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34,067 results on '"Shabbir, A"'

59. Revision/Conversion Surgeries After One Anastomosis Gastric Bypass—An Experts’ Modified Delphi Consensus

Author

Kermansaravi, Mohammad, Chiappetta, Sonja, Parmar, Chetan, Carbajo, Miguel A., Musella, Mario, Chevallier, Jean-Marc, Ribeiro, Rui, Ramos, Almino C., Weiner, Rudolf, Nimeri, Abdelrahman, Aarts, Edo, Abbas, Syed Imran, Bashir, Ahmad, Behrens, Estuardo, Billy, Helmuth, Cohen, Ricardo V., Caina, Daniel, De Luca, Maurizio, Dillemans, Bruno, Fobi, Mathias A. L., Neto, Manoel Galvao, Gawdat, Khaled, ElFawal, Mohamad Hayssam, Kasama, Kazunori, Kassir, Radwan, Khan, Amir, Kow, Lilian, Kular, Kul Deepak Singh, Lakdawala, Muffazal, Layani, Laurent, Lee, Wei-Jei, Luque-de-León, Enrique, Mahawar, Kamal, Almomani, Hazem, Miller, Karl, González, Juan Carlos Olivares, Prasad, Arun, Rheinwalt, Karl, Rutledge, Robert, Safadi, Bassem, Salminen, Paulina, Shabbir, Asim, Taskin, Halit Eren, Verboonen, Jose Sergio, Vilallonga, Ramon, Wang, Cunchuan, Shikora, Scott A., and Prager, Gerhard

Published
2024
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66. Predicting Confinement Effect of Carbon Fiber Reinforced Polymers on Strength of Concrete using Metaheuristics-based Artificial Neural Networks

Author

Wahab, Sarmed, Suleiman, Mohamed, Shabbir, Faisal, Mahmoudabadi, Nasim Shakouri, Waqas, Sarmad, Herl, Nouman, and Ahmad, Afaq

Subjects
Computer Science - Neural and Evolutionary Computing, Computer Science - Artificial Intelligence
Abstract

This article deals with the study of predicting the confinement effect of carbon fiber reinforced polymers (CFRPs) on concrete cylinder strength using metaheuristics-based artificial neural networks. A detailed database of 708 CFRP confined concrete cylinders is developed from previously published research with information on 8 parameters including geometrical parameters like the diameter (d) and height (h) of a cylinder, unconfined compressive strength of concrete (fco'), thickness (nt), the elastic modulus of CFRP (Ef), unconfined concrete strain confined concrete strain and the ultimate compressive strength of confined concrete fcc'. Three metaheuristic models are implemented including particle swarm optimization (PSO), grey wolf optimizer (GWO), and bat algorithm (BA). These algorithms are trained on the data using an objective function of mean square error and their predicted results are validated against the experimental studies and finite element analysis. The study shows that the hybrid model of PSO predicted the strength of CFRP-confined concrete cylinders with maximum accuracy of 99.13% and GWO predicted the results with an accuracy of 98.17%. The high accuracy of axial compressive strength predictions demonstrated that these prediction models are a reliable solution to the empirical methods. The prediction models are especially suitable for avoiding full-scale time-consuming experimental tests that make the process quick and economical., Comment: 28 Pages, 19 Figures

Published
2023

67. Shuffle series

Author

Ahmad, Khushdil, Dolores-Cuenca, Eric Rubiel, and Shabbir, Khurram

Subjects
Mathematics - Combinatorics, Mathematics - Algebraic Topology, 05A15 (Primary) 18M80, 05A10, 06A11
Abstract

We apply operad theory to enumerative combinatorics in order to count the number of shuffles between series-parallel posets and chains. We work with three types of shuffles, two of them noncommutative, for example a local deck-divider shuffle $A$ between $P$ and $Q$ is a shuffle of the posets in which, on every maximal chain $m\subset A$, the minimum and maximum elements belong to $P$ and no two consecutive points of $Q$ appear consecutively on $m$. The number of local deck-divider shuffles of $P$ and $Q$ differ from the number of local deck-divider shuffles of $Q$ and $P$. The generating functions whose $n$ coefficient counts shuffles between a poset $P$ and $1<2<\cdots

Published
2023

68. Unleashing Modified Deep Learning Models in Efficient COVID19 Detection

Author

Islam, Md Aminul, Shuvo, Shabbir Ahmed, Rony, Mohammad Abu Tareq, Raihan, M, and Sufian, Md Abu

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Neural and Evolutionary Computing
Abstract

The COVID19 pandemic, a unique and devastating respiratory disease outbreak, has affected global populations as the disease spreads rapidly. Recent Deep Learning breakthroughs may improve COVID19 prediction and forecasting as a tool of precise and fast detection, however, current methods are still being examined to achieve higher accuracy and precision. This study analyzed the collection contained 8055 CT image samples, 5427 of which were COVID cases and 2628 non COVID. The 9544 Xray samples included 4044 COVID patients and 5500 non COVID cases. The most accurate models are MobileNet V3 (97.872 percent), DenseNet201 (97.567 percent), and GoogleNet Inception V1 (97.643 percent). High accuracy indicates that these models can make many accurate predictions, as well as others, are also high for MobileNetV3 and DenseNet201. An extensive evaluation using accuracy, precision, and recall allows a comprehensive comparison to improve predictive models by combining loss optimization with scalable batch normalization in this study. Our analysis shows that these tactics improve model performance and resilience for advancing COVID19 prediction and detection and shows how Deep Learning can improve disease handling. The methods we suggest would strengthen healthcare systems, policymakers, and researchers to make educated decisions to reduce COVID19 and other contagious diseases. CCS CONCEPTS Covid,Deep Learning, Image Processing KEYWORDS Covid, Deep Learning, DenseNet201, MobileNet, ResNet, DenseNet, GoogleNet, Image Processing, Disease Detection.

Published
2023

69. Enhanced Graph Neural Networks with Ego-Centric Spectral Subgraph Embeddings Augmentation

Author

Said, Anwar, Shabbir, Mudassir, Derr, Tyler, Abbas, Waseem, and Koutsoukos, Xenofon

Subjects
Computer Science - Social and Information Networks, Computer Science - Machine Learning
Abstract

Graph Neural Networks (GNNs) have shown remarkable merit in performing various learning-based tasks in complex networks. The superior performance of GNNs often correlates with the availability and quality of node-level features in the input networks. However, for many network applications, such node-level information may be missing or unreliable, thereby limiting the applicability and efficacy of GNNs. To address this limitation, we present a novel approach denoted as Ego-centric Spectral subGraph Embedding Augmentation (ESGEA), which aims to enhance and design node features, particularly in scenarios where information is lacking. Our method leverages the topological structure of the local subgraph to create topology-aware node features. The subgraph features are generated using an efficient spectral graph embedding technique, and they serve as node features that capture the local topological organization of the network. The explicit node features, if present, are then enhanced with the subgraph embeddings in order to improve the overall performance. ESGEA is compatible with any GNN-based architecture and is effective even in the absence of node features. We evaluate the proposed method in a social network graph classification task where node attributes are unavailable, as well as in a node classification task where node features are corrupted or even absent. The evaluation results on seven datasets and eight baseline models indicate up to a 10% improvement in AUC and a 7% improvement in accuracy for graph and node classification tasks, respectively., Comment: 22nd IEEE International Conference on Machine Learning and Applications 2023

Published
2023

70. Bayesian inference methodology to characterize the dust emissivity at far-infrared and submillimeter frequencies

Author

Adak, Debabrata, Shaikh, Shabbir, Sinha, Srijita, Ghosh, Tuhin, Boulanger, Francois, Lagache, Guilaine, Souradeep, Tarun, and Miville-Deschênes, Marc-Antoine

Subjects
Astrophysics - Astrophysics of Galaxies, Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract

We present a Bayesian inference method to characterise the dust emission properties using the well-known dust-HI correlation in the diffuse interstellar medium at Planck frequencies $\nu \ge 217$ GHz. We use the Galactic HI map from the Galactic All-Sky Survey (GASS) as a template to trace the Galactic dust emission. We jointly infer the pixel-dependent dust emissivity and the zero level present in the Planck intensity maps. We use the Hamiltonian Monte Carlo technique to sample the high dimensional parameter space ($D \sim 10^3$). We demonstrate that the methodology leads to unbiased recovery of dust emissivity per pixel and the zero level when applied to realistic Planck sky simulations over a 6300 deg$^2$ area around the Southern Galactic pole. As an application on data, we analyse the Planck intensity map at 353 GHz to jointly infer the pixel-dependent dust emissivity at Nside=32 resolution (1.8\deg\ pixel size) and the global offset. We find that the spatially varying dust emissivity has a mean of 0.031 MJysr$^{-1} (10^{20} \mathrm{cm^{-2}})^{-1}$ and $1\sigma$ standard deviation of 0.007 MJysr$^{-1} (10^{20} \mathrm{cm^{-2}})^{-1}$. The mean dust emissivity increases monotonically with increasing mean HI column density. We find that the inferred global offset is consistent with the expected level of Cosmic Infrared Background (CIB) monopole added to the Planck data at 353 GHz. This method is useful in studying the line-of-sight variations of dust spectral energy distribution in the multi-phase interstellar medium., Comment: 19 pages, 14 figures, Accepted for publication in MNRAS

Published
2023
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72. Genotype × environment interactions analysis for chickpea grain yield and related traits by a mixed model approach

Author

Khan, Abdul Manan, Ali, Zulfiqar, Mohsin, Ata ul, Soomro, Aijaz Ahmed, Hussain, Khalid, Rafiq, Muhammad, Hussain, Niaz, Ahmad, Qadeer, Ahmed, Ishfaq, Khan, Muhammad Ijaz, Shaikh, Muhammad Naeem, Matloob, Amar, Akram, Zahid, Shabbir, Ghulam, Hassan, Mahmood ul, Waqas, Muhammad Umair, Hussain, Israr, Rani, Saima, Malik, Shahid Riaz, Rehman, Ata ur, Ramsay, Gavin, and Blanchard, Chris

Published
2024
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89. Reducing the Gap in Knowledge and Expectations between Clinicians and People with Polycystic Ovary Syndrome or Adrenal Conditions: Simulation via Instant Messaging—Birmingham Advance: Patient and Public Involvement (SIMBA-PPI) Study

Author

Melson, Eka, Rezai, Fatema, Pan, Carina, Ng, Sung Yat, Ogiliev, Tamzin, Blendis, Ella, Sheikh, Haaziq, Kaur, Harjeet, Cooper, Catherine, Abdelhameed, Farah, Pang, Francesca, Bhatt, Shreya, Shabbir, Dania, Olateju, Zahra, Radcliffe, Eloise, Balendran, Prashanthan, Radcliffe, Abby, Lau, Gar Mun, Davitadze, Meri, Zhou, Dengyi, Malhotra, Kashish, Gillett, Caroline, and Kempegowda, Punith

Published
2024
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