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Articles de revues sur le sujet « AI driven Networks »

Auteur : Grafiati

Publié le 7 juin 2025

Mis à jour le 2 août 2025

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1

D, Aishwaya. "AI Driven Phishing Detection Model." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 1023–25. https://doi.org/10.22214/ijraset.2025.70029.

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Phishing attacks are a significant cybersecurity threat as they trick people into revealing personal information through fake websites. This project introduces an integrated CNN-LSTM model to detect phishing URLs. It uses Convolutional Neural Networks (CNNs) to look for local patterns and Long Short-Term Memory (LSTM) networks to analyze the order of information in URLs. To further clarify, SHAP (SHapley Additive Explanations) and LIME (Local Interpretable Model-agnostic Explanations) are implemented, giving insights into how the model predicts. The trained model is served as a FastAPI/Flask w

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Madhura, G. K. "Quantum-Inspired Optimization and Resource Allocation in AI-Driven Data Centers and Edge Networks." Journal of Advances in Computational Intelligence Theory 7, no. 2 (2025): 19–25. https://doi.org/10.5281/zenodo.15228558.

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<em>This study presents a comprehensive review and synthesis of recent research advancements integrating quantum-inspired optimization and artificial intelligence (AI) in data centers and edge computing networks. With the exponential growth in data generation and the demand for real-time processing, AI-driven infrastructures face challenges in scalability, latency, and energy efficiency. Through the evaluation of 29 scholarly works authored or co-authored by Vinod Veeramachaneni, Srinivasa Rao Bittla, and Srimaan Yarram, the study highlights innovations across diagnostics in electrical systems

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Kashyap, Gaurav. "AI-Driven Smart Contracts for Blockchain Networks." ESP Journal of Engineering & Technology Advancements 3, no. 4 (2023): 85–90. https://doi.org/10.56472/25832646/jeta-v3i8p109.

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Sponner, Max, Bernd Waschneck, and Akash Kumar. "AI-Driven Performance Modeling for AI Inference Workloads." Electronics 11, no. 15 (2022): 2316. http://dx.doi.org/10.3390/electronics11152316.

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Deep Learning (DL) is moving towards deploying workloads not only in cloud datacenters, but also to the local devices. Although these are mostly limited to inference tasks, it still widens the range of possible target architectures significantly. Additionally, these new targets usually come with drastically reduced computation performance and memory sizes compared to the traditionally used architectures—and put the key optimization focus on the efficiency as they often depend on batteries. To help developers quickly estimate the performance of a neural network during its design phase, performa

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Hasan, Rakibul, Syeda Farjana Farabi, Md Kamruzzaman, Md Khokan BHUYAN, Sadia Islam Nilima, and Atia Shahana. "AI-Driven Strategies for Reducing Deforestation." American Journal of Engineering and Technology 6, no. 6 (2024): 6–20. http://dx.doi.org/10.37547/tajet/volume06issue06-02.

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Recent advancements in data science, coupled with the revolution in digital and satellite technology, have catalyzed the potential for artificial intelligence (AI) applications in forestry and wildlife sectors. Recognizing the critical importance of addressing land degradation and promoting regeneration for climate regulation, ecosystem services, and population well-being, there is a pressing need for effective land use planning and interventions. Traditional regression approaches often fail to capture underlying drivers' complexity and nonlinearity. In response, this research investigates the

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Kulkarni, Prof Rekha. "AI Driven Chatbot Counsellor." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem35303.

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Speech and written information are fundamental to human communication. As a result, the majority of spoken and textual communication takes place on digital platforms like Twitter, Facebook, and WhatsApp, among others. Our model employs dual recurrent neural networks (RNNs) to encode the information from text and audio sequences, as spoken language and sound constitute emotional discourse. The emotion class is then predicted by combining the data from the two sources. Due to the complexity of speech emotion recognition, models that use audio properties to generate powerful classifiers have beco

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V, Devaraj F., Nagadeepa S. M, Neha B. Nanjegowda, and Rakshitha D. H. "AI Driven Sentiment News Curation." International Journal for Research in Applied Science and Engineering Technology 13, no. 1 (2025): 152–55. https://doi.org/10.22214/ijraset.2025.66229.

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Abstract- This paper provides an in-depth review of sentiment analysis techniques applied in various domains, focusing on methodologies such as the VADER sentiment analysis model and Long Short-Term Memory (LSTM) networks. The survey discusses their respective advantages, including VADER's efficiency in handling real-time and news articles text and LSTM's ability to capture long-term dependencies in sequential data. Additionally, the paper explores the use of Bidirectional LSTM (BiLSTM) for improving sentiment classification accuracy and the Natural Language Toolkit (NLTK) for enabling diverse

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BS, Guru Prasad, Dr Kiran GM, and Dr Dinesha HA. "AI-Driven cyber security: Security intelligence modelling." International Journal of Multidisciplinary Research and Growth Evaluation 4, no. 6 (2023): 961–65. http://dx.doi.org/10.54660/.ijmrge.2023.4.6.961-965.

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The process of defending computer networks from cyber attacks or unintended, unauthorized access is known as cyber security. Organizations, businesses, and governments need cyber security solutions because cyber criminals pose a threat to everyone. Artificial intelligence promises to be a great solution for this. Security experts are better able to defend vulnerable networks and data from cyber attackers by combining the strengths of artificial intelligence and cyber security. This paper provides an introduction to the use of artificial intelligence in cyber security. AI-driven cyber security

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Balasaheb Balkhande, Et al. "Artificial Intelligence Driven Power Optimization in IOT-Enabled Wireless Sensor Networks." Journal of Electrical Systems 19, no. 2 (2024): 38–46. http://dx.doi.org/10.52783/jes.689.

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The widespread use of Wireless Sensor Networks (WSN) in Internet of Things (IoT) causes energy efficiency issues. This paper proposes an AI-based solution to this problem. The propose an AI-Driven Power Optimization framework for IoT-enabled WSN using Deep Q-Network (DQN) and Dynamic Voltage and Frequency Scaling (DVFS). These techniques can adapt to changing network conditions and reduce power consumption when used together. Sensor nodes provide environmental parameters, battery status, and network behavior data to the AI-driven framework DQN is implemented after data preprocessing to learn a

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Ullah, Amin, Qazi Mazhar Ul Haq, Zabeeh Ullah, Jaroslav Frnda, and Muhammad Shahid Anwar. "AI-Driven fetal distress monitoring SDN-IoMT networks." PLOS One 20, no. 7 (2025): e0328099. https://doi.org/10.1371/journal.pone.0328099.

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The healthcare industry is transforming with the integration of the Internet of Medical Things (IoMT) with AI-powered networks for improved clinical connectivity and advanced monitoring capabilities. However, IoMT devices struggle with traditional network infrastructure due to complexity and eterogeneous. Software-defined networking (SDN) is a powerful solution for efficiently managing and controlling IoMT. Additionally, the integration of artificial intelligence such as Deep Learning (DL) algorithms brings intelligence and decision-making capabilities to SDN-IoMT systems. This study focuses o

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Niranjana, Gurushankar. "AI-Driven Signal Processing for Mobile Communications." INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH AND CREATIVE TECHNOLOGY 10, no. 1 (2024): 1–6. https://doi.org/10.5281/zenodo.14541031.

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The relentless demand for higher data rates, lower latency, and massive connectivity in next-generation mobile networks (beyond 5G) necessitates innovative signal processing techniques. This paper delves into the intricacies of AI-driven signal processing in mobile communications, addressing challenges, solutions, and future directions. It also explores the transformative role of Artificial Intelligence (AI) in revolutionizing signal processing for future 6G systems. We examine how deep learning, reinforcement learning, and other AI paradigms are being applied to address key challenges such as

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Patil, Ravindra R., Rajnish Kaur Calay, Mohamad Y. Mustafa, and Somil Thakur. "Artificial Intelligence-Driven Innovations in Hydrogen Safety." Hydrogen 5, no. 2 (2024): 312–26. http://dx.doi.org/10.3390/hydrogen5020018.

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This review explores recent advancements in hydrogen gas (H2) safety through the lens of artificial intelligence (AI) techniques. As hydrogen gains prominence as a clean energy source, ensuring its safe handling becomes paramount. The paper critically evaluates the implementation of AI methodologies, including artificial neural networks (ANN), machine learning algorithms, computer vision (CV), and data fusion techniques, in enhancing hydrogen safety measures. By examining the integration of wireless sensor networks and AI for real-time monitoring and leveraging CV for interpreting visual indic

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Chinta, Sribala Vidyadhari, Zichong Wang, Avash Palikhe, et al. "AI-driven healthcare: Fairness in AI healthcare: A survey." PLOS Digital Health 4, no. 5 (2025): e0000864. https://doi.org/10.1371/journal.pdig.0000864.

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Artificial intelligence (AI) is rapidly advancing in healthcare, enhancing the efficiency and effectiveness of services across various specialties, including cardiology, ophthalmology, dermatology, emergency medicine, etc. AI applications have significantly improved diagnostic accuracy, treatment personalization, and patient outcome predictions by leveraging technologies such as machine learning, neural networks, and natural language processing. However, these advancements also introduce substantial ethical and fairness challenges, particularly related to biases in data and algorithms. These b

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Sk, Mr Shafiulilah. "AI-Driven Network Intrusion Detection System." International Journal for Research in Applied Science and Engineering Technology 13, no. 3 (2025): 1481–86. https://doi.org/10.22214/ijraset.2025.67539.

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In the evolving landscape of network security, conventional Intrusion Detection Systems (IDS) often fall short in addressing sophisticated and novel cyber threats. It provides an advanced approach to Network Intrusion Detection by leveraging Generative Adversarial Networks (GANs) to enhance detection accuracy and adaptability. The proposed system integrates GANs to generate synthetic attack patterns and improve anomaly detection capabilities. By training a GAN with diverse network traffic data, our method not only detects known threats but also identifies previously unseen attack vectors with

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Moro Visconti, Roberto. "Artificial Intelligence-Driven FinTech Valuation: A Scalable Multilayer Network Approach." FinTech 3, no. 3 (2024): 479–95. http://dx.doi.org/10.3390/fintech3030026.

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The integration of Artificial Intelligence (AI) in the FinTech industry has significantly reshaped operational workflows, product innovation, and risk management, all of which are pivotal to company valuation. This study investigates the impact of AI-enhanced multilayer networks on FinTech valuation, introducing a novel, scalable multilayer network model with AI-driven Copula Nodes that serve as connectors across operational layers. By incorporating AI, the research unveils a dynamic and interconnected approach to FinTech valuation, revealing new pathways for value co-creation through real-tim

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Liu, Xiaoyang. "Special Issue “Artificial Intelligence in Complex Networks”." Applied Sciences 14, no. 7 (2024): 2822. http://dx.doi.org/10.3390/app14072822.

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Mozo, Alberto, Amit Karamchandani, Sandra Gómez-Canaval, Mario Sanz, Jose Ignacio Moreno, and Antonio Pastor. "B5GEMINI: AI-Driven Network Digital Twin." Sensors 22, no. 11 (2022): 4106. http://dx.doi.org/10.3390/s22114106.

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Network Digital Twin (NDT) is a new technology that builds on the concept of Digital Twins (DT) to create a virtual representation of the physical objects of a telecommunications network. NDT bridges physical and virtual spaces to enable coordination and synchronization of physical parts while eliminating the need to directly interact with them. There is broad consensus that Artificial Intelligence (AI) and Machine Learning (ML) are among the key enablers to this technology. In this work, we present B5GEMINI, which is an NDT for 5G and beyond networks that makes an extensive use of AI and ML.

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Mozo, Alberto, Amit Karamchandani, Sandra Gómez-Canaval, Mario Sanz, Jose Ignacio Moreno, and Antonio Pastor. "B5GEMINI: AI-Driven Network Digital Twin." Sensors 22, no. 11 (2022): 4106. https://doi.org/10.3390/s22114106.

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Network Digital Twin (NDT) is a new technology that builds on the concept of Digital Twins (DT) to create a virtual representation of the physical objects of a telecommunications network. NDT bridges physical and virtual spaces to enable coordination and synchronization of physical parts while eliminating the need to directly interact with them. There is broad consensus that Artificial Intelligence (AI) and Machine Learning (ML) are among the key enablers to this technology. In this work, we present B5GEMINI, which is an NDT for 5G and beyond networks that makes an extensive use of AI and ML.

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Ankita, Sharma. "Network Automation and Orchestration: AI-Driven Self-Healing Networks and Zero-Touch Provisioning." European Journal of Advances in Engineering and Technology 10, no. 3 (2023): 98–104. https://doi.org/10.5281/zenodo.14168741.

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This study analyzes the changing dynamics of network automation and orchestration, highlighting the contributions of Artificial Intelligence (AI) and Machine Learning (ML) in improving network stability, scalability, and efficiency. We examine the development of self-healing networks for autonomous fault identification and rectification, SD-WAN automation for hybrid cloud settings, and zero-touch provisioning for efficient network administration. This investigation underscores the role of AI and ML in advancing the next generation of network automation, paving the way for progressively autonom

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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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Vemuri, Naveen, Naresh Thaneeru, and Venkata Manoj Tatikonda. "AI-Driven Predictive Maintenance in the Telecommunications Industry." Journal of Science & Technology 3, no. 2 (2022): 21–45. http://dx.doi.org/10.55662/jst.2022.3201.

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The rapid evolution of the telecommunications industry has heightened the demand for uninterrupted connectivity and network reliability. In this context, the integration of Artificial Intelligence (AI) in the form of predictive maintenance emerges as a pivotal solution. This research explores the impact of AI-driven predictive maintenance on the telecommunications sector, aiming to enhance network reliability and performance. The telecommunications industry serves as the backbone of global communication, and the importance of maintaining a robust and reliable network infrastructure cannot be o

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Vegesna, Dr Vinod. "AI-Driven Predictive Modelling for Cardiovascular Disease Risk Assessment." International Journal of Innovative Research in Advanced Engineering 11, no. 08 (2024): 760–65. http://dx.doi.org/10.26562/ijirae.2024.v1108.01.

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Advanced predictive modelling is necessary for early identification and risk assessment of cardiovascular disease (CVD), as it continues to be a significant cause of death globally. This study compares established algorithms with novel ideas to investigate AI-driven predictive modelling tools for estimating the risk of cardiovascular disease. Support vector machines (SVM), random forests, and logistic regression are examples of machine learning methods used in the current models. Although these have demonstrated effectiveness in managing organised clinical data, they frequently struggle to inc

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Bilen, Tuğçe, Berk Canberk, Vishal Sharma, Muhammad Fahim, and Trung Q. Duong. "AI-Driven Aeronautical Ad Hoc Networks for 6G Wireless: Challenges, Opportunities, and the Road Ahead." Sensors 22, no. 10 (2022): 3731. http://dx.doi.org/10.3390/s22103731.

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Aeronautical ad hoc network (AANET) has been considered a promising candidate to complete the vision of “Internet in the sky” by supporting high-speed broadband connections on airplanes for 6G networks. However, the specific characteristics of AANET restrict the applicability of conventional topology and routing management algorithms. Here, these conventional methodologies reduce the packet delivery success of AANET with higher transfer delay. At that point, the artificial intelligence (AI)-driven solutions have been adapted to AANET to provide intelligent frameworks and architectures to cope

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Verma, Muskan. "AI-Driven Cyber Attacks and 5G Networks: The New Age of Digital Threats." International Journal for Research in Applied Science and Engineering Technology 13, no. 2 (2025): 1469–75. https://doi.org/10.22214/ijraset.2025.67117.

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AI is experiencing rapid growth across various sectors due to advancements in technology [1]. The collaboration among artificial intelligence (AI) and 5G improves communication technologies. While 5G networks continue to roll out, offering unprecedented speed, low latency, and network density, they also introduce new vulnerabilities that can be exploited by hackers. One area of concern here is the use of AI for cyber-attacks. For some years now there has been a rise in AI-driven cyber-attacks. AI-driven cyber-attacks employ artificial intelligence (AI) and machine learning (ML) algorithms to l

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Arsalan, Hunnain. "Enhancing Predictive Healthcare Using AI-Driven Early Warning Systems." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 28 (2025): 29564–66. https://doi.org/10.1609/aaai.v39i28.35326.

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This research proposes an AI-driven early warning system to predict patient deterioration in real-time using electronic health records (EHRs) and wearable devices. Leveraging deep learning techniques, such as recurrent neural networks (RNNs) for sequential data and convolutional neural networks (CNNs) for pattern recognition, the system adapts dynamically through reinforcement learning. Evaluation strategies include retrospective and prospective studies in clinical settings, measuring prediction accuracy and impact on patient outcomes. If successful, this system has the potential to save lives

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Chelladurai, Callins Christiyana, Priyadharsini Kuluchamy, Sangeetha Santhavaliyan, and Samraj Lawrence T. "INTEGRATING AI-DRIVEN ON-CHIP NEURAL NETWORKS INTO SOC ARCHITECTURES." ICTACT Journal on Microelectronics 9, no. 3 (2023): 1640–45. https://doi.org/10.21917/ijme.2023.0284.

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In System-on-Chip (SoC) architectures, the integration of on-chip neural networks has emerged as a promising avenue for augmenting computational capabilities. This research addresses the imperative need to seamlessly embed AI-driven neural networks directly into SoC designs, paving the way for efficient, real-time processing of complex tasks. Current SoC architectures often grapple with limitations in handling intricate computations and real-time decision-making, prompting the exploration of innovative solutions. The research identifies a critical research gap in the seamless integration of on

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Ojha, Ayush Kumar. "Revolutionizing Enterprise Network Management: The Role of Ai-Driven Solutions in Modern Computer Networking." June-July 2024, no. 44 (June 27, 2024): 1–9. http://dx.doi.org/10.55529/jecnam.44.1.9.

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In the rapidly evolving landscape of enterprise network management, artificial intelligence (AI) is emerging as a transformative force. This paper, titled "Revolutionizing Enterprise Network Management: The Role of AI-Driven Solutions in Modern Computer Networking," delves into the significant impact of AI technologies on the efficiency, security, and scalability of enterprise networks. By integrating AI-driven solutions, organizations can achieve unprecedented levels of automation, predictive maintenance, and real-time anomaly detection, thus enhancing overall network performance. This study

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Satyanarayan Kanungo, Tolu Adedoja, Sourabh Sharma, Suresh Dodda, Suman Narne, Sathishkumar Chintala,. "Exploring AI-driven Innovations in Image Communication Systems for Enhanced Medical Imaging Applications." Journal of Electrical Systems 20, no. 3s (2024): 949–59. http://dx.doi.org/10.52783/jes.1409.

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Artificial intelligence (AI) has emerged as a promising avenue for enhancing medical imaging systems and improving clinical workflows. This research explores innovative applications of AI and deep learning for image communication networks in healthcare. Specifically, we develop an intelligent image compression framework that optimizes data transmission and speeds interpretation of radiology scans. Our approach combines convolutional neural networks, generative adversarial networks, and specialized image filters to balance communication efficiency, diagnostic accuracy, and system latency. Rigor

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Mao, Yingchi, Andri Pranolo, Leonel Hernandez, Aji Prasetya Wibawa, and Zalik Nuryana. "Artificial intelligence in mobile communication: A Survey." IOP Conference Series: Materials Science and Engineering 1212, no. 1 (2022): 012046. http://dx.doi.org/10.1088/1757-899x/1212/1/012046.

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Abstract In this paper, we elaborate on artificial intelligence (AI) techniques used to improve the performance of mobile communication. This article describes brief AI approaches in mobile communication, several classics AI techniques, and the current AI approaches in wireless communication. The techniques contain fuzzy logic, neural networks, reinforcement learning, and AI techniques implemented on mobile communication. Some keys or terms challenges between AI and future mobile communication, not only 5G generation issues but also how the sixth generation (6G) of mobile networks will be driv

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Ahmed, Ibrahim Adeiza, and Paul Boadu Asamoah. "AI-Driven Predictive Maintenance for Energy Infrastructure." International Journal of Research and Scientific Innovation XI, no. IX (2024): 507–28. http://dx.doi.org/10.51244/ijrsi.2024.1109048.

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The growing complexity and critical importance of energy infrastructure necessitate the adoption of advanced maintenance strategies to ensure reliability, efficiency, and sustainability. Traditional maintenance approaches, such as reactive and preventive maintenance, have proven inadequate in addressing the challenges posed by modern energy systems, particularly with the integration of renewable energy sources. This research explores the potential of artificial intelligence (AI)-driven predictive maintenance (PdM) as a transformative solution for the energy sector. By leveraging historical mai

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R, Sumanth. "AI-Driven Crop Disease Prediction and Management System." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem47236.

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Abstract—The agricultural sector faces critical challenges due to plant diseases, leading to reduced crop yields, economic losses, and food insecurity. Traditional plant disease detection methods are based on manual inspection, which is time consuming, subjec- tive, and prone to errors. This research presents an AI-powered system that utilizes deep learning, specifically Convolutional Neural Networks (CNNs), for efficient disease identification. The model processes plant leaf images to extract key features, classify diseases, and provide real-time predictions. Integrated with a web-based appli

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Arun Sugumar. "AI-Driven 5G Network Slicing: Revolutionizing Enterprise Connectivity." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 2 (2025): 2601–8. https://doi.org/10.32628/cseit25112729.

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This article explores the transformative impact of AI-enhanced 5G network slicing on enterprise connectivity across various industries. Network slicing represents a paradigm shift from traditional networking approaches, enabling the creation of multiple virtualized networks on shared physical infrastructure, each optimized for specific applications. While network slicing offers significant advantages over conventional models, its true potential emerges through artificial intelligence integration. The article examines how AI transforms network slicing from static configuration into dynamic, sel

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Patil, Pankaj Rambhau. "Elevating Performance Through AI-Driven Mock Interviews." International Journal for Research in Applied Science and Engineering Technology 12, no. 6 (2024): 1136–39. http://dx.doi.org/10.22214/ijraset.2024.63277.

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Abstract: This paper proposes an innovative AI-based mock interview platform designed to enhance interview preparedness by assessing candidates across three key dimensions: emotions, confidence, and knowledge. Utilizing deep learning convolutional neural networks, the system analyzes facial expressions to gauge emotional responses, while speech recognition and natural language processing evaluate the candidate's confidence levels. Additionally, semantic analysis and keyword mapping assess the candidate's knowledge by comparing responses with relevant online resources. This comprehensive approa

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Sheoran, Amit, Sonia Fahmy, Lianjie Cao, and Puneet Sharma. "AI-Driven Provisioning in the 5G Core." IEEE Internet Computing 25, no. 2 (2021): 18–25. http://dx.doi.org/10.1109/mic.2021.3056230.

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Vinay, M. "Transforming Investment Strategies: AI-Driven Portfolio Optimization." Journal of Research and Review in Digital Marketing and Communications 2, no. 1 (2024): 1–6. https://doi.org/10.5281/zenodo.13933431.

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<em>AI has transformed the approach to delivering financial services significantly with its ease of decision-making and risk control. The paper investigates how AI influences portfolio management by analysing, practical data and distinct models such as support vector machines (SVM), artificial neural networks (ANN), and reinforcement learning (RL). The effectiveness of these models is assessed relative to typical approaches such as Mean-Variance Optimization (MVO) with measures including return on investment (ROI), Sharpe ratio and maximum loss. Findings reveal that AI-inspired models bring la

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Rachit, Garg, and Devi Jayanthila. "Empowering cybersecurity: A deep dive into AI-driven security intelligence modelling." i-manager's Journal on Information Technology 12, no. 4 (2023): 1. http://dx.doi.org/10.26634/jit.12.4.20363.

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The term "cyber security" refers to the process of protecting computer networks from malicious online attacks or unauthorized access. Cyber security solutions are essential for organisations, enterprises, and governments due to the pervasive danger posed by cyber criminals. AI has immense potential as a viable solution for addressing this issue. By using the capabilities of artificial intelligence, security specialists can enhance their ability to protect susceptible networks and data from cyber assailants. This article provides an overview of the use of AI in the field of cyber security. AIdr

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Devarajan, Vinodkumar. "Advancing Data Center Reliability Through AI-Driven Predictive Maintenance." European Journal of Computer Science and Information Technology 13, no. 14 (2025): 102–14. https://doi.org/10.37745/ejcsit.2013/vol13n14102114.

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The evolution of data center maintenance has undergone a transformative shift from traditional reactive and scheduled maintenance to AI-driven predictive maintenance strategies. The integration of artificial intelligence and machine learning technologies enables precise failure prediction, optimizes resource allocation, and enhances operational reliability. Advanced sensor networks and sophisticated analytics pipelines process vast amounts of operational data, while machine learning models, including neural networks, support vector machines, and decision trees, provide accurate predictions of

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Middae, Vijaya lakshmi. "Enhancing Cloud Security with AI-Driven Big Data Analytics." American Journal of Engineering and Technology 07, no. 05 (2025): 185–91. https://doi.org/10.37547/tajet/volume07issue05-18.

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Since cloud computing is changing so rapidly, maintaining strong security is now a major issue for companies everywhere. Massive volumes of mixed data are constantly created in cloud environments at every layer, involving virtual machines, containers, storage, identity management and application activities. It is usually not possible for traditional security systems and old monitoring tools to manage vast and changing data flow in real time. Con- ventional methods fail to discover advanced persistent threats, attacks by team members and new vulnerabilities because they do not easily adjust to

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Lingli Deng, Chengcheng Feng, and Yuan Yao. "Data and knowledge dual-driven architecture for autonomous networks." ITU Journal on Future and Evolving Technologies 3, no. 3 (2022): 602–11. http://dx.doi.org/10.52953/wmup9519.

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The vision of autonomous network has become an industry consensus. Leading operators have moved from network automation to network intelligence, and the deep integration of network and AI technology as the main technical method enters the scale adoption in production networks. At the same time, the third-generation AI technology has ushered in a research and development boom driven by both data and knowledge. To build an architectural consensus to further guide technical standards for accelerating industrial cooperation, a data and knowledge dual-driven autonomous network architecture, as well

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Devi Rahmayanti. "Analysis of AI-Driven Modulation for Cognitive Cellular Networks : DNN Approach." International Journal of Mechanical, Electrical and Civil Engineering 1, no. 4 (2024): 86–101. https://doi.org/10.61132/ijmecie.v1i4.138.

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Objective: analyze the modulation scheme that can intelligently select the appropriate modulation model for service conditions to obtain a high Signal to Noise Ratio, as well as throughput efficiency on wireless networks through the DNN approach. Method: this study uses simulations with the Python language, through AI-Driven on BPSK, QPSK, 16-QAM, and 64-QAM modulation, to determine the SNR and Quality of Service (QoS) produced, both through conventional approaches and Deep Neuro Network (DNN). Researh Finding: AI-Driven modulation used for Cognitive Cellular Networks (CCN), through Deep Neuro

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Li, Zexu, Jingyi Wang, Song Zhao, Qingtian Wang, and Yue Wang. "Evolving Towards Artificial-Intelligence-Driven Sixth-Generation Mobile Networks: An End-to-End Framework, Key Technologies, and Opportunities." Applied Sciences 15, no. 6 (2025): 2920. https://doi.org/10.3390/app15062920.

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The incorporation of artificial intelligence (AI) into sixth-generation (6G) mobile networks is expected to revolutionize communication systems, transforming them into intelligent platforms that provide seamless connectivity and intelligent services. This paper explores the evolution of 6G architectures, as well as the enabling technologies required to integrate AI across the cloud, core network (CN), radio access network (RAN), and terminals. It begins by examining the necessity of embedding AI into 6G networks, making it a native capability. The analysis then outlines potential evolutionary

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Srinivasan Pakkirisamy. "AI-driven cloud ERP: The next frontier in predictive financial management." World Journal of Advanced Research and Reviews 26, no. 1 (2025): 4160–69. https://doi.org/10.30574/wjarr.2025.26.1.1516.

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This article explores groundbreaking advancements in AI-driven Cloud Enterprise Resource Planning (ERP) systems, focusing on Oracle Cloud ERP implementation. The integration of artificial intelligence with cloud-based ERP platforms represents a transformative evolution in financial management capabilities. Through the implementation of hybrid AI agents combining deep learning with Bayesian networks, a sophisticated fraud detection framework utilizing graph neural networks, and automated payment reconciliation through reinforcement learning, organizations can achieve enhanced financial precisio

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Vegesna, Dr Vinod. "AI-Driven Personalized Medicine: A Frame Work for Tailored Cancer Treatment." International Journal of Innovative Research in Advanced Engineering 11, no. 06 (2024): 747–52. http://dx.doi.org/10.26562/ijirae.2024.v1106.06.

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Regardless of the variability of the disease and the individual variances among patients, cancer treatment has historically taken a standardised approach, which frequently leads to less than ideal results. By customising treatment plans to each patient's distinct genetic, phenotypic, and clinical characteristics, AI-driven personalised medicine has the promise of completely changing the way cancer is treated. This study suggests a thorough framework for predicting the best course of treatment for each patient by combining multi-omics data, electronic health records (EHRs), and empirical eviden

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Mu, Yuanyuan, Shuangshuang Zheng, Lizhen Du, and Youqing Wang. "Semantic AI-Driven Knowledge Networks for Enhancing Linguistic Competence in Educational Management." International Journal on Semantic Web and Information Systems 21, no. 1 (2025): 1–23. https://doi.org/10.4018/ijswis.382223.

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Rapid information growth and digital transformation demand advanced educational management systems to foster more effective learning. This study explores the role of semantic artificial intelligence (AI)-driven knowledge networks in enhancing linguistic competence within educational management. By leveraging AI's semantic capabilities, such networks organize, analyze, and visualize linguistic data to support deeper knowledge sharing and language development. This interdisciplinary research empirically investigates the approach. A semester-long intervention involved 69 Chinese junior students m

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Wang, Zihan. "AI and Machine Learning Approaches to Adaptive Signal Processing in Future Wireless Networks." Applied and Computational Engineering 150, no. 1 (2025): 95–100. https://doi.org/10.54254/2755-2721/2025.22279.

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The rapid expansion of wireless communication networks, driven by the increasing demand for high-speed connectivity and the exponential growth of IoT devices, presents significant challenges to traditional signal processing methods. As Beyond 5G (B5G) and 6G technologies continue to evolve, wireless networks must address issues related to spectrum congestion, dynamic channel conditions, and interference management while maintaining low latency and high energy efficiency. Traditional signal processing approaches struggle to adapt to these dynamic environments, necessitating AI-driven adaptive s

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Song, Jialin. "Application Of Ai-Assisted Medical Imaging." Highlights in Science, Engineering and Technology 123 (December 24, 2024): 495–500. https://doi.org/10.54097/rf9fh276.

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Artificial Intelligence (AI) has rapidly gained widespread attention and has made unprecedented strides in recent years, significantly influencing various sectors, especially the medical field. Its applications have revolutionized healthcare by assisting doctors in making faster, more accurate, and data-driven decisions. This paper aims to provide an in-depth analysis of AI's role in medical imaging, focusing on its applications in interpreting X-rays, CT scans, and MRIs. These imaging techniques utilize cutting-edge image recognition algorithms, with Generative Adversarial Networks (GANs) and

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Kavitha Soppari, Shanmukha Saketh Naidu Nakka, Akram Mohammed, and Manoj Kumar Mogala. "A study on AI generated animated videos." World Journal of Advanced Research and Reviews 26, no. 2 (2025): 3347–55. https://doi.org/10.30574/wjarr.2025.26.2.1954.

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The field of character animation has undergone a significant transformation with the advent of artificial intelligence (AI). Traditional animation techniques relied on manual frame-by-frame drawing and motion capture, but recent advancements in AI-driven methodologies have revolutionized the process, making it more efficient, realistic, and scalable. This study explores the evolution of AI techniques in character animation, focusing on deep learning models such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), Variational Autoenc

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Researcher. "AI-DRIVEN NETWORK OPTIMIZATION FOR 5G AND BEYOND." International Journal of Computer Engineering and Technology (IJCET) 15, no. 6 (2024): 809–22. https://doi.org/10.5281/zenodo.14241899.

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This comprehensive article explores the transformative role of Artificial Intelligencein optimizing modern telecommunications networks, particularly focusing on 5G andfuture 6G systems. The article examines how AI-driven solutions revolutionize networkmanagement through advanced data analysis, pattern recognition, and real-timeoptimization capabilities. It investigates key technologies and techniques, includingmachine learning models, predictive analytics, and autonomous network operations,while assessing their impact on network efficiency, service quality, and operationalcosts. The article al

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Triplett, William J. "Artificial Intelligence, Cybersecurity, and Human Trafficking Networks." Cybersecurity and Innovative Technology Journal 2, no. 2 (2024): 112–17. https://doi.org/10.53889/citj.v2i2.556.

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This study aimed to examine the role of artificial intelligence (AI) in dealing with human trafficking by analyzing social networks. Human trafficking is a global concern that exists in the anonymity of social connections and online platforms. There are important transformative tools that facilitate the identification and disruption of trafficking networks, including AI techniques like natural language processing, social network analysis, and machine learning. The study tests AI applications, ethical considerations, and cybersecurity measures that importantly safeguards data integrity and prom

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Mariia, Gerr. "AI-Driven Content Curation and Its Impact on Media Diversity in Social Networks." Proceedings of the World Conference on Media and Communication 2, no. 1 (2025): 22–33. https://doi.org/10.33422/worldcmc.v2i1.1050.

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The rapid advancement of generative artificial intelligence (AI) has made AI-driven content curation a dominant force in shaping public discourse on social media. Platforms, including Facebook, YouTube, and TikTok employ recommendation algorithms to personalise content and increase user engagement. However, these systems also intensify concerns over media pluralism, algorithmic bias, and misinformation. By prioritising user preferences, they reinforce filter bubbles and restrict exposure to diverse viewpoints. As a result, democratic dialogue weakens, and public opinion formation becomes disto

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