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

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