A framework combining machine learning-based link prediction with science-driven analysis to identify technologies with breakthrough potential before they reach mainstream adoption.
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Applying machine learning to discover and optimize catalysts for methane dry reforming, a process that converts CO₂ and methane into synthesis gas for sustainable fuel production.
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A survey of how LLMs are being applied in computational social science — covering data bias, privacy concerns, integration challenges, and the current frontier of what these models can reliably do in …
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A comprehensive review of AI/ML techniques in cybersecurity, covering adversarial AI, automated threat intelligence, AI-driven security orchestration, and emerging paradigms shaping the intersection o…
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An overview of post-quantum cryptographic approaches for securing IoT devices against future quantum computer attacks, surveying lattice-based, hash-based, and code-based schemes suitable for resource…
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A review spanning quantum-enhanced classical ML, native quantum algorithms, and hybrid approaches — covering applications in optimization, drug discovery, and quantum-secured communications.
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A framework combining AI for threat analysis, blockchain as an immutable compliance ledger, and smart contracts for automated security policy enforcement and dynamic cyber defense adjustments.
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