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This study aims to evaluate the effect of the installed photovoltaic (PV) systems on the aesthetic perception of the mosque’s architectural form. It also aims to develop a framework for integrating PV cells with the various elements of the building type.

The study adopted a mixed-method approach comprising both qualitative and quantitative techniques for data collection procedures. This includes surveys, literature review, focus groups and an experiment.

The results revealed a negative impact of the building-applied PV panels on people’s perception of the mosque’s architectural form. However, integrating the PV cells with the mosque form was perceived as more aesthetically pleasing. Certain integrating PV strategies integrated more harmoniously with certain mosque styles.

This study is focused on limited styles of one building type. Extra research is needed to explore the differences between the different participated groups.

There is a lack of research that explores the ways the installed PV systems impact the users’ architectural aesthetic perception of the mosque. This study informs the design process and practice and construction industry by highlighting the opportunities PV systems, as a legitimate sustainable energy resource, offers to architects and manufacturers.

The aim of this paper is to study and evaluate the situation of different affordable housing projects with an eye toward developing a new affordable and sustainable housing model in the hot-arid climate of Jordan. There is a clear interest in providing affordable housing, yet sustainable and environmental issues are highly marginalized. To bridge this gap, and to meet Jordanian housing needs while tackling environmental problems, this research has analyzed the environmental issues of selected housing projects in Jordan, aiming to determine the existing problems. In addition, it proposes a solution through a sustainable and affordable housing model that was analyzed and compared to the previously studied housing projects.

A cross-sectional design strategy was adopted and a mixed design method was used. Information related to the physical and operation characteristics of buildings was collected through the review of “as built” drawings and other relevant documents. Further information was obtained from field surveys and personal interviews with architects and decision-makers in the housing sector. Energy consumption patterns of these housing projects were analyzed using the DesignBuilder simulation program. Water efficiency was assessed using the BRE Code Water Calculator. Based on the previous analysis, a new housing model was developed that was evaluated in terms of energy and water consumption.

The analysis shows a significant difference among different housing projects in terms of energy cooling and heating loads in different climatic regions in Jordan. Energy analysis proved that the proposed model is energy efficient in different locations and it can save up to 50.4 per cent of annual energy usage in comparison with existing projects. In addition, it can save around 43 per cent of water consumption by using a number of modifications for saving water.

Most of the housing initiatives focus on providing affordable housing, yet sustainable and environmental issues are highly marginalized. This research will bridge the gap by reducing the operation cost of affordable housing through adapting and implementing sustainable measures of design and construction.

As numerous research studies have investigated the effect of the built environment on human contentment, building regulations have advanced as a direct impact on indoor environmental quality (IEQ) to include thermal, lighting, air quality and acoustics systems. Yet, while IEQ and residents' satisfaction have been discussed thoroughly in previous research, only a few studies have researched the role of building regulations as motivating factors in the housing context, specifically in Jordan.

A mixed-method approach was adopted to address this issue involving genotype analysis for building morphology and simulation using Design Builder software. This helped to understand the impact of building regulations variables, including building setback, the height of an adjacent building, orientation and building geometry. Meanwhile, an online survey was conducted to include 410 residents spread out in various building categories (A, B, C and D).

The results of this study revealed that building regulation of setbacks, the height of adjacent buildings and orientation are significant parameters that directly affect IEQ and residents' satisfaction. In addition, based on this study, the matter was clear that the highest total satisfaction resulted based on the highest comfort level in terms of temperature and daylight obtained due to larger setback and lower building height. Yet, this finding undermined smart growth principles due to the limited scope of building regulation that focused only on spatial and physical dimensions, so improving to include environmental aspects such as passive design strategies that appreciate natural ventilation and lighting is necessary, which positively impact IEQ.

The concept of IEQ and residents' satisfaction have been discussed thoroughly, but only a few studies have researched the role of building regulations as motivating factors in the housing context specifically in Jordan.

The purpose of the paper is to compare the aesthetic responses of three groups (architects, engineers and non‐experts) to a set of non‐conventional structures.

A group of 150 respondents (divided into three equal sub‐groups of architects, structural engineers, and non‐experts) were selected to participate in the main study, which used 14 different non‐conventional structural systems. The images of these systems were derived from an earlier pilot study. The evaluation tool included 38 semantic items, also derived from the pilot study. Two statistical analyses were applied to the collected data: factor analysis and ANOVA.

Finds, first, that factor analysis revealed a set of factors identified by all participants as meaningful dimensions, by which they evaluate structural systems. Second, ANOVA revealed differences between the three groups when evaluating different structural systems using the identified factors. Differences between the groups were attributed to their different backgrounds and technical training.

The study argues that understanding the aesthetic experience of architects, engineers and ordinary users of structures is essential. First, it helps the designers to establish the basis for selecting appropriate structural methods and materials in relation to building design. Second, it would also help the designers to better understand the relationship between the structure and architecture in terms of a trade‐off between the technical and aesthetic issues. Third, it helps the designers to better understand how their designed structures are perceived by the public.

This study introduces an alternative approach to the study of the aesthetics of structures, with a focus on non‐conventional structures.

The impact of the coronavirus disease 2019 (COVID-19) pandemic on architectural education (AE) was investigated, and a framework was proposed to reduce the impacts' negative consequences.

Systematic literature review, bibliometric and content analyses were combined to gain an in-depth understanding of the effects of the pandemic on AE and projections for its future. Relevant documents were extracted from the Web of Science (WoS) database. Bibliometric connections in the context of AE and COVID-19 pandemic were explored using text-mining and content analysis was performed.

The challenges, development tendencies and collaboration networks in AE during the pandemic were quantitatively and qualitatively analyzed. The most influential articles, journals, authors and countries/regions were highlighted using a bibliometric analysis. The analysis of keyword tendencies and clusters indicates that new concepts have emerged in AE research during the pandemic involving online, in-person and hybrid education. Using content analysis of 57 subtopics, 39 (18) were categorized as having negative (positive) effects. A comprehensive mitigation framework was designed to reduce the impact of the pandemic on AE.

The study findings can enable practitioners to construct effective solutions to COVID-19- and other disaster-related problems regarding AE. The implications, obstacles and mitigation framework presented can help identify gaps in the literature and guide further research.

This paper presents the first bibliometric and content analysis of AE and COVID-19 pandemic-related studies published from January 2020 to June 2022 to highlight several research directions and academic development within the field.

The purpose of this research study is to extract and identify named entities from Hadith literature. Named entity recognition (NER) refers to the identification of the named entities in a computer readable text having an annotation of categorization tags for information extraction. NER is an active research area in information management and information retrieval systems. NER serves as a baseline for machines to understand the context of a given content and helps in knowledge extraction. Although NER is considered as a solved task in major languages such as English, in languages such as Urdu, NER is still a challenging task. Moreover, NER depends on the language and domain of study; thus, it is gaining the attention of researchers in different domains.

This paper proposes a knowledge extraction framework using finite-state transducers (FSTs) – KEFST – to extract the named entities. KEFST consists of five steps: content extraction, tokenization, part of speech tagging, multi-word detection and NER. An extensive empirical analysis using the data corpus of Urdu translation of Sahih Al-Bukhari, a widely known hadith book, reveals that the proposed method effectively recognizes the entities to obtain better results.

The significant performance in terms of f-measure, precision and recall validates that the proposed model outperforms the existing methods for NER in the relevant literature.

This research is novel in this regard that no previous work is proposed in the Urdu language to extract named entities using FSTs and no previous work is proposed for Urdu hadith data NER.

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.