Journal of Intelligent & Fuzzy Systems - Volume 35, issue 2

Authors: Luo, Peicong | Wang, Xiaoying

Article Type: Research Article

Abstract: With the construction and application of large-scale datacenters, the issue of resource allocation in cloud computing becomes a serious concern. Although the current static allocation method can make applications get corresponding resources, there still exist some shortcomings such as resource surpluses or shortages. This kind of problem is more crucial in real-time requirements of mobile cloud computing service. Therefore, it is necessary to establish a forecasting model to predict the future resource demands, and then perform on-demand distribution, which can effectively reduce the unnecessary daily network management fees and address the issues mentioned above. This paper focuses on CPU resource …forecasting, establishing three forecasting models including Markov chain, weighted Markov chain and stacking weighted Markov chain. By comparing and analyzing the experiment results, the most reasonable forecasting model is found and explained. Show more

Keywords: Mobile cloud computing, resource allocation, forecasting model, markov chain, CPU

DOI: 10.3233/JIFS-169675

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 2, pp. 1315-1324, 2018

Authors: Yan, Maoling | Liu, Pingzeng | Zhao, Rui | Liu, Lining | Chen, Weijie | Yu, Xueru | Zhang, Jianyong

Article Type: Research Article

Abstract: With the accelerated process of agricultural modernization, the accurate acquisition of agricultural environmental information has become a major trend. A field microclimate monitoring system based on wireless sensor network is constructed based on the latest concept model of the Internet of things and fuzzy control theory; it mainly composed of data acquisition system, data storage system and visualization platform for big data analysis. The data acquisition system is highly integrated with data acquisition and data transmission to obtain real-time data of farmland environment in different terrain areas, including meteorology, hydrology, soil, growth etc. Wireless transmission will transmit real-time data through …the GPRS network to the big data analysis platform. The big data analysis platform presents the site data information and analyzes the rule of historical data through visualization technology, realizes the meteorological disaster early warning and forecast, and provides effective decision-making service information based on the agricultural fuzzy theory. Finally, we analyze all kinds of hardware interference problems and software defects encountered in the debugging process, and propose new solutions through the experimental data obtained from actual production applications. It has been proved that the field microclimate monitoring system runs steadily and meets the demand of agricultural monitoring. Show more

Keywords: Wireless sensor networks, field microclimate, monitoring system

DOI: 10.3233/JIFS-169676

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 2, pp. 1325-1337, 2018

Authors: Wang, Hua | Wen, Yingyou | Zhao, Dazhe

Article Type: Research Article

Abstract: Wireless Sensor Networks (WSNs) are vulnerable to various localization attacks where attackers intended to provide improper beacons or manipulate the location determination. Attack classification for localization in WSNs is not only the condition, prerequisite and premise of threat analysis, but, more significantly, a vital part of the security anomaly detection. In this paper, a localization attack recognition method using a deep learning architecture was proposed. To enhance the classification performance, a good feature representation was established through combining location features with topological indexes based on the complex network theory. The ability of Stacked Denoising Autoencoder (SDA) to learn the underlying …features from input data was exploited. Back-propagation algorithm was performed to update weights through a stochastic gradient descent method. The proposed approach could efficiently distinguish the Sybil attacks, Replay attacks, Interference attacks, Collusion attacks and normal beacons. Extensive experiments demonstrated that the proposed algorithm can achieve an average classification accuracy of 94.39% and was more robust and efficient even in the existent of huge baneful beacons. Show more

Keywords: Security, wireless sensor networks, attack classification, deep learning

DOI: 10.3233/JIFS-169677

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 2, pp. 1339-1351, 2018

Authors: Xu, Zhi | Ding, Hongwei | Liu, Qianlin | Yang, Zhijun | Bao, Liyong | Zhan, Gang

Article Type: Research Article

Abstract: In order to solve the contradiction between wireless network application requirements and increasingly scarce spectrum resources, cognitive wireless network technology emerged. Based on the characteristics of wireless network nodes and combining multiple hybrid control strategies, this paper proposes a multi-priority dual-clock probability detection CSMA(MPDCPD-CSMA) protocol with a monitoring mechanism. The field-programmable gate array (FPGA) hardware circuit is used as an experimental research platform for the first time. Cognitive wireless network MAC protocol design and implementation. The design took full advantage of the flexibility of the FPGA, using a hardware description language Verilog HDL and schematic input combined with the QuartusII9.0 …circuit design. By comparing the statistical values of the circuit system with the theoretical values, it is verified that the design has the characteristics of good real-time performance, high reliability, and strong portability. It can effectively reduce system node energy consumption, improve system throughput, and can be applied to wireless networks. Show more

Keywords: cognitive wireless network, hybrid control strategy, field programmable gate array, schematic input, throughput

DOI: 10.3233/JIFS-169678

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 2, pp. 1353-1361, 2018

Authors: Wu, Guangsheng | Liu, Juan | Min, Wenwen

Article Type: Research Article

Abstract: Uncovering the potential treatment associations of the drug-disease pairs is a research focus of drug repositioning. However, it is time-consuming and costly to verify the potential treatment relation between a drug and a disease by “wet” experiment methods. Fortunately, along with the accumulation of large amount of data and the development of machine learning methods, lots of computational methods to predict the drug-disease treatment associations have been proposed. In order to build the prediction model based on machine learning techniques, both plenty of positive and negative training samples are required. In the case of biological experiments, however, we can only …verify whether a drug cures a disease, yet we are unable to answer whether a drug definitely cannot treat a disease. Correspondently, there are only positive and unlabeled samples in the data. Being lack of validated negative samples, most computational methods assume the unlabeled samples to be negative ones and randomly select some unlabeled samples and positive samples to train the prediction models. Obviously, the unlabeled samples are not necessarily negative, and some of them may be positive just remaining uncovered via experiments. In this paper, we propose a method called PUDrDi which directly make use of the positive and unlabeled samples to train a Biased-SVM classifier. Moreover, we combine the drug and disease features together to represent a drug-disease pair, in which we use chemical substructures and symptoms as the features to represent drugs and diseases respectively. The experiment results demonstrate that PUDrDi outperforms some other methods. The case study further shows the practicality of PUDrDi. Show more

Keywords: Drug repositioning, drug-disease treatment associations, unlabeled samples, machine learning, positive-unlabeled learning

DOI: 10.3233/JIFS-169679

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 2, pp. 1363-1373, 2018

Authors: Vinod Kumar, K. | Ranvijay,

Article Type: Research Article

Abstract: As the performance of modern multi-core processors is significantly increases, the total energy consumption in the systems also increases drastically. Dynamic Voltage and Frequency Scaling (DVFS) is considered as one of the efficient schemes for achieving the aim of energy saving. In this paper, we consider scheduling a task set, whose release times, deadlines and execution requirements are given, on DVFS-enabled multi-core processor system. Our main aim is to meet the execution requirements of all the tasks, and to minimizethe overall energy consumption on the processor with effective utilization of resources. Instead of seeking optimal solutions with high complexity, we …aim to design algorithms suitable for real-time systems, with good performances. We come up with a simple algorithm for task scheduling and energy awareness by considering deadline constraint. We further consider the distribution of deadline and task scheduling, which guarantee that all tasks meet their execution requirements, and tries to minimize the overall energy consumption. Case based simulations for various applications and task characteristics and evaluations using a practical processor’s power configuration indicate that our proposed algorithm has a less energy consumption performance and good resource utilization in terms of saving processor energy, though it has low complexity. Besides, the proposed algorithm is easy to be implemented in practical systems. Show more

Keywords: Realtime, DVFS, energy efficiency, DAG, multicore, resoure utilization

DOI: 10.3233/JIFS-169680

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 2, pp. 1375-1385, 2018

Authors: Bhuvanesh, A. | Jaya Christa, S.T. | Kannan, S. | Karuppasamy Pandiyan, M. | Gangatharan, K.

Article Type: Research Article

Abstract: Generation Expansion Planning (GEP) aims to define the least cost capacity expansion plan to meet forecasted demand inward a pre-defined reliability criterion and emission constraint over a planning horizon. This paper presents the application of Differential Evolution (DE), Opposition-based Differential Evolution (ODE) and Self-adaptive Differential Evolution (SaDE) algorithms to GEP problem, where the power generating system of an Indian state Tamil Nadu is taken as study region. GEP problem has been solved for short-term (6-years) and long-term (12-years) planning horizon by considering least-cost, reliable supply and lowest emission to the environment using DE, ODE and SaDE also validated by Dynamic …Programming (DP). GEP problem is solved for seven diverse cases such as, Case 1: Base case, Case 2: GEP with Energy Conservation (EC), Case 3: GEP with high penetration of Renewable Energy Sources (RES), Case 4: GEP with penalty costs on emissions from high emission plants (HEP), Case 5: GEP with energy storage technologies (EST), Case 6: Combination of Cases 2, 3&4 and Case 7: Combination of Cases 2, 3, 4&5. The results simultaneously provide the type and capacity of each power plant need to be expanded in each year of the planning horizon at least cost. Show more

Keywords: DE, emission cost, GEP, ODE, RES, SaDE and Tamil Nadu electricity sector

DOI: 10.3233/JIFS-169681

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 2, pp. 1387-1398, 2018

Authors: Jha, Sunil Kr. | Ahmad, Zulfiqar | Crowley, David E.

Article Type: Research Article

Abstract: Microbial activities are the indicators of soil strength. The present study explores the development of efficient predictive modeling systems for the estimation of specific soil microbial dynamics, phosphate solubilization (PS), bacterial population (BP), and 1-aminocyclopropane-1-carboxylate ACC-deaminase activity. More specifically, fuzzy c-means clustering (FCM)-FIS, Wang and Mendel’s (WM) fuzzy inference systems (FIS), adaptive neuro-fuzzy inference system (ANFIS), and subtractive clustering (SC) and have been implemented with the objective to achieve the best estimation accuracy of microbial dynamics. Experimental measurements were performed using controlled pot experiment using minimal salt media. Three experimental parameters, including temperature, pH, and incubation period have been …used as inputs of FCM-FIS, SC-FIS, ANFIS, and WM-FIS methods. The SC-FIS method has the best estimation accuracy for the PS (R2 of 0.99) and BP (R2 of 0.94) than the rest three FIS methods. Show more

Keywords: FCM-FIS, WM-FIS, ANFIS, SC-FIS, phosphate solubilizing bacteria, bacterial population, ACC-deaminase activity

DOI: 10.3233/JIFS-169682

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 2, pp. 1399-1406, 2018

Authors: Zhu, Lei | Liang, Xuefei | Wang, Lei | Wu, Xingrong

Article Type: Research Article

Abstract: Pythagorean fuzzy sets, which is based on intuitionistic fuzzy sets (IFSs), is an important tool to solve problems and has attracted a large number of researchers in different fields. As we know, studies have focused on interval-valued Pythagorean fuzzy set and aggregated operators. However, few studies focus on point operators. This paper introduces and discusses what is the pythagorean fuzzy point operators, study their properties and relationships, which is seen as the extensions of intuitionistic fuzzy sets. The uncertainty regarding to Pythagorean fuzzy set could be decreased if we use the pythagorean fuzzy point operators. In the end, pythagorean fuzzy …multi-attributes decision making based on analytic hierarchy procedure is put forward to cope with the complicated MADM (multi-attributes decision making) issues which can be very useful when we face the multi-level analysis. Show more

Keywords: Point operators, multi-criteria decision making, pythagorean fuzzy set, analytic hierarchy process

DOI: 10.3233/JIFS-169683

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 2, pp. 1407-1418, 2018

Authors: Seiti, H. | Hafezalkotob, A. | Najafi, S.E. | Khalaj, M.

Article Type: Research Article

Abstract: Failure Modes and Effects Analysis (FMEA) is a common technique used in several manufacturing and service industries for eliminating failures and potential problems using the evaluation of failure modes of a new or an existing product, process, or system. The risk analysis is carried out by calculating the Risk Priority Number (RPN), which is a product of three factors, Occurrence (O), Severity (S), and Detection (D). In the literature, modeling uncertainties are used to improve the FMEA process and overcome the inefficiencies of traditional RPN. One of the common uncertainties in FMEA is the epistemic uncertainty that is essentially modeled …using the Dempster-Shafer theory (DST). In this study, a novel risk-based fuzzy evidential approach is proposed by using interval-valued DST and fuzzy axiomatic design (FAD) to assess the risk of failure modes with fuzzy belief structures. The efficiency of the proposed model was investigated with the help of an example and the results are compared with riskless evaluations. Reviewing the results shows the information content of failure modes decrease relatively when risk is taken into account, in fact failure modes become relatively more critical than those in the case where no risk is considered. Show more

Keywords: Fuzzy belief structure, FMEA, risk of evaluations, fuzzy information axiom

DOI: 10.3233/JIFS-169684

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 2, pp. 1419-1430, 2018

Authors: Zhu, Xuhui | Ni, Zhiwei | Zhang, Gongrang | Jin, Feifei | Cheng, Meiying | Li, Jingming

Article Type: Research Article

Abstract: Diversity and accuracy of classifiers are widely recognized to be two key factors for a successful ensemble. The increase of diversity among classifiers must lead to the decrease of the average accuracy of that, and vice verse. Therefore, finding a tradeoff between the diversity and the accuracy of classifiers can make the ensemble perform the best. Existing ensemble pruning approaches always find the tradeoff using diversity measures and heuristic algorithms separately. Those ensemble pruning approaches based on diversity measures, using different strategies, cannot exactly find the tradeoff; Those approaches based on heuristic algorithms cannot also exhaustively search for that. To …address the issue, Combining Weak-link Co-evolution Binary Artificial Fish swarm algorithm and Complementarity measure for Ensemble Pruning (CWCBAFCEP) is proposed using a combination of the proposed Weak-link Co-evolution Binary Artificial Fish Swarm Algorithm (WCBAFSA) and COMplementarity measure (COM). First, the classifiers in a constructed initial pool of classifiers are pre-pruned using COM, which significantly reduce the computational complexity of ensemble pruning. Second, the final ensemble extracted from the remaining classifiers after pre-pruning can be efficiently achieved using the proposed WCBAFSA. Experimental results on 25 datasets from the UCI Machine Learning Repository demonstrate that CWCBAFCEP performs much better than the original ensemble and other state-of-the-art ensemble pruning approaches, and that its effectiveness and efficiency. It provides a new research idea for ensemble pruning. Show more

Keywords: Artificial fish swarm algorithm, weak-link co-evolution mechanism, complementarity measure, ensemble pruning

DOI: 10.3233/JIFS-169685

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 2, pp. 1431-1444, 2018

Authors: Singh, Sapam Jitu | Roy, Sudipta | Singh, Khumanthem Manglem | Khelchandra, Thongam

Article Type: Research Article

Abstract: This paper presents a technique of motion planning of robot using Fuzzy Method and Genetic Algorithm along with Three Path concept in a dynamic environment which contains both static and dynamic obstacles. The algorithm is divided into two phases. In the first phase , a path or two is generated by using Three Path Method or Fuzzy-GA while considering static obstacles only. The information about the distances and angles of obstacles from the robot is acquired by using the concept of Three Path. A collision free path is selected by using Three Path concept. When all the three paths are …blocked by static obstacles Fuzzy Method is used for obstacles avoidance. Genetic Algorithm is used to find optimal range of the linguistic values of the variables for the membership functions. In the second phase , the moving obstacles are avoided along the path/paths generated in the first phase by considering the velocities of the moving obstacles and the velocity of the robot. Results show that the technique of motion planning of mobile robots using Fuzzy-GA along with Three Path concept is computationally efficient as compared to other hybrid methods for motion planning in dynamic environment. Show more

Keywords: Mobile robots, motion planning, fuzzy computing, Genetic Algorithm, dynamic environment, obstacle avoidance

DOI: 10.3233/JIFS-169686

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 2, pp. 1445-1457, 2018

Authors: Amudha, J. | Nandakumar, Hitha

Article Type: Research Article

Abstract: The general characteristics observed in Autism is decrease in communication skill, interaction and shows behavioral changes. The reasons for these can be studied by understanding their visual sensory processing. The research work presented here uses image stimuli to study the behavior in children by understanding when and where they look. A Fuzzy based Eye Gaze Point estimation (FEGP) has been proposed which observes the gaze coordinates of the child, analyze the eye gaze parameters to assess the difference in visual perception of an autistic child in comparison to a normal child. The approach helps to identify the visual behavior difference …in autistic children with a performance level indicator, visualization and inferences that can be used to tune their learning programs with an attempt to meet their counterparts. Show more

Keywords: Autism, fuzzy system, visual perception, cognitive visual task, eye tracking

DOI: 10.3233/JIFS-169687

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 2, pp. 1459-1469, 2018

Authors: Li, Yaohui | Zhang, Quanyou | Wu, Yizhong | Wang, Shuting

Article Type: Research Article

Abstract: A Kriging-based global optimization method is proposed to solve black-box unconstrained design problems in this work. Firstly, the non-convex Kriging optimization problem is converted into the two convex programing problems by the canonical dual transform to quickly get global optimal solution. Then, PSO (Particle Swarm Optimization) algorithm is adopted to find next promising design point by exploring and optimizing the transformed problems. The proposed method not only reduces the computational burden, but also effectively balances local and global search behavior. Some well-known numerical test functions and a real engineering example are investigated to illustrate that the presented method can further …enhance the feasibility, validity and robustness of the optimization process in contrast with other global optimization algorithms. Show more

Keywords: Surrogate model, kriging, global optimization, dual transformation

DOI: 10.3233/JIFS-169688

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 2, pp. 1471-1482, 2018

Authors: Kostopoulos, Georgios | Karlos, Stamatis | Kotsiantis, Sotiris | Ragos, Omiros

Article Type: Research Article

Abstract: Nowadays, Semi-Supervised Learning lies at the core of the Machine Learning field trying to effectively exploit unlabeled data as much as possible, together with a small amount of labeled data aiming to improve the predictive performance. Depending on the nature of the output class, Semi-Supervised Classification and Semi-Supervised Regression constitute the basic components of Semi-Supervised Learning. Various studies deal with the implementation of Semi-Supervised Classification techniques in many real world problems over the last two decades in contrast with Semi-Supervised Regression, which is deemed to be a more general and slightly touched case. This survey aims to provide a detailed …review of Semi-Supervised Regression methods and implemented algorithms in recent years. Our in-depth study reveals the relatively few studies that deal with this specific problem. Moreover, we seek to classify these methods by proposing a schema and categorizing all the related methods that have been developed in recent years according to specific criteria. Show more

Keywords: Semi-supervised regression, parametric/non parametric methods, categorization, confidence meters

DOI: 10.3233/JIFS-169689

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 2, pp. 1483-1500, 2018

Authors: Ghosh, Rajdeep | Kumar, Vikas | Sinha, Nidul | Biswas, Saroj Kumar

Article Type: Research Article

Abstract: Brain Computer Interface (BCI) enables us to record and process the information generated by the brain and process them. Due to high variability of the Electroencephalogram (EEG) data, multiple trails are recorded for a particular task. The present work aims to improve the accuracy for motor imagery task classification by selecting the most prominent trail from the multiple trails recorded during motor imagery. In this paper, we propose a novel weight optimization algorithm for common spatial filtering (CSP) using evolutionary algorithms (i.e. cuckoo search algorithm (CSA), firefly algorithm (FA) and gravitational search algorithm (GSA)) to select the most prominent trial …from the multiple trails recorded for feature extraction. The features extracted from the selected trials were thus used for motor imagery task classification. The performance was evaluated on the extracted features from the selected trials using two classifiers namely linear discriminant analysis (LDA) and support vector machines (SVM). It is observed that FA with band power as a feature gives the best performance in comparison to the earlier reported methods i.e. average, error based and alternating direction method of multipliers (ADMM). Show more

Keywords: Brain computer interface, common spatial pattern, cuckoo search algorithm, electroencephalography, firefly algorithm, gravitational search algorithm, linear discriminant analysis, support vector machine

DOI: 10.3233/JIFS-169690

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 2, pp. 1501-1510, 2018

Authors: Lee, Donggil | Kim, Seonghun | Kim, Pyungkwan | Yang, Yongsu

Article Type: Research Article

Abstract: Sea squirts are cultivated mainly in Korea, Japan, and China. Sea squirt sorting during the harvesting process is labor-intensive and time-consuming as there is no automatic sorting technology for sea squirts. In this study, we developed and evaluated an automatic sea squirt sorting algorithm based on sea squirt color information analyzed using the hue-saturation-value (HSV) color model and the regression equation of the projected area and weight of the sea squirt. The developed algorithm recognizes sea squirts during the sorting process based on the threshold range of sea squirt color values and their weight based on measurements of the projected …area. In 100 repeated experiments conducted with mixed products containing sea squirts, mussels, and Styela clava , the average sea squirt recognition rate of the developed algorithm was 98.5%, and the sorting performance based on animal weight and grade was ≥95.5% at an average speed of 1,050 kg/h. Show more

Keywords: Sea squirt, sorting, image processing, HSV color model

DOI: 10.3233/JIFS-169691

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 2, pp. 1511-1518, 2018

Authors: Bharti, Shambhu Shankar | Gupta, Manish | Agarwal, Suneeta

Article Type: Research Article

Abstract: Voice activity detection (VAD) identifies the presence/absence of human speech in a frame of a given speech signal. Presence/Absence of human speech can easily be identified in clean speech signal but its accuracy decreases with decreasing Signal-to-Noise ratio (SNR) value. Robust VAD helps to enhance the efficiency of speech signal based automated applications like speech enhancement, speaker identification, hearing aid devices etc. In this paper, a new feature of speech signal- “Peak of Log Magnitude Spectrum (PLMS)” is introduced and used for VAD. This newly defined feature PLMS along with three existing acoustic features(MFCC;RASTA-PLP and Formant Frequency) are used to …train SVM classifier for VAD. Experimentally, it is found that coefficients of PLMS play most prominent role. Experimentally, it is also observed that the accuracy of the trained SVM classifier for VAD is the highest when compared with other state of the art methods (Sohn VAD and VAD G.729). Show more

Keywords: VAD, PLMS, SVM, MFCC, RASTA-PLP, Formant Frequency

DOI: 10.3233/JIFS-169692

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 2, pp. 1519-1530, 2018

Authors: Yadav, Harikesh Bahadur | Kumar, Sumit | Kumar, Yugal | Yadav, Dilip Kumar

Article Type: Research Article

Abstract: Decision-making is very important activities in the various applications of science, engineering, and technology. A decision can be derived in three manners by these applications: (1) by developing a mathematical model, (2) taking domain experts advice, (3) developing an expert system. However, accurate mathematical model may not be developed for the domain that might not be completely interpreted. Moreover, the problem with the second method is that the human intervention is not possible all the time and the expenditure of hiring a domain expert may be high. Decision-making, using expert system or controller induces great interest among the researchers and …professionals. Expert systems or controllers are capable enough to counter unpredictability, noise, and vagueness. Fuzzy set theory is commonly used in building the expert systems and controllers due to its ease and similarity to human reasoning. Therefore, the proposed approach is based on fuzzy logic for decision making. The proposed model is explained through a case study. The result of the proposed work is compared and judged by the results of earlier studies. The result depicts that the proposed method has a better performance and effectiveness than existing studies. Show more

Keywords: KC2, fuzzy rule, fuzzy decision tree, histogram

DOI: 10.3233/JIFS-169693

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 2, pp. 1531-1539, 2018

Authors: Gupta, Manish | Bharti, Shambhu Shankar | Agarwal, Suneeta

Article Type: Research Article

Abstract: Emotion is a property by which human beings and machines can be differentiated as machines are emotionless while human beings are not. If the emotion of a speaker is recognized then others can interact accordingly. This paper presents a new approach for recognizing all the six basic emotions (Happy, anger, fear, sadness, boredom and neutral) from the speech signals more effectively. To recognize the emotion of a speaker, pitch value and two wavelet packet feature vectors derived from speech signals are used. Principal Component Analysis (PCA) has been applied to reduce the dimension of feature vectors. Random Forest (RF) and …Support Vector Machine (SVM) classifiers are trained separately based on these reduced feature vectors. The experimental results show that the accuracy of emotion recognition with Random Forest classifier is 86.11% while with SVM classifier it is 84.41%. Experimentally, it is also found that clean speech of 1 sec duration is sufficient enough to recognize emotion of the speaker. Show more

Keywords: Pitch, emotions, speech recognition, SVM, Random Forest (RF)

DOI: 10.3233/JIFS-169694

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 2, pp. 1541-1553, 2018

Authors: Kamal, Md. S. | Trivdedi, Munesh C. | Alam, Jannat B. | Dey, Nilanjan | Ashour, Amira S. | Shi, Fuqian | Tavares, João Manuel R.S.

Article Type: Research Article

Abstract: Consensus is a significant part that supports the identification of unknown information about animals, plants and insects around the globe. It represents a small part of Deoxyribonucleic acid (DNA) known as the DNA segment that carries all the information for investigation and verification. However, excessive datasets are the major challenges to mine the accurate meaning of the experiments. The datasets are increasing exponentially in ever seconds. In the present article, a memory saving consensus finding approach is organized. The principal component analysis (PCA) and independent component (ICA) are used to pre-process the training datasets. A comparison is carried out between …these approaches with the Apriori algorithm. Furthermore, the push down automat (PDA) is applied for superior memory utilization. It iteratively frees the memory for storing targeted consensus by removing all the datasets that are not matched with the consensus. Afterward, the Apriori algorithm selects the desired consensus from limited values that are stored by the PDA. Finally, the Gauss-Seidel method is used to verify the consensus mathematically. Show more

Keywords: Push down automata, principal component analysis, independent component, big data, DNA

DOI: 10.3233/JIFS-169695

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 2, pp. 1555-1565, 2018

Authors: Sinha, Rupesh Kumar | Sahu, S.S.

Article Type: Research Article

Abstract: The augmented growing of visual cryptography in multimedia image transmission or data transmission over unsecured networks leads in safekeeping for confidential information. Generally two techniques are employed to afford secure transmission namely data hiding and cryptography. Cryptography is the main objective of recent research work in which the way of achieving secure transmission over the network be contingent on the interest of data encryption. This encryption process encrypts the constituent of data such as manuscript, image, audial, and audiovisual to make the data unconceivable or incomprehensible during transmission. A novel secret key generation based on Improved Bat Optimized Piecewise Linear …Chaotic Map is proposed for image encryption. Our proposed secret key is intended for image encryption owing to the progression of mixing, permutation, double diffusion and confusion with the size of 128 bit to perform secure transmission. The success of our proposed method is revealed by the tentative results and comparison with the existing techniques in terms of sensitivity analysis, Information Entropy, correlation coefficient and, Encryption speed. Show more

Keywords: Image encryption, cryptography, secure transmission, improved bat optimized piecewise linear chaotic map, permutation-confusion, double diffusion

DOI: 10.3233/JIFS-169696

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 2, pp. 1567-1578, 2018

Authors: KanagaSakthivel, B. | Devaraj, D. | Banu, R. Narmatha | Selvi, V. Agnes Idhaya

Article Type: Research Article

Abstract: A hybrid renewable energy scheme comprising of the wind and solar PV electric power systems with appropriate maximum power point tracking is presented in this paper. The maximum power point tracking for the wind generator is carried out using Adaptive Neuro Fuzzy Inference System. The MPPT technique adopted for the photovoltaic power generation system is the Incremental Conductance (IC) algorithm. A power flow control scheme based on fuzzy logic is developed to regulate the power transaction from the wind and solar power sources as well as for the battery charging and discharging. Based on the available velocity of wind and …solar insolation and based on the electrical demand different modes of operation are selected automatically using the ANFIS based control strategy. Considering the non linearity’s of the converters and the unpredictable nature of the renewable sources an advanced adaptive controller is necessary. The proposed ANFIS controller performs well and the proposed idea has been validated using MATLAB/Simulink and the simulation results are reported. Show more

Keywords: Hybrid energy system, wind energy, solar PV, ANFIS, SVPWM

DOI: 10.3233/JIFS-169697

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 2, pp. 1579-1595, 2018

Authors: Huang, Qing | Huang, Bo | Fang, Zhijun | Xiao, Meihua | Yu, Ying

Article Type: Research Article

Abstract: Benefited on the open source software movement, many code search tools are proposed to retrieve source code over the internet. However, the retrieved source code rarely meets user needs perfectly so that it has to be changed manually. This is because the retrieved source code is concretely over-specific to some particular context. To solve this problem, we propose an Abstract Change Pattern Model (ACPM) to ensure the context-specific source code general for various contexts. This model consists of the ACP abstracting and the ACP concretizing algorithms. The former exploits the abstractly context-aware change pattern from the code changes. Based on …the change pattern, the latter transforms the context-specific source code into the correct one meeting different user needs. To evaluate ACPM, we extract 7 topics and collect 5-6 code snippets per topic from the Github, while performing 5 different experiments where we explore 2 sensitivity-related rules and use them to raise the accuracy gradually. Our experimental results show that ACPM is feasible and practical with 73.84% accuracy. Show more

Keywords: Code search, program transformation, code change pattern

DOI: 10.3233/JIFS-169698

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 2, pp. 1597-1608, 2018

Authors: Almaslukh, Bandar | Al Muhtadi, Jalal | Artoli, Abdel Monim

Article Type: Research Article

Abstract: The online smartphone-based human activity recognition (HAR) has a variety of applications such as fitness tracking, healthcare…etc. Currently, the signals generated from smartphone-embedded sensors are used for HAR systems. The smartphone-embedded sensors are utilized in order to provide an unobtrusive platform for HAR. In this paper, we propose a deep convolution neural network (CNN) model that provides an effective and efficient smartphone-based HAR system. For automatic local features extraction from the raw time-series data, we use the CNN while simple time-domain statistical features are used to extract more distinguishable features. Furthermore, we explore the impact of a novel data augmentation …on the recognition accuracy of the proposed model. The performance of the proposed method is evaluated using two public data sets (UCI and WISDM) which are collected using smartphones. Experimentally, we show how the proposed model establishes the state-of-the-art performance using these datasets. Finally, to demonstrate the applicability of the proposed model for online smartphone-based HAR, the computational cost of the model is evaluated. Show more

Keywords: Deep learning, convolutional neural network, smartphone-based human activity recognition, data augmentation, HAR

DOI: 10.3233/JIFS-169699

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 2, pp. 1609-1620, 2018

Authors: Rathore, Arun | Patidar, N.P.

Article Type: Research Article

Abstract: Energy storage using batteries is emerging as a fundamental element of standalone power system based on non conventional energy sources like wind and solar to increase the penetration level of these sources. The planning of standalone power system incorporating renewable sources and storage necessitates a vigilant study on modeling of a storage system. In most of the planning study reported in literature pertaining to battery storage, charging efficiency (CE) of a battery is assumed to be fixed at constant value. However, CE and State of charge (SOC) of the battery both are correlated. In this paper, Interval Type(IT)-2 fuzzy logic …has been applied for determining CE of battery relative to a specific SOC. For evaluating reliability indices i.e. Expected Energy not served (EENS), probabilistic analysis using analytical method has been applied to the standalone power system, situated near Kandla Port in Gujarat, India. The effect of considering CE of battery as a function of SOC has been compared with the constant value of CE of battery for the different groups consisting of solar-battery storage, wind turbine(WT)-battery storage, and wind-solar battery storage systems. Show more

Keywords: Interval type-2 fuzzy logic, CE, SOC, reliability, EENS

DOI: 10.3233/JIFS-169700

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 2, pp. 1621-1632, 2018

Authors: Kumar, Ashish | Bhatnagar, Roheet | Srivastava, Sumit

Article Type: Research Article

Abstract: Even though finding out distance is the central core of k-Nearest Neighbor classification techniques, similarity measures are often favored against distance in various realistic scenarios and situation. Most of the similarity measures, which are used to classify an instance, are based on geometric model. Their effectiveness decreases with the increases in the number of dimensions. This paper establishes an efficient technique called ARSkNN for finding out class of any given instance using a measure based on an unique similarity, that does no longer compute distance, for k-NN classification. Our empirical results show that ARSkNN classification technique is better than the …previous established k-NN classifiers. The performance of algorithm was verified and validated on various datasets from different domains. Show more

Keywords: Data mining, classification, nearest neighbor, similarity measure

DOI: 10.3233/JIFS-169701

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 2, pp. 1633-1644, 2018

Authors: Kaur, Amanpreet | Sidhu, Jagroop Singh | Bhullar, Jaskarn Singh

Article Type: Research Article

Abstract: With the higher compression ratio, the decoded image produces annoying compression artifacts near block boundaries, ringing artifacts near original edges and corner outliers at block corner. These coding artifacts are caused by quantization and transformation process of discrete cosine transform (DCT). This paper proposes a novel deblocking algorithm that removes block discontinuities by taking into account the ringing, blurring and corner outlier artifacts. The proposed deblocking technique consists of two frequency related modes (smooth and detailed region mode) and corner outlier mode have been proposed and then applied median filter. The proposed technique has been applied to a number of …reconstructed images and their performance is compared with conventional methods on the basis of standard metrics such a PSNR-B, BBM and MOS. Experimental simulation results illustrate that proposed technique improves the perceptual quality of reconstructed images and thus outperforms the all existing methods. Show more

Keywords: Deblocking filter, post-processing, blocking artifacts, corner outlier, image compression

DOI: 10.3233/JIFS-169702

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 2, pp. 1645-1656, 2018

Authors: Vig, Vidhi | Kaur, Arvinder

Article Type: Research Article

Abstract: Recently, many software companies have shifted to shorter release cycles from the traditional multi-month release cycle. Evolution and transition of release cycles may affect the test effort in the system. This paper analyses 25 traditional releases containing 1210 classes and 69 rapid releases containing 2616 classes of four Open Source Java systems. Correlations between 48 Object Oriented metrics and 2 test metrics were evaluated to identify the best indicators of test effort. The results show that (i) correlation between OO and test metrics remain irrespective of release models, (ii) test effort required in Rapid Release (RR) models (shorter release …cycles) is slightly more as compared to Traditional Release (TR) models, (iii) Out of 18 machine learning algorithms instance based machine learning algorithms IBK and K star followed by Multi-Layer Perceptron (MLP) and additive regression are able to predict the test effort accurately in classes. Show more

Keywords: Release cycles, machine learning, prediction, software metrics, test effort

DOI: 10.3233/JIFS-169703

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 2, pp. 1657-1669, 2018

Authors: Moayedirad, Hojat | Shamsi Nejad, Mohammad Ali

Article Type: Research Article

Abstract: This paper models the single and bi-objective brain emotional intelligent controllers for the dual stator winding induction motor (DSWIM) drive. The main purpose of this paper is performance improvement of the DSWIM drive control system and power losses reduction of the inverter in the DSWIM drive at low speeds. In the vector control method, it is difficult to estimate flux at low speeds. To solve the mentioned problem, researchers have used from the free capacity of the two windings of the stator. This paper presents three proposed methods: 1. Using the idea of rotor flux compensation based on classical PI …controller at low speeds, the motor works in its standard operating mode; 2. Proposed Method 1 is reformed and improved based on the bi-objective brain emotional controller; and 3. Proposed Method 2 is improved using single-objective brain emotional controller in the speed control loops of the DSWIM drive. The proposed methods are simulated in MATLAB/Simulink software. Show more

Keywords: Bi-objective, dual stator, emotional intelligent controller, induction motor, low speeds

DOI: 10.3233/JIFS-169704

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 2, pp. 1671-1683, 2018

Authors: Zhang, Lili | Shao, Heshuai | Yao, Kai | Li, Qi | Wang, Huibin

Article Type: Research Article

Abstract: Due to the limited focus range of optical imaging system, and the locations or focus of different objects in the same scene are different, multiple objects cannot be focused at the same time. In order to solve this problem and make the underwater image clearer, we propose a fusion method based on the sparse matrix in this paper. Firstly, we transform the source image into sparse image by sparse transform and get the clearity of the image based on the sparsity. Then, the clearity image will be segmented into focus regions. After that, the focus regions and non-focus regions are fused …respectively based on different fusion algorithms. Finally, the focus regions and non-focus regions are combined to get the enhanced image. The experiments in the end show that the fusion method we proposed in this paper has higher information entropy, correlation entropy, standard deviation, and average gradient, so it can enhance the underwater multi-focus image and can be applied to the underwater object detection. Show more

Keywords: Sparse matrix, underwater multi-focus image, fusion, region segment

DOI: 10.3233/JIFS-169705

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 2, pp. 1685-1693, 2018

Authors: Zhou, Chengmin | Li, Fei | Cao, Wen | Wang, Cao | Wu, Yihuai

Article Type: Research Article

Abstract: Contrasted with common obstacle avoidance mode based on single sensor or solo algorithm, this article put forward an intelligent pattern based on Combination from CNN-based Deep Learning Method and liDAR-based Image Processing approach. As for Deep Learning method, a 10-layer Convolutional Neural Network (CNN) is designed which comes to a high recognition accuracy of 97 percent in Tensorflow and success rate of obstacle avoidance is over 90 percent. With regard to liDAR-based Image Processing approach, decision is made by a special method of counting the number of Point Cloud Data (PCD) which is generated by 2D liDAR and a success …rate over 90 percent is achieved as well. When two kinds of methods work together, a robust success rate of 100 percent is realized. Meanwhile, Inertial Measurement Unit (IMU) and Xbox360 are taken into consideration for Pose Estimation and Data Collection. Finally, all functions are integrated in Robot Operation System (ROS) on platform of nVidia Jetson TX1. Show more

Keywords: Obstacle avoidance, deep learning, collaborative system design, 2D liDAR, ROS

DOI: 10.3233/JIFS-169706

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 2, pp. 1695-1705, 2018

Authors: Tripathi, Ashish | Mishra, K.K. | Tiwari, Shailesh | Kumar, Naveen

Article Type: Research Article

Abstract: Software cost estimation is the process of predicting the most realistic and valid amount of effort necessary for the development of any software. The cost estimation of any software is a difficult assignment due to the involvement of many factors that anyhow affect the estimation process. In literature, many cost estimation models have been developed for more than a decade to maintain accuracy in estimation of the cost of software projects. But, it is found that these models are inefficient to estimate the exact cost of software development because of uncertainties and lack of accuracy associated with them. In this …paper, Alla F. Sheta models have been taken for optimization, which are the modified versions of the very famous Boehm’s COCOMO model. Parameters of the Sheta models have been tuned enough by the proposed method to estimate and minimize the consequences of different factors that affect the overall software development cost. Experimental work has been carried out in MATLAB environment and analysis of results is performed on the basis of Magnitude of Relative Error (MRE), Prediction (PRED) at 0.25, Value Accounted For (VAF) and Mean Magnitude of Relative Error (MMRE). Estimation accuracy of the proposed work is tested on NASA software project dataset. It is found that the proposed method shows good estimation capabilities over other state-of-the-art cost estimation models. Show more

Keywords: Software cost estimation, EAMD, COCOMO model, NASA dataset, natural phenomena

DOI: 10.3233/JIFS-169707

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 2, pp. 1707-1720, 2018

Authors: Bhuvaneshwari, B. | Rajeswari, A.

Article Type: Research Article

Abstract: 3D Reconstruction has been an enduring problem in Image understanding and computer vision. There is an increasing interest on 3D information in the general public due to rapid development of 3D imaging techniques, marketing of 3D movies and games, and low cost of depth cameras. Numerous algorithms that have been proposed for performing 3D reconstruction using different variants of Iterative Closest Point(ICP) algorithm focusses mainly on reducing the computation time. The accuracy of 3D reconstruction is not taken in to consideration. An efficient, accurate, real time and active 3D reconstruction method using Kinect sensor is developed in this paper focusing …on improving the accuracy of 3D reconstruction in less computation time. An Artificial Bee colony based ICP algorithm is proposed by incorporating several efficient variants of ICP algorithm. The proposed algorithm is intended to improve the accuracy and stability of the standard ICP algorithm. The performance of the proposed algorithm is satisfactory when compared with structured light technique and several ICP variants with respect to accuracy, complexity and computation speed. Show more

Keywords: 3D Reconstruction, iterative closest point, kinect sensor, artificial bee colony, structured light technique

DOI: 10.3233/JIFS-169708

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 2, pp. 1721-1732, 2018

Authors: Zhao, Hong | Hou, Chunning

Article Type: Research Article

Abstract: Smartphone has been used for recognizing the different motion activities. However, current studies focus on either improving algorithm factor or adjusting neural network structure factor rather than on time cost factor and actual application factor. A novel method to consider these four factors comprehensively enhancing recognition of motion state accuracy is proposed. An architecture of the Bi-LSTM neural network and the TensorFlow machine learning system are used to classify the motion state and evaluate its experimental results. In addition, the Bi-LSTM neural network is compared with other neural network structures. Meanwhile, using the data captured by the accelerometer sensor and …gyroscope sensor of the smartphone tests the Bi-LSTM neural network model. Experimental results show that using Bi-LSTM neural network and TensorFlow machine learning system to extract motion state characteristics, this method makes the motion state identification achieve 86.7% accuracy and the Bi-LSTM neural network model is better than other neural network models considering above four factors. The model of Bi-LSTM neural network can be used for other time-series fields such as signal recognition, action analysis, etc. This study provides a new method, which considers the four factors, to enhance the accuracy of the motion state classification. Show more

Keywords: Deep learning, Bi-LSTM neural network, motion state, sensors of smartphone, TensorFlow

DOI: 10.3233/JIFS-169709

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 2, pp. 1733-1742, 2018

Authors: Gujarathi, Pritam K. | Shah, Varsha A. | Lokhande, Makarand M.

Article Type: Research Article

Abstract: Conversion of the conventional vehicle (CV) into the plug-in hybrid electric vehicle (PHEV) is one of the promising solutions to improve transport sustainability and reduce outdoor air pollution. Energy management is crucial for the performance of PHEV. The paper presents combine rule based-artificial bee colony optimization algorithm for energy management of converted plug-in hybrid electric vehicle (CPHEV). The diesel operated parallel hybrid topology is considered for study with the designed electric powertrain. NOx and PM are considered as optimization parameters along with specific fuel consumption. The performance based on fuel consumption and emissions (NOx and PM) is analyzed by considering …sample Indian urban and highway driving cycle. The complete vehicle is simulated using MATLAB Simulink linked with coding. The results of converted PHEV obtained is compared with conventional one for both driving cycles for analysis of the fuel consumption and emissions considering real-time benchmarking norms. The results indicate that the combine rule based-artificial bee colony strategy keeps pollution under control required as per BSIII norms. Show more

Keywords: Artificial bee colony, emission, optimization, plug-in hybrid electric vehicle

DOI: 10.3233/JIFS-169710

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 2, pp. 1743-1753, 2018

Authors: Xia, Min | Zhang, Chong | Weng, Liguo | Liu, Jia | Wang, Ying

Article Type: Research Article

Abstract: Robot path planning is integral to many robotic applications. In this work, three optimization objectives are presented: path length, degree of path smoothness, and degree of security. Due to the lack of local search ability, the optimal solution set is difficult to be obtained with the traditional method especially when the search space is very irregular. And the simple local search algorithm is often trapped into local optimization. A new method with local search is introduced to improve the SPEA2 in this work. The proposed method sets up an external population dedicated to local search, which can increase the local …search ability of the method while retaining good global searching ability. In addition, the new crossover operator and the individual update strategy are used for proposed method. The simulation results shows that the proposed method is better than that of SPEA2, NSGA-2 and PESA. It was found that the model proposed in this work is practical for robot path planning. Show more

Keywords: Multi-objective evolutionary algorithm, SPEA2, robot path planning, adaptive crossover, local search

DOI: 10.3233/JIFS-169711

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 2, pp. 1755-1764, 2018

Authors: Sahoo, Samanway | Nandi, Subham Kumar | Barua, Sourav | Pallavi, | Bhowmik, Showmik | Malakar, Samir | Sarkar, Ram

Article Type: Research Article

Abstract: Handwritten word recognition is considered as an active research area since long because of its various real life applications. The key obstacle of this research problem is the huge variation of the writing styles of different individuals. In addition to that the complex shapes of alphabet make the recognition process more difficult. A holistic word recognition approach is proposed here in order to classify 80-class handwritten city name images written in Bangla script. Based on the negative refraction property of the light, a novel shape-based feature vector of size 186 is generated from each of the word images. Effectiveness of …the feature vector is tested on a database containing total 12000 handwritten word images having equal number of samples from each class. The proposed method achieves a reasonably good recognition accuracy of 87.50% which proves better while comparing with some of the recently published feature vectors used for similar job. The reported result is achieved by combining the classifiers namely Sequential Minimal Optimization (SMO), Simple Logistic and CV Parameter Selection embedded with SMO. To verify the robustness of the present method it is also applied on handwritten word images written in Roman and Devanagari scripts separately and it is found that our method obtains satisfactory result on the both the cases. Show more

Keywords: Word recognition, holistic approach, handwritten word, shape descriptor, Bangla script, negative refraction

DOI: 10.3233/JIFS-169712

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 2, pp. 1765-1777, 2018

Authors: Sharma, Aman | Rani, Rinkle

Article Type: Research Article

Abstract: Human Cancer Cell lines have gained a lot of attention since it helps in studying cancer biology and various treatment options. Recently various large-scale drug screening experiments were performed providing access to genomic and pharmacological data. This data helps in predicting drug responses which eventually contributes to the development of personalized cancer treatment. Heterogeneous nature of cancer raises the serious need for therapeutic agents with an essence of personalized treatment. Thus considering the assumption that similar drugs exhibit similar drug responses, we have developed kernelized similarity based regularization matrix factorization framework for predicting anti-cancer drug responses. Drug-Drug chemical structure similarity …and Tissue-Tissue similarity (gene expression) are taken as key descriptors to formulate the objective function. The kernel function is used to map non-linear relationships between drugs and tissues. Our aim is to provide an efficient anti-cancer drug response prediction approach to establish the protocol for personalized treatment and new drugs designing. The proposed framework is validated using publicly available tumor datasets: GDSC and CCLE. Proposed KSRMF is further compared with three states of art algorithms using GDSC and CCLE drug screens. We have also predicted missing drug response values in the dataset using KSRMF. KSRMF outperforms other counterparts even though gene mutation data is not incorporated while designing the approach. An average mean square error of 3.24 and 0.504 is achieved using GDSC and CCLE drug screens respectively. The obtained results show that the proposed framework has quite potential to improve anti-cancer drug response prediction. Our analysis showed how data integration can help in achieving the goal of personalized cancer treatment. Show more

Keywords: Matrix Factorization, Kernel, Drug Responses Prediction, Personalized Anti-Cancer Treatment

DOI: 10.3233/JIFS-169713

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 2, pp. 1779-1790, 2018

Authors: Giuffrè, Orazio | Granà, Anna | Tumminello, Maria Luisa | Sferlazza, Antonino

Article Type: Research Article

Abstract: The paper introduces a methodological approach based on genetic algorithms to calibrate microscopic traffic simulation models. The specific objective is to test an automated procedure utilizing genetic algorithms for assigning the most appropriate values to driver and vehicle parameters in AIMSUN. The genetic algorithm tool in MATLAB® and AIMSUN micro-simulation software were used. A subroutine in Python implemented the automatic interaction of AIMSUN with MATLAB® . Focus was made on two roundabouts selected as case studies. Empirical capacity functions based on summary random-effects estimates of critical headway and follow up headway derived from meta-analysis were used as reference for …calibration purposes. Objective functions were defined and the difference between the empirical capacity functions and simulated data were minimized. Some model parameters in AIMSUN, which can significantly affect the simulation outputs, were selected. A better match to the empirical capacity functions was reached with the genetic algorithm-based approach compared with that obtained using the default parameters of AIMSUN. Overall, GA performs well and can be recommended for calibrating microscopic simulation models and solving further traffic management applications that practioners usually face using traffic microsimulation in their professional activities. Show more

Keywords: Genetic algorithm, traffic microsimulation, AIMSUN, passenger car equivalent, roundabout

DOI: 10.3233/JIFS-169714

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 2, pp. 1791-1806, 2018

Authors: Gupta, Ranu | Pachauri, Rahul | Singh, Ashutosh

Article Type: Research Article

Abstract: A linear method based on local statistical parameters of the image to remove the speckles of ultrasound carotid artery medical image is presented in this article. Speckle is the main drawback of medical images and it should be removed before any further processing of images like edge detection and registration. The focus of this article is to filter the speckle efficiently and effectively even at higher density of noise. The filter is designed by keeping in mind that the local statistical parameters are important rather than global statistical parameters. The weighting factor is designed such that it is high for …similar areas and thus results into more smoothing without destroying the useful information, whereas it is low at the edges and thus less smoothing will be done. The filter is applied with the help of 5×5 sliding window. The noise ranging from 0.01–0.09 of variance is unnaturally inserted in the medical images through Matlab. The efficiency calculating parameters like Signal to Noise Ratio (SNR), Quality Index (Q), Mean Square Error (MSE), Similarity Index Measure (SSIM) and Edge Preserved Index (EPI) were used to evaluate the proposed technique. The suggested method is also compared with the existing local statistical mean variance filter for the said parameters in order to analyse the performance of the filter. Show more

Keywords: Medical images, ultrasound, speckle, local statistics

DOI: 10.3233/JIFS-169715

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 2, pp. 1807-1816, 2018

Article Type: Other

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 2, pp. 1817-1817, 2018

Authors: Pires, Danúbia | Serra, Ginalber

Article Type: Research Article

Abstract: A methodology to systems identification based on Evolving Fuzzy Kalman Filter, is proposed in this paper. The mathematical formulation using an evolving Takagi-Sugeno (TS) structure, is presented: the offline Gustafson Kessel (GK) algorithm is used for initial parametrization of antecedent of the fuzzy Kalman filter inference system, considering an initial data set; and an evolving version of the GK algorithm is developed for online parametrization of antecedent of the fuzzy Kalman filter inference system. A fuzzy recursive version of OKID (Observer/Kalman Filter Identification) algorithm is proposed for parametrizing the matrices A, B, C, D and K (state matrix, input influence …matrix, output influence matrix, direct transmission matrix, and Kalman gain matrix, respectively), in the consequent of the fuzzy Kalman filter inference system. Computational and experimental results from the estimation of the states and outputs of a dynamic system and a two-degree-of-freedom (2DoF) Helicopter, respectively, show the efficiency and applicability of the proposed methodology. Show more

Keywords: Evolving, fuzzy Kalman filter, Takagi-Sugeno

DOI: 10.3233/JIFS-17087

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 2, pp. 1819-1834, 2018

Authors: Zhang, Feng-Xia

Article Type: Research Article

Abstract: Distributivity equation has been widely studied involving different classes of logical connectives or aggregation operators, such as implications, uninorms, t-operators and their generalizations. In this paper, we follow on these works by investigating the distributivity for uninorms and Mayor’s aggregation operators.

Keywords: Aggregation operators, uninorms, Mayor’s aggregation operators, distributivity equation

DOI: 10.3233/JIFS-171286

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 2, pp. 1835-1849, 2018

Authors: Erginel, Nihal | Uluskan, Meryem | Küçük, Gamze | Altıntaş, Merve

Article Type: Research Article

Abstract: We propose project evaluation criteria by considering not only the Six Sigma approach, but also European Foundation for Quality Management (EFQM) model features, and introduce an evaluation model for completed Six Sigma projects. The proposed model uses seven main criteria: leadership, policies/strategies, main performance results, social results, employees, cooperation, and resources. These main criteria are enhanced by 18 sub-criteria. Due to the evaluation scale being based on human judgments, fuzzy set theory is an obvious methodology choice to account for uncertainty in the evaluation data. Type-2 fuzzy sets can provide flexibility for uncertainty by considering membership functions and their footprints. …We also propose an evaluation methodology for weighting the main and sub-criteria and for ranking completed Six Sigma projects through a fuzzy analytic network process (ANP) method with interval type-2 fuzzy sets. From our analysis, performance results were found to have the highest weight among the seven main criteria, while achieving project objectives was found to be the most effective criteria. Show more

Keywords: Six Sigma methodology, project evaluations, fuzzy sets, the EFQM model, interval type-2 fuzzy ANP

DOI: 10.3233/JIFS-171306

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 2, pp. 1851-1863, 2018

Authors: Ma, Ying | Zhu, Xiatian | Zhu, Shunzhi | Wu, Keshou | Chen, Yuming

Article Type: Research Article

Abstract: Recent studies have shown sparse representation learning is a potentially promising method in pattern classification, but very few focused on class imbalanced problems involved in its applications and practice. This problem is particularly important, since it causes suboptimal classification performances, especially when the cost of misclassifying a minority-class example is substantial. Unlike the prior test sample sparse representation on balanced data sets, which cannot reflect the data distribution in real applications, we proposed a novel sparse representation learning algorithm called Balanced Sparse Representation Classifier (BSRC), considering the contribution from heavily under-represented of minority classes. Our solution first estimates the contribution …of training sample in each class, and then identifies the nearest neighbors with the largest contributions. After that, the test data is expressed based on linear combination of all the nearest samples. Finally, the decision has been made according to sum of contribution for each class. Moreover, we also present the kernel extension of the proposed classifier to deal with complex data. Experimental results also show that with the proposed learning approach, it is possible to design better method to tackle the class imbalance problem in sparse representation learning. Show more

Keywords: Machine learning, sparse representation, class imbalance, classification

DOI: 10.3233/JIFS-171342

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 2, pp. 1865-1874, 2018

Authors: Zhou, Lintao | Wang, Yanfeng | Jiang, Yong

Article Type: Research Article

Abstract: Investment project assessment is one of the most critical activities in the investment process, which requires a trade-off between multiple attributes exhibiting vagueness and imprecision with the involvement of a group of experts. The multiple attribute group decision-making (MAGDM) method based on trapezoidal interval type-2 fuzzy sets (IT2FSs) is suitable for the decision makers to deal with this problem. However, some shortcomings in the arithmetic operations of trapezoidal IT2FSs, and some of ranking methods in some cases are invalid. In this paper, the arithmetic operations of trapezoidal IT2FSs are redefined, which can overcome some shortcomings of the ones developed in …existing literature. And then, a new ranking method of trapezoidal IT2FSs based on the incentre point of fuzzy sets is developed. In order to verify the proposed method, thirteen fuzzy sets are used in comparison with some of the existing methods, and the comparison results demonstrate the superiority of the proposed method. Finally, the proposed method is integrated into the technique for order preference by similarity to the ideal solution (TOPSIS) method and an illustrative example in investment project assessment is presented to evaluate the effectiveness of the proposed methods. Show more

Keywords: Investment project assessment, interval type-2 fuzzy sets, multiple attribute group decision-making

DOI: 10.3233/JIFS-171403

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 2, pp. 1875-1888, 2018

Authors: Bi, Yunrui | Sun, Zhe | Lu, Xiaobo | Sun, Zhixin | Liu, Di | Liu, Kun

Article Type: Research Article

Abstract: Traffic congestion has become a serious phenomenon in the cities. In order to achieve the effective control of intersections, multi-lane four-phase intersection is studied. The corresponding queue length model and vehicular delay model are established. Aiming at the dynamic uncertainty problem in the intersection, a type-2 fuzzy logic controller is designed. The green time of each phase is dynamically decided according to the real-time traffic information for purpose of achieving the smallest vehicular average delay, so as to enhance the traffic efficiency in the intersection. The excellent performance of the designed controller is confirmed through simulation experiments under different conditions. …Finally, in view of the difficulty of parameter settings in type-2 fuzzy controller, DNA evolutionary algorithm is applied to online optimize and adjust the parameters of membership function. One group of parameters is difficult to fit all traffic situations, so on-line optimization and adjustment is necessary for reflecting the real-time change of traffic flow in time, which is of great significance for the practical application. The experimental results indicate that the online optimized type-2 fuzzy traffic control method has better effect. Show more

Keywords: Type-2 fuzzy logic control, traffic signal control, optimization, DNA evolutionary algorithm

DOI: 10.3233/JIFS-171405

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 2, pp. 1889-1904, 2018

Authors: Akram, Muhammad | Sarwar, Musavarah | Borzooei, Rajab Ali

Article Type: Research Article

Abstract: A hypergraph is one of the most developing area for modeling various practical problems in different fields, including computer science, biological sciences, social networks and psychology. Our main discussion in this research paper is to apply the notion of intuitionistic fuzzy sets to extend the theory of hypergraphs. We introduce the concept of isomorphism, dual intuitionistic fuzzy hypergraph, intuitionistic fuzzy line graph and 2-section of an intuitionistic fuzzy hypergraph. We present some applications of intuitionistic fuzzy hypergraphs in planet surface networks, selection of authors of of intersecting communities in a social network and grouping of incompatible chemical substances. We design …certain algorithms to construct dual intuitionistic fuzzy hypergraph, intuitionistic fuzzy line graph and the selection of objects in decision-making problems. Show more

Keywords: Isomorphism, intuitionistic fuzzy hypergraph, planet surface network, social network, incompatible chemicals Mathematics Subject Classification 2010: 05C65, 05C85, 05C90, 03E72

DOI: 10.3233/JIFS-171443

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 2, pp. 1905-1922, 2018

Authors: Niroomand, Sadegh

Article Type: Research Article

Abstract: A linear programming with triangular intuitionistic fuzzy parameters is focused in this paper. As a shortcoming, all the solution approaches of the literature for this problem are constructed based on ranking functions, where, use of different ranking functions may result in different solutions. In this study for the first time an approach with no ranking function is developed for the problem. For this aim, the triangular intuitionistic fuzzy objective function is decomposed to a multi-objective function, and the problem is converted to a multi-objective crisp problem. As another contribution, in order to solve the obtained multi-objective problem for its efficient …solutions, a new multi-objective optimization approach was developed and suited to the obtained crisp multi-objective problem. The computational experiments of the study, show the superiority of the proposed multi-objective optimization approach over the existing approaches of the literature. Show more

Keywords: Linear programming, triangular intuitionistic fuzzy number, multi-objective optimization, fuzzy programming approach

DOI: 10.3233/JIFS-171504

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 2, pp. 1923-1934, 2018