AIMC Topic: Bees

Clear Filters Showing 41 to 50 of 91 articles

Nested Bee Hive: A Conceptual Multilayer Architecture for 6G in Futuristic Sustainable Smart Cities.

Sensors (Basel, Switzerland)
Several smart city ideas are introduced to manage various problems caused by overpopulation, but the futuristic smart city is a concept based on dense and artificial-intelligence-centric cities. Thus, massive device connectivity with huge data traffi...

SurferBot: a wave-propelled aquatic vibrobot.

Bioinspiration & biomimetics
Nature has evolved a vast array of strategies for propulsion at the air-fluid interface. Inspired by a survival mechanism initiated by the honeybee () trapped on the surface of water, we here present the: a centimeter-scale vibrating robotic device t...

Machine learning models identify gene predictors of waggle dance behaviour in honeybees.

Molecular ecology resources
The molecular characterization of complex behaviours is a challenging task as a range of different factors are often involved to produce the observed phenotype. An established approach is to look at the overall levels of expression of brain genes-or ...

In-Field Detection of American Foulbrood (AFB) by Electric Nose Using Classical Classification Techniques and Sequential Neural Networks.

Sensors (Basel, Switzerland)
American foulbrood is a dangerous bee disease that attacks the sealed brood. It quickly leads to the death of bee colonies. Efficient diagnosis of this disease is essential. As specific odours are produced when larvae rot, it was investigated whether...

Deep learning increases the availability of organism photographs taken by citizens in citizen science programs.

Scientific reports
Citizen science programs using organism photographs have become popular, but there are two problems related to photographs. One problem is the low quality of photographs. It is laborious to identify species in photographs taken outdoors because they ...

Machine learning approach for automatic recognition of tomato-pollinating bees based on their buzzing-sounds.

PLoS computational biology
Bee-mediated pollination greatly increases the size and weight of tomato fruits. Therefore, distinguishing between the local set of bees-those that are efficient pollinators-is essential to improve the economic returns for farmers. To achieve this, i...

Oscillations make a self-scaled model for honeybees' visual odometer reliable regardless of flight trajectory.

Journal of the Royal Society, Interface
Honeybees foraging and recruiting nest-mates by performing the waggle dance need to be able to gauge the flight distance to the food source regardless of the wind and terrain conditions. Previous authors have hypothesized that the foragers' visual od...

Can plants fool artificial intelligence? Using machine learning to compare between bee orchids and bees.

Plant signaling & behavior
Bee orchids have long been an excellent example of how dishonest signal works in plant-animal interaction. Many studies compared the flower structures that resemble female bees, leading toward pseudo-copulation of the male bees on the flower. Using M...

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...

Assessing the potential for deep learning and computer vision to identify bumble bee species from images.

Scientific reports
Pollinators are undergoing a global decline. Although vital to pollinator conservation and ecological research, species-level identification is expensive, time consuming, and requires specialized taxonomic training. However, deep learning and compute...