AIMC Topic: Neural Networks, Computer

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Recommendation of Business Models for Agriculture-Related Platforms Based on Deep Learning.

Computational intelligence and neuroscience
Agriculture is a basic and pillar industry. With the integration and development of Internet+, platform economy, and various industries, the business model of agriculture-related platforms is also constantly innovating. In this context, it is necessa...

Predicting Divorce Prospect Using Ensemble Learning: Support Vector Machine, Linear Model, and Neural Network.

Computational intelligence and neuroscience
A divorce is a legal step taken by married people to end their marriage. It occurs after a couple decides to no longer live together as husband and wife. Globally, the divorce rate has more than doubled from 1970 until 2008, with divorces per 1,000 m...

Application of image processing and soft computing strategies for non-destructive estimation of plum leaf area.

PloS one
Plant leaf area (LA) is a key metric in plant monitoring programs. Machine learning methods were used in this study to estimate the LA of four plum genotypes, including three greengage genotypes (Prunus domestica [subsp. italica var. claudiana.]) and...

Enhancing classification of cells procured from bone marrow aspirate smears using generative adversarial networks and sequential convolutional neural network.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Leukemia represents 30% of all pediatric cancers and is considered the most common malignancy affecting adults and children. Cell differential count obtained from bone marrow aspirate smears is crucial for diagnosing hematol...

Heuristic Attention Representation Learning for Self-Supervised Pretraining.

Sensors (Basel, Switzerland)
Recently, self-supervised learning methods have been shown to be very powerful and efficient for yielding robust representation learning by maximizing the similarity across different augmented views in embedding vector space. However, the main challe...

Non-Intrusive Fish Weight Estimation in Turbid Water Using Deep Learning and Regression Models.

Sensors (Basel, Switzerland)
Underwater fish monitoring is the one of the most challenging problems for efficiently feeding and harvesting fish, while still being environmentally friendly. The proposed 2D computer vision method is aimed at non-intrusively estimating the weight o...

Automatic Identification of Depression Using Facial Images with Deep Convolutional Neural Network.

Medical science monitor : international medical journal of experimental and clinical research
BACKGROUND Depression is a common disease worldwide, with about 280 million people having depression. The unique facial features of depression provide a basis for automatic recognition of depression with deep convolutional neural networks. MATERIAL A...

DeepBacs for multi-task bacterial image analysis using open-source deep learning approaches.

Communications biology
This work demonstrates and guides how to use a range of state-of-the-art artificial neural-networks to analyse bacterial microscopy images using the recently developed ZeroCostDL4Mic platform. We generated a database of image datasets used to train n...

Innovation of Platform Economy Business Model Driven by BP Neural Network and Artificial Intelligence Technology.

Computational intelligence and neuroscience
In order to enhance the competitiveness of enterprises, how to evaluate and enhance the competitiveness of B2B e-commerce enterprises and promote the orderly and healthy development of B2B e-commerce industry are discussed. This paper puts forward th...

An Efficient Machine Learning-Based Feature Optimization Model for the Detection of Dyslexia.

Computational intelligence and neuroscience
Dyslexia is among the most common neurological disorders in children. Detection of dyslexia therefore remains an important pursuit for the research works across various domains which is illustrated by the plethora of work presented in diverse scienti...