AI Learning Research Articles

Hydraulic modeling and real-time optimization of drilling fluids: A future perspective

The evolution of drilling operations in the oil and gas industry has highlighted the need for more efficient and adaptive management of drilling fluids, particularly in complex well environments. This paper presents a comprehensive review of hydraulic modeling and real-time optimization of drilling fluids, focusing on future perspectives and industry implications. Hydraulic modeling plays a crucial role in predicting pressure, fluid flow, and wellbore stability, while real-time optimization, powered by advanced sensors, AI, and machine learning, enables dynamic fluid management. The integration of these technologies allows for continuous monitoring and adjustment of fluid properties, improving efficiency, reducing downtime, and preventing formation damage. Future technological advancements, including digital twins and enhanced downhole sensors, are expected to improve well performance and cost-efficiency further. Strategic recommendations for industry adoption include investment in advanced technologies and further research into hydraulic modeling and real-time optimization for complex wells.

Optimizing talent management in creative industries: Theoretical insights into effective database utilization

This review paper explores the optimization of talent management in creative industries through the utilization of advanced database systems. Traditional talent management methods often lack the structure and objectivity provided by modern database-driven approaches, which leverage comprehensive talent profiles, advanced search algorithms, and robust analytics to enhance decision-making processes. Case studies from industry leaders like Pixar, Wieden+Kennedy, and Ubisoft illustrate the practical benefits of these technologies in recruitment, development, and overall talent optimization. The paper also discusses challenges such as data quality, privacy concerns, and resistance to change, providing practical recommendations for overcoming these obstacles. Creative industries can significantly improve their talent management practices by fostering a culture of data-driven decision-making and investing in comprehensive talent databases. Future research directions include the impact of AI and machine learning, integration of emerging technologies, and longitudinal studies on the effectiveness of database-driven approaches.

Automated System for Credibility Diagnosis of Forged Facial Images Using Ensemble Deep Learning and Explainable AI

Automated System for Credibility Diagnosis of Forged Facial Images Using Ensemble Deep Learning and Explainable AI

From Monitoring to Observability: A Paradigm Shift in IT Operations

This article explores the paradigm shift from traditional monitoring to observability in IT operations, driven by the increasing complexity of modern distributed systems, data overload, evolving cybersecurity threats, and the need for proactive problem-solving. Observability platforms, built on the four pillars of events, metrics, traces, and logs, provide comprehensive insights into system behavior and performance. Adopting observability practices offers numerous benefits, including automated discovery of system components, dependency mapping, topology visualization, correlative intelligence, faster root cause diagnosis, and more accurate anomaly detection through auto-baselining. Organizations that embrace observability report significant improvements in incident response times, system reliability, and overall operational efficiency. The article guides successfully transitioning to an observability-focused approach by embracing automation, fostering a culture of observability, implementing the right tools, and leveraging AI and machine learning for advanced analytics and insights.

Artificial Neural Network to Predict Curvature Light Shelf Design Related Daylighting Optimization on Office Spaces

Energy Optimization in building design field now has been revolutionized due to AI and machine learning applications. Leveraging daylight to reduce artificial lighting consumption holds promise for significant energy savings, yet the nonlinear nature of daylight patterns poses challenges in prediction and optimization. This study proposes a novel approach to automated light shelf design using machine learning algorithms, specifically artificial neural networks (ANNs) such as recurrent neural networks (RNN) by long short term memory (LSTM), to accelerate daylighting simulation and optimization processes. The methodology employed two distinct approaches: Firstly, we employed the theoretical-analytical approach to explore methods for utilizing machine learning in natural lighting and light shelf parameters. Second, the practical and applied method involved creating a predictive model for designing the light shelf using appropriate AI and ML techniques. This model is based on an office geometry model at the El Arish weather file in Egypt, four-dimensional training room models with three internal zones-oriented south. Rhinoceros and Grasshopper, two parametric simulation tools, are used to normalize and optimize light shelf parameters like geometry cross-section, curvature surface, width, height position, depth, tilt angle, and reflectance materials. Then, the Galapagos plug-in and Colibri2 are used for dataset creation and optimization. The results demonstrate that automated light shelf operation has a significant impact on internal daylighting quality. RNNs enable rapid prediction of optimization models, reducing time consumption in the early design phase. ML facilitates decision-making by generating evaluative criteria from user-selected design options. RNNs were classified as good and bad and used LSTM to enhance prediction accuracy for efficient illuminance values metric in zones 1 and 2. Challenges include increasing the simulation procedure's efficiency. The results of model accuracy reached 99%. Hence, future research should prioritize resolving the previously identified concerns. In conclusion, this study underscores the importance of ML-driven approaches in early design phases to optimize building energy consumption and pave the way for sustainable architectural practices.

Effects of students using different learning approaches for learning computational thinking and AI applications

Effects of students using different learning approaches for learning computational thinking and AI applications

Application of deep learning for rice leaf disease detection in the Mekong Delta

The Mekong River Delta, the largest rice-producing region in Vietnam with an annual output of over 25 million tons, plays a vital role in ensuring food security both within the country and globally. In recent years, it has undergone significant transformation in rice cultivation, which aims to support farmers here to plant rice more effectively. However, severe weather conditions and soil degradation have negatively impacted rice growth. Additionally, rice is highly susceptible to various diseases that must be identified and prevented promptly. As a result, leveraging technology such as AI and deep learning to diagnose rice diseases based on leaf symptoms is essential. This paper utilizes an image dataset of three common rice leaf diseases—leaf smut, brown spot, and bacterial leaf blight—and applies deep learning networks (MobileNet and ResNet) to evaluate and select the best model. A diagnostic program is then developed to detect these diseases. Experimental results show that the MobileNetV3-Small model (a variant of the MobileNet network) is the most optimal, offering fast training time, high accuracy, and acceptable levels of loss and error.

Finding the human in an era of machine intelligence: A flat ontological analysis of generative AI and language learning

Amid the optimism of generative Artificial Intelligence (gen-AI) in language education, there remains a weak connection to learning practices. The emergence of gen-AI has preceded considerations of how it should be applied in teaching and learning. However, while gen-AI has been justified in terms of the possibilities to enhance learner agency by expanding opportunities to engage with language, such as through the generation of content or the translation of texts, it can also take power away from learners. How can learners be self-determining in light of how choices become increasingly guided by Artificial Intelligence? In this paper, I conceive the arrangements of humans and software as an assemblage of complex and dynamic social, and technical processes. Drawing on a flat ontology, where all agents (human and non-human, material and subjective) have equal ontological status, I argue that learner agency has its origins in the messy and lively interactions between heterogenous actors. In particular, I consider active and passive affects as being part of the same process: active when we bring something into effect ourselves, passive when our self-determination is changed not by our own power, but through external forces acting on it (such as gen-AI). From this, I explore the constraining and enabling potential of artificial intelligence. Finally, I extend this discussion to the emergence of learner agency.

Historical insights at scale: A corpus-wide machine learning analysis of early modern astronomic tables.

Understanding the evolution and dissemination of human knowledge over time faces challenges due to the abundance of historical materials and limited specialist resources. However, the digitization of historical archives presents an opportunity for AI-supported analysis. This study advances historical analysis by using an atomization-recomposition method that relies on unsupervised machine learning and explainable AI techniques. Focusing on the "Sacrobosco Collection," consisting of 359 early modern printed editions of astronomy textbooks from European universities (1472-1650), totaling 76,000 pages, our analysis uncovers temporal and geographic patterns in knowledge transformation. We highlight the relevant role of astronomy textbooks in shaping a unified mathematical culture, driven by competition among educational institutions and market dynamics. This approach deepens our understanding by grounding insights in historical context, integrating with traditional methodologies. Case studies illustrate how communities embraced scientific advancements, reshaping astronomic and geographical views and exploring scientific roots amidst a changing world.

Using Machine Deep Learning AI to Improve Forecasting of Tax Payments for Corporations

Using Machine Deep Learning AI to Improve Forecasting of Tax Payments for Corporations

Defining AI companions: a research agenda—from artificial companions for learning to general artificial companions for Global Harwell

This paper examines and outlines the future exploration of AI companions, focusing on three main areas. Firstly, it defines AI companions as sophisticated AI entities designed for supporting and enhancing human experiences in daily activities, such as learning, working, and others. They encompass emotional, social, and practical aspects of daily life while fostering interactions and relationships with humans. Secondly, the paper provides a historical review of AI companions for learning (i.e., AI learning companions), offering an overview of their conceptualization, development, and utilization in educational contexts and insights into their potential future trajectory. Lastly, a research agenda is presented, which includes AI learning companions—outlining key questions, challenges, and goals for integrating AI companions into learning environments—and the pursuit of Global ‘Harwell’ (a portmanteau of ‘harmony’ and ‘wellbeing’). This suggests that, in addition to transforming education, AI companions can contribute to individual wellbeing as well as broader humanitarian objectives. Beyond contributing to economic growth and efficiency, it is fundamentally important to address the most pressing global challenges or human crises of our time, foster understanding and cooperation among researchers and practitioners in different fields, and hence pave the way toward a future world marked by Global Harwell. To further advocate and demonstrate the pivotal role of AI companions in achieving these genuine objectives, we propose the General Artificial Companion Hypothesis. All these endeavors, however, begin with AI companions for learning. As Mandela said, “Education is the most powerful weapon which you can use to change the world.”

Systematic Review on Citizen Science and Artificial Intelligence for Vector-Borne Diseases

Abstract. Vector-borne diseases (VBDs) pose a significant threat to public health globally. VBDs are a significant public health concern worldwide, with infections such as Malaria, Dengue Fever, Zika Virus, and Lyme Disease posing a threat to global health security. There is a need for innovative and effective strategies to control these diseases. One potential solution lies in the integration of citizen science and artificial intelligence technologies. Citizen science, which involves the participation of volunteers in scientific research, can greatly contribute to data collection and monitoring efforts for vector-borne diseases. Artificial intelligence can enhance the analysis of this data, leading to improved disease surveillance, prediction, and control strategies. Citizen Science involves active public participation in scientific research, data collection, and analysis, while AI and Machine Learning (ML) techniques offer powerful tools for processing and interpreting large datasets. By leveraging the power of citizen science and artificial intelligence, we can harness the collective efforts of volunteers and advanced technology to better understand, track, and mitigate the spread of vector-borne diseases. Through the combination of citizen science and artificial intelligence, a more comprehensive and efficient approach can be taken to gather data on vector-borne diseases, analyze the data, and inform public health interventions. This systematic review aims to explore the role of citizen science and artificial intelligence in addressing the challenges associated with vector-borne diseases. It will examine the existing literature on the use of citizen science and artificial intelligence in vector-borne disease research, including their applications, benefits, and limitations, in order to provide insights and recommendations for future research and public health strategies.

Enhancing Quality Assurance in Annuities: A Risk Management Approach with AI and Machine Learning

Enhancing Quality Assurance in Annuities: A Risk Management Approach with AI and Machine Learning

Adaptation in Global Semiconductor Supply Chains: Insights from the COVID-19 Pandemic and Strategic Responses

Abstract—This study examines the global semiconductor supply chain disruptions triggered by the COVID-19 pandemic, particularly focusing on their impact on the automotive and electronics industries. The research highlights that these disruptions were exacerbated by systemic vulnerabilities such as just-in-time manufacturing, geopolitical tensions, and reliance on complex global interdependencies. Through case studies of companies like Tesla and Hyundai, the study demonstrates the critical role of adaptability and supply chain resilience in mitigating the effects of semiconductor shortages. Firms that diversified suppliers or employed technological innovations, like software adjustments, were better equipped to manage the crisis. Additionally, the study underscores the importance of international cooperation, government intervention, and technological advancements in forecasting and supply chain management to build more resilient systems. The research identifies significant gaps in understanding the long-term resilience of semiconductor supply chains, calling for sustainable, scalable solutions to manage future disruptions. Keywords— Semiconductor shortages, global supply chain disruptions, automotive and electronics industries, resilience, geopolitical tensions, technological innovation, government policy, circular economy principles, AI, and machine learning in supply chains.

How Personalization Is Shaping Consumer Behavior : An Analysis of Data-Driven Marketing Trends

The paper “How Personalization Is Shaping Consumer Behaviour: An Analysis of Data-Driven Marketing Trends” seeks to also analyse data mining in marketing and the extent to which personalization contributes to consumer behaviour. This relates to how with data and technology the use of Artificial Intelligence can replace and transform marketing and the ability for businesses to offer personalized experience. This means that through email marketers, consumer intimacy will be possible in some ways, which include but not limited to the following: product communication or advertisement. At the same time, the article raises questions, such as security of personal information and the benefits, which may be hazardous, resulting from constant request. The use of AI and machine Learning in personalization in marketing will grow stronger to improve the customers experience and meanwhile ensure a balanced and ethical approach.

A Review on the Influence of Deep Learning and Generative AI in the Fashion Industry

Incorporating deep learning models has marked a significant advancement in integrating trends and technology within the fashion industry. These models are extensively applied in the realm of image recognition, product recommendation, and trend prediction, employing deep learning techniques such as Convolutional Neural Networks (CNNs), Generative Adversarial Networks (GANs), Recurrent Neural Networks (RNNs), and Autoencoders. This paper aims to cover various aspects of the textile industry’s supply chain processes, highlighting these deep learning techniques' present influence and potential future directions. It includes a comprehensive analysis of some of the most recent and well-recognized studies in the industry that focus on different parts of a product’s lifecycle in the industry, such as Design and Trend Forecasting, Production and Quality Control, Marketing and Sales, and Distribution and Retail. While deep learning has significantly improved the efficiency of processes across the supply chain, our review highlights some of the existing challenges, such as dependency on large datasets, manual annotation needs, and limitations in creative design generation, encouraging future research to focus on more sophisticated models incorporating multimodal data and personalized factors like body types and aesthetic preferences. Additionally, areas like sewing pattern generation, body-aware designs, and ethical sourcing are critical areas of the fashion industry that require further exploration.

The Rise of Biochemical Sensors: Technology for Real-Time Health Monitoring (Applications and Future Scope)

Biochemical sensors integrated into wearable health technology represent one of the most transformative innovations of the 21st century. This review delves into the evolution, principles, and real-time applications of biochemical sensors and their role in personalized health monitoring. Wearable biochemical sensors, capable of continuously measuring various biomarkers like glucose, lactate, cortisol, and electrolytes, are revolutionizing healthcare by enabling proactive management of chronic diseases, including diabetes, cardiovascular disorders, and mental health issues. Advances in biosensing technologies, coupled with the use of AI and machine learning algorithms, have enhanced the sensitivity and accuracy of these devices, ensuring that critical health data is available in real-time. From glucose monitoring devices like Abbott's FreeStyle Libre to the latest nanomaterial-based sensors, these innovations are reshaping healthcare delivery by shifting the focus from hospital-centered treatments to patient-centric, continuous monitoring systems. This review provides an in-depth analysis of the technological advancements, challenges, and future directions in biochemical sensors, focusing on key technologies such as electrochemical, optical, and enzymatic sensors. It also highlights the critical role of AI in interpreting the complex data generated by these sensors, paving the way for more efficient diagnostics and predictive healthcare models. Furthermore, the paper explores how these sensors have been applied in infectious disease detection, particularly during the COVID-19 pandemic, and discusses their potential to enhance global health surveillance systems. In conclusion, wearable biochemical sensors represent a significant leap forward in the pursuit of personalized medicine, offering real-time diagnostics and timely interventions for disease management. The future of healthcare is closely tied to the ongoing innovations in sensor technology, with the promise of even more advanced and multifunctional devices on the horizon.

A Sustainable Educational Tool for Engineering Education Based on Learning Styles, AI, and Neural Networks Aligning with the UN 2030 Agenda for Sustainable Development

This study addresses the United Nations 2030 Agenda Sustainable Development Goals 4, 8, 10, and 12 by developing a resource-efficient tool that promotes equitable quality education and lifelong learning opportunities, supports decent work and economic growth, reduces inequalities, and ensures sustainable consumption and production patterns. This study contributes to sustainable education by providing a tool that is designed to be easy to use, easy to modify, and resource-efficient, making it accessible to institutions with limited technological resources. The tool uses artificial intelligence and a long short-term memory (LSTM) neural network to provide personalized teaching, adapting to the unique learning styles of its users. A custom survey adapted from the Felder–Silverman model was used to track weekly learning style transitions among 72 engineering students at the Faculty of Engineering at the University of Los Llanos. These data were used to build the LSTM model to predict learning style transitions over a 16-week semester. Two interfaces were created: one for instructors, integrating the LSTM model, and one for students, incorporating a custom survey. An OpenAI API-powered chat was also built into both interfaces to provide study advice to students according to their styles and enable professors to personalize their teaching methodologies in engineering education.

Updating a Code for Teaching Ethical Visualizations

The prevalent paradigm for understanding what constitutes a ‘good’ data visualization, and what we are likely teaching business students, relates to a conventional wisdom of efficiency, clarity, transparency, and faithful representation of truth. Teaching about the ethics of visualizations seems to be largely absent from business school curricula. This paper suggests that business students who make and use visualizations should be taught to use a code of ethics for visualizations based not only on conventional wisdom, but also on ideas from the Machine Learning/AI and ‘ethical visualizations’ communities. Elaborating on Sheppard’s code, the code suggested here incorporates ideas about bias, transparency, user agency, and privacy. Ethical awareness and practice become even more important as more data collection, processing, and visualization is shifted to Machine Learning and Generative AI. A threshold model is adapted to understand when visualization decisions require more scrutiny. Ideas from the code and the threshold model are used to analyze choices made by the CDC’s COVID-19 Rapid Response Team for an early pandemic visualization that circulated widely beyond the initial report in which it appeared. This and other examples underscore the need for business professionals to consider their visualizations from the perspective of the code.

Internet of Things and Cloud Computing-based Disease Diagnosis using Optimized Improved Generative Adversarial Network in Smart Healthcare System

ABSTRACT The integration of IoT and cloud services enhances communication and quality of life, while predictive analytics powered by AI and deep learning enables proactive healthcare. Deep learning, a subset of machine learning, efficiently analyzes vast datasets, offering rapid disease prediction. Leveraging recurrent neural networks on electronic health records improves accuracy for timely intervention and preventative care. In this manuscript, Internet of Things and Cloud Computing-based Disease Diagnosis using Optimized Improved Generative Adversarial Network in Smart Healthcare System (IOT-CC-DD-OICAN-SHS) is proposed. Initially, an Internet of Things (IoT) device collects diabetes, chronic kidney disease, and heart disease data from patients via wearable devices and intelligent sensors and then saves the patient’s large data in the cloud. These cloud data are pre-processed to turn them into a suitable format. The pre-processed dataset is sent into the Improved Generative Adversarial Network (IGAN), which reliably classifies the data as disease-free or diseased. Then, IGAN was optimized using the Flamingo Search optimization algorithm (FSOA). The proposed technique is implemented in Java using Cloud Sim and examined utilizing several performance metrics. The proposed method attains greater accuracy and specificity with lower execution time compared to existing methodologies, IoT-C-SHMS-HDP-DL, PPEDL‐MDTC and CSO-CLSTM-DD-SHS respectively.