Innovative Intelligence Research Articles

The Generative Generic-Field Design Method Based on Design Cognition and Knowledge Reasoning

Large language model (LLM) and Crowd Intelligent Innovation (CII) are reshaping the field of engineering design and becoming a new design context. Generative generic-field design can solve more general design problems innovatively by integrating multi-domain design knowledge. However, there is a lack of knowledge representation and design process model in line with the design cognition of the new context. It is urgent to develop generative generic-field design methods to improve the feasibility, innovation, and empathy of design results. This study proposes a method based on design cognition and knowledge reasoning. Firstly, through the problem formulation, a generative universal domain design framework and knowledge base are constructed. Secondly, the knowledge-based discrete physical structure set generation method and system architecture generation method are proposed. Finally, the application tool Intelligent Design Assistant (IDA) is developed, verified, and discussed through an engineering design case. According to the design results and discussion, the design scheme is feasible and reflects empathy for the fuzzy original design requirements. Therefore, the method proposed in this paper is an effective technical scheme of generative generic-field engineering design in line with the design cognition in the new context.

Machine Learning and AI Innovations with Python: Trends and Future Directions

This comprehensive article examines the current state and future directions of machine learning and artificial intelligence innovations using Python. It provides an in-depth analysis of recent advancements in popular Python-based libraries such as TensorFlow, PyTorch, and scikit-learn, highlighting key features and performance improvements. The article explores emerging machine learning techniques, including federated learning, few-shot learning, and explainable AI, discussing their principles, Python implementations, and real-world applications. Furthermore, the article investigates future trends in AI development with Python, considering potential technological advancements, new libraries and frameworks, and emerging research areas such as quantum machine learning and neurosymbolic AI. It also addresses the challenges and opportunities facing Python in the evolving landscape of AI, including performance optimization, ethical considerations, and the increasing demand for production-ready AI solutions. By synthesizing current developments and future prospects, this article offers valuable insights for researchers, developers, and organizations seeking to leverage Python's capabilities in the rapidly advancing field of artificial intelligence and machine learning.

Can urban digital intelligence transformation promote corporate green innovation? Evidence from China

Can urban digital intelligence transformation (DIT) facilitate corporate green innovation (CGI)? This research uses the staggered difference-in-differences (DID) approach to study how urban DIT, represented by the Artificial Intelligence Innovation Development Pilot Zones (AIIDPZs), affects CGI. Regression results indicate that urban DIT promotes both the quantity and quality of CGI. Mechanisms analysis reveals that urban DIT promotes CGI by facilitating digital transformation, increasing firms’ investment in R&D, and facilitating information sharing. Heterogeneity analysis shows that urban DIT has a more significant effect on promoting green innovation in non-SOEs, industries with lower pollution levels, and enterprises in regions with high intellectual property protection (IPP). This study offers new perspectives on the role of urn promoting green development and provides empirical support for policy-making.

Use of Artificial Intelligence in Fashion Sales Techniques

This article discusses the prominent elements of artificial intelligence innovation in fashion sales techniques and details artificial intelligence-supported sales strategies such as virtual reality, augmented reality, virtual try-on rooms, smart mirrors, artificial intelligence-supported style consultants, visual search technologies and chatbot. This study was written with the compilation method. The compilation method is written with the aim of collecting the literature written so far on the research subjects from a scientific perspective and making a collective contribution to the literature. While virtual reality and augmented reality offer customers interactive shopping experiences and the opportunity to try products in their own environment without going to the store, virtual trial rooms and smart mirrors; Studies have shown that it has the potential to make online and face-to-face shopping easier, faster and more interactive. It has been stated that visual search technologies enable customers to easily find the products in the style they want, and artificial intelligence-supported style consultants and chatbots play an important role in optimizing the shopping experience by making personalized suggestions to customers. New and innovative solutions can be produced by conducting studies that encourage interdisciplinary collaboration between fashion designers, software developers and academicians. In this period when we live intertwined with technology, artificial intelligence-focused modules can be added to academic curricula for students studying in the field of design, and these modules will provide benefits for students to gain knowledge and skills on how to integrate artificial intelligence technologies into fashion design. The effective use of artificial intelligence in fashion sales techniques will provide a competitive advantage against competitors in the industry.

Preliminary Investigation of Context-Aware AAC with Automated Just-in-Time Cloze Phrase Response Options for Social Participation from Children on the Autism Spectrum.

Social participation for emerging symbolic communicators on the autism spectrum is often restricted. This is due in part to the time and effort required for both children and partners to use traditional augmentative and alternative communication (AAC) technologies during fast-paced social routines. Innovations in artificial intelligence provide the potential for context-aware AAC technology that can provide just-in-time (JIT) communication options based on linguistic input from partners to minimize the time and effort needed to use AAC technologies for social participation. This preliminary study used an alternating treatment design to compare the effects of a context-aware AAC prototype with automated cloze phrase response options to traditional AAC for supporting three young children who were emerging symbolic communicators on the autism spectrum in participating within a social routine. Visual analysis and effect size estimates suggest the context-aware AAC condition resulted in increases in linguistic participation, vocal approximations, and visual attention for all three children. While this study was only an initial exploration and results are preliminary, context-aware AAC technologies have the potential to enhance participation and communication outcomes for young emerging symbolic communicators on the autism spectrum and more research is needed.

Energy Security, Intelligence and Innovation Quality: Evidence From China's New Energy Demonstration City Policy

ABSTRACTThe development of new energy and the application of intelligent technologies are crucial for ensuring energy security and promoting high‐quality economic growth. This article utilises a sample of A‐share listed enterprises in China from 2008 to 2020, employing a difference‐in‐differences (DID) approach to explore the impact of new energy demonstration cities (NEDC) on innovation quality and focuses on the role of intelligent development. It is found that the construction of NEDC contributes to the improvement of enterprise innovation quality. Corporate intelligence transformation further enhances the effect of the NEDC policy, demonstrating a synergistic relationship with new energy transition. Additionally, compared with collaborative innovation, the improvement effect of NEDC policy on enterprise self‐innovation quality is more significant. Non‐state‐owned enterprises, those in the growth stage, with a solid technological foundation and located in non‐resource‐based cities are more likely to achieve improvements in innovation quality during the energy transition. Our work provides empirical evidence and valuable insights for ensuring energy security and facilitating intelligent innovation development.

Artificial intelligence innovation and corporate environmental investment: A contingent view

Existing research has begun to focus on the negative impacts caused by artificial intelligence (AI), but few studies have examined the influence of AI on corporate environmental activities. This study, using 15,779 observations from 3,097 Chinese listed firms from 2008 to 2022, finds that corporate AI innovation has a significant negative impact on CEI. Additionally, we discover that firm size strengthens the negative relationship between AI innovation and CEI, while foreign direct investment (FDI) intensity weakens this negative relationship. This study extends the research on AI in the field of corporate environmentally friendly behavior.

How intelligence organisations innovate

ABSTRACT Practitioners and academics recognize the importance of innovative intelligence organisations. However, we know little about how intelligence organisations innovate and what challenges they are being confronted with. Drawing upon intelligence studies as well as innovation and organisational studies this paper forwards a conceptual framework to answer the question: how do intelligence organisations innovate? To address this, we introduce the concept of innovation capability, an organisation’s potential to innovate. The paper identifies seven attributes of innovation capability and explores these attributes within intelligence organisations. The attributes are: (1) vision and strategy, (2) competence base, (3) organisational intelligence, (4) creativity and idea management, (5) organisational structure, (6) culture and climate and (7) management of technology. The paper concludes with an agenda for further research.

Transforming banking: Examining the role of AI technology innovation in boosting banks financial performance

With the ongoing advancement of technology, artificial intelligence is increasingly being integrated into banking and finance, with the transformative potential to reshape the financial performance of economies worldwide. This research investigates the dynamic relationship between banking and finance artificial intelligence technology innovation and banks' financial performance across 20 countries using the feasible generalized least squares and the generalized method of moments techniques. The results show that banking and finance artificial intelligence technology innovation positively impacts banks' return on assets, highlighting its role in enhancing financial performance. The interaction term between artificial intelligence innovation and economic growth emphasizes their collaborative positive impact on financial performance. Mediation analysis highlights information and communication technology development's role in transforming artificial intelligence innovation into improved financial outcomes. Considering lagged effects, initial innovation surges correlate with improved financial performance, but prolonged exposure leads to diminishing returns. Moreover, the findings indicate that non-performing loans negatively affect financial performance, underscoring the importance of asset quality. Additionally, regulatory capital and economic growth are positively associated with financial performance, while government regulations exhibit a negative impact. This study highlights the essential role of artificial intelligence technology innovation in banking and finance, emphasizing the need to consider economic and technological factors for maximizing its benefits in enhancing financial performance. Policy recommendations include promoting an artificial intelligence innovation ecosystem, adapting regulatory frameworks, and investing in information and communication technology infrastructure to harness artificial intelligence innovation's benefits while addressing associated challenges.

Application of Commonly Used Artificial Intelligence Tools in Field of Publishing

With the rapid development of technology, artificial intelligence (AI) has penetrated various industries, including the publishing industry. This article provides a detailed introduction to the current application status of commonly used AI tools in the field of publishing. Among them, the automated typesetting technology represented by Fangzheng Academic Publishing Cloud Service Platform has shown a dual leap in efficiency and quality compared to traditional typesetting methods, and has brought comprehensive innovations in intelligence, personalization, and cost control; Intelligent editing, represented by Grammarly, Heima Proofreading Software, and Shanfeng Software, has demonstrated their respective advantages in efficiency, accuracy, personalization, and technological innovation, providing strong support and assistance for editors. It is an indispensable and important tool in the field of publishing.

An automated social media manager based on artificial intelligence

In today's world of social media, where brands and companies constantly interact with their audiences, the automation of content management processes is becoming increasingly important. Manual content creation, publication, and analysis on social networks require significant resources and time. Automated social media management systems based on artificial intelligence help simplify these processes, enhancing efficiency and optimizing audience engagement strategies. Artificial intelligence in social media marketing systems allows for the automation of tasks such as creating text posts, scheduling publications across different platforms, and analyzing user interactions. Artificial intelligence can analyze audience behavior, predict the optimal time for posts, and recommend changes to content strategies for maximum reach. This not only saves time but also improves the effectiveness of marketing campaigns through more accurate analytical data. However, the automation of social media marketing also faces challenges. The primary issues include the complexity of integrating different social networks into a single management system and the limitations in generating high-quality content that meets the emotional and stylistic requirements of the brand. Additionally, a major challenge is content personalization based on data analysis, which must account for each audience segment's unique needs and behavioral characteristics. Despite these challenges, the prospects for developing automated social media marketing managers are promising due to ongoing innovations in artificial intelligence and machine learning. Future research and development will focus on improving the quality of automated content generation, enhancing user interfaces, and integrating new social platforms to expand multi-channel marketing campaign management capabilities. Thus, the automation of social media marketing is a promising field that will enable brands and companies to optimize their audience engagement strategies while significantly reducing the time and resources required for content creation, publication, and analysis. This work presents the structure of the main entities of an automated social media management system based on artificial intelligence, including users, professions, questions, answers, settings, and integration with Telegram. The developed system enables efficient content management, automates publications, and personalizes audience interaction based on user responses. The system's described key components, attributes, and relationships provide scalability and flexibility, allowing brands and companies to optimize their marketing strategies and client interactions.

Named entity recognition and its role in unstructured data analysis

In today's digital world, where vast amounts of unstructured data are generated every day, the ability to efficiently process this information is key for many industries. Unstructured data, which includes text files, emails, video, audio, images, and other forms of media, is the bulk of digital data and requires specialized tools to analyze it. Natural Language Processing (NLP) and Named Entity Recognition (NER) are two key technologies that enable the transformation of unstructured data into structured information that can be used for a variety of applications. Natural Language Processing enables machines to understand, interpret, manipulate and generate human language, opening up possibilities for deep analysis of textual data. This includes identifying key words, phrases, themes, and emotional nuances in texts. NER, as an important component of Natural Language Processing, specializes in identifying and classifying named entities in the text into certain categories, such as names of persons, organizations, locations, dates, times, and others. This allows you to automate the processes of sorting, categorizing and analyzing information. However, working with Natural Language Processing and Named Entity Recognition faces a number of challenges. The large volume and variety of data make it difficult to collect, store and analyze it. Lack of standardization can lead to problems with interoperability and integration of different data sources. In addition, there are challenges related to the recognition of named entities, in particular, distinguishing between the same names belonging to different persons and understanding the context in which the names are used. Despite these challenges, the outlook for Natural Language Processing and Named Entity Recognition looks bright, with continued innovations in artificial intelligence and machine learning promising to improve the accuracy and efficiency of these technologies in the future.

Exploring the LCC Hypothesis in the Nordic Region: The Role of AI Innovation, Environmental Taxes, and Financial Accessibility via Panel ARDL

This study investigates the impact of artificial intelligence (AI) innovation on environmental sustainability in the Nordic region. Additionally, it tests the Load Capacity Curve (LCC) hypothesis by incorporating factors such as financial accessibility, environmental tax, and urbanization, using data spanning from 1990 to 2020. The methodology includes the Cross-Sectional Dependence test and the slope homogeneity test, revealing issues of heterogeneity and cross-sectional dependence. Furthermore, first and second-generation panel unit root assessments indicate that the variables are free from unit root problems. Panel Cointegration tests confirm that the variables are cointegrated in the long run. To analyze both short-run and long-run relationships, the study employs the Panel Autoregressive Distributed Lag (ARDL) model. The results from the Panel ARDL model support the LCC hypothesis in the Nordic region, showing a U-shaped relationship between income and load capacity factor. Moreover, AI innovation and environmental tax significantly and positively contribute to environmental sustainability in both the short and long run. In contrast, higher financial accessibility and urbanization degrade environmental sustainability over these timeframes. To validate the robustness of the Panel ARDL estimations, the study also uses Fully Modified OLS, Dynamic OLS, and Fixed Effects OLS approaches, all of which corroborate the ARDL findings. The study employs the D-H causality test to explore causal relationships among the variables. The test results reveal a unidirectional causal relationship between income and AI innovation to the load capacity factor and a bidirectional causal relationship between financial accessibility and the load capacity factor, as well as between urbanization and the load capacity factor. However, no causal relationship is found between environmental tax and the load capacity factor.

Revolutionizing Indian Healthcare: The Impact of Digital Health, AI, Telemedicine, and Data Security

Introduction: The Indian healthcare system is in the middle of a transformative shift, driven by innovations in digital health, artificial intelligence (AI), telemedicine, and data security. These technologies are restructuring healthcare delivery, with substantial implications for patient safety, access to care, and overall efficiency. Understanding their impact is important for stakeholders seeking to navigate and leverage these innovations for improved healthcare outcomes. Methodology: This review involved a thorough analysis of current literature, case studies, and statistical data. Sources were selected based on their relevance to digital health, AI, telemedicine, and data security within the Indian healthcare context. The analysis focused on examining the benefits and challenges associated with the integration of these technologies, as well as the influence of government policies on their adoption. Results: The review underlines both- the significant benefits and challenges of integrating these technologies into the Indian healthcare system. While advancements have led to improved patient safety, increased access to care, and enhanced efficiency, they also present challenges related to infrastructure, training, and data privacy. The role of government policies has been pivotal in facilitating the adoption and growth of these technologies, providing necessary regulatory support and strategic initiatives. Conclusion: The review underscores the transformative potential of digital health, AI, telemedicine, and data security in revolutionizing Indian healthcare. However, realizing this potential requires continued government support, strategic implementation, and addressing the challenges identified. A balanced approach is essential to leverage these technologies effectively for the betterment of the nation’s healthcare system.

Analyzing the Nexus between AI Innovation and Ecological Footprint in Nordic Region: Impact of Banking Development and Stock Market Capitalization using Panel ARDL method

This study investigates the impact of Artificial Intelligence (AI) innovation on the ecological footprint in the Nordic region from 1990 to 2020, alongside the effects of banking development, stock market capitalization, economic growth, and urbanization. Utilizing the STIRPAT model, the study incorporates cross-sectional dependence and slope homogeneity tests, revealing issues of heterogeneity and cross-sectional dependence. The analysis employs both first and second-generation panel unit root tests, confirming that the variables are free from unit root problems. Panel cointegration tests demonstrate that the variables are cointegrated in the long run. To explore the short- and long-term relationships, the study utilizes the Panel Autoregressive Distributed Lag (ARDL) model. The Panel ARDL results indicate that economic growth, stock market capitalization, and urbanization positively correlate with the ecological footprint in both the short and long run. Conversely, AI innovation and banking development negatively correlate with the ecological footprint. To validate the Panel ARDL estimations, robustness checks are performed using Fully Modified OLS, Dynamic OLS, and Fixed Effects with OLS, all of which support the initial findings. Furthermore, the study employs the D-H causality test to identify causal relationships. The results show a unidirectional causal relationship between AI innovation, stock market capitalization, urbanization, and the ecological footprint. In contrast, a bidirectional causal relationship exists between economic growth and the ecological footprint, as well as between banking development and the ecological footprint.

Leveraging ChatGPT for Personalized Learning: A systematic Review in Educational Settings

In today’s digital world, personalization is a leading point for each learner to strengthen their educational achievements. Considering innovations in artificial intelligence (AI), particularly in the area of natural language processing, Chat GPT has sparked awareness as a viable mechanism for supporting personalized learning. The aim of the systematic review is to identify the present-day status of research on using Chat GPT for independent learning in educational settings. The research effort combines works that study Chat GPT application in learning environments, summarizing the results of a broad survey of scholarly literature. The significance of the Chat GPT in the learning path was identified from the previous studies. Also, the study discusses the practical difficulties and challenges of this technology. The study highlights some potential improvements by the Chat GPT, like improving the individual learning experience, offering seamless feedback and error corrections, encouraging study, and finding study metrics. Also, the study identified some potential challenges, for example, data privacy, bias, accessibility of databases, and similarity between answers. There is a possibility for further research on this topic in advance to get more valuable ideas in the academic field. This comprehensive study examines a collection of AI-based personalized learning ideas to clearly enlighten the advantages and disadvantages of utilizing Chat GPT in an academic environment. Practitioners can gain a clear idea by referring to the article, which will maximize the usage of technology in the near future.

Enhancing cervical cancer cytology screening via artificial intelligence innovation

A double-check process helps prevent errors and ensures quality control. However, it may lead to decreased personal accountability, reduced effort, and declining quality checks. Introducing an artificial intelligence (AI)-based system in such scenarios could effectively address the risk of oversights. This study introduces an innovative AI-integrated workflow for cervical cytology screening that substantially improves efficiency and reduces the burden on cytologists. The AI model prioritizes cases for review based on anomaly scores and streamlines the first screening process to approximately 10 s per case. The model enhances the identification of high-risk cases via detailed microscopic observation, high anomaly scores cases, and a targeted review of low-score cases. The workflow highlights its capability for rapid, accurate, and less labor-intensive evaluations, demonstrating the potential to transform cervical cancer screening. This study highlights the importance of AI in modern medical diagnostics, particularly in areas with a high demand for accuracy and efficiency.

Exploring the Path of Digital Intelligence Innovation and Productivity Enhancement in the Greater Bay Area from a Multidimensional Perspective

“New quality productivity” is an important term put forward by General Secretary Xi Jinping in September 2023 during his visit and research in Heilongjiang. It is a form of productivity relative to traditional productivity, characterized by high technology, high energy efficiency and high quality, with science and technology innovation as the core driving force, and with a wide range of permeability and integration. To develop new quality productivity, it is inseparable from scientific and technological innovation and the development of new industries. Digital economy and intelligence are important paths to develop new quality productivity. The purpose of this paper is to explore the path and its impact of digital intelligence innovation on productivity enhancement in the Greater Bay Area from a multi-dimensional perspective. The study first starts with the policy environment, outlines the policy background of the Greater Bay Area, comprehends the formation and development of the digital economy, and analyzes in depth how policies promote digital innovation. On this basis, the paper explores the specific impact of digital intelligence innovation on productivity and its path from three perspectives: technological, social and cultural. Under the technological perspective, this paper focuses on the technology-driven mechanism of digital intelligence innovation, including its direct impact on the optimization of industrial structure and productivity enhancement, and analyzes in detail the breakthroughs in major technological fields and their role in promoting the regional economy. Through technological innovation cases, it demonstrates how digital innovation can enhance productivity at the technological level and inject new vitality into the economy of the Greater Bay Area. The social perspective focuses on how digital intelligence innovation can enhance social governance, drive social benefits and public services, and explore the role of digital intelligence innovation in promoting social inclusion and narrowing the digital divide. By analyzing the specific practices and effects at the social level, it reveals the important role of digital intelligence innovation in social development. The cultural perspective analyzes the integration of digital intelligence innovation with regional culture and explores its role in promoting the cultural industry. It demonstrates the specific practices of the integration of culture and technology through typical cases, emphasizes the importance of regional cultural identity and innovation atmosphere, and proposes how to further enhance the productivity of the Greater Bay Area through cultural innovation. In the comprehensive analysis section, this paper integrates the analysis results from various perspectives and proposes the future development trend of digital intelligence innovation and its long-term impact on productivity enhancement in the Greater Bay Area. Finally, based on the findings, this paper proposes a series of policy recommendations and optimization paths, including strengthening policy support, promoting technological research and development, and fostering social inclusiveness and cultural innovation, with a view to providing scientific references and practical guidance for the future development of the digital intelligence economy in the Greater Bay Area.

Toward Fully Automated Personalized Orthopedic Treatments: Innovations and Interdisciplinary Gaps.

Personalized orthopedic devices are increasingly favored for their potential to enhance long-term treatment success. Despite significant advancements across various disciplines, the seamless integration and full automation of personalized orthopedic treatments remain elusive. This paper identifies key interdisciplinary gaps in integrating and automating advanced technologies for personalized orthopedic treatment. It begins by outlining the standard clinical practices in orthopedic treatments and the extent of personalization achievable. The paper then explores recent innovations in artificial intelligence, biomaterials, genomic and proteomic analyses, lab-on-a-chip, medical imaging, image-based biomechanical finite element modeling, biomimicry, 3D printing and bioprinting, and implantable sensors, emphasizing their contributions to personalized treatments. Tentative strategies or solutions are proposed to address the interdisciplinary gaps by utilizing innovative technologies. The key findings highlight the need for the non-invasive quantitative assessment of bone quality, patient-specific biocompatibility, and device designs that address individual biological and mechanical conditions. This comprehensive review underscores the transformative potential of these technologies and the importance of multidisciplinary collaboration to integrate and automate them into a cohesive, intelligent system for personalized orthopedic treatments.

A Comprehensive Survey on Emerging Assistive Technologies for Visually Impaired Persons: Lighting the Path with Visible Light Communications and Artificial Intelligence Innovations.

In the context in which severe visual impairment significantly affects human life, this article emphasizes the potential of Artificial Intelligence (AI) and Visible Light Communications (VLC) in developing future assistive technologies. Toward this path, the article summarizes the features of some commercial assistance solutions, and debates the characteristics of VLC and AI, emphasizing their compatibility with blind individuals' needs. Additionally, this work highlights the AI potential in the efficient early detection of eye diseases. This article also reviews the existing work oriented toward VLC integration in blind persons' assistive applications, showing the existing progress and emphasizing the high potential associated with VLC use. In the end, this work provides a roadmap toward the development of an integrated AI-based VLC assistance solution for visually impaired people, pointing out the high potential and some of the steps to follow. As far as we know, this is the first comprehensive work which focuses on the integration of AI and VLC technologies in visually impaired persons' assistance domain.