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This research aims to examine the impact of religiosity and other control variables on Muslims’ environmental preservation and economic growth choices in 33 nations.

The study uses data from the World Values Survey (Waves 4–7) with a large sample size of 30,242 individuals. Logistic regression analysis is used to analyze the data, and the robustness principle is applied using the marginal effect of interaction variables method to select a viable model.

This study reveals that different aspects of religiosity – cognitive, affective and behavioral – positively impact the tendency of Muslims in 33 countries to prioritize environmental protection over economic progress. However, these influences vary significantly, as seen through odds ratios. In essence, the degree of religious devotion in these nations affects individuals’ leaning toward environmental preservation. This impact is further shaped by other factors such as politics, governance, economic development, environmental measures and legal frameworks.

The practical implication of this study is the development of an alternative theory that explains the conditions and categories under which religious beliefs and attitudes can influence the preferences of Muslims concerning environmental issues and economic growth.

This study fills a void in the body of literature by examining the nonlinear relationship between religiosity and individual Muslim preferences for environmental preservation and economic growth. It offers a framework for comprehending religion’s impact on Muslims’ redistributive individual preferences in these fields.

The Saudi 2030 Housing Program Vision aims to increase the population of Riyadh City, the capital of the Kingdom of Saudi Arabia, to between 15 and 20 million people. This paper aims to predict the demand for residential units in Riyadh City by 2030 in line with this vision.

This paper adopts a statistical modeling approach to estimate the residential demands for Riyadh City. Several population growth models, including the nonlinear quadratic polynomial spline regression model, the sigmoidal logistic power model and the exponential model, are tested and applied to Riyadh to estimate the expected population in 2030. The growth model closest to the Kingdom’s goal of reaching between 15 and 20 million people in 2030 is selected, and the paper predicts the required number of residential units for the population obtained from the selected model. Desktop database research is conducted to obtain the data required for the modeling and analytical stage.

The exponential model predicts a population of 16,476,470 in Riyadh City by 2030, and as a result, 2,636,235 household units are needed. This number of housing units required in Riyadh City exceeds the available residential units by almost 1,370,000, representing 108% of the available residential units in Riyadh in 2020.

This study provides valuable insights into the demand for residential units in Riyadh City by 2030 in line with the Saudi 2030 Housing Program Vision, filling the gap in prior research. The findings suggest that significant efforts are required to meet the housing demand in Riyadh City by 2030, and policymakers and stakeholders need to take appropriate measures to address this issue.

Internet of Things (IoT) is a network, which provides the connection with various physical objects such as smart machines, smart home appliance and so on. The physical objects are allocated with a unique internet address, namely, Internet Protocol, which is used to perform the data broadcasting with the external objects using the internet. The sudden increment in the number of attacks generated by intruders, causes security-related problems in IoT devices while performing the communication. The main purpose of this paper is to develop an effective attack detection to enhance the robustness against the attackers in IoT.

In this research, the lasso regression algorithm is proposed along with ensemble classifier for identifying the IoT attacks. The lasso algorithm is used for the process of feature selection that modeled fewer parameters for the sparse models. The type of regression is analyzed for showing higher levels when certain parts of model selection is needed for parameter elimination. The lasso regression obtains the subset for predictors to lower the prediction error with respect to the quantitative response variable. The lasso does not impose a constraint for modeling the parameters caused the coefficients with some variables shrink as zero. The selected features are classified by using an ensemble classifier, that is important for linear and nonlinear types of data in the dataset, and the models are combined for handling these data types.

The lasso regression with ensemble classifier–based attack classification comprises distributed denial-of-service and Mirai botnet attacks which achieved an improved accuracy of 99.981% than the conventional deep neural network (DNN) methods.

Here, an efficient lasso regression algorithm is developed for extracting the features to perform the network anomaly detection using ensemble classifier.

Genre classification of songs based on lyrics is a challenging task even for humans, however, state-of-the-art natural language processing has recently offered advanced solutions to this task. The purpose of this study is to advance the understanding and application of natural language processing and deep learning in the domain of music genre classification, while also contributing to the broader themes of global knowledge and communication, and sustainable preservation of cultural heritage.

The main contribution of this study is the development and evaluation of various machine and deep learning models for song genre classification. Additionally, we investigated the effect of different word embeddings, including Global Vectors for Word Representation (GloVe) and Word2Vec, on the classification performance. The tested models range from benchmarks such as logistic regression, support vector machine and random forest, to more complex neural network architectures and transformer-based models, such as recurrent neural network, long short-term memory, bidirectional long short-term memory and bidirectional encoder representations from transformers (BERT).

The authors conducted experiments on both English and multilingual data sets for genre classification. The results show that the BERT model achieved the best accuracy on the English data set, whereas cross-lingual language model pretraining based on RoBERTa (XLM-RoBERTa) performed the best on the multilingual data set. This study found that songs in the metal genre were the most accurately labeled, as their text style and topics were the most distinct from other genres. On the contrary, songs from the pop and rock genres were more challenging to differentiate. This study also compared the impact of different word embeddings on the classification task and found that models with GloVe word embeddings outperformed Word2Vec and the learning embedding layer.

This study presents the implementation, testing and comparison of various machine and deep learning models for genre classification. The results demonstrate that transformer models, including BERT, robustly optimized BERT pretraining approach, distilled bidirectional encoder representations from transformers, bidirectional and auto-regressive transformers and XLM-RoBERTa, outperformed other models.

Distancing people socially as a precautionary measure against the mushrooming of COVID-19’s health and economic crisis has deleteriously affected the performance of the eatery industry to a great extent. Many food outlets failed to cope up with crisis and opted to move out, and many still vie to survive through pandemic. It becomes vital for researchers to understand what factors influence the performance and survival of eateries during the pandemic? The study makes an attempt to fabricate new factors which affect the performance and contribute significantly towards the survival of eateries in this new COVID-19-prone world.

The present study is a cross-sectional analysis with the sample of 150 eateries from the walled city of Punjab (India), i.e. Amritsar. Factor analysis is employed to scrutinise the factors which influence the performance of eateries during the pandemic, and to analyse factors which contribute significantly for the survival of eateries, logistic regression is performed.

The empirical analysis reveals that at prior psychological factor, followed by turnover factor, external factor, financial factor and marketing factor influence the performance of eateries during the pandemic. Only three factors, namely turnover factor, external factor and financial factor, turned up to be significant towards the survival rate of an eatery. The marketing factor which is a crucial contributor for survival of business in literature has turned out to be insignificant during the times of pandemic.

With the arrival of pandemic, the preference of people has changed, and the business environment in which entities operate has turned more complex. The present study is one of the pioneer attempts to evaluate whether the factors responsible for performance or survival of an eatery during normal times is relevant during the pandemic as well. The study contributes to the literature of eatery industry by adding a new variable namely psychological factor, i.e. changes witnessed in customers’ preference to visit an eatery.

As many businesses faced economic disruption due to the Covid-19 pandemic and sought financial relief, existing bank relationships became critical to getting a loan. This study examines factors associated with the development of personal relationships of rural small businesses with community bank representatives.

We applied a mixed-method approach. We employed descriptive statistics, principal factor analysis and logistic regression for data analysis. We distributed an online survey to rural small businesses in five states in the United States. Key informant interviews with community bank representatives supplemented the survey results.

A business owner’s trust in a banker was positively associated with the establishment of a business–bank relationship. However, an analysis of individual trust’s components revealed that the nature of trust is complex, and a failure of one or more components may lead to decreased trustworthiness in a banker. Small businesses that preferred personal communication with a bank were more inclined to relationship banking.

Due to the relatively small sample size and cross-sectional data, our results may not be conclusive but should be viewed as preliminary and as suggestions for future research. Bankers should be aware of the importance of trust for small business owners and of the actions that lead to increased trustworthiness.

The study extends the existing knowledge on the business–bank relationship by focusing mainly on social (instead of economic) factors associated with the establishment of the business–bank relationship in times of crisis and high uncertainty.

Total risk score (TRS) is considered one of the main indicators for risk evaluation. Several studies attempted to employ different types of risk indices for the evaluation of cost overrun causes. Hence, this study aims at performing a comparative analysis to evaluate the efficiency of three different approaches for TRS calculation.

Thirty-eight unique causes of cost overrun in urban-related construction projects were identified and a survey was conducted among construction professionals in Iran. The TRS for each cost overrun cause is calculated using single-attribute (SA), double-attribute (DA), and multiple-attribute (MA) approaches, and eventually, causes were ranked. Furthermore, principal component analysis (PCA), logistic regression analysis (LRA), and K-means clustering are utilized to compare the differences in the generated TRS using different approaches.

The results revealed that the TRS generated through the MA approach demonstrated the highest efficiency in terms of generating correlation between causes and their identified latent constructs, prediction capability, and classification of the influential causes in the same group.

The originality of this study primarily stems from the adoption of statistical approaches in the evaluation of the recently introduced TRS calculation approach in comparison to traditional ones. Additionally, this study proposed a modified application of the relative importance index (RII) for risk prioritization. The results from this study are expected to fulfill the gap in previous literature toward exploring the most efficient TRS calculation approach for those researchers and practitioners who seek to utilize them as a measure to identify the influential cost overrun causes.

Given the heated academic and policy debate regarding the fate of garment manufacturing in a high-wage developed economy in the 21st century, this study aims to explore the production and export strategies of apparel “Made in Ireland.”

A logistic regression analysis of 4,000 apparel items at the stock keeping unit (SKU) level sold in the market from January 2018 to December 2021 was conducted to evaluate the production and export strategy of apparel “Made in Ireland” versus foreign-made imported items sold in Ireland.

The statistical results showed that Ireland’s apparel manufacturing sector survived the market competition by leveraging non-price competing factors, such as distinct product assortment, cultural heritage, history and traditional craftsmanship.

The findings challenged the conclusions of the classic trade and economic development theories regarding the trajectory of the garment manufacturing sector and called for a rethink about the strategies for expanding garment manufacturing in a high-wage developed country in today’s global economy.

Advanced big data analysis and machine learning methods are concurrently used to unleash the value of the data generated by government hotline and help devise intelligent applications including automated process management, standard construction and more accurate dispatched orders to build high-quality government service platforms as more widely data-driven methods are in the process.

In this study, based on the influence of the record specifications of texts related to work orders generated by the government hotline, machine learning tools are implemented and compared to optimize classify dispatching tasks by performing exploratory studies on the hotline work order text, including linguistics analysis of text feature processing, new word discovery, text clustering and text classification.

The complexity of the content of the work order is reduced by applying more standardized writing specifications based on combining text grammar numerical features. So, order dispatch success prediction accuracy rate reaches 89.6 per cent after running the LSTM model.

The proposed method can help improve the current dispatching processes run by the government hotline, better guide staff to standardize the writing format of work orders, improve the accuracy of order dispatching and provide innovative support to the current mechanism.

This study aims to assess the household food insecurity in Malaysia during the initial phase of the movement control order (MCO) to provide insights into the prevalence and predictors of food insecurity in this context.

The research used an online cross-sectional survey between March 28 and April 28, 2020. The study collected data from the Radimer/Cornell Hunger Scale and a food insecurity instrument. Analytical tools included chi-square and logistic regression models.

Of the 411 participating households, 54.3% were food-secure, while 45.7% experienced varying food insecurity. Among these, 29.9% reported mild hunger-associated food insecurity, 8.5% experienced individual food insecurity and 7.3% reported child hunger. The study identified predictors for food insecurity, including household income, as those with total income of < RM 2,300 had 13 times greater odds (odds ratio [OR] 13.8; confidence interval [CI] 5.9–32.1; p < 0.001) than those with income of RM 5,600, marital status as divorced (OR 4.4; 95% CI 1.0–19.9; p-value = 0.05) or married (OR 1.04; 95% CI 0.52–2.1) compared to those who are single. Self-employed respondents had three times greater odds of living in a household experiencing food insecurity (OR 3.58; 95% CI 1.6–7.7; p-value = 0.001) than those in the private sector (OR 1.48; 95% CI 0.85–2.61) or experiencing job loss (OR 1.39; 95% CI 0.62–3.1) compared with those who reported being in full-time government employment.

This study acknowledged limitations, such as not considering various dimensions of food insecurity, such as coping strategies, nutritional support, diet quality and well-being, due to the complexity of the issue.

The study underscores the importance of targeted support for vulnerable groups and fostering collaborative efforts to address household food insecurity during crises like the MCOs.

The research offers insights into how to address household food insecurity and its impact on society.

It identifies predictors, quantifies increased odds and emphasizes the necessity of targeted policies and collaborative approaches for fostering resilient recovery and promoting well-being in vulnerable populations.