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618 results on '"Khan, Yusuf"'

2. LASERS in Orthodontics

Author

Khajuria, Amit Kumar, Prasantha, G.S., Mathew, Silju, Madhavi, Khan, Yusuf, and Adeeba

Published
2016

4. Machine Learning For Classification Of Antithetical Emotional States

Author

Sharma, Jeevanshi, Maheshwari, Rajat, and Khan, Yusuf Uzzaman

Subjects
Computer Science - Machine Learning, Computer Science - Human-Computer Interaction
Abstract

Emotion Classification through EEG signals has achieved many advancements. However, the problems like lack of data and learning the important features and patterns have always been areas with scope for improvement both computationally and in prediction accuracy. This works analyses the baseline machine learning classifiers' performance on DEAP Dataset along with a tabular learning approach that provided state-of-the-art comparable results leveraging the performance boost due to its deep learning architecture without deploying heavy neural networks., Comment: 11 pages

Published
2022

15. List of contributors

Author

Abdalla, Amira, primary, Benissan-Messan, Dathe, additional, Chen, Yupeng, additional, Grassie, Kevin, additional, Han, Biao, additional, Han, Lin, additional, Jayasuriya, Chathuraka T., additional, Ji, Jenny, additional, Khan, Yusuf, additional, Kilian, Emily, additional, Korcari, Antonion, additional, Kwok, Bryan, additional, Lin, Xiao, additional, Liu, Huinan, additional, Loiselle, Alayna E., additional, Nichols, Anne E.C., additional, O’Neil, Meghan, additional, Payne, Emma, additional, Quint, Jacob, additional, Samandari, Mohamadmahdi, additional, Sapowadia, Avin, additional, Seo, Catherine, additional, Shen, Jie, additional, Tamayol, Ali, additional, Trivedi, Jay, additional, Wang, Chao, additional, Wu, Kang, additional, Xiao, Ding, additional, Xu, Pengcheng, additional, Yang, Lei, additional, Yau, Anne, additional, Yeh, Kaitlyn, additional, Yin, Jiewei, additional, Zhang, Chaoxing, additional, Zhang, Wuxia, additional, Zhou, Libo, additional, and Zhu, Hua, additional

Published
2022
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18. Phase Locked Feature Based BCI Speller for P300 Analysis

Author

Nausheen, Khan, Ayesha Tooba, Khan, Yusuf Uzzaman, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martin, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Singh, Pradeep Kumar, editor, Suryadevara, Nagender Kumar, editor, Sharma, Sudhir Kumar, editor, and Singh, Amit Prakash, editor

Published
2020
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28. The potential role of mechanotransduction in the management of pediatric calvarial bone flap repair.

Author

Anderson, Hanna, Hersh, David S., and Khan, Yusuf

Abstract

Pediatric patients suffering traumatic brain injuries may require a decompressive craniectomy to accommodate brain swelling by removing a portion of the skull. Once the brain swelling subsides, the preserved calvarial bone flap is ideally replaced as an autograft during a cranioplasty to restore protection of the brain, as it can reintegrate and grow with the patient during immature skeletal development. However, pediatric patients exhibit a high prevalence of calvarial bone flap resorption post‐cranioplasty, causing functional and cosmetic morbidity. This review examines possible solutions for mitigating pediatric calvarial bone flap resorption by delineating methods of stimulating mechanosensitive cell populations with mechanical forces. Mechanotransduction plays a critical role in three main cell types involved with calvarial bone repair, including mesenchymal stem cells, osteoblasts, and dural cells, through mechanisms that could be exploited to promote osteogenesis. In particular, physiologically relevant mechanical forces, including substrate deformation, external forces, and ultrasound, can be used as tools to stimulate bone repair in both in vitro and in vivo systems. Ultimately, combating pediatric calvarial flap resorption may require a combinatorial approach using both cell therapy and bioengineering strategies. [ABSTRACT FROM AUTHOR]

Published
2024
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35. A fast and novel deep learning approach for automatic classification of epileptic seizures using spectrograms.

Author

Khan, Mudassir Hasan, Khan, Yusuf Uzzaman, Sarfraz, Mohammad, and Al Maathidi, Muhammad Mazin

Subjects
*EPILEPSY, *DEEP learning, *AUTOMATIC classification, *SPECTROGRAMS, *MACHINE learning, *FEATURE selection
Abstract

Epilepsy has now become a serious global health issue. Approximately 1% of people globally experience epileptic seizures. Predicting seizures before they start helps use medication to avoid them. Using modern computer tools, machine learning, and deep learning techniques, EEG has been used to predict seizures. The main difficulties in designing epilepsy prediction algorithms are feature selection and classification. Automatic detection and prediction of epileptic seizures are carried out much faster through deep learning techniques than traditional approaches. Designing a deep learning network from scratch or using a pre-trained network not only depends on the size, variability of data, complexity of the network, number of layers, network parameters, depth, etc. This work presents A Fast and Novel Deep Learning Approach for the Automatic Classification of Epileptic Seizures using Spectrograms. The seizure data was acquired from the TUH EEG Corpus. Pre-trained networks were used on four sets of spectrogram images of normal and seizure data and reorganized within the networks, tuned on the parameters, such as Convolution layers, Classification layers, etc. The above networks were trained and tested for each set of spectrograms, and the performance was evaluated based on testing accuracy and time for the training and validation process. The proposed method achieved remarkable accuracy with the capability of retrieving the signal information at each instant. Tuned ResNet-18 achieved an average accuracy of 98.9%, with an operating time of 1056 to 3606 seconds, while modified AlexNet proved to be the fastest, with an operating time of 421 to 425 seconds with an average accuracy of 98.2 for various features selected. [ABSTRACT FROM AUTHOR]

Published
2023
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44. Biomimetic Electroconductive Nanofibrous Matrices for Skeletal Muscle Regenerative Engineering

Author

Tang, Xiaoyan, Saveh-Shemshaki, Nikoo, Kan, Ho-Man, Khan, Yusuf, and Laurencin, Cato T.

Abstract

Background: The regeneration of the muscles of the rotator cuff represents a grand challenge in musculoskeletal regenerative engineering. Several types of matrices have been proposed for skeletal muscle regeneration. However, biomimetic matrices to promote muscle regeneration and mimic native muscle tissue have not been successfully engineered. Besides topographical cues, an electrical stimulus may serve as a critical cue to improve interactions between materials and cells in scenarios fostering muscle regeneration. In this in vitro study, we engineered a novel stimulus-responsive conductive nanocomposite matrix and studied its ability to regulate muscle cell adhesion, proliferation, and differentiation. Electroconductive nanocomposite matrices demonstrated tunable conductivity and biocompatibility. Under the optimum concentration of conductive material, the matrices facilitated muscle cell adhesion, proliferation, and differentiation. Importantly, aligned conductive fibrous matrices were effective in promoting myoblast differentiation by upregulation of myogenic markers. The results demonstrated a promising potential of aligned conductive fibrous matrices for skeletal muscle regenerative engineering. Lay Summary: Around 40% of the human body mass consists of skeletal muscle. Musculoskeletal disorders such as muscle atrophy and fatty infiltration after rotator cuff injury lead to disability and pain and increase the rate of retear after rotator cuff surgery. The study showed the potential of novel engineered matrix to regenerate skeletal muscle by utilizing conductive material and nanofiber-based matrices. The incorporation of conductive material and aligned nanofibers as electrical and topographical cues significantly impacted cell viability and differentiation to support muscle regeneration. Future Work: The study demonstrated that electroconductive nanocomposite matrix can favorably modulate myoblast proliferation and differentiation. Future study will investigate the in vivo efficacy of the engineered matrix using a rat rotator cuff tear model to understand the ability of the engineered matrix in reducing the fatty infiltration.

Published
2024
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