Publications by authors named "Md Azam Hossain"
Phenotyping is used in plant breeding to identify genotypes with desirable characteristics, such as drought tolerance, disease resistance, and high-yield potentials. It may also be used to evaluate the effect of environmental circumstances, such as drought, heat, and salt, on plant growth and development. Wheat spike density measure is one of the most important agronomic factors relating to wheat phenotyping.
View Article and Find Full Text PDFArticle Synopsis
- Electroencephalography (EEG) is a method used to study human behavior by monitoring brain activity during various tasks, while machine learning (ML) helps recognize these activities.
- This study aimed to see how well an EEG-based ML model could categorize daily activities like resting, walking, and reading, and to use explainable AI techniques to understand which EEG features were most important in these classifications.
- The research involved 75 healthy participants and found that ML models like Random Forest and Gradient Boosting performed excellently in differentiating activities, with clear correlations found between machine learning results and EEG spectral data, suggesting potential benefits for healthcare and rehabilitation applications.
Three-dimensional video services delivered through wireless communication channels have to deal with numerous challenges due to the limitations of both the transmission channel's bandwidth and receiving devices. Adverse channel conditions, delays, or jitters can result in bit errors and packet losses, which can alter the appearance of stereoscopic 3D (S3D) video. Due to the perception of dissimilar patterns by the two human eyes, they can not be fused into a stable composite pattern in the brain and hence try to dominate by suppressing each other.
View Article and Find Full Text PDFState-of-the-art healthcare technologies are incorporating advanced Artificial Intelligence (AI) models, allowing for rapid and easy disease diagnosis. However, most AI models are considered "black boxes," because there is no explanation for the decisions made by these models. Users may find it challenging to comprehend and interpret the results.
View Article and Find Full Text PDFElectroencephalography (EEG) is immediate and sensitive to neurological changes resulting from sleep stages and is considered a computing tool for understanding the association between neurological outcomes and sleep stages. EEG is expected to be an efficient approach for sleep stage prediction outside a highly equipped clinical setting compared with multimodal physiological signal-based polysomnography. This study aims to quantify the neurological EEG-biomarkers and predict five-class sleep stages using sleep EEG data.
View Article and Find Full Text PDF