Journal of Intelligent & Fuzzy Systems - Volume 32, issue 4

Authors: Thampi, Sabu M. | El-Alfy, El-Sayed M.

Article Type: Editorial

DOI: 10.3233/JIFS-169221

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 4, pp. 2791-2796, 2017

Authors: Deepthi, P.S. | Thampi, Sabu M.

Article Type: Research Article

Abstract: Microarray technologies help to observe the expression levels of thousands of genes. Analysis of gene expression data arising from these experiments provides insight into different subtypes of diseases and functions of genes. Gene expression data are characterized by a large number of genes and a few samples. Employing traditional supervised classifiers for prediction requires adequate labeled data. However, the limited number of samples make the prediction of disease subtypes a difficult task. Hence, we investigate the potential of semi-supervised learning to delineate the tissue samples from a few labeled data. The available labeled samples were exploited to guide the clustering …of unlabeled samples. A classification system by integrating feature selection techniques with semi-supervised fuzzy c-means algorithm was built. The system was evaluated using publicly available gene expression datasets and results showed that a few labeled tissue samples can assist in the accurate prediction of disease subtypes. Show more

Keywords: Gene expression, clustering, semi-supervised fuzzy c-means

DOI: 10.3233/JIFS-169222

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 4, pp. 2797-2805, 2017

Authors: Vidyarthi, Ankit | Mittal, Namita

Article Type: Research Article

Abstract: In machine learning based disease diagnosis, extraction of relevant and informative features from medical image slices is vital aspect. Extracted features represent the descriptive nature of the imaging modality for machine learning. Texture description, is one such method which is used to extract the informative aspect of the object. In this paper a new texture based feature extraction algorithm is proposed for extracting relevant and informative features from brain MR Images having tumor. Suggested algorithm is based on finding the texture description using nine different variants of texture objects. Subsequently, the intermediate texture index matrix is formed using texture objects …with high pass and low pass spiral filters. The resultant two index matrix are used to generate the Texture Co-occurrence Matrix (TOM). TOM helps to extract the spatial and spectral domain features that forms the hybrid feature set for brain MRI classification. Using TOM, an experimentation is performed with a dataset of 660 T1-weighted post contrast brain MR Images having 5 different types of malignant tumors. Experimental results suggest that proposed method gives significant results in abnormality classification when compared with state-of-art GLCM and Run length algorithms. Show more

Keywords: Texture, Texture Co-occurrence Matrix, texture objects, brain tumor, classification

DOI: 10.3233/JIFS-169223

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 4, pp. 2807-2818, 2017

Authors: Punitha, Stephan | Ravi, Subban | Anousouya Devi, M. | Vaishnavi, Jothimani

Article Type: Research Article

Abstract: Breast cancer is one of the most commonly occurring cancers among women globally. The accurate detection and classification of the abnormalities such as masses and microcalcifications in mammograms is a challenging task for the radiologist without which the survival rate of the breast cancer patients may increase worldwide. This paper presents a novel Computer Aided Diagnosis (CAD) system which uses Cellular Neural Network (CNN) technique, which is optimized using Particle Swarm Optimization (PSO) for detection and Particle Swarm Optimised Probabilistic Neural Network (PSOPNN) for the classification of breast masses as benign or malignant. The breast mass texture feature extraction is …carried out using Gray Level Co-occurrence Matrix (GLCM) and the optimal texture features are selected using a particle swarm optimized feature selection. The performance of the proposed system can be evaluated using the True Positive (TP), True Negative (TN), False Positive (FP), False Negative (FN) values. Show more

Keywords: Gray Level Co-Occurrence Matrix (GLCM), Cellular Neural Network (CNN), Digital Mammography, Particle Swarm Optimized Probabilistic Neural Network (PSOPNN)

DOI: 10.3233/JIFS-169224

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 4, pp. 2819-2828, 2017

Authors: Patil, Sarika B. | Narote, Abbhilasha S. | Narote, Sandipann P.

Article Type: Research Article

Abstract: Digital fundus photography plays a major role in the diagnosis of different retinal pathologies like hypertension, diabetic retinopathy and Glaucoma. To identify abnormal components on the retina, retinal features should be detected accurately. Retinal vessel structure is one of the important landmarks of the retina. So precise detection of retinal vessel structure is imperative. This paper presents a simple, robust retinal vessel extraction approach based on the line detectors and morphological operations. As vessel detection is basically a problem of a line detection, the green channel retinal image is applied to morphological opening using a line as structuring element. The …resultant image is again applied with the line detectors and thresholded using Otsu’s thresholding. The proposed algorithm overcomes the fundamental issues of scale and orientation avoiding the need of multiple thresholds with improved values of performance measure as compared to the state of the art techniques. The proposed algorithm is applied on 3 standard databases-HRF (healthy and Diabetic), DIARETDB1 and DRIVE. Area under the ROC curve (AUC) of 97% was achieved with 91% Sensitivity and 97% Specificity for DRIVE dataset. The proposed algorithm achieved an Accuracy of 97%, Sensitivity of 85 % and Specificity of 97% for HRF database. On DIARETDB1 database too observed very good results. Show more

Keywords: DIARETDB1, DRIVE, HRF database, fundus image, retinopathy, vessel detection

DOI: 10.3233/JIFS-169225

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 4, pp. 2829-2836, 2017

Authors: Mane, V.M. | Jadhav, D.V. | Shirbahadurkar, S.D.

Article Type: Research Article

Abstract: One of the major eye diseases called Diabetic retinopathy (DR), which causes loss of sight if it is not noticed in the early hours. In order to keep the patient’s vision, the early detection and periodic screening of DR plays an important role in eye diagnosis by examining the deformity in retinal fundus images. During the early detection of DR, ophthalmologists identify the lesions called microaneurysms that emerge as the first symptom of the disease. The various test methods availability and the handlings of all these test methods for detection of DR are not possible in rural areas. The automatic …DR detection system offers the potential to be used in large-scale screening programs. This paper presents a hybrid classifier and region-dependent integrated features for detection of DR automatically. In the proposed hybrid classifier, holoentropy enabled decision tree is combined with a feed forward neural network using the proposed score level fusion method. The performance is evaluated and compared with existing classification algorithms using sensitivity, specificity, and accuracy. Two different databases such as DIARETDB0 and DIARETDB1 are utilized for the experimentation. From the experimental results, proposed technique obtained the accuracy of 98.70%, which is better as compared with existing algorithms. Show more

Keywords: Feature extraction, fusion, holoentropy, neural networks, classification

DOI: 10.3233/JIFS-169226

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 4, pp. 2837-2845, 2017

Authors: Devi, Salam Shuleenda | Singha, Joyeeta | Sharma, Manish | Laskar, Rabul Hussain

Article Type: Research Article

Abstract: Manual analyzing and interpreting of the microscopic images of thin blood smears for diagnosis of the malaria is a tedious and challenging task. This paper aims to develop a computer assisted system for quantification of erythrocytes in microscopic images of thin blood smears. The proposed method consists of preprocessing, segmentation, morphological filtering, cell separation and clump cell segmentation. The major issues, required to be addressed to enhance the performance of the system are cell separation (i.e. isolated and clump erythrocytes classification) and clump cell segmentation. The geometric features such as cell area, compactness ratio and aspect ratio have been used …to define the feature set. Further, the performance of the system in classifying the isolated and clump erythrocytes is evaluated for the different classifiers such as Naive Bayes, k -NN and SVM. Moreover, the clump erythrocytes are segmented using marker controlled watershed with h-minima as internal marker. Based on the experimental results, it may be concluded that the proposed model provides satisfactory results with an accuracy of 98.02% in comparison to the state of art method. Show more

Keywords: Erythrocyte, segmentation, feature extraction, cell separation, clump erythrocyte

DOI: 10.3233/JIFS-169227

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 4, pp. 2847-2856, 2017

Authors: Agarwal, Shivangi | Singh, Vijander | Rani, Asha | Mittal, A.P.

Article Type: Research Article

Abstract: The traditional signal processing algorithms suffer from large execution delay for real time issues, therefore implementation of high speed algorithms is needed. The present work aims to implement multiplier less Savitzky Golay smoothing filter (SGSF) based on distributed arithmetic (DA) for pre-processing of Electro-oculographic (EOG) signals such that speed is increased along with reduction in chip area. The filter used should be efficient enough to remove the artifacts along with least deformation from the actual signal. Savitzky-Golay (SG) filter is widely employed in biomedical signal analysis but its fast and efficient implementation is not proposed yet for EOG analysis. SGSF …is selected so that disease diagnosis using saccade detection of EOG signal can be done accurately. The efficiency of proposed filter is tested in terms of signal-to-signal-plus-noise ratio (SSNR) and real time computations. It is observed from the analysis that DA based architecture increases the processing speed, reduces the chip area and original features of filtered signal are preserved. Show more

Keywords: Electro-oculography, Savitzky golay filter, distributed arithmetic

DOI: 10.3233/JIFS-169228

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 4, pp. 2857-2862, 2017

Authors: Sreedhar, K.C. | Faruk, M.N. | Venkateswarlu, B.

Article Type: Research Article

Abstract: Cloud computing plays a predominant role in storage technologies. It enables the tenant user to deploy their infrastructure without any investment. Cloud storage offers flexibility with storage and sharing facilities using the Internet platform. Storing sensitive information such as clinical data requires high privacy preservation and is associated with serious concern over data privacy on the cloud platform. Privacy preservation becomes the most adherent issue when a large volume of data is stored in public clouds. Subtree anonymization using the bottom–up generalization (BUG) and top–down specialization (TDS) approaches has been widely adopted for anonymizing data sets. This ensures individual data …privacy; however, it causes potential violations when the new update is received, and it suffers from valuing the k -anonymity parameter. In this proposed model, a pseudo-identity was anticipated to accomplish privacy preservation with maximum data utility on incremental data sets. Initially, the Data Set (DS) was partitioned in the preprocessing stage; subsequently, the processed data sets were clustered into groups. The genetic model was used for indexing and updating incremental data sets. This was consistent with repeatedly modified data sets. In the evaluation process, an incremental and distributed DS was deployed, and our model exhibited efficient and optimal performance for privacy preservation in comparison with existing models. Show more

Keywords: Subtree anonymization, bottom–up generalization, top–down specialization, k-anonymity, data set partitioning

DOI: 10.3233/JIFS-169229

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 4, pp. 2863-2873, 2017

Authors: Baig, Mirza M. | Awais, Mian M. | El-Alfy, El-Sayed M.

Article Type: Research Article

Abstract: This paper presents a cascade of ensemble-based artificial neural network for multi-class intrusion detection (CANID) in computer network traffic. The proposed system learns a number of neural-networks connected as a cascade with each network trained using a small sample of training examples. The proposed cascade structure uses the trained neural network as a filter to partition the training data and hence a relatively small sample of training examples are used along with a boosting-based learning algorithm to learn an optimal set of neural network parameters for each successive partition. The performance of the proposed approach is evaluated and compared on …the standard KDD CUP 1999 dataset as well as a very recent dataset, UNSW-NB15, composed of contemporary synthesized attack activities. Experimental results show that our proposed approach can efficiently detect various types of cyber attacks in computer networks. Show more

Keywords: Intrusion detection, artificial neural network, cascading classifiers, ensemble learning, AdaBoost

DOI: 10.3233/JIFS-169230

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 4, pp. 2875-2883, 2017