57 papers indexed here
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Dynamic Topology Optimization (DTO) is a powerful technique for designing complex systems, particularly those exhibiting dynamic behavior, such as protein folding and cellular automata. Traditional topology optimization methods often rely on manually crafted constraints and iterative refinement, limiting their adaptabi...
Protein folding prediction is a critical challenge in bioinformatics, demanding significant computational resources. This research explores the potential of quantum computing to accelerate and enhance this process. We formulate the protein folding problem as a quantum optimization problem, employing quantum simulation...
This paper presents a novel geometric simulation method, termed Wave Function Collapse (WFC), designed to model complex biological processes. Traditional computational methods often struggle to effectively represent the intricate dynamics of biological systems due to their inherent complexity. WFC utilizes quantum-insp...
This paper proposes a novel approach to artificial intelligence by integrating neural networks and symbolic reasoning, resulting in Neural Symbolic Hybrid Systems. The core claim is that combining the strengths of both paradigms—the pattern recognition capabilities of neural networks and the logical inference capabilit...
This paper proposes a novel engine for Explainable Artificial Intelligence (XAI) focused on causal inference. The core claim is to construct an AI engine capable of performing causal reasoning and providing more reliable explanations for decision-making. The engine leverages a mechanism based on structural causal model...
Algorithmic bias presents a significant challenge to the responsible deployment of machine learning models across various sectors. This paper proposes a novel framework for algorithmic fairness auditing that utilizes Explainable AI (XAI) techniques to enhance transparency and accountability. The core idea is to systema...
This paper proposes a novel approach to machine learning utilizing Cognitive Graph Representation (CGR). CGR fundamentally shifts the paradigm by modeling knowledge and relationships within machine learning models as cognitive graphs. These graphs explicitly incorporate uncertainty, inference chains, and metacognitive...
Explainable AI (XAI) is a rapidly growing field focused on making AI models more transparent and understandable to humans. However, many existing XAI techniques primarily focus on post-hoc explanations, often without a deep understanding of the underlying causal relationships driving the model's decisions. This paper p...
Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without directly exchanging data. However, existing FL systems are vulnerable to various attacks, including model poisoning and Byzantine attacks, which can compromise model integrity and user privacy....
Federated learning (FL) offers a promising approach to machine learning by enabling collaborative model training across decentralized devices without directly exchanging data. However, traditional FL methods often struggle to provide strong differential privacy guarantees, especially when group membership and data hete...
This paper presents a novel approach to decentralized federated learning that leverages graph consensus algorithms to achieve robust and privacy-preserving model aggregation. The core idea is to represent participating devices as nodes within a graph and utilize graph consensus protocols for model update exchange and a...
Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without directly exchanging data, thereby preserving user privacy. However, FL systems are vulnerable to Byzantine attacks, where malicious participants can inject corrupted model updates to compromise...
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