AIMC Topic: Mathematics

Clear Filters Showing 21 to 30 of 60 articles

Effectiveness of Artificial Intelligence (AI) in Improving Pupils' Deep Learning in Primary School Mathematics Teaching in Fujian Province.

Computational intelligence and neuroscience
Several primary school students in Fujian Province have perceived studying mathematics as challenging. To deal with this issue, computer technology advancements, specifically artificial intelligence (AI), present an opportunity to evaluate individual...

A Classification Method for Electronic Components Based on Siamese Network.

Sensors (Basel, Switzerland)
In the field of electronics manufacturing, electronic component classification facilitates the management and recycling of the functional and valuable electronic components in electronic waste. Current electronic component classification methods are ...

A Method for Evaluating the Quality of Mathematics Education Based on Artificial Neural Network.

Computational and mathematical methods in medicine
Teaching quality evaluation (TQE) is an important link in the process of school teaching management. Evaluation indicators and teaching quality have a complicated and nonlinear connection that is influenced by several variables. Some of these drawbac...

A neural network solves, explains, and generates university math problems by program synthesis and few-shot learning at human level.

Proceedings of the National Academy of Sciences of the United States of America
We demonstrate that a neural network pretrained on text and fine-tuned on code solves mathematics course problems, explains solutions, and generates questions at a human level. We automatically synthesize programs using few-shot learning and OpenAI's...

Optimization Model of Mathematics Instructional Mode Based on Deep Learning Algorithm.

Computational intelligence and neuroscience
This paper proposes corresponding teaching methods and instructional modes based on predecessors' research on mathematics instructional mode and the current state of mathematics teaching. In addition, this paper constructs a teaching evaluation model...

Study on OBE Teaching Concept in the Context of Deep Learning for the Construction of University Mathematics Microcourses.

Computational intelligence and neuroscience
Outcome-Based Education (OBE) is a goal-based educational system in which each part of education is around outcomes. By the end of the course, every student should have achieved the goal. Outcome-Based Education (OBE) involves various teaching method...

An RNA-based theory of natural universal computation.

Journal of theoretical biology
Life is confronted with computation problems in a variety of domains including animal behavior, single-cell behavior, and embryonic development. Yet we currently do not know of a naturally existing biological system that is capable of universal compu...

The mathematics of erythema: Development of machine learning models for artificial intelligence assisted measurement and severity scoring of radiation induced dermatitis.

Computers in biology and medicine
Although significant advancements in computer-aided diagnostics using artificial intelligence (AI) have been made, to date, no viable method for radiation-induced skin reaction (RISR) analysis and classification is available. The objective of this si...

A global neural network learning machine: Coupled integer and fractional calculus operator with an adaptive learning scheme.

Neural networks : the official journal of the International Neural Network Society
Find the global optimal solution of the model is one promising research topic in computational intelligent community. Dependent on analogies to natural processes, the evolutionary swarm intelligent algorithms are widely used for solving global optimi...

Statistical guarantees for regularized neural networks.

Neural networks : the official journal of the International Neural Network Society
Neural networks have become standard tools in the analysis of data, but they lack comprehensive mathematical theories. For example, there are very few statistical guarantees for learning neural networks from data, especially for classes of estimators...