Journal of Intelligent & Fuzzy Systems - Volume Pre-press, issue Pre-press

Authors: Davarpanah, Seyed Hashem

Article Type: Research Article

Abstract: A normal human brain holds a high level of bilateral reflection symmetry. On the sagittal view, the brain can be separated into the left and the right hemispheres with approximately identical anatomical properties, so that symmetrical mirror pixels are almost similar. As a result, the symmetry information can be used to enhance results of brain segmentation methods. In this paper, I introduced a new version of the Fuzzy C-Mean (FCM) segmentation method which is called Genetic Spatial Possibilistic Fuzzy C-Mean (GSPFCM). GSPFCM integrates symmetry information with SPFCM. It is an extension of Possibilistic Fuzzy C-Mean (PFCM) on 3D Magnetic Resonance …(MR) images. GSPFCM uses the spatial information and fuzzy membership values. Spatial and possibilistic information were added in order to solve the noise sensibility defect of FCM. To integrate the symmetry information, I first extracted the Mid-Sagittal Surface using a proposed genetic algorithm. According to this algorithm, inside each axial slice, a Thin-Plate Spline (TPS) surface was constructed and a genetic algorithm was applied to fit this TPS surface to the brain data. Then, the symmetry degree of each symmetry pair voxels was calculated. Finally, the membership values in SPFCM were updated based on the corresponding symmetrical values. The efficiency of GSPFCM, was evaluated using both simulated and real Magnetic Resonance Images (MRI), and was compared to the state-of-the-art methods. My results showed images with different degrees of Intensity Non-Uniformity (INU) and different levels of noise were segmented efficiently by the GSPFCM. Show more

Keywords: 3D brain MR segmentation, Mid-Sagittal Surface, Fuzzy C-Mean, genetic algorithm, fractal dimension, possibilistic information, spatial information

DOI: 10.3233/JIFS-191258

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2020

Authors: Navdeep, | Singh, Vijander | Rani, Asha | Goyal, Sonal

Article Type: Research Article

Abstract: This paper presents an improved hyper smoothing function based methodology for efficient edge detection. The main aim of this work is to obtain localized edges of noisy and blurred images without duplicate ones and integrating them into meaningful object boundaries. Therefore, logarithmic hyper-smoothing function is introduced in local binary pattern leading to improved hyperfunction based local binary pattern (IHLBP) algorithm. The proposed technique uses an improved counting scheme to correctly evaluate the number of image points having pixel value greater than or equal to the central pixel. The IHLBP algorithm is tested on synthetic images, radiography images, real-life pictures from …USC-SIPL and BSDS database<s>.< /s>Improved local binary pattern (ILBP), hyper local binary pattern (HLBP), Canny and Sobel methods are also used for comparative analysis. The results reveal that the proposed algorithm performs well on all synthetic and real images in the presence of blur and salt & pepper noise. Thus IHLBP proves to be an effective approach for edge detection in comparison to conventional methods. Show more

Keywords: Edge detection, digital radiography images, real images, noise images

DOI: 10.3233/JIFS-179713

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2020

Authors: Yanmaz, Ozgur | Turgut, Yakup | Can, Emine Nisa | Kahraman, Cengiz

Article Type: Research Article

Abstract: Classical multi-criteria decision making (MCDM) methods have been extended to their fuzzy versions under uncertainty in the literature. Besides, these ordinary fuzzy MCDM methods have been further extended to their new versions through the recently developed types of ordinary fuzzy sets. This study extends the evaluation based on distance from the average solution (EDAS) method by using interval-valued Pythagorean fuzzy numbers to solve fuzzy multi-criteria group decision- making problems with a larger membership domain providing more flexibility. An illustrative example of the car selection problem is given to show the effectiveness and applicability of the proposed model and results are …compared with intuitionistic interval-valued fuzzy EDAS method. A sensitivity analysis is also performed to reveal the effect of the weights on alternative rankings. Show more

Keywords: EDAS, pythagorean, fuzzy, car selection

DOI: 10.3233/JIFS-182667

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2020

Authors: Arif, Muhammad | Wang, Guojun | Peng, Tao | Balas, Valentina Emilia | Geman, Oana | Chen, Jianer

Article Type: Research Article

Abstract: Vehicular Ad-hoc Networks (VANETs) have unique capabilities and are associated with Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communications to reduce traffic dynamics and provide support to the providers and the drivers to rely on security and communication alerts. Our proposed method is only supported by V2V and V2I communications and neighbourhood localization records, which are summarized in an adaptive adjustment of the forwarded information related to the messages(data communication). We optimize these message information, and only the relevant information is transmitted to the relevant vehicles, and all other unnecessary information is terminated. Because, irrelevant information causes many problems, like unnecessary resource …utilization and energy consumption. In this case, the driver will be promptly reminded to take appropriate measures. A swarm intelligence algorithm such as the Artificial Bee Colony (ABC) is utilized to optimize the communication mechanism. In this paper, we present a high-level framework for ABC based on the fuzzy logic for the VANETs. We prove the validity and efficiency of the proposed method. Our proposed method achieves higher optimization accuracy in information broadcasting with minimum delay and with the lowest energy consumption. Show more

Keywords: Communication, vehicles, optimization, fuzzy logic, ad-hoc network, artificial bee colony

DOI: 10.3233/JIFS-179697

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2020

Authors: Sujitha, P. | Simon, Philomina

Article Type: Research Article

Abstract: For the last three decades human activity recognition has shown a huge technological advancement due to less expensive RGB-D cameras and the increase in the large volume of video data. As a result of the increase in number of surveillance cameras, manual annotation becomes difficult and need for automatic recognition and annotation of video arises. In this paper, we introduce a computationally and storage efficient method for recognizing human activities from depth videos and a new frame selection method based on the mean value of motion energy. We extract normal vectors from the points in the boundary curve. Then polynormals …are obtained by sequentially attaching the normals from a neighborhood of each of the points in the boundary curve. These polynormals from a spatio-temporal cuboid constructed from the input video and it is pooled to form the Super Normal vectors. These Super Normal vectors are the final feature vectors, which are given as input to the classifier. The classifier used is lib-linear SVM. The results on MSRAction3D dataset show that the algorithm we put forward is fast and the accuracy obtained is comparable with the existing methods. The method which we proposed here gives an accuracy of 88% while taking whole frames and 89.82% when frame selection method is applied. The proposed method is also tested on UTD-MHAD dataset. Show more

Keywords: ??

DOI: 10.3233/JIFS-179706

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-9, 2020

Authors: Sourav, R. | Dayanand, V. | Gowtham, R. | Sini Raj, P. | Senthilkumar, M.

Article Type: Research Article

Abstract:  Human-Elephant Conflict (HEC) and its mitigation have always been a serious conservation issue in India. It occurs mainly due to the encroachment of forests by humans as part of societal development. Consequently, these human settlements are highly affected by the intrusion of wild elephants as they cause extensive crop-raiding, injuries and even death in many cases. HEC is a growing problem in rural areas of India which shares a border with forests and other elephant habitats. Based on the studies, it is very explicit that HEC is an important conservation issue which affects the peaceful co-existence of both humans and …elephants near the forest areas. The desirable solution for this problem would be to facilitate co-existence among humans and elephants, but this often fails because of technical difficulties. Hence, this paper presents an end-to-end technological solution to facilitate smoother coexistence of humans and elephants. The proposed work deploys a live video surveillance system along with deep learning strategies to effectively detect the presence of elephants. From the numerical analysis, it is revealed that the post-training accuracy of the deep learning model used in the proposed approach is evaluated at 98.7% and outperforms an an out-of-the-box image detector. The layered approach used in the proposed work improves resource management which is a major bottleneck in real-time deployment scenarios. Show more

Keywords: Human elephant conflict, machine learning, convolutional neural network, support vector machine

DOI: 10.3233/JIFS-179710

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-8, 2020

Authors: Shiji, T. P. | Remya, S. | Lakshmanan, Rekha | Pratab, Thara | Thomas, Vinu

Article Type: Research Article

Abstract: Intelligent lesion detection system for medical ultrasound images are aimed at reducing physicians’ effort during cancer diagnosis process. Automatic separation and classification of tumours in ultrasound images is challenging owing to the low contrast and noisy behavior of the image. A Computer aided detection (CAD) system that automatically segment and classify breast tumours in ultrasound (US) images is proposed in this paper. The proposed method is invariant to scale changes and does not require an operator defined initial region of interest. Wavelet modulus maxima points of the US image are analyzed to extract the tumour seed point. The lesions segmented …using a region-based approach are classified using a support vector machine (SVM) classifier. Evaluation of various performance measures show that the performance of the proposed CAD system is promising. Show more

Keywords: Breast ultrasound, Shearlet transform, tumour detection, wavelet modulus maxima, SVM classifier

DOI: 10.3233/JIFS-179709

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2020

Authors: Yang, Xiangfeng | Ni, Yaodong

Article Type: Research Article

Abstract:  Along with increasing and extension of uncertain set theory in decision making, ordering and ranking of uncertain sets have significantly become relevant, that is, an uncertain set needs to be evaluated and compared with the others. This paper introduces the size relation between two uncertain sets. It derives a formula to calculate the uncertain measure that an uncertain set is less than or equal to another one. Moreover, some examples are given to illustrate the size relation of uncertain sets. Finally, clustering is proposed as an application.

Keywords: Uncertainty theory, uncertain set, membership function, size relation, clustering

DOI: 10.3233/JIFS-190342

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-7, 2020

Authors: Ramachandran, Sivakumar | Kochitty, Shymol | Vinekar, Anand | John, Renu

Article Type: Research Article

Abstract: The identification of landmark features such as optic disc is of high prognostic significance in diagnosing various ophthalmic diseases. A retinal fundus photograph provides a non-invasive observation of the optic disc. The wide variability present in fundus images poses difficulties in its detection and further analysis. The reported work is a part of the fundus image screening for the diagnosis of Retinopathy of Prematurity (ROP), a sight threatening disorder seen in preterm infants. The diagnostic procedure for this disease estimates blood vessel tortuosity in a pre-defined area around the optic disc. Hence accurate optic disc localization is very important for …the disease diagnosis. In this paper, we present an optic disc localization technique using a deep neural network based framework. The proposed system relies on the underlying architecture of YOLOv3, a fully convolutional neural network pipeline for object detection and localization. The new approach is tested in 10 different data sets and has achieved an overall accuracy of 99.25%, outperforming other deep learning-based OD detection methods. The test results guarantees the robustness of the proposed technique, and hence may be deployed to assist medical experts for disease diagnosis. Show more

Keywords: Optic disc, deep learning, convolutional neural network, retinal images, ROP diagnosis

DOI: 10.3233/JIFS-179708

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2020

Authors: Alpana, | Chand, Satish

Article Type: Research Article

Abstract: Coal is a primary natural resource of fuel that is efficiently used for electricity generation, steel or iron production, and household usage. Characterization is needed for industries to understand the quality of coal before shipping. Currently, industries follow chemical, microscopical, and machine-based analysis as the gold standard for coal characterization. These conventional analyses of coal are an indispensable method over the years and have tested by high skilled petrologists. Though, these types of optical or machine-dependent recognition of coal category samples are quite slow, expensive, and restricted by subjective analyses with less accuracy. The main aim of this research is …to propose an accurate, time, and cost-effective machine learning-based automated characterization system by analyzing coal color and textural features. This paper comes up with a quantitative approach toward the characterization of dissimilar types of coal for better utilization in industries. The proposed ensemble learning coal characterization method provides an accuracy of around 97% and takes less computational time than conventional methods. Hence, the proposed automated coal characterization system provides support to industries in the development of computer-aided assessment of coal category samples. Show more

Keywords: Coal, HSV, GLCM, image processing, machine learning

DOI: 10.3233/JIFS-179707

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2020