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The authors adopt the resource-based view (RBV) and information processing theory to discover the problems that impact the capital structure of financial institutions in the UK. Five firm-level explanatory variables (profitability, size, tangibility, age, and growth) were selected. The relevant capital structure measure was then regressed against the dependent variable leverage (debt-to-equity ratio). Consequently, correlation and multivariate regressions are applied to firm financial data from the selected financial institutions during the fiscal years 2011–2022. The primary conclusions of the study indicate that important information resources management variables for financial institutions in the UK are profitability and size. While the two other factors, profitability and growth, exhibit negative associations with capital structure, the remaining four variables, tangibility, size, age, and profitability, did not. The study reveals that optimal determinants of information resources management enhance financial performance in the case of top UK banks.

Traffic flow detection plays a significant part in freeway traffic surveillance systems. Currently, effective autonomous traffic analysis is a challenging task due to the complexity of traffic delays, despite the significant investment spent by authorities in monitoring and analysing traffic congestion. This study builds an intelligent analytic method based on machine‐learning algorithms to investigate and predict road traffic flows in four locations in the United Kingdom (London, Yorkshire and the Humber, North East, and North West) with a range of relevant factors. While aiming to conduct the study, the dataset ‘estimated annual average daily flows (AADFs) Data—major and minor roads’ from the UK government was used. Machine‐learning algorithms are used for this research and classification applied consists of Logistic Regression, Decision Trees, Random Forests, K‐Nearest Neighbors, and Gradient Boosting. Each of these algorithms achieves an accuracy of over 93% and the F1 score of over 95%, with Random Forest outperforming the other algorithms. This analytical approach helps to focus attention on critical areas to reduce traffic flows on major and minor roads in the area. In summary, the findings on traffic analysis have been discussed in detail to demonstrate the practical insights of this study.

Scientists are developing hand gesture recognition systems to improve authentic, efficient, and effortless human–computer interactions without additional gadgets, particularly for the speech-impaired community, which relies on hand gestures as their only mode of communication. Unfortunately, the speech-impaired community has been underrepresented in the majority of human–computer interaction research, such as natural language processing and other automation fields, which makes it more difficult for them to interact with systems and people through these advanced systems. This system’s algorithm is in two phases. The first step is the Region of Interest Segmentation, based on the color space segmentation technique, with a pre-set color range that will remove pixels (hand) of the region of interest from the background (pixels not in the desired area of interest). The system’s second phase is inputting the segmented images into a Convolutional Neural Network (CNN) model for image categorization. For image training, we utilized the Python Keras package. The system proved the need for image segmentation in hand gesture recognition. The performance of the optimal model is 58 percent which is about 10 percent higher than the accuracy obtained without image segmentation.

The paper discusses cybersecurity for children (particularly teenagers) and focuses on Social Media’s impact using a theoretical approach. Many social media users are unaware of their Cybersecurity in Social Media and all-round digital privacy and do not understand the importance of developing privacythrough taking both digital and physical measures. We identify seven categories of hacking motivations through multimedia platforms: Emotions, Financial gains, Entertainment, Proficiency for jobs, Hacktivism, Espionage, and Cyber-warfare, particularly for children. As vulnerable people, they can be the principal victims. We explore various methods used for hacking, such as Sexting, Facebook depression, and Influence on buying advertisements. In our findings, we demonstrate that the most critical protection method is to fully understand the digital footprint left behind and its possible consequences. The users should know this as a self-protection mechanism to mitigate security issues before problems occur. It means adopting the same mindset and attitude of protecting oneself in the online world as in the real world.

The advancement in technology has fostered the prevalence of the Internet of Things (IoT), which enhances healthcare business quality, offers a seamless customer experience, and maximizes turnovers and profits. Consequently, omnichannel services have emerged by integrating online and offline channels and providing customers with more real-time information and services to increase their engagement. Healthcare wearable devices appear as a salient tool to connect healthcare providers and patients and thus become an essential part of the omnichannel environment. Along with this trend, the ethical concerns while using these devices have increasingly intensified and are significant barriers to market expansion. Nevertheless, there is a lack of studies discussing the role of wearables in omnichannel hospital supply chain management and examining the influence of those above concerns on healthcare wearables adoption. Therefore, this study explores these gaps through an integrated approach. Furthermore, we proposed a framework integrating the traditional statistical and machine learning-based approach to analyze a large amount of data; and thereby facilitate a data-driven analytic model to manage omnichannel healthcare supply chain businesses.

This paper proposes that high value on the work-life balance, compensation, career opportunity and fitness of culture and management style would improve job satisfaction. A turnover risk prediction model based on the random forest is constructed to understand the turnover risk feature and identify risk. Using a sample of 17,724 online reviews of employees from Glassdoor, the positive effect of antecedents, the job satisfaction variable as a mediator, and the unemployment rate variable as a moderator is verified. Finally, job satisfaction is identified as the most important feature for predicting turnover based on the random forest algorithm.

'Hearables' have become important in the aging population. This study investigates whether smart technologies help middle-aged and elderly people accept hearing aid devices in smart cities of China. The authors adopt the PLS-SEM framework to analyze the factors that affect behavioral intention towards adopting hearing aids in smart cities. In order to avoid common method bias, Harman's single factor method is also carried out to make sure the instrument does not introduce a bias. The findings suggest that perceived playfulness and perceived usefulness are principal determinants of hearing aids adoption. In contrast, perceived ease of use, a factor always stressed in literature, does not matter significantly. The results reveal that smart technologies enable patients to access professional services and instructions playfully, which reduces obstacles to adopt hearing aids. This study provides novel insights for policymakers and manufacturers to expand hearing aid adoption by facilitating smart infrastructure and technologies.

The survival rate of breast cancer prediction has been a significant issue for researchers. Nowadays, the health care industry has completely transformed by using modern technologies and their applications for medical services. Among those technologies, machine learning is one of them, which has gained attention by people that its new advanced technology would give accurate results by using modeling methods for prediction. As this is a branch of artificial intelligence, it employs various statics, probabilistic and optimistic tools. This is applied to medical services, especially which are based on proteomic and genomic measurements. The aim is to use the dataset of cancer treatment and predict the results of patients by using machine learning with its modeling methods for accurate results. Recently experts have even used this machine learning in cancer for prognosis and forecasting.