AHCI RESEARCH GROUP
Publications
Papers published in international journals,
proceedings of conferences, workshops and books.
OUR RESEARCH
Scientific Publications
How to
Here you can find the complete list of our publications.
You can use the tag cloud to select only the papers dealing with specific research topics.
You can expand the Abstract, Links and BibTex record for each paper.
You can use the tag cloud to select only the papers dealing with specific research topics.
You can expand the Abstract, Links and BibTex record for each paper.
2025
Banafa, A.
Artificial intelligence in action: Real-world applications and innovations Book
River Publishers, 2025, ISBN: 978-877004619-0 (ISBN); 978-877004620-6 (ISBN).
Abstract | Links | BibTeX | Tags: 5G, Affective Computing, AGI, AI, AI alignments, AI Ethics, AI hallucinations, AI hype, AI models, Alexa, ANI, ASI, Augmented Reality, Autoencoders, Autonomic computing, Autonomous Cars, Autoregressive models, Big Data, Big Data Analytics, Bitcoin, Blockchain, C3PO, Casual AI, Causal reasoning, ChatGPT, Cloud computing, Collective AI, Compression engines, Computer vision, Conditional Automation, Convolutional neural networks (CNNs), Cryptocurrency, Cybersecurity, Deceptive AI, Deep learning, Digital transformation, Driver Assistance, Driverless Cars, Drones, Elon Musk, Entanglement, Environment and sustainability, Ethereum, Explainable AI, Facebook, Facial Recognition, Feedforward. Neural Networks, Fog Computing, Full Automation, Future of AI, General AI, Generative Adversarial Networks (GANs), Generative AI, Google, Green AI, High Automation, Hybrid Blockchain, IEEE, Industrial Internet of Things (IIoT), Internet of things (IoT), Jarvis, Java, JavaScript, Long Short-Term Memory Networks, LTE, machine learning, Microsoft, MultiModal AI, Narrow AI, Natural disasters, Natural Language Generation (NLG), Natural Language Processing (NLP), NetFlix, Network Security, Neural Networks, Nuclear, Nuclear AI, NYTimes, Objective-driven AI, Open Source, Partial Automation, PayPal, Perfect AI, Private Blockchain, Private Cloud Computing, Programming languages, Python, Quantum Communications, Quantum Computing, Quantum Cryptography, Quantum internet, Quantum Machine Learning (QML), R2D2, Reactive machines. limited memory, Recurrent Neural Networks, Responsible AI, Robots, Sci-Fi movies, Self-Aware, Semiconductorâ??s, Sensate AI, Siri, Small Data, Smart Contracts. Hybrid Cloud Computing, Smart Devices, Sovereign AI, Super AI, Superposition, TensorFlow, Theory of Mind, Thick Data, Twitter, Variational Autoencoders (VAEs), Virtual Reality, Voice user interface (VUI), Wearable computing devices (WCD), Wearable Technology, Wi-Fi, XAI, Zero-Trust Model
@book{banafa_artificial_2025,
title = {Artificial intelligence in action: Real-world applications and innovations},
author = {A. Banafa},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105000403587&partnerID=40&md5=4b0d94be48194a942b22bef63f36d3bf},
isbn = {978-877004619-0 (ISBN); 978-877004620-6 (ISBN)},
year = {2025},
date = {2025-01-01},
publisher = {River Publishers},
series = {Artificial Intelligence in Action: Real-World Applications and Innovations},
abstract = {This comprehensive book dives deep into the current landscape of AI, exploring its fundamental principles, development challenges, potential risks, and the cutting-edge breakthroughs that are propelling it forward. Artificial intelligence (AI) is rapidly transforming industries and societies worldwide through groundbreaking innovations and real-world applications. Starting with the core concepts, the book examines the various types of AI systems, generative AI models, and the complexities of machine learning. It delves into the programming languages driving AI development, data pipelines, model creation and deployment processes, while shedding light on issues like AI hallucinations and the intricate path of machine unlearning. The book then showcases the remarkable real-world applications of AI across diverse domains. From preventing job displacement and promoting environmental sustainability, to enhancing disaster response, drone technology, and even nuclear energy innovation, it highlights how AI is tackling complex challenges and driving positive change. The book also explores the double-edged nature of AI, recognizing its tremendous potential while cautioning about the risks of misuse, unintended consequences, and the urgent need for responsible development practices. It examines the intersection of AI and fields like operating system design, warfare, and semiconductor technology, underscoring the wide-ranging implications of this transformative force. As the quest for artificial general intelligence (AGI) and superintelligent AI systems intensifies, the book delves into cutting-edge research, emerging trends, and the pursuit of multimodal, explainable, and causally aware AI systems. It explores the symbiotic relationship between AI and human creativity, the rise of user-friendly "casual AI," and the potential of AI to tackle open-ended tasks. This is an essential guide for understanding the profound impact of AI on our world today and its potential to shape our future. From the frontiers of innovation to the challenges of responsible development, this book offers a comprehensive and insightful exploration of the remarkable real-world applications and innovations driving the AI revolution. © 2025 River Publishers. All rights reserved.},
keywords = {5G, Affective Computing, AGI, AI, AI alignments, AI Ethics, AI hallucinations, AI hype, AI models, Alexa, ANI, ASI, Augmented Reality, Autoencoders, Autonomic computing, Autonomous Cars, Autoregressive models, Big Data, Big Data Analytics, Bitcoin, Blockchain, C3PO, Casual AI, Causal reasoning, ChatGPT, Cloud computing, Collective AI, Compression engines, Computer vision, Conditional Automation, Convolutional neural networks (CNNs), Cryptocurrency, Cybersecurity, Deceptive AI, Deep learning, Digital transformation, Driver Assistance, Driverless Cars, Drones, Elon Musk, Entanglement, Environment and sustainability, Ethereum, Explainable AI, Facebook, Facial Recognition, Feedforward. Neural Networks, Fog Computing, Full Automation, Future of AI, General AI, Generative Adversarial Networks (GANs), Generative AI, Google, Green AI, High Automation, Hybrid Blockchain, IEEE, Industrial Internet of Things (IIoT), Internet of things (IoT), Jarvis, Java, JavaScript, Long Short-Term Memory Networks, LTE, machine learning, Microsoft, MultiModal AI, Narrow AI, Natural disasters, Natural Language Generation (NLG), Natural Language Processing (NLP), NetFlix, Network Security, Neural Networks, Nuclear, Nuclear AI, NYTimes, Objective-driven AI, Open Source, Partial Automation, PayPal, Perfect AI, Private Blockchain, Private Cloud Computing, Programming languages, Python, Quantum Communications, Quantum Computing, Quantum Cryptography, Quantum internet, Quantum Machine Learning (QML), R2D2, Reactive machines. limited memory, Recurrent Neural Networks, Responsible AI, Robots, Sci-Fi movies, Self-Aware, Semiconductorâ??s, Sensate AI, Siri, Small Data, Smart Contracts. Hybrid Cloud Computing, Smart Devices, Sovereign AI, Super AI, Superposition, TensorFlow, Theory of Mind, Thick Data, Twitter, Variational Autoencoders (VAEs), Virtual Reality, Voice user interface (VUI), Wearable computing devices (WCD), Wearable Technology, Wi-Fi, XAI, Zero-Trust Model},
pubstate = {published},
tppubtype = {book}
}
This comprehensive book dives deep into the current landscape of AI, exploring its fundamental principles, development challenges, potential risks, and the cutting-edge breakthroughs that are propelling it forward. Artificial intelligence (AI) is rapidly transforming industries and societies worldwide through groundbreaking innovations and real-world applications. Starting with the core concepts, the book examines the various types of AI systems, generative AI models, and the complexities of machine learning. It delves into the programming languages driving AI development, data pipelines, model creation and deployment processes, while shedding light on issues like AI hallucinations and the intricate path of machine unlearning. The book then showcases the remarkable real-world applications of AI across diverse domains. From preventing job displacement and promoting environmental sustainability, to enhancing disaster response, drone technology, and even nuclear energy innovation, it highlights how AI is tackling complex challenges and driving positive change. The book also explores the double-edged nature of AI, recognizing its tremendous potential while cautioning about the risks of misuse, unintended consequences, and the urgent need for responsible development practices. It examines the intersection of AI and fields like operating system design, warfare, and semiconductor technology, underscoring the wide-ranging implications of this transformative force. As the quest for artificial general intelligence (AGI) and superintelligent AI systems intensifies, the book delves into cutting-edge research, emerging trends, and the pursuit of multimodal, explainable, and causally aware AI systems. It explores the symbiotic relationship between AI and human creativity, the rise of user-friendly "casual AI," and the potential of AI to tackle open-ended tasks. This is an essential guide for understanding the profound impact of AI on our world today and its potential to shape our future. From the frontiers of innovation to the challenges of responsible development, this book offers a comprehensive and insightful exploration of the remarkable real-world applications and innovations driving the AI revolution. © 2025 River Publishers. All rights reserved.
2024
Bandara, E.; Foytik, P.; Shetty, S.; Hassanzadeh, A.
Generative-AI(with Custom-Trained Meta's Llama2 LLM), Blockchain, NFT, Federated Learning and PBOM Enabled Data Security Architecture for Metaverse on 5G/6G Environment Proceedings Article
In: Proc. - IEEE Int. Conf. Mob. Ad-Hoc Smart Syst., MASS, pp. 118–124, Institute of Electrical and Electronics Engineers Inc., 2024, ISBN: 979-835036399-9 (ISBN).
Abstract | Links | BibTeX | Tags: 5G, 6G, Adversarial machine learning, Bill of materials, Block-chain, Blockchain, Curricula, Data privacy, Distance education, Federated learning, Generative adversarial networks, Generative-AI, Hardware security, Llama2, LLM, Medium access control, Metaverse, Metaverses, Network Security, Nft, Non-fungible token, Personnel training, Problem oriented languages, Reference architecture, Steganography
@inproceedings{bandara_generative-aicustom-trained_2024,
title = {Generative-AI(with Custom-Trained Meta's Llama2 LLM), Blockchain, NFT, Federated Learning and PBOM Enabled Data Security Architecture for Metaverse on 5G/6G Environment},
author = {E. Bandara and P. Foytik and S. Shetty and A. Hassanzadeh},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85210243120&doi=10.1109%2fMASS62177.2024.00026&partnerID=40&md5=70d21ac1e9c7b886da14825376919cac},
doi = {10.1109/MASS62177.2024.00026},
isbn = {979-835036399-9 (ISBN)},
year = {2024},
date = {2024-01-01},
booktitle = {Proc. - IEEE Int. Conf. Mob. Ad-Hoc Smart Syst., MASS},
pages = {118–124},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {The Metaverse is an integrated network of 3D virtual worlds accessible through a virtual reality headset. Its impact on data privacy and security is increasingly recognized as a major concern. There is a growing interest in developing a reference architecture that describes the four core aspects of its data: acquisition, storage, sharing, and interoperability. Establishing a secure data architecture is imperative to manage users' personal data and facilitate trusted AR/VR and AI/ML solutions within the Metaverse. This paper details a reference architecture empowered by Generative-AI, Blockchain, Federated Learning, and Non-Fungible Tokens (NFTs). Within this archi-tecture, various resource providers collaborate via the blockchain network. Handling personal user data and resource provider identities is executed through a Self-Sovereign Identity-enabled privacy-preserving framework. AR/NR devices in the Metaverse are represented as NFT tokens available for user purchase. Software updates and supply-chain verification for these devices are managed using a Software Bill of Materials (SBOM) and a Pipeline Bill of Materials (PBOM) verification system. Moreover, a custom-trained Llama2 LLM from Meta has been integrated to generate PBOMs for AR/NR devices' software updates, thereby preventing malware intrusions and data breaches. This Llama2-13B LLM has been quantized and fine-tuned using Qlora to ensure optimal performance on consumer-grade hardware. The provenance of AI/ML models used in the Metaverse is encapsu-lated as Model Card objects, allowing external parties to audit and verify them, thus mitigating adversarial learning attacks within these models. To the best of our knowledge, this is the very first research effort aimed at standardizing PBOM schemas and integrating Language Model algorithms for the generation of PBOMs. Additionally, a proposed mechanism facilitates different AI/ML providers in training their machine learning models using a privacy-preserving federated learning approach. Authorization of communications among AR/VR devices in the Metaverse is conducted through a Zero-Trust security-enabled rule engine. A system testbed has been implemented within a 5G environment, utilizing Ericsson new Radio with Open5GS 5G core. © 2024 IEEE.},
keywords = {5G, 6G, Adversarial machine learning, Bill of materials, Block-chain, Blockchain, Curricula, Data privacy, Distance education, Federated learning, Generative adversarial networks, Generative-AI, Hardware security, Llama2, LLM, Medium access control, Metaverse, Metaverses, Network Security, Nft, Non-fungible token, Personnel training, Problem oriented languages, Reference architecture, Steganography},
pubstate = {published},
tppubtype = {inproceedings}
}
The Metaverse is an integrated network of 3D virtual worlds accessible through a virtual reality headset. Its impact on data privacy and security is increasingly recognized as a major concern. There is a growing interest in developing a reference architecture that describes the four core aspects of its data: acquisition, storage, sharing, and interoperability. Establishing a secure data architecture is imperative to manage users' personal data and facilitate trusted AR/VR and AI/ML solutions within the Metaverse. This paper details a reference architecture empowered by Generative-AI, Blockchain, Federated Learning, and Non-Fungible Tokens (NFTs). Within this archi-tecture, various resource providers collaborate via the blockchain network. Handling personal user data and resource provider identities is executed through a Self-Sovereign Identity-enabled privacy-preserving framework. AR/NR devices in the Metaverse are represented as NFT tokens available for user purchase. Software updates and supply-chain verification for these devices are managed using a Software Bill of Materials (SBOM) and a Pipeline Bill of Materials (PBOM) verification system. Moreover, a custom-trained Llama2 LLM from Meta has been integrated to generate PBOMs for AR/NR devices' software updates, thereby preventing malware intrusions and data breaches. This Llama2-13B LLM has been quantized and fine-tuned using Qlora to ensure optimal performance on consumer-grade hardware. The provenance of AI/ML models used in the Metaverse is encapsu-lated as Model Card objects, allowing external parties to audit and verify them, thus mitigating adversarial learning attacks within these models. To the best of our knowledge, this is the very first research effort aimed at standardizing PBOM schemas and integrating Language Model algorithms for the generation of PBOMs. Additionally, a proposed mechanism facilitates different AI/ML providers in training their machine learning models using a privacy-preserving federated learning approach. Authorization of communications among AR/VR devices in the Metaverse is conducted through a Zero-Trust security-enabled rule engine. A system testbed has been implemented within a 5G environment, utilizing Ericsson new Radio with Open5GS 5G core. © 2024 IEEE.
Chandrashekar, N. Donekal; Lee, A.; Azab, M.; Gracanin, D.
Understanding User Behavior for Enhancing Cybersecurity Training with Immersive Gamified Platforms Journal Article
In: Information (Switzerland), vol. 15, no. 12, 2024, ISSN: 20782489 (ISSN).
Abstract | Links | BibTeX | Tags: Artificial intelligence, Critical infrastructures, Cyber attacks, Cyber security, Cyber systems, Cyber-attacks, Cybersecurity, Decisions makings, Digital infrastructures, digital twin, Extended reality, Gamification, Immersive, Network Security, simulation, Technical vulnerabilities, Training, user behavior, User behaviors
@article{donekal_chandrashekar_understanding_2024,
title = {Understanding User Behavior for Enhancing Cybersecurity Training with Immersive Gamified Platforms},
author = {N. Donekal Chandrashekar and A. Lee and M. Azab and D. Gracanin},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85213435167&doi=10.3390%2finfo15120814&partnerID=40&md5=134c43c7238bae4923468bc6e46c860d},
doi = {10.3390/info15120814},
issn = {20782489 (ISSN)},
year = {2024},
date = {2024-01-01},
journal = {Information (Switzerland)},
volume = {15},
number = {12},
abstract = {In modern digital infrastructure, cyber systems are foundational, making resilience against sophisticated attacks essential. Traditional cybersecurity defenses primarily address technical vulnerabilities; however, the human element, particularly decision-making during cyber attacks, adds complexities that current behavioral studies fail to capture adequately. Existing approaches, including theoretical models, game theory, and simulators, rely on retrospective data and static scenarios. These methods often miss the real-time, context-specific nature of user responses during cyber threats. To address these limitations, this work introduces a framework that combines Extended Reality (XR) and Generative Artificial Intelligence (Gen-AI) within a gamified platform. This framework enables continuous, high-fidelity data collection on user behavior in dynamic attack scenarios. It includes three core modules: the Player Behavior Module (PBM), Gamification Module (GM), and Simulation Module (SM). Together, these modules create an immersive, responsive environment for studying user interactions. A case study in a simulated critical infrastructure environment demonstrates the framework’s effectiveness in capturing realistic user behaviors under cyber attack, with potential applications for improving response strategies and resilience across critical sectors. This work lays the foundation for adaptive cybersecurity training and user-centered development across critical infrastructure. © 2024 by the authors.},
keywords = {Artificial intelligence, Critical infrastructures, Cyber attacks, Cyber security, Cyber systems, Cyber-attacks, Cybersecurity, Decisions makings, Digital infrastructures, digital twin, Extended reality, Gamification, Immersive, Network Security, simulation, Technical vulnerabilities, Training, user behavior, User behaviors},
pubstate = {published},
tppubtype = {article}
}
In modern digital infrastructure, cyber systems are foundational, making resilience against sophisticated attacks essential. Traditional cybersecurity defenses primarily address technical vulnerabilities; however, the human element, particularly decision-making during cyber attacks, adds complexities that current behavioral studies fail to capture adequately. Existing approaches, including theoretical models, game theory, and simulators, rely on retrospective data and static scenarios. These methods often miss the real-time, context-specific nature of user responses during cyber threats. To address these limitations, this work introduces a framework that combines Extended Reality (XR) and Generative Artificial Intelligence (Gen-AI) within a gamified platform. This framework enables continuous, high-fidelity data collection on user behavior in dynamic attack scenarios. It includes three core modules: the Player Behavior Module (PBM), Gamification Module (GM), and Simulation Module (SM). Together, these modules create an immersive, responsive environment for studying user interactions. A case study in a simulated critical infrastructure environment demonstrates the framework’s effectiveness in capturing realistic user behaviors under cyber attack, with potential applications for improving response strategies and resilience across critical sectors. This work lays the foundation for adaptive cybersecurity training and user-centered development across critical infrastructure. © 2024 by the authors.
Sehgal, V.; Sekaran, N.
Virtual Recording Generation Using Generative AI and Carla Simulator Proceedings Article
In: SAE Techni. Paper., SAE International, 2024, ISBN: 01487191 (ISSN).
Abstract | Links | BibTeX | Tags: Access control, Air cushion vehicles, Associative storage, Augmented Reality, Automobile driver simulators, Automobile drivers, Automobile simulators, Automobile testing, Autonomous Vehicles, benchmarking, Computer testing, Condition, Continuous functions, Dynamic random access storage, Formal concept analysis, HDCP, Language Model, Luminescent devices, Network Security, Operational test, Operational use, Problem oriented languages, Randomisation, Real-world drivings, Sailing vessels, Ships, Test condition, UNIX, Vehicle modelling, Virtual addresses
@inproceedings{sehgal_virtual_2024,
title = {Virtual Recording Generation Using Generative AI and Carla Simulator},
author = {V. Sehgal and N. Sekaran},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85213320680&doi=10.4271%2f2024-28-0261&partnerID=40&md5=37a924cf9beda31f2c23b3a2cdf575d2},
doi = {10.4271/2024-28-0261},
isbn = {01487191 (ISSN)},
year = {2024},
date = {2024-01-01},
booktitle = {SAE Techni. Paper.},
publisher = {SAE International},
abstract = {To establish and validate new systems incorporated into next generation vehicles, it is important to understand actual scenarios which the autonomous vehicles will likely encounter. Consequently, to do this, it is important to run Field Operational Tests (FOT). FOT is undertaken with many vehicles and large acquisition areas ensuing the capability and suitability of a continuous function, thus guaranteeing the randomization of test conditions. FOT and Use case(a software testing technique designed to ensure that the system under test meets and exceeds the stakeholders' expectations) scenario recordings capture is very expensive, due to the amount of necessary material (vehicles, measurement equipment/objectives, headcount, data storage capacity/complexity, trained drivers/professionals) and all-time robust working vehicle setup is not always available, moreover mileage is directly proportional to time, along with that it cannot be scaled up due to physical limitations. During the early development phase, ground truth data is not available, and data that can be reused from other projects may not match 100% with current project requirements. All event scenarios/weather conditions cannot be ensured during recording capture, in such cases synthetic/virtual recording comes very handy which can accurately mimic real conditions on test bench and can very well address the before mentioned constraints. Car Learning to Act (CARLA) [1] is an autonomous open-source driving simulator, used for the development, training, and validation of autonomous driving systems is extended for generation of synthetic/virtual data/recordings, by integrating Generative Artificial Intelligence (Gen AI), particularly Generative Adversarial Networks (GANs) [2] and Retrieval Augmented Generation (RAG) [3] which are deep learning models. The process of creating synthetic data using vehicle models becomes more efficient and reliable as Gen AI can hold and reproduce much more data in scenario development than a developer or tester. A Large Language Model (LLM) [4] takes user input in the form of user prompts and generate scenarios that are used to produce a vast amount of high-quality, distinct, and realistic driving scenarios that closely resemble real-world driving data. Gen AI [5] empowers the user to generate not only dynamic environment conditions (such as different weather conditions and lighting conditions) but also dynamic elements like the behavior of other vehicles and pedestrians. Synthetic/Virtual recording [6] generated using Gen AI can be used to train and validate virtual vehicle models, FOT/Use case data which is used to indirectly prove real-world performance of functionality of tasks such as object detection, object recognition, image segmentation, and decision-making algorithms in autonomous vehicles. Augmenting LLM with CARLA involves training generative models on real-world driving data using RAG which allows the model to generate new, synthetic instances that resemble real-world conditions/scenarios. © 2024 SAE International. All Rights Reserved.},
keywords = {Access control, Air cushion vehicles, Associative storage, Augmented Reality, Automobile driver simulators, Automobile drivers, Automobile simulators, Automobile testing, Autonomous Vehicles, benchmarking, Computer testing, Condition, Continuous functions, Dynamic random access storage, Formal concept analysis, HDCP, Language Model, Luminescent devices, Network Security, Operational test, Operational use, Problem oriented languages, Randomisation, Real-world drivings, Sailing vessels, Ships, Test condition, UNIX, Vehicle modelling, Virtual addresses},
pubstate = {published},
tppubtype = {inproceedings}
}
To establish and validate new systems incorporated into next generation vehicles, it is important to understand actual scenarios which the autonomous vehicles will likely encounter. Consequently, to do this, it is important to run Field Operational Tests (FOT). FOT is undertaken with many vehicles and large acquisition areas ensuing the capability and suitability of a continuous function, thus guaranteeing the randomization of test conditions. FOT and Use case(a software testing technique designed to ensure that the system under test meets and exceeds the stakeholders' expectations) scenario recordings capture is very expensive, due to the amount of necessary material (vehicles, measurement equipment/objectives, headcount, data storage capacity/complexity, trained drivers/professionals) and all-time robust working vehicle setup is not always available, moreover mileage is directly proportional to time, along with that it cannot be scaled up due to physical limitations. During the early development phase, ground truth data is not available, and data that can be reused from other projects may not match 100% with current project requirements. All event scenarios/weather conditions cannot be ensured during recording capture, in such cases synthetic/virtual recording comes very handy which can accurately mimic real conditions on test bench and can very well address the before mentioned constraints. Car Learning to Act (CARLA) [1] is an autonomous open-source driving simulator, used for the development, training, and validation of autonomous driving systems is extended for generation of synthetic/virtual data/recordings, by integrating Generative Artificial Intelligence (Gen AI), particularly Generative Adversarial Networks (GANs) [2] and Retrieval Augmented Generation (RAG) [3] which are deep learning models. The process of creating synthetic data using vehicle models becomes more efficient and reliable as Gen AI can hold and reproduce much more data in scenario development than a developer or tester. A Large Language Model (LLM) [4] takes user input in the form of user prompts and generate scenarios that are used to produce a vast amount of high-quality, distinct, and realistic driving scenarios that closely resemble real-world driving data. Gen AI [5] empowers the user to generate not only dynamic environment conditions (such as different weather conditions and lighting conditions) but also dynamic elements like the behavior of other vehicles and pedestrians. Synthetic/Virtual recording [6] generated using Gen AI can be used to train and validate virtual vehicle models, FOT/Use case data which is used to indirectly prove real-world performance of functionality of tasks such as object detection, object recognition, image segmentation, and decision-making algorithms in autonomous vehicles. Augmenting LLM with CARLA involves training generative models on real-world driving data using RAG which allows the model to generate new, synthetic instances that resemble real-world conditions/scenarios. © 2024 SAE International. All Rights Reserved.
2023
Banafa, A.
Transformative AI: Responsible, Transparent, and Trustworthy AI Systems Book
River Publishers, 2023, ISBN: 978-877004018-1 (ISBN); 978-877004019-8 (ISBN).
Abstract | Links | BibTeX | Tags: 5G, Affective Computing, AI, AI Ethics, Alexa, Augment Reality, Autoencoders, Autonomous Cars, Autoregressive models, Big Data, Big Data Analytics, Bitcoin, Blockchain, C3PO, ChatGPT, Cloud computing, CNN, Computer vision, Conditional Automation, Convolutional Neural Networks, Cryptocurrency, Cybersecurity, Deep learning, Digital transformation, Driver Assistance, Driverless Cars, Entanglement, Ethereum, Explainable AI. Environment and sustainability, Facebook, Facial Recognition, Feedforward. Neural Networks, Fog Computing, Full Automation, General AI, Generative Adversarial Networks (GANs), Generative AI, Google, High Automation, Hybrid Blockchain, IEEE, IIoT, Industrial Internet of Things, Internet of Things, IoT, Jarvis, Long Short-Term Memory Networks, LTE, Machin Learning, Microsoft, Narrow AI, Natural Language Generation (NLG), Natural Language Processing (NLP), NetFlix, Network Security, Neural Networks, NYTimes, Open Source, Partial Automation, PayPal, Private Blockchain, Private Cloud Computing, Quantum Communications, Quantum Computing, Quantum Cryptography, Quantum Internet. Wearable Computing Devices (WCD). Autonomic Computing, Quantum Machine Learning (QML), R2D2, Reactive Machines . Limited Memory, Recurrent Neural Networks, Robots, Sci-Fi movies, Self-Aware, Siri, Small Data, Smart Contracts. Hybrid Cloud Computing, Smart Devices, Super AI, Superposition, Theory of Mind, Thick Data, Twitter, Variational Autoencoders (VAEs), Virtual Reality, Voice User Interface, VUI, Wearable Technology, Wi-Fi, Zero-Trust Model
@book{banafa_transformative_2023,
title = {Transformative AI: Responsible, Transparent, and Trustworthy AI Systems},
author = {A. Banafa},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85180544759&partnerID=40&md5=c1fcd00f4b40e16156d9877185f66554},
isbn = {978-877004018-1 (ISBN); 978-877004019-8 (ISBN)},
year = {2023},
date = {2023-01-01},
publisher = {River Publishers},
series = {Transformative AI: Responsible, Transparent, and Trustworthy AI Systems},
abstract = {Transformative AI provides a comprehensive overview of the latest trends, challenges, applications, and opportunities in the field of Artificial Intelligence. The book covers the state of the art in AI research, including machine learning, natural language processing, computer vision, and robotics, and explores how these technologies are transforming various industries and domains, such as healthcare, finance, education, and entertainment. The book also addresses the challenges that come with the widespread adoption of AI, including ethical concerns, bias, and the impact on jobs and society. It provides insights into how to mitigate these challenges and how to design AI systems that are responsible, transparent, and trustworthy. The book offers a forward-looking perspective on the future of AI, exploring the emerging trends and applications that are likely to shape the next decade of AI innovation. It also provides practical guidance for businesses and individuals on how to leverage the power of AI to create new products, services, and opportunities. Overall, the book is an essential read for anyone who wants to stay ahead of the curve in the rapidly evolving field of Artificial Intelligence and understand the impact that this transformative technology will have on our lives in the coming years. © 2024 River Publishers. All rights reserved.},
keywords = {5G, Affective Computing, AI, AI Ethics, Alexa, Augment Reality, Autoencoders, Autonomous Cars, Autoregressive models, Big Data, Big Data Analytics, Bitcoin, Blockchain, C3PO, ChatGPT, Cloud computing, CNN, Computer vision, Conditional Automation, Convolutional Neural Networks, Cryptocurrency, Cybersecurity, Deep learning, Digital transformation, Driver Assistance, Driverless Cars, Entanglement, Ethereum, Explainable AI. Environment and sustainability, Facebook, Facial Recognition, Feedforward. Neural Networks, Fog Computing, Full Automation, General AI, Generative Adversarial Networks (GANs), Generative AI, Google, High Automation, Hybrid Blockchain, IEEE, IIoT, Industrial Internet of Things, Internet of Things, IoT, Jarvis, Long Short-Term Memory Networks, LTE, Machin Learning, Microsoft, Narrow AI, Natural Language Generation (NLG), Natural Language Processing (NLP), NetFlix, Network Security, Neural Networks, NYTimes, Open Source, Partial Automation, PayPal, Private Blockchain, Private Cloud Computing, Quantum Communications, Quantum Computing, Quantum Cryptography, Quantum Internet. Wearable Computing Devices (WCD). Autonomic Computing, Quantum Machine Learning (QML), R2D2, Reactive Machines . Limited Memory, Recurrent Neural Networks, Robots, Sci-Fi movies, Self-Aware, Siri, Small Data, Smart Contracts. Hybrid Cloud Computing, Smart Devices, Super AI, Superposition, Theory of Mind, Thick Data, Twitter, Variational Autoencoders (VAEs), Virtual Reality, Voice User Interface, VUI, Wearable Technology, Wi-Fi, Zero-Trust Model},
pubstate = {published},
tppubtype = {book}
}
Transformative AI provides a comprehensive overview of the latest trends, challenges, applications, and opportunities in the field of Artificial Intelligence. The book covers the state of the art in AI research, including machine learning, natural language processing, computer vision, and robotics, and explores how these technologies are transforming various industries and domains, such as healthcare, finance, education, and entertainment. The book also addresses the challenges that come with the widespread adoption of AI, including ethical concerns, bias, and the impact on jobs and society. It provides insights into how to mitigate these challenges and how to design AI systems that are responsible, transparent, and trustworthy. The book offers a forward-looking perspective on the future of AI, exploring the emerging trends and applications that are likely to shape the next decade of AI innovation. It also provides practical guidance for businesses and individuals on how to leverage the power of AI to create new products, services, and opportunities. Overall, the book is an essential read for anyone who wants to stay ahead of the curve in the rapidly evolving field of Artificial Intelligence and understand the impact that this transformative technology will have on our lives in the coming years. © 2024 River Publishers. All rights reserved.