Category: Applied Soft Computing

Heart failure (HF) has become a global public health issue due to its high prevalence and the significant cost of treatment. Today, one of the major challenges faced by clinicians is understanding the complex HF self-care process to develop suitable interventions. The self-care process of maintaining physiologic stability and manage the disease symptoms of HF patients is influenced by several factors, complicating the process for patients, caregivers and managers. In this research, the objective was to reduce the complexity of HF self-care process and to optimize it as a critical success factor (CSFs). Therefore, fuzzy decision-making trial and evaluation laboratory (DEMATEL) method were used to identify CSFs in HF self-care in an uncertain environment. Based on the literature review on HF self-care, 10 factors were identified as affecting HF self-care. A questionnaire, specifically designed for double-sided comparison DEMATEL Technique, covered these factors. Four experts were selected t evaluation using snowball sampling method. These experts assessed the direct associations between influential factors in HF self-care. The initial assessments were then turned into triangular fuzzy numbers (TFNs), and group opinions were fused; thus, the total relation matrix with TFNs elements was obtained. Defuzzification of the fuzzy total relation matrix was performed by converting fuzzy data into crisp scores (CFCS). After extracting the results of the model, influential factors were categorized as cause or effect, with cause factors identified as CSFs in HF self-care. Five influential factors were identified as CSFs by the proposed method, including F₄ (cultural beliefs and values) > F₉ (improving patient’s self-efficacy and ensuring HF self-care by patients (expecting proper self-care and long-term effort to maintain HF self-care)) > F₇ (family and friends support and social relation network to support patients with HF) > F₈ (easy access to care (regular outpatient clinic visits with a professional HF nurse and easy access to care in order to support HF self-care, guidance and training)) > F6(confidence of patients and caregivers regarding HF self-care). Based on the results of this study, the optimization of HF self-care can be efficiently simplified into optimizing five CSFs. Therefore, to improve HF self-care process, it is suggested that decision makers and managers in the design of interventions focus on these five factors as CSFs, particularly under conditions with limited resources.

In this paper, we present a novel complex neural fuzzy approach to multi-class prediction. The complex neural fuzzy system (CNFS) is proposed using complex fuzzy sets (CFSs), fuzzy causalities and multi-swarm machine learning. In general, CFSs are regarded as advanced fuzzy sets with membership degrees defined in the unit disc of the complex plane, in contrast to regular fuzzy sets with membership degrees in the real-valued unit interval [0,1]. The proposed model is composed of the premises designed by CFSs, the consequences designed by Takagi-Sugeno linear functions, and a fuzzy causality layer connecting the premises toward the consequences, and it is able to perform prediction of multiple targets. The usage of fuzzy causality in the proposed model makes difference to traditional fuzzy models using If-Then rules, and gives the freedom and flexibility for model construction. To optimize the proposed CNFS, we present a hybrid learning scheme using the particle swarm optimization with multiple swarms (denoted as PSOmsw) and the Kalman filtering algorithm (denoted as KFA). In the hybrid learning method, the KFA updates the consequence parameters while the PSOmsw evolves the rest parameters of the model. The proposed approach has been tested with experiments using several real-world stock market datasets. Compared with other methods, the proposed approach has shown excellent performance.

Decomposition is a representative method for handling many-objective optimization problems with evolutionary algorithms. Classical decomposition scheme relies on a set of uniformly distributed reference vectors to divide the objective space into multiple subregions. This scheme often works poorly when the problem has an irregular Pareto front due to the inconsistency between the distribution of reference vectors and the shape of Pareto fronts. We propose in this paper an adaptive weighted decomposition based many-objective evolutionary algorithm to tackle complicated many-objective problems whose Pareto fronts may or may not be regular. Unlike traditional decomposition based algorithms that use a pre-defined set of reference vectors, the reference vectors in the proposed algorithm are produced from the population during the search. The experiments show that the performance of the proposed algorithm is competitive with other state-of-the-art algorithms and is less-sensitive to the irregularity of the Pareto fronts.

Today’s ever-increasing application of high-dimensional data sets makes it necessary to find a way to fully comprehend them. One of these ways is visualizing data sets. However, visualizing more than 3-dimensional data sets in a fathomable way has always been a serious challenge for researchers in this field. There are some visualizing methods already available such as parallel coordinates, scatter plot matrix, RadViz, bubble charts, heatmaps, Sammon mapping and self organizing maps. In this paper, an axis-based method (called Nasseh method) is introduced in which familiar elements of visualization of 1-, 2- and 3-dimensional data sets are used to visualize higher dimensional data sets so that it will be easier to explore the data sets in the corresponding dimensions. Nasseh method can be used in many applications from illustrating points in high-dimensional geometry to visualizing estimated Pareto-fronts for many-objective optimization problems.

Image watermarking has emerged as a useful method for solving security issues like authenticity, copyright protection and rightful ownership of digital data. Existing watermarking schemes use either a binary or grayscale image as a watermark. This paper proposes a new robust and adaptive watermarking scheme in which both the host and watermark are the color images of the same size and dimension. The security of the proposed watermarking scheme is enhanced by scrambling both color host and watermark images using Arnold chaotic map. The host image is decomposed by redundant discrete wavelet transform (RDWT) into four sub-bands of the same dimension, and then approximate sub-band undergoes singular value decomposition (SVD) to obtain the principal component (PC). The scrambled watermark is then directly inserted into a principal component of scrambled host image, using an artificial bee colony optimized adaptive multi-scaling factor, obtained by considering both the host and watermark image perceptual quality to overcome the tradeoff between imperceptibility and robustness of the watermarked image. The hybridization of RDWT-SVD provides an advantage of no shift-invariant to achieve higher embedding capacity in the host image and preserving the imperceptibility and robustness by exploiting SVD properties. To measure the imperceptibility and robustness of the proposed scheme, both qualitative and quantitative evaluation parameters like peak signal to noise ratio (PSNR), structural similarity index metric (SSIM) and normalized cross-correlation (NC) are used. Experiments are performed against several image processing attacks and the results are analyzed and compared with other related existing watermarking schemes which clearly depict the usefulness of the proposed scheme. At the same time, the proposed scheme overcomes the major security problem of false positive error (FPE) that mostly occurs in existing SVD based watermarking schemes.

The power management system for electronic vehicles selectively activates Electronic Control Units (ECUs) in the electronic control system according to time-series vehicle data and predefined operation states. However, at an operation state transition, the energy overheads used for the selective ECU activation could be higher than the energy saved by deactivating ECUs. To prevent these energy-inefficient state transitions, we apply two main ideas to our proposed algorithm: (A) unacceptable state transitions and (B) adaptive training speed. For the unacceptable transitions, our energy model evaluates the breakeven time where energy saving equals to energy overheads. Based on the breakeven time, our algorithm classifies training dataset as unacceptable and acceptable event sets. Especially when the algorithm trains neural networks for the two event sets, the adaptive training speed expedites its training speed based on a history of training errors. Consequently, without violating in-vehicle time constraints, the algorithm could provide real-time predictions and save energy overheads by avoiding unacceptable transitions. In the simulation results on real driving datasets, our algorithm improves the energy dissipation of the electronic control system by 5% to 7%.

Combinatorial optimization problems (COPs) are discrete problems arising from aerospace, bioinformatics, manufacturing, and other fields. One of the classic COPs is the scheduling problem. Moreover, these problems are usually multimodal optimization problems with a quantity of global and local optima. As a result, many search algorithms can easily become trapped into local optima. In this article, we propose a multi-center variable-scale search algorithm for solving both single-objective and multi-objective COPs. The algorithm consists of two distinct points. First, the multi-center strategy chooses several individuals with better performance as the only parents of the next generation, which means that there are a number of separate searching areas around the searching center. Second, the next generation of the population is produced by a variable-scale strategy with an exponential equation based on the searching center. The equation is designed to control the neighborhood scale, and adaptively realize the large-scale and small-scale searches at different search stages to balance the maintenance of diversity and convergence speed. In addition, an approach of adjusting centers is proposed concerning the number and distribution of centers for solving multi-objective COPs. Finally, the proposed algorithm is applied to three COPs, including the well-known flexible job shop scheduling problem, the unrelated parallel machine scheduling problem, and the test task scheduling problem. Both the single-objective optimization algorithm and the multi-objective optimization algorithm demonstrate competitive performance compared with existing methods.