Papers by Nawazish Naveed
Applied Computer Science, Sep 30, 2021
Breast cancer is commonest type of cancers among women. Early diagnosis plays a significant role ... more Breast cancer is commonest type of cancers among women. Early diagnosis plays a significant role in reducing the fatality rate. The main objective of this study is to propose an efficient approach to classify breast cancer tumor into either benign or malignant based on digitized image of a fine needle aspirate (FNA) of a breast mass represented by the Wisconsin Breast Cancer Dataset. Two wrapper-based feature selection methods, namely, sequential forward selection(SFS) and sequential backward selection (SBS) are used to identify the most discriminant features which can contribute to improve the classification performance. The feed forward neural network (FFNN) is used as a classification algorithm. The learning algorithm hyper-parameters are optimized using the grid search process. After selecting the optimal classification model, the data is divided into training set and testing set and the performance was evaluated. The feature space is reduced from nine feature to seven and six features using SFS and SBS respectively. The highest classification accuracy recorded was 99.03% with FFNN using the seven SFS selected features. While accuracy recorded with the six SBS selected features was 98.54%. The obtained results indicate that the proposed approach is effective in terms of feature space reduction leading to better accuracy and efficient classification model.
Applied Computer Science
Breast cancer is commonest type of cancers among women. Early diagnosis plays a significant role ... more Breast cancer is commonest type of cancers among women. Early diagnosis plays a significant role in reducing the fatality rate. The main objective of this study is to propose an efficient approach to classify breast cancer tumor into either benign or malignant based on digitized image of a fine needle aspirate (FNA) of a breast mass represented by the Wisconsin Breast Cancer Dataset. Two wrapper-based feature selection methods, namely, sequential forward selection(SFS) and sequential backward selection (SBS) are used to identify the most discriminant features which can contribute to improve the classification performance. The feed forward neural network (FFNN) is used as a classification algorithm. The learning algorithm hyper-parameters are optimized using the grid search process. After selecting the optimal classification model, the data is divided into training set and testing set and the performance was evaluated. The feature space is reduced from nine feature to seven and six f...
International Journal of Physical Sciences, Apr 18, 2011
Breast cancer detection and diagnosis is a critical and complex procedure that demands high degre... more Breast cancer detection and diagnosis is a critical and complex procedure that demands high degree of accuracy. In computer aided diagnostic systems, the breast cancer detection is a two stage procedure. First, to classify the malignant and benign mammograms, while in second stage, the type of abnormality is detected. The classifier ensemble optimization is a method that can be applied to increase the classification accuracy at both stages. In this paper, we have proposed a novel technique to enhance the classification of malignant and benign mammograms using multi-classification of malignant mammograms into six abnormality classes. DWT (discrete wavelet transformation) features are extracted from preprocessed images and passed through different classifiers. To improve accuracy, results generated by various classifiers are ensembled. Mammograms declared as malignant by ensemble classifiers are divided into six classes. The ensemble classifiers are further used for multiclassification using one against all technique for classification. Output of all ensemble classifiers is combined by product, median and mean rule. It has been observed that the accuracy of classification of abnormalities is more than 97% in case of mean rule. Mammographic Institute Society Analysis [MIAS] dataset is used for experimentation.
Optimal control strategies for a heroin epidemic model with age-dependent susceptibility and recovery-age
AIMS Mathematics, 2021
AIMS Mathematics, 2021
In the present manuscript, an age-structured heroin epidemic model is formulated with the assumpt... more In the present manuscript, an age-structured heroin epidemic model is formulated with the assumption that susceptibility and recovery are age-dependent. Keeping in view some control measures for heroin addiction, a control problem for further analysis is presented. The main results are the existence of control variables, sensitivities, adjoint system and the setting of an optimal control problem. We used the techniques of weak derivatives and a general principle of Pontryagin's type for obtaining the optimal control problem. To compare our results, we demonstrated sample simulations which show the effect of control on the entire population.
Semantic Based Ontology Driven Feature Interaction Detection & Modeling
Cancer Detection and Classification of Mammograms using Machine Learning Techniques
Optik, 2016
Active control strategy is a powerful control technique in synchronizing chaotic/hyperchaotic sys... more Active control strategy is a powerful control technique in synchronizing chaotic/hyperchaotic systems. Until now, active control techniques have been employed to synchronize chaotic systems with the same orders. The present study overcomes the limitations of synchronization of chaotic systems of similar dimensions using active control. In this article, the authors investigate the synchronization problem for a drive-response chaotic system with different orders under the effect of both unknown model uncertainties and external disturbance. Based on the Lyapunov stability theory and Routh-Hurwitz criterion, a robust generalized active control approach is proposed and sufficient algebraic conditions are derived to compute a suitable linear controller gain matrix that guarantees the globally exponentially stable synchronization. Two examples are presented to illustrate the main results, namely reduced-order synchronization between the hyperchaotic Lu and the unified chaotic systems and the increased-order synchronization between the unified chaotic and the hyperchaotic Lu systems. There are three main contributions of the present study: (a) generalization of the active control for synchronization of a class of chaotic systems with different orders; (b) a recursive approach is proposed to compute a suitable linear controller gain matrix and (c) reduced (increased) order synchronization under the effect of both unknown model uncertainties and external disturbances. A comparative study has been done with our results of the previously published work in terms of synchronization speed and quality. Finally, numerical simulations are given to verify the effectiveness of the proposed reduced (increased) order active synchronization approach. Future applications of the proposed reduced (increased) order synchronization approach is discussed.
Diagnosis of Vertebral Column Disorders Using Machine Learning Classifiers
2013 International Conference on Information Science and Applications (ICISA), 2013
Multi Domain Features Based Classification of Mammogram Images Using SVM and MLP
2009 Fourth International Conference on Innovative Computing, Information and Control (ICICIC), 2009
Abstract Breast cancer is the most common cancer diagnosed among US women. In this paper we have ... more Abstract Breast cancer is the most common cancer diagnosed among US women. In this paper we have done some experiments for tumor detection in digital mammogram images. First of all, we have described a method that segments the breast image automatically. As a preprocessing, we have used fuzzy based noise removal filter that removes noise. Then for segmentation, we have provided a background removal method. We have extracted eight different multi domains features. For accurate classification, we have used two different ...
Heart disease classification ensemble optimization using Genetic algorithm
2011 IEEE 14th International Multitopic Conference, 2011
... Benish Fida, Muhammad Nazir, Nawazish Naveed, Sheeraz Akram University Institute of Informati... more ... Benish Fida, Muhammad Nazir, Nawazish Naveed, Sheeraz Akram University Institute of Information Technology, PMAS-Arid Agriculture University Rawalpindi, Pakistan ... pattern data to construct an intelligent and efficient heart disease prediction system [9]. In [10], Yana et al. ...
Efficient gender classification methodology using DWT and PCA
2011 IEEE 14th International Multitopic Conference, 2011
Recognition of gender from face images has accomplished great popularity and also enlightened som... more Recognition of gender from face images has accomplished great popularity and also enlightened some new research problems. In this paper, we presented a new technique for gender classification using DWT and PCA. The technique has shown performance better than existing gender classification techniques. Experiments were carried out on standard face database used in various existing works of literature. Our proposed
Opposition based Particle Swarm Optimization with student T mutation (OSTPSO)
2012 4th Conference on Data Mining and Optimization (DMO), 2012
ABSTRACT Particle swarm optimization (PSO) is a stochastic algorithm, used for the optimization p... more ABSTRACT Particle swarm optimization (PSO) is a stochastic algorithm, used for the optimization problems, proposed by Kennedy [1] in 1995. PSO is a recognized algorithm for optimization problems, but suffers from premature convergence. This paper presents an Opposition-based PSO (OPSO) to accelerate the convergence of PSO and at the same time, avoid early convergence. The proposed OPSO method is coupled with the student T mutation. Results from the experiment performed on the standard benchmark functions show an improvement on the performance of PSO.
Microscopy Research and Technique, 2011
Breast cancer is the most common cancer diagnosed among women. In this article, support vector ma... more Breast cancer is the most common cancer diagnosed among women. In this article, support vector machine is used to classify digital mammogram images into malignant and benign. Wiener filter is used to handle the possible quantum noise, which is more likely to occur in mammograms. Stack‐based connected component method is proposed for background removal, and the image is enhanced using retinax method. Seeded region growing algorithm is used to remove the pectoral muscle part of the mammogram. We have extracted 13 different multidomains' features for classification. Results show the superiority of the proposed algorithm in terms of sensitivity, specificity, and accuracy. We have used MIAS database of mammography for experimentation. Microsc. Res. Tech., 2011. © 2011 Wiley Periodicals, Inc.
sersc.org
Classification has emerged as a leading technique for problem solution and optimization. Classifi... more Classification has emerged as a leading technique for problem solution and optimization. Classification has been used extensively in several problems domain. Automated gender classification is a significant area of research and has great potential. It offers several industrial applications in near future such as monitoring, surveillance, commercial profiling and human computer interaction. Different methods have been proposed for gender classification like gait, iris and hand shape. However, majority of techniques for gender classification are based on facial information. In this paper, a comparative study of gender classification using different techniques is presented. The major emphasis of this work is on the critical evaluation of different techniques used for gender classification. The comparative evaluation has highlighted major strengths and limitations of these existing techniques. Taking an overview of these major problems, our research is focused on summarizing the literature by highlighting its strengths and limitations. This study also presents several future research areas in the domain of gender classification.
Computerized Medical Imaging and Graphics, 2006
A conventional FCM algorithm does not fully utilize the spatial information in the image. In this... more A conventional FCM algorithm does not fully utilize the spatial information in the image. In this paper, we present a fuzzy c-means (FCM) algorithm that incorporates spatial information into the membership function for clustering. The spatial function is the summation of the membership function in the neighborhood of each pixel under consideration. The advantages of the new method are the following: (1) it yields regions more homogeneous than those of other methods, (2) it reduces the spurious blobs, (3) it removes noisy spots, and (4) it is less sensitive to noise than other techniques. This technique is a powerful method for noisy image segmentation and works for both single and multiple-feature data with spatial information.
Quantum and impulse noise filtering from breast mammogram images
Computer Methods and Programs in Biomedicine, 2012
DCT Features Based Malignancy and Abnormality Type Detection Method for Mammograms
ijicic.org
Proceedings of the 3rd …, May 1, 2009
Video segmentation can be considered as a clustering process that classifies one video succession... more Video segmentation can be considered as a clustering process that classifies one video succession into several objects. Spatial information enhances the quality of clustering which is not utilized in the conventional FCM. Normally fuzzy c-mean (FCM) algorithm is ...
Image segmentation has been and is likely to be an important component of the content-based image... more Image segmentation has been and is likely to be an important component of the content-based image acquisition and retrieval systems. This paper describes a new method for segmentation of color images. The proposed method uses two phases segmentation processes. In the 1 st phase, segmentation is performed with the help of cluster validity measures and Spatial Fuzzy C-Mean (sFCM). HSV model helps in the decomposition of color image then FCM is applied separately on each component of HSV model. In the 2 nd phase, for fine tuning, Kohonen's Self Organizing Map (SOM) neural network along with wavelets is used. SOM is a computationally expensive network. It has been observed that if SOM training performed on the wavelet-transformed image, then not only it reduces SOM training time but in this way makes more compact segments. The advantages of new method are: (i) it yields regions more homogeneous than those of other methods for color images; (ii) it reduces the spurious blobs; and (iii) it removes noisy spots. The technique presented in this paper is a powerful method for noisy color image segmentation and works for both single and multiple-feature data. Experiments were performed on standard color images. Experiments show better performance of the proposed method when compared with other approaches in practice.
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Papers by Nawazish Naveed