Machine learning methods for sign language recognition: a critical review and analysis.

Sign language is an essential tool to bridge the communication gap between normal and hearing-impaired people. However, the diversity of over 7000 present-day sign languages with variability in motion position, hand shape, and position of body parts making automatic sign language recognition (ASLR) a complex system. In order to overcome such complexity, researchers are investigating better ways of developing ASLR systems to seek intelligent solutions and have demonstrated remarkable success. This paper aims to analyse the research published on intelligent systems in sign language recognition over the past two decades. A total of 649 publications related to decision support and intelligent systems on sign language recognition (SLR) are extracted from the Scopus database and analysed. The extracted publications are analysed using bibliometric VOSViewer software to (1) obtain the publications temporal and regional distributions, (2) create the cooperation networks between affiliations and authors and identify productive institutions in this context. Moreover, reviews of techniques for vision-based sign language recognition are presented. Various features extraction and classification techniques used in SLR to achieve good results are discussed. The literature review presented in this paper shows the importance of incorporating intelligent solutions into the sign language recognition systems and reveals that perfect intelligent systems for sign language recognition are still an open problem. Overall, it is expected that this study will facilitate knowledge accumulation and creation of intelligent-based SLR and provide readers, researchers, and practitioners a roadmap to guide future direction

Recent Developments and Challenges on AC Microgrids Fault Detection and Protection Systems–A Review

The protection of AC microgrids (MGs) is an issue of paramount importance to ensure their reliable and safe operation. Designing reliable protection mechanism, however, is not a trivial task, as many practical issues need to be considered. The operation mode of MGs, which can be grid-connected or islanded, employed control strategy and practical limitations of the power electronic converters that are utilized to interface renewable energy sources and the grid, are some of the practical constraints that make fault detection, classification, and coordination in MGs different from legacy grid protection. This article aims to present the state-of-the-art of the latest research and developments, including the challenges and issues in the field of AC MG protection. A broad overview of the available fault detection, fault classification, and fault location techniques for AC MG protection and coordination are presented. Moreover, the available methods are classified, and their advantages and disadvantages are discussed

AI and OR in management of operations: history and trends

The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested

Fuzzy averaging filter for impulse noise reduction in colour images with a correction step

[EN] In this paper we propose a fuzzy detection and reduction method for impulse noise in colour images. Detection is based on the fuzzyfication of a well-known statistic called ROD. The noise degrees obtained are used to reduce impulses by employing a fuzzy averaging between the input colour vector and a robust estimate of noise-free colour vector within the input neighbourhood. Fuzzy averaging has some advantages in terms of both noise reduction and detail preservation in front of detect and replace approaches because of threshold based decisions of the latter. However, robustness of the former is lower. We solve this problem by including a correction mechanism that checks the fuzzy noise degree of the output and replaces it with a robust colour vector either when noise has not been properly reduced or when a colour artefact has been introduced. We carry out a thorough study of the method parameter setting and give a convenient and robust setting. Experimental results show that our approach is very robust in front of four different types of impulse noise.The authors are very grateful to the reviewers for their valuable suggestions. Valentin Gregori and Samuel Morillas acknowledges the support of Ministry of Economy and Competitiveness of Spain under grant MTM 2015-64373-P (MINECO/FEDER, UE). Bernardino Roig and Almanzor Sapena acknowledges the support of Generalitat Valencians under grant AICO/2017/059.Gregori Gregori, V.; Morillas, S.; Roig, B.; Sapena Piera, A. (2018). Fuzzy averaging filter for impulse noise reduction in colour images with a correction step. Journal of Visual Communication and Image Representation. 55:518-528. https://doi.org/10.1016/j.jvcir.2018.06.025S5185285

Fuzzy Decision Making and Soft Computing Applications

This Special Issue collects original research articles discussing cutting-edge work as well as perspectives on future directions in the whole range of theoretical and practical aspects in these research areas: i) Theory of fuzzy systems and soft computing; ii) Learning procedures; iii) Decision-making applications employing fuzzy logic and soft computing

RACFIS: A new rapid adaptive complex fuzzy inference system for regression modelling

The theory of complex fuzzy sets has made great breakthroughs in recent times. Complex fuzzy theory (CFT) allows a fuzzy set to include more information with the help of its two-dimensional rule-base, which is of great potential for improving the related fuzzy system performance while managing the size of the associated rule-bases. In this paper, a new rapid adaptive complex fuzzy inference system (RACFIS) is proposed by redesigning the optimization policy of the earlier complex neuro-fuzzy system (CNFS) algorithm. Improvements include a new three-parameter Quasi-hyperbolic momentum (QHM) optimization method to replace the original particle swarm optimization (PSO), and unsupervised learning is introduced, for the first time, into the complex neuro-fuzzy model to pre-train the antecedent parameters for a better global optimum as well as a faster convergence. Experimental results show that RACFIS performs very well on all data sets, obtaining excellent prediction accuracies with on average 10 times lower epoch numbers (as compared with all benchmark models) and a reduction in the size of the rule-bases by nearly 20 % ∼30% (as compared with non-complex fuzzy models). RACFIS also possesses a strong generalization capability that outperforms all the benchmark algorithms employed in the simulation experiments. In addition, a mean impact value (MIV) algorithm based on radial basis function (RBF) network is also employed to select variables with higher relevance in order to mitigate the drawbacks caused by the higher dimensionality of the data

Neuroevolutional Methods for Decision Support Under Uncertainty

The article presents a comparative analysis of the fundamental neuroevolutional methods, which are widely applied for the intellectualization of the decision making support systems under uncertainty. Based on this analysis the new neuroevolutionary method is introduced. It is intended to modify both the topology and the parameters of the neural network, and not to impose additional constraints on the individual. The results of the experimental evaluation of the performance of the methods based on the series of benchmark tasks of adaptive control, classification and restoration of damaged data are carried out. As criteria of the methods evaluation the number of failures and the total number of evolution epochs are used