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Vol. 1 No. 2 (2024): Volume-I, Number II, 2024

  • Post-Quantum Cryptography: Securing AI Systems against Quantum Threats

    Danny Smith (Author)
    1-17
    Abstract  38

    As artificial intelligence (AI) systems increasingly underpin critical applications in healthcare, finance, defense, and infrastructure, ensuring their security has become paramount. However, the rapid advancement of quantum computing poses a significant threat to classical cryptographic schemes that currently safeguard these systems. Quantum algorithms such as Shor’s and Grover’s threaten ... read more

  • AI-Driven Autonomous Vehicles: Safety, Ethics, and Regulatory Challenges

    Isha Mehra (Author)
    18-31
    Abstract  27

    Artificial Intelligence (AI) is revolutionizing the automotive industry by enabling autonomous vehicles (AVs) that have the potential to dramatically improve road safety, reduce traffic congestion, and increase mobility access. Despite these promising benefits, the deployment of AI-driven AVs raises complex challenges that span technical safety concerns, ethical dilemmas, and regulatory uncerta ... read more

  • The Role of AI in Climate Change Mitigation: Predictive Models for Sustainability

    Soumya Jyoti Pratihari (Author)
    32-43
    Abstract  27

    Climate change represents one of the most pressing global challenges of the 21st century, demanding innovative and scalable solutions to mitigate its adverse impacts. Artificial Intelligence (AI) has emerged as a transformative tool in environmental science, offering advanced predictive capabilities that can enhance climate change mitigation efforts. This paper explores the pivotal role of AI-d ... read more

  • TinyML: Deploying Machine Learning on Microcontrollers for IoT Applications

    Ron Wainbuch (Author)
    44-57
    Abstract  33

    The rapid proliferation of Internet of Things (IoT) devices has created an urgent need for intelligent data processing directly on resource-constrained hardware. Tiny Machine Learning (TinyML) addresses this challenge by enabling the deployment of machine learning models on microcontrollers and other low-power embedded systems with limited memory, processing power, and energy resources. This pa ... read more

  • Brain-Computer Interfaces (BCIs) and AI: The Future of Human-Machine Symbiosis

    Jabez Ivan Joshiraj (Author)
    58-65
    Abstract  26

    Brain-Computer Interfaces (BCIs) represent a transformative technology that enables direct communication between the human brain and external devices, offering unprecedented opportunities for enhancing human-machine interaction. The integration of Artificial Intelligence (AI) with BCIs has the potential to significantly improve the accuracy, adaptability, and usability of these systems, fosteri ... read more

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