Development Of Intelligence Research Articles

Determinants of AI performance and their effect on national GDP: an empirical study

Abstract. Artificial intelligence is one of the most widely discussed topics in recent years, remarkably following the pandemic. During the pandemic, numerous industries and organizations have focused on the development of artificial intelligence due to the shift to remote learning and working. This shift also caused expanded investments and research on AI driven by the growing demand of for AI-powered tools across various sectors such as education, healthcare, and business. This paper analyses the determinants of AI performance and examines the relationship between a countrys AI performance and its economic growth, measured by Gross Domestic Product (GDP). This paper employs Pearson Pairwise Correlation Multiple Linear Regression, and Simple Linear Regression models with a sample of 62 different countries to uncover the most influential factors impacting a country's AI performance and its subsequent influence on GDP. During the process of constructing the multiple linear regression model, this paper also evaluates the collinearity diagnostics such as VIF and tolerance level to prevent multicollinearity. This study finds that a countrys innovation skill, featuring its research and development capabilities, is the primary driver influencing its success in AI development. Subsequently, this paper concludes that advanced AI performance can significantly contribute to a countrys economic growth, further emphasizing the importance of progressive AI development.

Decoding Scholarcy website: A Study on its Research Summarization Efficiency

The development of artificial intelligence (AI) has revolutionized various facets of education, particularly the research domain. With the rapid growth of scholarly literature, the need for an efficient tool for summarization has become increasingly paramount. This study proposes a quantitative investigation into the efficiency of Scholarcy (www.scholarcy.com), an AI-powered summarizer used to aid researchers in summarizing academic literature. Thirty-five postgraduate students were involved in a survey research design. After five weeks of using Scholarcy, participants were asked to complete a survey questionnaire via their email addresses to give feedback on this tool. The results indicated a positive reception when postgraduates found this AI summarizer convenient and useful for reading academic papers. On the other hand, the participants identified several drawbacks when using it including concerns about summary quality, the absence of a mobile application, and high cost, along with suggestions for further application. With the highlights of both benefits and limitations, the study hopes to contribute valuable insights into the development of AI-powered summarization tools in the future.

Artificial Intelligence: Intensifying or Mitigating Unemployment?

The rapid development of Artificial Intelligence (AI) is simultaneously fostering a proliferation of novel job opportunities while rendering some traditional roles obsolete and specific skills outdated. Previous research has failed to consider the short-, medium-, and long-term variations in AI’s impact on unemployment, which may lead to an incomplete understanding of the AI-employment relationship. This paper examines daily data from January 4, 2013, to August 12, 2024, utilising advanced wavelet-based Quantile on Quantile Regression (QQR) methodology to assess AI’s impact on the Unemployment Index (UI) across quantiles and time scales, with a sample size of 2820 drawn from a larger dataset totalling 4241 observations. The conclusions reveal that AI generally positively impacts UI in the short term, especially with AI at 0.6-0.7 quantiles, as automation replaces workers faster than new job roles emerge and skills transform. However, in the medium term, positive and negative effects balance as new jobs and skills emerge through continuous industrial restructuring. In the long run, AI predominantly mitigates UI by further enhancing economic development, fostering skill upgrading, and facilitating market adjustments, but this result does not hold during AI at 0.7 quantiles and UI at the highest quantiles, such as Coronavirus Disease 2019 (COVID-19). Under new technological revolution and industrial transformation, we formulate China-specific suggestions to avert potential AI-induced unemployment crisis from short-term, medium-term, long-term, and sector-specific perspectives.

Development and cultural adaptation of the Saudi pediatric speech intelligibility test.

To report the development and administration of the Saudi pediatric speech intelligibility (SPSI) test. This study was carried out at King Abdullah Ear Specialist Center, King Saud University, Riyadh, Saudi Arabia, between 2021 and 2023. A pilot study was carried out in 5 regions of Saudi Arabia including 100 normal hearing children aged 7-11 years old, with 20 children from each region. The final test materials were voice-recorded by a native Saudi radio announcer, and a software-based application for administering the SPSI was developed. During the pilot study, minor adjustments were carried out to the test for more clarity. The developed SPSI software is an android-based application that includes features like automatic result calculation, data saving, and individual user accounts for examiners to review results. The SPSI test comprises 4-word cards and 2 sentence cards, each with 5 pictures, and operates in 3 modes: quiet, background noise, and competing sentences. The SPSI test has been developed in an android-based software using the Saudi local dialects. It can facilitate comprehensive and accurate assessment of speech intelligibility, particularly in children with hearing impairments.

The Evolution of Artificial Intelligence in Medical Imaging: From Computer Science to Machine and Deep Learning.

Artificial intelligence (AI), the wide spectrum of technologies aiming to give machines or computers the ability to perform human-like cognitive functions, began in the 1940s with the first abstract models of intelligent machines. Soon after, in the 1950s and 1960s, machine learning algorithms such as neural networks and decision trees ignited significant enthusiasm. More recent advancements include the refinement of learning algorithms, the development of convolutional neural networks to efficiently analyze images, and methods to synthesize new images. This renewed enthusiasm was also due to the increase in computational power with graphical processing units and the availability of large digital databases to be mined by neural networks. AI soon began to be applied in medicine, first through expert systems designed to support the clinician's decision and later with neural networks for the detection, classification, or segmentation of malignant lesions in medical images. A recent prospective clinical trial demonstrated the non-inferiority of AI alone compared with a double reading by two radiologists on screening mammography. Natural language processing, recurrent neural networks, transformers, and generative models have both improved the capabilities of making an automated reading of medical images and moved AI to new domains, including the text analysis of electronic health records, image self-labeling, and self-reporting. The availability of open-source and free libraries, as well as powerful computing resources, has greatly facilitated the adoption of deep learning by researchers and clinicians. Key concerns surrounding AI in healthcare include the need for clinical trials to demonstrate efficacy, the perception of AI tools as 'black boxes' that require greater interpretability and explainability, and ethical issues related to ensuring fairness and trustworthiness in AI systems. Thanks to its versatility and impressive results, AI is one of the most promising resources for frontier research and applications in medicine, in particular for oncological applications.

IDL-LTSOJ: Research and implementation of an intelligent online judge system utilizing DNN for defect localization

The evolution of artificial intelligence has thrust the Online Judge (OJ) systems into the forefront of research, particularly within programming education, with a focus on enhancing performance and efficiency. Addressing the shortcomings of the current OJ systems in coarse defect localization granularity and heavy task scheduling architecture, this paper introduces an innovative Integrated Intelligent Defect Localization and Lightweight Task Scheduling Online Judge (IDL-LTSOJ) system. Firstly, to achieve token-level fine-grained defect localization, a Deep Fine-Grained Defect Localization (Deep-FGDL) deep neural network model is developed. By integrating Bidirectional Long Short-Term Memory (BiLSTM) and Bidirectional Gated Recurrent Unit (BiGRU), this model extracts fine-grained information from the abstract syntax tree (AST) of code, enabling more accurate defect localization. Subsequently, we propose a lightweight task scheduling architecture to tackle issues, such as limited concurrency in task evaluation and high equipment costs. This architecture integrates a Kafka messaging system with an optimized task distribution strategy to enable concurrent execution of evaluation tasks, substantially enhancing system evaluation efficiency. The experimental results demonstrate that the Deep-FGDL model improves the accuracy by 35.9% in the Top-20 rank compared to traditional machine learning benchmark methods for fine-grained defect localization tasks. Moreover, the lightweight task scheduling strategy notably reduces response time by nearly 6000ms when handling 120 task volumes, which represents a significant improvement in evaluation efficiency over centralized evaluation methods.

Generating the assembly instructions of helicopter subassemblies using the hierarchical pruning strategy and large language model

Assembly instructions are process documents in detail describing the operation steps, materials, tools, fixtures, and assembly sequences in assembly procedures. Due to assembly instructions including numerous contents, and the content being easy for workers to understand, process designers need to spend lots of time thinking and authoring assembly instructions to ensure that workers can complete the assembly task according to the assembly instructions. Focusing on the difficulties of the variety of assembly instructions and the process factors implicit in the standard languages of assembly instructions, a method of assembly instruction generation for helicopter subassemblies is proposed. First, a data representation model of multi-source heterogeneous knowledge and information based on knowledge graphs is designed and established. Then, a hierarchical pruning VF3 algorithm is presented to reuse assembly instructions according to hybrid similarity. Finally, a process factor revision model based on RoBERTa-BiLSTM-CRF is proposed to generate revised assembly instructions. Helicopter subassemblies, which contain 11,240 assembly procedures, are used to evaluate the performance of the method for generating assembly instructions. The proposed method greatly reduces the time cost of assembly instruction authoring and promotes the intelligent development of assembly process design.

Unveiling the Dynamic Mechanisms of Generative AI in English Language Learning: A Hybrid Study Based on fsQCA and System Dynamics

The burgeoning development of generative artificial intelligence (GenAI) has unleashed transformative potential in reshaping English language education. However, the complex interplay of learner, technology, pedagogy, and contextual factors that shape the effectiveness of GenAI-assisted language learning remains underexplored. This study employed a novel mixed-methods approach, integrating qualitative comparative analysis (QCA) and system dynamics (SD) modeling, to unravel the multi-dimensional, dynamic mechanisms underlying the impact of GenAI on English learning outcomes in higher education. Leveraging a sample of 33 English classes at the Harbin Institute of Technology, the QCA results revealed four distinct configurational paths to high and low learning effectiveness, highlighting the necessary and sufficient conditions for optimal GenAI integration. The SD simulation further captured the emergent, nonlinear feedback processes among learner attributes, human–computer interaction, pedagogical practices, and ethical considerations, shedding light on the temporal evolution of the GenAI-empowered language-learning ecosystem. The findings contribute to the theoretical advancement of intelligent language education by constructing an integrative framework encompassing learner, technology, pedagogy, and context dimensions. Practical implications are generated to guide the responsible design, implementation, and optimization of GenAI in English language education, paving the way for learner-centric, adaptive learning experiences in the intelligence era.

A multimodal educational robots driven via dynamic attention.

With the development of artificial intelligence and robotics technology, the application of educational robots in teaching is becoming increasingly popular. However, effectively evaluating and optimizing multimodal educational robots remains a challenge. This study introduces Res-ALBEF, a multimodal educational robot framework driven by dynamic attention. Res-ALBEF enhances the ALBEF (Align Before Fuse) method by incorporating residual connections to align visual and textual data more effectively before fusion. In addition, the model integrates a VGG19-based convolutional network for image feature extraction and utilizes a dynamic attention mechanism to dynamically focus on relevant parts of multimodal inputs. Our model was trained using a diverse dataset consisting of 50,000 multimodal educational instances, covering a variety of subjects and instructional content. The evaluation on an independent validation set of 10,000 samples demonstrated significant performance improvements: the model achieved an overall accuracy of 97.38% in educational content recognition. These results highlight the model's ability to improve alignment and fusion of multimodal information, making it a robust solution for multimodal educational robots.

Artificial Intelligence: Communication, Technology, and Society (a Systematic Literature Review)

The development of artificial intelligence (AI) technology has transformed the way various social sectors operate, including healthcare, communications, and decision-making. The integration of AI into these domains not only opens up new opportunities for efficiency and innovation, but also presents significant challenges. Among these challenges, the emergence of algorithmic bias, inadequate data protection, technical limitations, and complex ethical dilemmas are of major concern. These issues affect the reliability and fairness of the use of AI technologies, potentially harming individuals and society as a whole. This study aims to investigate the barriers associated with the implementation of AI in society and provide theoretical and methodological solutions to address these challenges. This study uses a systematic literature review methodology to review 40 journals, with the aim of identifying key issues and suggesting solutions for the ethical and successful integration of AI. The findings show that the potential of AI is often hampered by biased algorithms, inadequate privacy laws, and a lack of transparency in decision-making processes. The study recommends the use of qualitative and quantitative methodologies, including experimental research and case studies, to investigate practical applications of AI. In addition, a multidisciplinary approach is suggested to address the technical and social dimensions of AI applications.

Trends in the industry of clinical trials

Background. The clinical trial (CT) industry is subject to changes, some of which are rapidly developing and their directions can be predicted, while others develop gradually, forming stable tendencies, which requires analysis to confirm and predict them. According to the generally accepted time gradation, there are an operational (up to 1 month), short-term (up to 1 year), medium-term (up to 5 years), long-term (up to 20 years), and long-term (over 20 years) prognoses. A short-term forecast is common in CT industry.Objective: to identify trends in the development of CT industry until the end of 2024.Material and methods. We searched publications in Russian and English segments of the Internet in open access sources in ClinicalTrials.gov, PubMed/MEDLINE, Google Scholar, Academia, ResearchGate, CyberLeninka, eLibrary databases by key query “trends in clinical trials” for the period from January to March 2023. The query “history of clinical trials” was also used in Russian part of the Internet through Google search. The analysis included publications on the history of CT development over 1936–2023. The total number of sources analyzed was 59 (9 Russian and 50 English).Results. The tendencies in CT development can be divided into two groups. The first one is related to CT organization and conducting, while another is associated with development of innovative drugs. In the first group, the trends have persisted since 2022 and are expressed in the ongoing digitalization of operational activities, a shift from centralized research to decentralization, while the protocol design has changed towards patient-centricity. In the second group, the number of expected drugs has decreased and a shift towards biological drugs, gene and cell therapy has become more pronounced.Conclusion. Trends in CI are characterized by a number of innovations, primarily related to digitalization, the development of telemedicine technologies, mathematical modeling, artificial intelligence, virtual CI, decentralization and patient-centricity.

Exploring the Landscape of Explainable Artificial Intelligence (XAI): A Systematic Review of Techniques and Applications

Artificial intelligence (AI) encompasses the development of systems that perform tasks typically requiring human intelligence, such as reasoning and learning. Despite its widespread use, AI often raises trust issues due to the opacity of its decision-making processes. This challenge has led to the development of explainable artificial intelligence (XAI), which aims to enhance user understanding and trust by providing clear explanations of AI decisions and processes. This paper reviews existing XAI research, focusing on its application in the healthcare sector, particularly in medical and medicinal contexts. Our analysis is organized around key properties of XAI—understandability, comprehensibility, transparency, interpretability, and explainability—providing a comprehensive overview of XAI techniques and their practical implications.

THE INNOVATIVE IMPACT OF GENERATIVE ARTIFICIAL INTELLIGENCE ON DIGITAL BUSINESS TRANSFORMATION

In the context of intensifying competition and evolving market dynamics, the deployment of cutting-edge technologies has become not merely a discretionary choice, but an indispensable imperative for any enterprise aspiring to achieve successful growth. Generative artificial intelligence, with its substantial potential for automation, personalisation and optimisation of business processes, is emerging as a highly promising avenue of digital transformation. This study is dedicated to investigating approaches and delineating strategies for aligning generative artificial intelligence with the requirements of digital business transformation. The research examines the development of artificial intelligence, with a focus on symbolic artificial intelligence, machine learning, deep learning and generative artificial intelligence. In addition, it considers the impact of these developments on business processes. The article identifies the potential benefits and challenges associated with the adaptation of generative artificial intelligence to the needs of modern business, in the areas of marketing, sales and data analysis. The utilisation of diverse methodologies and techniques, including prompts, fine-tuning, and the incorporation of interactive guidance systems, can enhance the efficacy and precision of generative AI in a business setting, thereby facilitating optimal outcomes in a multitude of tasks. The authors put forth the proposition of employing generative artificial intelligence technology in conjunction with Retrieval-Augmented Generation, with the objective of enhancing the quality and relevance of responses to user queries. Additionally, they advocate for the utilisation of agents or orchestration tools to provide guidance to models. The successful implementation of generative artificial intelligence hinges on three key factors: the clear definition of objectives, the selection of suitable tools and technologies, and the assurance of managerial and staff support. The implementation of generative artificial intelligence will contribute to increased efficiency through the automation of routine tasks, enhanced competitiveness through personalisation and innovation, optimised cost structures that increase profitability, and expanded opportunities for research and development.

Harnessing the Power of ChatGPT in Cardiovascular Medicine: Innovations, Challenges, and Future Directions.

Cardiovascular diseases remain the leading cause of morbidity and mortality globally, posing significant challenges to public health. The rapid evolution of artificial intelligence (AI), particularly with large language models such as ChatGPT, has introduced transformative possibilities in cardiovascular medicine. This review examines ChatGPT's broad applications in enhancing clinical decision-making-covering symptom analysis, risk assessment, and differential diagnosis; advancing medical education for both healthcare professionals and patients; and supporting research and academic communication. Key challenges associated with ChatGPT, including potential inaccuracies, ethical considerations, data privacy concerns, and inherent biases, are discussed. Future directions emphasize improving training data quality, developing specialized models, refining AI technology, and establishing regulatory frameworks to enhance ChatGPT's clinical utility and mitigate associated risks. As cardiovascular medicine embraces AI, ChatGPT stands out as a powerful tool with substantial potential to improve therapeutic outcomes, elevate care quality, and advance research innovation. Fully understanding and harnessing this potential is essential for the future of cardiovascular health.

GPS Tracking and Indoor Navigation System

This project is an idea of developing an application based on a navigation system formed by Graph Data Structure. An indoor positioning system (IPS) can be thought of as a global positioning system (GPS) for indoor venues. While GPS is used outdoors, IPS can detect real-time locations to accurately determine the coordinates of people or assets inside a building. These coordinates are typically represented visually by a blue dot on a digital indoor map to provide additional context to users and features such as wayfinding. The project aims to have certain objectives such as a tracking system, navigation system that is inside a small area of a building. This idea aims to track the location of any particular device within a building and show a navigation to that device in an efficient manner. Not only tracking of devices but also solving a problem of finding a particular room in the building through data of different paths that is stored. This project comes under the intelligent system design and development domain and falls under the sector of analysing and designing hardware, software and operating systems for computers. The main objective of this system design is to navigate through the area and find places to reach with ease and as fast as possible, it is helpful for people who are new at that place and even finding a user in that particular vicinity by live tracking technique.

Mastering Data Pipelines for Al: A Beginner's Guide to Building Efficient Workflows

This comprehensive article explores the critical role of data pipelines in Artificial Intelligence (AI) development, emphasizing their importance in ensuring high-quality, relevant data for machine learning models. It covers key stages of the data pipeline process, including data ingestion, cleaning and preprocessing, transformation and feature engineering, and storage and loading. The article discusses various tools, techniques, and best practices for each stage, addressing challenges in handling diverse data sources, scalability issues, and the need for efficient data management. It highlights the significance of robust data pipelines in enhancing AI model performance and reliability, while also considering the dynamic nature of the AI field and the necessity for continuous learning and adaptation.

FORMATION OF STRATEGIC DIRECTIONS FOR THE USE OF ARTIFICIAL INTELLIGENCE IN THE ENTERPRISE TO ACHIEVE THE GOALS OF SUSTAINABLE DEVELOPMENT

Rapid artificial intelligence (AI) development opens new opportunities for enterprises to achieve sustainable development goals. This article summarizes the research results of applying AI to optimize production processes, reduce environmental impact, increase productivity, and strengthen social responsibility. The article aims to highlight the potential of AI for achieving enterprises' sustainable development goals and identify effective strategies for its application to increase economic efficiency, social responsibility, and environmental sustainability. A qualitative analysis of statistical data, official regulatory documents, books, and articles from accredited journals was used to achieve the goal. The obtained results showed that AI could be used to analyze production processes and forecast the demand for raw materials, optimize supply chains and reduce energy consumption, identify potential sustainability risks and develop strategies to prevent them, identify defects in production and control product quality in real-time, create personalized services and products for customers. The study highlights the importance of AI's ethical and safe use to ensure its positive impact on society and the environment. The practical value of the article lies in the systematic review of studies on the effects of AI on the sustainable development of enterprises. The obtained results can be used to improve production processes, increase enterprises' competitiveness, and promote sustainable development.

The suitability of skilled talent training in higher vocational colleges based on AI and 5G network connection in the context of educational transformation and upgrading

The development of artificial intelligence (AI) and 5G network technology has changed the production and lifestyle of people. AI also has promoted the transformation of talent training mode under the integration of college industry and education. In the context of the current transformation of education, AI and 5G networks are increasingly used in the education industry. This paper optimizes and upgrades the training mode of skilled talents in higher vocational colleges by using its advanced methods and technologies of information display. This means is helpful to analyze and solve a series of objective problems such as the single training form of the current talent training mode. This paper utilizes the principles and laws of industry university research (IUR) collaboration for reference to construct and optimize the talent training mode based on the analysis of the requirements of talent training and the role of each subject in talent training. Then, the ecological talent training environment can be realized. In the analysis of talent training mode under the cooperation of production and education, the correlation coefficients of network construction, environment construction, scientific research funds, scientific research level, and policy support were 0.618, 0.576, 0.493, 0.785, and 0.451, respectively. This showed that the scientific research level had the greatest impact on talent training in the talent training mode of IUR collaboration, while policy support had less impact on talent training compared with other factors. The combination of AI and 5G network technology with the talent training mode of IUR cooperation can effectively analyze the influencing factors and problems of the talent training mode. The hybrid method is of great significance to the talent training strategy and fitting degree.

Challenges of Artificial Intelligence (AI) in Education

With the continuous development of artificial intelligence (AI) technology, AI technology has been widely used in many industry fields, also in the field of education, while the application of AI in the field of education also faces many challenges. This paper gives the in-depth analysis of the impact of AI technology in the field of education in order to promote the in-depth application of it in the field of education.

The odyssey of Turkish school leaders in Africa: An expedition into professional identity, challenges, and cultural adaptations

This phenomenological study explores the experiences of Turkish school leaders in international schools across 20 African countries, offering a novel perspective on educational leadership beyond the Anglo-Saxon context. In-depth interviews were conducted with 20 leaders selected through maximum variation sampling. The analysis revealed four themes: (i) factors that lead to working abroad, (ii) challenges of working abroad, (iii) perceived cultural similarities and differences, and (iv) reflections on international experiences. Findings illuminate a complex interplay of personal, altruistic, and professional factors driving these leaders’ decisions to work abroad. Participants reported navigating significant challenges at individual, professional, and environmental levels, necessitating the development of cultural intelligence, adaptive leadership styles, and crisis management skills. The study highlights a profound transformation in leaders’ professional identities from “outsiders” to “cultural mediators,” underscoring the critical role of culturally responsive leadership in diverse educational contexts. Results reveal how African sociocultural settings shape expatriate leaders’ mindsets, fostering more collaborative approaches and deeper cross-cultural appreciation. This highlights the evolution of leaders’ cultural understanding and effective communication skills, emphasizing the need for targeted support and professional development in cultural sensitivity and adaptive leadership. This research enhances the understanding of leadership complexities in multicultural settings, providing insights for global educational leadership and future research in cross-cultural management.