10 results on '"Imtiaz SM"'
Search Results
2. Market Blended Insight: Modeling Propensity to Buy with the Semantic Web
- Author
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Salvadores, Manuel, Zuo, Landong, Imtiaz, SM Hazzaz, Darlington, John, Gibbins, Nicholas, Shadbolt, Nigel R, Dobree, James, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Sheth, Amit, editor, Staab, Steffen, editor, Dean, Mike, editor, Paolucci, Massimo, editor, Maynard, Diana, editor, Finin, Timothy, editor, and Thirunarayan, Krishnaprasad, editor
- Published
- 2008
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3. Outcome of Myringoplasty in Underlay Technique
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Razzak, Md Abdur, primary, Murshed, KM Mamun, primary, Sobhan, AKMA, primary, Hossain, Md Rakib, primary, and Imtiaz, SM Nafeez, primary
- Published
- 2019
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4. Swin Transformer and the Unet Architecture to Correct Motion Artifacts in Magnetic Resonance Image Reconstruction.
- Author
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Hossain MB, Shinde RK, Imtiaz SM, Hossain FMF, Jeon SH, Kwon KC, and Kim N
- Abstract
We present a deep learning-based method that corrects motion artifacts and thus accelerates data acquisition and reconstruction of magnetic resonance images. The novel model, the Motion Artifact Correction by Swin Network (MACS-Net), uses a Swin transformer layer as the fundamental block and the Unet architecture as the neural network backbone. We employ a hierarchical transformer with shifted windows to extract multiscale contextual features during encoding. A new dual upsampling technique is employed to enhance the spatial resolutions of feature maps in the Swin transformer-based decoder layer. A raw magnetic resonance imaging dataset is used for network training and testing; the data contain various motion artifacts with ground truth images of the same subjects. The results were compared to six state-of-the-art MRI image motion correction methods using two types of motions. When motions were brief (within 5 s), the method reduced the average normalized root mean square error (NRMSE) from 45.25% to 17.51%, increased the mean structural similarity index measure (SSIM) from 79.43% to 91.72%, and increased the peak signal-to-noise ratio (PSNR) from 18.24 to 26.57 dB. Similarly, when motions were extended from 5 to 10 s, our approach decreased the average NRMSE from 60.30% to 21.04%, improved the mean SSIM from 33.86% to 90.33%, and increased the PSNR from 15.64 to 24.99 dB. The anatomical structures of the corrected images and the motion-free brain data were similar., Competing Interests: The authors declare that there is no conflict of interest regarding the publication of this paper., (Copyright © 2024 Md. Biddut Hossain et al.)
- Published
- 2024
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5. A Hybrid Residual Attention Convolutional Neural Network for Compressed Sensing Magnetic Resonance Image Reconstruction.
- Author
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Hossain MB, Kwon KC, Shinde RK, Imtiaz SM, and Kim N
- Abstract
We propose a dual-domain deep learning technique for accelerating compressed sensing magnetic resonance image reconstruction. An advanced convolutional neural network with residual connectivity and an attention mechanism was developed for frequency and image domains. First, the sensor domain subnetwork estimates the unmeasured frequencies of k-space to reduce aliasing artifacts. Second, the image domain subnetwork performs a pixel-wise operation to remove blur and noisy artifacts. The skip connections efficiently concatenate the feature maps to alleviate the vanishing gradient problem. An attention gate in each decoder layer enhances network generalizability and speeds up image reconstruction by eliminating irrelevant activations. The proposed technique reconstructs real-valued clinical images from sparsely sampled k-spaces that are identical to the reference images. The performance of this novel approach was compared with state-of-the-art direct mapping, single-domain, and multi-domain methods. With acceleration factors (AFs) of 4 and 5, our method improved the mean peak signal-to-noise ratio (PSNR) to 8.67 and 9.23, respectively, compared with the single-domain Unet model; similarly, our approach increased the average PSNR to 3.72 and 4.61, respectively, compared with the multi-domain W-net. Remarkably, using an AF of 6, it enhanced the PSNR by 9.87 ± 1.55 and 6.60 ± 0.38 compared with Unet and W-net, respectively.
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- 2023
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6. High-Quality 3D Visualization System for Light-Field Microscopy with Fine-Scale Shape Measurement through Accurate 3D Surface Data.
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Kwon KH, Erdenebat MU, Kim N, Khuderchuluun A, Imtiaz SM, Kim MY, and Kwon KC
- Abstract
We propose a light-field microscopy display system that provides improved image quality and realistic three-dimensional (3D) measurement information. Our approach acquires both high-resolution two-dimensional (2D) and light-field images of the specimen sequentially. We put forward a matting Laplacian-based depth estimation algorithm to obtain nearly realistic 3D surface data, allowing the calculation of depth data, which is relatively close to the actual surface, and measurement information from the light-field images of specimens. High-reliability area data of the focus measure map and spatial affinity information of the matting Laplacian are used to estimate nearly realistic depths. This process represents a reference value for the light-field microscopy depth range that was not previously available. A 3D model is regenerated by combining the depth data and the high-resolution 2D image. The element image array is rendered through a simplified direction-reversal calculation method, which depends on user interaction from the 3D model and is displayed on the 3D display device. We confirm that the proposed system increases the accuracy of depth estimation and measurement and improves the quality of visualization and 3D display images.
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- 2023
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7. De-Aliasing and Accelerated Sparse Magnetic Resonance Image Reconstruction Using Fully Dense CNN with Attention Gates.
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Hossain MB, Kwon KC, Imtiaz SM, Nam OS, Jeon SH, and Kim N
- Abstract
When sparsely sampled data are used to accelerate magnetic resonance imaging (MRI), conventional reconstruction approaches produce significant artifacts that obscure the content of the image. To remove aliasing artifacts, we propose an advanced convolutional neural network (CNN) called fully dense attention CNN (FDA-CNN). We updated the Unet model with the fully dense connectivity and attention mechanism for MRI reconstruction. The main benefit of FDA-CNN is that an attention gate in each decoder layer increases the learning process by focusing on the relevant image features and provides a better generalization of the network by reducing irrelevant activations. Moreover, densely interconnected convolutional layers reuse the feature maps and prevent the vanishing gradient problem. Additionally, we also implement a new, proficient under-sampling pattern in the phase direction that takes low and high frequencies from the k-space both randomly and non-randomly. The performance of FDA-CNN was evaluated quantitatively and qualitatively with three different sub-sampling masks and datasets. Compared with five current deep learning-based and two compressed sensing MRI reconstruction techniques, the proposed method performed better as it reconstructed smoother and brighter images. Furthermore, FDA-CNN improved the mean PSNR by 2 dB, SSIM by 0.35, and VIFP by 0.37 compared with Unet for the acceleration factor of 5.
- Published
- 2022
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8. Depth Estimation for Integral Imaging Microscopy Using a 3D-2D CNN with a Weighted Median Filter.
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Imtiaz SM, Kwon KC, Hossain MB, Alam MS, Jeon SH, and Kim N
- Subjects
- Microscopy, Neural Networks, Computer
- Abstract
This study proposes a robust depth map framework based on a convolutional neural network (CNN) to calculate disparities using multi-direction epipolar plane images (EPIs). A combination of three-dimensional (3D) and two-dimensional (2D) CNN-based deep learning networks is used to extract the features from each input stream separately. The 3D convolutional blocks are adapted according to the disparity of different directions of epipolar images, and 2D-CNNs are employed to minimize data loss. Finally, the multi-stream networks are merged to restore the depth information. A fully convolutional approach is scalable, which can handle any size of input and is less prone to overfitting. However, there is some noise in the direction of the edge. A weighted median filtering (WMF) is used to acquire the boundary information and improve the accuracy of the results to overcome this issue. Experimental results indicate that the suggested deep learning network architecture outperforms other architectures in terms of depth estimation accuracy.
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- 2022
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9. Trajectory-Based Air-Writing Recognition Using Deep Neural Network and Depth Sensor.
- Author
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Alam MS, Kwon KC, Alam MA, Abbass MY, Imtiaz SM, and Kim N
- Abstract
Trajectory-based writing system refers to writing a linguistic character or word in free space by moving a finger, marker, or handheld device. It is widely applicable where traditional pen-up and pen-down writing systems are troublesome. Due to the simple writing style, it has a great advantage over the gesture-based system. However, it is a challenging task because of the non-uniform characters and different writing styles. In this research, we developed an air-writing recognition system using three-dimensional (3D) trajectories collected by a depth camera that tracks the fingertip. For better feature selection, the nearest neighbor and root point translation was used to normalize the trajectory. We employed the long short-term memory (LSTM) and a convolutional neural network (CNN) as a recognizer. The model was tested and verified by the self-collected dataset. To evaluate the robustness of our model, we also employed the 6D motion gesture (6DMG) alphanumeric character dataset and achieved 99.32% accuracy which is the highest to date. Hence, it verifies that the proposed model is invariant for digits and characters. Moreover, we publish a dataset containing 21,000 digits; which solves the lack of dataset in the current research.
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- 2020
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10. The Potential Involvement of an ATP-Dependent Potassium Channel-Opening Mechanism in the Smooth Muscle Relaxant Properties of Tamarix dioica Roxb .
- Author
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Imtiaz SM, Aleem A, Saqib F, Ormenisan AN, Neculau AE, and Anastasiu CV
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- Adenosine Triphosphate genetics, Animals, Antidiarrheals chemistry, Antidiarrheals pharmacology, Cardiovascular Diseases drug therapy, Flavonoids chemistry, Gallic Acid chemistry, Gastrointestinal Diseases drug therapy, Humans, Neuromuscular Agents chemistry, Plant Extracts pharmacology, Potassium Channels drug effects, Rats, Saponins chemistry, Tannins chemistry, Neuromuscular Agents pharmacology, Plant Extracts chemistry, Potassium Channels genetics, Tamaricaceae chemistry
- Abstract
Background : Tamarix dioica is traditionally used to manage various disorders related to smooth muscle in the gastrointestinal, respiratory, and cardiovascular systems. This study was planned to establish a pharmacological basis for the uses of Tamarix dioica in certain medical conditions related to the digestive, respiratory, and cardiovascular systems, and to explore the underlying mechanisms. Methods : A phytochemical study was performed by preliminary methods, followed by HPLC-DAD and spectrometric methods. In vivo evaluation of a crude hydromethanolic extract of T. dioica (TdCr) was done with a castor-oil-provoked diarrheal model in rats to determine its antidiarrheal effect. Ex vivo experiments were done by using isolated tissues to determine the effects on smooth and cardiac muscles and explore the possible mechanisms. Results : TdCr tested positive for flavonoids, saponins, phenols, and tannins as methanolic solvable constituents in a preliminary study. The maximum quantity of gallic acid equivalent (GAE), phenolic, and quercetin equivalent (QE) flavonoid content found was 146 ± 0.001 μg GAE/mg extract and 36.17 ± 2.35 μg QE/mg extract. Quantification based on HPLC-DAD (reverse phase) exposed the presence of rutin at the highest concentration, followed by catechin, gallic acid, myricetin, kaempferol, and apigenin in TdCr. In vivo experiments showed the significant antidiarrheal effect of TdCr (100, 200, and 400 mg/kg) in the diarrheal (castor-oil-provoked) model. Ex vivo experiments revealed spasmolytic, bronchodilatory, and vasorelaxant activities as well as partial cardiac depressant activity, which may be potentiated by a potassium channel opener mechanism, similar to that of cromakalim. The potassium channel (K
ATP channel)-opening activity was further confirmed by repeating the experiments in glibenclamide-pretreated tissues. Conclusions : In vivo and ex vivo studies of T. dioica provided evidence of the antidiarrheal, spasmolytic, bronchodilator, vasorelaxant, and partial cardiodepressant properties facilitated through the opening of the KATP channel., Competing Interests: The authors declare no conflict of interest.- Published
- 2019
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