Christian Versloot, Maria Iacob and Klaas Sikkel
Utility strikes have spawned companies specializing in providing a priori analyses of the underground. Geophysical techniques such as Ground Penetrating Radar (GPR) are harnessed…
Abstract
Utility strikes have spawned companies specializing in providing a priori analyses of the underground. Geophysical techniques such as Ground Penetrating Radar (GPR) are harnessed for this purpose. However, analyzing GPR data is labour-intensive and repetitive. It may therefore be worthwhile to amplify this process by means of Machine Learning (ML). In this work, harnessing the ADR design science methodology, an Intelligence Amplification (IA) system is designed that uses ML for decision-making with respect to utility material type. It is driven by three novel classes of Convolutional Neural Networks (CNNs) trained for this purpose, which yield accuracies of 81.5% with outliers of 86%. The tool is grounded in the available literature on IA, ML and GPR and is embedded into a generic analysis process. Early validation activities confirm its business value.
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Throstur Olaf Sigurjonsson, Robert H. Haraldsson and Jordan Mitchell
Throstur Olaf Sigurjonsson, Robert H. Haraldsson and Jordan Mitchell
The purpose of this study is to examine the state of research into adoption of machine learning systems within the health sector, to identify themes that have been studied and…
Abstract
Purpose
The purpose of this study is to examine the state of research into adoption of machine learning systems within the health sector, to identify themes that have been studied and observe the important gaps in the literature that can inform a research agenda going forward.
Design/methodology/approach
A systematic literature strategy was utilized to identify and analyze scientific papers between 2012 and 2022. A total of 28 articles were identified and reviewed.
Findings
The outcomes reveal that while advances in machine learning have the potential to improve service access and delivery, there have been sporadic growth of literature in this area which is perhaps surprising given the immense potential of machine learning within the health sector. The findings further reveal that themes such as recordkeeping, drugs development and streamlining of treatment have primarily been focused on by the majority of authors in this area.
Research limitations/implications
The search was limited to journal articles published in English, resulting in the exclusion of studies disseminated through alternative channels, such as conferences, and those published in languages other than English. Considering that scholars in developing nations may encounter less difficulty in disseminating their work through alternative channels and that numerous emerging nations employ languages other than English, it is plausible that certain research has been overlooked in the present investigation.
Originality/value
This review provides insights into future research avenues for theory, content and context on adoption of machine learning within the health sector.
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Giuliana Isabella, Marcos Inácio Severo de Almeida and Jose Afonso Mazzon
This study aims to evaluate Artificial Intelligence (AI) research in the hospitality industry based on the service AI framework (mechanical-thinking-feeling) and highlight…
Abstract
Purpose
This study aims to evaluate Artificial Intelligence (AI) research in the hospitality industry based on the service AI framework (mechanical-thinking-feeling) and highlight prospective avenues for future inquiry in this growing domain.
Design/methodology/approach
This paper conceptualizes timely concepts supported by research spanning multiple domains.
Findings
This research introduces a novel classification for the domain of AI hospitality research. This classification encompasses prediction and pattern recognition, computer vision, NLP, behavioral research, and synthetic data generation. Based on this classification, this study identifies and elaborates upon five emerging research topics, each linked to a corresponding set of research questions. These focal points encompass the realms of interpretable AI, controllable AI, AI ethics, collaborative AI, and synthetic data generation.
Originality/value
This viewpoint provides a foundational framework and a directional compass for future research in AI within the hospitality industry. It pushes the industry forward with a balanced approach to leveraging AI to augment human potential and enrich customer experiences. Both the classification and the research agenda would contribute to the body of knowledge that will guide the industry toward a future where technology and human service coalesce to create unparalleled value for all stakeholders.
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Li Chen, Dirk Ifenthaler, Jane Yin-Kim Yau and Wenting Sun
The study aims to identify the status quo of artificial intelligence in entrepreneurship education with a view to identifying potential research gaps, especially in the adoption…
Abstract
Purpose
The study aims to identify the status quo of artificial intelligence in entrepreneurship education with a view to identifying potential research gaps, especially in the adoption of certain intelligent technologies and pedagogical designs applied in this domain.
Design/methodology/approach
A scoping review was conducted using six inclusive and exclusive criteria agreed upon by the author team. The collected studies, which focused on the adoption of AI in entrepreneurship education, were analysed by the team with regards to various aspects including the definition of intelligent technology, research question, educational purpose, research method, sample size, research quality and publication. The results of this analysis were presented in tables and figures.
Findings
Educators introduced big data and algorithms of machine learning in entrepreneurship education. Big data analytics use multimodal data to improve the effectiveness of entrepreneurship education and spot entrepreneurial opportunities. Entrepreneurial analytics analysis entrepreneurial projects with low costs and high effectiveness. Machine learning releases educators’ burdens and improves the accuracy of the assessment. However, AI in entrepreneurship education needs more sophisticated pedagogical designs in diagnosis, prediction, intervention, prevention and recommendation, combined with specific entrepreneurial learning content and entrepreneurial procedure, obeying entrepreneurial pedagogy.
Originality/value
This study holds significant implications as it can shift the focus of entrepreneurs and educators towards the educational potential of artificial intelligence, prompting them to consider the ways in which it can be used effectively. By providing valuable insights, the study can stimulate further research and exploration, potentially opening up new avenues for the application of artificial intelligence in entrepreneurship education.
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Harleen Kaur and Vinita Kumari
Diabetes is a major metabolic disorder which can affect entire body system adversely. Undiagnosed diabetes can increase the risk of cardiac stroke, diabetic nephropathy and other…
Abstract
Diabetes is a major metabolic disorder which can affect entire body system adversely. Undiagnosed diabetes can increase the risk of cardiac stroke, diabetic nephropathy and other disorders. All over the world millions of people are affected by this disease. Early detection of diabetes is very important to maintain a healthy life. This disease is a reason of global concern as the cases of diabetes are rising rapidly. Machine learning (ML) is a computational method for automatic learning from experience and improves the performance to make more accurate predictions. In the current research we have utilized machine learning technique in Pima Indian diabetes dataset to develop trends and detect patterns with risk factors using R data manipulation tool. To classify the patients into diabetic and non-diabetic we have developed and analyzed five different predictive models using R data manipulation tool. For this purpose we used supervised machine learning algorithms namely linear kernel support vector machine (SVM-linear), radial basis function (RBF) kernel support vector machine, k-nearest neighbour (k-NN), artificial neural network (ANN) and multifactor dimensionality reduction (MDR).
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Samar Al Adem, Paul Childerhouse, Temitope Egbelakin and Bill Wang
The purpose of this paper is to identify the key drivers and challenges to supply chain collaboration in the humanitarian sector; to appraise the relationships between…
Abstract
Purpose
The purpose of this paper is to identify the key drivers and challenges to supply chain collaboration in the humanitarian sector; to appraise the relationships between international non-governmental organizations (INGOs) and local non-governmental organizations (LNGOs) during disaster relief; and to explore the humanitarian context in regard to supply chain collaboration.
Design/methodology/approach
Literature from both the commercial and humanitarian sectors is discussed in the context of vertical partnerships. A Jordanian study spanning a network of 26 international and LNGOs is explored via semi-structured interviews.
Findings
The research provides valuable insights on the challenges facing LNGOs and INGOs when developing partnerships. Contextual factors, including host governmental policies and the social-economic setting of a disaster directly affect the motivations for supply chain collaboration between LNGOs and INGOs.
Research limitations/implications
The research is built on interviewees with 30 humanitarian professionals working in one country during an extended crisis. The majority of the empirical data are only from one actor’s perspective, thus further research into dyadic and network relationships is required. Approaches to addressing the diverse cultural and decision-making perspectives of LNGOs and INGOs warrant further investigation.
Practical implications
Recognizing the motives and challenges to vertical partnerships between LNGOs and INGOs will assist the managers, both at the strategic and operational levels, to find solutions and evolve strategies to build effective partnerships. Compromise and consideration for partner’s drivers and cultural views are essential for effective joint humanitarian relief initiatives.
Originality/value
This paper extends supply chain collaboration to a humanitarian context. Overcoming the challenges facing collaborative efforts and complementary nature of the drivers provide a means to achieve effective partnerships. Despite the uniqueness of the humanitarian context, such as the secondary nature of cost and dynamic demand, the core principles of collaboration still hold.