AIMC Topic: India

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Evaluation of different spectral indices for wheat lodging assessment using machine learning algorithms.

Scientific reports
Wheat lodging is a recurrent phenomenon that significantly affects grain yield and impedes the harvesting efficiency. Therefore, the precise and rapid assessment of wheat lodging is crucial in minimizing its impact on grain yield and quality. Recentl...

Comparative analysis of machine learning approaches for heatwave event prediction in India.

Scientific reports
Heatwaves, are identified as prolonged durations of unusually high temperatures, which pose significant threats to human health, animal health and agriculture. With the increasing frequency and intensity of heatwaves driven by climate change, accurat...

Leveraging machine learning for monitoring afforestation in mining areas: evaluating Tata Steel's restoration efforts in Noamundi, India.

Environmental monitoring and assessment
Mining activities have long been associated with significant environmental impacts, including deforestation, habitat degradation, and biodiversity loss, necessitating targeted strategies like afforestation to mitigate ecological damage. Tata Steel's ...

Decoding nutrient dynamics in coastal aquifers: Machine learning insights into submarine groundwater discharge and seawater intrusion in south India.

Chemosphere
Coastal aquifers are vulnerable to natural and human-induced processes that impact their resilience and ecosystems. Submarine Groundwater Discharge (SGD) and Seawater Intrusion (SWI) play crucial roles in transporting nutrients and contaminants into ...

Artificial intelligence-driven green innovation for sustainable development: Empirical insights from India's renewable energy transition.

Journal of environmental management
This paper explores the contribution of artificial intelligence (AI), green technology innovation (GTI), and renewable energy generation (REG) to sustainable development in India, with a specific focus on their alignment with the Sustainable Developm...

Soil moisture mapping in Indian tropical islands with C-band SAR and artificial neural network models.

Environmental monitoring and assessment
This study aims at analyzing the patterns of soil moisture in the South Andaman district using an integrated approach that incorporates Sentinel-1A C-band synthetic aperture radar (SAR) data and other auxiliary data from Sentinel-2A and Landsat 8. A ...

NeuroEmo: A neuroimaging-based fMRI dataset to extract temporal affective brain dynamics for Indian movie video clips stimuli using dynamic functional connectivity approach with graph convolution neural network (DFC-GCNN).

Computers in biology and medicine
FMRI, a non-invasive neuroimaging technique, can detect emotional brain activation patterns. It allows researchers to observe functional changes in the brain, making it a valuable tool for emotion recognition. For improved emotion recognition systems...

Enhancing particulate matter prediction in Delhi: insights from statistical and machine learning models.

Environmental monitoring and assessment
This study advances our approach to modeling particulate matter levels-specifically, PM and PM-in Delhi's dynamic urban environment through an extensive evaluation of traditional time series models (ARIMAX, SARIMAX) and machine learning models (RF, S...

Phase recognition in manual Small-Incision cataract surgery with MS-TCN + + on the novel SICS-105 dataset.

Scientific reports
Manual Small-Incision Cataract Surgery (SICS) is a prevalent technique in low- and middle-income countries (LMICs) but understudied with respect to computer assisted surgery. This prospective cross-sectional study introduces the first SICS video data...

A comparative analysis of logistic regression (LR) and artificial neural network (ANN) models for predicting antimicrobial resistance in surgical ICU patients: Insights from real-world evidence in India.

The International journal of risk & safety in medicine
BackgroundMachine learning approaches for the prediction of antimicrobial resistance (AMR) are gaining attention but are yet to be commonly applied in practice.ObjectiveThis study aims to predict the AMR in surgical intensive care unit patients using...