Advances In Artificial Intelligence Technology Research Articles

Abstract PO5-05-09: Testing AI-Predicted Protein Motifs that Direct Constitutive Genomic AR Activity in Endocrine Resistant Breast Cancer

Abstract Testing AI-Predicted Protein Motifs that Direct Constitutive Genomic AR Activity in Endocrine-Resistant Breast Cancer Ashfia F. Khan 1,2, Anthony S. Peidl 1,2, Shaymaa Bahnassy 3, Henry Vo2, Micah B. Castillo 2, Sarah K Herzog4,5, Suzanne AW Fuqua4,6, Preethi Gunaratne 2, Xiaolian Gao 2, Subash C. Pakhrin7, Tasneem Bawa-Khalfe1,2 1 Center for Nuclear Receptors & Cell Signaling, University of Houston, Houston, TX 2 Department of Biology & Biochemistry, University of Houston, Houston, TX 3 Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC 4 Lester & Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 5 Program in Integrative Molecular and Biomedical Sciences, Baylor College of Medicine, Houston, TX 6 Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX 7 Department of Computer Science & Engineering Technology, University of Houston-Downtown, Houston, TX Background: Endocrine therapy (ET) remains the first-line treatment for hormone-receptor positive (HR+) breast cancer (BCa). Approximately 15–20% of HR+ BCa are intrinsically resistant to ET, and 30–40% of patients acquire resistance. Resistance to ET (ET-R) supports cancer progression with reduced disease-free survival and greater incidence of metastatic disease. Hence, therapeutic strategies for ET-R HR+ BCa remain an overarching challenge. The androgen receptor (AR) is emerging as an attractive alternative target for BCa subtypes, and elevated AR levels drive HR+ BCa progression. Targeting AR in HR+ BCa is proving difficult with preclinical studies showing conflicting results for AR antagonists. Yet clinical trials with several AR-targeting drugs are ongoing. Our recent report highlights a unique constitutively active modified AR population that drives HR+ BCa metastatic properties and is insensitive to AR inhibitors. Our current objectives are to 1) use a novel machine-learning model to predict AR modifications and 2) establish a strategy to identify patients with high modified AR levels. Methods: An advanced artificial intelligence (AI) tool and mid-throughput microfluidic peptide array technology were used to map modification domains on AR. SUMO post-translational modification of AR (SUMO-AR) was eliminated in HR+ BCa using CRISPR-Cas9 technology. RNA-seq was employed to identify a unique gene signature for SUMO-AR, and comparative bioinformatic analysis stratified patients with high versus low SUMO-AR. Results: A novel deep-learning AI platform SumoPred-PLM is trained to identify consensus, non-consensus, and SUMO2/3-specific motifs on AR. We verified SUMO2/3-specific sites on AR with a mid-throughput microfluidic peptide array. The identified SUMO2/3-acceptor site of AR is important for HR+ BCa cell pathophysiology; loss of this SUMO2/3-acceptor site impacts endogenous AR SUMOylation, cell morphology, and proliferation/apoptosis. Using both high and low SUMO-AR BCa lines, a unique SUMO-AR gene profile was established. Our SUMO-AR gene signature identifies HR+ BCa patients with greater susceptibility to metastatic progression. Conclusion: Our studies present a unique pipeline that incorporates deep-learning AI technology to identify vulnerable motifs in AR for future drug discovery. Drug screens are currently ongoing. In addition, we establish a SUMO-AR gene signature that stratifies HR+ BCa patients with high/low SUMO-AR and predicts disease progression. We expect the results could be utilized to identify responders to AR inhibitors in ongoing clinical trials. Citation Format: Ashfia Khan, Anthony Peidl, Shaymaa Bahnassy, Henry Vo, Micah Castillo, Sarah Herzog, Suzanne Fuqua, Preethi Gunaratne, Xiaolian Gao, Subash Pakhrin, Tasneem Bawa-Khalfe. Testing AI-Predicted Protein Motifs that Direct Constitutive Genomic AR Activity in Endocrine Resistant Breast Cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-05-09.

Abstract PO2-28-06: An on-line deep learning decision support tool, iBRISK, aimed at improving breast cancer risk estimation and reducing unnecessary biopsies for BI-RADS 4 patients

Abstract Probability of malignancy (POM) for Breast Imaging Reporting and Data System (BI-RADS) category 4 designated breast lesions ranges from 2% – 95% and contributes to a high unnecessary biopsy rate. This is as most clinicians often stick to the biopsy option to rule in or out breast cancer early; withholding biopsy could be risky, and biopsies of BI-RADS 4 lesions serve as a quality metric and performance standard. At 21.1%, biopsy-proven positive predictive value (PPV3) rates for BI-RADS 4 have not improved for decades, translating to high false-positive rates of mammography. Unnecessary biopsies are a big issue in the management of BI-RADS 4 lesions with negative implications including increased medical costs, healthcare wastes, unnecessary psychological burdens to the patients, and potential complications and risks. Objectives 1. Optimize the precision breast cancer risk assessment tool, iBRISK, that utilizes artificial intelligence (AI) technologies, including natural language processing (NLP), image processing, and deep learning, with clinical risk factors and imaging features. 2. Develop a user-friendly web interface for iBRISK to facilitate clinicians or insurers in estimating cancer risk and making informed biopsy decisions for BI-RADS 4 lesions. Methods Our intelligent-augmented breast cancer risk calculator (iBRISK) model was trained on multimodal data collected from 10,778 patients, including demographic factors, historical and clinical characteristics, mammographic features, and pathologic signatures. We validated iBRISK using 4,200 patients from multiple leading hospitals, including Houston Methodist Neal Cancer Center, the University of Texas MD Anderson Cancer Center, and the University of Texas Health San Antonio MD Anderson Mays Cancer Center. The iBRISK was connected to a backend server which is linked to a frontend web user interface using technologies like Hypertext Preprocessor (PHP) for the backend application running on an APACHE HTTP server and React-JavaScript for the front-end web application that communicates with the backend using Representational State Transfer Application Programming Interface (RESTful API) and JavaScript Object Notation (JSON) format. The communication was secured using Secure Sockets Layer (SSL) encryption. Results The iBRISK model demonstrated high sensitivity in malignancy prediction and achieved an accuracy of 89.5%, area under the receiver operating characteristic curve of 0.93 (95% CI: 0.92-0.95), sensitivity of 100%, and specificity of 81%. Only 0.16% of lesions determined to have low POM by the model were malignant. Our multi-center study shows that iBRISK achieves at least 50% reduction in unnecessary biopsies of BI-RADS 4 cases. Data elements for the required 20 features are entered into the interface using a variety of imputation methods including direct text, dropdown menus and radio button selections. The user-friendly web interface provides risk scores (0 – 1), risk levels (low, medium, and high), and associated biopsy recommendations. Conclusion The user-friendly iBRISK web interface is proposed as an adjunct to the BI-RADS system, enhancing the precision of BI-RADS 4 lesion cancer risk stratification. It is expected to reduce unnecessary biopsies, lower health costs, and enhance the quality of health care. This approach aims to tackle a critical issue in breast cancer diagnosis by leveraging advanced AI technologies and big data and providing clinicians with a tool to make more informed and precise biopsy decisions for BI-RADS 4 lesions. Citation Format: Chika Ezeana, Xiaohui Yu, Zhihao Wan, Tiancheng He, Tejal Patel, Virginia Kaklamani, Maryam Elmi, Erika Brigmon, Pamela Otto, Kenneth Kist, Lin Wang, Joe Ensor, Heather Speck, Ya-Chen Shih, Bumyang Kim, I-Wen Pan, David Spak, Wei Yang, Jenny Chang, Stephen Wong. An on-line deep learning decision support tool, iBRISK, aimed at improving breast cancer risk estimation and reducing unnecessary biopsies for BI-RADS 4 patients [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-28-06.

Transforming academic librarianship through AI reskilling: Insights from the GPT-4 exploration program

The emergence of Artificial Intelligence (AI) in academic librarianship necessitates a corresponding evolution in librarians' competencies. This case study examines the GPT-4 Exploration Program at the University of New Mexico's College of University Libraries and Learning Sciences, which aimed to foster a culture of continuous learning and innovation by providing hands-on experience with advanced AI technology. Drawing on Malcolm Knowles' Adult Learning Principles, the program's structure and implementation are analyzed, highlighting the diverse range of projects undertaken by participants. The study reveals that effective AI reskilling involves cultivating a culture of continuous learning, adaptability, and collaborative exploration, anchored in a practical, hands-on approach. Participants reported significant improvements in AI literacy and confidence in applying AI tools to their work. The program's success demonstrates the potential of well-structured reskilling initiatives in empowering library professionals to navigate the evolving landscape of AI in academic libraries. The case study offers insights and strategies for other academic libraries seeking to develop AI reskilling initiatives, emphasizing the importance of fostering a supportive learning environment, addressing technical and ethical challenges, and ensuring the long-term sustainability of such programs. The GPT-4 Exploration Program serves as a blueprint for integrating AI into library services and operations.

Leveraging artificial intelligence for enhanced supply chain optimization

This study provides a comprehensive review of the integration of Artificial Intelligence (AI) into Supply Chain Management (SCM), focusing on its impact on operational efficiency, strategic innovation, and sustainability. Employing a systematic literature review and content analysis methodology, the research synthesizes findings from peer-reviewed articles and conference papers published between 2013 and 2023. The study identifies key advancements in AI technologies, such as machine learning, natural language processing, and robotics, and their applications across various supply chain processes including demand forecasting, inventory management, and logistics optimization. Key findings reveal that AI significantly enhances supply chain efficiency by improving decision-making, reducing costs, and optimizing resource allocation. However, challenges such as data privacy concerns, ethical considerations, and the need for skilled personnel emerge as critical factors influencing AI adoption in SCM. The future outlook for AI-enhanced supply chains is promising, with potential for further innovation and resilience, albeit contingent upon addressing existing challenges. The study concludes with strategic recommendations for practitioners and policymakers, emphasizing the importance of fostering a culture of innovation, developing digital competencies, and creating supportive regulatory frameworks for AI integration. Directions for future research include exploring the long-term impacts of AI on supply chain sustainability, ethical implications of autonomous systems, and the interplay between AI and emerging technologies. This research contributes to the academic discourse on AI in SCM, offering insights for enhancing supply chain operations in the digital age.

Intelligent Ideological and Political Education: The New Role of Artificial Intelligence

Intelligent political and ideological education has steadily gained popularity in today's educational landscape due to the rapid advancement of artificial intelligence technology. This research aims to investigate the emerging role of artificial intelligence in political and ideological education. First of all, we clearly define and explain the concept of intelligent ideological and political education, and deeply analyze the basic connotation and characteristics of intelligent ideological and political education including its important value. Secondly,we analyze the application status of artificial intelligence technology in ideological and political education, and give two representative typical cases.At the same time, we also analyzed the innovative practice of artificial intelligence technology from two aspects of moral education and political education.Furthermore, given the difficulties AI technology faces in the context of political and ideological education, we suggest appropriate responses,This study concludes with a discussion on artificial intelligence's development tendency in ideological education going forward.Our prediction is that as artificial intelligence technology advances and becomes more sophisticated, intelligent education will serve as a strong basis for comprehensive innovation in educational methods, content, and management. This will enable the development of exceptional talent who possess a strong sense of national identity, global perspective, and social responsibility.

Artificial intelligence vs. semi-automated segmentation for assessment of dental periapical lesion volume index score: A cone-beam CT study

IntroductionCone beam computed tomography periapical volume index (CBCTPAVI) is a categorisation tool to assess periapical lesion size in three-dimensions and predict treatment outcomes. This index was determined using a time-consuming semi-automatic segmentation technique. This study compared artificial intelligence (AI) with semi-automated segmentation to determine AI's ability to accurately determine CBCTPAVI score. MethodsCBCTPAVI scores for 500 tooth roots were determined using both the semi-automatic segmentation technique in three-dimensional imaging analysis software (Mimics Research™) and AI (Diagnocat™). A confusion matrix was created to compare the CBCTPAVI score by the AI with the semi-automatic segmentation technique. Evaluation metrics, precision, recall, F1-score (2×precision×recallprecision+recall), and overall accuracy were determined. ResultsIn 84.4 % (n = 422) of cases the AI classified CBCTPAVI score the same as the semi-automated technique. AI was unable to classify any lesion as index 1 or 2, due to its limitation in small volume measurement. When lesions classified as index 1 and 2 by the semi-automatic segmentation technique were excluded, the AI demonstrated levels of precision, recall and F1-score, all above 0.85, for indices 0, 3–6; and accuracy over 90 %. ConclusionsDiagnocat™ with its ability to determine CBCTPAVI score in approximately 2 min following upload of the CBCT could be an excellent and efficient tool to facilitate better monitoring and assessment of periapical lesions in everyday clinical practice and/or radiographic reporting. However, to assess three-dimensional healing of smaller lesions (with scores 1 and 2), further advancements in AI technologies are needed.

Artificial Intelligence and Human Resource Management: A Bibliometric Analysis

In recent times, the fusion of artificial intelligence (AI) within the human resource function of organizations has been witnessing a notable surge. The rapid advancements in AI technology are bringing about a transformative impact on information processing across diverse domains within human resource management (HRM). This paper seeks to carry out a bibliometric study on the scientific research of AI and HRM, presenting key aspects of academic research in this field. A bibliometric analysis methodology was employed, retrieving a total of 144 articles published between 2018 and 2022 from SCOPUS. These articles were analyzed using VOS viewer and Publish or Perish software. Using thorough bibliometric techniques, this study provides insights into various aspects. The accessibility of articles within SCOPUS affects the database's limitations, which in turn can impact various bibliometric aspects. Despite these limitations, the utilization of software tools allows for a detailed perspective and insight into the analyzed publications, enabling a detailed exploration of bibliometric aspects.

Synergizing Zoological Research and AI in Business: Unveiling Biological Strategies for Sustainable Innovation

This paper explores the symbiotic relationship between zoological research and artificial intelligence (AI)-driven business management, elucidating how their integration can unlock profound biological insights with transformative implications for various industries. With advancements in AI technologies and an increasing emphasis on sustainability, businesses are increasingly turning to interdisciplinary approaches to optimize operations and enhance decision-making processes. Leveraging insights from zoological research, which encompasses the study of diverse animal species and their ecological interactions, presents a novel avenue for innovation and strategic development in the business landscape. The integration of zoological research with AI-driven business management offers multifaceted benefits. Firstly, it provides businesses with a deeper understanding of complex biological systems, facilitating informed decision-making regarding resource management, product development, and sustainability initiatives. Zoological studies offer insights into the intricate interdependencies within ecosystems, shedding light on ecological dynamics, species behaviors, and evolutionary patterns. By integrating these insights into AI algorithms, businesses can develop predictive models to anticipate environmental changes, species migrations, and biodiversity trends, thereby enhancing resilience and adaptive capacity. Furthermore, the synergy between zoological research and AI enables businesses to optimize processes across various domains, ranging from supply chain management to customer engagement. For instance, by analyzing animal foraging behaviors and social structures, businesses can derive inspiration for efficient routing algorithms, inventory optimization strategies, and collaborative network designs. Moreover, AI-driven data analytics can streamline market research efforts by identifying consumer preferences, market trends, and emerging opportunities, drawing parallels with animal behavior studies that elucidate patterns of resource utilization and adaptation. In addition to operational enhancements, the integration of zoological insights with AI-driven business management fosters innovation and product development. By studying biological adaptations and evolutionary processes, businesses can derive inspiration for biomimetic design principles, leading to the creation of sustainable materials, energy-efficient technologies, and biologically inspired innovations. Moreover, by leveraging AI techniques such as machine learning and natural language processing, businesses can analyze vast datasets of biological literature and ecological observations to identify novel bioactive compounds, genetic sequences, and ecological niches with commercial potential. Importantly, the incorporation of zoological research into AI-driven business management fosters a paradigm shift towards holistic and ethical business practices. By recognizing the interconnectedness of ecological systems and economic activities, businesses can develop strategies that prioritize environmental stewardship, biodiversity conservation, and social responsibility. Zoological insights inform businesses about the intrinsic value of biodiversity, highlighting the importance of preserving natural habitats, protecting endangered species, and mitigating anthropogenic impacts on ecosystems. Through AI-powered simulations and scenario analyses, businesses can assess the long-term consequences of their decisions on ecological integrity, societal well-being, and corporate reputation, fostering a culture of responsible innovation and sustainable growth. However, the integration of zoological research with AI-driven business management also poses challenges and ethical considerations. Ensuring the ethical treatment of animals in research and development processes, safeguarding data privacy and security, and addressing biases in AI algorithms are paramount concerns that require interdisciplinary collaboration and stakeholder engagement. Moreover, fostering inclusivity and diversity in both scientific research and business leadership is essential for promoting equitable outcomes and fostering innovation. In conclusion, the integration of zoological research with AI-driven business management holds immense potential for unlocking biological insights and driving sustainable innovation in diverse industries. By bridging the gap between biological sciences and business practices, this interdisciplinary approach offers a pathway towards a more resilient, adaptive, and ethically conscious future. Through collaborative efforts among scientists, entrepreneurs, policymakers, and community stakeholders, we can harness the power of nature-inspired solutions to address complex challenges and create positive societal impact.

Non-speech emotion recognition based on back propagation feed forward networks

Non-speech emotion recognition involves identifying emotions conveyed through non-verbal vocalizations such as laughter, crying, and other sound signals, which play a crucial role in emotional expression and transmission. This paper employs a nine-category discrete emotion model encompassing happy, sad, angry, peaceful, fearful, loving, hateful, brave, and neutral. A proprietary non-speech dataset comprising 2337 instances was utilized, with 384-dimensional feature vectors extracted. The traditional Backpropagation Neural Network (BPNN) algorithm achieved a recognition rate of 87.7% on the non-speech dataset. In contrast, the proposed Whale Optimization Algorithm - Backpropagation Neural Network (WOA-BPNN) algorithm, applied to a self-made non-speech dataset, demonstrated a remarkable accuracy of 98.6%. Notably, even without facial emotional cues, non-speech sounds effectively convey dynamic information, and the proposed algorithm excels in their recognition. The study underscores the importance of non-speech emotional signals in communication, especially with the continuous advancement of artificial intelligence technology. The abstract thus encapsulates the paper’s focus on leveraging AI algorithms for high-precision non-speech emotion recognition.

Chatbots for Well-Being: Exploring the Impact of Artificial Intelligence on Mood Enhancement and Mental Health

Introduction Over the past few years, Psychiatry has undergone a significant transformation with the integration of Artificial Intelligence (AI). This shift has been driven by the increasing demand for mental health services, as well as advances in AI technology. AI analyzes extensive datasets, including text, voice, and behavioral data, aiding in mental health diagnosis and treatment. Consequently, a range of AI-based interventions has been developed, including chatbots, virtual therapists and apps featuring cognitive-behavioral therapy (CBT) modules. Notably, chatbots, as conversational agents, have emerged as valuable tools, assisting users in monitoring emotions and providing evidence-based resources, well-being support, psychoeducation and adaptive coping strategies.ObjectivesThis study aims to investigate the impact of AI chatbots on improving mental health, evaluate their strengths and weaknesses and explore their potential for early detection and intervention in mental health issues.Methods A literature review was conducted through PubMed and Google Scholar databases, using keywords ‘artificial intelligence’, ‘chatbot’ and ‘mental health’. The selection focused on the most relevant articles published between January 2021 and September 2023.ResultsMental health chatbots are highly personalized, with a primary focus on addressing issues such as depression or anxiety within specific clinical population groups. Through the integration of Natural Language Processing (NLP) techniques and rule-based AI algorithms, these chatbots closely simulate human interactions and effectively instruct users in therapeutic techniques. While chatbots integrating CBT principles have gained widespread use and extensive research attention, some also incorporate alternative therapeutic approaches, including dialectical behavior therapy, motivational interviewing, acceptance and commitment therapy, positive psychology or mindfulness-based stress reduction. AI chatbots provide substantial advantages in terms of accessibility, cost-effectiveness and improved access to mental health support services. Nonetheless, they also exhibit limitations, including the absence of human connection, limited expertise, potential for misdiagnosis, privacy concerns, risk of bias and limitations in risk assessment accuracy.ConclusionsAI-based chatbots hold the potential to enhance patient outcomes by enabling early detection and intervention in mental health issues. However, their implementation in mental health should be approached with caution. Further studies are essential to thoroughly evaluate their effectiveness and safety.Disclosure of InterestNone Declared

The Transformative Impact of AI in Finance and Banking

The transformative impact of artificial intelligence (AI) in finance and banking is pro7 found, revolutionizing the way financial institutions operate, interact with customers, and make 8 decisions. This article explores the technological landscape that has paved the way for AI adoption, including falling data storage costs, data availability, advancements in machine learning, cost reduction, regulatory compliance, competitive advantage, risk management, customer experience, fraud detection, increased connectivity, and rapid advances in AI technologies. It discusses how AI is enhancing customer support, improving security through fraud detection algorithms, and enhancing credit scoring accuracy through machine learning. The value creation potential of AI in banking includes unlocking up to $1 trillion of incremental value annually through personalized services, cost reduction via automation, and uncovering new opportunities. Additionally, it provides examples of AI applications in banking, including personalization, automation, and insight extraction, showcasing how AI is transforming the industry. Despite these advancements, the article acknowledges challenges such as a lack of clear strategy, legacy systems, and fragmented data assets and proposes solutions like promoting a growth mindset and responsible AI deployment. It also addresses current issues in the banking sector, such as cyberattacks, voice cloning, and fraud, emphasizing the importance of AI in addressing these challenges. Overall, the article highlights the transformative potential of AI in finance and banking, urging banks to embrace an AI-first mindset for sustained growth and innovation.

The Role of Artificial Intelligence in Formal and Informal Education for Students

Abstract: Artificial Intelligence (AI) is transforming the field of online learning by providing personalized and efficient learning experiences for students. AI-powered online learning platforms can analyze data on students’ learning styles, preferences, progress, and performance, and use this information to tailor the curriculum, content, feedback, and support for each student. AI can also enhance the engagement and interaction of online learners by using chat bots, virtual assistants, and intelligent content recommendations. According to a report by eLearning Industry 1 , AI is expected to grow by 48% in the eLearning market by 2024, reaching a value of $3.68 billion. Some of the factors driving this growth are the increasing demand for personalized learning, the availability of big data and cloud computing, and the advancement of AI technologies. However, there are also some challenges and limitations that need to be addressed, such as the ethical, legal, and social implications of AI, the quality and reliability of AI systems, and the need for human oversight and intervention. Artificial Intelligence (AI) has the potential to revolutionize the way we teach and learn, making education more personalized, efficient, and effective. In this paper, we explore the role of AI in both formal and informal education for students. The fundamental goal of AI research includes the representation of knowledge, reasoning, planning, learning and processing, and the capability to use objects [2].We discuss the benefits and challenges of implementing AI in education and provide examples of how AI is being used in education today

PEMANFAATAN ESTIMASI POSE GERAK PADA PENARI TRANCE DALAM RITUAL ADAT NGALAKSA

This research aims detected the motions poses of trance dancers as a computer vision model in the trance dancers performing arts. The annual ceremonies of the Ngalaksa ritual and the Tarawangsa art as cultural traditions in Rancakalong Tourism Village, located in Sumedang, West Java. This ritual serves as a reverent to the Rice Goddess, with the farmers engaging in ceremonial processions and traditional Ngalaksa rites. Accompanied by the melodic strains of Tarawangsa music, participants dancing in rhythmic dance, some even entering trance states. On the other hand, the advancement of artificial intelligence technology is rapid. This research used machine learning for detecting the motion poses of dancers experiencing trance. The research endeavors to discern motion poses within trance dancers during Ngalaksa rituals for understanding the dynamics motion of traditional Ngalaksa rituals. Machine learning methods applied particularly computer vision utilizing GluonCV model. The study analyzes a dataset comprising motion poses of dancers in Rancakalong Village. Data gathering involves firsthand observation, field participation, and in-depth interviews with informans, including Saehu, dancers and Arak-Arakan participants. The research findings show the accuracy of pose estimation within the Ngalaksa ritual, though refinement of detection models for both trance and non-trance dancers warrants further research.

ANALYSIS OF ARTIFICIAL INTELLIGENCE BASED SYSTEMS FOR AUTOMATED GENERATION OF DIGITAL CONTENT

This paper is aimed at analyzing the contemporary challenges surrounding the integration of generative models API of artificial intelligence (AI) into a unified information system to facilitate automated generation of digital content. Against the backdrop of rapid advancements in AI technologies and the escalating demand for diverse and personalized digital content, the integration of API-based generative models emerges as a pivotal engine for progress in this domain. The findings of this research validate the significance of incorporating API-based generative AI models into a unified system, serving as a fundamental stride towards automating the process of digital content creation to meet contemporary market demands. Moreover, it underscores the potential of such integration in fostering the development of personalized and innovative solutions for end-users. The further effort of our research team will be aimed at the implementation of this concept and further exploration of its applicability across diverse domains.

Autonomous Vehicle Technology and Technology Acceptance: The Role of Technological Readiness on Consumers' Attitudes Towards Driverless Cars and Intention to Use in the Future

Autonomous (driverless) cars, which have entered the automotive industry with the developments in automotive and the advancement of artificial intelligence technologies, are rapidly finding a place in the marketing field. At this point, there are factors affecting consumers' concerns and willingness to use autonomous vehicles. In order to discover these factors, the readiness of consumers and the aspects in which they are ready for this technology are issues that need to be investigated. As a result of this situation, consumers' readiness to use autonomous vehicles, their attitudes toward using them, and their intentions to use them in the future are essential. This study aims to reveal the factors affecting consumers' attitudes and intentions towards using autonomous cars. Research data was collected via an online survey method. The convenience sampling method was used in the research. The research model was tested by structural equation modeling using Smart PLS. As a result of the research, it was found that discomfort and distrust dimensions significantly and negatively affected consumers' attitudes towards usage. It was found that the dimensions of optimism, innovativeness, and anthropomorphism significantly and positively affected consumers' attitudes toward use, and users' attitudes towards use significantly and positively affected their intention to use. The research results show that brands that put autonomous cars on the market should give importance to improvements in the dimensions of optimism, innovation, and anthropomorphism and should make improvements that will eliminate consumers' discomfort and insecurity.

Overview of State Estimation Methods and Research Progress of Power Lithium Batteries for Electric Vehicles

This paper presents a comprehensive research framework for state estimation of power lithium batteries by summarizing both model-based and non-model-based approaches. It provides an in-depth analysis of various methods, supported by experimental results. In the first part, the paper surveys model-based state estimation techniques, including the ECM (Equivalent Circuit Model), electrochemical models, and data-driven models. It systematically explains the principles behind these models and their respective applications. Additionally, the study delves into model-independent state estimation, emphasizing the utilization of machine learning algorithms in battery state estimation and the advancements in artificial intelligence technology. The potential of hybrid methods and intelligent algorithms in state estimation is also explored, highlighting possible future directions. The findings indicate that while model-based methods can attain high estimation accuracy in specific scenarios, they are constrained by model complexity and parameter uncertainty. The application of fusion method and intelligent algorithm further improves the performance of state estimation and provides strong support for real-time monitoring and prediction of power lithium batteries.

The emotional impact of generative AI: negative emotions and perception of threat

ABSTRACT Generative Artificial Intelligence (AI) is a rapidly expanding field that aims to develop machines capable of performing tasks that were previously considered unique to humans, such as learning, reasoning, problem-solving, and decision-making. The recent release of several tools based on AI (e.g. ChatGPT) has sparked debates on the potential of this technology and garnered widespread attention in the mainstream media. Using a socio-psychological approach, in three studies (total N = 410), we demonstrate that when faced with Generative AI’s ability to reproduce the complexity of human cognitive capabilities, participants reported significantly higher negative emotions than those in the control group. In turn, negative emotions elicited by a specific type of AI (e.g. generative AI) were associated to the perception of threat extended to AI technologies as a whole, understood as a threat to various aspects of human life, including jobs, resources, identity, uniqueness, and value. Our findings emphasise the importance of considering emotional and societal impacts when developing and deploying advanced AI technologies and implementing responsible guidelines to minimise adverse effects. As AI technology advances, addressing public concerns and regulating its usage is crucial for the benefit of society. To achieve this goal, collaboration between experts, policymakers, and the public is necessary.

Impact of Artificial Intelligence on New Global Order: A Nepalese Security Perspective

The novelty of Artificial Intelligence (AI) and nascent geo-tech interests of powerful countries have largely influenced foreign policy, while ‘techno-geopolitics’ and the emergence of the ‘AI world order’ have constantly challenged the world order milieu. The changing global power dynamics, including the escalating Russia-Ukraine war, the Israel-Palestine conflict and China’s rising clout in tech and diplomatic spheres have induced specific geo-political challenges to the US-led global order. In this context, this research primarily unfolds whether the remaking of new global order can fundamentally be signified by the end of bipolar or unipolar world order, while the advancement of AI technology and geo-tech interests of tech powers have contributed to a remaking of new global order. As the powerful countries have fundamentally concentrated on marshaling AI in foreign policy, both AI and foreign policy have been closely interlinked. This research aims to explore the impact of AI on new global order and corresponding security concerns, particularly Nepalese security concerns. Since both AI and the new global order are relatively vast fields, this research focuses on tech foreign policy that is directly linked with the balance of power and the corresponding international order. This study adopts an analytical descriptive research method. It relates AI ethics and global tech concerns, considering the global need, beginning with the notion of multilateral tech diplomacy, and inquiring whether the tech foreign policy is truly functional. Despite varying challenges to the new global order, rational ‘geopolitical balancing’ and techno-economic cooperation in ‘better-functioning relations’ with immediate neighbors and other superpowers drive Nepal’s security architecture.

Automatic identification of stone-handling behaviour in Japanese macaques using LabGym artificial intelligence.

The latest advances in artificial intelligence technology have opened doors to the video analysis of complex behaviours. In light of this, ethologists are actively exploring the potential of these innovations to streamline the time-intensive behavioural analysis process using video data. Several tools have been developed for this purpose in primatology in the past decade. Nonetheless, each tool grapples with technical constraints. To address these limitations, we have established a comprehensive protocol designed to harness the capabilities of a cutting-edge artificial intelligence-assisted software, LabGym. The primary objective of this study was to evaluate the suitability of LabGym for the analysis of primate behaviour, focusing on Japanese macaques as our model subjects. First, we developed a model that accurately detects Japanese macaques, allowing us to analyse their actions using LabGym. Our behavioural analysis model succeeded in recognising stone-handling-like behaviours on video. However, the absence of quantitative data within the specified time frame limits the ability of our study to draw definitive conclusions regarding the quality of the behavioural analysis. Nevertheless, to the best of our knowledge, this study represents the first instance of applying the LabGym tool specifically for the analysis of primate behaviours, with our model focusing on the automated recognition and categorisation of specific behaviours in Japanese macaques. It lays the groundwork for future research in this promising field to complexify our model using the latest version of LabGym and associated tools, such as multi-class detection and interactive behaviour analysis.

CMNet: deep learning model for colon polyp segmentation based on dual-branch structure.

Colon cancer is one of the top three diseases in gastrointestinal cancers, and colon polyps are an important trigger of colon cancer. Early diagnosis and removal of colon polyps can avoid the incidence of colon cancer. Currently, colon polyp removal surgery is mainly based on artificial-intelligence (AI) colonoscopy, supplemented by deep-learning technology to help doctors remove colon polyps. With the development of deep learning, the use of advanced AI technology to assist in medical diagnosis has become mainstream and can maximize the doctor's diagnostic time and help doctors to better formulate medical plans. We propose a deep-learning model for segmenting colon polyps. The model adopts a dual-branch structure, combines a convolutional neural network (CNN) with a transformer, and replaces ordinary convolution with deeply separable convolution based on ResNet; a stripe pooling module is introduced to obtain more effective information. The aggregated attention module (AAM) is proposed for high-dimensional semantic information, which effectively combines two different structures for the high-dimensional information fusion problem. Deep supervision and multi-scale training are added in the model training process to enhance the learning effect and generalization performance of the model. The experimental results show that the proposed dual-branch structure is significantly better than the single-branch structure, and the model using the AAM has a significant performance improvement over the model not using the AAM. Our model leads 1.1% and 1.5% in mIoU and mDice, respectively, when compared with state-of-the-art models in a fivefold cross-validation on the Kvasir-SEG dataset. We propose and validate a deep learning model for segmenting colon polyps, using a dual-branch network structure. Our results demonstrate the feasibility of complementing traditional CNNs and transformer with each other. And we verified the feasibility of fusing different structures on high-dimensional semantics and successfully retained the high-dimensional information of different structures effectively.