Hybrid ACO and TOFA feature selection approach for text classification

With the highly increasing availability of text data on the Internet, the process of selecting an appropriate set of features for text classification becomes more important, for not only reducing the dimensionality of the feature space, but also for improving the classification performance. This paper proposes a novel feature selection approach to improve the performance of text classifier based on an integration of Ant Colony Optimization algorithm (ACO) and Trace Oriented Feature Analysis (TOFA). ACO is metaheuristic search algorithm derived by the study of foraging behavior of real ants, specifically the pheromone communication to find the shortest path to the food source. TOFA is a unified optimization framework developed to integrate and unify several state-of-the-art dimension reduction algorithms through optimization framework. It has been shown in previous research that ACO is one of the promising approaches for optimization and feature selection problems. TOFA is capable of dealing with large scale text data and can be applied to several text analysis applications such as text classification, clustering and retrieval. For classification performance yet effective, the proposed approach makes use of TOFA and classifier performance as heuristic information of ACO. The results on Reuters and Brown public datasets demonstrate the effectiveness of the proposed approach. © 2012 IEEE

Analysis of heat transfer for unsteady MHD free convection flow of rotating Jeffrey nanofluid saturated in a porous medium

In this article, the influence of thermal radiation on unsteady magnetohydrodynamics (MHD) free convection flow of rotating Jeffrey nanofluid passing through a porous medium is studied. The silver nanoparticles (AgNPs) are dispersed in the Kerosene Oil (KO) which is chosen as conventional base fluid. Appropriate dimensionless variables are used and the system of equations is transformed into dimensionless form. The resulting problem is solved using the Laplace transform technique. The impact of pertinent parameters including volume fraction φ, material parameters of Jeffrey fluid λ1, λ, rotation parameter r, Hartmann number Ha, permeability parameter K, Grashof number Gr, Prandtl number Pr, radiation parameter Rd and dimensionless time t on velocity and temperature profiles are presented graphically with comprehensive discussions. It is observed that, the rotation parameter, due to the Coriolis force, tends to decrease the primary velocity but reverse effect is observed in the secondary velocity. It is also observed that, the Lorentz force retards the fluid flow for both primary and secondary velocities. The expressions for skin friction and Nusselt number are also evaluated for different values of emerging parameters. A comparative study with the existing published work is provided in order to verify the present results. An excellent agreement is found

Diario oficial del Ministerio de Marina: Año LXVII Número 141 - 1974 junio 24

In this paper, we present and illustrate a frozen Jacobian multistep iterative method to solve systems of nonlinear equations associated with initial value problems (IVPs) and boundary value problems (BVPs). We have used Jacobi-Gauss-Lobatto collocation (J-GL-C) methods to discretize the IVPs and BVPs. Frozen Jacobian multistep iterative methods are computationally very efficient. They require only one inversion of the Jacobian in the form of LU-factorization. The LU factors can then be used repeatedly in the multistep part to solve other linear systems. The convergence order of the proposed iterative method is , where is the number of steps. The validity, accuracy, and efficiency of our proposed frozen Jacobian multistep iterative method is illustrated by solving fifteen IVPs and BVPs. It has been observed that, in all the test problems, with one exception in this paper, a single application of the proposed method is enough to obtain highly accurate numerical solutions. In addition, we present a comprehensive comparison of J-GL-C methods on a collection of test problems

Arabic Translation, Validation and Cultural Adaptation of the 7-Item Hamilton Depression Rating Scale in Two Community Samples

Objectives: Depression is a common mental disorder, the severity of which is frequently assessed via interview-based clinical scales such as the 7-item Hamilton Depression Rating Scale (HAMD-7). The current study aimed to translate and examine the validity of an Arabic version of the HAMD-7 scale. Methods: This study took place between February and March 2016 in the Psychiatry Department of King Saud University, Riyadh, Saudi Arabia. The HAMD-7 scale was translated into Arabic using forward and backward translation methods. A total of 153 Arabic speakers were recruited to test the translated scale, including 57 medical students and 96 members of the general public. The Arabic version of the HAMD-7 scale was completed by trained investigators during face-toface interviews with the participants. In order to assess convergent validity, participants also completed an Arabic version of the self-assessed Patient Health Questionnaire-9 (PHQ-9) scale. Subsequently, the test-retest reliability of the translated HAMD-7 scale was evaluated two weeks later during a second interview. Results: Overall, HAMD-7 scores were positively correlated with PHQ-9 scores (r = 0.633–0.749). Moreover, the translated HAMD-7 scale proved to be reliable in terms of test-retest reliability (intra-class correlation coefficient: 0.807; P <0.001). With regards to internal consistency, the Cronbach’s α values ranged between 0.607–0.756. Conclusion: The Arabic HAMD-7 scale was found to be reliable and valid among two samples of Arabic speakers in Saudi Arabia. However, further research among Arab-speaking patients diagnosed with depression is needed in order to establish its usefulness in assessing the severity of depressive symptoms. Keywords: Psychiatry; Depression; Psychometrics; Validity and Reliability; Translation; Questionnaire Design; Saudi Arabia

An insight into imbalanced Big Data classification: outcomes and challenges

Big Data applications are emerging during the last years, and researchers from many disciplines are aware of the high advantages related to the knowledge extraction from this type of problem. However, traditional learning approaches cannot be directly applied due to scalability issues. To overcome this issue, the MapReduce framework has arisen as a “de facto” solution. Basically, it carries out a “divide-and-conquer” distributed procedure in a fault-tolerant way to adapt for commodity hardware. Being still a recent discipline, few research has been conducted on imbalanced classification for Big Data. The reasons behind this are mainly the difficulties in adapting standard techniques to the MapReduce programming style. Additionally, inner problems of imbalanced data, namely lack of data and small disjuncts, are accentuated during the data partitioning to fit the MapReduce programming style. This paper is designed under three main pillars. First, to present the first outcomes for imbalanced classification in Big Data problems, introducing the current research state of this area. Second, to analyze the behavior of standard pre-processing techniques in this particular framework. Finally, taking into account the experimental results obtained throughout this work, we will carry out a discussion on the challenges and future directions for the topic.This work has been partially supported by the Spanish Ministry of Science and Technology under Projects TIN2014-57251-P and TIN2015-68454-R, the Andalusian Research Plan P11-TIC-7765, the Foundation BBVA Project 75/2016 BigDaPTOOLS, and the National Science Foundation (NSF) Grant IIS-1447795

The impact of diabetes mellitus on the emergence of multi-drug resistant tuberculosis and treatment failure in TB-diabetes comorbid patients: a systematic review and meta-analysis

BackgroundThe existence of Type 2 Diabetes Mellitus (DM) in tuberculosis (TB) patients is very dangerous for the health of patients. One of the major concerns is the emergence of MDR-TB in such patients. It is suspected that the development of MDR-TB further worsens the treatment outcomes of TB such as treatment failure and thus, causes disease progression.AimTo investigate the impact of DM on the Emergence of MDR-TB and Treatment Failure in TB-DM comorbid patients.MethodologyThe PubMed database was systematically searched until April 03, 2022 (date last searched). Thirty studies met the inclusion criteria and were included in this study after a proper selection process.ResultsTuberculosis-Diabetes Mellitus patients were at higher risk to develop MDR-TB as compared to TB-non-DM patients (HR 0.81, 95% CI: 0.60–0.96, p &lt; 0.001). Heterogeneity observed among included studies was moderate (I2 = 38%). No significant change was observed in the results after sub-group analysis by study design (HR 0.81, 95% CI: 0.61–0.96, p &lt; 0.000). In the case of treatment failure, TB-DM patients were at higher risk to experience treatment failure rates as compared to TB-non-DM patients (HR 0.46, 95% CI: 0.27–0.67, p &lt; 0.001).ConclusionThe results showed that DM had a significant impact on the emergence of MDR-TB in TB-diabetes comorbid patients as compared to TB-non-DM patients. DM enhanced the risk of TB treatment failure rates in TB-diabetes patients as compared to TB-non-DM patients. Our study highlights the need for earlier screening of MDR-TB, thorough MDR-TB monitoring, and designing proper and effective treatment strategies to prevent disease progression

On exact special solutions for the stochastic regularized long wave-Burgers equation

Inc, Mustafa/0000-0003-4996-8373WOS:000566264500001In this paper, we will analyze the Regularized Long Wave-Burgers equation with conformable derivative (cd). Some white noise functional solutions for this equation are obtained by using white noise analysis, Hermite transforms, and the modified sub-equation method. These solutions include exact stochastic trigonometric functions, hyperbolic functions solutions and wave solutions. This study emphasizes that the modified fractional sub-equation method is sufficient to solve the stochastic nonlinear equations in mathematical physics

The deterministic and stochastic solutions of the Schrodinger equation with time conformable derivative in birefrigent fibers

Inc, Mustafa/0000-0003-4996-8373In this manuscript, the deterministic and stochastic nonlinear Schrodinger equation with time conformable derivative is analysed in birefrigent fibers. Hermite transforms, white noise analysis and the modified fractional sub-equation method are used to obtain white noise functional solutions for this equation. These solutions consists of exact stochastic hyperbolic functions, trigonometric functions and wave solutions

Soret and Dufour effects on doubly diffusive convection of nanofluid over a wedge in the presence of thermal radiation and suction

This paper investigates the effects of thermal radiation, Dufour, and Soret effects on doubly diffusive convective heat transfer of nanoliquid over a wedge in the presence of wall suction. The governing equations are transformed to nonlinear ordinary differential equations using similarity transformation. The resulting system is solved numerically by the fourth-order Runge-Kutta-Gill method with a shooting technique and a Newton-Raphson method. The solutions are expressed in terms of velocity, temperature, solutal concentration, and volume fraction profiles. The effects of pertinent parameters involved in the problem such as wedge angle, thermal radiation, Brownian motion, thermophoresis, Soret number, and Dufour number on the skin friction coefficient, local Nusselt number, and local Sherwood number are discussed in detail. © 2019 Sharif University of Technology. All rights reserved