Artificial Intelligence Technology Research Articles

Leveraging artificial intelligence to enhance psychological capital in learning organizations: a viewpoint

Purpose This viewpoint article presents perspectives on the intersection of AI and the diverse elements of Psychological Capital (PsyCap) and is a foundation for discussion on this juncture. It also exhibits the advantages of integrating AI and PsyCap in learning organizations and the challenges and future directions associated with the same. Design/methodology/approach The article exhibits the juncture of Artificial Intelligence (AI) and Psychological Capital (PsyCap) in context to learning organizations. It also highlights the various AI tools that can be used to manage diverse PsyCap attributes in learning organizations. The approach is to present a starting point for discussion on the intersection of the two concepts and how learning organizations can leverage them to create sustained competitive advantage. Findings Artificial Intelligence Systems can be used to enhance self-efficacy, promote optimism, cultivate hope and build resilience in learning organizations. A strategic approach with continued evaluation of AI tools is the key. However, challenges like privacy and unethical issues, biasness and management of human- AI intervention have to be taken into consideration for effective integration of AI tools and Psychological Capital. Originality/value The research in the domain of Artificial Intelligence and related technologies is not a new phenomenon. However, there is a possibility to understand that AI is not a competitor to human ingenuity but a facilitator to it. Through this article an effort is made to ignite the conversation on the topic and it can be a starting point for future research on the integration of the two perspectives.

An Intelligent Kick Detection Model for Large-Hole Ultra-Deep Wells in the Sichuan Basin

The Sichuan Basin has abundant deep and ultra-deep natural gas resources, making it a primary target for exploration and the development of China’s oil and gas industry. However, during the drilling of ultra-deep wells in the Sichuan Basin, complex geological conditions frequently lead to gas kicks, posing significant challenges to well control and safety. Compared to traditional kick detection methods, artificial intelligence technology can improve the accuracy and timeliness of kick detection. However, there are limited real-world kick data available from drilling operations, and the datasets are extremely imbalanced, making it difficult to train intelligent models with sufficient accuracy and generalization capabilities. To address this issue, this paper proposes a kick data augmentation method based on a time-series generative adversarial network (TimeGAN). This method generates synthetic kick samples from real datasets and then employs a long short-term memory (LSTM) neural network to extract multivariate time-series features of surface drilling parameters. A multilayer perceptron (MLP) network is used for data classification tasks, constructing an intelligent kick detection model. Using real drilling data from ultra-deep wells in the SY block of the Sichuan Basin, the effects of k-fold cross-validation, data dimensionality, various imbalanced data handling techniques, and the sample imbalance ratio on the model’s kick detection performance are analyzed. Ablation experiments are also conducted to assess the contribution of each module in identifying kick. The results show that TimeGAN outperforms other imbalanced data handling techniques. The accuracy, recall, precision, and F1-score of the kick identification model are highest when the sample imbalance ratio is at 1 but decrease as the imbalance ratio increases. This indicates that maintaining a balance between positive and negative samples is essential for training a reliable intelligent kick detection model. The trained model is applied during the drilling of seven ultra-deep wells in Sichuan, demonstrating its effectiveness and accuracy in real-world kick detection.

The Role of AI in Data Engineering and Integration in Cloud Computing

This article presents a comprehensive analysis of the transformative role of Artificial Intelligence (AI) in revolutionizing data engineering and integration processes within cloud computing environments. The article examines the implementation of AI-driven solutions across multiple dimensions, including automated pipeline generation, intelligent schema matching, anomaly detection, and real-time data integration. Through a mixed-methods approach incorporating both quantitative and qualitative analyses, the article demonstrates significant improvements in data processing efficiency, with organizations achieving up to 67% reduction in processing time and 89% enhancement in accuracy. The article encompasses case studies from financial services, healthcare, and e-commerce sectors, providing concrete evidence of practical applications and scalability. Key findings reveal that AI-powered systems substantially outperform traditional approaches in cost efficiency, scalability, and data quality management, while simultaneously reducing operational overhead. The article also addresses implementation challenges, including legacy system integration and initial deployment complexities, offering strategic insights for organizations pursuing AI integration in their data engineering workflows. These article contribute to the broader understanding of how AI technologies can be effectively leveraged to address the growing challenges of data management in cloud computing environments, while providing a framework for future developments in this rapidly evolving field.

Benefits and Risks of AI in Health Care: Narrative Review

Background The integration of artificial intelligence (AI) into health care has the potential to transform the industry, but it also raises ethical, regulatory, and safety concerns. This review paper provides an in-depth examination of the benefits and risks associated with AI in health care, with a focus on issues like biases, transparency, data privacy, and safety. Objective This study aims to evaluate the advantages and drawbacks of incorporating AI in health care. This assessment centers on the potential biases in AI algorithms, transparency challenges, data privacy issues, and safety risks in health care settings. Methods Studies included in this review were selected based on their relevance to AI applications in health care, focusing on ethical, regulatory, and safety considerations. Inclusion criteria encompassed peer-reviewed articles, reviews, and relevant research papers published in English. Exclusion criteria included non–peer-reviewed articles, editorials, and studies not directly related to AI in health care. A comprehensive literature search was conducted across 8 databases: OVID MEDLINE, OVID Embase, OVID PsycINFO, EBSCO CINAHL Plus with Full Text, ProQuest Sociological Abstracts, ProQuest Philosopher’s Index, ProQuest Advanced Technologies & Aerospace, and Wiley Cochrane Library. The search was last updated on June 23, 2023. Results were synthesized using qualitative methods to identify key themes and findings related to the benefits and risks of AI in health care. Results The literature search yielded 8796 articles. After removing duplicates and applying the inclusion and exclusion criteria, 44 studies were included in the qualitative synthesis. This review highlights the significant promise that AI holds in health care, such as enhancing health care delivery by providing more accurate diagnoses, personalized treatment plans, and efficient resource allocation. However, persistent concerns remain, including biases ingrained in AI algorithms, a lack of transparency in decision-making, potential compromises of patient data privacy, and safety risks associated with AI implementation in clinical settings. Conclusions In conclusion, while AI presents the opportunity for a health care revolution, it is imperative to address the ethical, regulatory, and safety challenges linked to its integration. Proactive measures are required to ensure that AI technologies are developed and deployed responsibly, striking a balance between innovation and the safeguarding of patient well-being.

AI in Healthcare Supply Chain Management: Enhancing Efficiency and Reducing Costs with Predictive Analytics

This paper explores the transformative role of artificial intelligence (AI) and predictive analytics in enhancing operational efficiency within healthcare supply chains. By leveraging AI-driven business analytics, healthcare organizations can optimize inventory management, improve demand forecasting, and streamline supply chain processes. The study presents a comprehensive review of recent advancements, challenges, and opportunities in the integration of AI technologies, focusing on their application in various healthcare contexts. Through systematic analysis of existing literature, the findings emphasize the significance of adopting AI and predictive analytics for effective decision-making, cost reduction, and improved service delivery in healthcare. The research highlights the need for organizations to embrace digital transformation and foster a collaborative approach in the implementation of AI-driven solutions to enhance overall supply chain resilience.

Building a better lawyer: Experimental evidence that artificial intelligence can increase legal work efficiency

AbstractRapidly improving artificial intelligence (AI) technologies have created opportunities for human–machine cooperation in legal practice. We provide evidence from an experiment with law students (N = 206) on the causal impact of machine assistance on the efficiency of legal task completion in a private law setting with natural language inputs and multidimensional AI outputs. We tested two forms of machine assistance: AI‐generated summaries of legal complaints and AI‐generated text highlighting within those complaints. AI‐generated highlighting reduced task completion time by 30% without any reduction in measured quality indicators compared to no AI assistance. AI‐generated summaries produced no change in performance metrics. AI summaries and AI highlighting together improved efficiency but not as much as AI highlighting alone. Our results show that AI support can dramatically increase the efficiency of legal task completion, but finding the optimal form of AI assistance is a fine‐tuning exercise. Currently, AI‐generated highlighting is not readily available from state‐of‐the‐art, consumer‐facing large language models, but our work suggests that this capability should be prioritized in the development of legal AI products.

Introduction of AI Technology for Objective Physical Function Assessment

Objective physical function assessment is crucial for determining patient eligibility for treatment and adjusting the treatment intensity. Existing assessments, such as performance status, are not well standardized, despite their frequent use in daily clinical practice. This paper explored how artificial intelligence (AI) could predict physical function scores from various patient data sources and reviewed methods to measure objective physical function using this technology. This review included relevant articles published in English that were retrieved from PubMed. These studies utilized AI technology to predict physical function indices from patient data extracted from videos, sensors, or electronic health records, thereby eliminating manual measurements. Studies that used AI technology solely to automate traditional evaluations were excluded. These technologies are recommended for future clinical systems that perform repeated objective physical function assessments in all patients without requiring extra time, personnel, or resources. This enables the detection of minimal changes in a patient’s condition, enabling early intervention and enhanced outcomes.

Use of Artificial Intelligence in the Banking Industry: A Case Study of Pakistan

This study investigates the adoption and impact of Artificial Intelligence (AI) in Pakistan’s banking sector, with a focus on Habib Bank Limited (HBL), Faysal Bank, and Bank Alfalah. The objective is to understand the extent to which AI technologies have transformed core activities within customer service, operations, fraud detection, and risk management in these banks. Data were collected from semi-structured interviews with ten respondents, selected based on their experience with AI implementation. This qualitative methodology allows for in-depth insights into the functionality and challenges associated with AI in banking. Results reveal that AI has notably improved operational efficiency by streamlining risk management and enabling real-time fraud detection. Additionally, AI-driven chatbots and virtual assistants have enhanced customer experiences by offering faster and more personalized services. Despite these benefits, the study identifies significant challenges, particularly regarding data privacy and regulatory compliance, as well as the need for human resources to adapt to AI-enabled tools. Key pressures in the industry include building capabilities to work alongside AI while addressing concerns over automation. To maximize the advantages of AI, the study emphasizes the need for comprehensive policies that balance technological adoption with ethical considerations, data security, and staff training. Strategic planning in Pakistan’s banking sector will be crucial to harnessing AI’s potential while managing its risks.

Investigating Adoption Determinants, Obstacles, and Interventions for AI Implementation in Emirati Media Organizations

This research explores the integration and impact of artificial intelligence (AI) within Emirati media organizations, focusing on adoption determinants, obstacles, and interventions. Utilizing a mixed-methods approach, the study incorporates qualitative data from 21 in-depth interviews with media professionals and a thematic analysis of 30 scholarly articles published between 2019 and 2024. The research aims to provide a nuanced understanding of AI's transformative effects on the UAE media landscape. Key findings reveal that AI is significantly enhancing content creation, distribution, and audience engagement processes within Emirati media organizations. AI technologies such as machine learning, natural language processing, and predictive analytics are being leveraged to improve operational efficiency, streamline workflows, and deliver personalized content to audiences. These advancements are driving competitive advantages and elevating the overall quality of media output. Despite these benefits, several challenges hinder widespread AI adoption. Technological barriers, including limited access to advanced AI tools and infrastructure, pose significant obstacles. Additionally, there is a pronounced shortage of skilled personnel capable of implementing and managing AI systems, which exacerbates concerns over job displacement and workforce anxiety. Ethical considerations, such as ensuring unbiased AI algorithms and protecting user privacy, are also critical issues that require attention. The study underscores the importance of continuous training and upskilling for media professionals to adapt to the evolving technological landscape. It highlights the role of robust ethical guidelines and governance frameworks in promoting responsible AI use. Government support, through targeted policies, funding initiatives, and incentives, is identified as a crucial factor in facilitating AI integration and addressing adoption barriers. The research concludes by providing strategic recommendations for media organizations and policymakers. It advocates for the development of comprehensive AI adoption roadmaps that include capacity-building initiatives, ethical oversight, and collaborative efforts between industry stakeholders. By addressing these areas, Emirati media organizations can harness the full potential of AI to drive innovation, improve audience engagement, and maintain competitiveness in a rapidly digitizing global media environment. This study contributes to the growing body of knowledge on AI in media, offering valuable insights into the specific context of the UAE. It provides a foundation for future research on AI's role in media and serves as a guide for organizations seeking to navigate the complexities of AI adoption.

The Double-Edged Sword of Artificial Intelligence (AI) in Education

Objective: The purpose of this paper is to conduct a thorough review and investigation on the function and importance of AI in education. The development of technology in the sector, and the emergence of raising interest to apply this AI technologies with a view to enable disruptive efficiencies across all aspects of education. Methodology: The study uses a systematic review of literature for exploring the existing literatures and research findings about AI implementation in Education. For learning it tells you good & bad about AI of bunchy resources, And changes because of the whether educational practices. Results: The results suggest that AI can greatly improve the personalization of learning, being able to provide tailored activities beyond traditional strategies. But the review also highlights worries over inequalities that may arise if only a specialist few can call upon top-notch AI tools possibly deepening of the achievement gap. Furthermore, a sanguine reliance on automated solutions could compromise the core human element in education and stymie social-emotional development for our students. Originality/value: Given the tendency for increased transparency, accountability and potential bias of AI systems in this critical domain leading to possible discrimination practices (e.g. admissions, grading etc) it is felt that the paper has great value contribution. There needs to be a spirit among policymakers, educators and developers working together to ensure that AI in education is implemented following certain principles.

AI Orientation, Capabilities, and Business Value: Case Study

ABSTRACT In this study, artificial intelligence (AI) orientation, AI capabilities, as well as process-oriented dynamic capabilities (PDCs) within the realm of AI business value creation, are unpacked through multiple case studies in Hong Kong. We propose a conceptual framework suggesting that AI resources enable organizations to develop PDCs, manifesting in several abilities, thereby contributing to business value. In addition, the case study’s findings indicate that AI capabilities developed by organizations correlate with their AI orientation, which is their overall strategic direction and aspiration of employing AI technology. Apart from basic AI capabilities, AI-oriented organizations would develop advanced AI capabilities. The proposed conceptual framework and findings can guide and assist practitioners in utilizing AI resources and building AI capabilities. This study also enriches the growing body of research on AI and contributes to the limited understanding of AI capabilities in the extant literature.

Enhancement of Patient Engagement and Healthcare Delivery Through the Utilization of Artificial Intelligence (AI) Technologies

Background: Patient engagement refers to the actively involvement of individuals in their own treatment, decision-making, and partnering with healthcare providers. Due to the progress of Artificial Intelligence (AI), there is a groundbreaking shift happening in healthcare that is bringing numerous benefits to patients and health systems. AI technologies are not only used as instruments in healthcare but also evolving into collaborators for diagnosing and treating patients, as well as enhancing the quality of services, offering a personalized, timely, and interactive healthcare experience. This article delves into how AI is changing patient engagement by increasing effectiveness and catering to individual patient requirements. Methods: The research made use of previously published materials on artificial intelligence, data, and robotic technologies in healthcare settings to explore ways in which patients can be effectively involved in the advancement of these technologies in healthcare settings. Results: AI-powered decision support systems improve healthcare operations by giving instant access to data analysis and medical advice, ultimately aiding in making decisions based on real-time evidence. AI enables proactive healthcare interventions by detecting potential health issues in real time, allowing for remote patient monitoring. Motivating patients to actively participate can result in improved adherence to the treatment plan. Overall, patient engagement appears to be the most well-developed and progressive concept for enabling patients to participate actively in their healthcare. Conclusions: AI technologies are more than just tools in healthcare; they are evolving into collaborators in patient involvement, providing a personalized, proactive and engaging healthcare journey. Further evidence is required to comprehend how patients engage in the process and if this leads to better quality of care. In the dynamic health sector, change management is essential for continuously updating and adjusting healthcare facilities to address the evolving needs of patients. Health systems that do not adjust to changes in a timely manner are unsuccessful.

The Impact of AI and VR/AR Technologies on Yuan Blue-and-White Porcelain Art

Yuan blue-and-white porcelain, a treasure of Chinese ceramic art, is renowned worldwide for its unique artistic value and cultural significance. With the continuous advancement of artificial intelligence (AI) technology, the artistic innovation of Yuan blue-and-white porcelain is experiencing new opportunities. AI technology not only improves the efficiency and quality of creation but also breaks through traditional physical limitations, enabling the creation of more diverse and creative artworks. By integrating augmented reality (AR) and virtual reality (VR) technologies, AI provides new ways to display and interact with Yuan blue-and-white porcelain art. In non-physical artistic innovation, AI generates new designs through deep learning algorithms, breaking free from the constraints of traditional handmade processes and creating diverse works. Technologies like generative adversarial networks (GANs) can produce highly realistic Yuan blue-and-white designs, while convolutional neural networks (CNNs) can quickly generate design sketches, enhancing creative efficiency. AI can also optimize designs, improving the artistic quality and visual effects of the works.AR and VR technologies offer immersive experiences and interactivity for the audience. Through virtual exhibition spaces and AR applications, viewers can appreciate Yuan blue-and-white artworks as if they were physically present, enhancing interactivity and visual appeal. VR technology, through virtual exhibitions and other means, enhances the viewing experience and impact of the artworks. The application of AI technology in the non-physical artistic innovation of Yuan blue-and-white porcelain not only enhances creation efficiency and quality but also brings limitless possibilities to artistic creation. The combination of AR and VR technologies further enhances the visual appeal and interactivity of Yuan blue-and-white art. As technology continues to advance, AI will play an increasingly important role in the artistic innovation of Yuan blue-and-white porcelain, creating more splendid artworks.

Adaptive Arithmetic Coding-Based Encoding Method Toward High-Density DNA Storage.

With the rapid advancement of big data and artificial intelligence technologies, the limitations inherent in traditional storage media for accommodating vast amounts of data have become increasingly evident. DNA storage is an innovative approach harnessing DNA and other biomolecules as storage mediums, endowed with superior characteristics including expansive capacity, remarkable density, minimal energy requirements, and unparalleled longevity. Central to the efficient DNA storage is the process of DNA coding, whereby digital information is converted into sequences of DNA bases. A novel encoding method based on adaptive arithmetic coding (AAC) has been introduced, delineating the encoding process into three distinct phases: compression, error correction, and mapping. Prediction by Partial Matching (PPM)-based AAC in the compression phase serves to compress data and enhance storage density. Subsequently, the error correction phase relies on octal Hamming code to rectify errors and safeguard data integrity. The mapping phase employs a "3-2 code" mapping relationship to ensure adherence to biochemical constraints. The proposed method was verified by encoding different formats of files such as text, pictures, and audio. The results indicated that the average coding density of bases can be up to 3.25 per nucleotide, the GC content (which includes guanine [G] and cytosine [C]) can be stabilized at 50% and the homopolymer length is restricted to no more than 2. Simulation experimental results corroborate the method's efficacy in preserving data integrity during both reading and writing operations, augmenting storage density, and exhibiting robust error correction capabilities.

Research on an Eye Control Method Based on the Fusion of Facial Expression and Gaze Intention Recognition

With the deep integration of psychology and artificial intelligence technology and other related technologies, eye control technology has achieved certain results at the practical application level. However, it is found that the accuracy of the current single-modal eye control technology is still not high, which is mainly caused by the inaccurate eye movement detection caused by the high randomness of eye movements in the process of human–computer interaction. Therefore, this study will propose an intent recognition method that fuses facial expressions and eye movement information and expects to complete an eye control method based on the fusion of facial expression and eye movement information based on the multimodal intent recognition dataset, including facial expressions and eye movement information constructed in this study. Based on the self-attention fusion strategy, the fused features are calculated, and the multi-layer perceptron is used to classify the fused features, so as to realize the mutual attention between different features, and improve the accuracy of intention recognition by enhancing the weight of effective features in a targeted manner. In order to solve the problem of inaccurate eye movement detection, an improved YOLOv5 model was proposed, and the accuracy of the model detection was improved by adding two strategies: a small target layer and a CA attention mechanism. At the same time, the corresponding eye movement behavior discrimination algorithm was combined for each eye movement action to realize the output of eye behavior instructions. Finally, the experimental verification of the eye–computer interaction scheme combining the intention recognition model and the eye movement detection model showed that the accuracy of the eye-controlled manipulator to perform various tasks could reach more than 95 percent based on this scheme.

Integrating Artificial Intelligence into Traditional Sports: A Case Study of the Asian Games and Future Directions for Modernization

Abstract: This paper explores the integration of Artificial Intelligence (AI) into traditional sports, addressing a critical gap in existing research through a qualitative case study of the Asian Games. AI's application in modern sports has demonstrated its potential to enhance event management, athlete preparation, and scoring fairness. However, its adoption in traditional sports, such as Wushu and Chinese wrestling, remains limited. This study examines the challenges and opportunities associated with applying AI to traditional sports, offering development strategies to modernize these disciplines. By analyzing the impact of AI in modern competitive sports, the research proposes specific recommendations for integrating AI into traditional sports, aiming to preserve their cultural heritage while advancing their global relevance. Limitations include the focus on a single event and the exclusion of the most recent AI developments. Future research should consider a broader range of global sporting events and incorporate the latest advancements in AI technology. This study provides a theoretical and practical foundation for the application of AI in traditional sports, with implications for future technological innovation and the evolution of the sports industry.

Artificial intelligence, medications, pharmacogenomics, and ethics.

Artificial Intelligence (AI) and Machine Learning (ML) are revolutionizing various scientific and clinical disciplines including pharmacogenomics (PGx) by enabling the analysis of complex datasets and the development of predictive models. The integration of AI and ML with PGx has the potential to provide more precise, data-driven insights into new drug targets, drug efficacy, drug selection, and risk of adverse events. While significant effort to develop and validate these tools remain, ongoing advancements in AI technologies, coupled with improvements in data quality and depth is anticipated to drive the transition of these tools into clinical practice and delivery of individualized treatments and improved patient outcomes. The successful development and integration of AI-assisted PGx tools will require careful consideration of ethical, legal, and social issues (ELSI) in research and clinical practice. This paper explores the intersection of PGx with AI, highlighting current research and potential clinical applications, and ELSI including privacy, oversight, patient and provider knowledge and acceptance, and the impact on patient-provider relationship and new roles.

Synergies between Machine Learning, Artificial Intelligence, and Game Theory for Complex Decision-Making

The special focus of this paper is to discuss a likely intersection of machine learning, artificial intelligence (AI) technology, and game theory, pointing at the importance of this synthesis both in mathematics and engineering. As these domains develop various means of addressing decision-making problems become more and more sophisticated and can be used in different areas such as economics, security, and social sciences. We will also address selected game-theoretic issues including the concept of decision making in terms of Nash equilibria or in the distinction of games as being cooperative or non-cooperative and how they work in synergy with machine learning approaches bringing in reinforcements and deep learning to leverage forecasting and strategizing. The paper makes references to problem-oriented branches of studies such as autonomous systems or market strategies stressing the importance of the novel direction for further studies. Within the scope of machine learning and game theory the goal is to implement better complex models which utilize real world intricacies for enhancing decision making within a populated agent environment.

Comparative Analysis of Object Detection Models for Sheet Music Recognition: A Focus on YOLO and OMR Technologies

Abstract. As Artificial Intelligence (AI) technologies are developing rapidly and are widely used in various domains, it is efficient and convenient for composers to make music using AI to convert sheet music to audio. This research aims to compare the performance of different models in identifying individual notes within sheet music. Compared to traditional technologies like Optical Music Recognition (OMR), deep learning models have a significant advantage in processing blurry images with high efficiency. In the research process, three different models are used in searching for musical notes: OMR, You Only Look Once (YOLO)v5, and YOLOv8. The evaluation index consists of recognition accuracy, mean Average Precision (mAP), inference speed, and parameter quantity. After the experiment, it is found that the YOLO model performs best with high accuracy and fast speed. Based on the above analyses, the thesis finds that the YOLO model can be an efficient tool in composing music, with further research.

A feminist legal analysis of non-consensual sexualized deepfakes: contextualizing its impact as AI-generated image-based violence under EU law

ABSTRACT This article focuses on the legal contextualization of the generation of fake audiovisual sexual content based on replacing and shuffling facial or voice data using artificial intelligence (AI) technology without the previous consent, commonly known as ‘non-consensual sexualized deepfakes'. The legal analysis will be limited to examination of the impact of non-consensual sexualized deepfakes on the gender equality spectrum and gender-based violence context. For this reason, the analysis will be built upon the European AI Act – the applied legal framework when AI software is used – the European Directive on combating violence against women and domestic violence (GBV Directive) – which criminalizes the generation of sexualized deepfakes – and the Digital Service Act (DSA) – the legislative framework regulating online platforms, where mostly sexualized deepfakes are distributed. To better understand how sexualized deepfakes are generated, we will examine their generation and detection techniques through a short overview from a technical lens. The methodology used is built on the feminist critical approach – based on the ‘King Kong’ theory and abolitionist feminism – to the doctrinal analysis of the relevant European legislation. After having captured the harms of deepfake technology, legal solutions will be provided through this cross-disciplinary perspective.