Advances In Artificial Intelligence Technology Research Articles

Empowering Diversity in Education: A Web-Based Tool for Real-Time Sign Language Detection

People worldwide have severe hearing loss and there is little to no technological involvement to empower diversity and inclusion in the education system. This study aims to provide an understanding of the needs of the community of such individuals and a possible solution to the problem using current advances in artificial intelligence technologies. In particular, we will present a solution to the problem through a real-time sign language detection web application. First, the structural and functional aspects of the application are described, followed by the software architecture needs. The technological aspect of the application is entirely defined by expressing all the significant implementations of each programming language contained behind the software diagrams and architecture. This study will provide the latest technologies in the market combined with a well-planned and developed image detection application that is scalable, portable, maintainable, and efficient in empowering diversity in education. This technologically up-to-date application is delivered as a full-stack web application. The application produces results that waver between 85%-95% quality off detection; it consists of a bi-partite backend, one for creating and compiling models, namely Python, TensorFlow, and Jupyter Notebooks, and the other part held by Node.JS to power the front-end built with React and TensorFlow's JS. This study presents a fully developed web-based application that has the potential to impact and improve diversity in education significantly.

The role of artificial intelligence in revolutionizing personalized medicine: A comprehensive review of techniques and applications

This comprehensive review explores the transformative role of Artificial Intelligence (AI) in revolutionizing personalized medicine, focusing on techniques, applications, and future directions. Personalized medicine, which tailors medical interventions to individual characteristics, has significantly advanced through the integration of AI technologies. AI's capabilities in data integration, genomic and molecular data analysis, predictive modeling, and personalized treatment development are highlighted. The review also addresses the challenges and limitations of AI in personalized medicine, such as data privacy, bias, and the need for seamless clinical integration. Furthermore, it discusses future trends, including advances in AI technologies, the integration of multi-omics data, and the importance of ethical and regulatory considerations. The historical evolution of personalized medicine is traced, emphasizing key milestones that have led to current innovations. This review underscores AI's pivotal role in enhancing the precision and effectiveness of personalized healthcare, offering insights into how AI-driven approaches can shape the future of medicine.

Potential strength and weakness of artificial intelligence integration in emergency radiology: a review of diagnostic utilizations and applications in patient care optimization.

Artificial intelligence (AI) and its recent increasing healthcare integration has created both new opportunities and challenges in the practice of radiology and medical imaging. Recent advancements in AI technology have allowed for more workplace efficiency, higher diagnostic accuracy, and overall improvements in patient care. Limitations of AI such as data imbalances, the unclear nature of AI algorithms, and the challenges in detecting certain diseases make it difficult for its widespread adoption. This review article presents cases involving the use of AI models to diagnose intracranial hemorrhage, spinal fractures, and rib fractures, while discussing how certain factors like, type, location, size, presence of artifacts, calcification, and post-surgical changes, affect AI model performance and accuracy. While the use of artificial intelligence has the potential to improve the practice of emergency radiology, it is important to address its limitations to maximize its advantages while ensuring the safety of patients overall.

The Role of AI in Enhancing Marketing Communication: Implications for Policy and Development in Indonesian Higher Education

This research delves into the transformative impact of Artificial Intelligence (AI) on marketing communication within the Indonesian higher education sector. By adopting advanced AI technologies like machine learning algorithms, universities aim to enhance customer engagement, understand student behavior, and personalize marketing strategies. The study combines quantitative data, showcasing the extent of AI adoption and its outcomes, with qualitative insights to highlight the effectiveness of AI in improving key marketing metrics. Post-AI integration, there were significant increases in student inquiries, application rates, and enrollment numbers, underscoring the tangible benefits of AI-driven marketing strategies. The findings emphasize the strategic shift towards data-driven decision-making and personalized engagement, positioning Indonesian universities at the forefront of innovative marketing practices. This research contributes valuable insights for academia, industry practitioners, and policymakers looking to leverage AI for strategic marketing initiatives in higher education.

Research on the Application of Machine Learning in Cancer Prediction and Diagnosis

Due to the continuous development and advancement of artificial intelligence technology, the application of machine learning in the medical field, particularly in cancer treatment and prediction, is becoming increasingly widespread and profound. By utilizing machine learning algorithms, healthcare professionals can analyze large volumes of medical data more accurately through specific models. This paper discusses the application of machine learning in cancer diagnosis and treatment, with special attention to prediction, diagnosis and treatment. By analyzing a large number of clinical and genomic data, machine learning, which provides support for early diagnosis, reveals the complex pattern of cancer development. Deep learning technology can help doctors diagnose and predict cancer more accurately in medical image analysis and genome data analysis. Ensemble learning methods such as the random forest model improve the accuracy of prediction. It also introduces semi supervised learning, which provides a new perspective for cancer prediction: enhancing model training by using unlabeled data. The paper also introduces the application of cox-net and survival support vector machine in survival analysis.

Blockchain and Artificial Intelligence Non-Formal Education System (BANFES)

The resurgence of the Taliban in Afghanistan has significantly exacerbated educational challenges for marginalized women and girls, deepening gender disparities and impeding socio-economic development. Addressing these issues, this article introduces the Blockchain and Artificial Intelligence Non-Formal Education System (BANFES), an innovative educational solution specifically designed for Afghan girls deprived of formal schooling. BANFES leverages advanced artificial intelligence technologies, including personalized data analysis, to provide customized learning experiences. Additionally, blockchain technology ensures secure record management and data integrity, facilitating a decentralized educational ecosystem where various nodes offer hybrid learning methodologies without intermediaries. This system not only adapts to individual learning speeds and styles to enhance engagement and outcomes but also employs an independent assessment mechanism to evaluate learners. Such evaluations promote transparency and maintain the quality and reputation of educational contributions within the network. The BANFES initiative also addresses implementation challenges, including local distrust and integration with existing educational structures, providing a robust model to overcome barriers to education. Furthermore, the paper explores the scalability of BANFES, proposing its application as a global strategy for non-formal education systems facing similar geopolitical and infrastructural challenges. By creating a secure, flexible, and learner-focused environment, BANFES aims to empower Afghan women and girls with essential skills for personal and professional growth, thus fostering socioeconomic advancement within their communities and setting a new standard for informal education worldwide.

Fairness mechanism and stackelberg strategy for multi-agent systems: A case study of legends of the three kingdoms

Alongside the rapid advancement of artificial intelligence technologies, the complexity of society continues to escalate, and the design of multi-agent system rules becomes increasingly crucial, optimize participant engagement and addressing the issue of fairness and equilibrium win rates. The card game Legends of the Three Kingdoms (LTK), as a complex multi-agent system, represents an important abstraction of real-world scenarios, requiring the implementation of proper incentive mechanisms. Based on the mechanism and Stackelberg, this paper optimizes player strategies and system fairness. Firstly, we constructed a multi-agent Stackelberg game model for team battles. Then, we analyzed the impact of decision-making factors on players. Subsequently, we defined three kinds of fairness and improved the incentive mechanism. The results indicate that our mechanism optimizes participants' game behaviors, enhancing fairness among team players. Win rates were improved from 56.2% VS 43.8% to 51.4% VS 48.6%. With three different fairness measures, the win percentage fairness increased by 72 %, and the first elimination fairness increased by about 79 %. Our research provides a reference for understanding and analyzing complex computational models and facilitates the resolution of various resource allocation and system design issues.

Will ChatGPT be Useful for Korean Neurologists in Clinical Practice?

Background: Since the emergence of artificial intelligence (AI), such as ChatGPT, there has been extensive research on its utility in the medical field. This study evaluates the potential of utilizing the ChatGPT AI platform to provide specialized advice to Korean neurologists.Methods: Through a multifaceted approach, it assesses both the knowledge and practical clinical utility using neurology training evaluation questions and clinical case reports.Results: The accuracy of ChatGPT in answering medical exam questions (45.1%) is comparable to or slightly lower than previous studies. The study also examines performance of ChatGPT in providing advice based on clinical case reports, finding that while it can offer suitable diagnostic suggestions and propose relevant tests in some cases (8/12), its overall diagnostic accuracy is limited (2/12). Limitations include the platform’s text-based nature, which hinders the interpretation of image-based questions, and the uneven distribution of case reports across subfields.Conclusions: Despite these limitations, ongoing advancements in AI technology offer prospects for improved performance and expanded applications in the medical field, warranting continued research and monitoring of AI’s role in medicine.

The practical applications of federated learning across various domains

With the advancement of artificial intelligence technology, a vast amount of data is transmitted during the model training process, significantly increasing the risk of data leakage. In an era where data privacy is highly valued, protecting data from leakage has become an urgent issue. Federated Learning (FL) has thus been proposed and applied across various fields. This paper presents the applications of FL in five key areas: healthcare, urban transportation, computer vision, Industrial Internet of Things (IIoT), and 5G networks. This paper discusses the feasibility of implementing FL for privacy protection in the aforementioned five real-world application scenarios and analyzes its accuracy and effiency. Additionally, it compares the FL framework with traditional frameworks, exploring the improvements FL has made in terms of privacy protection and performance, as well as the existing shortcomings of the FL framework. Further discussions are provided on potential future improvements. Moreover, this paper offers an outlook on current research trends and the developmental prospects in this research field.

Robotic technology in Autism intervention: Design and application of the CARS cat-like robot

This study introduces a novel robotic system designed to aid individuals with Autism Spectrum Disorder (ASD) in enhancing their social communication and interaction skills. Incorporating advanced artificial intelligence, sensors, and image recognition technologies, the robot simulates human social interactions and provides tailored feedback, aiming to improve the quality of life for individuals with ASD. Through a detailed examination of hardware design, software functionalities, and data processing mechanisms, this paper evaluates the system's practical applications and identifies ongoing challenges and potential improvements. The findings suggest that while the robotic system significantly enhances social interaction and learning for autistic individuals, challenges such as technological adaptability, cost, and physical design require further innovation. This research contributes to the field of assistive technologies by offering insights into how robotic systems can be optimized to meet the unique needs of the autism community, thus paving the way for more effective therapeutic tools.

INTEGRATION OF ARTIFICIAL INTELLIGENCE IN HOTEL SERVICES: TRENDS AND DEVELOPMENTS (THE ISRAELI CASE)

The integration of artificial intelligence in hotel services is an important and rapidly developing field. The article "Integration of Artificial Intelligence in Hotel Services: Trends and Developments" explores the current possibilities and trends in utilizing advanced artificial intelligence technologies in the hotel industry. The article discusses the use of smart algorithms for data analysis, the implementation of robots and smart technologies to enhance guest experiences, the utilization of support systems and personalized self-service, and the improvement of management and operational processes in hotels . It presents examples and research from the industry that demonstrate the benefits of leveraging AI and machine learning in hotel services and offers scenarios and tools for successful implementation of this advanced technology in the field. The article provides an in-depth and up-to-date insight into the use of artificial intelligence in the hotel industry and offers ideas and recommendations for experts and businesses in the field to effectively implement this technology in hotel operations.

Overview of Pest Detection and Recognition Algorithms

Detecting and recognizing pests are paramount for ensuring the healthy growth of crops, maintaining ecological balance, and enhancing food production. With the advancement of artificial intelligence technologies, traditional pest detection and recognition algorithms based on manually selected pest features have gradually been substituted by deep learning-based algorithms. In this review paper, we first introduce the primary neural network architectures and evaluation metrics in the field of pest detection and pest recognition. Subsequently, we summarize widely used public datasets for pest detection and recognition. Following this, we present various pest detection and recognition algorithms proposed in recent years, providing detailed descriptions of each algorithm and their respective performance metrics. Finally, we outline the challenges that current deep learning-based pest detection and recognition algorithms encounter and propose future research directions for related algorithms.

Leveraging Copernicus for Ice Block Detection: A Step Towards Safer Arctic Navigation

Background The Arctic is becoming more accessible due to ice melting, posing challenges for navigation due to floating ice blocks. Detecting these ice blocks is crucial for ensuring the safety of vessels navigating these waters. Methods This study introduces a novel method for detecting ice blocks in the Arctic using Artificial Intelligence (AI) and Synthetic Aperture Radar (SAR) satellite images from the Sentinel-1 mission of the Copernicus program. Our approach combines state-of-the-art image segmentation and deep learning techniques. We utilize a Convolutional Neural Network (CNN) classifier to accurately locate ice blocks within the images and retrieve their geographic coordinates. The method's performance is validated using precision, recall, and F1-score metrics. Results Our CNN classifier demonstrates robust performance in detecting ice blocks, validated through precision, recall, and F1-score metrics. The practical application of our technology was showcased in the AI-ARC project’s Arctic demo, receiving positive feedback from coast guards across various European countries. The system provides near-real-time alerts about detected ice blocks, allowing for timely route adjustments and reducing collision risks. Conclusions The developed system significantly contributes to Arctic navigation safety by providing accurate and timely detection of ice blocks. This work underscores the transformative potential of AI in environmental monitoring and maritime safety. Future refinements will be based on user feedback and advancements in AI technology, enhancing the system's effectiveness and reliability.

ChatGPT Over My Friends: Japanese English-as-a-Foreign-Language Learners’ Preferences for Editing and Proofreading Strategies

This study contrasts 33 Japanese English-as-a-foreign-language learners’ experiences using writing groups and artificial intelligence technology (ChatGPT-3.5) for editing and proofreading academic writing assignments. Recent advancements in artificial intelligence technology have significantly influenced academic writing, teaching and learning, prompting researchers to explore the practical application of these technologies in educational settings. In this study, participants edited and proofread their writing in class by using a writing group and experimenting with ChatGPT. After each activity, participants answered a questionnaire about their experiences. In the final questionnaire, participants compared their experiences of each activity and reported on which one they preferred. The results indicate that students mostly prefer using artificial intelligence technology for editing and proofreading. However, they acknowledge some value in writing groups. They assert that technology provides effective feedback, improving clarity and cohesion in their writing. Additionally, the findings reveal preferences for specific prompts to enhance their writing. Overall, this research demonstrates how teachers can use artificial intelligence in language classrooms to improve writing and practices, while emphasizing the importance of ethical implementation.

CLIP2TF:Multimodal video–text retrieval for adolescent education

With the rapid advancement of artificial intelligence technology, particularly within the sphere of adolescent education, a continual emergence of new challenges and opportunities is observed. The current educational system increasingly requires the automation of teaching activities detection and evaluation, offering fresh perspectives for enhancing the quality of adolescent education. Although large-scale models are receiving significant attention in educational research, their high demand for computational resources and limitations in specific applications constrain their widespread use in analyzing educational video content, especially when handling multimodal data. Current multimodal contrastive learning methods, which integrate video, audio, and text information, have achieved certain successes in video–text retrieval tasks. However, these methods typically employ simpler weighted fusion strategies and fail to avoid noise and information redundancy. Therefore, our study proposes a novel network framework, CLIP2TF, which includes an efficient audio–visual fusion encoder. It aims to dynamically interact and integrate visual and audio features, further enhancing the visual features that may be missing or insufficient in specific teaching scenarios while effectively reducing redundant information transfer during the modality fusion process. Through ablation experiments on the MSRVTT and MSVD datasets, we first demonstrate the effectiveness of CLIP2TF in video–text retrieval tasks. Subsequent tests on teaching video datasets further proves the applicability of the proposed method. This research not only showcases the potential of artificial intelligence in the automated assessment of teaching quality but also provides new directions for research in related fields studies.

Innovative research on AI-assisted teaching models for college English listening and speaking courses

This paper explores the innovative application of artificial intelligence (AI) in the construction of teaching models for college English listening and speaking courses. By leveraging advanced AI technologies, educators can enhance the effectiveness of language instruction and provide personalized learning experiences. This study examines the theoretical foundations, practical implementations, and the impact of AI-assisted teaching on student engagement and performance. Through comprehensive analysis and discussion, we highlight the potential of AI to transform traditional language education, address challenges, and improve learning outcomes. The findings suggest that integrating AI into college English courses offers significant advantages in terms of adaptability, interactivity, and efficiency, paving the way for future educational innovations.

Redefining Enterprise Data Management with AI-Powered Automation

In today's rapidly evolving digital landscape, the volume of enterprise data has surged exponentially, posing significant challenges in effective data management. Traditional data management techniques are becoming increasingly inadequate to handle the complexity and scale of modern enterprise data. This paper presents an innovative approach to revolutionize enterprise data management through AI-powered automation, a solution that enhances accuracy, efficiency, and decision-making processes within organizations. By leveraging advanced artificial intelligence technologies, such as machine learning, natural language processing, and predictive analytics, our proposed system aims to streamline data processing, ensure data quality, and provide real-time insights. This paper will discuss the limitations of existing data management systems, illustrate the novel methodologies integrated within our AI-driven framework, and demonstrate the system's efficacy through empirical results. The transformative potential of AI in automating data management processes not only addresses current challenges but also sets a foundation for future advancements in the field. As enterprises strive to maintain a competitive edge, the adoption of AI-powered automation for data management is not merely an option but a necessity for sustaining growth and innovation.

Artificial Intelligence and Its Challenges To Elections In Indonesia: A Legal Analysis

The improper utilization of AI technology poses difficulties to democracy, particularly the growing threat of unjust elections, exemplified by the deployment of bot accounts and deep fakes during electoral processes. Hence, it is crucial to build a strong and comprehensive framework to regulate the utilization of AI technology in Indonesia's political process. This article analyzes four main topics: (a) the advancement of AI technology and its connection to elections; (b) the influence of AI technology on election principles; (c) the pressing need for regulating AI in elections; and (d) the possibilities and difficulties of regulating AI technology within Indonesia's legal framework. The paper employs doctrinal legal research to examine the necessity of regulating AI technology in the context of conducting elections, taking into account the constitutional framework, established principles, and democratic election norms. The result shows that irresponsible use of AI technology remains a menace to democratic election ideals, and Indonesia must establish adequate legal mechanisms to tackle the problems stemming from the improper use of AI technology in the political process. The regulation of AI technology can be initiated by introducing a bill specifically focused on artificial intelligence (AI). This process should also involve the synchronization and harmonization of election rules, including election laws, laws governing the election of governors, regents, and mayors, laws concerning political parties, and other implementing regulations such as those established by the General Election Commission and the Election Supervisory Board.

Applying ChatGPT to writing scientific articles on the use of telemedicine: Opportunities and limitations

In the rapidly evolving world of technology, artificial intelligence (AI) has significantly integrated into various aspects of our lives, including health-care, education, finance, transportation, and entertainment. Notably, AI has also impacted the writing of textual works such as scientific papers, professional opinions, and educational texts. This study investigates the application of OpenAI’s ChatGPT language model in writing scientific articles on telemedicine, specifically in the areas of cardiology, oncology, and remote medical examination. The study uses ChatGPT versions 3.5 and 4 to create articles using three different prompts. The created articles were evaluated based on the reliability of the cited literature references, the impact factor (IF) of the journal in which the sources were published, and the relevance of the sources. The sources were divided into three categories: reliable, semi-reliable, and completely fictitious. The results demonstrate that ChatGPT can produce semantically coherent and error-free texts indistinguishable from human-written texts. However, the reliability of literary references varies significantly. ChatGPT 4, benefitting from its larger training dataset, generates a higher percentage of reliable sources compared to ChatGPT 3.5. The IF analysis indicates the prevalence of high-impact journals among reliable sources, which emphasizes the effectiveness of the model in selecting quality references. The study highlights the need for caution when using AI to write scientific articles due to the potential for biased, unverified, and inaccurate information. It is important to critically evaluate and vet AI-generated content. In addition, the study emphasizes that the correct use of AI and thoughtful drafting of prompts can improve the efficiency and quality of scientific papers. Future advancements in AI technology are expected to further minimize errors and biases.

Reading Strategies in the AI Age: Enhancing Comprehension and Engagement with Advanced Technologies

The advent of artificial intelligence (AI) has transformed various facets of daily life, including how individuals approach reading and comprehension. This article explores the evolving landscape of reading strategies in the AI age, evaluating the impact of AI tools and applications on reading habits, comprehension techniques, and educational outcomes. With the rapid advancements in AI technologies, traditional reading methods are being supplemented, and in some cases, entirely replaced by AI-driven solutions. The discussion emphasizes AI-driven recommendations, which tailor reading materials to individual preferences and comprehension levels, thereby fostering more personalized and efficient learning experiences. Additionally, this article delves into the integration of AI in educational settings, where adaptive learning systems and intelligent tutoring platforms provide customized guidance and real-time feedback, significantly enhancing student engagement and achievement. However, the incorporation of AI in reading and education also presents potential challenges, including issues of data privacy, the digital divide, and the ethical implications of relying heavily on technology for cognitive development. By critically examining these multifaceted impacts, this article aims to provide a comprehensive understanding of how AI is reshaping reading strategies and to highlight the considerations necessary for harnessing its potential responsibly. Keywords: Artificial Intelligence, Reading Strategies, Comprehension, Personalized Learning, Educational Technology Proceedings Citation Format Ademola, E.O. (2024): Reading Strategies in the AI Age: Enhancing Comprehension and Engagement with Advanced Technologies. Proceedings of the 38th iSTEAMS Multidisciplinary Bespoke Conference. 15th–19th July, 2024. University of Ghana, Accra, Ghana. Pp 105-124. dx.doi.org/10.22624/AIMS/ACCRABESPOKE2024P13