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The digital marketing landscape is rapidly evolving, but the integration of visual content still heavily depends on human expertise. Driven by the quest for innovative marketing strategies that resonate with family-oriented consumers, this study seeks to bridge this gap by applying machine learning to analyze visual content in the maternity and baby care product sector.

This study incorporates a range of machine learning techniques – including open science framework feature detection, panoptic segmentation, customized instance segmentation, and face detection calculation methods – to analyze and predict the appeal of images, thereby enhancing user engagement and parent-child intimacy.

The exploration of various ML models, such as DT, LightGBM, RIPPER algorithm, and CNNs, has offered a comparative analysis that addresses a methodological gap in the existing literature, which frequently depends on isolated model evaluations. According to our quadrant analysis with respect to engagement rate and parent-child intimacy, the selection of a model for real-world applications depends on balancing performance and interpretability.

The proposed system offers a series of actionable recommendations designed to enhance customer engagement and foster brand loyalty. This study contributes to image design in maternity and baby care marketing and provides analytical insights for recommendation systems.

The purpose of this study is to investigate and analyse the potential of existing buildings in the UK to contribute to the net-zero emissions target. Specifically, it aims to address the significant emissions from building fabrics which pose a threat to achieving these targets if not properly addressed.

The study, based on a literature review and ten (10) case studies, explored five investigative approaches for evaluating building fabric: thermal imaging, in situ U-value testing, airtightness testing, energy assessment and condensation risk analysis. Cross-case analysis was used to evaluate both case studies using each approach. These methodologies were pivotal in assessing buildings’ existing condition and energy consumption and contributing to the UK’s net-zero ambitions.

Findings reveal that incorporating the earlier approaches into the building fabric showed great benefits. Significant temperature regulation issues were identified, energy consumption decreased by 15% after improvements, poor insulation and artistry quality affected the U-values of buildings. Implementing retrofits such as solar panels, air vents, insulation, heat recovery and air-sourced heat pumps significantly improved thermal performance while reducing energy consumption. Pulse technology proved effective in measuring airtightness, even in extremely airtight houses, and high airflow and moisture management were essential in preserving historic building fabric.

The research stresses the need to understand investigative approaches’ strengths, limitations and synergies for cost-effective energy performance strategies. It emphasizes the urgency of eliminating carbon dioxide (CO₂) and greenhouse gas emissions to combat global warming and meet the 1.5° C threshold.

This case study involves interviews with radiologists of various hospitals and with company personnel. Both primary and secondary data sources have been used. The first-hand perspective from the radiologists highlighted the challenges they face concerning time and the patient load. The company personnel highlighted using machine learning for used cases to make the platform more robust and accurate. This case has been tested with MBA students.

An emerging health-care artificial intelligence (AI) start-up, DeepTek.AI, wants to expand its reach in the radiology market. The company intends to leverage technology to assist radiologists in diagnostics. India's health-care sector faces the challenge of needing more trained doctors and nurses to meet the ever-increasing needs of patients. This case study revolves around the radiologists' concerns about implementing the new technology and its ease of use. The features and benefits of integrating AI in diagnostics are the need of the hour, but the reliability of results needs to be ascertained for adopting it.

This case was written for marketing applications and practices, trends in marketing, marketing strategy and technology adoption in marketing courses at the post-graduate level. Consumer adoption of finance, hospitality, travel and health-care technology is vital for increasing the company's market share and growth prospects. The students will have an opportunity to understand the challenges and the opportunities.

Computer vision (CV) offers a promising approach to transforming the conventional in-person inspection practices prevalent within the construction industry. However, the reliance on centralized systems in current CV-based inspections introduces a vulnerability to potential data manipulation. Unreliable inspection records make it challenging for safety managers to make timely decisions to ensure safety compliance. To address this issue, this paper proposes a blockchain (BC) and CV-based framework to enhance safety inspections at construction sites.

This study adopted a BC-enhanced CV approach. By leveraging CV and BC, safety conditions are automatically identified from site images and can be reliably recorded as safety inspection data through the BC network. Additionally, by using this data, smart contracts coordinate inspection tasks, assign responsibilities and verify safety performance, managing the entire safety inspection process remotely.

A case study confirms the framework’s applicability and efficacy in facilitating remote and reliable safety inspections. The proposed framework is envisaged to greatly improve current safety inspection practices and, in doing so, contribute to reduced accidents and injuries in the construction industry.

This study provides novel and practical guidance for integrating CV and BC in construction safety inspection. It fulfills an identified need to study how to leverage CV-based inspection results for remotely managing the safety inspection process using BC. This work not only takes a significant step towards data-driven decision-making in the safety inspection process, but also paves the way for future studies aiming to develop tamper-proof data management systems for industrial inspections and audits.

This chapter focuses on how alternative data can change the nature of financial forecasting through improved short-term forecasting techniques and decreased informativeness from longer term sources. Increased use of social media data leads the charge in transforming this transition. Alternative data are data not from standard financial statements or formal reports. This chapter looks at alternative data from new sources (e.g., social media, Internet of Things [IoT], and digital footprints) and alternative data from new collection methods like web scraping for textual analysis, image analysis, and vocal analysis). It first discusses standard data in financial forecasting. Next, this chapter examines alternative data in financial forecasting. Finally, it discusses alternative data used in studying finance more broadly.

This study aims to draw lessons from the financial technology (FinTech) ecosystem literature through a systematic literature review.

This study systematically studied a sample of 134 articles from the Scopus database to assess the pattern of research development within the scope of the FinTech ecosystem over the last 15 years (2008–2023).

The results obtained indicated that the current research focus leads to several aspects: digital technology and financial inclusion, FinTech and customer behavior, FinTech ecosystem, business model, as well as aspects of governance and regulation. In the effort to develop Islamic FinTech, some aspects that might be targeted include aspects of business development and the Islamic FinTech ecosystem in general, extending financial inclusion to governance and managerial implementation of Islamic FinTech itself.

This research has limitations because it did not focus on the study of more specialized sectors, such as insurance or microfinance institutions, in adopting FinTech, requiring the use of other specifications of institutions in addition to Islamic banking.

This research has substantial theoretical implications in mapping the intellectual structure of Islamic FinTech research, which has been underexplored by previous researchers, as well as providing essential information about which sectors should be prioritized to encourage inclusiveness and overall performance of financial institutions.

This research explores more deeply with a comprehensive approach so that it becomes a pioneer in the study of FinTech ecosystem literature for the development of Islamic FinTech.

Financial technology (fintech) refers to using new technology to improve and automate the delivery and use of financial services. This chapter provides a brief introduction to fintech. It also includes the book's purpose, distinguishing features, intended audience, and structure. A synopsis of Chapters 2 through 23 is offered. The chapter concludes that fintech is constantly evolving and is reshaping finance. Fintechs offer a new paradigm of growth.

Based on the comprehensive definition of accounting of Carnegie et al. (2021a, 2021b), this study examines how visual imagery can expand and enhance accountability to stakeholders and create room for more human-centric accounts. This study aims to understand how this use can elucidate and prompt interpretations of rhetorical features aimed at envisioning legitimacy and being perceived as accountable.

Following a methodological interpretative approach, this paper draws on a qualitative case study based on a Portuguese charity, the Santa Casa da Misericordia do Porto, from 2019 to 2021, including the COVID-19 crisis period, analysing visual rhetoric in annual and sustainability reports.

The study illuminates how the visual images interact and evoke shared cultural understandings, shaping meanings that can symbolically foster organisational legitimacy and envisions accountability. These symbolic and emotive elements capture and make visible social impacts and reflect broader societal concerns.

The study of visual images within the accounting context can enrich the understanding of accounting as a technical, social and moral practice, while expanding the scope of accountability and promoting a more human-centred approach to accounting. It also adds to the literature on the persuasiveness and rhetoric of accounting and reporting visualisations and on charities’ accountability in crisis period.

This study aims to investigate the performance analysis, science mapping and future direction of artificial intelligence (AI) technology, applications, tools and software used to preserve, curate and predict the historical value of cultural heritage.

This study uses the bibliometric research method and utilizes the Scopus database to gather data. The keywords used are “artificial intelligence” and “cultural heritage,” resulting in 718 data sets spanning from 2001 to 2023. The data is restricted to the years 2001−2023, is in English language and encompasses all types of documents, including conference papers, articles, book chapters, lecture notes, reviews and editorials.

The performance analysis of research on the use of AI to aid in the preservation of cultural heritage has been ongoing since 2001, and research in this area continues to grow. The countries contributing to this research include Italy, China, Greece, Spain and the UK, with Italy being the most prolific in terms of authored works. The research primarily falls under the disciplines of computer science, mathematics, engineering, social sciences and arts and humanities, respectively. Document types mainly consist of articles and proceedings. In the science mapping process, five clusters have been identified. These clusters are labeled according to the contributions of AI tools, software, apps and technology to cultural heritage preservation. The clusters include “conservation assessment,” “exhibition and visualization,” “software solutions,” “virtual exhibition” and “metadata and database.” The future direction of research lies in extended reality, which integrates virtual reality (VR), augmented reality (AR) and mixed reality (MR); virtual restoration and preservation; 3D printing; as well as the utilization of robotics, drones and the Internet of Things (IoT) for mapping, conserving and monitoring historical sites and cultural heritage sites.

The cultural heritage institution can use this result as a source to develop AI-based strategic planning for curating, preservation, preventing and presenting cultural heritages. Researchers and academicians will get insight and deeper understanding on the research trend and use the interdisciplinary of AI and cultural heritage for expanding collaboration.

This study will help to reveal the trend and evolution of AI and cultural heritage. The finding also will fill the knowledge gap on the research on AI and cultural heritage.

Some similar bibliometric studies have been conducted; however, there are still limited studies on contribution of AI to preserve cultural heritage in wider view. The value of this study is the cluster in which AI is used to preserve, curate, present and assess cultural heritages.

To investigate whether the actual effects of eight drape characteristics of virtual fabrics can be manifested in the Style 3D software.

Image analysis was conducted using MATLAB software to obtain the drape characteristics of virtual fabrics. Pair the drape characteristics of the real and virtual fabrics for difference. The S-W method was used to conduct a normality test to obtain the correlation of paired samples. A paired sample t-test was performed to obtain the significance values.

The simulation restoration performance of the drape coefficient, number of undulations, maximum undulation angle, minimum undulation angle and undulation angle uniformity was good. However, there are differences in the simulation performance of the other three indicators: maximum undulation amplitude, minimum undulation amplitude and undulation amplitude uniformity compared to the drape characteristics of real fabrics.

Provides reference value for the improvement of Style3D software in virtual fabric simulation and finds the main influential parameters and their impact levels that contribute to the realistic representation of virtual fabrics in software. It provides a theoretical basis for course teaching in digital fashion.