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This paper aims to present a framework to detect phishing websites using stacking model. Phishing is a type of fraud to access users’ credentials. The attackers access users’ personal and sensitive information for monetary purposes. Phishing affects diverse fields, such as e-commerce, online business, banking and digital marketing, and is ordinarily carried out by sending spam emails and developing identical websites resembling the original websites. As people surf the targeted website, the phishers hijack their personal information.

Features of phishing data set are analysed by using feature selection techniques including information gain, gain ratio, Relief-F and recursive feature elimination (RFE) for feature selection. Two features are proposed combining the strongest and weakest attributes. Principal component analysis with diverse machine learning algorithms including (random forest [RF], neural network [NN], bagging, support vector machine, Naïve Bayes and k-nearest neighbour) is applied on proposed and remaining features. Afterwards, two stacking models: Stacking1 (RF + NN + Bagging) and Stacking2 (kNN + RF + Bagging) are applied by combining highest scoring classifiers to improve the classification accuracy.

The proposed features played an important role in improving the accuracy of all the classifiers. The results show that RFE plays an important role to remove the least important feature from the data set. Furthermore, Stacking1 (RF + NN + Bagging) outperformed all other classifiers in terms of classification accuracy to detect phishing website with 97.4% accuracy.

This research is novel in this regard that no previous research focusses on using feed forward NN and ensemble learners for detecting phishing websites.

This review deals with the pros and cons of ultraviolet (UV) radiation on human beings and the role of textile clothing and the chemicals used for textiles to protect from their harmful effects.

UV radiation (UVR) which has further divided into UVA, UVB, and UVC. Almost 100% of UVC and major portion of UVB are bounced back to stratosphere by ozone layer while UVA enters the earth atmosphere. Excessive exposure of solar or artificial UVR exhibit potential risks to human health. UVR is a major carcinogen and excessive exposure of solar radiation in sunlight can cause cancer in the lip, skin squamous cell, basal cell and cutaneous melanoma, particularly in people with the fair skin.

This article aims to provide a comprehensive overview of the harmful effects of UVR on human skin, factors affecting UV irradiance and factors affecting UV protection offered by textile clothing.

Effect of fiber properties, yarn properties, fabric construction, fabric treatments and laundering has been reviewed along with the identification of gaps in the reported research. A comparison of inorganic and organic UV absorbers has also been given along with different testing and evaluation methods for UV protective clothing.

The purpose of this paper is to develop an Islamic religiosity measurement which can be applied in many various sectors and fields.

The religiosity measurement developed by the authors had undergone systematic qualitative and quantitative approaches taking into consideration the expert opinion survey in ensuring the measurement content validity and reliability.

The study found that Islamic religiosity measurement is multi-dimensional. The dimensions found were beliefs and commitment or practice.

The research limitation of the study is that the research is in its exploratory stages and needs to be replicated and to be tested in different contextual settings.

The instrument was developed through a rigorous systematic database search, qualitative and quantitative scale development stages which can be used as the basis in measuring Islamic religiosity.