Book Chapters

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Browsing Book Chapters by browse.metadata.impactarea "Biophotonics"

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    Post-Quantum Cryptography: Number Theoretic Foundations and Future-Proof Protocols
    (InTechOpen, 2025-11) Mpofu, Kelvin T; Mthunzi-Kufa, Patience; Carpentieri, B; Shams, M
    As quantum computing continues its rapid progression, traditional cryptographic schemes based on the hardness of integer factorization and discrete logarithms, such as RSA and ECC, face obsolescence. This chapter explores the number theoretic underpinnings of Post-Quantum Cryptography (PQC), surveying emerging quantum-resistant protocols including lattice-based, code-based, multivariate polynomial, and isogeny-based cryptography. We analyze the mathematical assumptions behind their security, the role of hard problems in number theory (e.g., shortest vector problem, syndrome decoding, supersingular isogeny graphs), and the computational implications of these approaches. Furthermore, the chapter discusses the transition from classical cryptography to PQC within the NIST standardization process and how these cryptographic primitives align with the modern number theoretic and algorithmic challenges. Designed for both researchers and educators, this chapter aims to bridge theory and practice while emphasizing the continuing centrality of number theory in shaping the future of secure communication in the quantum era.
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    Recent advances in artificial intelligence and machine learning based biosensing technologies
    (Intec, 2025) Mpofu, Kelvin T; Mthunzi-Kufa, Patience; Karakuş, S; Küçükdeniz, T; Evran, S
    Advancements in artificial intelligence (AI) and machine learning (ML) have transformed biosensing technologies, enhancing data acquisition, analysis, and interpretation in biomedical diagnostics. This chapter explores AI integration into biosensing, focusing on natural language processing (NLP), large language models (LLMs), data augmentation, and various learning paradigms. These technologies improve biosensor sensitivity, precision, and real-time adaptability. NLP automates biomedical text extraction, while LLMs facilitate complex decision-making using vast datasets. Data augmentation mitigates dataset limitations, strengthening ML model training and reducing overfitting. Supervised learning drives predictive models for disease detection, whereas unsupervised learning uncovers hidden biomarker patterns. Reinforcement learning optimizes sensor operations, calibration, and autonomous control in dynamic environments. The chapter discusses case studies, emerging trends, and challenges in AI-driven biosensing. AI’s convergence with edge computing and Internet of Things (IoT)-enabled biosensors enhances real-time data processing, reducing latency and expanding accessibility in resource-limited settings. Ethical concerns, including data privacy, model interpretability, and regulatory compliance, must be addressed for responsible AI applications in biosensing. Future research should focus on developing AI models resilient to bias, capable of continuous learning, and optimized for low-power, portable biosensors. Addressing these challenges will enable AI-powered biosensing to advance precision medicine and improve global healthcare outcomes. Through interdisciplinary approaches, AI and ML will continue to drive the evolution of next-generation diagnostic solutions.
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    Recent Advances in Quantum Biosensing Technologies
    (InTechOpen, 2024-12) Mpofu, Kelvin T; Mthunzi-Kufa, Patience; Karakuş, S
    Recent advances in biosensing technologies have revolutionized the field of biomedical diagnostics and environmental monitoring. This chapter reviews cutting-edge developments in quantum sensing and quantum biosensing, with examples including diamond defect sensing and quantum plasmonic biosensing, among other novel methodologies. Diamond defect sensing, leveraging nitrogen-vacancy centers in diamond, offers unparalleled sensitivity and precision in detecting magnetic and electric fields at the nanoscale. Quantum plasmonic biosensing, combining the unique properties of plasmons and quantum mechanics, enhances sensitivity and specificity, enabling the detection of biomolecules at ultra-low concentrations. Additionally, advancements in other quantum biosensing technologies, such as quantum dot-based sensors and single-photon detection, will be discussed, highlighting their potential applications in real-time, high-resolution biosensing. These innovative approaches promise to significantly improve the accuracy, speed, and versatility of biosensing, paving the way for new diagnostic tools and environmental monitoring solutions. The chapter will delve into the principles behind these technologies, their current applications, and the future directions they may take, providing a comprehensive overview of the transformative impact of quantum biosensing on medical diagnostics and beyond.