Relevant bibliographies by topics / AI driven Networks

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'AI driven Networks'

Author: Grafiati
Published: 7 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "AI driven Networks"

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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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Dissertations / Theses on the topic "AI driven Networks"

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Mennborg, Alexander. "AI-Driven Image Manipulation : Image Outpainting Applied on Fashion Images." Thesis, Luleå tekniska universitet, Institutionen för system- och rymdteknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-85148.

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The e-commerce industry frequently has to deal with displaying product images in a website where the images are provided by the selling partners. The images in question can have drastically different aspect ratios and resolutions which makes it harder to present them while maintaining a coherent user experience. Manipulating images by cropping can sometimes result in parts of the foreground (i.e. product or person within the image) to be cut off. Image outpainting is a technique that allows images to be extended past its boundaries and can be used to alter the aspect ratio of images. Together
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Perera, Jayasuriya Kuranage Menuka. "AI-driven Zero-Touch solutions for resource management in cloud-native 5G networks." Electronic Thesis or Diss., Ecole nationale supérieure Mines-Télécom Atlantique Bretagne Pays de la Loire, 2024. http://www.theses.fr/2024IMTA0427.

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Le déploiement des réseaux 5G a introduit des architectures cloud-native et des systèmes de gestion automatisés, offrant aux fournisseurs de services de communication une infrastructure évolutive, flexible et agile. Ces avancées permettent une allocation dynamique des ressources, augmentant celles-ci en période de forte demande et les réduisant en période de faible utilisation, optimisant ainsi les CapEx et OpEx. Cependant, une observabilité limitée et une caractérisation insuffisante des charges de travail entravent la gestion des ressources. Une surprovisionnement pendant les périodes creuse
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Ishida, Shoichi. "Development of an AI-Driven Organic Synthesis Planning Approach with Retrosynthesis Knowledge." Doctoral thesis, Kyoto University, 2021. http://hdl.handle.net/2433/263605.

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TOMA, ANDREA. "PHY-layer Security in Cognitive Radio Networks through Learning Deep Generative Models: an AI-based approach." Doctoral thesis, Università degli studi di Genova, 2020. http://hdl.handle.net/11567/1003576.

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Recently, Cognitive Radio (CR) has been intended as an intelligent radio endowed with cognition which can be developed by implementing Artificial Intelligence (AI) techniques. Specifically, data-driven Self-Awareness (SA) functionalities, such as detection of spectrum abnormalities, can be effectively implemented as shown by the proposed research. One important application is PHY-layer security since it is essential to establish secure wireless communications against external jamming attacks. In this framework, signals are non-stationary and features from such kind of dynamic spectrum, with m
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Perez, Joan. "Spatial structures in India in the age of globalisation : a data-driven approach." Thesis, Avignon, 2015. http://www.theses.fr/2015AVIG1151/document.

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Les pays qui se sont insérés plus tardivement dans l'économie mondiale subissent généralement les effets de la mondialisation de manière accrue.De ce point de vue, les BRIC, comparés aux autres pays émergents, possèdent un certain poids dans l’économie mondiale et représentent doncun potentiel de marché important. Avec une croissance économique qui devrait dans un avenir proche dépasser celle de la Chine, l’Inde sembleêtre un remarquable cas d’étude. Cependant, les clichés persistent dans un pays où deux aspects seulement sont le plus souvent mis en avant.D’une part, l’Inde est considérée comm
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SORO, FRANCESCA. "An AI and data-driven approach to unwanted network traffic inspection." Doctoral thesis, Politecnico di Torino, 2022. http://hdl.handle.net/11583/2950486.

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Johansson, Linn, and Marcus Persson. "Make way for the Artificial Intelligence train : A qualitative case study investigating the internationalization of Swedish AI firms, as well as the drivers and barriers affecting their process." Thesis, Linnéuniversitetet, Institutionen för marknadsföring (MF), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-76507.

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The purpose of this thesis was to investigate and analyse the internationalization of Swedish SMEs operating within the artificial intelligence industry, as well as to identify the drivers and barriers that influence the process. The methodology chosen for the research was to conduct a qualitative multiple case study with different Swedish AI firms with international operations through an abductive approach. The theoretical framework was established based on different theories related to internationalization, drivers and barriers. With the theoretical framework, a conceptual model was created
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SYED, MUHAMMAD FARRUKH SHAHID. "Data-Driven Approach based on Deep Learning and Probabilistic Models for PHY-Layer Security in AI-enabled Cognitive Radio IoT." Doctoral thesis, Università degli studi di Genova, 2021. http://hdl.handle.net/11567/1048543.

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Cognitive Radio Internet of Things (CR-IoT) has revolutionized almost every field of life and reshaped the technological world. Several tiny devices are seamlessly connected in a CR-IoT network to perform various tasks in many applications. Nevertheless, CR-IoT surfers from malicious attacks that pulverize communication and perturb network performance. Therefore, recently it is envisaged to introduce higher-level Artificial Intelligence (AI) by incorporating Self-Awareness (SA) capabilities into CR-IoT objects to facilitate CR-IoT networks to establish secure transmission against vicious attac
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Djikic, Addi. "Segmentation and Depth Estimation of Urban Road Using Monocular Camera and Convolutional Neural Networks." Thesis, KTH, Robotik, perception och lärande, RPL, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-235496.

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Deep learning for safe autonomous transport is rapidly emerging. Fast and robust perception for autonomous vehicles will be crucial for future navigation in urban areas with high traffic and human interplay. Previous work focuses on extracting full image depth maps, or finding specific road features such as lanes. However, in urban environments lanes are not always present, and sensors such as LiDAR with 3D point clouds provide a quite sparse depth perception of road with demanding algorithmic approaches. In this thesis we derive a novel convolutional neural network that we call AutoNet. It is
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Teng, Sin Yong. "Intelligent Energy-Savings and Process Improvement Strategies in Energy-Intensive Industries." Doctoral thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2020. http://www.nusl.cz/ntk/nusl-433427.

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S tím, jak se neustále vyvíjejí nové technologie pro energeticky náročná průmyslová odvětví, stávající zařízení postupně zaostávají v efektivitě a produktivitě. Tvrdá konkurence na trhu a legislativa v oblasti životního prostředí nutí tato tradiční zařízení k ukončení provozu a k odstavení. Zlepšování procesu a projekty modernizace jsou zásadní v udržování provozních výkonů těchto zařízení. Současné přístupy pro zlepšování procesů jsou hlavně: integrace procesů, optimalizace procesů a intenzifikace procesů. Obecně se v těchto oblastech využívá matematické optimalizace, zkušeností řešitele a pr
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Books on the topic "AI driven Networks"

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Zhai, Xiaoming, and Joseph Krajcik, eds. Uses of Artificial Intelligence in STEM Education. Oxford University PressOxford, 2024. http://dx.doi.org/10.1093/oso/9780198882077.001.0001.

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Abstract In the age of rapid technological advancements, the integration of artificial intelligence (AI), machine learning (ML), and large language models (LLMs) in science, technology, engineering, and mathematics (STEM) education has emerged as a transformative force, reshaping pedagogical approaches and assessment methodologies. This book, comprising twenty-six chapters, delves deep into the multifaceted realm of AI-driven STEM education. It begins by exploring the challenges and opportunities of AI-based STEM education, emphasizing the intricate balance between human tasks and technologica
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Kumar, Sunil, Glenford Mapp, and Korhan Cengiz, eds. Intelligent Network Design Driven by Big Data Analytics, IoT, AI and Cloud Computing. Institution of Engineering and Technology, 2022. http://dx.doi.org/10.1049/pbpc054e.

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Kumar, Sunil, Korhan Cengiz, and Glenford Mapp. Intelligent Network Design Driven by Big Data Analytics, IoT, AI and Cloud Computing. Institution of Engineering & Technology, 2022.

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Kumar, Sunil, Korhan Cengiz, and Glenford Mapp. Intelligent Network Design Driven by Big Data Analytics, IoT, AI and Cloud Computing. Institution of Engineering & Technology, 2022.

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Mohan Kumar, Dr S. Artificial Intelligence: Foundations, Applications, and the Generative Future. Magestic Technology Solutions (P) Ltd, Chennai, Tamil Nadu, India, 2025. https://doi.org/10.47716/978-93-92090-63-9.

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Artificial Intelligence (AI) is no longer confined to laboratories and theoretical models; it is now a driving force behind the digital transformation of society. Artificial Intelligence: Foundations, Applications, and the Generative Future provides an in-depth journey through AI’s essential principles, its wide-ranging applications, and the revolutionary impact of generative technologies. The book methodically builds knowledge from classical AI concepts like intelligent agents, search strategies, and knowledge representation, to advanced learning models including neural networks and machine l
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ELIV-MarketPlace 2018. VDI Verlag, 2018. http://dx.doi.org/10.51202/9783181023389.

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Alle Entwicklungen rund ums Auto und das vieldiskutierte vollautomatisierte Fahren sind sehr sehr spannend. Hier können Sie die neuesten Entwicklungen nachlesen. Inhalt (Auszüge) HAF und VAF: Anforderungen, Realisierungen und Ausblicke Artifical Intelligence in the Driver’s Seat / AI – enabling autonomous driving and transportation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1 P. de Boer, NVIDIA, USA AF und VAF: Realisierungen und Ausblicke Artificial Intelligence: Today ́s and tomorrow ́s opportunities, how do we adjust to it?. .
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Muggleton, Stephen, and Nicholas Chater, eds. Human-Like Machine Intelligence. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780198862536.001.0001.

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In recent years there has been increasing excitement concerning the potential of Artificial Intelligence to transform human society. This book addresses the leading edge of research in this area. The research described aims to address present incompatibilities of Human and Machine reasoning and learning approaches. According to the influential US funding agency DARPA (originator of the Internet and Self-Driving Cars) this new area represents the Third Wave of Artificial Intelligence (3AI, 2020s–2030s), and is being actively investigated in the US, Europe and China. The EPSRC’s UK network on Hu
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Bentley, Peter J. Digitized. Oxford University Press, 2012. http://dx.doi.org/10.1093/oso/9780199693795.001.0001.

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There's a hidden science that affects every part of your life, a science so powerful that you would be hard-pressed to find a single human being on the planet unaffected by its achievements. It is the science behind computers, the machines which drive the supply and creation of power, food, medicine, money, communication, entertainment, and most goods our stores. It has transformed societies with the Internet, the digitization of information, mobile phone networks, and GPS technologies. Written in friendly and approachable language, Digitized provides a window onto the mysterious field from wh
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Wright, Dawn J., and Christian Harder, eds. GIS for Science, Volume 3: Maps for Saving the Planet. Esri Press, 2021. http://dx.doi.org/10.17128/9781589486713.

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GIS for Science: Maps for Saving the Planet, Volume 3, highlights real-world examples of scientists creating maps about saving life on Earth and preserving biodiversity. With Earth and the natural world at risk from various forces, geographic information system (GIS) mapping is essential for driving scientifically conscious decision-making about how to protect life on Earth. In volume 3 of GIS for Science, explore a collection of maps from scientists working to save the planet through documenting and protecting its biodiversity. In this volume, learn how GIS and data mapping are used in tandem
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Book chapters on the topic "AI driven Networks"

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Nguyen, Hien. "AI Driven User Authentication." In Wireless Networks. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-98064-1_14.

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Agarwal, Pratiksha, Arun Gupta, and Mohuya Chakraborty. "Ai-Driven Customer Retention." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-97-8457-8_27.

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Singh, Juhi, Ashvin Sharma, Harish Yadav, and Aryan. "AI-Driven Loan Approval Model." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-1981-8_24.

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Díaz-Santos, Sonia, Pino Caballero-Gil, and Cándido Caballero-Gil. "AI-Driven Wearables for Driver Health and Safety." In Lecture Notes in Networks and Systems. Springer Nature Switzerland, 2024. https://doi.org/10.1007/978-3-031-77571-0_36.

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Agzayal, Yasser, and Mohammed Bouhorma. "AI-Driven Cyber Risk Management Framework." In Lecture Notes in Networks and Systems. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-53824-7_51.

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Shukla, Vinay, Satyam Omar, Naval Bihani, et al. "AI Driven Fraud-Detection in Fintech." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-3247-3_50.

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Tripathy, Binodini, and Niva Das. "AI Driven Automatic Voice Based Gender Detection." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-4071-3_8.

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Sheth, Kashvi Ankitbhai, Rujuta Prajakt Kulkarni, K. Tamilarasi, and S. Pavithra. "AI-Driven Fashion Recommendation System: Exploring StyleSavan." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-0924-6_46.

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Dhabliya, Dharmesh, S. A. Sivakumar, Deepti Khubalkar, Pramod Kumar Sharma, Pranali S. Kshirsagar, and Jyoti Hindurao Jadhav. "AI-Driven Decision Systems for Adaptive Cybersecurity." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-3939-7_30.

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Ouafiq, El Mehdi, Rachid Saadane, and Abdellah Chehri. "Data-Driven Agriculture and Role of AI in Smart Farming." In Data-Driven Intelligence in Wireless Networks. CRC Press, 2023. http://dx.doi.org/10.1201/9781003216971-6.

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Conference papers on the topic "AI driven Networks"

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Natalino, Carlos. "AI-Driven Optical Network Automation: Opportunities and Challenges." In Photonic Networks and Devices. Optica Publishing Group, 2024. https://doi.org/10.1364/networks.2024.new2c.1.

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Sonkar, Piyush, Debabrata Dalai, and B. S. Manoj. "AI-driven Satellite Network Routing and Planning." In 2024 IEEE Future Networks World Forum (FNWF). IEEE, 2024. https://doi.org/10.1109/fnwf63303.2024.11028826.

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Keerthi, Pidikiti, Srisai Satya Santosh Y, and Iniyan S. "Lung Cancer Classification Using Deep Neural Networks." In 2025 AI-Driven Smart Healthcare for Society 5.0. IEEE, 2025. https://doi.org/10.1109/ieeeconf64992.2025.10963162.

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Singh, Kamred Udham, Ankit Kumar, Neeraj Varshney, Linesh Raja, Teekam Singh, and Amit Kumar Dewangan. "AI-Driven Security Enhancements in Wireless Sensor Networks." In 2024 IEEE International Conference on Contemporary Computing and Communications (InC4). IEEE, 2024. http://dx.doi.org/10.1109/inc460750.2024.10649103.

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Mallik, Biswadip Basu, Sudeshna Das, Anshit Mukherjee, Sohini Banerjee, and Avishek Gupta. "Adaptive Multiscale Attention Networks (ASMA-Net) for Enhanced Diabetic Retinopathy." In 2025 AI-Driven Smart Healthcare for Society 5.0. IEEE, 2025. https://doi.org/10.1109/ieeeconf64992.2025.10963200.

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Das, Arpan, Barnali Kundu, Taniska Sarkar, Suman Ghosh, Banshari Deb Majumder, and Debasrree Saha. "Identification of Dental Diseases Using Convolutional and Regional Convolutional Neural Networks." In 2025 AI-Driven Smart Healthcare for Society 5.0. IEEE, 2025. https://doi.org/10.1109/ieeeconf64992.2025.10962868.

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Kashania, Madhu, Ankit Rajpal, Sheetal Rajpal, and Naveen Kumar. "Revealing the Unseen: Explainable AI-Driven Masked Face Recognition." In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10649984.

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N V, Poornima, G. Ahmed Zeeshan, Sanathkumar S, Malik Bader Alazzam, Shamim Ahmad Khan, and Ponni Valavan M. "Energy-Efficient Protocols for Remote Patient Monitoring Using Wireless Sensor Networks in Telehealth Applications." In 2025 AI-Driven Smart Healthcare for Society 5.0. IEEE, 2025. https://doi.org/10.1109/ieeeconf64992.2025.10963250.

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Marry, Prabhakar, Arukali Preethi, Kandhi Bhuvan, Aluvala Ravali, and Cherupalli Linesh. "Interview Brilliance: Harnessing AI for Confidence-Driven Evaluation." In 2024 5th International Conference on Image Processing and Capsule Networks (ICIPCN). IEEE, 2024. http://dx.doi.org/10.1109/icipcn63822.2024.00066.

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Paeleke, Leonard, Navid Keshtiarast, Paul Seehofer, et al. "Demo: Testing AI-Driven Mac Learning in Autonomic Networks." In 2024 IEEE 32nd International Conference on Network Protocols (ICNP). IEEE, 2024. https://doi.org/10.1109/icnp61940.2024.10858521.

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Reports on the topic "AI driven Networks"

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Pasupuleti, Murali Krishna. Quantum Intelligence: Machine Learning Algorithms for Secure Quantum Networks. National Education Services, 2025. https://doi.org/10.62311/nesx/rr925.

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Abstract: As quantum computing and quantum communication technologies advance, securing quantum networks against emerging cyber threats has become a critical challenge. Traditional cryptographic methods are vulnerable to quantum attacks, necessitating the development of AI-driven security solutions. This research explores the integration of machine learning (ML) algorithms with quantum cryptographic frameworks to enhance Quantum Key Distribution (QKD), post-quantum cryptography (PQC), and real-time threat detection. AI-powered quantum security mechanisms, including neural network-based quantum
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Pasupuleti, Murali Krishna. Neuromorphic Nanotech: 2D Materials for Energy-Efficient Edge Computing. National Education Services, 2025. https://doi.org/10.62311/nesx/rr325.

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Abstract The demand for energy-efficient, real-time computing is driving the evolution of neuromorphic computing and edge AI systems. Traditional silicon-based processors struggle with power inefficiencies, memory bottlenecks, and scalability limitations, making them unsuitable for next-generation low-power AI applications. This research report explores how 2D materials, such as graphene, transition metal dichalcogenides (TMDs), black phosphorus, and MXenes, are enabling the development of neuromorphic architectures that mimic biological neural networks for high-speed, ultra-low-power computat
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Pasupuleti, Murali Krishna. Smart Nanomaterials and AI-Integrated Grids for Sustainable Renewable Energy. National Education Services, 2025. https://doi.org/10.62311/nesx/rr1025.

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Abstract: The transition to sustainable and intelligent renewable energy systems is being driven by advancements in smart nanomaterials and AI-integrated smart grids. Nanotechnology has enabled the development of high-performance energy materials, such as graphene, perovskites, quantum dots, and MXenes, which enhance the efficiency, durability, and scalability of renewable energy solutions. Simultaneously, AI-driven smart grids leverage machine learning, deep learning, and digital twins to optimize energy distribution, predictive maintenance, and real-time load balancing in renewable energy ne
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Pasupuleti, Murali Krishna. Next-Generation Extended Reality (XR): A Unified Framework for Integrating AR, VR, and AI-driven Immersive Technologies. National Education Services, 2025. https://doi.org/10.62311/nesx/rrv325.

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Abstract: Extended Reality (XR), encompassing Augmented Reality (AR), Virtual Reality (VR), and Mixed Reality (MR), is evolving into a transformative technology with applications in healthcare, education, industrial training, smart cities, and entertainment. This research presents a unified framework integrating AI-driven XR technologies with computer vision, deep learning, cloud computing, and 5G connectivity to enhance immersion, interactivity, and scalability. AI-powered neural rendering, real-time physics simulation, spatial computing, and gesture recognition enable more realistic and adap
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5

Pasupuleti, Murali Krishna. Decentralized Creativity: AI-Infused Blockchain for Secure and Transparent Digital Innovation. National Education Services, 2025. https://doi.org/10.62311/nesx/rrvi125.

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Abstract The convergence of artificial intelligence (AI) and blockchain technology is transforming the creative economy by enabling secure, transparent, and decentralized innovation in digital content creation, intellectual property management, and monetization. Traditional creative industries are often constrained by centralized platforms, opaque copyright enforcement, and unfair revenue distribution, which limit the autonomy and financial benefits of creators. By leveraging blockchain’s immutable ledger, smart contracts, and non-fungible tokens (NFTs), digital assets can be authenticated, to
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6

Loecker, Florian, Amanah Ramadiah, and Kimmo Soramäki. Countering Consumer Fraud and Scams with National Fraud Portals. FNA, 2024. http://dx.doi.org/10.69701/oppl1525.

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In this paper, we argue that setting up a new Digital Public Infrastructure - a National Fraud Portal (NFP) - is the only way to address fraud and consumer scams efficiently. NFPs provide a technological solution as a shared facility for banks, law enforcement, the Financial Intelligence Unit (FIU), the central bank, the conduct supervisor, and other stakeholders. Further down the line, NFPs can connect to one another as cross-border criminal activity increases (a likely consequence of suppressing fraud domestically). The National Fraud Portal (NFP) enables: The real-time tracing and tracking
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