AIMC Topic: Problem Solving

Clear Filters Showing 71 to 80 of 146 articles

A New Initialization Approach in Particle Swarm Optimization for Global Optimization Problems.

Computational intelligence and neuroscience
Particle swarm optimization (PSO) algorithm is a population-based intelligent stochastic search technique used to search for food with the intrinsic manner of bee swarming. PSO is widely used to solve the diverse problems of optimization. Initializat...

Vision-Language-Knowledge Co-Embedding for Visual Commonsense Reasoning.

Sensors (Basel, Switzerland)
Visual commonsense reasoning is an intelligent task performed to decide the most appropriate answer to a question while providing the rationale or reason for the answer when an image, a natural language question, and candidate responses are given. Fo...

Solving infinite-horizon optimalcontrol problems of the time-delayedsystems by a feed forward neural network model.

Network (Bristol, England)
A numerical method using neural network for solving infinite-horizon time-delayed optimal control problems is studied. The problem is first transformed, using a Páde approximation, to one without a time-delayed argument. By a suitable change of varia...

Review of Temporal Reasoning in the Clinical Domain for Timeline Extraction: Where we are and where we need to be.

Journal of biomedical informatics
Understanding a patient's medical history, such as how long symptoms last or when a procedure was performed, is vital to diagnosing problems and providing good care. Frequently, important information regarding a patient's medical timeline is buried i...

Exploring the spatial reasoning ability of neural models in human IQ tests.

Neural networks : the official journal of the International Neural Network Society
Although neural models have performed impressively well on various tasks such as image recognition and question answering, their reasoning ability has been measured in only few studies. In this work, we focus on spatial reasoning and explore the spat...

A Two-Timescale Duplex Neurodynamic Approach to Mixed-Integer Optimization.

IEEE transactions on neural networks and learning systems
This article presents a two-timescale duplex neurodynamic approach to mixed-integer optimization, based on a biconvex optimization problem reformulation with additional bilinear equality or inequality constraints. The proposed approach employs two re...

Efficient crowdsourcing of crowd-generated microtasks.

PloS one
Allowing members of the crowd to propose novel microtasks for one another is an effective way to combine the efficiencies of traditional microtask work with the inventiveness and hypothesis generation potential of human workers. However, microtask pr...

Parallelograms revisited: Exploring the limitations of vector space models for simple analogies.

Cognition
Classic psychological theories have demonstrated the power and limitations of spatial representations, providing geometric tools for reasoning about the similarity of objects and showing that human intuitions sometimes violate the constraints of geom...

Using Machine Learning to Train a Wearable Device for Measuring Students' Cognitive Load during Problem-Solving Activities Based on Electrodermal Activity, Body Temperature, and Heart Rate: Development of a Cognitive Load Tracker for Both Personal and Classroom Use.

Sensors (Basel, Switzerland)
Automated tracking of physical fitness has sparked a health revolution by allowing individuals to track their own physical activity and health in real time. This concept is beginning to be applied to tracking of cognitive load. It is well known that ...

Graph transform learning.

Neural networks : the official journal of the International Neural Network Society
Transform learning is a new representation learning framework where we learn an operator/transform that analyses the data to generate the coefficient/representation. We propose a variant of it called the graph transform learning; in this we explicitl...