Predicting the development cost of TFT-LCD manufacturing equipment with artificial intelligence models

Real-World Surveillance of FDA-Cleared Artificial Intelligence Models: Rationale and Logistics.

Developing a reliable parametric cost model at the conceptual stage of the project is crucial for project managers and decision makers. Existing methods, such as probabilistic and statistical algorithms have been developed for project cost prediction. However, these methods are unable to produce accurate results for conceptual cost prediction due to small and unstable data samples. Artificial intelligence (AI) and machine learning (ML) algorithms include numerous models and algorithms for supervised regression applications. Therefore, a comparative analysis for AI models is required to guide practitioners to the appropriate model. The article focuses on investigating 20 AI techniques which are conducted for conceptual cost modeling, such as fuzzy logic model, artificial neural networks, multiple regression analysis, case-based reasoning, hybrid models, such as genetic fuzzy model, and ensemble methods such as scalable boosting trees (XGBoost) and random forest. Field canals improvement projects (FCIPs) are used as an actual case study to analyze the performance of the applied ML models. Out of 20 AI techniques, the results show that the most accurate and suitable method is XGBoost with 9.091% and 0.929 based on mean absolute percentage error and adjusted R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , respectively. Nonlinear adaptability, handling missing values and outliers, model interpretation, and uncertainty have been discussed for the 20 developed AI models. In addition, this study presents a publicly open dataset for FCIPs to be used for future models' validation and analysis.

Solar energy-based technologies have developed rapidly in recent years, however, the inability to appropriately estimate solar energy resources is still a major drawback for these technologies. In this study, eight different artificial intelligence (AI) models namely; convolutional neural network (CNN), artificial neural network (ANN), long short-term memory recurrent model (LSTM), eXtreme gradient boost algorithm (XG Boost), multiple linear regression (MLR), polynomial regression (PLR), decision tree regression (DTR), and random forest regression (RFR) are designed and compared for solar irradiance prediction. Additionally, two hybrid deep neural network models (ANN-CNN and CNN-LSTM-ANN) are developed in this study for the same task. This study is novel as each of the AI models developed was used to estimate solar irradiance considering different timesteps (hourly, every minute, and daily average). Also, different solar irradiance datasets (from six countries in Africa) measured with various instruments were used to train/test the AI models. With the aim to check if there is a universal AI model for solar irradiance estimation in developing countries, the results of this study show that various AI models are suitable for different solar irradiance estimation tasks. However, XG boost has a consistently high performance for all the case studies and is the best model for 10 of the 13 case studies considered in this paper. The result of this study also shows that the prediction of hourly solar irradiance is more accurate for the models when compared to daily average and minutes timestep. The specific performance of each model for all the case studies is explicated in the paper.

Artificial Intelligence: The Future of Maxillofacial Prognosis and Diagnosis?

Artificial Intelligence (AI) has emerged as a disruptive force, transforming industries and revolutionizing business processes. Among the domains significantly impacted, project management holds immense potential for transformation. AI’s integration promises intelligent automation, data-driven decision-making, and predictive capabilities, addressing challenges in traditional project management methodologies. Successful AI implementations have revolutionized project management, improving forecasting, resource allocation, and risk assessment. Despite the benefits, challenges hinder AI adoption, including data quality, investment uncertainties, workforce readiness, and change management. However, AI’s potential to address gaps in project management is substantial, enabling data-driven decisions, real-time monitoring, resource optimization, and enhanced agility. This research examines AI’s evolution, successful implementations, challenges, and potential implications, providing insights for effective AI integration in project management. Key Takeaways: • Project management is one domain that stands out as highly impacted by artificial intelligence, offering immense potential for transformation through AI integration. • AI-driven solutions in project management promise intelligent automation, data-driven decision-making, and enhanced predictive capabilities, addressing traditional methodological limitations. • Despite challenges, AI holds substantial potential to address existing gaps in project management practices.

Hybrid ANN-CBR model for disputed change orders in construction projects

Quantum computing (QC) is not a futuristic notion in agriculture, though its full potential has yet to be realized. QC is an emerging field at the intersection of physics and computer science that holds immense potential to revolutionize various sectors, including agriculture production and artificial intelligence (AI) modeling. While QC is still in the early stages of development and practical applications within agriculture are not yet widespread, researchers are actively exploring its potential benefits in various agricultural domains, including crop optimization, livestock breeding, and environmental monitoring. QC harnesses the principles of quantum mechanics to perform computations using quantum bits or qubits, which can exist in multiple states simultaneously. Unlike classical computers, which rely on binary bits representing 0 or 1, quantum computers exploit phenomena such as superposition and entanglement to process information in parallel, potentially offering exponential speedup for certain types of problems. In agriculture production, particularly in animal science, QC offers promising avenues for optimizing processes and enhancing productivity. Quantum algorithms can analyze vast amounts of genomic data to improve breeding programs, leading to the development of more resilient and productive livestock breeds. Furthermore, QC can facilitate precision farming techniques by modeling complex environmental factors and animal behavior to optimize feeding strategies, disease management, and overall farm management practices. Moreover, QC can significantly benefit AI modeling by accelerating computations and enabling more efficient training of AI models. Quantum algorithms can enhance the performance of AI algorithms in various tasks, including pattern recognition, natural language processing, and predictive analytics. By leveraging quantum-enhanced optimization algorithms, AI models can achieve better convergence and accuracy, leading to more effective decision-making and problem-solving capabilities. While hybrid intelligent models also represent a novel frontier in agriculture, QC has the potential to expedite the merging of mechanistic and AI modeling paradigms, facilitating a more holistic understanding of complex systems in agriculture and beyond. By integrating mechanistic models, which describe the underlying physical processes, with AI models, which learn patterns from data, quantum computing can enable comprehensive simulations and predictions of agricultural systems. This fusion of modeling paradigms can lead to more accurate and robust predictions of crop yields, livestock performance, and environmental impacts, facilitating informed decision-making for farmers and policymakers. The application of QC in agriculture, however, requires interdisciplinary collaborations between physicists, computer scientists, agronomists/animal scientists, and AI researchers. These collaborations can drive the development of quantum algorithms tailored to agricultural applications, the integration of quantum-enhanced AI techniques into existing modeling frameworks, and the deployment of QC resources in real-world agricultural systems. Ultimately, harnessing the power of QC holds the potential to revolutionize agriculture production practices, including regenerative agriculture, and advance AI modeling capabilities, paving the way for a more sustainable and efficient agricultural industry.

In recent years, the development and implementation of artificial intelligence (AI) have attracted tremendous attention. The implementation of active control systems for building structures can be improved by using an AI controller. Non-AI controllers such as the Linear Quadratic Regulator (LQR) controller require full state variables of the structure to be measured, which is rarely feasible. To address this problem, two AI models, namely, artificial neural network (ANN) and fuzzy logic (FL), have been tried as AI-based controller in various studies. In the present study, both AI models were investigated to see their practicality and effectiveness. The AI models were implemented to control an active mass damper (AMD) in a three-story prototype-sized building. The simulation results from the structure with an LQR controller were used as benchmark and training data for the AI models. The results of the study demonstrated that although both AI models could reduce the structure responses, ANN was more practical and effective compared to FL as an AI-based controller for the given structure. Furthermore, the effectiveness of an ANN-based AMD was also shown by the experimental results.

This study evaluated if medical doctors could identify more hemorrhage events during chart review in a clinical setting when assisted by an artificial intelligence (AI) model and medical doctors' perception of using the AI model. To develop the AI model, sentences from 900 electronic health records were labeled as positive or negative for hemorrhage and categorized into one of 12 anatomical locations. The AI model was evaluated on a test cohort consisting of 566 admissions. Using eye-tracking technology, we investigated medical doctors' reading workflow during manual chart review. Moreover, we performed a clinical use study where medical doctors read two admissions with and without AI assistance to evaluate performance when using and perception of using the AI model. The AI model had a sensitivity of 93.7% and a specificity of 98.1% on the test cohort. In the use studies, we found that medical doctors missed more than 33% of relevant sentences when doing chart review without AI assistance. Hemorrhage events described in paragraphs were more often overlooked compared with bullet-pointed hemorrhage mentions. With AI-assisted chart review, medical doctors identified 48 and 49 percentage points more hemorrhage events than without assistance in two admissions, and they were generally positive toward using the AI model as a supporting tool. Medical doctors identified more hemorrhage events with AI-assisted chart review and they were generally positive toward using the AI model.

The use of Artificial Intelligence (AI) with the non-intrusive SonReb method, which combines Ultrasonic Pulse Velocity (UPV) and Rebound Number (RN) to predict concrete compressive strength, has attracted increasing attention in recent years. This study introduces a novel approach to improve AI models for predicting concrete strength, making them more suitable for future practical applications. One of the key challenge in AI models is the number of input parameters; while more inputs often improve accuracy, they are typically impractical for most applications dealing with existing structures (e.g., requiring detailed concrete mix design information that is often unavailable). SonReb AI-based models which use only two input parameters (UPV and RN) have shown reasonable accuracy, but their general use is limited by adoption of different testing standards which precludes the development of large databases. This study aims to improve two-parameter SonReb-based AI models through the addition of a binary input variable that represents the type of the specimen geometry (cube or cylinder) and investigates the effect of model architecture by comparing three different AI algorithms: Artificial Neural Networks (ANN), Deep Neural Networks (DNN), and Adaptive Neuro-Fuzzy Inference Systems (ANFIS). Six AI models were developed using 514 data points from experimental tests and collected data, and an unbiased data splitting method was applied for training and testing. The results showed that including specimen geometry improved model accuracy across all AI algorithms. The results of this study show that regardless of AI architecture, the proposed novel approach not only improves the accuracy of models, but also enables the use of larger databases containing both cubic and cylindrical specimens.

The study of groundwater quality is typically conducted using water quality indices such as the Groundwater Quality Index (GQI) or the GroundWater Quality Index (GWQI). The indices are calculated using field data and a scoring system that uses ratios of the constituents to the prescribed standards and weights based on each constituent’s relative importance. The results obtained by this procedure suffer from inherent subjectivity, and consequently may have some conflicts between different water quality indices. An innovative feature drives this research to mitigate the conflicts in the results of GQI and GWQI by using the predictive power of artificial intelligence (AI) models and the integration of multiple water quality indicators into one representative index using the concept of data fusion through the catastrophe theory. This study employed a two-level AI modeling strategy. In Level 1, three indices were calculated: GQI, GWQI, and a data-fusion index based on four pollutants including manganese (Mn), arsenic (As), lead (Pb), and iron (Fe). Further data fusion was applied at Level 2 using supervised learning methods, including Mamdani fuzzy logic (MFL), support vector machine (SVM), artificial neural network (ANN), and random forest (RF), with calculated GQI and GWQI indices at Level 1 as inputs, and data-fused indices target values derived from Level 1 fusion as targets. We applied these methods to the Gulfepe-Zarinabad subbasin in northwest Iran. The results show that all AI models performed reasonably well, and the difference between models was negligible based on the root mean square errors (RMSE), and the coefficient of determination (r2) metrics. RF (r2 = 0.995 and RMSE = 0.006 in the test phase) and MFL (r = 0.921 and RMSE = 0.022 in the test phase) had the best and worst performances, respectively. The results indicate that AI models mitigate the conflicts between GQI and GWQI results. The method presented in this study can also be applied to modeling other aquifers.

Aim: The majority of machine learning (ML) models in healthcare are built on retrospective data, much of which is collected without explicit patient consent for use in artificial intelligence (AI) and ML applications. The primary aim of this study was to evaluate whether clinicians and scientific researchers themselves consent to provide their own data for the training of ML models. Materials and Methods: This survey was conducted through an anonymous online survey, utilizing platforms such as Telegram, LinkedIn, and Viber. The target audience comprised specific international groups, primarily Russian, German, and English-speaking, of clinicians and scientific researchers. These participants ranged in their levels of expertise and experience, from beginners to veterans. The survey centered on a singular, pivotal question: “Do You Consent to the Use of Your Biological and Private Data for Training Machine Learning and AI Models?” Respondents had the option to choose from three responses: “Yes” and “No”. Results: The survey was conducted in January 2024. A total of 119 unique and verified individuals participated in the survey. The results revealed that only 50% of respondents (63 persons) expressed consent to provide their own data for the training of ML and AI models. Conclusion: In the development of ML and AI models, particularly open-source ones, it is crucial to ascertain whether participants are willing to provide their private data. While ML algorithms can transform the nature of data, it is important to remember that the primary owner of this data is the individual. Our findings show that in 50% of the cases, even participants from scientific research and clinical backgrounds – individuals typically accountable for ensuring data quality in AI and ML model development – do not consent to the use of their data in AI and ML settings. This highlights the need for more stringent consent processes and ethical considerations in the utilization of personal data in AI and ML research.

Data-based groundwater quality estimation and uncertainty analysis for irrigation agriculture

Background: The evolving business environment and technological advancements in the UK construction industry, particularly Artificial Intelligence (AI), are reshaping project management processes, including risk management (RM). As the industry continues to evolve, project managers need to develop AI competencies to effectively mitigate emerging risks. Background: The UK construction industry is undergoing a transformation, driven by technological advancements, particularly the integration of Artificial Intelligence (AI). This shift is reshaping traditional project management practices, including risk management (RM). With the increasing complexity of construction projects and evolving risks, it is essential for project managers to develop AI competencies to effectively navigate and mitigate emerging risks. Aim: This review aimed to evaluate the significance of AI competence for project managers and its impact on risk management practices within the UK construction industry. Methods: Using diverse databases including ProQuest, MEDLINE, and IEEE Xplore, a comprehensive literature review was conducted focused on studies from 2010 to the present related to AI competencies for project managers and risk management in the UK construction industry. The articles were initially screened based on relevant and specific keywords including Boolean operators and followed by full-text review to determine final choice articles used for this review. The findings were summarized using the narrative synthesis approach to report the findings in line with the review objectives. Results: The findings of this review highlight the critical role of AI competence among project managers in enhancing risk management (RM) practices within the UK construction industry. While AI integration improves decision-making, efficiency, and accuracy while reducing human error, the effectiveness of these benefits hinges on project managers’ ability to competently utilize AI tools. The findings were thematically categorized into: (1) AI in the construction industry, (2) AI integration in RM practices, (3) benefits and challenges of AI adoption, and (4) assessing project managers' AI competencies. Case studies, such as IBM’s Watson Explorer and RiskLens, underscore the necessity of AI competence for optimizing RM strategies. Conclusion: AI is revolutionizing RM practices by offering data-driven insights and predictive risk analysis within the UK construction industry. To harness AI's full potential, project managers must develop AI-related competencies. However, for successful adoption of AI in construction, addressing challenges related to competency, ethics, and cost is crucial. AI competencies development among project managers is key to maximizing AI’s benefits, ensuring effective risk management and successful projects.

Purpose The advent of digital technology has increased both the autonomy and complexity of intelligent machines. As data functionality becomes more advanced, the demand for machines to engage in teamwork alongside humans is rising, leading to a transformation of intelligent machines from tools to teammates. As such, human-autonomy team (HAT) is a new concept in digital transformation, and HAT-related research has driven the application and development of artificial intelligence in industry and production. This study explores the prospects of HAT in project management through a systematic literature review of published articles, highlighting current research themes and proposing directions for future research. Design/methodology/approach While the systematic literature review follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, VOSviewer was employed to assist in keywords co-occurrence analysis to visualize the connections and networks of included articles. A total of 38 publications were retrieved from both Scopus and Web of Science databases to examine the annual publication trends, geographical distribution, research methods and keywords co-occurrence analysis. Findings The results revealed four mainstream research themes: (1) simulation of human–robotic system integration in project management, (2) algorithm design for human-centered artificial intelligence (AI), (3) the impact of digital transformation on teams towards agile project execution and (4) generative AI model for risk analysis in project management. Originality/value From both theoretical and practical implications, this study would deepen the role and effectiveness of HAT in project management strategies. The proposed framework suggested potential future directions such as (1) expanded testing scenarios, (2) human factors in evaluation criteria, (3) agile transition issues in HATs and (4) standards for AI model applications in project management. Ultimately, this study would foster a dialogue among researchers and practitioners by encouraging a synergistic approach to the implementation of HAT solutions in project settings.