AIMC Topic: Seasons

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Winter wheat yield prediction using convolutional neural networks from environmental and phenological data.

Scientific reports
Crop yield forecasting depends on many interactive factors, including crop genotype, weather, soil, and management practices. This study analyzes the performance of machine learning and deep learning methods for winter wheat yield prediction using an...

The research of ARIMA, GM(1,1), and LSTM models for prediction of TB cases in China.

PloS one
BACKGROUND AND OBJECTIVE: Tuberculosis (Tuberculosis, TB) is a public health problem in China, which not only endangers the population's health but also affects economic and social development. It requires an accurate prediction analysis to help to m...

Learning robust perceptive locomotion for quadrupedal robots in the wild.

Science robotics
Legged robots that can operate autonomously in remote and hazardous environments will greatly increase opportunities for exploration into underexplored areas. Exteroceptive perception is crucial for fast and energy-efficient locomotion: Perceiving th...

MARIDA: A benchmark for Marine Debris detection from Sentinel-2 remote sensing data.

PloS one
Currently, a significant amount of research is focused on detecting Marine Debris and assessing its spectral behaviour via remote sensing, ultimately aiming at new operational monitoring solutions. Here, we introduce a Marine Debris Archive (MARIDA),...

Deep learning assisted distinguishing of honey seasonal changes using quadruple voltammetric electrodes.

Talanta
The work presents innovative quadruple disk iridium, platinum, and iridium-platinum voltammetric electrodes with a special design, dedicated to the testing of samples with a complex organic composition. Noble metal wires are tightened in one silver r...

Crop loss identification at field parcel scale using satellite remote sensing and machine learning.

PloS one
Identifying crop loss at field parcel scale using satellite images is challenging: first, crop loss is caused by many factors during the growing season; second, reliable reference data about crop loss are lacking; third, there are many ways to define...

Water tank and swimming pool detection based on remote sensing and deep learning: Relationship with socioeconomic level and applications in dengue control.

PloS one
Studies have shown that areas with lower socioeconomic standings are often more vulnerable to dengue and similar deadly diseases that can be spread through mosquitoes. This study aims to detect water tanks installed on rooftops and swimming pools in ...

Analyzing the Check-In Behavior of Visitors through Machine Learning Model by Mining Social Network's Big Data.

Computational and mathematical methods in medicine
The current article paper is aimed at assessing and comparing the seasonal check-in behavior of individuals in Shanghai, China, using location-based social network (LBSN) data and a variety of spatiotemporal analytic techniques. The article demonstra...

The Short-Term Load Forecasting Using an Artificial Neural Network Approach with Periodic and Nonperiodic Factors: A Case Study of Tai'an, Shandong Province, China.

Computational intelligence and neuroscience
Accurate electricity load forecasting is an important prerequisite for stable electricity system operation. In this paper, it is found that daily and weekly variations are prominent by the power spectrum analysis of the historical loads collected hou...

Application of Neural Network Model Based on Multispecies Evolutionary Genetic Algorithm to Planning and Design of Diverse Plant Landscape.

Computational intelligence and neuroscience
In order to explore the feasibility of applying neural network model to landscape planning, based on the multispecies evolutionary genetic algorithm, a neural network model is proposed in this paper for the system design of diverse plant landscape pl...