AIMC Topic: Fishes

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Development of machine learning-based shelf-life prediction models for multiple marine fish species and construction of a real-time prediction platform.

Food chemistry
At least 10 million tons of seafood products are spoiled or damaged during transportation or storage every year worldwide. Monitoring the freshness of seafood in real time has become especially important. In this study, four machine learning algorith...

Deep Learning-Based Fish Detection Using Above-Water Infrared Camera for Deep-Sea Aquaculture: A Comparison Study.

Sensors (Basel, Switzerland)
Long-term, automated fish detection provides invaluable data for deep-sea aquaculture, which is crucial for safe and efficient seawater aquafarming. In this paper, we used an infrared camera installed on a deep-sea truss-structure net cage to collect...

Bio-QSARs 2.0: Unlocking a new level of predictive power for machine learning-based ecotoxicity predictions by exploiting chemical and biological information.

Environment international
Practical, legal, and ethical reasons necessitate the development of methods to replace animal experiments. Computational techniques to acquire information that traditionally relied on animal testing are considered a crucial pillar among these so-cal...

Robot motor learning shows emergence of frequency-modulated, robust swimming with an invariant Strouhal number.

Journal of the Royal Society, Interface
Fish locomotion emerges from diverse interactions among deformable structures, surrounding fluids and neuromuscular activations, i.e. fluid-structure interactions (FSI) controlled by fish's motor systems. Previous studies suggested that such motor-co...

Integration of lanthanide MOFs/methylcellulose-based fluorescent sensor arrays and deep learning for fish freshness monitoring.

International journal of biological macromolecules
Preserving fish meat poses a significant challenge due to its high protein and low fat content. This study introduces a novel approach that utilizes a common type of lanthanide metal-organic frameworks (Ln-MOFs), EuMOFs, in combination with 5-fluores...

Predicting the long-term collective behaviour of fish pairs with deep learning.

Journal of the Royal Society, Interface
Modern computing has enhanced our understanding of how social interactions shape collective behaviour in animal societies. Although analytical models dominate in studying collective behaviour, this study introduces a deep learning model to assess soc...

Fast-Swimming Soft Robotic Fish Actuated by Bionic Muscle.

Soft robotics
Soft underwater swimming robots actuated by smart materials have unique advantages in exploring the ocean, such as low noise, high flexibility, and friendly environment interaction ability. However, most of them typically exhibit limited swimming spe...

Effect of Light-Emitting Grid Panel on Indoor Aquaculture for Measuring Fish Growth.

Sensors (Basel, Switzerland)
This study is related to Smart Aqua Farm, which combines artificial intelligence (AI) and Internet of things (IoT) technology. This study aimed to monitor fish growth in indoor aquaculture while automatically measuring the average size and area in re...

Recent Advances in Bioimage Analysis Methods for Detecting Skeletal Deformities in Biomedical and Aquaculture Fish Species.

Biomolecules
Detecting skeletal or bone-related deformities in model and aquaculture fish is vital for numerous biomedical studies. In biomedical research, model fish with bone-related disorders are potential indicators of various chemically induced toxins in the...

A Novel Machine-Learning Framework Based on a Hierarchy of Dispute Models for the Identification of Fish Species Using Multi-Mode Spectroscopy.

Sensors (Basel, Switzerland)
Seafood mislabeling rates of approximately 20% have been reported globally. Traditional methods for fish species identification, such as DNA analysis and polymerase chain reaction (PCR), are expensive and time-consuming, and require skilled technicia...