AI Learning Research Articles

Tour Sentiments Personalized Recommendations System Using AI and Deep Learning

In a world saturated with travel information overload and generic recommendations, "Tour Sentiments: A Personalized Travel Recommendation System Using AI " emerges as a groundbreaking initiative. This project seeks to transform travel planning by harnessing the capabilities of Artificial Intelligence and real-time google Maps with street view and duration of travel. The primary goal of 'Tour Sentiments' is to create a personalized travel recommendation system that adapts to the changing emotions and preferences of travelers. By employing real-time sentiment analysis using google gemini, the system aims to offer tailored suggestions that align closely with individuals' current feelings and desires, departing from conventional one-size-fits-all approaches. Beyond mere efficiency, the ultimate vision of 'Tour Sentiments' is to elevate the travel experience, infusing it with joy, discovery, and lasting memories. Through the amalgamation of AI and sentiment analysis, the project aspires to make global exploration more accessible and captivating, setting a new standard for personalized travel guidance and enriching journeys worldwide.

고등학교 『중국 문화』 AI 융합 교수-학습 방안 제안

This study proposed AI convergence Chinese cultural education through the creation of Chinese food culture and AI learning models. AI convergence Chinese culture education can directly and indirectly experience make learners learn Chinese culture using various media such as textbooks and Internet materials. First of all, AI-based platforms can be used to understand the concept of machine learning and to find out the characteristics of Chinese culture. Secondly the information learned on the SW education platform is reprocessed and applied in connection with other subjects based on the later period of Chinese culture. Thirdly the educational plan can evaluate the understanding of Chinese culture. It is expected that AI convergence Chinese cultural education can be used in various fields such as Chinese language subject content development and customized curriculum development in the future.

중학교 기술교육을 위한 인공지능 학습과정 시각화 중심 인공지능 교육프로그램 개발

This research develops a middle school AI education program that can understand the overall context of the AI learning process by visualizing the AI learning process using Orange3, an AI tool, and combines it with traditional technology education subjects. The development process followed the five-step process of analysis, design, development, implementation, and evaluation according to the ‘ADDIE model’. In the analysis stage, middle school AI education content standards, middle school technology and home-economics curriculum and content elements, domestic AI-education-related content, and learner level were analyzed. In the design stage, the AI education content and learners were selected, and five technology education subject learning topics that were suitable for organizing with the corresponding education content were selected. Then, learning goals were set, class schedules were organized, and teaching and learning methods were also selected. In the development stage, an AI education program centered on visualization of AI learning process was developed for total of 15 sessions, including learning topics, contents, goals, class sessions, teaching and learning methods, student understanding test sheets, teaching materials, and activity sheets. This program was revised after evaluating its validity by AI education experts with a master's degree or higher in AI education. In the implementation stage, a preliminary test for this AI education program was conducted with 14 first-year students at A middle school in Gyeonggi-do, and the results of student understanding test were also collected to determine effectiveness and modifications and supplements. In the evaluation stage, the results of the student understanding test collected above were analyzed to modify and supplement this AI education program.

Human intelligence and artificial intelligence and the challenges of biases in ai algorithms

This article acknowledges the profound transformations that Artificial Intelligence imposes on society. A descriptive-exploratory study aims to discuss algorithmic biases and understand their impacts on society. The article starts from the understanding of human intelligence and learning from a pluralistic perspective, based on the analysis of literary works and scientific articles. This approach provides a context in which AI and machine learning can be conceived from an innovation perspective for the common good. The critical analysis emphasizes the need for ethical approaches in the development of these systems. The topics discussed highlight the importance of a multidimensional approach in mitigating algorithmic biases. From data selection to audits and accountability, diversity of perspectives, both in datasets and development teams, is crucial. The implementation of continuous training and human supervision reflects a continuous commitment to transparency and fairness in artificial intelligence. These integrated strategies are essential for the ethical, transparent, and equitable development of AI. This holistic approach, involving diverse skills and people, continuous training, and vigilant oversight, is vital to ensure the ethical use of AI for the collective well-being.

IoT Based Smart Irrigation System using Artificial Intelligence

This proposed of the project introduces an innovative IoT-based AI-driven solution engineered to revolutionize water management in agricultural contexts, catering to both large-scale piped irrigation networks and micro-irrigation systems. Our proposed system leverages cutting-edge AI algorithms to forecast dynamic crop water requirements, seamlessly integrating real-time soil moisture data to optimize water utilization and amplify crop yields. By amalgamating AI-driven predictive analytics with instantaneous sensor feedback, our system facilitates proactive water resource management, curbing wastage and mitigating environmental repercussions. Moreover, the integration of Deep Learning-based pest detection augments the system's capabilities by safeguarding crops against potential threats, thus fostering sustainable agricultural practices. Additionally, our solution pioneers the incorporation of Deep Learning-based crop production for the detection of wild animals, thereby enabling timely alerts through buzzer sound notifications, thereby ensuring comprehensive crop protection. This amalgamation of IoT, AI, and Deep Learning technologies promises to significantly enhance agricultural sustainability, optimize resource utilization, and mitigate ecological impact, thus paving the way for a more resilient and productive agricultural ecosystem.

REVIEW ON DESKTOP ASSISTANT FOR VISUALLY IMPAIRED: MIME.AI

The study begins by examining the historical evolution of desktop assistants, highlighting key milestones and breakthroughs in assistive technology. It then delves into the core functionalities that make these desktop assistants valuable tools for the visually impaired. These functionalities include speech synthesis, screen reading, voice commands, and tactile feedback mechanisms. The review addresses the challenges and limitations associated with current desktop assistant technologies for the visually impaired. Mime.ai is a model which includes key aspects typically associated with desktop assistants for the visually impaired like Text-to-Speech technology, voice commands, AI and Machine Learning Integration, Web Accessibility and Compatibility with Other Assistive Technologies. It examines factors such as learnability, efficiency, memorability, errors, and user satisfaction, providing insights into the overall usability of the assistant. The review assesses seamless integration of the assistant with screen readers, braille displays, magnification software, and productivity tools. Finally, the review considers the impact of the desktop assistant on the daily lives of visually impaired users. It presents user feedback and testimonials regarding the utility, effectiveness, and overall satisfaction with the assistant, highlighting its potential to improve accessibility and productivity for this user group. Key Words: Natural Language Processing, Neural Network, Voice Commands, Text-to-Speech technology.

Green and sustainable AI research: an integrated thematic and topic modeling analysis

This investigation delves into Green AI and Sustainable AI literature through a dual-analytical approach, combining thematic analysis with BERTopic modeling to reveal both broad thematic clusters and nuanced emerging topics. It identifies three major thematic clusters: (1) Responsible AI for Sustainable Development, focusing on integrating sustainability and ethics within AI technologies; (2) Advancements in Green AI for Energy Optimization, centering on energy efficiency; and (3) Big Data-Driven Computational Advances, emphasizing AI’s influence on socio-economic and environmental aspects. Concurrently, BERTopic modeling uncovers five emerging topics: Ethical Eco-Intelligence, Sustainable Neural Computing, Ethical Healthcare Intelligence, AI Learning Quest, and Cognitive AI Innovation, indicating a trend toward embedding ethical and sustainability considerations into AI research. The study reveals novel intersections between Sustainable and Ethical AI and Green Computing, indicating significant research trends and identifying Ethical Healthcare Intelligence and AI Learning Quest as evolving areas within AI’s socio-economic and societal impacts. The study advocates for a unified approach to innovation in AI, promoting environmental sustainability and ethical integrity to foster responsible AI development. This aligns with the Sustainable Development Goals, emphasizing the need for ecological balance, societal welfare, and responsible innovation. This refined focus underscores the critical need for integrating ethical and environmental considerations into the AI development lifecycle, offering insights for future research directions and policy interventions.

Advances in Autonomous Robotics: Integrating AI and Machine Learning for Enhanced Automation and Control in Industrial Applications.

Advances in Autonomous Robotics: Integrating AI and Machine Learning for Enhanced Automation and Control in Industrial Applications.

AI and Machine Learning for Optimal Crop Yield Optimization in the USA

AI and Machine Learning for Optimal Crop Yield Optimization in the USA

Advancements and Challenges in Email Spam and Malware Filtering Utilizing AI and Machine Learning

Advancements and Challenges in Email Spam and Malware Filtering Utilizing AI and Machine Learning

Data Preprocessing Techniques for AI and Machine Learning Readiness: Scoping Review of Wearable Sensor Data in Cancer Care.

Wearable sensors are increasingly being explored in health care, including in cancer care, for their potential in continuously monitoring patients. Despite their growing adoption, significant challenges remain in the quality and consistency of data collected from wearable sensors. Moreover, preprocessing pipelines to clean, transform, normalize, and standardize raw data have not yet been fully optimized. This study aims to conduct a scoping review of preprocessing techniques used on raw wearable sensor data in cancer care, specifically focusing on methods implemented to ensure their readiness for artificial intelligence and machine learning (AI/ML) applications. We sought to understand the current landscape of approaches for handling issues, such as noise, missing values, normalization or standardization, and transformation, as well as techniques for extracting meaningful features from raw sensor outputs and converting them into usable formats for subsequent AI/ML analysis. We systematically searched IEEE Xplore, PubMed, Embase, and Scopus to identify potentially relevant studies for this review. The eligibility criteria included (1) mobile health and wearable sensor studies in cancer, (2) written and published in English, (3) published between January 2018 and December 2023, (4) full text available rather than abstracts, and (5) original studies published in peer-reviewed journals or conferences. The initial search yielded 2147 articles, of which 20 (0.93%) met the inclusion criteria. Three major categories of preprocessing techniques were identified: data transformation (used in 12/20, 60% of selected studies), data normalization and standardization (used in 8/20, 40% of the selected studies), and data cleaning (used in 8/20, 40% of the selected studies). Transformation methods aimed to convert raw data into more informative formats for analysis, such as by segmenting sensor streams or extracting statistical features. Normalization and standardization techniques usually normalize the range of features to improve comparability and model convergence. Cleaning methods focused on enhancing data reliability by handling artifacts like missing values, outliers, and inconsistencies. While wearable sensors are gaining traction in cancer care, realizing their full potential hinges on the ability to reliably translate raw outputs into high-quality data suitable for AI/ML applications. This review found that researchers are using various preprocessing techniques to address this challenge, but there remains a lack of standardized best practices. Our findings suggest a pressing need to develop and adopt uniform data quality and preprocessing workflows of wearable sensor data that can support the breadth of cancer research and varied patient populations. Given the diverse preprocessing techniques identified in the literature, there is an urgency for a framework that can guide researchers and clinicians in preparing wearable sensor data for AI/ML applications. For the scoping review as well as our research, we propose a general framework for preprocessing wearable sensor data, designed to be adaptable across different disease settings, moving beyond cancer care.

Introduction for the special issue of “Ideologies of AI and the consolidation of power”: Naming power

This introductory essay for the special issue of First Monday, “Ideologies of AI and the consolidation of power,” considers how power operates in AI and machine learning research and publication. Drawing on themes from the seven contributions to this special issue, we argue that what can and cannot be said inside of mainstream computer science publications appears to be constrained by the power, wealth, and ideology of a small cohort of industrialists. The result is that shaping discourse about the AI industry is itself a form of power that cannot be named inside of computer science. We argue that naming and grappling with this power, and the troubled history of core commitments behind the pursuit of general artificial intelligence, is necessary for the integrity of the field and the well-being of the people whose lives are impacted by AI.

Exploring Factors That Support Pre-service Teachers’ Engagement in Learning Artificial Intelligence

AbstractArtificial intelligence (AI) is becoming increasingly relevant, and students need to understand the concept. To design an effective AI program for schools, we need to find ways to expose students to AI knowledge, provide AI learning opportunities, and create engaging AI experiences. However, there is a lack of trained teachers who can facilitate students’ AI learning, so we need to focus on developing the capacity of pre-service teachers to teach AI. Since engagement is known to enhance learning, it is necessary to explore how pre-service teachers engage in learning AI. This study aimed to investigate pre-service teachers’ engagement with learning AI after a 4-week AI program at a university. Thirty-five participants took part in the study and reported their perception of engagement with learning AI on a 7-factor scale. The factors assessed in the survey included engagement (cognitive—critical thinking and creativity, behavioral, and social), attitude towards AI, anxiety towards AI, AI readiness, self-transcendent goals, and confidence in learning AI. We used a structural equation modeling approach to test the relationships in our hypothesized model using SmartPLS 4.0. The results of our study supported all our hypotheses, with attitude, anxiety, readiness, self-transcendent goals, and confidence being found to influence engagement. We discuss our findings and consider their implications for practice and policy.

Automotive intelligence: Unleashing the potential of AI beyond advance driver assisting system, a comprehensive review

Big data, AI, and machine learning have ushered in a transformative era in computer processing and application. AI's capacity to emulate human cognition spans a spectrum of industries, from rudimentary tasks to intricate decision-making processes. Projections indicate that within a short span, AI technology will bestow computers with human-like capabilities, rendering it indispensable. This advancement has substantially bolstered industry autonomy, fortifying security and safety through the integration of electronics, internet connectivity, advanced algorithms, and cutting-edge processing chips.In this context, Automated Guided Vehicles (AGVs) leverage AI-powered systems and sensors to optimize performance and proactively thwart potential failures. Going beyond Advanced Driver Assistance Systems (ADAS), this review scrutinizes the broader spectrum of AI applications. Specifically, it synthesizes pertinent studies encompassing AI's impact on car emissions, predictive maintenance, security, and the realm of connected vehicles.While AGVs can utilize AI algorithms to chart emission-efficient routes and engineer energy-conservative vehicles, AI, through diverse algorithms, proficiently anticipates component failures, substantially enhancing the cost-effectiveness of predictive maintenance and bolstering vehicle reliability. Moreover, AI integrated with sensor technology safeguards vehicles against a spectrum of threats. Neural networks and machine learning further automate security measures for both cargo and passengers. Concurrently, AI and machine learning fortify traffic control and enhance vehicle communication within connected vehicle frameworks, albeit necessitating fortifications in data security, encryption, and system efficiency.This survey paper comprehensively covers the expanse of automotive AI and machine learning applications, extending beyond ADAS. It underscores that the evolving mobility landscape necessitates a thorough evaluation of network architecture, connectivity, and performance metrics, encompassing AGV technology, networking protocols, and swarm dynamics. In this vein, the paper advocates for experts to address existing literature gaps and proactively enhance automotive systems.

AI-Based Customer Churn Prediction Model for Business Markets in the USA: Exploring the Use of AI and Machine Learning Technologies in Preventing Customer Churn

Understanding consumer churn is pivotal for companies in the USA to develop efficient strategies for consumer retention and reduce its negative effects on revenue and profitability. To start with, understanding client churn entails pinpointing the factors that contribute to it. This research paper delved into the application of machine learning algorithms such as Random Forests and Decision Trees for designing churn prediction models and exploring key factors that churn probabilities. The dataset used in this study was sourced from the prominent UCI repository of machine learning databases, preserved at the University of California, Irvine. This dataset provided extensive information on a total of 3333 clients, facilitating in-depth analysis and insights. Models performance evaluation comprised examining the model's efficiency using a confusion matrix. Random Forest seemed to be a relatively better performing model than Decision Tree for this specific classification task. In particular, Random Forest attained higher accuracy (96.25%), precision (91.49), Recall (83.49%), F-measure (0.87), and Phi coefficient (0.85). By deploying Random Forest and Decision Tree models, government companies can get an in-depth comprehension of the factors that lead to consumer churn. As a result, this information may enable them to tailor targeted retention strategies and interventions. By effectively retaining consumers, government organizations can maintain a stable customer base, leading to sustained revenue and economic growth.

Predictive modeling for breast cancer classification in the context of Bangladeshi patients by use of machine learning approach with explainable AI

Breast cancer has rapidly increased in prevalence in recent years, making it one of the leading causes of mortality worldwide. Among all cancers, it is by far the most common. Diagnosing this illness manually requires significant time and expertise. Since detecting breast cancer is a time-consuming process, preventing its further spread can be aided by creating machine-based forecasts. Machine learning and Explainable AI are crucial in classification as they not only provide accurate predictions but also offer insights into how the model arrives at its decisions, aiding in the understanding and trustworthiness of the classification results. In this study, we evaluate and compare the classification accuracy, precision, recall, and F1 scores of five different machine learning methods using a primary dataset (500 patients from Dhaka Medical College Hospital). Five different supervised machine learning techniques, including decision tree, random forest, logistic regression, naive bayes, and XGBoost, have been used to achieve optimal results on our dataset. Additionally, this study applied SHAP analysis to the XGBoost model to interpret the model’s predictions and understand the impact of each feature on the model’s output. We compared the accuracy with which several algorithms classified the data, as well as contrasted with other literature in this field. After final evaluation, this study found that XGBoost achieved the best model accuracy, which is 97%.

Harmonizing Innovation: A Journey through the History of Computational Music

The history of computational music traces a fascinating journey of innovation and creativity, intertwining disciplines such as computer science, mathematics, music theory, and psychology. From its inception in the mid-20th century to its current state of sophistication, computational music has evolved into a multifaceted field encompassing algorithmic composition, digital signal processing, machine learning, and interactive music systems. Pioneering works such as Roads' "The Computer Music Tutorial" and Biles' "GenJam" exemplify the early endeavors in digital sound synthesis and algorithmic composition. The roots of computational music extend back to the 1950s and 1960s with the pioneering efforts of Max Mathews, John Chowning, and Lejaren Hiller, laying the groundwork for future advancements. Technological breakthroughs in the 1970s and 1980s, including the advent of digital audio workstations and FM synthesis, further propelled the field forward. The 1990s and 2000s witnessed democratization and accessibility with the rise of affordable software tools and online collaboration platforms. Today, computational music continues to evolve with the emergence of deep learning and generative AI techniques, albeit accompanied by ethical considerations regarding copyright ownership and algorithmic bias. In conclusion, the history of computational music underscores the transformative power of technology in shaping musical expression and invites us to explore new frontiers where creativity knows no bounds.

THE PROGRESS IN THE RESEARCH OF MACHINE LEARNING IN SPORTS MEDICINE

To explore the prospects and challenges of applying artificial intelligence and its machine learning subfield in sports medicine, to drive knowledge innovation in this domain. Research Content includes Applications of machine learning in sports medicine: Clustering and classifying athlete data, developing predictive models to optimize training and prevent injuries, and providing interpretable decision support for medical professionals. Challenges of machine learning in sports medicine: Issues with data availability and quality, model interpretability and transparency, as well as the integration with existing workflows. In summary, the potential of AI and machine learning in sports medicine is immense, but to fully harness their transformative value, interdisciplinary collaboration, data sharing, rigorous validation, and the establishment of ethical guidelines are essential. Only through these collective efforts can the field optimize athlete training, prevent injuries, and drive overall innovation in sports medicine. KEYWORD: Machine Learning,Sports Medicine , Artificial intelligence ,Knowledge Representation, Decision Support

Advanced Techniques for Fraud Detection in Online Transactions: Leveraging AI and Machine Learning Approaches

Advanced Techniques for Fraud Detection in Online Transactions: Leveraging AI and Machine Learning Approaches

Abstract LB391: Deep learning AI predicts HRD and platinum response from histologic slides

Abstract Background: Cancers with homologous recombination deficiency (HRD) can benefit from platinum salts and PARP inhibitors. Standard diagnostic tests, including FDA-approved companion diagnostics, for detecting HRD require molecular profiling, which is not universally available with global testing rates lowest among minority, rural, and other underserved populations. Methods: We trained DeepHRD, a deep-learning platform for predicting HRD from hematoxylin and eosin (H&E)-stained histopathological slides, using primary breast (n=1,008) and ovarian (n=459) cancers from The Cancer Genome Atlas (TCGA). DeepHRD was compared to four standard HRD molecular tests using breast (n=349) and ovarian (n=141) cancers from multiple external and independent datasets, including clinical cohorts with platinum complete response, progression-free survival (PFS) and overall survival (OS) endpoints. Results: DeepHRD detected HRD from held-out H&E-stained breast cancer slides in TCGA with an AUC of 0.81 ([0.77-0.85]; 95% confidence interval). This performance was confirmed in two independent primary breast cancer cohorts (AUC=0.76; [0.71-0.82]). In an external platinum-treated metastatic breast cancer cohort, samples detected as HRD had a higher complete response (AUC=0.76; [0.54-0.93]) with 3.7-fold increase in median PFS (14.4 versus 3.9 months; p-value=0.0019) and hazard ratio (HR) of 0.45 (p=0.0047) after correcting for PAM50 molecular subtype and age at diagnosis. This deep-learning classifier outperformed four genomic HRD tests used in the clinic, including standard HRD score, BRCA1/2, 26-HR gene panel and single-base substitution signature 3 (SBS3). Multiresolution spatial mapping identified morphological features utilized by DeepHRD for detecting HRD, notably enriched for neoplastic and necrotic tissues, and a higher macrophage density. Through transfer learning to high-grade serous-ovarian cancer, DeepHRD-positive samples exhibited better overall survival after TCGA first-line (HR=0.46; p=0.030) and an external neoadjuvant (HR=0.49; p=0.015) platinum-treated cohorts. Conclusion: In summary, DeepHRD exhibits consistent hazard ratios ranging from 0.45 to 0.49 across the three clinical cohorts and captures 1.8- to 3.1-fold more HRD-positive breast and ovarian cancer patients. DeepHRD-positive breast cancer patients that received platinum exhibited better complete response and PFS. Similarly, DeepHRD-positive platinum-treated ovarian cancer patients had a better OS. DeepHRD’s ability to detect HRD from digital H&E slides provides an important precision oncology tool that can be utilized in resource-constrained and underserved areas where genomic testing is generally not existent. Citation Format: Erik N. Bergstrom, Ammal Abbasi, Marcos Diaz-Gay, Loïck Galland, Sylvain Ladoire, Scott M. Lippman, Ludmil B. Alexandrov. Deep learning AI predicts HRD and platinum response from histologic slides [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB391.