Journal of Intelligent & Fuzzy Systems - Volume 36, issue 5

Authors: Ahmad, Ibtihaj | Hussain, Farhan | Khan, Shoab Ahmad | Akram, Usman | Jeon, Gwanggil

Article Type: Research Article

Abstract:  3D Cardiac Magnetic Resonance Imaging (MRI) is widely used for the diagnosis of cardiac diseases such as congenital heart defect, left ventricular hypertrophy and left atrium hypertrophy etc. It is one of the noninvasive technique to examine cardiac anatomy. However this technique is semi- automatic, i.e. the images obtained directly from MRI machine have to be segmented manually. This includes the segmentation of chambers and vessels, which is quite complex and requires specialized technical knowledge. Without proper segmentation, it is extremely difficult for medical staff to examine the data. This paper suggest a fully automatic method for cardiac chamber segmentation …(Left Atrium and Left Ventricle pair) in 3D cardiac MRI based on artificial intelligence. The proposed method identifies the junction of Left Atrium (LA) and Left Ventricle (LV) using neural networks. The features used for this purpose are based on shape, size and position. Then it uses traditional methods to track and stack the upper and lower slices based on neighborhood. I.e. a 3D model of the segmented LA and LV is reconstructed from the 2D format. This enhanced 3D image model helps in deducing quality information for the diagnosis of various heart diseases. The proposed algorithm shows acceptable performances for all planes of LV and LA. We have achieved 91.57% mean segmentation accuracy. The proposed algorithm is not effected by the thickness of the slices. It is simple and computationally less intensive than existing algorithms. Show more

Keywords: Cardiac MRI segmentation, left ventricle segmentation, left atrium segmentation, heart chamber segmentation, format conversion, image quality enhancement

DOI: 10.3233/JIFS-169974

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 5, pp. 4153-4164, 2019

Authors: Arunkumari, T. | Indragandhi, V.

Article Type: Research Article

Abstract:  In this manuscript a DC non-isolated converter model with high static voltage gain module is presented. The proposed converter has the feature of stable frequency and stable output voltage. It also achieves high voltage conversion, high efficiency, low voltage stress and less switching loss. The voltage tripler technique is implemented in the proposed model. The designed converter model attains high static gain with reduced duty cycle. The proposed single switch converter is controlled by fuzzy-PI controlled technique. The working process of the converter under Continuous Conduction Mode (CCM) is explained. The 30 V input source is boosted up to 400 V. The …simulation of the presented converter is done with MATLAB simulink. The hardware prototype is also tested and results are analysed. Show more

Keywords: Continuous conduction mode, duty-cycle, high voltage gain, fuzzy-PI, voltage tripler

DOI: 10.3233/JIFS-169975

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 5, pp. 4165-4176, 2019

Authors: Sukor, Abdul Syafiq Abdull | Zakaria, Ammar | Rahim, Norasmadi Abdul | Kamarudin, Latifah Munirah | Setchi, Rossi | Nishizaki, Hiromitsu

Article Type: Research Article

Abstract:  Accurate activity recognition plays a major role in smart homes to provide assistance and support for users, especially elderly and cognitively impaired people. To realize this task, knowledge-driven approaches are one of the emerging research areas that have shown interesting advantages and features. However, several limitations have been associated with these approaches. The produced models are usually incomplete to capture all types of human activities. This resulted in the limited ability to accurately infer users’ activities. This paper presents an alternative approach by combining knowledge-driven with data-driven reasoning to allow activity models to evolve and adapt automatically based on users’ …particularities. Firstly, a knowledge-driven reasoning is presented for inferring an initial activity model. The model is then trained using data-driven techniques to produce a dynamic activity model that learns users’ varying action. This approach has been evaluated using a publicly available dataset and the experimental results show the learned activity model yields significantly higher recognition rates compared to the initial activity model. Show more

Keywords: A ctivity recognition, knowledge-driven approaches, data-driven approaches, activity model, hybrid reasoning

DOI: 10.3233/JIFS-169976

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 5, pp. 4177-4188, 2019

Authors: Natarajan, Sivaramakrishnan | Vairavasundaram, Subramaniyaswamy | Ravi, Logesh

Article Type: Research Article

Abstract: Rapid web growth and associated applications have proven of colossal importance for recommender systems. In the current digital world, a recommender system aims to acquire high-level prediction-based accuracy. However, many studies have suggested diversity-based recommendations are required for high-level accuracy. Group recommendation systems (GRS) recommend lists of items to a group of users according to their social activities, such as planning for a holiday tour, watching movies, etc. Using GRS, preferences/choices shared by users affected all the available aggregation with GRS leads to information loss and negatively affects ‘diversity.’ To handle the problem of ‘information loss,’ which is caused by …aggregation, this paper proposes fuzzy-based GRS and argues that communicating such hesitant information will prove beneficial to generating recommendations. To find the valuable suggestions, greater focus must be dedicated to avoiding lack of variety and interest in the complete list of recommendations. In this article, we propose a novel Parallel Computing Group Recommendation System, which quantifies different approaches, chooses the right approach for group recommendation, and quickly generates optimal results. This proposed approach is an ensemble model of parallel ranking and matrix factorization that facilitates a diversified group recommendation list. Experimental evaluation signals that our model achieves higher diversity positively packed with user satisfaction. Show more

Keywords: Recommender systems, matrix factorization, collaborative filtering, parallel computing, fuzzy sets, diversity introduction

DOI: 10.3233/JIFS-169977

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 5, pp. 4189-4199, 2019

Authors: Tan, Yao | Shum, Hubert P. H. | Chao, Fei | Vijayakumar, V. | Yang, Longzhi

Article Type: Research Article

Abstract: Fuzzy inference systems have been successfully applied to many real-world applications. Traditional fuzzy inference systems are only applicable to problems with dense rule bases covering the entire problem domains, whilst fuzzy rule interpolation (FRI) works with sparse rule bases that do not cover certain inputs. Thanks to its ability to work with a rule base with less number of rules, FRI approaches have been utilised as a means to reduce system complexity for complex fuzzy models. This is implemented by removing the rules that can be approximated by their neighbours. Most of the existing fuzzy rule base generation and simplification …approaches only target dense rule bases for traditional fuzzy inference systems. This paper proposes a new sparse fuzzy rule base generation method to support FRI. In particular, this approach uses curvature values to identify important rules that cannot be accurately approximated by their neighbouring ones for initialising a compact rule base. The initialised rule base is then optimised using an optimisation algorithm by fine-tuning the membership functions of the involved fuzzy sets. Experiments with a simulation model and a real-world application demonstrate the working principle and the actual performance of the proposed system, with results comparable to the traditional methods using rule bases with more rules. Show more

Keywords: Fuzzy inference, fuzzy interpolation, sparse rule base generation, curvature

DOI: 10.3233/JIFS-169978

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 5, pp. 4201-4214, 2019

Authors: Wang, Jiaqi | Karuppiah, Marimuthu | Kumari, Saru | Kong, Zhiyin | Shi, Wenbo

Article Type: Research Article

Abstract:  In recent years, a large number of spectrum mechanisms have been proposed, but these mechanisms ignore the security issues that arise during the design of the mechanism. In this paper, two secure models for sealed-bid spectrum auction are given based on Wang’s generic spectrum auction mechanism. One is the basic model and another improved model based on the basic model is proposed, which maximizes Social welfare while it is a Privacy-preserving Spectrum auction mechanism with public Verification namely SPSV. The SPSV scheme achieves the properties of maximizing the social welfare but also, by using the double paillier cryptosystem, it is …privacy-preserving for bidders’ bids without revealing any sensitive information to auctioneer or agent during the entire spectrum auction. Oblivious transfer is applied to ensure the anonymity of bidders. Furthermore, the use of inequality comparison proof also provides the public verification of winner group to verify the comparison relationship between winner groups and losing groups. At last, the performance analysis are given. Show more

Keywords: Spectrum auction, social welfare, privacy-preserving, public verification

DOI: 10.3233/JIFS-169979

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 5, pp. 4215-4226, 2019

Authors: Veeramuthu, A. | Meenakshi, S. | Ashok Kumar, K.

Article Type: Research Article

Abstract: Brain tumor image segmentation is process of locating the interesting area in terms of objects, like tumor and extracting it for the further process of the image and getting the boundaries of the image for analysis. The bio-medical brain tumor image segmentation is a great challenging field for the today world active researchers with the standardized image datasets and various metrics used for evaluating and comparing the performance of the new algorithm with existing segmentation algorithms. In recent development, these problems are addressed using various image manipulation tools and rapid growth of computer hardware enhancement. Image segmentation was done in …three ways: (1) Manual-based (2) Semi-automated-based (3) Fully automated-based. But still be a short of research in the field of brain tumor segmentation and accurate identification of tumor cells. To overcome all the above-mentioned challenges and complexity of the brain tumor segmentation, it need to understand the pre-processing of the image like, registering the image, correction of bias in image, and non-brain tissue removal. In this paper, we propose a new methodology for segmenting the brain tumor from the affected brain image in a significantly efficient way by using deep learning method. Show more

Keywords: Segmentation, metrics, manual-based, semi-automated, automated, deep learning

DOI: 10.3233/JIFS-169980

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 5, pp. 4227-4234, 2019

Authors: Asaithambi, Sasikumar | Rajappa, Muthaiah | Ravi, Logesh

Article Type: Research Article

Abstract: Guaranteeing the reliability of cyber-physical systems (CPS) requires analog integrated circuits for correct functioning. Analog integrated circuits capture the continuous signal and amplify the signal for further processing in CPS applications. This paper presents the hybrid swarm intelligence based approach for determining the optimal transistors sizes and bias current values of CMOS differential amplifier and an operational amplifier. We proposed the simplex search based global optimization method called a hybrid grey wolf optimization (GWO) for solving amplifiers circuit sizing problems. Simplex and GWO techniques were combined to improve the local search capabilities of the optimization method. Our main aim is …to optimize the transistor size and bias current values using hybrid GWO algorithm for an optimal design of the CMOS amplifiers. CMOS 180 nm technology was utilized to finding the circuit performance using proposed optimization approach. Simulation result shows that the proposed method provides the better result for circuit performance parameters such as DC gain, phase margin, unity gain bandwidth and power dissipation. Show more

Keywords: Cyber-physical systems, CMOS amplifiers, circuit design optimization, grey wolf optimization, simplex method, circuit sizing

DOI: 10.3233/JIFS-169981

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 5, pp. 4235-4245, 2019

Authors: Ramya, K.C. | Vinoth Kumar, K. | Irfan, Muhammad | Mesforush, Shaghayegh | Mohanasundaram, K. | Vijayakumar, V.

Article Type: Research Article

Abstract: The demand for electricity is increasing very rapidly due to the vast development in industrialization. Generally, at present, for electric power generation, Renewable Energy Sources (RES) are considered as a better alternative option than conventional energy sources. Among the various RES, Solar and wind energy are available in abundantly and hence they can be recognized as a reliable source of power generation. More over this type of Hybrid solar and wind energy systems can be used for rural electrification and modernization of remote area. However, there will be problem of power quality issues such as harmonics, sag etc., hence, this …work proposed a novel methodology to improve the power quality of the grid system interfaced with hybrid wind-solar system. In this proposed methodology, shunt active power filter with fuzzy logic based control strategy is introduced to minimize the harmonics present in the system. The proposed topology is validated through dynamic simulation using the MATLAB/Simulink Power System Toolbox. Simulation results demonstrate that the proposed system injects power into the grid from hybrid system with harmonic mitigation. This approach also eliminates the need of additional power conditioning equipment for the improvement of power quality. Show more

Keywords: Power quality, active power filter, fuzzy controller, harmonics compensation

DOI: 10.3233/JIFS-169982

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 5, pp. 4247-4256, 2019

Authors: Zhang, Xiaodan | Gong, Yanping | Spece, Michael

Article Type: Research Article

Abstract: The trustworthiness of consumer evaluation is an important prerequisite for reference to make a decision. Hence, a trust evaluator must recognize biased information (referred to as false recommendation), and do so dynamically. Drawing on the sociological concept of trust fusion, a new trust evaluating model is proposed, one built upon (i) Bayesian updating of the trust evaluation with each transaction, and (ii) the identification and correction of purposefully misleading evaluations according to improved evidence theory. Simulations show that the algorithm’s trust value increases slowly with successful transactions, but drops rapidly with a failed transaction, capturing the notion that trust is …hard to establish, yet easy to destroy. Further simulations demonstrate the model has good robustness and error tolerance of trust evaluation against false recommendations at varying levels of deception. The algorithm effectively and robustly compensates for deception. Show more

Keywords: Trust update mechanism, online trust, false recommendation, trust evaluation

DOI: 10.3233/JIFS-169983

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 5, pp. 4257-4264, 2019