Intelligent Decision Technologies

There is no universally accepted methodology to determine how much confidence one should place in the output of a classification system. Leap and Bauer [15] present a confidence paradigm based on the assumptions that system confidence acts like, or can be modeled as value and that indication confidence can be modeled as a function of the posterior probability estimates. This paper extends the paradigm to include out-of-library considerations. In addition, a novel out-of-library detector is presented. Developing the out-of-library detector involves bounding and discretizing the feature space and assigning each discrete point to either in-library or out-of-library classes based upon Mahalanobis distance from the in-library target classes. Application of the confidence paradigm to the out-of-library detector leads us to the demonstration of a new concept called out-of-library non-declarations. The extended paradigm is applied to a synthetic data set as well as an automatic target recognition data set. In all cases, the results show performance that tracks well with previous studies found in the literature and demonstrate positive steps toward fuller development of a theoretical framework that unites the viewpoints of the classification system developer and its user.

CogInfoCom is a novel interdisciplinary field of research – seen as a result of the merging of cognitive aspects and infocommunicative technologies in recent years. Research on CogInfoCom has enabled researchers and developers in engineering to explore the degree of co-existence of human and artificial intelligence in different fields. Such interdisciplinary research also contributes to the mapping out of innovative engineering applications enabled by CogInfoCom technologies.

In the new era of computational intelligence, the ever-expanding abundance of information storage and processing power enables researchers and users to tackle applications on various scientific domains of Decision Support Systems (DSS). The general focus of this special issue is to provide insights on how computational intelligence methodologies and algorithms can be employed in real world applications, so as to produce information systems capable of solving important and hard decision problems. This special issue on "Innovative Decision Systems" contains extended versions of seven (7) papers selected from the 12th EANN/7th AIAI 2011 Joint Conference. The manuscripts were accepted for publication, after passing through a peer review process by at least two independent academic referees. AIAI and EANN are two well-established annual events, technically sponsored by the Technical Committee 12, Working Group 12.5 (TC12-WG12.5) of the International Federation for Information Processing (IFIP) and the International Neural Network Society (INNS) respectively. The 2011 event was held on September 15–18 2011 in Corfu, Greece.

Several algorithms of the top-k retrieval problem have been introduced in recent years. Unfortunately, current top-k query processing techniques focus on Boolean queries, and cannot be applied to the large Data Bases (DB) seen the gigantic number of data. In this paper, we propose an intelligent approach for top-k flexible queries taking into account another degree of granularity in the process of the evaluation of the query. It consists of two steps: 1) Generate a Meta-DB formed by a set of clusters resulting of a preliminary fuzzy clustering on the data. This set represents a reduced view of the initial DB and permits to deduct the semantics of the initial DB, and 2) Generate automatically the top k results using this Meta-DB and a new operator called stratified operator. We prove that this approach is optimum sight that the evaluation of the query is not done on the set of starting data which are enormous but rather by using the Meta-DB. Moreover, this approach can be applied to the large DB and that it does not require modifying the SQL language.

MapReduce is a widespread programming model used for overcoming processing limits of current hardware resources. In the MapReduce paradigm, large amount of data distributed into multiple parts and each part processed on different processing units and finally the results of all units combined to obtain the ultimate result. Scheduling is an important aspect that affects overall processing quality, hence finding suitable algorithm based on properties of jobs is essential to acquire maximum performance. The basic FIFO algorithm for job scheduling does not meet maximum efficiency. Therefore, many of new schedulers proposed so far to improve quality metrics of the MapReduce system. The scheduling methods can be designed for job scheduling, map or reduce task scheduling or both. In this paper, we select high quality studies that concern the MapReduce scheduling problem and then classify them based on their main concerning quality measure and subsequently review selected studies. Finally, we discuss research trends and provide a roadmap of the scheduling problem for researchers.

This study aims to determine the effect of augmented reality-based mobile software on students’ academic achievement in the technical drawing lesson. Quantitative method was used in the study. In order to determine the effect of augmented reality-based mobile software developed on the students’ academic achievement, a static group pre-test-posttest experimental design was used. The study group consists of 84 engineering students. Thirty-seven of the students participating in the study are female and 47 are male. Academic achievement test was used in the study. T -test was used in data analysis. In the results of study it was concluded that the use of augmented reality technology in technical drawing lessons increased students’ academic achievement.

Krill herd (KH) is a stochastic nature-inspired optimization algorithm, it has been successfully used to solve many involved optimization problems. Occasionally, poor exploration (diversification) capability affects the performance of krill herd algorithm (KHA). In this paper, we proposed a new hybridization strategy, namely, hybrid the krill herd algorithm with the harmony search (HS) algorithm (Harmony-KHA), to improve the data clustering technique. This hybridization strategy seeks to enhance the global (diversification) search capability of the KH algorithm to obtain the best global optima. The proposed algorithm are conducted through the addition of the global search operator from the HS algorithm in order to improve the exploration around the optimal solution in KH and thus kill individuals move towards the global best solution. The proposed algorithm is applied to keep the best krill individuals during the updating positions of the krill individuals. Experiments were conducted using four standard datasets from the UCI Machine Learning Repository, which is commonly used in the domain of data clustering. The results showed that the proposed hybrid the KH algorithm with the HS algorithm (Harmony-KHA) is produced very accurate clusters especially in the large dataset. Furthermore, the Harmony-KHA obtained a high convergence rate and it can overcome the other comparative algorithms. The proposed algorithm is compared with other well-known based on data clustering algorithms including the original KH algorithm.