Soft Computing is dedicated to system solutions based on soft computing techniques. It provides rapid dissemination of important results in soft computing technologies, a fusion of research in evolutionary algorithms and genetic programming, neural science and neural net systems, fuzzy set theory and fuzzy systems, and chaos theory and chaotic systems.
Soft Computing encourages the integration of soft computing techniques and tools into both everyday and advanced applications. By linking the ideas and techniques of soft computing with other disciplines, the journal serves as a unifying platform that fosters comparisons, extensions, and new applications. As a result, the journal is an international forum for all scientists and engineers engaged in research and development in this fast growing field.
The book covers a broad spectrum of soft computing techniques, theoretical and practical applications employing knowledge and intelligence to find solutions for world industrial, economic and medical problems. The combination of such intelligent systems tools and a large number of applications introduce a need for a synergy of scientific and technological disciplines in order to show the great potential of Soft Computing in all domains.
The series "Advances in Intelligent Systems andComputing" contains publications on theory, applications, and designmethods of Intelligent Systems and Intelligent Computing. Virtually alldisciplines such as engineering, natural sciences, computer and informationscience, ICT, economics, business, e-commerce, environment, healthcare, lifescience are covered. The list of topics spans all the areas of modernintelligent systems and computing such as: computational intelligence, softcomputing including neural networks, fuzzy systems, evolutionary computing andthe fusion of these paradigms, social intelligence, ambient intelligence,computational neuroscience, artificial life, virtual worlds and society,cognitive science and systems, Perception and Vision, DNA and immune basedsystems, self-organizing and adaptive systems, e-Learning and teaching,human-centered and human-centric computing, recommender systems, intelligentcontrol, robotics and mechatronics including human-machine teaming,knowledge-based paradigms, learning paradigms, machine ethics, intelligent dataanalysis, knowledge management, intelligent agents, intelligent decision makingand support, intelligent network security, trust management, interactiveentertainment, Web intelligence and multimedia.
Using soft computing solves the problem of computational complexity and efficiency in the classification. Techniques of soft computing include Genetic Algorithms, Genetic Programming, Classifier Systems, Evolution Strategies, artificial life, and a few others, which are used here.
People have started using techniques of soft computing like fuzzy sets theory, neural nets, fuzzy neuro system, adaptive neuro-fuzzy inference system (ANFIS), for driving various numerical simulation analysis.
Soft computing has helped in modeling the processes of machines with the help of artificial intelligence. Also, there are certain areas where soft computing is in budding stages only and is expected to see a massive evolution:
With the advancement of technologies such as the Internet of Things (IoT) and the wide applications of mobile technologies, organizations are generating large amounts of data in different formats at a faster rate than before. In addition, data content, processing, analytical models and the management of big data transformation have also generated huge challenges and opportunities. Advanced soft computing methods and decision-making techniques can be used to extract useful information and obtain effective manufacturing intelligence. They can then allow for equipment automation and combine with decision-making techniques to adopt useful rules and patterns from the big data. Moreover, they can detect potential failures in early stages under certain circumstances, diagnose defects, control advanced equipment/processes, decrease cycle time and costs, and increase productive rate. Applications exist in a wide range of fields such as artificial intelligence (AI), robotics, Internet of Things (IoT), autonomous vehicle, 3D printing, nanotechnology, materials science, energy storage, etc.
This Special Issue aims to collect research results concerning the latest developments, problems and challenges of the applications of soft computing in the manufacturing process of Industry 4.0. Original research and review articles are welcome.
Abstract:Air quality models simulate the atmospheric environment systems and provide increased domain knowledge and reliable forecasting. They provide early warnings to the population and reduce the number of measuring stations. Due to the complexity and non-linear behavior associated with air quality data, soft computing models became popular in air quality modeling (AQM). This study critically investigates, analyses, and summarizes the existing soft computing modeling approaches. Among the many soft computing techniques in AQM, this article reviews and discusses artificial neural network (ANN), support vector machine (SVM), evolutionary ANN and SVM, the fuzzy logic model, neuro-fuzzy systems, the deep learning model, ensemble, and other hybrid models. Besides, it sheds light on employed input variables, data processing approaches, and targeted objective functions during modeling. It was observed that many advanced, reliable, and self-organized soft computing models like functional network, genetic programming, type-2 fuzzy logic, genetic fuzzy, genetic neuro-fuzzy, and case-based reasoning are rarely explored in AQM. Therefore, the partially explored and unexplored soft computing techniques can be appropriate choices for research in the field of air quality modeling. The discussion in this paper will help to determine the suitability and appropriateness of a particular model for a specific modeling context.Keywords: Adaptive neuro-fuzzy inference system; artificial neural networks; air quality model; deep learning; ensemble model; evolutionary techniques; fuzzy logic model; review; soft computing model; support vector machine
Contributions covering all theoretical developments and practical applications in artificial intelligence and soft computing techniques, including but not limited to the following technical areas, are invited:
Fuzzy logic, one of the techniques of representation in artificial intelligence, is a well-known method in soft computing that allows the treatment of strong constraints generated by the inaccuracy that characterizes the data obtained from sensors. Fuzzy control is an intelligent control technique, characteristic of intelligent control systems, which serves as an alternative to conventional control techniques, and the construction of a mathematical model is not necessary. The article presents two intelligent control applications, Agent-based modeling and fuzzy logic for simulating pedestrian crowds in panic decision-making and Fuzzy controller for mobile robot.
In the case of Fuzzy Applied Cell Control Technology soft computing implementations, the synthesis of fuzzy automata (controllers) is sufficiently flexible, being practically a problem of emulating the typical phases of the algorithm, for the model of the given problem. The ability to program a problem is important in this case. However, some considerations are needed, which must be considered when structuring a fuzzy control system, regardless of the form in which it will be implemented. The configuration of a fuzzy controller, intended to lead a process, considers the conventional decomposition of its dynamics, corresponding to the evolution strategies adopted in the modeling stage.
Implementing the application in the Fuzzy Applied Cell Control Technology soft computing language. The problem is to obtain a symbolic description of the position of an object, based on two sensors for measuring distance, c1 and c2, represented in Figure 14. The purpose of this application is to characterize the detected object by determining its position and orientation by analyzing two fuzzy subsets. The syntax sets of terms are close, quite_close, quite_far, far and left, front, right.
Social media are computer-mediated tools that allow people to create, share or exchange information, ideas, pictures, audio or videos in virtual communities by using open Internet. Among online social networking services, there exist very interesting and challengeable research works on how to improve an efficient social media computing and how to make an effective social network analysis and mining from the perspectives of both academia and industry. Therefore, social computing, as a research discipline, is emerging for handling those kind of data generated from social media. Normally, various social computing related techniques include statistical approaches, graph based approaches and so forth. However, a human nature is present in the social networks. This implies that the social networks are human-like-full of imprecise relations and connections between individuals, vague terms, groups and individuals with indefinite descriptions and characteristics of interests [1]. In order to better cope with these burning issues, advances on soft computing technologies, such as fuzzy set, formal concept analysis and rough set theories, probabilistic computing, as well as neural network and system, are paving a road to more valuable and feasible solutions to the emerging social media and big data, finally bringing a brilliant future of wisdom and intelligent social media network. This survey will be carried out for SNA from following various aspects, i.e., network representation, reputation/position analysis of users, social relationships characterization, topological structure analysis, social data analysis.
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