Fuzzy Adaptive Tuning of a Particle Swarm Optimization Algorithm for Variable-Strength Combinatorial Test Suite Generation

Combinatorial interaction testing is an important software testing technique that has seen lots of recent interest. It can reduce the number of test cases needed by considering interactions between combinations of input parameters. Empirical evidence shows that it effectively detects faults, in particular, for highly configurable software systems. In real-world software testing, the input variables may vary in how strongly they interact, variable strength combinatorial interaction testing (VS-CIT) can exploit this for higher effectiveness. The generation of variable strength test suites is a non-deterministic polynomial-time (NP) hard computational problem \cite{BestounKamalFuzzy2017}. Research has shown that stochastic population-based algorithms such as particle swarm optimization (PSO) can be efficient compared to alternatives for VS-CIT problems. Nevertheless, they require detailed control for the exploitation and exploration trade-off to avoid premature convergence (i.e. being trapped in local optima) as well as to enhance the solution diversity. Here, we present a new variant of PSO based on Mamdani fuzzy inference system \cite{Camastra2015,TSAKIRIDIS2017257,KHOSRAVANIAN2016280}, to permit adaptive selection of its global and local search operations. We detail the design of this combined algorithm and evaluate it through experiments on multiple synthetic and benchmark problems. We conclude that fuzzy adaptive selection of global and local search operations is, at least, feasible as it performs only second-best to a discrete variant of PSO, called DPSO. Concerning obtaining the best mean test suite size, the fuzzy adaptation even outperforms DPSO occasionally. We discuss the reasons behind this performance and outline relevant areas of future work.Comment: 21 page

Echocardiography in the evaluation of chest pain in the emergency department

Background: A challenge for clinicians in emergency departments (EDs) is rapid identification of those patients with chest pain who require admission and urgent management and those with low clinical risk who can be discharged safely from the ED. This study was designed with an aim to evaluate the ability of two-dimensional transthoracic echocardiography (2D-TTE) to determine causes of acute chest pain in patients presenting to the ED in order to decide whether hospital admission and further investigations were needed. Material/Methods: A total of 250 consecutive patients admitted with chest pain, were enrolled in this prospective study. Patients were divided into three groups: high risk, moderate risk, and low risk of cardiac events, according to cardiovascular risk factors. 2D-TTE was obtained using the HI vision Avius ultrasound unit (Hitachi). Statistical analysis was performed with the Statistical Package for the Social Sciences (SPSS), version 20. Results: Ischemic and/or non-ischemic heart diseases (IHD and/or NIHD) were detected in 147 (86.5%), 13 (7.6%), and 10 (5.9%) patients with high, moderate, and low risk, respectively. 2D-TTE was characterized by sensitivity of 85.86%, specificity of 100%, and positive predictive value (PPV) of 100% for detecting causes of chest pain. Conclusions: 2D-TTE increased specificity and sensitivity of detecting causes of chest pain, when compared to patient history, clinical findings, and electrocardiography (ECG). 2D-TTE can be used to help determine the need for hospital admission, to confirm or exclude diagnosis, and guide urgent therapy

Level-crossing rate and average duration of fades for mobile radio channel with hyperbolically distributed scatterers

In this paper we study the geometrical and time-variant wireless vector channel model with hyperbolically distributed scatterers for a macrocell mobile environment. In this study we investigate the level-crossing rate (LCR), the average duration of fades (ADF), the probability density function (PDF), the cumulative distribution function (CDF) and the autocorrelation functions (ACF) of this recently-proposed model. The simulated results are verified against the analytical Clarke's channel model. In this paper we study the geometrical and time-variant wireless vector channel model with hyperbolically distributed scatterers for a macrocell mobile environment. In this study we investigate the level-crossing rate (LCR), the average duration of fades (ADF), the probability density function (PDF), the cumulative distribution function (CDF) and the autocorrelation functions (ACF) of this recently-proposed model. The simulated results are verified against the analytical Clarke's channel model

Adsorption of Mn (II) ion on polyvinyl alcohol/chitosan dry blending from aqueous solution

AbstractThe present study explored the ability of polyvinyl alcohol/chitosan (PVA/CS) binary dry blend as an adsorbent for removal of Mn (II) ion from aqueous solution. We will study how the solid-state shear ball milling is employed to prepare the polyvinyl alcohol/chitosan hydrogel crosslinked without using the crosslinking agent. The PVA/CS was characterized by LDS, X-ray diffraction, FTIR and SEM. The results revealed that during the process of ball milling at ambient temperature, a PVA/CS blending can be effectively pulverized resulting in crosslinking of PVA/CS hydrogel having nanosized particles. Also, the removal of Mn (II) ion has been found to be pH, adsorbent dosage and contact time dependent and the optimum pH was 6.0. From the testing of equilibrium data by Langmuir and Freundlich isotherm models, it was found that the adsorption equilibrium was fitted well by Freundlich isotherm model. In addition, the kinetic adsorption was fitted well by the pseudo-second-order kinetic model. The thermodynamic study indicated that the adsorption of Mn (II) ion onto PVA/CS was spontaneous and endothermic in nature. Also, it showed that the PVA/CS binary blend was a good adsorbent for the removal of Mn (II) ion from aqueous solution

Context for Personalized Web Services

The paper discusses the way context is used for Web services personalization. A Web service is an accessible application that other applications and humans can discover and trigger to satisfy various needs such as car rental. Context is the information that characterizes the interactions between humans, applications, and the surrounding environment. Web services are personalized so that users\u27 preferences can be accommodated. Preferences are of different types varying from when the execution of a Web service should start to where the outcome of this execution should be delivered. Besides users\u27 preferences, this paper highlights that the resources on which the Web services are performed have an impact on Web services personalization

Time-frequency analysis of normal and abnormal biological signals

Due to the non-stationary, multicomponent nature of biomedical signals, the use of time-frequency analysis can be inevitable for these signals. The choice of the proper time-frequency distribution (TFD) that can reveal the exact multicomponent structure of biological signals is vital in many applications, including the diagnosis of medical abnormalities. In this paper, the instantaneous frequency (IF) estimation using four well-known TFDs is applied for analyzing biological signals. These TFDs are: the Wigner-Ville distribution (WVD), the Choi-Williams distribution (CWD), the Exponential T-distribution (ETD) and the Hyperbolic T-distribution (HTD). Their performance over normal and abnormal biological signals as well as over multicomponent frequency modulation (FM) signals in additive Gaussian noise was compared. Moreover, the feasibility of utilizing the wavelet transform (WT) in IF estimation is also studied. The biological signals considered in this work are the surface electromyogram (SEMG) with the presence of ECG noise and abnormal cardiac signals. The abnormal cardiac signals were taken from a patient with malignant ventricular arrhythmia, and a patient with supraventricular arrhythmia. Simulation results showed that the HTD has a superior performance, in terms of resolution and cross-terms reduction, as compared to other time-frequency distributions

Inappropriate prescribing in hospitalized elderly patients

Inappropriate prescribing (IP) is a major healthcare problem in elderly patients. The risk of this problem increases during hospitalization. This is due to increase morbidity and thus increases the use of medications by the inpatients. This study will clarify the problem of IP for elderly people during hospitalization and will identify the different types of it. It also will highlight some tools that are used to assess the different types of IP and the prevalence of it in elderly patients during hospitalization. Finally, the study will address the consequences of IP in the elderly inpatients and the risks associated with the use of some potentially inappropriate medications (PIMs) in the elderly.

Data Clustering Using Wave Atom

Clustering of huge spatial databases is an important issue which tries to track the densely regions in the feature space to be used in data mining, knowledge discovery, or efficient information retrieval. Clustering approach should be efficient and can detect clusters of arbitrary shapes because spatial objects cannot be simply abstracted as isolated points they have different boundary, size, volume, and location. In this paper we use discrete wave atom transformation technique in clustering to achieve more accurate result. By using multi-resolution transformation like wavelet and wave atom we can effectively identify arbitrary shape clusters at different degrees of accuracy. Experimental results on very large data sets show the efficiency and effectiveness of the proposed wave atom bases clustering approach compared to other recent clustering methods. Experimental result shows that we get more accurate result and denoised output than others