Breast cancer diagnosis using a hybrid genetic algorithm for feature selection based on mutual information

Feature Selection is the process of selecting a subset of relevant features (i.e. predictors) for use in the construction of predictive models. This paper proposes a hybrid feature selection approach to breast cancer diagnosis which combines a Genetic Algorithm (GA) with Mutual Information (MI) for selecting the best combination of cancer predictors, with maximal discriminative capability. The selected features are then input into a classifier to predict whether a patient has breast cancer. Using a publicly available breast cancer dataset, experiments were performed to evaluate the performance of the Genetic Algorithm based on the Mutual Information approach with two different machine learning classifiers, namely the k-Nearest Neighbor (KNN), and Support vector machine (SVM), each tuned using different distance measures and kernel functions, respectively. The results revealed that the proposed hybrid approach is highly accurate for predicting breast cancer, and it is very promising for predicting other cancers using clinical data

Combining Islamic Equity Portfolios and Digital Currencies: Evidence from Portfolio Diversification

Digital currencies are unregulated and potentially have a destabilizing effect coupled with increased concerns over capital gains and losses in a high volatility environment. When added to a portfolio, this currency may have certain driving factors in terms of return and risks in the case of portfolio diversification. In this study, from the Sharia angle, we follow the position of Monzer Kahf (Fatwa on Bitcoin (by Monzer Kahf). http://lightuponlight.com/blog/fatwa-on-bitcoin-by-monzer-kahf/. Accessed 03 Feb 2020, 2017) who explained that Bitcoin is considered “Like any other currency”. It should be used under the “same conditions of exchanging currencies”. Therefore, we explore the effects of adding digital currencies to an Islamic portfolio by relying on a mean-variance efficient frontier and comparing the risk-return of portfolios with and without digital currencies for different scenarios. The results show that by adding digital currencies to Shariah-compliant portfolios, its performance improves; but this depends more or less on the increase in returns than in the reduction of total risk. Specifically, digital currencies may have a big role in bringing high risks with speculative effect in portfolio diversification. Therefore, we provide some recommendations to investors and regulators to secure these currencies in Islamic capital markets

Computational Intelligence for Solving Complex Optimization Problems

Complex optimization issues may now be solved using computational intelligence (CI), which has shown to be a powerful and diverse discipline. Traditional optimization approaches frequently struggle to offer efficient and effective solutions because real-world situations are becoming more complicated. Evolutionary algorithms, neural networks, fuzzy systems, and swarm intelligence are just a few examples of the many methods that fall under the umbrella of computational intelligence and are inspired by both natural and artificial intelligence. This abstract examines how computational intelligence techniques are used to solve complicated optimization issues, highlighting their benefits, drawbacks, and most recent developments. In this, computational intelligence techniques provide a potent and adaptable solution for resolving challenging optimization issues. They are highly adapted for dealing with the non-linear connections, uncertainties, and multi-objective situations that arise in real-world problems. The limits of computational intelligence have recently been pushed by recent developments in hybrid techniques and metaheuristics, even if obstacles in algorithm design and parameter tuning still exist. Computational intelligence is anticipated to play an increasingly significant role in tackling complicated optimization issues and fostering innovation across a variety of disciplines as technology continues to advance

The what and where of adding channel noise to the Hodgkin-Huxley equations

One of the most celebrated successes in computational biology is the Hodgkin-Huxley framework for modeling electrically active cells. This framework, expressed through a set of differential equations, synthesizes the impact of ionic currents on a cell's voltage -- and the highly nonlinear impact of that voltage back on the currents themselves -- into the rapid push and pull of the action potential. Latter studies confirmed that these cellular dynamics are orchestrated by individual ion channels, whose conformational changes regulate the conductance of each ionic current. Thus, kinetic equations familiar from physical chemistry are the natural setting for describing conductances; for small-to-moderate numbers of channels, these will predict fluctuations in conductances and stochasticity in the resulting action potentials. At first glance, the kinetic equations provide a far more complex (and higher-dimensional) description than the original Hodgkin-Huxley equations. This has prompted more than a decade of efforts to capture channel fluctuations with noise terms added to the Hodgkin-Huxley equations. Many of these approaches, while intuitively appealing, produce quantitative errors when compared to kinetic equations; others, as only very recently demonstrated, are both accurate and relatively simple. We review what works, what doesn't, and why, seeking to build a bridge to well-established results for the deterministic Hodgkin-Huxley equations. As such, we hope that this review will speed emerging studies of how channel noise modulates electrophysiological dynamics and function. We supply user-friendly Matlab simulation code of these stochastic versions of the Hodgkin-Huxley equations on the ModelDB website (accession number 138950) and http://www.amath.washington.edu/~etsb/tutorials.html.Comment: 14 pages, 3 figures, review articl

The Love of Money and Pay Level Satisfaction: Measurement and Functional Equivalence in 29 Geopolitical Entities around the World

Demonstrating the equivalence of constructs is a key requirement for cross-cultural empirical research. The major purpose of this paper is to demonstrate how to assess measurement and functional equivalence or invariance using the 9-item, 3-factor Love of Money Scale (LOMS, a second-order factor model) and the 4-item, 1-factor Pay Level Satisfaction Scale (PLSS, a first-order factor model) across 29 samples in six continents (N = 5973). In step 1, we tested the configural, metric and scalar invariance of the LOMS and 17 samples achieved measurement invariance. In step 2, we applied the same procedures to the PLSS and nine samples achieved measurement invariance. Five samples (Brazil, China, South Africa, Spain and the USA) passed the measurement invariance criteria for both measures. In step 3, we found that for these two measures, common method variance was non-significant. In step 4, we tested the functional equivalence between the Love of Money Scale and Pay Level Satisfaction Scale. We achieved functional equivalence for these two scales in all five samples. The results of this study suggest the critical importance of evaluating and establishing measurement equivalence in cross-cultural studies. Suggestions for remedying measurement non-equivalence are offered

Barriers to formal healthcare utilisation among poor older people under the livelihood empowerment against poverty programme in the Atwima Nwabiagya District of Ghana

Abstract: Background: Even though there is a growing literature on barriers to formal healthcare use among older people, little is known from the perspective of vulnerable older people in Ghana. Involving poor older people under the Livelihood Empowerment Against Poverty (LEAP) programme, this study explores barriers to formal healthcare use in the Atwima Nwabiagya District of Ghana. Methods: Interviews and focus group discussions were conducted with 30 poor older people, 15 caregivers and 15 formal healthcare providers in the Atwima Nwabiagya District of Ghana. Data were analysed using the thematic analytical framework, and presented based on an a posteriori inductive reduction approach. Results: Four main barriers to formal healthcare use were identified: physical accessibility barriers (poor transport system and poor architecture of facilities), economic barriers (low income coupled with high charges, and non-comprehensive nature of the National Health Insurance Scheme [NHIS]), social barriers (communication/language difficulties and poor family support) and unfriendly nature of healthcare environment barriers (poor attitude of healthcare providers). Conclusions: Considering these barriers, removing them would require concerted efforts and substantial financial investment by stakeholders. We argue that improvement in rural transport services, implementation of free healthcare for poor older people, strengthening of family support systems, recruitment of language translators at the health facilities and establishment of attitudinal change programmes would lessen barriers to formal healthcare use among poor older people. This study has implications for health equity and health policy framework in Ghana

Expressed concerns of Yemeni adolescents

This study examined the concerns of adolescents in the Republic of Yemen. A short version of the Mooney Problem Check List was administered to 150 13-to 17-year-old males and females. Results indicated that the major concerns and problems reported by Yemeni adolescents were related to their vocational and educational future, recreational activities, religious matters, and school curriculum and teaching methods. Problems related to social life, family, and health and physical issues were less prominent. Results also showed that though there were similarities in the number of concerns expressed by males and females, males reported more difficulties with their vocational and educational future, marriage and sexual matters, and finances and employment, while females reported more problems with recreational activities, personal relationships, and healt

A multivariate feature selection framework for high dimensional biomedical data classification

High dimensional biomedical data are becoming common in various predictive models developed for disease diagnosis and prognosis. Extracting knowledge from high dimensional data which contain a large number of features and a small sample size presents intrinsic challenges for classification models. Genetic Algorithms can be successfully adopted to efficiently search through high dimensional spaces, and multivariate classification methods can be utilized to evaluate combinations of features for constructing optimized predictive models. This paper proposes a framework which can be adopted for building prediction models for high dimensional biomedical data. The proposed framework comprises of three main phases. The feature filtering phase which filters out the noisy features; the feature selection phase which is based on multivariate machine learning techniques and the Genetic Algorithm to evaluate the filtered features and select the most informative subsets of features for achieving maximum classification performance; and the predictive modeling phase during which machine learning algorithms are trained on the selected features to construct a reliable prediction model. Experiments were conducted using four high dimensional biomedical datasets including protein and gene expression data. The results revealed optimistic performances for the multivariate selection approaches which utilize classification measurements based on implicit assumptions

Assessment of Natural Radioactivity Levels and Radiation Hazards in Agricultural and Virgin Soil in the State of Kedah, North of Malaysia

The activity concentrations of naturally occurring radionuclides 226Ra, 232Th, and 40K were determined in 30 agricultural and virgin soil samples randomly collected from Kedah, north of Malaysia, at a fertile soil depth of 0–30 cm. Gamma-ray spectrometry was applied using high-purity germanium (HPGe) gamma-ray detector and a PC-based MCA. The mean radioactivity concentrations of 226Ra, 232Th, and 40K were found to be 102.08 ± 3.96, 133.96 ± 2.92, and 325.87 ± 9.83 Bq kg−1, respectively, in agricultural soils and 65.24 ± 2.00, 83.39 ± 2.27, and 136.98 ± 9.76 Bq kg−1, respectively, in virgin soils. The radioactivity concentrations in agricultural soils are higher than those in virgin soils and compared with those reported in other countries. The mean values of radium equivalent activity (), absorbed dose rates (nGy h−1), annual effective dose equivalent, and external hazard index () are 458.785 Bq kg−1, 141.62 nGy h−1, and 0.169 mSv y−1, respectively, in agricultural soils and 214.293 Bq kg−1, 87.47 nGy h−1, and 0.106 mSv y−1, respectively, in virgin soils, with average of 0.525. Results were discussed and compared with those reported in similar studies and with internationally recommended values