D-Index & Metrics Best Publications
Muhammad Attique Khan

Muhammad Attique Khan

D-Index & Metrics

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Computer Science D-index 36 Citations 3,929 132 World Ranking 5536 National Ranking 3

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Pattern recognition

His primary areas of study are Artificial intelligence, Feature selection, Pattern recognition, Support vector machine and Feature extraction. His work in the fields of Artificial intelligence, such as Segmentation, Preprocessor and Selection, overlaps with other areas such as Image fusion. His study in Feature selection is interdisciplinary in nature, drawing from both Transfer of learning, Discriminant, Deep learning and Convolutional neural network.

His Pattern recognition research is multidisciplinary, relying on both Pixel, Background subtraction and Correlation coefficient. His research integrates issues of Euclidean distance, Joint entropy and Biometrics in his study of Support vector machine. His studies in Feature extraction integrate themes in fields like Machine learning, Activity recognition, False positive rate and Pattern recognition.

His most cited work include:

  • Detection and classification of citrus diseases in agriculture based on optimized weighted segmentation and feature selection (94 citations)
  • Detection and classification of citrus diseases in agriculture based on optimized weighted segmentation and feature selection (94 citations)
  • An automated detection and classification of citrus plant diseases using image processing techniques: A review (70 citations)

What are the main themes of his work throughout his whole career to date?

Muhammad Attique Khan mainly focuses on Artificial intelligence, Pattern recognition, Feature selection, Deep learning and Feature extraction. Artificial intelligence connects with themes related to Machine learning in his study. His Pattern recognition study combines topics from a wide range of disciplines, such as Entropy and Biometrics.

His Feature selection study deals with Skin cancer intersecting with Normalization. His work in Feature extraction tackles topics such as Medical imaging which are related to areas like Lesion segmentation and Computer vision. His research integrates issues of Pixel, Medical diagnosis, Preprocessor and Capsule endoscopy in his study of Segmentation.

He most often published in these fields:

  • Artificial intelligence (100.00%)
  • Pattern recognition (75.65%)
  • Feature selection (43.48%)

What were the highlights of his more recent work (between 2020-2021)?

  • Artificial intelligence (100.00%)
  • Pattern recognition (75.65%)
  • Deep learning (29.57%)

In recent papers he was focusing on the following fields of study:

His primary scientific interests are in Artificial intelligence, Pattern recognition, Deep learning, Feature selection and Convolutional neural network. His research in Artificial intelligence tackles topics such as Machine learning which are related to areas like Decision support system. His Pattern recognition research includes elements of Feature, Entropy and Selection.

His Deep learning research is multidisciplinary, incorporating perspectives in Classifier, Kernel extreme learning machine, Disease and Multiclass classification. His Feature selection research is multidisciplinary, incorporating elements of Cardiac disorders and Plethysmograph. His Feature extraction study combines topics in areas such as Cancer, Medical physics and Medical imaging.

Between 2020 and 2021, his most popular works were:

  • Gastric Tract Infections Detection and Classification from Wireless Capsule Endoscopy using Computer Vision Techniques: A Review. (9 citations)
  • Prediction of COVID-19 - Pneumonia based on Selected Deep Features and One Class Kernel Extreme Learning Machine. (8 citations)
  • Microscopic brain tumor detection and classification using 3D CNN and feature selection architecture (8 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Machine learning
  • Pattern recognition

Artificial intelligence, Pattern recognition, Deep learning, Feature selection and Feature extraction are his primary areas of study. His Pattern recognition study frequently draws parallels with other fields, such as Pixel. The various areas that Muhammad Attique Khan examines in his Deep learning study include Kernel extreme learning machine and Convolutional neural network.

His Feature selection study incorporates themes from Contrast, Selection and Support vector machine. His work carried out in the field of Support vector machine brings together such families of science as RGB color model and Fitness function. His biological study spans a wide range of topics, including Cancer and Segmentation.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

Detection and classification of citrus diseases in agriculture based on optimized weighted segmentation and feature selection

Muhammad Sharif;Muhammad Attique Khan;Muhammad Attique Khan;Zahid Iqbal;Muhammad Faisal Azam.
Computers and Electronics in Agriculture (2018)

127 Citations

An automated detection and classification of citrus plant diseases using image processing techniques: A review

Zahid Iqbal;Muhammad Attique Khan;Muhammad Attique Khan;Muhammad Sharif;Jamal Hussain Shah.
Computers and Electronics in Agriculture (2018)

105 Citations

A framework for offline signature verification system: Best features selection approach

Muhammad Sharif;Muhammad Attique Khan;Muhammad Faisal;Mussarat Yasmin.
Pattern Recognition Letters (2018)

96 Citations

Critical limb ischemia: a global epidemic. A critical analysis of current treatment unmasks the clinical and economic costs of CLI

David E Allie;Chris J Hebert;Mitchell D Lirtzman;Charles H Wyatt.
Eurointervention (2005)

96 Citations

Active deep neural network features selection for segmentation and recognition of brain tumors using MRI images

Muhammad Irfan Sharif;Jian Ping Li;Muhammad Attique Khan;Muhammad Asim Saleem.
Pattern Recognition Letters (2020)

86 Citations

Multimodal Brain Tumor Classification Using Deep Learning and Robust Feature Selection: A Machine Learning Application for Radiologists.

Muhammad Attique Khan;Imran Ashraf;Majed Alhaisoni;Robertas Damaševičius;Robertas Damaševičius.
Diagnostics (Basel, Switzerland) (2020)

85 Citations

Brain tumor detection and classification: A framework of marker-based watershed algorithm and multilevel priority features selection.

Muhammad A. Khan;Ikram U. Lali;Amjad Rehman;Mubashar Ishaq.
Microscopy Research and Technique (2019)

84 Citations

An improved strategy for skin lesion detection and classification using uniform segmentation and feature selection based approach.

Muhammad Nasir;Muhammad Attique Khan;Muhammad Sharif;Ikram Ullah Lali.
Microscopy Research and Technique (2018)

83 Citations

License number plate recognition system using entropy-based features selection approach with SVM

Muhammad Attique Khan;Muhammad Sharif;Muhammad Younus Javed;Tallha Akram.
Iet Image Processing (2018)

81 Citations

A framework of human detection and action recognition based on uniform segmentation and combination of Euclidean distance and joint entropy-based features selection

Muhammad Sharif;Muhammad Attique Khan;Tallha Akram;Muhammad Younus Javed.
Eurasip Journal on Image and Video Processing (2017)

80 Citations

Best Scientists Citing Muhammad Attique Khan

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COMSATS University Islamabad

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Yudong Zhang

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U. Rajendra Acharya

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Ngee Ann Polytechnic

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David Taniar

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Mazin Abed Mohammed

Mazin Abed Mohammed

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Suresh Chandra Satapathy

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KIIT University

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Michael R. Jaff

Michael R. Jaff

Boston Scientific (United States)

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Mohammed A. Gondal

Mohammed A. Gondal

King Fahd University of Petroleum and Minerals

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Muhammad Asif

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Thomas Zeller

Thomas Zeller

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Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking d-index is inferred from publications deemed to belong to the considered discipline.

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