Soft computing

About: Soft computing is a research topic. Over the lifetime, 6710 publications have been published within this topic receiving 118508 citations.

Fuzzy Cognitive Map Learning Based on Nonlinear Hebbian Rule

Abstract: Fuzzy Cognitive Map (FCM) is a soft computing technique for modeling systems. It combines synergistically the theories of neural networks and fuzzy logic. The methodology of developing FCMs is easily adaptable but relies on human experience and knowledge, and thus FCMs exhibit weaknesses and dependence on human experts. The critical dependence on the expert’s opinion and knowledge, and the potential convergence to undesired steady states are deficiencies of FCMs. In order to overcome these deficiencies and improve the efficiency and robustness of FCM a possible solution is the utilization of learning methods. This research work proposes the utilization of the unsupervised Hebbian algorithm to nonlinear units for training FCMs. Using the proposed learning procedure, the FCM modifies its fuzzy causal web as causal patterns change and as experts update their causal knowledge.

Harmony search for generalized orienteering problem: best touring in China

Abstract: In order to overcome the drawbacks of mathematical optimization techniques, soft computing algorithms have been vigorously introduced during the past decade. However, there are still some possibilities of devising new algorithms based on analogies with natural phenomena. A nature-inspired algorithm, mimicking the improvisation process of music players, has been recently developed and named Harmony Search (HS). The algorithm has been successfully applied to various engineering optimization problems. In this paper, the HS was applied to a TSP-like NP-hard Generalized Orienteering Problem (GOP) which is to find the utmost route under the total distance limit while satisfying multiple goals. Example area of the GOP is eastern part of China. The results of HS showed that the algorithm could find good solutions when compared to those of artificial neural network.

Artificial Intelligence and Soft Computing - ICAISC 2004

Abstract: Information system design and optimum sizing is a very complex task. Theoretical research and practitioners often tackle the optimization problem by applying specific techniques for the optimization of individual design phases, usually leading to local optima. Conversely, this paper proposes the definition of a design methodology based on an evolutionary approach to the optimization of the client/server-farm distributed structure, which is typical of a distributed information technology (IT) architecture. The optimization problem consists of finding the minimum-cost physical systems that satisfy all architectural requirements given by the designer. The proposed methodology allows for the identification of the architectural solution that minimizes costs, against different information system requirements and multiple design alternatives, thorough a genetic-based exploration of the solution space. Experimental results show that costs can be significantly reduced with respect to conventional approaches adopted by IT designers and available in the professional literature.

A survey on indexing techniques for big data: taxonomy and performance evaluation

Abstract: The explosive growth in volume, velocity, and diversity of data produced by mobile devices and cloud applications has contributed to the abundance of data or `big data.' Available solutions for efficient data storage and management cannot fulfill the needs of such heterogeneous data where the amount of data is continuously increasing. For efficient retrieval and management, existing indexing solutions become inefficient with the rapidly growing index size and seek time and an optimized index scheme is required for big data. Regarding real-world applications, the indexing issue with big data in cloud computing is widespread in healthcare, enterprises, scientific experiments, and social networks. To date, diverse soft computing, machine learning, and other techniques in terms of artificial intelligence have been utilized to satisfy the indexing requirements, yet in the literature, there is no reported state-of-the-art survey investigating the performance and consequences of techniques for solving indexing in big data issues as they enter cloud computing. The objective of this paper is to investigate and examine the existing indexing techniques for big data. Taxonomy of indexing techniques is developed to provide insight to enable researchers understand and select a technique as a basis to design an indexing mechanism with reduced time and space consumption for BD-MCC. In this study, 48 indexing techniques have been studied and compared based on 60 articles related to the topic. The indexing techniques' performance is analyzed based on their characteristics and big data indexing requirements. The main contribution of this study is taxonomy of categorized indexing techniques based on their method. The categories are non-artificial intelligence, artificial intelligence, and collaborative artificial intelligence indexing methods. In addition, the significance of different procedures and performance is analyzed, besides limitations of each technique. In conclusion, several key future research topics with potential to accelerate the progress and deployment of artificial intelligence-based cooperative indexing in BD-MCC are elaborated on.

System and Method for Extremely Efficient Image and Pattern Recognition and Artificial Intelligence Platform

Abstract: Specification covers new algorithms, methods, and systems for: Artificial Intelligence; the first application of General-AI. (versus Specific, Vertical, or Narrow-AI) (as humans can do) (which also includes Explainable-AI or XAI); addition of reasoning, inference, and cognitive layers/engines to learning module/engine/layer; soft computing; Information Principle; Stratification; Incremental Enlargement Principle; deep-level/detailed recognition, e.g., image recognition (e.g., for action, gesture, emotion, expression, biometrics, fingerprint, tilted or partial-face, OCR, relationship, position, pattern, and object); Big Data analytics; machine learning; crowd-sourcing; classification; clustering; SVM; similarity measures; Enhanced Boltzmann Machines; Enhanced Convolutional Neural Networks; optimization; search engine; ranking; semantic web; context analysis; question-answering system; soft, fuzzy, or un-sharp boundaries/impreciseness/ambiguities/fuzziness in class or set, e.g., for language analysis; Natural Language Processing (NLP); Computing-with-Words (CWW); parsing; machine translation; music, sound, speech, or speaker recognition; video search and analysis (e.g., “intelligent tracking”, with detailed recognition); image annotation; image or color correction; data reliability; Z-Number; Z-Web; Z-Factor; rules engine; playing games; control system; autonomous vehicles or drones; self-diagnosis and self-repair robots; system diagnosis; medical diagnosis/images; genetics; drug discovery; biomedicine; data mining; event prediction; financial forecasting (e.g., for stocks); economics; risk assessment; fraud detection (e.g., for cryptocurrency); e-mail management; database management; indexing and join operation; memory management; data compression; event-centric social network; social behavior; drone/satellite vision/navigation; smart city/home/appliances/IoT; and Image Ad and Referral Networks, for e-commerce, e.g., 3D shoe recognition, from any view angle.