In the design of fuzzy rule-based models, in the presence of high-dimensional data, we are faced with conceptual and algorithmic challenges. Conceptually, as the dimensionality of data increases, the notion of distance starts to be questionable, resulting in the well-known concentration effect. This has a direct detrimental effect given the fact that the distance is used in fuzzy clustering, using which the condition parts of the rules are being formed. Computationally, with the increase of dimensionality, the computing overhead becomes significant and has to be carefully addressed. In this article, we advocate a construction of distributed fuzzy rule-based models, where instead of a single monolithic (multivariable) rule-based model, we construct a collection of low-dimensional (in particular, 1- or 2-D) rule-based models and aggregate their results through some linear transformations. A suite of experimental studies realized using publicly available data are reported along with a comparative analysis engaging accuracy criteria and computing overhead. Interestingly, building distributed models exhibits some tangible benefits over the construction of the monolithic rule-based models. In terms of accuracy and computing costs, the gains of 1-D rule-based models with optimal linkage matrix (on average) are around 43.46% and 98.85%, respectively.
The recent trend in the development of neurofuzzy systems has profoundly emphasized the importance of synergy between the fundamentals of fuzzy sets and neural networks. The resulting frameworks of the neurofuzzy systems took advantage of an array of learning mechanisms primarily originating within the theory of neurocomputing and the use of fuzzy models (predominantly rule-based systems) being well established in the realm of fuzzy sets. Ideally, one can anticipate that neurofuzzy systems should fully exploit the linkages between these two technologies while strongly preserving their evident identities (plasticity or learning abilities to be shared by the transparency and full interpretability of the resulting neurofuzzy constructs). Interestingly, this synergy still becomes a target yet to be satisfied. This study is an attempt to address the fundamental interpretability challenge of neurofuzzy systems. Our underlying conjecture is that the transparency of any neurofuzzy system links directly with the logic fabric of the system so the logic fundamentals of the underlying architecture become of primordial relevance. Having this in mind the development of neurofuzzy models hinges on a collection of logic driven processing units named here fuzzy (logic) neurons. These are conceptually simple logic-oriented elements that come with a well-defined semantics and plasticity. Owing to their diversity, such neurons form essential building blocks of the networks. The study revisits the existing categories of logic neurons, provides with their taxonomy, helps understand their functional features and sheds light on their behavior when being treated as computational components of any neurofuzzy architecture. The two main categories of aggregative and reference neurons are deeply rooted in the fundamental operations encountered in the technology of fuzzy sets (including logic operations, linguistic modifiers, and logic reference operations). The developed heterogeneous networks come with a well-defined semantics and high interpretability (which directly translates into the rule-based representation of the networks). As the network takes advantage of various logic neurons, this imposes an immediate requirement of structural optimization, which in this study is addressed by utilizing various mechanisms of genetic optimization (genetic algorithms). We discuss the development of the networks, elaborate on the interpretation aspects and include a number of illustrative numeric examples.
In semantic-based image classification, learning concepts from features is an ongoing challenge for researchers and practitioners in different communities such as pattern recognition, machine learning and image analysis, among others. Concepts are used to add knowledge to the image descriptions linking high- and low-level numerical interpretation of the image content. Augmented descriptions are useful to perform more ‘intelligent’ processing on large-scale image databases. The semantic component casts the classification into the supervised or learning-from-examples paradigm, in which the classifier obtains knowledge by generalising specific facts presented in a number of design samples (or training patterns). Consequently, selection of suitable samples becomes a critical design step. The introduced framework exploits the capability of support vector classifiers to learn from relatively small number of patterns. Classifiers make decisions based on low-level descriptions containing only some image content information (e.g. colour, texture, shape). Therefore there is a clear drawback in collecting image samples by just using random visual observation and ignoring any low-level feature similarity. Moreover, this sort of approach set-up could lead to sub-optimal training data sets. The presented framework uses unsupervised learning to organise images based on low-level similarity, in effort to assist a professional annotator in picking positive and negative samples for a given concept. Active learning to refine the classifier model follows this initial design step. The framework shows promising results as an efficient approach in selecting design samples for semantic image description and classification.
Microbial fuel cells (MFCs) are envisioned as one of the most promising alternative renewable energy sources because they can generate electric current continuously while treating waste. Terrestrial Microbial Fuel Cells (TMFCs) can be inoculated and work on the use of soil, which further extends the application areas of MFCs. Energy supply, as a primary influential factor determining the lifetime of Wireless Sensor Network (WSN) nodes, remains an open challenge in sensor networks. In theory, sensor nodes powered by MFCs have an eternal life. However, low power density and high internal resistance of MFCs are two pronounced problems in their operation. A single-hop WSN powered by a TMFC experimental setup was designed and experimented with. Power generation performance of the proposed TMFC, the relationships between the performance of the power generation and the environment temperature, the water content of the soil by weight were measured by experiments. Results show that the TMFC can achieve good power generation performance under special environmental conditions. Furthermore, the experiments with sensor data acquisition and wireless transmission of the TMFC powering WSN were carried out. We demonstrate that the obtained experimental results validate the feasibility of TMFCs powering WSNs.
In this paper, the authors propose and investigate a new category of neurofuzzy networks-fuzzy polynomial neural networks (FPNNs)-and develop a comprehensive design methodology involving mechanisms of genetic optimization and, in particular, genetic algorithms (GAs). The conventional FPNNs developed so far are based on mechanisms of self-organization and evolutionary optimization. The design of the network exploits the extended group method of data handling, with some essential parameters of the network being provided by the designer and kept fixed throughout the overall development process. This restriction may hamper a possibility of producing an optimal architecture of the model. The design proposed in this paper addresses this issue. The augmented genetically optimized FPNN (gFPNN) results in a structurally optimized structure and comes with a higher level of flexibility in comparison with the one encountered in the conventional FPNN. The GA-based design procedure that is applied to each layer of FPNN leads to the selection of the preferred nodes (or fuzzy polynomial neurons) available within the FPNN. In the sequel, two general optimization mechanisms are explored. First, the structural optimization is realized via GAs, whereas the ensuing, detailed parametric optimization is carried out in the setting of a standard least-square-method-based learning. The performance of gFPNN is quantified through experimentation where a number of modeling benchmarks are being used, i.e., synthetic and experimental data already experimented within fuzzy or neurofuzzy modeling. The obtained results demonstrate superiority of the proposed networks over the existing fuzzy and neural models
The papers in included in this special issue examines new and novel communication methods for smart e-health systems. Recent developments in the healthcare domain have facilitated the integration of several technologies for smart, cost-effective, reliable, and pervasive health monitoring of chronic diseases. Current research efforts focus on developing real-time communication methods, mostly for body area networks (BANs) that are used to deliver patients´ information effectively. These efforts are limited to communication within a BAN; however, less attention has been paid to connect multiple BANs to remote servers in real time. In addition, there is a limited study on the integration of BANs with different technologies including mobile cloud computing—a technology that may assist in storing and processing the huge amount of BAN data at competitive costs. Machine to machine is also considered to be a valuable paradigm in delivering BAN data to a remote server/cloud for further analysis. This may assist in reducing risks and cost of remote health monitoring. Unlike conventional research in BANs where researchers focused on individual networks, there is a need to develop innovative communication methods with a focus on complete and smart e-health systems. This smart e-health system must integrate the aforementioned technologies with multiple BANs, and provide rich healthcare services to end users. It calls for research on versatile topics ranging from physical and medium access control protocols to BANs coexistence, traffic characterization, cloud resource allocation, and cloud monitoring and maintenance.
