publications:
- title: "A Unified Model for Concurrent Debugging"
  author:
  - name: "S. Irfan Hyder"
    link: "http://"
  - name: "John Werth"
    link: "https://researchr.org/alias/john-werth"
  - name: "James C. Browne"
    link: "https://researchr.org/alias/james-c.-browne"
  year: "1993"
  tags:
  - "meta-model"
  - "C++"
  - "debugging"
  - "Meta-Environment"
  researchr: "https://researchr.org/publication/HyderWB93"
  cites: 0
  citedby: 0
  pages: "58-67"
  booktitle: "icpp"
  kind: "inproceedings"
  key: "HyderWB93"
- title: "Feature Based Image Classification by using Principal Component Analysis"
  author:
  - name: "Imran Sarwar Bajwa"
    link: "https://researchr.org/alias/imran-sarwar-bajwa"
  - name: "M. Shahid Naweed"
    link: "https://researchr.org/alias/m.-shahid-naweed"
  - name: "M. Nadim Asif"
    link: "https://researchr.org/alias/m.-nadim-asif"
  - name: "S. Irfan Hyder"
    link: "http://"
  year: "2009"
  month: "April"
  links:
    "url": "/brokenurl#www.icgst.com/gvip/volume9/Issue2/P1150729002.pdf"
  tags:
  - "rule-based"
  - "classification"
  - "testing"
  - "analysis"
  researchr: "https://researchr.org/publication/bajwa2009feature-1"
  cites: 0
  citedby: 0
  journal: "Journal of Graphics, Vision and Image Processing"
  volume: "9"
  number: "2"
  kind: "article"
  key: "bajwa2009feature-1"
- title: "Feature Based Image Classification by using Principal Component Analysis"
  author:
  - name: "Imran Sarwar Bajwa"
    link: "https://sites.google.com/view/isbajwa/my-website"
  - name: "M. Shahid Naweed"
    link: "https://researchr.org/alias/m.-shahid-naweed"
  - name: "M. Nadim Asif"
    link: "https://researchr.org/alias/m.-nadim-asif"
  - name: "S. Irfan Hyder"
    link: "http://"
  year: "2009"
  month: "April"
  abstract: "Classification of different types of cloud images is the primary issue used to forecast precipitation and other weather constituents. A PCA based classification system has been presented in this paper to classify the different types of single-layered and multi-layered clouds. Principal Component Analysis (PCA) provides enhanced accuracy in features based image identification and classification as compared to other techniques. PCA is a feature based classification technique that is characteristically used for image recognition. PCA is based on principal features of an image and these features discreetly represent an image. The used approach in this research uses the principal features of an image to identify different cloud image types with better accuracy. A classifier system has also been designed to exhibit this enhancement. The designed system reads features of gray-level images to create an image space. This image space is used for classification of images. In testing phase, a new cloud image is classified by comparing it with the specified image space using the PCA algorithm."
  links:
    "url": "/brokenurl#www.icgst.com/gvip/volume9/Issue2/P1150729002.pdf"
  tags:
  - "rule-based"
  - "classification"
  - "design research"
  - "testing"
  - "analysis"
  - "type system"
  - "systematic-approach"
  researchr: "https://researchr.org/publication/bajwa2009feature"
  cites: 0
  citedby: 0
  journal: "Journal of Graphics, Vision and Image Processing"
  volume: "9"
  number: "2"
  kind: "article"
  key: "bajwa2009feature"
- title: "Cloud Classification Using PCA"
  author:
  - name: "Imran Sarwar Bajwa"
    link: "http://www.pafkiet.edu.pk/Default.aspx?tabid=291"
  - name: "S. Irfan Hyder"
    link: "http://"
  year: "2005"
  month: "Feburary"
  abstract: "An automatic classification system is presented, which discriminates the different types of single-layered clouds using Principal Component Analysis (PCA) with enhanced accuracy and provides fast processing speed as compared to other techniques. PCA is an image classification technique, which is typically used for face recognition. PCA can be used to identify the image features called principal components. A principal component is a peculiar feature of an image. The approach described in report uses this PCA capability for enhancing the accuracy of cloud image analysis. To demonstrate this enhancement, a software classifier system has been developed that incorporates PCA capability for better discrimination of cloud images. The system is first trained by cloud images. In training phase, system reads major principal features of the different cloud images to produce an image space. In testing phase, a new cloud image can be classified by comparing it with the specified image space using the PCA algorithm. Weather forecasting applications use various pattern recognition techniques to analyze clouds? information and other meteorological parameters. Neural Networks is an often-used methodology for image processing. Some statistical methodologies like FDA, RBFNN and SVM are also being used for image analysis. These methodologies require more training time and have limited accuracy of about 70%. This level of accuracy often degrades classification of clouds, and hence the accuracy of rain and other weather predictions is reduced. Better accuracy in cloud classification means accurate categorization of clouds according to high, mid and low levels. These high, mid and low-level clouds are further classified in their particular sub classes. PCA can easily handle a large amount of data due to its capability of reducing data dimensionality and complexity, thus getting better results. PCA algorithm provides a more accurate cloud classification that yield better and concise forecasting of rain."
  links:
    "url": "http://www.pafkiet.edu.pk/LinkClick.aspx?fileticket=1HiSWq6Dipk%3d&tabid=291&mid=1060"
  tags:
  - "classification"
  - "software components"
  - "software component"
  - "testing"
  - "analysis"
  - "type system"
  - "data-flow"
  - "data-flow analysis"
  - "systematic-approach"
  researchr: "https://researchr.org/publication/hyder2005cloud-0"
  cites: 0
  citedby: 0
  school: "College of Computing and Information Sciences"
  type: "MS(Computer Science) Thesis"
  address: "PAF-KIET, City Campus, 28-D P.E.C.H.S, Karachi, Pakistan"
  kind: "mastersthesis"
  key: "hyder2005cloud-0"
- title: "PCA Based Classification of Single Layered Cloud Types"
  author:
  - name: "Imran Sarwar Bajwa"
    link: "https://sites.google.com/view/isbajwa/my-website"
  - name: "S. Irfan Hyder"
    link: "http://"
  year: "2005"
  month: "July"
  abstract: "The paper presents an automatic classification system, which discriminates the different types of single-layered clouds using Principal Component Analysis (PCA) with enhanced accuracy as compared to other techniques. PCA is an image classification technique, which is typically used for face recognition. PCA can be used to identify the image features called principal components. A principal component is a peculiar feature of an image. The approach described in this paper uses this PCA capability for enhancing the accuracy of cloud image analysis. To demonstrate this enhancement, a software classifier system has been developed that incorporates PCA capability for better discrimination of cloud images. The system is first trained by cloud images. In training phase, system reads major principal features of the different cloud images to produce an image space. In testing phase, a new cloud image can be classified by comparing it with the specified image space using the PCA algorithm."
  links:
    "url": "http://pafkiet.edu.pk/LinkClick.aspx?fileticket=hLyTG0rv8JI%3D&tabid=149&mid=1544"
  tags:
  - "rule-based"
  - "classification"
  - "software components"
  - "software component"
  - "testing"
  - "analysis"
  - "type system"
  - "systematic-approach"
  researchr: "https://researchr.org/publication/hyder2005based"
  cites: 0
  citedby: 0
  journal: "Journal of Market Forces"
  volume: "1"
  number: "2"
  kind: "article"
  key: "hyder2005based"