Journal of Intelligent & Fuzzy Systems - Volume 41, issue 5

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

Article Type: Editorial

DOI: 10.3233/JIFS-189845

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 5, pp. 5221-5234, 2021

Authors: Krishnan, Sajitha | Amudha, J. | Tejwani, Sushma

Article Type: Research Article

Abstract: It is quite alarming that the increase of glaucoma is due to the lack of awareness of the disease and the cost for glaucoma screening. The primary eye care centers need to include a comprehensive glaucoma screening and include machine learning models to elaborate it as decision support system. The proposed system considers the state of art of eye gaze features to understand cognitive processing, direction and restriction of visual field. There is no significant difference in global and local ratio and skewness value of fixation duration and saccade amplitude, which suggest that there is no difference in cognitive processing. …The significance value of saccadic extent along vertical axis, Horizontal Vertical ratio (HV ratio), convex hull area and saccadic direction show that there is restriction in vertical visual field. The statistical measures (p < 0.05) and Spearman correlation coefficient with class label validate the results. The proposed system compared the performance of seven classifiers: Naïve Bayes classifier, linear and kernel Support Vector classifiers, decision tree classifier, Adaboost, random forest and eXtreme Gradient Boosting (XGBoost) classifier. The discrimination of eye gaze features of glaucoma and normal is efficiently done by XGBoost with accuracy 1.0. The decision support system is cost-effective and portable. Show more

Keywords: Restriction, quality of life, decision support system, eye tracking, glaucoma

DOI: 10.3233/JIFS-189846

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 5, pp. 5235-5242, 2021

Authors: Naik, Amrita | Edla, Damodar Reddy

Article Type: Research Article

Abstract: Lung cancer is the most common cancer throughout the world and identification of malignant tumors at an early stage is needed for diagnosis and treatment of patient thus avoiding the progression to a later stage. In recent times, deep learning architectures such as CNN have shown promising results in effectively identifying malignant tumors in CT scans. In this paper, we combine the CNN features with texture features such as Haralick and Gray level run length matrix features to gather benefits of high level and spatial features extracted from the lung nodules to improve the accuracy of classification. These features are …further classified using SVM classifier instead of softmax classifier in order to reduce the overfitting problem. Our model was validated on LUNA dataset and achieved an accuracy of 93.53%, sensitivity of 86.62%, the specificity of 96.55%, and positive predictive value of 94.02%. Show more

Keywords: CNN, GLCM, GLRLM, SVM

DOI: 10.3233/JIFS-189847

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 5, pp. 5243-5251, 2021

Authors: Sethy, Prabira Kumar | Pandey, Chanki | Khan, Mohammad Rafique | Behera, Santi Kumari | Vijaykumar, K. | Panigrahi, Sibarama

Article Type: Research Article

Abstract: In the last decade, there have been extensive reports of world health organization (WHO) on breast cancer. About 2.1 million women are affected every year and it is the second most leading cause of cancer death in women. Initial detection and diagnosis of cancer appreciably increase the chance of saving lives and reduce treatment costs. In this paper, we perform a survey of the techniques utilized in breast cancer detection and diagnosis in image processing, machine learning (ML), and deep learning (DL). We also proposed a novel computer-vision based cost-effective method for breast cancer detection and diagnosis. Along with the …detection and diagnosis of breast cancer, our proposed method is capable of finding the exact position of the abnormality present in the breast that will help in breast-conserving surgery or partial mastectomy. The proposed method is the simplest and cost-effective approach that has produced highly accurate and useful outcomes when compared with the existing approach. Show more

Keywords: Breast cancer, computer vision, mammography, support vector machine (SVM), HOG features

DOI: 10.3233/JIFS-189848

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 5, pp. 5253-5263, 2021

Authors: Jaiseeli, C. | Raajan, N.R.

Article Type: Research Article

Abstract: The neurological disorders are developed in adults due to reduced visual perception. Opto Kinetic Nystagmus (OKN) is a clinical method to detect visual perception. For objective measurements, a computational algorithm based OKN detection is preferable rather than clinical practice. In this paper, a memory-efficient Subsampled Lucas-Kanade Optical Flow (SLKOF) is proposed. The proposal employs the Subsampling of images for various levels. The proposal deals with the computation of OKN gain for different image Subsampling factors using the MATLAB platform. The experimental set up to observe OKN is done using computer-based rotation control of the drum through a stepper motor. The …results are compared with the well established Lucas-Kanade (LK) method for Optical flow. It is observed that OKN gain corresponds to 1/4th of a subsampled image of the SLKOF method correlates with the LK method for the majority of the cases. This validation evidently elucidates that the proposal is computationally efficient. Show more

Keywords: Opto Kinetic Nystagmus, Lucas-Kanade Optical Flow, eye movements, subsampling, objective method

DOI: 10.3233/JIFS-189849

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 5, pp. 5265-5274, 2021

Authors: Babu, Tina | Singh, Tripty | Gupta, Deepa | Hameed, Shahin

Article Type: Research Article

Abstract: Colon cancer is one of the highest cancer diagnosis mortality rates worldwide. However, relying on the expertise of pathologists is a demanding and time-consuming process for histopathological analysis. The automated diagnosis of colon cancer from biopsy examination played an important role for patients and prognosis. As conventional handcrafted feature extraction requires specialized experience to select realistic features, deep learning processes have been chosen as abstract high-level features may be extracted automatically. This paper presents the colon cancer detection system using transfer learning architectures to automatically extract high-level features from colon biopsy images for automated diagnosis of patients and prognosis. In …this study, the image features are extracted from a pre-trained convolutional neural network (CNN) and used to train the Bayesian optimized Support Vector Machine classifier. Moreover, Alexnet, VGG-16, and Inception-V3 pre-trained neural networks were used to analyze the best network for colon cancer detection. Furthermore, the proposed framework is evaluated using four datasets: two are collected from Indian hospitals (with different magnifications 4X, 10X, 20X, and 40X) and the other two are public colon image datasets. Compared with the existing classifiers and methods using public datasets, the test results evaluated the Inception-V3 network with the accuracy range from 96.5% - 99% as best suited for the proposed framework. Show more

Keywords: Transfer learning, features, CNN, colon cancer, classification

DOI: 10.3233/JIFS-189850

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 5, pp. 5275-5286, 2021

Authors: Agrawal, Rahul | Bajaj, Preeti

Article Type: Research Article

Abstract: Brain-Computer Interface provides and simplifies the communication channel for the physically disabled individuals suffering from severe brain injury related to brain stroke and lost ability to speak. It helps these patients to connect with the outside world. In the proposed work, the electroencephalogram signal is used as an input source taken from Bonn University database that is divided into three class of data consisting of 247 samples each. It is further processed by Tunable Q-Wavelet Transform signal decomposition technique where the signals are subdivided into various sub-bands depending on the value of Q-factor, redundancy factor, and number of sub-bands. A …novel custom technique uses Q-factor of 3, redundancy value of 3 & 12 number of sub-bands for high pass filtering as well as Q-factor of 1, redundancy value of 3 & 7 number of sub-bands for low pass filtering combined with nine statistical measures for feature extraction purpose. The classification is performed by using multi-class support vector machine giving the accuracy of 99.59%. The accuracy performs best when compared with the existing research results Furthermore, the comparative study has been performed on the same dataset by using deep neural network along with support vector machine giving an accuracy of 100%. Other evaluation parameters such as precision, sensitivity, specificity, and F1 score are also calculated. The classified data help transform the signal into three communication messages that will help solve the speech impairment of disabled individuals. Show more

Keywords: Brain computer interface (BCI), Electroencephalogram (EEG), Tunable Q wavelet Transform (TQWT), Support vector machine (SVM), Deep Neural network (DNN) etc

DOI: 10.3233/JIFS-189851

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 5, pp. 5287-5297, 2021

Authors: Rudhra, B | Malu, G | Sherly, Elizabeth | Mathew, Robert

Article Type: Research Article

Abstract:  Normal Pressure Hydrocephalus (NPH), an Atypical Parkinsonian syndrome, is a neurological syndrome that mainly affects elderly people. This syndrome shows the symptoms of Parkinson’s disease (PD), such as walking impairment, dementia, impaired bladder control, and mental impairment. The Magnetic Resonance Imaging (MRI) is the aptest modality for the detection of the abnormal build-up of cerebrospinal fluid in the brain’s cavities or ventricles, which is the major cause of NPH. This work aims to develop an automated biomarker for NPH segmentation and classification (NPH-SC) that efficiently detect hydrocephalus using a deep learning-based approach. Removal of non-cerebral tissues (skull, scalp, and dura) …and noise from brain images by skull stripping, unsharp-mask based edge sharpening, segmentation by marker-based watershed algorithm, and labelling are performed to improve the accuracy of the CNN based classification system. The brain ventricles are extracted using the external and internal markers and then fed into the convolutional neural networks (CNN) for classification. This automated NPH-SC model achieved a sensitivity of 96%, a specificity of 100%, and a validation accuracy of 97%. The prediction system, with the help of a CNN classifier, is used for the calculation of test accuracy of the system and obtained promising 98% accuracy. Show more

Keywords: Structural magnetic resonance imaging, normal pressure hydrocephalus, convolutional neural networks

DOI: 10.3233/JIFS-189852

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 5, pp. 5299-5307, 2021

Authors: Preethi, D. | Vimala, J. | Rajareega, S. | Al-Tahan, M.

Article Type: Research Article

Abstract:  This article deals with a fuzzy hypercompositional structure called fuzzy hyperlattice ordered δ - group ( FHLO δ - G ) , the extension of the fuzzy hypercompositional structure namely fuzzy hyperlattice ordered group (FHLOG ). Using FHLO δ - G , we can involve one additional non-empty set δ with FHLOG , which helps to develop new results and applications. The structural characteristics and properties of FHLO δ - G are analysed. Furthermore, an application of FHLO δ - G …for ABO blood group system is proposed. Show more

Keywords: Lattice ordered group, fuzzy lattice ordered group, fuzzy hyperlattice, fuzzy hyperlattice ordered group, 𝒜ℬ𝒪 blood group system

DOI: 10.3233/JIFS-189853

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 5, pp. 5309-5315, 2021

Authors: Sukumaran, Poornima | Govardhanan, Kousalya

Article Type: Research Article

Abstract: Voice processing has proven to be an eminent way of recognizing the various emotions of the people. The objective of this research is to identify the presence of Autism Spectrum Disorder (ASD) and to analyze the emotions of autistic children through their voices. The presented automated voice-based system can detect and classify seven basic emotions (anger, disgust, neutral, happiness, calmness, fear and sadness) expressed by children through source parameters associated with their voices. Various prime voice features such as Mel-frequency Cepstral Coefficients (MFCC) and Spectrogram are extracted and utilized to train a Multi-layer Perceptron (MLP) Classifier to identify possible emotions …exhibited by the children thereby assessing their behavioral state. This proposed work therefore helps in the examination of emotions in autistic children that can be used to assess the kind of training and care required to enhance their lifestyle. Show more

Keywords: Emotion recognition, Autism Spectrum Disorder, voice processing, MFCC

DOI: 10.3233/JIFS-189854

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 5, pp. 5317-5326, 2021