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COVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The main purpose of this work is, COVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The COVID-19 diagnosis is required to detect at the beginning stage and special attention should be given to them. The fastest way to detect the COVID-19 infected patients is detecting through radiology and radiography images. The few early studies describe the particular abnormalities of the infected patients in the chest radiograms. Even though some of the challenges occur in concluding the viral infection traces in X-ray images, the convolutional neural network (CNN) can determine the patterns of data between the normal and infected X-rays that increase the detection rate. Therefore, the researchers are focusing on developing a deep learning-based detection model.

The main intention of this proposal is to develop the enhanced lung segmentation and classification of diagnosing the COVID-19. The main processes of the proposed model are image pre-processing, lung segmentation and deep classification. Initially, the image enhancement is performed by contrast enhancement and filtering approaches. Once the image is pre-processed, the optimal lung segmentation is done by the adaptive fuzzy-based region growing (AFRG) technique, in which the constant function for fusion is optimized by the modified deer hunting optimization algorithm (M-DHOA). Further, a well-performing deep learning algorithm termed adaptive CNN (A-CNN) is adopted for performing the classification, in which the hidden neurons are tuned by the proposed DHOA to enhance the detection accuracy. The simulation results illustrate that the proposed model has more possibilities to increase the COVID-19 testing methods on the publicly available data sets.

From the experimental analysis, the accuracy of the proposed M-DHOA–CNN was 5.84%, 5.23%, 6.25% and 8.33% superior to recurrent neural network, neural networks, support vector machine and K-nearest neighbor, respectively. Thus, the segmentation and classification performance of the developed COVID-19 diagnosis by AFRG and A-CNN has outperformed the existing techniques.

This paper adopts the latest optimization algorithm called M-DHOA to improve the performance of lung segmentation and classification in COVID-19 diagnosis using adaptive K-means with region growing fusion and A-CNN. To the best of the authors’ knowledge, this is the first work that uses M-DHOA for improved segmentation and classification steps for increasing the convergence rate of diagnosis.

An abundance of techniques has been presented so forth for waste classification but, they deliver inefficient results with low accuracy. Their achievement on various repositories is different and also, there is insufficiency of high-scale databases for training. The purpose of the study is to provide high security.

In this research, optimization-assisted federated learning (FL) is introduced for thermoplastic waste segregation and classification. The deep learning (DL) network trained by Archimedes Henry gas solubility optimization (AHGSO) is used for the classification of plastic and resin types. The deep quantum neural networks (DQNN) is used for first-level classification and the deep max-out network (DMN) is employed for second-level classification. This developed AHGSO is obtained by blending the features of Archimedes optimization algorithm (AOA) and Henry gas solubility optimization (HGSO). The entities included in this approach are nodes and servers. Local training is carried out depending on local data and updations to the server are performed. Then, the model is aggregated at the server. Thereafter, each node downloads the global model and the update training is executed depending on the downloaded global and the local model till it achieves the satisfied condition. Finally, local update and aggregation at the server is altered based on the average method. The Data tag suite (DATS_2022) dataset is used for multilevel thermoplastic waste segregation and classification.

By using the DQNN in first-level classification the designed optimization-assisted FL has gained an accuracy of 0.930, mean average precision (MAP) of 0.933, false positive rate (FPR) of 0.213, loss function of 0.211, mean square error (MSE) of 0.328 and root mean square error (RMSE) of 0.572. In the second level classification, by using DMN the accuracy, MAP, FPR, loss function, MSE and RMSE are 0.932, 0.935, 0.093, 0.068, 0.303 and 0.551.

The multilevel thermoplastic waste segregation and classification using the proposed model is accurate and improves the effectiveness of the classification.

This study aims to reduce data bias during human activity and increase the accuracy of activity recognition.

A convolutional neural network and a bidirectional long short-term memory model are used to automatically capture feature information of time series from raw sensor data and use a self-attention mechanism to learn select potential relationships of essential time points. The proposed model has been evaluated on six publicly available data sets and verified that the performance is significantly improved by combining the self-attentive mechanism with deep convolutional networks and recursive layers.

The proposed method significantly improves accuracy over the state-of-the-art method between different data sets, demonstrating the superiority of the proposed method in intelligent sensor systems.

Using deep learning frameworks, especially activity recognition using self-attention mechanisms, greatly improves recognition accuracy.

Full adder cells are building blocks of arithmetic circuits and affect the performance of the entire digital system. The purpose of this study is to provide a low-power and high-performance full adder cell.

Approximate computing is a novel paradigm that is used to design low-power and high-performance circuits. In this paper, a novel 1-bit approximate full adder cell is presented using the combination of complementary metal-oxide-semiconductor, transmission gate and pass transistor logic styles.

Simulation results confirm the superiority of the proposed design in terms of power consumption and power–delay product (PDP) criteria compared to state-of-the-art circuits. Also, the proposed full adder cell is applied in an 8-bit ripple carry adder to accomplish image processing applications including image blending, motion detection and edge detection. The results confirm that the proposed cell has premier compromise and outperforms its counterparts.

The proposed cell consists of only 11 transistors and decreases the switching activity remarkably. Therefore, it is a low-power and low-PDP cell.

The aim of this research is to understand the relationship between cultural buildings, economic powers and social justice and equality in architecture and how this relationship has evolved over the last hundred years. This research seeks to identify architectural and urban elements that enhance social justice and equality to inform architectural and urban designs and public policies.

The author explores the relationship between case studies of museums, cultural centers and libraries, and economic powers between 1920 and 2020 in Stockholm, Sweden. The author conducts a historical analysis and combines it with statistical and geographically referenced information in a Geographic Information System, archival data and in situ observations of selected buildings in the city. The author leverages the median income of household data from Statistics Sweden, with the geographical location of main public buildings and the headquarters of main companies operating in Sweden.

This analysis presents a gradual commercialization of cultural buildings in terms of location, inner layout and management, and the parallel filtering and transforming of the role of users. The author assesses how these cultural buildings gradually conformed to a system in the city and engaged with the market from a more local and national level to global networks. Findings show a cluster of large public buildings in the center of Stockholm, the largest global companies' headquarters and high-income median households. Results show that large shares of the low-income population now live far away from these buildings and the increasing commercialization of cultural space and inequalities.

This research provides a novel image of urban inequalities in Stockholm focusing on cultural buildings and their relationship with economic powers over the last hundred years. Cultural buildings could be a tool to support equality and stronger democracy beyond their primary use. Public cultural buildings offer a compromise between generating revenue for the private sector while catering to the needs and interests of large numbers of people. Therefore, policymakers should consider emphasizing the construction of more engaging public cultural buildings in more distributed locations.

Adequate means for easily viewing, browsing and searching knowledge graphs (KGs) are a crucial, still limiting factor. Therefore, this paper aims to present virtual properties as valuable user interface (UI) concept for ontologies and KGs able to improve these issues. Virtual properties provide shortcuts on a KG that can enrich the scope of a class with other information beyond its direct neighborhood.

Virtual properties can be defined as enhancements of shapes constraint language (SHACL) property shapes. Their values are computed on demand via protocol and RDF query language (SPARQL) queries. An approach is demonstrated that can help to identify suitable virtual property candidates. Virtual properties can be realized as integral functionality of generic, frame-based UIs, which can automatically provide views and masks for viewing and searching a KG.

The virtual property approach has been implemented at Bosch and is usable by more than 100,000 Bosch employees in a productive deployment, which proves the maturity and relevance of the approach for Bosch. It has successfully been demonstrated that virtual properties can significantly improve KG UIs by enriching the scope of a class with information beyond its direct neighborhood.

SHACL-defined virtual properties and their automatic identification are a novel concept. To the best of the author’s knowledge, no such approach has been established nor standardized so far.

This study aims to test mutual fund superiority, comparing the performance of 646 Islamic mutual funds with 475 ethical funds and conventional proxies.

This study uses statistical methods including paired t-statistics of independent samples, one-way Bonferroni test–analysis of variance–F-statistic for testing means equality, the chi-squared test for median equality and regression models corrected for heteroscedasticity. These methods are used to identify superiority of mutual funds and to validate the significance of the results.

The findings confirm the superiority of conventional funds over ethical funds and ethical funds over Islamic funds. Both ethical and Islamic funds, however, outperform conventional proxies during some recessionary periods. Moreover, stronger performance is recorded for Islamic funds in Europe and North America regions and across age and asset allocation categories, but limited support for reversal fund size, composition focus and reversed price effect.

These findings should assist investors when deciding to invest and motivate Islamic and ethical funds to improve their portfolio formation and asset allocation strategies set by their professional managers.

The originality of this study is in its comprehensive approach in that it compares the performance of funds after accounting for such characteristics as fund objectives, size, age, asset allocation, geographical investment focus, fund composition focus, share price levels and the effect of global crises. This study approach is not only original and productive in documenting Islamic funds’ performance for the past three decades (1990–2022) but can also update the literature on these characteristics collectively and individually.

This paper aims to examine how neighborhood characteristics (income, population composition) and individual building attributes (ownership) affect the recovery period of single-family housing and determine their correlations with property abandonment and changes in residential land use after natural disaster.

This empirical study focuses on Valley Fire, one of the California’s most destructive wildfires in 2015, and uses assessor, community, demographic and sales data to measure recovery of a panel of single-family houses located in Lake County in California between 2012 and 2020. Several regression and correlation models will be developed to test different hypotheses.

This study found that: Recovery period is longer than what expected in most existing literature; ownership status significantly affects recovery period; income level is not a significant factor for shortening the recovery period; and minorities may need more assistance for constant recovery. Findings of this research will help identify at risk communities to avoid uneven housing recovery and lower the rate of property abandonment.

Housing recovery is key to revitalizing communities following major natural disasters. The sociodemographic characteristics of each neighborhood have significant impact on the duration of recovery and possible property abandonment. Understanding how home and neighborhood characteristics affect recovery will help planners prevent long-lasting adverse effects of natural disasters.

This chapter investigates the cost of public debt for firms using a comprehensive sample consisting of 17,368 industrial bond issues from 1970 to 2011. The empirical evidence shows that yield spreads for seasoned bond issues are significantly lower than those for initial bond issues. This seasoning effect is robust across different sample periods, subsamples, and model specifications. On average, the yield spreads for seasoned bond issues are around 50 bps lower than those for initial bond issues. This difference cannot be explained by other bond and firm characteristics. The seasoning effect is more pronounced for firms with higher levels of uncertainty, lower information disclosure quality, and longer time intervals between the first and subsequent issues. Our empirical findings provide supportive evidence for the extant theories that aim to rationalize the information role in determining the cost of capital.

Background: Rising food bank usage in the UK suggests a growing prevalence of food insecurity. However, a formalised, representative measure of food insecurity was not collected in the UK until 2019, over a decade after the initial proliferation of food bank demand. In the absence of a direct measure of food insecurity, this article identifies and summarises longitudinal proxy indicators of UK food insecurity to gain insight into the growth of insecure access to food in the 21st century.

Methods: A rapid evidence synthesis of academic and grey literature (2005–present) identified candidate proxy longitudinal markers of food insecurity. These were assessed to gain insight into the prevalence of, or conditions associated with, food insecurity.

Results: Food bank data clearly demonstrates increased food insecurity. However, this data reflects an unrepresentative, fractional proportion of the food insecure population without accounting for mild/moderate insecurity, or those in need not accessing provision. Economic indicators demonstrate that a period of poor overall UK growth since 2005 has disproportionately impacted the poorest households, likely increasing vulnerability and incidence of food insecurity. This vulnerability has been exacerbated by welfare reform for some households. The COVID-19 pandemic has dramatically intensified vulnerabilities and food insecurity. Diet-related health outcomes suggest a reduction in diet quantity/quality. The causes of diet-related disease are complex and diverse; however, evidence of socio-economic inequalities in their incidence suggests poverty, and by extension, food insecurity, as key determinants.

Conclusion: Proxy measures of food insecurity suggest a significant increase since 2005, particularly for severe food insecurity. Proxy measures are inadequate to robustly assess the prevalence of food insecurity in the UK. Failure to collect standardised, representative data at the point at which food bank usage increased significantly impairs attempts to determine the full prevalence of food insecurity, understand the causes, and identify those most at risk.