Applications of computational intelligence techniques to software engineering problems

Innovations in Systems and Software Engineering -     

From computational intelligence to Web intelligence

The authors explore three topics in computational intelligence: machine translation, machine learning and user interface design and speculate on their effects on Web intelligence. Systems that can communicate naturally and learn from interactions will power Web intelligence's long term success. The large number of problems requiring Web-specific solutions demand a sustained and complementary effort to advance fundamental machine-learning research and incorporate a learning component into every Internet interaction. Traditional forms of machine translation either translate poorly, require resources that grow exponentially with the number of languages translated, or simplify language excessively. Recent success in statistical, nonlinguistic, and hybrid machine translation suggests that systems based on these technologies can achieve better results with a large annotated language corpus. Adapting existing computational intelligence solutions, when appropriate for Web intelligence applications, must incorporate a robust notion of learning that will scale to the Web, adapt to individual user requirements, and personalize interfaces.     

Picture fuzzy clustering: a new computational intelligence method

Fuzzy clustering especially fuzzy \(C\)-means (FCM) is considered as a useful tool in the processes of pattern recognition and knowledge discovery from a database; thus being applied to various crucial, socioeconomic applications. Nevertheless, the clustering quality of FCM is not high since this algorithm is deployed on the basis of the traditional fuzzy sets, which have some limitations in the membership representation, the determination of hesitancy and the vagueness of prototype parameters. Various improvement versions of FCM on some extensions of the traditional fuzzy sets have been proposed to tackle with those limitations. In this paper, we consider another improvement of FCM on the picture fuzzy sets, which is a generalization of the traditional fuzzy sets and the intuitionistic fuzzy sets, and present a novel picture fuzzy clustering algorithm, the so-called FC-PFS. A numerical example on the IRIS dataset is conducted to illustrate the activities of the proposed algorithm. The experimental results on various benchmark datasets of UCI Machine Learning Repository under different scenarios of parameters of the algorithm reveal that FC-PFS has better clustering quality than some relevant clustering algorithms such as FCM, IFCM, KFCM and KIFCM.     

计算智能在移动机器人路径规划中的应用综述

移动机器人路径规划是建立在机器人定位与避障研究之上,进一步对机器人行为的深入.在给出人工神经网络(ANN)、模糊逻辑(FL)、遗传算法(GA)等计算智能原理性方法的基础上,从一般意义讨论了各类计算智能方法用于路径规划的切入点,研究了各类算法的实现机理与设计思想.最后结合目前的技术发展趋势,对路径规划问题未来可能的研究发展方向进行了探讨.     

Advances in computational intelligence

Neural Computing and Applications -     

Exploring spatial data through computational intelligence: a joint perspective

The dramatic increase in geospatial data occasioned by developments in digital mapping, remote sensing, IT, and widespread generalization of Geographic Information Systems (GIS), emphasises the importance of exploring new approaches to spatial analysis and modelling. This favours the creation of new knowledge and eventually helps the process of scientific discovery. In this context the special nature of spatial data is particularly relevant and should be taken into account (e.g. observations are not independent and data uncertainty and errors are often spatially structured). The tolerance of imprecision and uncertainty makes soft computing a potentially very useful tool in the GIS environment. Computational Intelligence (or Soft computing) fits particularly well with GIS applications in those cases where computationally hard problems cannot be solved by classical algorithmic approaches.     

Learning patterns in ambient intelligence environments: a survey

It is essential for environments that aim at helping people in their daily life that they have some sort of Ambient Intelligence. Learning the preferences and habits of users then becomes an important step in allowing a system to provide such personalized services. Thus far, the exploration of these issues by the scientific community has not been extensive, but interest in the area is growing. Ambient Intelligence environments have special characteristics that have to be taken into account during the learning process. We identify these characteristics and use them to highlight the strengths and weaknesses of developments so far, providing direction to encourage further development in this specific area of Ambient Intelligence.     

Solving the minimum bisection problem using a biologically inspired computational model

The traditional trend of DNA computing aims at solving computationally intractable problems. The minimum bisection problem (MBP) is a well-known NP-hard problem, which is intended to partition the vertices of a given graph into two equal halves so as to minimize the number of those edges with exactly one end in each half. Based on a biologically inspired computational model, this paper describes a novel algorithm for the minimum bisection problem, which requires a time cost and a DNA strand length that are linearly proportional to the instance size.     

Applications of artificial intelligence in intelligent manufacturing: a review

Based on research into the applications of artificial intelligence (AI) technology in the manufacturing industry in recent years, we analyze the rapid development of core technologies in the new era of ‘Internet plus AI’, which is triggering a great change in the models, means, and ecosystems of the manufacturing industry, as well as in the development of AI. We then propose new models, means, and forms of intelligent manufacturing, intelligent manufacturing system architecture, and intelligent manufacturing technology system, based on the integration of AI technology with information communications, manufacturing, and related product technology. Moreover, from the perspectives of intelligent manufacturing application technology, industry, and application demonstration, the current development in intelligent manufacturing is discussed. Finally, suggestions for the application of AI in intelligent manufacturing in China are presented.     

Market segmentation of organ donors in Egypt: a bio-inspired computational intelligence approach

First performed in 1954, organ transplantation is a universally practiced clinical procedure. This study uses ant colony optimization (ACO), radial basis function neural network (RBFNN), Kohonen’s self-organizing maps (SOM), and support vector machines (SVMs) to examine the effect of various cognitive, psychographic, and attitudinal factors on organ donation. ACO, RBFNN, SOM, and SVMs are compared to a standard statistical method (linear discriminant analysis [LDA]). The variable sets considered are altruistic values, perceived risks/benefits, knowledge, attitudes toward organ donation, and intention to donate organs. The paper shows how it is possible to identify various dimensions of organ donation behavior by uncovering complex patterns in the dataset and also shows the classification and clustering abilities of machine-learning systems.     

计算智能的新框架:生物网络结构

由神经、内分泌和免疫这3个生物系统可分别引发人工神经网络（ANN）、人工内分泌系（AES）和人工免疫系统（AIS），它们三者所组成的“神经内分泌免疫网络”在生物系统中扮演着维持动态平衡的重要角色．为此，提出一种将ANN、AES和AIS进行集成的计算智能的新框架，称之为“生物网络结构”，并讨论了其中应着重关注的研究方向和思路，从而使人们在此框架的基础上研究其理论和应用技术，更好地满足复杂系统对计算智能的应用需求．     

A perspective on computational intelligence education

Presents an interview with Joe Rothermich, a Vice President at Lincoln Vale, LLC, an alternative asset management firm.     

Applications of structure from motion: a survey

Structure from motion (SfM) has been an active research area in computer vision for decades and numerous practical applications are benefiting from this research. While no previous work has tried to summarize the applications appearing in the literature, this paper deals with a comprehensive overview of recent applications of SfM by classifying them into 10 categories, namely augmented reality, autonomous navigation/guidance, motion capture, hand-eye calibration, image/video processing, image-based 3D modeling, remote sensing, image organization/browsing, segmentation and recognition, and military applications. The goal is to provide insights for researchers to position their work more appropriately in the context of existing techniques, and to perceive both new applications and relevant research problems.     

Quantization of games: Towards quantum artificial intelligence

We discuss the impact of quantum game theory on information processing and the emerging information society. The framework, that we establish, encompasses various particular models considered in the field of artificial intelligence. This paper provides insight into the following issues: detailed analysis of a quantum algorithm solving Newcombs’ paradox, the Elitzur–Vaidman circuit breaker and the Metropolis algorithm is presented.