Objective: To highlight association of coronary artery disease on angiograms and high altitude-re... more Objective: To highlight association of coronary artery disease on angiograms and high altitude-related ECG abnormalities that is thought to be ischemic in origin. Place and Duration of Study: This was a cross sectional study done in Armed Force Institute of Cardiology/National Institute of Heart Disease from Oct 2016 to Oct 2021. Methodology: This was a cross sectional study done in AFIC/NIHD from Oct 2016–Oct 2021 (5years). A total of 103 patients at a range of 9000 to 22000 feet in altitude, with new ECG changes were selected via consecutive sampling. Data was analyzed by SPSS version-23. Descriptive statistics were run to present categorical data in frequencies and percentages. Chi-square and Fisher Exact Test was applied to find the association between study variables at 95% CI and 5% margin of error (α= 5%). Results: The data was collected from a total of 103 respondents, mean age (years) of the respondents was 30.57±6.27, and mean duration of stay (days) at high altitude was 6...
2021 International Joint Conference on Neural Networks (IJCNN), 2021
The paper proposes a new text recognition network for scene-text images. Many state-of-the-art me... more The paper proposes a new text recognition network for scene-text images. Many state-of-the-art methods employ the attention mechanism either in the text encoder or decoder for the text alignment. Although the encoder-based attention yields promising results, these schemes inherit noticeable limitations. They perform the feature extraction (FE) and visual attention (VA) sequentially, which bounds the attention mechanism to rely only on the FE final single-scale output. Moreover, the utilization of the attention process is limited by only applying it directly to the single scale feature-maps. To address these issues, we propose a new multi-scale and encoder-based attention network for text recognition that performs the multi-scale FE and VA in parallel. The multi-scale channels also undergo regular fusion with each other to develop the coordinated knowledge together. Quantitative evaluation and robustness analysis on the standard benchmarks demonstrate that the proposed network outperforms the state-of-the-art in most cases.
IEEE Transactions on Circuits and Systems for Video Technology, 2020
This paper proposes a novel approach for crowd counting in low to high density scenarios in stati... more This paper proposes a novel approach for crowd counting in low to high density scenarios in static images. Current approaches cannot handle huge crowd diversity well and thus perform poorly in extreme cases, where the crowd density in different regions of an image is either too low or too high, leading to crowd underestimation or overestimation. The proposed solution is based on the observation that detecting and handling such extreme cases in a specialized way leads to better crowd estimation. Additionally, existing methods find it hard to differentiate between the actual crowd and the cluttered background regions, resulting in further count overestimation. To address these issues, we propose a simple yet effective modular approach, where an input image is first subdivided into fixedsize patches and then fed to a four-way classification module labeling each image patch as low, medium, high-dense or nocrowd. This module also provides a count for each label, which is then analyzed via a specifically devised novel decision module to decide whether the image belongs to any of the two extreme cases (very low or very high density) or a normal case. Images, specified as high-or low-density extreme or a normal case, pass through dedicated zooming or normal patch-making blocks respectively before routing to the regressor in the form of fixed-size patches for crowd estimate. Extensive experimental evaluations demonstrate that the proposed approach outperforms the state-of-the-art methods on four benchmarks under most of the evaluation criteria. Index Terms-Crowd counting, crowd density, cluttered background, decision module, four-way classification, zooming or normal patch-making blocks.
Objective: To highlight association of coronary artery disease on angiograms and high altitude-re... more Objective: To highlight association of coronary artery disease on angiograms and high altitude-related ECG abnormalities that is thought to be ischemic in origin. Place and Duration of Study: This was a cross sectional study done in Armed Force Institute of Cardiology/National Institute of Heart Disease from Oct 2016 to Oct 2021. Methodology: This was a cross sectional study done in AFIC/NIHD from Oct 2016–Oct 2021 (5years). A total of 103 patients at a range of 9000 to 22000 feet in altitude, with new ECG changes were selected via consecutive sampling. Data was analyzed by SPSS version-23. Descriptive statistics were run to present categorical data in frequencies and percentages. Chi-square and Fisher Exact Test was applied to find the association between study variables at 95% CI and 5% margin of error (α= 5%). Results: The data was collected from a total of 103 respondents, mean age (years) of the respondents was 30.57±6.27, and mean duration of stay (days) at high altitude was 6...
2021 International Joint Conference on Neural Networks (IJCNN), 2021
The paper proposes a new text recognition network for scene-text images. Many state-of-the-art me... more The paper proposes a new text recognition network for scene-text images. Many state-of-the-art methods employ the attention mechanism either in the text encoder or decoder for the text alignment. Although the encoder-based attention yields promising results, these schemes inherit noticeable limitations. They perform the feature extraction (FE) and visual attention (VA) sequentially, which bounds the attention mechanism to rely only on the FE final single-scale output. Moreover, the utilization of the attention process is limited by only applying it directly to the single scale feature-maps. To address these issues, we propose a new multi-scale and encoder-based attention network for text recognition that performs the multi-scale FE and VA in parallel. The multi-scale channels also undergo regular fusion with each other to develop the coordinated knowledge together. Quantitative evaluation and robustness analysis on the standard benchmarks demonstrate that the proposed network outperforms the state-of-the-art in most cases.
IEEE Transactions on Circuits and Systems for Video Technology, 2020
This paper proposes a novel approach for crowd counting in low to high density scenarios in stati... more This paper proposes a novel approach for crowd counting in low to high density scenarios in static images. Current approaches cannot handle huge crowd diversity well and thus perform poorly in extreme cases, where the crowd density in different regions of an image is either too low or too high, leading to crowd underestimation or overestimation. The proposed solution is based on the observation that detecting and handling such extreme cases in a specialized way leads to better crowd estimation. Additionally, existing methods find it hard to differentiate between the actual crowd and the cluttered background regions, resulting in further count overestimation. To address these issues, we propose a simple yet effective modular approach, where an input image is first subdivided into fixedsize patches and then fed to a four-way classification module labeling each image patch as low, medium, high-dense or nocrowd. This module also provides a count for each label, which is then analyzed via a specifically devised novel decision module to decide whether the image belongs to any of the two extreme cases (very low or very high density) or a normal case. Images, specified as high-or low-density extreme or a normal case, pass through dedicated zooming or normal patch-making blocks respectively before routing to the regressor in the form of fixed-size patches for crowd estimate. Extensive experimental evaluations demonstrate that the proposed approach outperforms the state-of-the-art methods on four benchmarks under most of the evaluation criteria. Index Terms-Crowd counting, crowd density, cluttered background, decision module, four-way classification, zooming or normal patch-making blocks.
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Papers by Aplha Bravo