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One of the impediments to the wide dissemination of software estimation and measurement practices is the significant overhead imposed by these practices on the project and development team. Despite significant investment in research, the lightweight estimation of development effort is still an unsolved problem in software engineering. This study proposes a new, lightweight effort estimation model aimed at iterative development environments, as Agile Processes. The model is based on Radial Basis Functions. It is experimented in two semi-industrial projects carried out using a customized version of Extreme Programming (XP). The results are promising and evidence that the proposed model can be developed incrementally and from scratch for new projects without resorting itself to historic data.
Inspired by the idealism embodied in Russell and Whitehead’s “Principia Mathematica,” Wiener marched along a different and unique path toward sciences of intelligence and behavior which culminated at “Cybernetics: Or Control and Communication in the Animal and the Machine” 70 years ago. Since then, we have witnessed the birth of Cognitive Science, Artificial Intelligence (AI), Computational Intelligence, and many other new research fields and disciplines, all of which have been catalyzed by Cybernetics. The IEEE Systems, Man, AND Cybernetics Society and this Transactions on Cybernetics have become the focal point of the broad cybernetics community by promoting the theory, practice, and interdisciplinary aspects of systems science and engineering, human-machine systems, and cybernetics principles. It is a time of celebration and reflection.
The six articles in this special issue provide a snapshot of the current research trends in multiobjective evolutionary algorithms for data mining. The main issues and challenges in this domain have been highlighted and directions of future research work have also been provided,
In this study, we are concerned with the role of information granulation in processes of data mining in databases. By its nature, data mining pursuits are very much oriented towards end-users and imply that any results need to be easily interpretable. Granulation of information promotes this interpretability and channels all pursuits of data mining (that are otherwise computationally intensive and thus highly prohibitive) towards more efficient processing and feasible processing of information granules. First, we discuss the essence of information granulation and afterwards elaborate on the main approaches to the design of information granules. We distinguish between user-driven, data-driven and hybrid methods of information granulation. Several main classes of membership function of information granules — fuzzy sets are investigated and contrasted in terms of some selection criteria such as parametric flexibility and sensitivity of the ensuing information granules. We revisit two fundamental concep ts in data mining such as associations and rules in the setting of information granules. Associations are direction-free constructs that capture the most essential components of the overall structure in database. The relevance of associations is expressed by counting the amount of data standing behind the Cartesian products of the information granules contributing to the construction of the associations. The proposed methodology of data mining comprises two phases. First, associations are constructed and the most essential (relevant) ones are collected in the form of a data mining agenda. Second, some of them are converted into direction-driven constructs, that is rules. The idea of consistency of the rules is discussed in detail.
In the article we have discussed an approach to time series modelling based on Fuzzy Cognitive Maps (FCMs). We have introduced FCM design method that is based on replicated ordered time series data points. We named this representation method history h, where h is number of consecutive data points we gather. Custom procedure for concepts/nodes extraction follows the same convention. The objective of the study reported in this paper was to investigate how increasing h influences modelling accuracy. We have shown on a selection of 12 time series that the higher the h, the smaller the error. Increasing h improves modelâs quality without increasing FCMâs size. The method is stable - gains are comparable for FCMs of different sizes.
Energy harvesting (EH) technique has been proposed as a favorable solution for addressing the power supply exhaustion in a wireless sensor node and prolong the operating time for a wireless sensor network. Thermoelectric energy generator (TEG) is a valuable device converting the waste heat into electricity which can be collected and stored for electronics. In this paper, the thermal energy from human body is captured and converted to the low electrical energy by means of thermoelectric energy harvester. The aim of presented work is utilizing the converted electricity to power the related electronic device and to extend the working life of a sensor node. Considering the related characteristics of TEG used for human, a type of a novel power management system is designed and presented to harvest generated electricity. The proposed circuit is developed based on off-the-shelf commercial chips, LTC3108 and BQ25504. It can accept the lowest input voltage of 20 mV, which is more suitable for human thermoelectric energy harvesting. Through experiments, developed energy harvesting system can effectively power the sensor to intermittently transmit the data as well as perform the converted energy storage. Compared to the independent commercial chips applications and other microcontroller-based energy harvesting systems, the designed thermoelectric energy harvester system presents the advantages not only in high energy storage utilization rate but also the ultra-low input voltage characteristic. Since the heat from human body is harvested, therefore, the system can possibly be used to power the sensor placed on human body and has practical applications such as physiological parameter monitoring.
Methods of qualitative analysis, such as qualitative classification, have gained importance as an essential complement of existing quantitative analysis in numerous fields. Only a few models have been developed to deal with qualitative inputs in the form of type‐2 fuzzy(T2F) sets properly, given that traditional defuzzification method like the Karnik–Mendel algorithm performs dimensionality reduction at the cost of loss of information. To improve the situation, we define the expected value and variance of T2F set in this paper. By using a combination of them, we transfer the vertical three‐dimensional uncertainty of T2F set to horizontal range uncertainty without much distortion of information. Additionally, current classification models are unsuitable to the partial classification problem if an output is not fully assigned to a single class. We build a comprehensive qualitative classification model based on fuzzy support vector machine (FSVM) combined with type‐2 fuzzy expected regression (FER) to solve the partial classification problem as mentioned. This classifier (i.e. FER‐FSVM) makes it possible to achieve the discrimination of output while characterizing membership for each class in terms of multidimensional qualitative inputs (attributes) in the form of T2F sets. FER‐FSVM also can self‐learn the data structure and shift between FER or FSVM for classification automatically, thus largely improving the efficiency of the classification process. The new model is almost 7 times more efficient than FSVM, as shown by our empirical experiments. © 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.
