Intelligence Literature Research Articles

Artificial intelligence in veterinary diagnostic imaging: A literature review.

Artificial intelligence in veterinary medicine is an emerging field. Machine learning, a subfield of artificial intelligence, allows computer programs to analyze large imaging datasets and learn to perform tasks relevant to veterinary diagnostic imaging. This review summarizes the small, yet growing body of artificial intelligence literature in veterinary imaging, provides necessary background to understand these papers, and provides author commentary on the state of the field. To date, less than 40 peer-reviewed publications have utilized machine learning to perform imaging-associated tasks across multiple anatomic regions in veterinary clinical and biomedical research. Major challenges in this field include collection and cleaning of sufficient image data, selection of high-quality ground truth labels, formation of relationships between veterinary and machine learning professionals, and closure of the gap between academic uses of artificial intelligence and currently available commercial products. Further development of artificial intelligence has the potential to help meet the growing need for radiological services through applications in workflow, quality control, and image interpretation for both general practitioners and radiologists.

The Causal Plausibility Decision in Healthcare.

The explosion of interest in exploiting machine learning techniques in healthcare has brought the issue of inferring causation from observational data to centre stage. In our work in supporting the health decisions of the individual person/patient-as-person at the point of care, we cannot avoid making decisions about which options are to be included or excluded in a decision support tool. Should the researcher's routine injunction to use their findings 'with caution', because of methodological limitations, lead to inclusion or exclusion? The task is one of deciding, first on causal plausibility, and then on causality. Like all decisions these are both sensitive to error preferences (trade-offs). We engage selectively with the Artificial Intelligence (AI) literature on the causality challenge and on the closely associated issue of the 'explainability' now demanded of 'black box' AI. Our commitment to embracing 'lifestyle' as well as 'medical' options for the individual person, leads us to highlight the key issue as that of who is to make the preference- sensitive decisions on causal plausibility and causality.

How the different explanation classes impact trust calibration: The case of clinical decision support systems

Machine learning has made rapid advances in safety-critical applications, such as traffic control, finance, and healthcare. With the criticality of decisions they support and the potential consequences of following their recommendations, it also became critical to provide users with explanations to interpret machine learning models in general, and black-box models in particular. However, despite the agreement on explainability as a necessity, there is little evidence on how recent advances in eXplainable Artificial Intelligence literature (XAI) can be applied in collaborative decision-making tasks, i.e., human decision-maker and an AI system working together, to contribute to the process of trust calibration effectively. This research conducts an empirical study to evaluate four XAI classes for their impact on trust calibration. We take clinical decision support systems as a case study and adopt a within-subject design followed by semi-structured interviews. We gave participants clinical scenarios and XAI interfaces as a basis for decision-making and rating tasks. Our study involved 41 medical practitioners who use clinical decision support systems frequently. We found that users perceive the contribution of explanations to trust calibration differently according to the XAI class and to whether XAI interface design fits their job constraints and scope. We revealed additional requirements on how explanations shall be instantiated and designed to help a better trust calibration. Finally, we build on our findings and present guidelines for designing XAI interfaces.

Code and Data Sharing Practices in the Radiology Artificial Intelligence Literature: A Meta-Research Study.

To evaluate code and data sharing practices in original artificial intelligence (AI) scientific manuscripts published in the Radiological Society of North America (RSNA) journals suite from 2017 through 2021. A retrospective meta-research study was conducted of articles published in the RSNA journals suite from January 1, 2017, through December 31, 2021. A total of 218 articles were included and evaluated for code sharing practices, reproducibility of shared code, and data sharing practices. Categorical comparisons were conducted using Fisher exact tests with respect to year and journal of publication, author affiliation(s), and type of algorithm used. Of the 218 included articles, 73 (34%) shared code, with 24 (33% of code sharing articles and 11% of all articles) sharing reproducible code. Radiology and Radiology: Artificial Intelligence published the most code sharing articles (48 [66%] and 21 [29%], respectively). Twenty-nine articles (13%) shared data, and 12 of these articles (41% of data sharing articles) shared complete experimental data by using only public domain datasets. Four of the 218 articles (2%) shared both code and complete experimental data. Code sharing rates were statistically higher in 2020 and 2021 compared with earlier years (P < .01) and were higher in Radiology and Radiology: Artificial Intelligence compared with other journals (P < .01). Original AI scientific articles in the RSNA journals suite had low rates of code and data sharing, emphasizing the need for open-source code and data to achieve transparent and reproducible science.Keywords: Meta-Analysis, AI in Education, Machine LearningSupplemental material is available for this article.© RSNA, 2022.

Artificial intelligence in marketing: A meta‐analytic review

AbstractThis study synthesizes the artificial intelligence literature into a Meta‐analytic framework based on the theory of reasoned action and the unified theory of acceptance and use of technology 2, and examines concrete relationships between the constructs of this framework. This meta‐review also performed a moderation analysis to investigate the possible reasons for inconsistent findings across studies. The findings suggest that three methodological moderators (sample type, gender dominance, and publication type), and one contextual moderator (level of country's technology advancement) lead to inconsistencies in the relationships between study constructs. Academically, this review synthesizes the artificial intelligence literature and resolves inconsistencies in the literature and also adds constructs to both theory of reasoned action and the unified theory of acceptance and use of technology 2. Practically, this meta‐analysis offers multiple implications for businesses interested in enhancing customer adoption of artificial intelligence. Especially, companies can increase customers' adoption of artificial intelligence by making it more user‐friendly, and advantageous and by adding pleasing features to it.

The Enriching Effects of Spouse’s Emotion Regulation Ability on Employee’s Leader-Member Exchange

Although work and family lives are increasingly intertwined in modern life, our understanding of how spouse’s emotional abilities can permeate employee’s work life remains limited. Integrating Work-Home Resources Model with emotional intelligence literatures, we investigate how spouse’s emotion regulation ability (ERA) facilitates employees’ leader-member exchange (LMX) across three mixed-methods studies. Study 1 employed an exploratory qualitative empirical study to identify the work-related benefits of spouse’s ERA. Study 2 used data from independent spouse-employee-supervisor triads measured at two separate time points to demonstrate that the indirect effect of spouse’s ERA on LMX via employees’ personal resources (i.e., psychological capital and emotion management knowledge) was conditional at higher, as opposed to lower, spouse’s family role overload. Study 3 replicated experimentally the interactive effects of spouse’s ERA and family role overload on employees’ personal resources, which in turn shape LMX quality. Collectively, our findings extend theoretical insights and empirical evidence to the important and complex role of affect in LMX development from the lens of family-to-work enrichment process.

Adverse outcome pathway from activation of the AhR to breast cancer-related death.

Adverse outcome pathways (AOPs) are formalized and structured linear concepts that connect one molecular initiating event (MIE) to an adverse outcome (AO) via different key events (KE) through key event relationships (KER). They are mainly used in eco-toxicology toxicology, and regulatory health issues. AOPs must respond to specific guidelines from the Organization for Economic Co-operation and Development (OECD) to weight the evidence between each KE. Breast cancer is the deadliest cancer in women with a poor prognosis in case of metastatic breast cancer. The role of the environments in the formation of metastasis has been suggested. We hypothesized that activation of the AhR (MIE), a xenobiotic receptor, could lead to breast cancer related death (AO), through different KEs, constituting a new AOP. An artificial intelligence tool (AOP-helpfinder), which screens the available literature, was used to collect all existing scientific abstracts to build a novel AOP, using a list of key words. Four hundred and seven abstracts were found containing at least a word from our MIE list and either one word from our AO or KE list. A manual curation retained 113 pertinent articles, which were also screened using PubTator. From these analyses, an AOP was created linking the activation of the AhR to breast cancer related death through decreased apoptosis, inflammation, endothelial cell migration, angiogenesis, and invasion. These KEs promote an increased tumor growth, angiogenesis and migration which leads to breast cancer metastasis and breast cancer related death. The evidence of the proposed AOP was weighted using the tailored Bradford Hill criteria and the OECD guidelines. The confidence in our AOP was considered strong. An in vitro validation must be carried out, but our review proposes a strong relationship between AhR activation and breast cancer-related death with an innovative use of an artificial intelligence literature search.

Automatic characterization of human embryos at day 4 post-insemination from time-lapse imaging using supervised contrastive learning and inductive transfer learning techniques

Background: Embryo morphology is a predictive marker for implantation success and ultimately live births. Viability evaluation and quality grading are commonly used to select the embryo with the highest implantation potential. However, the traditional method of manual embryo assessment is time-consuming and highly susceptible to inter- and intra-observer variability. Automation of this process results in more objective and accurate predictions.Method: In this paper, we propose a novel methodology based on deep learning to automatically evaluate the morphological appearance of human embryos from time-lapse imaging. A supervised contrastive learning framework is implemented to predict embryo viability at day 4 and day 5, and an inductive transfer approach is applied to classify embryo quality at both times.Results: Results showed that both methods outperformed conventional approaches and improved state-of-the-art embryology results for an independent test set. The viability result achieved an accuracy of 0.8103 and 0.9330 and the quality results reached values of 0.7500 and 0.8001 for day 4 and day 5, respectively. Furthermore, qualitative results kept consistency with the clinical interpretation.Conclusions: The proposed methods are up to date with the artificial intelligence literature and have been proven to be promising. Furthermore, our findings represent a breakthrough in the field of embryology in that they study the possibilities of embryo selection at day 4. Moreover, the grad-CAMs findings are directly in line with embryologists’ decisions. Finally, our results demonstrated excellent potential for the inclusion of the models in clinical practice.

Stable Near-Optimal Control of Nonlinear Switched Discrete-Time Systems: An Optimistic Planning-Based Approach

Originating in the artificial intelligence literature, optimistic planning(OP) is an algorithm that generates near-optimal control inputs for generic nonlinear discrete-time systems whose input set is finite. This technique is therefore relevant for the near-optimal control of nonlinear switched systems for which the switching signal is the control, and no continuous input is present. However, OP exhibits several limitations, which prevent its desired application in a standard control engineering context, as it requires for instance that the stage cost takes values in [0,1], an unnatural prerequisite, and that the cost function be discounted. In this paper, we modify OP to overcome these limitations, and we call the new algorithm OPmin. New near-optimality and performance guarantees for OPmin are derived, which have major advantages compared to those originally given for OP. We also prove that a system whose inputs are generated by OPmin in a receding-horizon fashion exhibits stability properties. As a result, OPmin provides a new tool for the near-optimal, stable control of nonlinear switched discrete-time systems for generic cost functions.

Methodology for Business Intelligence Solutions in Internet Banking Companies

Business intelligence in the banking industry has been studied extensively in the last decade; however, business executives still do not perceive efficiency in the decision-making process since the management and treatment of information are very time-consuming for the deliverer, generating costs in the process. On the other hand, there is no formal methodology for developing business intelligence solutions in this sector. This work aims to optimize decision-making in a business unit that works with internet banking companies, reducing the time, the number of people, and the costs involved in decision-making. To meet the objective, basic and applied research was conducted. The basic research allowed the construction of a new methodology from a study of critical success factors and approaches from the business intelligence literature. The applied research involved the implementation of a business intelligence solution applying the new methodology in a pre-experimental study. Thirty decision-making processes were analyzed using pre-test and post-test data. Tools such as a stopwatch and observation were used to collect and record data on time spent, the number of people, and the decision-making costs. This information was processed in the specialized Minitab18 statistical software, which allowed the observation and confirmation of relevant results regarding time reduction, the number of people, and the costs generated. Therefore, it was concluded that the business intelligence solution, applying the new methodology, optimized decision making in the business unit that works with internet banking for companies.

An intelligent literature review: adopting inductive approach to define machine learning applications in the clinical domain

Big data analytics utilizes different techniques to transform large volumes of big datasets. The analytics techniques utilize various computational methods such as Machine Learning (ML) for converting raw data into valuable insights. The ML assists individuals in performing work activities intelligently, which empowers decision-makers. Since academics and industry practitioners have growing interests in ML, various existing review studies have explored different applications of ML for enhancing knowledge about specific problem domains. However, in most of the cases existing studies suffer from the limitations of employing a holistic, automated approach. While several researchers developed various techniques to automate the systematic literature review process, they also seemed to lack transparency and guidance for future researchers. This research aims to promote the utilization of intelligent literature reviews for researchers by introducing a step-by-step automated framework. We offer an intelligent literature review to obtain in-depth analytical insight of ML applications in the clinical domain to (a) develop the intelligent literature framework using traditional literature and Latent Dirichlet Allocation (LDA) topic modeling, (b) analyze research documents using traditional systematic literature review revealing ML applications, and (c) identify topics from documents using LDA topic modeling. We used a PRISMA framework for the review to harness samples sourced from four major databases (e.g., IEEE, PubMed, Scopus, and Google Scholar) published between 2016 and 2021 (September). The framework comprises two stages—(a) traditional systematic literature review consisting of three stages (planning, conducting, and reporting) and (b) LDA topic modeling that consists of three steps (pre-processing, topic modeling, and post-processing). The intelligent literature review framework transparently and reliably reviewed 305 sample documents.

Artificial intelligence-based literature data warehouse for vaccine safety

Objective: To establish a sustainable updated literature data warehouse for global vaccine safety assessment, and provide data support for evidence-based vaccine safety assessment. Methods: Semi-automated construction and updating of a literature data warehouse were achieved through the continuous integration of standard operating steps of evidence-based reviews with artificial intelligence technologies. Following the standard procedure of a systematic literature review, the literatures about vaccine safety assessment published before November 29, 2020 were retrieved from 9 databases including OVID, Scopus, Web of Science, Cochrane Library, and ClinicalTrails.org in English and Wanfang, CNKI, VIP, and SinoMed in Chinese. Literatures were screened for two rounds in a semi-automatic manner (by artificial intelligence literature processing system and manual work) according to the inclusion/exclusion criteria. Furthermore, the literatures were classified according to the types of vaccines and adverse events. The updating strategy was established, and the literature data warehouse was updated regularly. Experts were organized to select specific vaccine safety topics and carry out special demonstration studies. Results: More than 0.41 million articles were retrieved. According to the inclusion/exclusion criteria, 23 304 articles were included after two rounds of screening. At present, we have selected and completed three prior topics as demonstration studies, including the systematic review of "DPT (diphtheria, pertussis and tetanus) vaccine and encephalopathy/encephalitis", and the classified management of literatures about allergic purpura and brachial plexus neuritis. Conclusions: The sustainable updated literature data warehouse of vaccine safety can provide high-quality research data for vaccine safety research, including evidence support for immunization related policy-making and adjustment and vaccine safety-related methodological research or clinical tool development; and further demonstration studies can provide references for building a new methodological framework system for timely and efficient completion of the evidence-based assessment of vaccine safety.

Family firm succession through the lens of technology intelligence

Succession is often a critical event in the lifecycle of any organization. Consequently, an extensive body of literature explores family firm succession, and includes studies on integrated succession frameworks. Notwithstanding the large, illuminating body of literature, we believe that family firm succession is worth a fresh reexamination and consolidation to translate the findings into a powerful planning and communication tool, especially as all firms are facing a ‘new normal’ and their leaders are seeking novel perspectives and approaches amidst the COVID-19 disruption. We thus offer a systemic and systematic framework for family firm succession planning, incorporating ideas from the technology intelligence literature. Since relationship building can ease the succession process and relationships are built through communication, we also present a succession roadmap as a communication tool that can assist in making this process part of the regular business routines.

An intelligence classic that almost never was –Roberta Wohlstetter’s Pearl Harbor: Warning and Decision

ABSTRACT Roberta Wohlstetter’s Pearl Harbor: Warning and Decision is a classic of intelligence literature that also made a major contribution to American strategic thought during the early Cold War. In 1959, however, the government classified Wohlstetter’s finished manuscript – written under an Air Force contract – just as it was on the verge of publication. Based on multiple archival sources, this article describes how lobbying by Wohlstetter’s employer, the RAND Corporation, and government insiders ultimately led Washington to reverse its decision and authorize the release of Wohlstetter’s work, thus illuminating the changing priorities, tensions and dilemmas inherent in government secrecy.

Research on Literary Translation Based on the Improved Optimization Model

Machine translation is widely used in people’s daily lives and production, occupying an important position. In order to improve the accuracy of the literary intelligent translation, research on literary intelligent translation is based on the improved optimization model. Based on semantic features, the semantic ontology optimization model including an encoder and a decoder is created by machine translation. In order to improve the accuracy of the intelligent translation literature of the semantic ontology optimization model, the conversion layer, including the forward neural network layer, residual connection layer, and normalization layer, is added between the encoder and decoder of the semantic ontology optimization model. An improved optimization model is established, and syntax conversion is realized by using the conversion layer, which completes the intelligent translation of literature. It is found that the BLEU value of using this method to translate literary sentences can reach 17.23 when the number of training steps is set as 8000, and the training time is low. The translation result has a low correlation misalignment rate, which can meet the user’s literary translation needs.

Artificial intelligence: a systematic review of methods and applications in hospitality and tourism

PurposeSeveral review articles have been published within the Artificial Intelligence (AI) literature that have explored a range of applications within the tourism and hospitality sectors. However, how efficiently the applied AI methods and algorithms have performed with respect to the type of applications and the multimodal sets of data domains have not yet been reviewed. Therefore, this paper aims to review and analyse the established AI methods in hospitality/tourism, ranging from data modelling for demand forecasting, tourism destination and behaviour pattern to enhanced customer service and experience.Design/methodology/approachThe approach was to systematically review the relationship between AI methods and hospitality/tourism through a comprehensive literature review of papers published between 2010 and 2021. In total, 146 articles were identified and then critically analysed through content analysis into themes, including “AI methods” and “AI applications”.FindingsThe review discovered new knowledge in identifying AI methods concerning the settings and available multimodal data sets in hospitality and tourism. Moreover, AI applications fostering the tourism/hospitality industries were identified. It also proposes novel personalised AI modelling development for smart tourism platforms to precisely predict tourism choice behaviour patterns.Practical implicationsThis review paper offers researchers and practitioners a broad understanding of the proper selection of AI methods that can potentially improve decision-making and decision-support in the tourism/hospitality industries.Originality/valueThis paper contributes to the tourism/hospitality literature with an interdisciplinary approach that reflects on theoretical/practical developments for data collection, data analysis and data modelling using AI-driven technology.

Artificial intelligence (AI) literature in patents: a global landscape

Artificial intelligence (AI) literature in patents: a global landscape

Italian Intelligence Studies Literature - Understanding the State of Play - A Comparative Perspective

ABSTRACT Contributions to the Italian intelligence studies literature have seen an expansion since the major reform of Italian intelligence introduced in 2007. Italian civil society also increasingly recognizes the importance of intelligence. This paper first explores Italian intelligence studies literature as it stands today before considering the academic research. The six main categories of Italian intelligence literature are presented within: history, sources of intelligence, international relations and geopolitics, theories of intelligence and theoretical frameworks, great figures and memoirs, law enforcement, and the fight against criminal organizations. A short comparative analysis between the Italian literature and international intelligence studies is drawn which includes a comparison of categories, topics, key questions, and projects. The goal of this paper, therefore, is to draw a likely depiction of the state of Italian intelligence studies.

The Canadian national intelligence culture: A minimalist and defensive national intelligence apparatus

This paper seeks to understand the nature and characteristics of Canadian national intelligence culture post–Cold War using the analytical insights of the strategic culture and intelligence culture literature. Previous studies have focused on an organizational description or historical studies of Canadian intelligence during the Cold War or after 9/11. Yet, no studies have examined the characterization of a national intelligence culture in Canada and proposed to contextualize the Canadian intelligence system in light of its national intelligence culture. Building on a culturalist approach of national intelligence systems, this paper proposes an operationalization of the national intelligence culture concept drawn on the strategic and intelligence culture literature. The paper concludes that Canada’s national intelligence culture is mostly defensive and minimalist. However, we note that recent changes in the Canadian intelligence apparatus have led to a gradual evolution of Canadian intelligence from defensive to offensive.

A look back and a leap forward: a review and synthesis of big data and artificial intelligence literature in hospitality and tourism

ABSTRACT In reaction to the growing attention paid to big data and artificial intelligence in hospitality and tourism research, we systematically reviewed 270 relevant studies to identify topical themes and trends. We first briefly reviewed the emergence definition of big data. Next, we introduced the methodology of literature collection and presented results of bibliometric analysis. Then, we identified types of big data used and the application of artificial intelligence in big data usage in hospitality and tourism research, followed by unveiling major themes of big data and artificial intelligence research in extant literature such as forecasting, industry development, marketing, performance analysis, consumer behaviors, attitudes, and so on. In addition, implications and challenges of applying big data and artificial intelligence in hospitality and tourism research and new directions for future research are identified. Finally, we discuss limitations of our review, proposing future research directions for scholars.