Breast cancer is one of the leading causes of death in the current age. It often results in subpar living conditions for a patient as they have to go through expensive and painful treatments to fight this cancer. One in eight women all over the world is affected by this disease. Almost half a million women annually do not survive this fight and die from this disease. Machine learning algorithms have proven to outperform all existing solutions for the prediction of breast cancer using models built on the previously available data. In this paper, a novel approach named BCD-WERT is proposed that utilizes the Extremely Randomized Tree and Whale Optimization Algorithm (WOA) for efficient feature selection and classification. WOA reduces the dimensionality of the dataset and extracts the relevant features for accurate classification. Experimental results on state-of-the-art comprehensive dataset demonstrated improved performance in comparison with eight other machine learning algorithms: Support Vector Machine (SVM), Random Forest, Kernel Support Vector Machine, Decision Tree, Logistic Regression, Stochastic Gradient Descent, Gaussian Naive Bayes and k-Nearest Neighbor. BCD-WERT outperformed all with the highest accuracy rate of 99.30% followed by SVM achieving 98.60% accuracy. Experimental results also reveal the effectiveness of feature selection techniques in improving prediction accuracy.

Purpose The purpose of this study is to propose a sustainable quality assessment approach (model) for the e-learning systems keeping software perspective under consideration. E-learning is becoming mainstream due to its accessibility, state-of-the-art learning, training ease and cost effectiveness. However, the poor quality of e-learning systems is one of the major causes of several failures reported. Moreover, this arena lacks well-defined quality assessment measures. Hence, it is quite difficult to measure the overall quality of an e-learning system effectively. Design/methodology/approach A pragmatic mixed-model philosophy was adopted for this study. A systematic literature review was performed to identify existing e-learning quality models and frameworks. Semi-structured interviews were conducted with e-learning experts following empirical investigations to identify the crucial quality characteristics of e-learning systems. Various statistical tests like principal component analysis, logistic regression, chi-square and analysis of means were applied to analyze the empirical data. These led to an adequate set of quality indicators that can be used by higher education institutions to assure the quality of e-learning systems. Findings A sustainable quality assessment model for the information delivery in e-learning systems in software perspective has been proposed by exploring the state-of-the-art quality assessment/evaluation models and frameworks proposed for the e-learning systems. The proposed model can be used to assess and improve the process of information discovery and delivery of e-learning. Originality/value The results obtained led to conclude that very limited attention is given to the quality of e-learning tools despite the importance of quality and its effect on e-learning system adoption and promotion. Moreover, the identified models and frameworks do not adequately address quality of e-learning systems from a software perspective.

Only 5-10% of people infected with Mycobacterium tuberculosis develop active tuberculosis which suggests a role of genetic variation in host immunity. Genetic variants in TLRs are potential indicator for host susceptibility and outcome of several diseases. We explored the association of nonsynonymous genetic variants (Met1Val) with Toll-like receptor 8 in Pakistani population. Genotypic and allelic distribution of TLR8 polymorphism (rs3764880) in patients with TB and healthy donors from different areas of southern Punjab, Pakistan, was determined. Results provide that our population is highly influenced by TLR8 Met1Val SNP for TB, and G allele appeared to increase TB susceptibility. Mutant genotype GG or G/- and G allele was significantly higher among all the categories of cases than in controls. Among different levels of bacillary load and genotypes, GG or G/- and G allele significantly supports the incidence of 2 + class for bacterial load.

The determination of the seven elements was performed by Perkin Elmer Atomic Absorption (AA) spectrophotometer. The present study highlights the importance of seven heavy metals residual concentration including Cd, Cr, Cu, Fe, Mn, Ni and Zn in milk of Camel, Cattle, Buffalo, Sheep and Goat from various areas of Khyber Pakhtunkhwa (KPK), Pakistan. It revealed that milk of camel comprising of high levels of Zn (5.150 ± 0.021 mg/kg), Mn (0.094 ± 0.003 mg/kg) and Fe (1.580 ± 0.530 mg/kg) with a definite correlation. In the milk of buffalo, high concentration of noxious heavy metals including Cu (0.223 ± 0.010 mg/kg) and Cd (0.117 ± 0.086 mg/kg) were found whereas in goat milk, high Ni (1.152 ± 0.045 mg/kg) and Cr (1.152 ± 0.045 mg/kg) was observed and detected. The analysis showed that camel and buffalo have similar high concentration of heavy metals. Overall results showed that milk of cattle shows higher concentration of Zn, Mn and Fe along with Buffalo.

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