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Institution

HITEC University

EducationRawalpindi, Pakistan
About: HITEC University is a education organization based out in Rawalpindi, Pakistan. It is known for research contribution in the topics: Nanofluid & Heat transfer. The organization has 339 authors who have published 773 publications receiving 11036 citations.


Papers
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Journal ArticleDOI
TL;DR: The proposed hybrid method for detection and classification of diseases in citrus plants outperforms the existing methods and achieves 97% classification accuracy on citrus disease image gallery dataset, 89% on combined dataset and 90.4% on the authors' local dataset.

274 citations

Journal ArticleDOI
TL;DR: A survey on the different methods relevant to citrus plants leaves diseases detection and the classification reveals that the adoption of automated detection and classification methods for citrus plants diseases is still in its infancy and new tools are needed to fully automate the detection and Classification processes.

251 citations

Journal ArticleDOI
TL;DR: In this article, a new model is proposed to investigate the effects of nano-ferroliquid under the influence of low oscillating over a stretchable rotating disk, where the basic governing equations are formulated under the effect of magnetic field and the resulting system of partial differential equations is first reduced in non-dimensional form by using proper transformations and then reduced coupled system of differential equations are solved analytically by means of homotopy analysis method.

198 citations

Journal ArticleDOI
06 Aug 2020
TL;DR: An automated multimodal classification method using deep learning for brain tumor type classification using two pre-trained convolutional neural network models for feature extraction and a correntropy-based joint learning approach for the selection of best features.
Abstract: Manual identification of brain tumors is an error-prone and tedious process for radiologists; therefore, it is crucial to adopt an automated system. The binary classification process, such as malignant or benign is relatively trivial; whereas, the multimodal brain tumors classification (T1, T2, T1CE, and Flair) is a challenging task for radiologists. Here, we present an automated multimodal classification method using deep learning for brain tumor type classification. The proposed method consists of five core steps. In the first step, the linear contrast stretching is employed using edge-based histogram equalization and discrete cosine transform (DCT). In the second step, deep learning feature extraction is performed. By utilizing transfer learning, two pre-trained convolutional neural network (CNN) models, namely VGG16 and VGG19, were used for feature extraction. In the third step, a correntropy-based joint learning approach was implemented along with the extreme learning machine (ELM) for the selection of best features. In the fourth step, the partial least square (PLS)-based robust covariant features were fused in one matrix. The combined matrix was fed to ELM for final classification. The proposed method was validated on the BraTS datasets and an accuracy of 97.8%, 96.9%, 92.5% for BraTs2015, BraTs2017, and BraTs2018, respectively, was achieved.

196 citations

Journal ArticleDOI
TL;DR: In this article, a comprehensive review on the preparation techniques as well as the applications of NePCMs in various fields is presented, which will intend readers to provide some insight to explore the further applications and essential properties.

177 citations


Authors

Showing all 342 results

NameH-indexPapersCitations
Muhammad Usman61120324848
Sam Kwong5955717019
Mahbub Hassan432737817
Syed Tauseef Mohyud-Din423916567
Muhammad Attique Khan362083453
Stephen J. Sangwine351044092
Muhammad Ayub322273626
Tariq Mahmood311563342
Umar Khan291442301
Naveed Ahmed281061970
Muhammad Abid273773214
Muhammad Younas262693099
Muhammad Hamid23611329
Rashid Mehmood22631231
Jawad Ahmad211071398
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
20228
2021116
2020118
201982
2018110
2017119