Artificial Intelligence Strategies Research Articles

The evaluation of AI integration in innovative digital marketing strategies

Purpose- This study aims to provide a bibliometric review of publications where the terms 'digital marketing' and 'artificial intelligence' are used together. Leading publications, authors, countries, and institutions in the Web of Science (WoS) database have been examined to achieve this goal. Additionally, this article investigates the combined use of digital marketing and artificial intelligence. Furthermore, it aims to offer insights into artificial intelligence strategies for marketing that businesses can employ. Methodology- The research employs the technique of bibliometric analysis. The Bibliometrix package within R Studio and its web-based component, Biblioshiny, were utilized for analysis. Searches were conducted in the Web of Science database using the keywords 'Digital Marketing' and 'Artificial Intelligence' in the title, abstract, and keywords sections. Findings- As a result of the analysis, a total of 60 publications authored by 140 researchers and distributed across 46 journals between 2017 and 2023 were identified. Examination of the included publications reveals frequent usage of terms such as 'artificial intelligence,' 'creativity,' 'analytics,' 'impact,' 'expertise,' 'social networks,' 'big data,' 'governance,' 'success,' and 'AI.' Upon scrutinizing the authors' countries, India emerged as the leading contributor, followed by Spain and the USA. Moreover, Finland (370), Spain (92), and France (58) had the highest citation counts. Conclusion- This research aims to contribute to researchers interested in working in digital marketing and artificial intelligence by examining its past and present. For this purpose, 60 relevant studies from the literature were systematically reviewed and analyzed across various categories. Additionally, the examined publications' conceptual, intellectual, and social structures were illuminated. Keywords: Digital marketing, artificial intelligence, AI systems, bibliometric analysis, bibliometrix. JEL Codes: M15, M30, M31

Unsupervised active–transfer learning for automated landslide mapping

Detailed landslide inventories are required for multiple purposes including disaster damage assessments, susceptibility mapping for spatial planning, and disaster risk reduction. Active learning is an artificial intelligence strategy that can achieve good performances in landslide mapping by training a machine-learning model with a reduced number of landslide/non-landslide observations, which can save time and effort in labeling training instances. Nevertheless, active-learning models are unstable at the beginning of sample selection due to the limited initial knowledge of landslide distribution. Transfer learning can help make the learner robust by transferring a landslide model trained on an existing landslide inventory from a different, but geographically similar source area, to the unseen target area. In order to adjust a transferred machine-learning model to the possibly unique environmental characteristics of the unseen area, we proposed a new framework called Unsupervised Active-Transfer Learning (UATL). This framework used a weight function to combine the landslide model transferred from the source area, with a model trained on a small, but increasing number of landslide/non-landslide observations from the target area to efficiently build a more robust learner. We examined two methods, adaptive UATL and regular UATL, which differed in the way they assign weights to the combined learners. We evaluated our proposed new methods by comparing them with three benchmark methods (active learning only, model transfer only, and the model trained in the unseen area itself) by means of the partial area under the receiver operating characteristic (ROC) curve (AUROC) as the evaluation criterion. The results showed that the new methods, and especially adaptive UATL, can achieve good predictive performances. With only about 235 training instances from the target area, the partial AUROC obtained from adaptive UATL was only 2% lower than that obtained from the model trained in the target area itself, and consistently outperformed the other two benchmarks. Overall, we suggest that the framework proposed can be applied to the natural hazards management workflow for assisting in emergency response, especially in data-scarce regions (e.g., mountainous areas and developing countries).

Artificial Intelligent in UAE (A Study on the Uses and Attitudes of AI in Media Companies)

The purpose of this research is to explore the attitudes of potential usage of Artificial Intelligence (AI) in media companies in the United Arab Emirates. The UAE has given more prominence to AI in recent years especially after launching the UAE Strategy for Artificial Intelligence in 2017 which aims toward achieving the country’s continental vision in making the UAE the pioneering in the field of AI by investing this technology in various sectors and integrate a smart digital system for better problem solving (UAE Government Portal, 2020). In addition, the media sector shall have it’s share of AI investment as H.E. Omar Sultan Al Olama, Minister of State for Artificial Intelligence, indicated that this will benefit the industry by enriching media content and supporting media research (Masterminds, 2019).

The impact of artificial intelligence on modern education: prospects and challenges

This paper examines the intricate links between artificial intelligence (AI) and education, delving into both theoretical and practical aspects while evaluating possible ramifications for labor market dynamics, professional activity, and wider educational paradigms. Our research methodology involved analyzing relevant scientific literature, classifying data, consulting with subject matter experts, and synthesizing the results. Our research suggests that AI has the ability to greatly improve pedagogical processes, personalize learning experiences to meet individual student needs, and successfully address the time and financial limitations that are inherent in traditional educational models. However, our study also reveals challenges related to data confidentiality, potential plagiarism and fraud associated with AI use, and socioeconomic disparities resulting from unequal technology access. Additionally, we identified a significant gap in current AI usage standards legislation. It is essential for researchers, educators, and policymakers to recognize the potential risks of AI implementation in educational settings and proactively develop strategies that prioritize ethics, safety, and effectiveness. With labor market trends favoring specialists knowledgeable in utilizing AI tools, a consequent change in curricula is expected. In response to our findings, we recommend the creation of new academic disciplines that concentrate on the cultivation of AI expertise; the establishment of comprehensive national AI strategies; the crafting of retraining roadmaps for those who may be affected by AI automation; the inclusion of online AI courses in existing educational programs; and the promotion of grant funding for future AI research. Our future research will concentrate on reducing the potential negative impacts of integrating AI into educational systems.

Synthetic Graphic Well Log Generation Using an Enhanced Deep Learning Workflow: Imbalanced Multiclass Data, Sample Size, and Scalability Challenges

Summary The present study introduces an enhanced deep learning (DL) workflow based on transfer learning (TL) for producing high-resolution synthetic graphic well logs (SGWLs). To examine the scalability of the proposed workflow, a carbonate reservoir with a high geological heterogeneity has been chosen as the case study, and the developed workflow is evaluated on unseen data (i.e., blind well). Data sources include conventional well logs and graphical well logs (GWLs) from neighboring wells. During drilling operations, GWLs are standard practice for collecting data. GWL provides a rapid visual representation of subsurface lithofacies to establish geological correlations. This investigation examines five wells in a southwest Iranian oil field. Due to subsurface geological heterogeneities, the primary challenge of this research lies in addressing the imbalanced facies distribution. The traditional artificial intelligence strategies that manage imbalanced data [e.g., the modified synthetic minority oversampling technique (M-SMOTE) and Tomek link (TKL)] are mainly designed to solve binary problems. However, to adapt these methods to the upcoming imbalanced multiclass situation, one-vs.-one (OVO) and one-vs.-all (OVA) decomposition strategies and ad-hoc techniques are used. Well-known VGG16-1D and ResNet18-1D are used as adaptive very-deep algorithms. Additionally, to highlight the robustness and efficiency of these algorithms, shallow learning approaches of support vector machine (SVM) and random forest (RF) as conventional facies classification methods are also used. The other main challenge is the need for enough data points to train the very deep algorithms, resolved through TL. After identifying a blind well, the other four wells’ data are entered for model training. The average kappa statistic and F-measure, as appropriate imbalance data evaluation metrics, are implemented to assess the designed workflows’ performance. The numerical and visual comparison analysis shows that the VGG16-1D TL model performs better on the blind well data set when combined with the OVA scheme as a decomposition technique and TKL as a binary imbalance data combat tactic. An average kappa statistic of 86.33% and a mean F-measure of 92.09% demonstrate designed workflow superiority. Considering the prevalence of different imbalanced facies distributions, the developed scalable workflow can be efficient and productive for generating SGWL.

Policy initiatives for Artificial Intelligence-enabled government: An analysis of national strategies in Europe

Governments have been putting forward various proposals to stimulate and facilitate research on Artificial Intelligence (AI), develop new solutions, and adopt these technologies within their economy and society. Despite this enthusiasm, however, the adoption and deployment of AI technologies within public administrations face many barriers, limiting administrations from drawing on the benefits of these technologies. These barriers include the lack of quality data, ethical concerns, unawareness of what AI could mean, lack of expertise, legal limitations, the need for inter-organisational collaboration, and others. AI strategy documents describe plans and goals to overcome the barriers to introducing AI in societies. Drawing on an analysis of 26 AI national strategy documents in Europe analysed through the policy instrument lens, this study shows that there is a strong focus on initiatives to improve data-related aspects and collaboration with the private sector, and that there are limited initiatives to improve internal capacity or funding.

The cold-start problem in nascent AI strategy: Kickstarting data network effects

While many artificial intelligence (AI) strategies are successful, countless others fail. Why do some strategies succeed while others fail? We adopt a network effects (NEs) perspective to conceptualize AI strategies, highlighting the AI context’s specifics. We argue that nascent AI strategies’ success depends on data NEs: companies establishing a functional “running system” to capitalize on these effects. However, this presents a challenge known as the cold-start problem (CSP), which involves initiating and accelerating a virtuous cycle: more data benefits the AI system, enhancing performance, which then attracts more data. In this paper, we examine the CSP in nascent AI strategy, exploring how it can be understood in terms of its technological and business dimensions and ultimately be overcome to kick-start a virtuous cycle of data NEs. By drawing insights from existing literature and practitioner interviews, we present a research agenda to encourage further investigation into overcoming the CSP.

Strategies based on artificial intelligence for the detection of fake news

The article examines artificial intelligence (AI) strategies to combat fake news, highlighting the rise in misinformation, especially during the pandemic, and its negative impact on public decision-making. The accelerated spread of fake news in the face of truth underlines the urgency of effective detection methods. Through a systematic literature review, the use of machine learning, deep learning, and natural language processing (NLP) to automate the identification of fake news is explored, highlighting key data sets such as BuzzFeedNews, LIAR, and BS Detector, among others, essential to train detection algorithms. The study discusses various AI approaches and techniques applied to detection, including convolutional neural networks (CNN), bidirectional LSTM, and the combination of CNN with LSTM, showing significant improvements in accuracy and efficiency. However, the limitations of these techniques are pointed out, such as the volatility of the training data and the difficulty of adapting models to different misinformation contexts. The conclusion highlights AI as a vital tool against fake news, emphasizing the need to advance research and develop more sophisticated technologies to strengthen disinformation detection and protect information integrity in society. The fight against fake news is complex, but AI-based strategies show a promising path toward practical solutions.

International Market Access Strategies for Artificial Intelligence–Based Medical Devices: Can We Standardize the Process to Faster Patient Access?

The reimbursement of artificial intelligence (AI)–based medical devices (MDs) is a complex and challenging issue, depending on national market access pathways. We aim to analyze these pathways and propose recommendations for standardized market access processes. In the USA, companies sell AI-based MDs to each health insurance provider separately. In Europe, Germany is a pioneer in digital MD reimbursement, followed by France. However, it varies markedly without specific pathways in other European countries. In Asia and Australia, health insurance reimburses them. Despite important variations in assessment policies, reimbursement restrictions, and a lack of transparency in decision-making processes, collaboration among health technology assessment agencies at an international level can enhance evidence-based coverage decision-making. Regarding current barriers to market access standardization, understanding each country’s needs and building high-quality evidence are mandatory. Solutions to promote standardized market access pathways are as follows: (1) accelerating approval; (2) standardizing health technology assessment; (3) generating health economics evidence; and (4) expanding insurance coverage. These perspectives aim to change paradigm from market access to patient access through a standardized assessment of AI-based MDs.

The European map of artificial intelligence development policies: a comparative analysis

Abstract Objective The aim of this paper is to discuss the management aspect of artificial intelligence development policy by the national regulators of the 27 European Union (EU) member states. The solutions formulated by three of them—Germany, France (as a pioneers), and Poland—are analyzed in depth. Conclusions The obtained results allow us to conclude that out of 27 EU countries, only one has not yet prepared its strategic project on artificial intelligence (AI) development, while among the existing policies one can find significant differences in the approach to the matter of strategic management. Methodology The research methods used are content analysis and comparative analysis of selected source documents. In the course of the deliberations presented, key legal acts concerning the areas of AI and modern technologies are cited. Practical applications The presented work formulates a starting point for further research and directions of changes in the management of AI development policy. The attention of subsequent researchers should focus on the possibility of a detailed analysis of other documents published by EU member states, a comparison of policies of other countries across the world, and even an attempt to examine the global dimension of AI strategies. Originality The article addresses the problem of creating law only following, rather than preceding, the emergence of technological changes, the lack of systemic regulation of AI tools and mechanisms, as well as the absence of integrated forms of international cooperation with a strategic and long-term dimension.

Artificial Intelligence and Firm Strategy: New Perspectives and Opportunities

Artificial Intelligence and Firm Strategy: New Perspectives and Opportunities

A Comprehensive Study Assessing the Transformative Role of Artificial Intelligence in India\'s Governance Policy Framework

Abstract: Artificial Intelligence (AI) has gained significant prominence worldwide, and India is actively embracing its potential for transforming various sectors. This paper comprehensively studies the intersection between artificial intelligence and Indian government policies. It explores the opportunities, challenges, and implications of AI implementation in the Indian context, and discusses the evolving role of the Indian government in harnessing AI technologies. The paper addresses the challenges and risks associated with AI implementation in India, including ethical considerations, socioeconomic implications, privacy concerns, workforce capacity building, and infrastructure requirements. This section underscores the need for appropriate policies and regulatory frameworks to address these challenges effectively. The paper further examines the Indian government's policies and initiatives on AI, including the national AI strategy, policy frameworks, AI centres of excellence, startup ecosystem, and international collaboration efforts. It delves into key policy considerations such as data governance, ethical and responsible AI, regulation and standards, skills development, and inclusivity.

Three Challenges for AI-Assisted Decision-Making.

Artificial intelligence (AI) has the potential to improve human decision-making by providing decision recommendations and problem-relevant information to assist human decision-makers. However, the full realization of the potential of human-AI collaboration continues to face several challenges. First, the conditions that support complementarity (i.e., situations in which the performance of a human with AI assistance exceeds the performance of an unassisted human or the AI in isolation) must be understood. This task requires humans to be able to recognize situations in which the AI should be leveraged and to develop new AI systems that can learn to complement the human decision-maker. Second, human mental models of the AI, which contain both expectations of the AI and reliance strategies, must be accurately assessed. Third, the effects of different design choices for human-AI interaction must be understood, including both the timing of AI assistance and the amount of model information that should be presented to the human decision-maker to avoid cognitive overload and ineffective reliance strategies. In response to each of these three challenges, we present an interdisciplinary perspective based on recent empirical and theoretical findings and discuss new research directions.

A Systematic Literature Review and a Conceptual Framework Proposition for Advanced Persistent Threats (APT) Detection for Mobile Devices Using Artificial Intelligence Techniques

Advanced persistent threat (APT) refers to a specific form of targeted attack used by a well-organized and skilled adversary to remain undetected while systematically and continuously exfiltrating sensitive data. Various APT attack vectors exist, including social engineering techniques such as spear phishing, watering holes, SQL injection, and application repackaging. Various sensors and services are essential for a smartphone to assist in user behavior that involves sensitive information. Resultantly, smartphones have become the main target of APT attacks. Due to the vulnerability of smartphone sensors, several challenges have emerged, including the inadequacy of current methods for detecting APTs. Nevertheless, several existing APT solutions, strategies, and implementations have failed to provide comprehensive solutions. Detecting APT attacks remains challenging due to the lack of attention given to human behavioral factors contributing to APTs, the ambiguity of APT attack trails, and the absence of a clear attack fingerprint. In addition, there is a lack of studies using game theory or fuzzy logic as an artificial intelligence (AI) strategy for detecting APT attacks on smartphone sensors, besides the limited understanding of the attack that may be employed due to the complex nature of APT attacks. Accordingly, this study aimed to deliver a systematic review to report on the extant research concerning APT detection for mobile sensors, applications, and user behavior. The study presents an overview of works performed between 2012 and 2023. In total, 1351 papers were reviewed during the primary search. Subsequently, these papers were processed according to their titles, abstracts, and contents. The resulting papers were selected to address the research questions. A conceptual framework is proposed to incorporate the situational awareness model in line with adopting game theory as an AI technique used to generate APT-based tactics, techniques, and procedures (TTPs) and normal TTPs and cognitive decision making. This framework enhances security awareness and facilitates the detection of APT attacks on smartphone sensors, applications, and user behavior. It supports researchers in exploring the most significant papers on APTs related to mobile sensors, services, applications, and detection techniques using AI.

A Path Towards an Internet of Things and Artificial Intelligence Regulatory Framework

As technology is the driver of the economy, it is necessary to follow emerging technological trends and to create appropriate conditions for its adoption and implementation as a human-centred technology. In this regard, rules and standards for the Internet of Things (IoT) and Artificial Intelligence (AI) should be established to best use the benefits of technology and to prevent or minimize the consequences of technology misuse. The fifth industrial revolution (Industry 5.0) has already begun, although Industry 4.0 is still developing. Consequently, the original attention has shifted from IoT to AI, with the IoT debate now being a prerequisite for the AI debate. As AI is transforming our lives, a growing number of countries have considered or already adopted national AI strategies. However, in many developing countries, national AI strategies and initiatives for establishing AI and IoT regulation and legislation frameworks yet need to be discussed. The subject of this paper is the research of existing initiatives related to establishing the IoT and AI regulatory and legislative framework in the EU and its applicability in developing countries. Index Terms-- AI, IoT, Artificial Intelligence, Internet of Things, Legislative, Regulatory, Regulation.

AI in Health Science: A Perspective.

By helping practitioners understand complicated and varied types of data, Artificial Intelligence (AI) has influenced medical practice deeply. It is the use of a computer to mimic intelligent behaviour. Many medical professions, particularly those reliant on imaging or surgery, are progressively developing AI. While AI cognitive component outperforms human intellect, it lacks awareness, emotions, intuition, and adaptability. With minimum human participation, AI is quickly growing in healthcare, and numerous AI applications have been created to address current issues. This article explains AI, its various elements and how to utilize them in healthcare. It also offers practical suggestions for developing an AI strategy to assist the digital healthcare transition.

Comprehensive review of EEG data classification techniques for ADHD detection using machine learning and deep learning

Children who have Attention-Deficit/Hyperactivity Disorder (ADHD) have a chronic behavioral disease. Children with ADHD have a hard time focusing and controlling their actions. One of the most difficult problems in controlling and treating this condition is early detection. There is yet to be discovered a reliable professional procedure for early detection of this condition. The electroencephalogram (EEG) is a useful neuroimaging technique for researching ADHD; one of the key goals is to define the EEG of ADHD youngsters. Numerous methods based on EEG signals have been put out in the literature to address this issue since they are an effective neuroimaging approach for studying ADHD. The best recording formats and channels for diagnosing ADHD, however, have not been the subject of many research. Machine learning (ML) and Artificial Intelligence (AI) strategies for identifying ADHD using EEG-based tools are discussed in this paper. Although, in the case of ADHD, the utilization of ML and AI approaches is restricted. However, the data clearly imply that combining EEG technologies with ML/AI may be utilized to detect ADHD. For categorizing adult ADHD subtypes based on EEG power spectra, ML algorithms that incorporate several classifiers are presented. A widely used deep learning (DL) method is the convolutional neural network (CNN). The use of DL approaches in ADHD research, on the other hand, is currently restricted. EEG has been used in studies to look for ADHD neurological connections. Recent advances in deep learning algorithms, particularly CNN, are anticipated to overcome the issue.

Digital Transformation, Enterprise Artificial Intelligence Strategy for Big Data Utilization, Innovation and Collaboration

Digital Transformation, Enterprise Artificial Intelligence Strategy for Big Data Utilization, Innovation and Collaboration

Machine Learning-Driven Discovery of Key Descriptors for CO2 Activation over Two-Dimensional Transition Metal Carbides and Nitrides.

Fusing high-throughput quantum mechanical screening techniques with modern artificial intelligence strategies is among the most fundamental ─yet revolutionary─ science activities, capable of opening new horizons in catalyst discovery. Here, we apply this strategy to the process of finding appropriate key descriptors for CO2 activation over two-dimensional transition metal (TM) carbides/nitrides (MXenes). Various machine learning (ML) models are developed to screen over 114 pure and defective MXenes, where the random forest regressor (RFR) ML scheme exhibits the best predictive performance for the CO2 adsorption energy, with a mean absolute error ± standard deviation of 0.16 ± 0.01 and 0.42 ± 0.06 eV for training and test data sets, respectively. Feature importance analysis revealed d-band center (εd), surface metal electronegativity (χM), and valence electron number of metal atoms (MV) as key descriptors for CO2 activation. These findings furnish a fundamental basis for designing novel MXene-based catalysts through the prediction of potential indicators for CO2 activation and their posterior usage.

Artificial intelligence in Corneal Topography: A Short Article in Enhancing Eye Care

The eye is a critical part of the human being, as it provides complete vision and the ability to receive and process visual details, and any deficiency in it may affect vision and loss of sight. Corneal topography is one of the essential diagnostic tools in the field of ophthalmology, as it can provide important information about the cornea and the problems that appear in it. Artificial intelligence strategies contribute to the development of the healthcare domain through a group of approaches that have a significant and vital impact on improving the field of ophthalmology. The primary purpose of this paper is to highlight the efficiency of artificial intelligence in extracting features from corneal topography and how these techniques contribute to helping ophthalmologists diagnose corneal topography. Furthermore, the focus is on the performance of AI algorithms, their diagnostic capabilities, and their importance in helping physicians and patients. The effects of this paper confirm the effectiveness and efficiency of artificial intelligence algorithms in the clinical diagnosis of various eye concerns.