AIMC Topic: Neural Networks, Computer

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Prediction of mustard yield using different machine learning techniques: a case study of Rajasthan, India.

International journal of biometeorology
Mustard is the second most important edible oilseed after groundnut for India. Adverse weather drastically reduces the mustard yield. Weather variables affect the crop differently during different stages of development. Weather influence on crop yiel...

Improving DNA-Binding Protein Prediction Using Three-Part Sequence-Order Feature Extraction and a Deep Neural Network Algorithm.

Journal of chemical information and modeling
Identification of the DNA-binding protein (DBP) helps dig out information embedded in the DNA-protein interaction, which is significant to understanding the mechanisms of DNA replication, transcription, and repair. Although existing computational met...

PA-Tran: Learning to Estimate 3D Hand Pose with Partial Annotation.

Sensors (Basel, Switzerland)
This paper tackles a novel and challenging problem-3D hand pose estimation (HPE) from a single RGB image using partial annotation. Most HPE methods ignore the fact that the keypoints could be partially visible (e.g., under occlusions). In contrast, w...

Altering Fish Behavior by Sensing Swarm Patterns of Fish in an Artificial Aquatic Environment Using an Interactive Robotic Fish.

Sensors (Basel, Switzerland)
Numerous studies have been conducted to prove the calming and stress-reducing effects on humans of visiting aquatic environments. As a result, many institutions have utilized fish to provide entertainment and treat patients. The most common issue in ...

Molecular Toxicity Virtual Screening Applying a Quantized Computational SNN-Based Framework.

Molecules (Basel, Switzerland)
Spiking neural networks are biologically inspired machine learning algorithms attracting researchers' attention for their applicability to alternative energy-efficient hardware other than traditional computers. In the current work, spiking neural net...

MRI image synthesis for fluid-attenuated inversion recovery and diffusion-weighted images with deep learning.

Physical and engineering sciences in medicine
This study aims to synthesize fluid-attenuated inversion recovery (FLAIR) and diffusion-weighted images (DWI) with a deep conditional adversarial network from T1- and T2-weighted magnetic resonance imaging (MRI) images. A total of 1980 images of 102 ...

Explainable Deep Learning-Assisted Photochromic Sensor for β-Lactam Antibiotic Identification.

Analytical chemistry
Photochromic sensors have the advantages of diverse isomers for multi-analysis, providing more sensing information and possessing more recognition units and more sensitivity to external stimulations, but they present enormous complexity with various ...

Combining Group-Contribution Concept and Graph Neural Networks Toward Interpretable Molecular Property Models.

Journal of chemical information and modeling
Quantitative structure-property relationships (QSPRs) are important tools to facilitate and accelerate the discovery of compounds with desired properties. While many QSPRs have been developed, they are associated with various shortcomings such as a l...

An Overview of Deep Learning Methods for Left Ventricle Segmentation.

Computational intelligence and neuroscience
Cardiac health diseases are one of the key causes of death around the globe. The number of heart patients has considerably increased during the pandemic. Therefore, it is crucial to assess and analyze the medical and cardiac images. Deep learning arc...

Improved UNet Deep Learning Model for Automatic Detection of Lung Cancer Nodules.

Computational intelligence and neuroscience
Uncontrolled cell growth in the two spongy lung organs in the chest is the most prevalent kind of cancer. When cells from the lungs spread to other tissues and organs, this is referred to as metastasis. This work uses image processing, deep learning,...