar. "AI-Powered Decision-Making in Cloud Transformation: Enhancing Scalability and Resilience Through Predictive Analytics." Nanotechnology Perceptions 20, S1 (2024): 1223–32. https://doi.org/10.5281/zenodo.15270406.

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Research Background: The fast acceptance of cloud technologies has transformed IT infrastructure management; nonetheless, especially in multi-hybrid cloud transformation projects, enterprises find great difficulty guaranteeing scalability and reliability. In dynamic cloud systems, conventional decision-making methods can find it difficult to foresee the unknowns that impacts negatively the digital transformation initiatives and maximize resource allocation. Predictive analytics driven by artificial intelligence (AI) offers a transforming solution since it lets companies aggressively manage thi

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Zülfikaroğlu, Sarp. "AI-Driven Strategic Management and Decision Making for Energy Sector." Next Frontier For Life Sciences and AI 8, no. 1 (2024): 91. http://dx.doi.org/10.62802/q7rkdb54.

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The energy sector faces unprecedented challenges, including volatile market conditions, fluctuating resource availability, and the urgent need for sustainable energy transition. Artificial intelligence (AI) offers transformative potential in addressing these challenges by enhancing strategic management and decision-making processes. This research explores the integration of AI-driven tools and methodologies into strategic management practices in the energy sector, focusing on optimization, predictive analytics, and automated decision systems. By leveraging machine learning, neural networks, an

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Tamtam, Fadoua, and Amina Tourabi. "Advancing Digital Supply Chains through Generative AI: A Strategic Framework with the ELECTRE III Method." Complex Systems Informatics and Modeling Quarterly, no. 43 (July 31, 2025): 17–33. https://doi.org/10.7250/csimq.2025-43.02.

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This study evaluates the role of Generative AI in optimizing digital supply chain performance, focusing on IoT integration, predictive analytics, and blockchain security. The primary objective is to determine which AI-driven initiatives offer the greatest benefits in enhancing resilience and operational efficiency. A structured multi-criteria decision-making approach is applied using the ELECTRE III method, leveraging quantitative data from DHL’s operational records (2022–2025). The evaluation is conducted with a panel of 18 industry experts, including logistics professionals and AI specialist

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Fardin Sabahat Khan, Abdullah Al Masum, Jamaldeen Adam, Md Rashidul Karim, and Sadia Afrin. "AI in Healthcare Supply Chain Management: Enhancing Efficiency and Reducing Costs with Predictive Analytics." Journal of Computer Science and Technology Studies 6, no. 5 (2024): 85–93. http://dx.doi.org/10.32996/jcsts.2024.6.5.8.

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This paper explores the transformative role of artificial intelligence (AI) and predictive analytics in enhancing operational efficiency within healthcare supply chains. By leveraging AI-driven business analytics, healthcare organizations can optimize inventory management, improve demand forecasting, and streamline supply chain processes. The study presents a comprehensive review of recent advancements, challenges, and opportunities in the integration of AI technologies, focusing on their application in various healthcare contexts. Through systematic analysis of existing literature, the findin

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ABDULRAHMAN, IBRAHIM ABDUL, UZOAMAKA C. OGOR, GABRIEL TOSIN AYODELE, CHIDOZIE ANADOZIE, and JACOB ALEBIOSU. "AI-Driven Threat Intelligence and Automated Incident Response: Enhancing Cyber Resilience through Predictive Analytics." Research Journal in Civil, Industrial and Mechanical Engineering 2, no. 1 (2025): 16–32. https://doi.org/10.61424/rjcime.v2i1.236.

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Cybersecurity is a critical concern for organizations as the complexity and volume of cyber threats continue to grow. Traditional methods of threat detection and incident response, such as signature-based detection and rule-based systems, are increasingly ineffective against sophisticated and evolving attacks. This study explores the integration of Artificial Intelligence (AI) and Machine Learning (ML) in enhancing threat intelligence and automating incident response. By leveraging predictive analytics, anomaly detection, and real-time data processing, AI-driven systems offer significant impro

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Riad, Meriem, Mohamed Naimi, and Chafik Okar. "Enhancing Supply Chain Resilience Through Artificial Intelligence: Developing a Comprehensive Conceptual Framework for AI Implementation and Supply Chain Optimization." Logistics 8, no. 4 (2024): 111. http://dx.doi.org/10.3390/logistics8040111.

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Background: Amid growing global uncertainty and increasingly complex disruptions, the ability of supply chains to rapidly adapt and recover is critical. The incorporation of artificial intelligence (AI) into supply chain management represents a transformative strategy for enhancing resilience. By harnessing advanced AI technologies, such as machine learning, predictive analytics, and real-time data processing, organizations can more effectively anticipate, respond to, and recover from disruptions.AI improves demand forecasting accuracy, optimizes inventory management, and increases real-time v

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Garg, Shally. "Real-Time Disaster Response with AIOps: Intelligent Infrastructure Monitoring and Optimization." International Journal of Multidisciplinary Research and Growth Evaluation. 5, no. 5 (2024): 1101–7. https://doi.org/10.54660/.ijmrge.2024.5.5.1101-1107.

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This paper explores leveraging AIOps for disaster resilience through real-time infrastructure monitoring and emergency response optimization. By integrating AI-driven predictive analytics, multi-source data (satellite, IoT, geospatial), and edge computing, AIOps can enhance decision-making during disasters. The paper highlights the importance of explainable AI (XAI) for building trust, addressing challenges like real-time data processing, scalability, and security. Future trends include autonomous response systems, deep learning for predictive management, and adaptive decision-making framework

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Pasupuleti, Murali Krishna. "Next-Gen Food Security: AI and Biotech Innovations for Sustainable Agriculture." International Journal of Academic and Industrial Research Innovations(IJAIRI) 05, no. 05 (2025): 16–28. https://doi.org/10.62311/nesx/rp05113.

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Abstract: This research paper explores the transformative integration of artificial intelligence (AI) and biotechnology in advancing sustainable agriculture and ensuring global food security. It examines how AI-driven tools such as machine learning, remote sensing, and precision analytics, alongside biotechnological innovations like CRISPR gene editing, biofortification, and synthetic biology, are revolutionizing farming practices, crop improvement, and resource management. The study highlights case-based evidence and emerging models that demonstrate increased yield, climate resilience, and re

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Carlos Domínguez and María Beltrán. "AI-Powered Predictive Analytics for Disaster Response in Smart Cities." International Journal of Emerging Trends and Innovation (IJETI) 1, no. 1 (2025): 10–21. https://doi.org/10.64056/nnb4na74.

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As urban centers increasingly evolve into smart cities, the challenges of disaster preparedness and response demand intelligent and data-driven strategies. Predictive analytics powered by Artificial Intelligence (AI) has emerged as a vital tool for enhancing resilience and response capabilities in the face of natural and human-made disasters. This paper investigates how AI-driven predictive analytics can support disaster response in smart cities through real-world case studies, including flood forecasting in the Netherlands, earthquake prediction in Japan, and COVID-19 pandemic modeling in Sou

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Jalili, Abdul Qawi, and Anton Dziatkovskii. "State data security backed by Artificial Intelligence and Zero Knowledge Proofs in the context of sanctions and economic pressure." Economic Annals-ХХI 202, no. 3-4 (2023): 4–16. http://dx.doi.org/10.21003/ea.v202-01.

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This research paper aims to elucidate the intricate relationship between artificial intelligence (AI), state data security, and the volatile circumstances induced by sanctions and economic pressure. By undertaking a comprehensive literature review, the study not only offers a historical context of state data security mechanisms but also delves deeply into the advancements provided by AI-driven solutions. The work serves as a crucial reference for policymakers, cybersecurity experts, and academic researchers, laying a foundation for the nuanced understanding of AI’s capabilities and limitations

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Gomaa, Prof Dr Attia Hussien. "RCM 4.0: A Novel Digital Framework for Reliability-Centered Maintenance in Smart Industrial Systems." International Journal of Emerging Science and Engineering 13, no. 5 (2025): 32–43. https://doi.org/10.35940/ijese.e2595.13050425.

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Reliability-Centered Maintenance (RCM) 4.0 introduces an AI-driven digital framework that integrates Artificial Intelligence (AI), the Industrial Internet of Things (IIoT), Digital Twins, and Big Data Analytics to enhance Reliability, Availability, Maintainability, and Safety (RAMS) in Smart Industrial Systems. As industrial environments grow increasingly complex and data-driven, traditional maintenance strategies struggle to deliver the agility and precision required for intelligent asset management. This study presents RCM 4.0 as a self-optimizing, predictive maintenance paradigm, transformi

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