World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
56
Citations
9880
World Ranking
2885
National Ranking
24

Overview

Muhammad Sharif is affiliated with King Fahd University of Petroleum and Minerals in Saudi Arabia. Sharif's research output spans multiple interdisciplinary fields combining computer science and medicine, with a focus on artificial intelligence applications in imaging and diagnostics.

The scientist's main research areas include:

  • Computer Science
  • Medicine

Within these broader categories, Sharif's work frequently addresses subfields such as:

  • Computer Vision and Pattern Recognition
  • Plant Science
  • Artificial Intelligence
  • Radiology, Nuclear Medicine and Imaging
  • Neurology

The research topics covered highlight a concentration on applied AI techniques in medical and agricultural contexts, including:

  • Smart Agriculture and AI
  • Digital Imaging for Blood Diseases
  • Anomaly Detection Techniques and Applications
  • COVID-19 diagnosis using AI
  • Brain Tumor Detection and Classification
  • Spectroscopy and Chemometric Analyses
  • AI in cancer detection

Sharif has published research in several notable scientific venues, frequently contributing to:

  • IEEE Access
  • Computers, Materials & Continua
  • Multimedia Tools and Applications
  • Sensors
  • Neural Computing and Applications

Examples of recent published papers include:

  • "Brain tumor detection and classification using machine learning: a comprehensive survey" (2021) in Complex & Intelligent Systems
  • "Skin Lesion Segmentation and Multiclass Classification Using Deep Learning Features and Improved Moth Flame Optimization" (2021) in Diagnostics
  • "Attributes based skin lesion detection and recognition: A mask RCNN and transfer learning-based deep learning framework" (2021) in Pattern Recognition Letters
  • "Multi-Class Skin Lesion Detection and Classification via Teledermatology" (2021) in IEEE Journal of Biomedical and Health Informatics
  • "A framework of human action recognition using length control features fusion and weighted entropy-variances based feature selection" (2020) in Image and Vision Computing

Collaborative work involves frequent co-authors who appear repeatedly in Sharif's publications. These co-authors include:

  • Muhammad Attique Khan
  • Seifedine Kadry
  • Javeria Amin
  • Muhammad Almas Anjum
  • Mussarat Yasmin

The distribution of publications and collaborations points to a multidisciplinary approach, integrating domain knowledge in AI-driven medical diagnostics and agriculture-related computer vision techniques.

Best Publications

  • A distinctive approach in brain tumor detection and classification using MRI

    Javeria Amin;Muhammad Sharif;Mussarat Yasmin;Steven Lawrence Fernandes

  • 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

  • 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

  • Brain tumor detection and classification using machine learning: a comprehensive survey

    Javaria Amin;Javaria Amin;Muhammad Sharif;Anandakumar Haldorai;Mussarat Yasmin

  • Brain tumor detection using statistical and machine learning method.

    Javaria Amin;Muhammad Sharif;Mudassar Raza;Tanzila Saba

  • A citrus fruits and leaves dataset for detection and classification of citrus diseases through machine learning.

    Hafiz Tayyab Rauf;Basharat Ali Saleem;M. Ikram Ullah Lali;Muhammad Attique Khan

  • A decision support system for multimodal brain tumor classification using deep learning

    Muhammad Imran Sharif;Muhammad Attique Khan;Musaed Alhussein;Khursheed Aurangzeb

  • Skin lesion segmentation and multiclass classification using deep learning features and improved moth flame optimization

    Muhammad Attique Khan;Muhammad Sharif;Tallha Akram;Robertas Damaševičius

  • Symptom based automated detection of citrus diseases using color histogram and textural descriptors

    H. Ali;M.I. Lali;M.Z. Nawaz;M. Sharif

  • CCDF: Automatic system for segmentation and recognition of fruit crops diseases based on correlation coefficient and deep CNN features

    Muhammad Attique Khan;Tallha Akram;Muhammad Sharif;Muhammad Awais

  • Attributes based skin lesion detection and recognition: A mask RCNN and transfer learning-based deep learning framework

    Muhammad Attique Khan;Tallha Akram;Yu-Dong Zhang;Muhammad Sharif

  • Brain tumor classification based on DWT fusion of MRI sequences using convolutional neural network

    Javaria Amin;Muhammad Sharif;Nadia Gul;Mussarat Yasmin

  • An Optimized Method for Segmentation and Classification of Apple Diseases Based on Strong Correlation and Genetic Algorithm Based Feature Selection

    Muhammad Attique Khan;M Ikram Ullah Lali;Muhammad Sharif;Kashif Javed

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

    Muhammad Sharif;Muhammad Attique Khan;Muhammad Faisal;Mussarat Yasmin

  • An integrated design of particle swarm optimization (PSO) with fusion of features for detection of brain tumor

    Muhammad Sharif;Javaria Amin;Mudassar Raza;Mussarat Yasmin

  • Brain tumor detection: a long short-term memory (LSTM)-based learning model

    Javaria Amin;Muhammad Sharif;Mudassar Raza;Tanzila Saba

  • 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

  • Detection of Brain Tumor based on Features Fusion and Machine Learning

    Javeria Amin;Muhammad Sharif;Mudassar Raza;Mussarat Yasmin

  • Multi-Model Deep Neural Network based Features Extraction and Optimal Selection Approach for Skin Lesion Classification

    Muhammad Attique Khan;Muhammad Younus Javed;Muhammad Sharif;Tanzila Saba

  • Brain tumor segmentation and classification by improved binomial thresholding and multi-features selection

    Muhammad Sharif;Uroosha Tanvir;Ehsan Ullah Munir;Muhammad Attique Khan

  • Multi-Class Skin Lesion Detection and Classification via Teledermatology

    Muhammad Attique Khan;Khan Muhammad;Muhammad Sharif;Tallha Akram

  • Hand-crafted and deep convolutional neural network features fusion and selection strategy: An application to intelligent human action recognition

    Muhammad Attique Khan;Muhammad Attique Khan;Muhammad Sharif;Tallha Akram;Mudassar Raza

  • Developed Newton-Raphson based deep features selection framework for skin lesion recognition

    Muhammad Attique Khan;Muhammad Sharif;Tallha Akram;Syed Ahmad Chan Bukhari

  • Skin lesion segmentation and classification: A unified framework of deep neural network features fusion and selection

    Muhammad Attique Khan;Muhammad Imran Sharif;Mudassar Raza;Almas Anjum

  • A framework of human action recognition using length control features fusion and weighted entropy-variances based feature selection

    Farhat Afza;Muhammad Attique Khan;Muhammad Sharif;Seifedine Nimer Kadry

  • From ECG signals to images: a transformation based approach for deep learning.

    Mahwish Naz;Jamal Hussain Shah;Muhammad Attique Khan;Muhammad Sharif

  • Object detection and classification: a joint selection and fusion strategy of deep convolutional neural network and SIFT point features

    Muhammad Rashid;Muhammad Attique Khan;Muhammad Sharif;Mudassar Raza

  • Intelligent fusion-assisted skin lesion localization and classification for smart healthcare

    Muhammad Attique Khan;Khan Muhammad;Muhammad Sharif;Tallha Akram

  • A two-stream deep neural network-based intelligent system for complex skin cancer types classification

    Muhammad Attique Khan;Muhammad Sharif;Tallha Akram;Seifedine Kadry

  • An implementation of optimized framework for action classification using multilayers neural network on selected fused features

    Muhammad Attique Khan;Muhammad Attique Khan;Tallha Akram;Muhammad Sharif;Muhammad Younus Javed

  • An automated system for cucumber leaf diseased spot detection and classification using improved saliency method and deep features selection

    Muhammad Attique Khan;Muhammad Attique Khan;Tallha Akram;Muhammad Sharif;Kashif Javed

  • Appearance based pedestrians’ gender recognition by employing stacked auto encoders in deep learning

    Mudassar Raza;Muhammad Sharif;Mussarat Yasmin;Muhammad Attique Khan

  • Quantum Machine Learning Architecture for COVID-19 Classification Based on Synthetic Data Generation Using Conditional Adversarial Neural Network.

    Javaria Amin;Muhammad Sharif;Nadia Gul;Seifedine Kadry

  • Deep neural network features fusion and selection based on PLS regression with an application for crops diseases classification

    Farah Saeed;Muhammad Attique Khan;Muhammad Sharif;Mamta Mittal

  • Convolutional neural network with batch normalization for glioma and stroke lesion detection using MRI

    Javaria Amin;Javaria Amin;Muhammad Sharif;Muhammad Almas Anjum;Mudassar Raza

  • A deep neural network and classical features based scheme for objects recognition: an application for machine inspection

    Nazar Hussain;Muhammad Attique Khan;Muhammad Sharif;Sajid Ali Khan

Frequent Co-Authors

Xiao-Feng Wu
Xiao-Feng Wu Dalian Institute of Chemical Physics
Muhammad Attique Khan
Muhammad Attique Khan Prince Mohammad bin Fahd University
Tanzila Saba
Tanzila Saba Prince Sultan University
Mussarat Yasmin
Mussarat Yasmin University of Gujrat
Mudassar Raza
Mudassar Raza Namal College
Matthias Beller
Matthias Beller Leibniz Institute for Catalysis
Anke Spannenberg
Anke Spannenberg Leibniz Institute for Catalysis
Amjad Rehman
Amjad Rehman Prince Sultan University
Alexander Villinger
Alexander Villinger University of Rostock
Tallha Akram
Tallha Akram Prince Sattam Bin Abdulaziz University

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