The rapid evolution of AI technology has led to promising developments in this field, particularly through the utilization of machine learning and deep learning models. The diverse implementation of AI algorithms was developed from various aspects of radiosurgery, including the successful detection of ...
As HuRAI is developed based on energy-efficient SNN computational models, it serves as an effective solution for energy-aware mobile robots. To the best of our knowledge, this is the first work that successfully applied SNNs for the aforementioned tasks with competitive accuracies to their ANN ...
Biophysically detailed multi-compartment models are powerful tools to explore computational principles of the brain and also serve as a theoretical framework to generate algorithms for artificial intelligence (AI) systems. However, the expensive computational cost severely limits the applications in both th...
Classification of medical documents and knowledge extraction from them for meta-researches JavaScript SoleLogics.jl Public Computational logic in Julia! Julia 12 SoleData.jl Public Manage unstructured and multimodal datasets! Julia 11 SoleModels.jl Public Symbolic modeling in Julia! Julia...
For example, there have been successes in achieving bioinspired hierarchical composites, in using semi-supervised approaches with graph neural networks, and in implementing natural language inputs for generative design of architected materials. Concurrently, machine learning (ML) models have been used in ...
information can be continuously collected with the implementations of AI applications, building automation systems and Internet of Things (IoT) [11]. This provides good opportunities to develop data-driven models for data resources when looking at retrofit scenarios. It is likely that the adaptation ...
in using semi-supervised approaches with graph neural networks, and in implementing natural language inputs for generative design of architected materials. Concurrently, machine learning (ML) models have been used in other material platforms for the prediction of a multitude of mechanical properties incl...
We believe that the ultimate goal of a simulation method or theory is to predict phenomena not seen before, and that Generative AI should be subject to these same standards before it is deemed useful for chemistry. We suggest that to overcome these challenges, future AI models need to ...
for 3.5 years in two leading Japanese universities. He is focusing on some of the emerging technologies such as the Internet of Things (IoT), Artificial Intelligence (AI) Model Optimization Techniques, Prompt Engineering for Large Language Models (LLMs), Efficient, Explainable and Edge AI, Block...
For example, there have been successes in achieving bioinspired hierarchical composites, in using semi-supervised approaches with graph neural networks, and in implementing natural language inputs for generative design of architected materials. Concurrently,machine learning(ML) models have been used in oth...