Core Of Artificial Intelligence Research Articles

Artificial intelligence ethical awareness according to the core competency level among dental hygiene students

Objectives: This study investigated the ethical awareness regarding artificial intelligence (AI) among dental hygiene students based on core competency levels and proposed a competency-based educational approach to improve AI ethical awareness. Methods: Eighty-six dental hygiene students participated in the study and provided informed consent. The core competency survey tool included innovation, communication, relations, and services. The AI ​​ethical awareness survey tool was divided into eight categories, each with 24 questions: responsibility, stability and reliability, non-discrimination, transparency and explainability, people-centered service, employment, tolerance and limits, and robot rights. Results: The group with high core competency had higher levels of AI ethical awareness (p<0.05), particularly in terms of responsibility, transparency, and peoplecentered service. The level of AI ethical awareness was significantly correlated with the relationship competency and service competency (p<0.05). Conclusions: These results highlight the association between AI ethical awareness and core competencies. These results suggest that competency-based education in universities is critical for improving AI ethical awareness. Furthermore, we intend to use the findings as preliminary data to suggest a direction for competency-centered education to improve AI ethical awareness.

Application of Fuzzy Reasoning with Fusion Rules in Intelligent Information Processing

The core of artificial intelligence (AI) research is intelligent information processing, and fuzzy reasoning and fuzzy neural networks are significant research areas in models of intelligent information processing. The study examines the robustness of fuzzy reasoning by using the property characteristics of triangular modal operators and implication operators, which are used to analyse the Lipschitz property of fuzzy operators and its corresponding property characteristics of triangular modal operators and implication operators. Inference operators are used to investigate the robustness and error-control capabilities of fuzzy operators for fuzzy reasoning. The results showed that the robustness of quasi-copula fuzzy reasoning with fusion rules is better, and the corresponding maximum output perturbation is lowest at 0.36 when the fuzzy operator is T[Formula: see text]&R[Formula: see text] and the corresponding maximum output error is lowest at 0.41 when the fuzzy operators are T[Formula: see text]&R[Formula: see text] and T[Formula: see text]&R[Formula: see text]. This shows the optimal performance of the fuzzy associative memory model with fuzzy rule for quasi-copula fuzzy operators and can improve the theoretical technology. The best-performance fuzzy copula operator of the fuzzy associative memory model can enhance the theoretical foundation and technical support for the processing of intelligent information.

P-554 Artificial intelligence (AI) for embryo genetic risk assessment: a novel form of preimplantation genetic screening

Abstract Study question Can an AI-based embryo ploidy screening tool effectively provide a genetic risk assessment on Day 5 embryos? Summary answer AI screening results showed strong predictive value for early detection of at-risk embryos warranting confirmatory diagnostic testing, based only on time lapse video assessment. What is known already Euploid embryo transfer is the dogma of IVF success. Embryo evaluation relies extensively on diagnostic preimplantation genetic testing for aneuploidy (PGT-A) by means of trophectoderm biopsy. Though pivotal, this presents substantial practical and financial limitations. There is demand for an alternative genetic screening method that provides a quantitative embryo genetic risk assessment for patients who opt out of invasive diagnostic testing, or who intend to undergo fresh transfer. These results can provide information for understanding your aneuploidy risk for more precise prognosis counseling and testing. This is important since diagnostic tests may incur excess financial burden, inconclusive outcomes, and time-to-results. Study design, size, duration Time lapse videos (N = 5,000) were used for AI training. The AI core architecture was a video transformer model for spatio-temporal video sampling. Known ploidy and live-birth results were used as ground truth, with maternal age and embryo quality scores incorporated as input parameters during calibrated logistic regression. A blind test dataset (N = 708 embryos; euploid=352; aneuploid=356; mean ± SD patient age: 35.9 ± 5.4 years) was used to evaluate predictive accuracy. Participants/materials, setting, methods The AI outputs a scalar (1-99) that associates with euploidy likelihood. The AI’s screening value was determined by its specificity (correct prediction of aneuploid), sensitivity (correct prediction of euploid), false positive, and false negative rate. We determined optimal AI score thresholds that identified high aneuploidy and euploidy risk categories (AI score <33; AI score >66, respectively). Main results and the role of chance Specificity of the AI screening test was 83.0%, with a sensitivity of 56.0%. The false positive rate was 26.7% and the false negative rate was 29.3%. The AI’s high specificity relative to its sensitivity shows its clinical value to deselect embryos with the strongest risk of genetic abnormality, and for the prioritization of embryos for confirmatory PGT. Boxplot visualization of the full AI score distribution showed successful discrimination between aneuploid and euploid embryos (mean AI score for aneuploid and euploid were 51.3 and 43.5, respectively) with appropriately minimized overlapping of the interquartile range. The optimal AI score thresholds for detecting embryos with the highest and lowest likelihoods of euploidy were >66 /<33, respectively, representing 71% likelihood of euploidy for the >66 group and 72% aneuploidy for the <33 group. Limitations, reasons for caution The screening does not provide diagnostic genetic testing on the chromosome level. The influence of mosaicism on the false positive/negative rates was not assessed. Wider implications of the findings Results support the use of an AI ploidy screening test for effective decision-making and triage of at-risk embryos for transfer or confirmatory diagnostic testing via PGT-A. Trial registration number Not applicable

Pengembangan Jiwa Wawasan Kebangsaan Yang Dipengaruhi Oleh Kemajuan Teknologi AI

Artificial intelligence is one of the most talked about technologies today. The advanced technology that allows computers to mimic human behaviour is what defines artificial intelligence. Advanced technology that allows computers to mimic human behaviour is at the core of artificial intelligence. The utilisation of this technology is widespread in all industries around the world. The purpose of this research is to find out how influential the advancement of AI Technology is on the insight of the archipelago. The method used is a qualitative method that emphasises qualitative information data. Qualitative research method is a research method that focuses on understanding a social phenomenon that occurs in society. The result of this discussion is that the development of the times has made technological advances increasingly sophisticated. Technology can facilitate the quick and easy acquisition of information about history, culture, values, and the latest national developments. Technology understands the diversity and uniqueness of each country, so it is expected to give birth to a spirit of solidarity to achieve common progress.

A review of the multi-dimensional application of machine learning to improve the integrated intelligence of laser powder bed fusion

Laser powder bed fusion (LPBF) as one of the most promising additive manufacturing (AM) technologies, has been widely used to produce metal parts and applied in fields such as medical and aerospace. However, the full development of LPBF is still limited by the existence of a limited variety of materials suitable for LPBF, a single structure of printed components, defects in the processing, and complex post-processing. Machine learning (ML), as the core of artificial intelligence (AI), is expected to be an effective tool for LPBF related researches due to its ability to find latent relationships among numerous research issues. This paper provides a newly comprehensive review of ML applications to LPBF. The ML-assisted three stages of LPBF, including the pre-processing, in-situ processing and post-processing, are systematically reviewed. In the pre-processing phase, the application of ML in designing lightweight structures as well as high-performance materials are analyzed in detail. The optimization on the identification of the process and real-time monitoring during the in-situ processing stage are thoroughly discussed. In the post-processing stage, the material-structure-performance (MSP) relationship and their optimization are summarized. Based on the comprehensive review, challenges and perspectives for ML multiscale application development are eventually envisaged. This work contributes a great help to utilize ML to AM and suggests meaningful guidelines for future ML applications to manufacturing work.

Performance modeling on DaVinci AI core

The extensive use of Deep Neural Networks (DNNs) encourages people to design domain-specific hardware called Artificial Intelligence (AI) processors. The novel hardware makes optimizations challenging without a proper performance model that reveals working details and performance implications. This paper presents a performance model, Verrocchio, for Huawei DaVinci AI Core, which predicts the execution time of real-world DaVinci kernels. We propose specially-crafted micro-benchmarks to identify contention source, runtime behaviors, and bandwidth sharing, which significantly determine performance. Since DaVinci Core adopts a binary semaphore mechanism for synchronization, Verrocchio views each instruction as a discrete event and manages its execution time based on the programming logic. For evaluation, Verrocchio achieves average error rates of 2.62% and 2.30% in sample kernels for single-core and double-core execution. We demonstrate an optimizing process of matrix multiplications with Verrocchio, achieving speedups of 1.70× for operators and 1.53× for applications and error rates of 5.06% and 5.25%.

Perspective on machine learning in energy material discovery

Perspective on machine learning in energy material discovery

School Mode Choice Classification Model Exploitation Though Artificial Intelligence Classification Application

Human behavior pattern recognition exploited using Artificial Neural Networks (ANN) is at the core of Artificial Intelligence (AI) classification applications. School trip transport forecasting mode selection based upon ANN forecasting ability provided an easy-to-use scientific toolkit for sustainable urban policies designing and implementation. The paper capitalized acknowledged forecasting ability of ANN to recognize and classify behavior patterns leading to parental decisions of school mode choice. Main stages of this research included the conduction of an extended questionnaire survey in 512 parents of school students in Thessaloniki (northern Greece) and the investigation whether ANN forecasting model could classify school mode choice. Research provided promising results (forecasting ability ranging from 76% to 93%) on school mode parental selection forecasting models based on ANN classifier, providing a solid proof of concept for further investigation.

From Emotion AI to Cognitive AI

Survey/review study From Emotion AI to Cognitive AI Guoying Zhao *, Yante Li , and Qianru Xu University of Oulu, Pentti Kaiteran Katu 1, Linnanmaa 90570, Finland * Correspondence: guoying.zhao@oulu.fi Received: 22 September 2022 Accepted: 28 November 2022 Published: 22 December 2022 Abstract: Cognitive computing is recognized as the next era of computing. In order to make hardware and software systems more human-like, emotion artificial intelligence (AI) and cognitive AI which simulate human intelligence are the core of real AI. The current boom of sentiment analysis and affective computing in computer science gives rise to the rapid development of emotion AI. However, the research of cognitive AI has just started in the past few years. In this visionary paper, we briefly review the current development in emotion AI, introduce the concept of cognitive AI, and propose the envisioned future of cognitive AI, which intends to let computers think, reason, and make decisions in similar ways that humans do. The important aspect of cognitive AI in terms of engagement, regulation, decision making, and discovery are further discussed. Finally, we propose important directions for constructing future cognitive AI, including data and knowledge mining, multi-modal AI explainability, hybrid AI, and potential ethical challenges.

Commissioning of an Artificial Intelligence (AI) Tool for Automated Head and Neck Intensity Modulated Radiation Therapy (IMRT) Treatment Planning

<h3>Purpose/Objective(s)</h3> Radiation treatment planning for head and neck (HN) cancers is challenging due to complex anatomy and labor-intensive inverse optimization. Recently we developed an Artificial Intelligence (AI) tool to generate IMRT plans for bilateral planning target volumes (PTVs) with promising results in both plan quality and efficiency. In this study, we describe the clinical commissioning process of this tool. <h3>Materials/Methods</h3> The AI tool was interfaced to the commercial treatment planning system (TPS) in the clinical environment. Essential information about patient anatomy was collected through vendor-supported scripts and transmitted to a designated workstation. This workstation was configured to perform AI core functions to avoid modification and burden on TPS workstations. The AI-generated optimal fluence maps were then imported into TPS for leaf sequencing, dose calculation, and optional fine-tuning. Planners could examine the plan dose and make further adjustments as clinically needed. The workflow and performance were evaluated and validated by experienced physicians and physicists specializing in HN treatment. Training, validation, and testing datasets of the AI tool consisted of 200, 16, and 15 retrospective cases. The commissioning dataset of the AI tool used 36 recent cases of various disease sites in HN. The commissioning AI plans were evaluated based on the corresponding clinical plans regarding critical dose-volume endpoints and 3D dose distributions. All plans were normalized to 44Gy covering 95% of PTV. Wilcoxon signed-rank tests were performed with a significance level of 0.05. <h3>Results</h3> The average AI planning time was 10 to 15min per case with default settings from start to finish. The dose distributions were comparable to clinical plans with slightly better dose conformity and slightly worse but acceptable dose heterogeneity and maximum dose. OAR sparing was comparable. <h3>Conclusion</h3> The AI planning tool for HN was commissioned in our clinic. The commissioning process demonstrates outstanding performance and robustness of the AI tool and provides sufficient validation and confidence for clinical use. Table. Statistics of dosimetric endpoints (median ± std) of the 36 commissioning cases.

Application of machine learning in the diagnosis of keratoconus

The diagnosis of keratoconus is closely related to artificial intelligence. Machine learning is the core of artificial intelligence, machine learning models combined with corneal information are effective supplements in keratoconus diagnosis and provide decision support. These artificial intelligence systems can bring efficiency, reduce artificial misjudgment and effectively reduce manpower costs. In this paper, by summarizing the previous machine learning models that assisted the diagnosis of keratoconus, we may help researchers understand the relevant knowledge in the field of intelligent diagnosis of keratoconus, which is of great significance for further machine learning research.

Review of Artificial Intelligence and Machine Learning Technologies: Classification, Restrictions, Opportunities and Challenges

Artificial intelligence (AI) is an evolving set of technologies used for solving a wide range of applied issues. The core of AI is machine learning (ML)—a complex of algorithms and methods that address the problems of classification, clustering, and forecasting. The practical application of AI&amp;ML holds promising prospects. Therefore, the researches in this area are intensive. However, the industrial applications of AI and its more intensive use in society are not widespread at the present time. The challenges of widespread AI applications need to be considered from both the AI (internal problems) and the societal (external problems) perspective. This consideration will identify the priority steps for more intensive practical application of AI technologies, their introduction, and involvement in industry and society. The article presents the identification and discussion of the challenges of the employment of AI technologies in the economy and society of resource-based countries. The systematization of AI&amp;ML technologies is implemented based on publications in these areas. This systematization allows for the specification of the organizational, personnel, social and technological limitations. This paper outlines the directions of studies in AI and ML, which will allow us to overcome some of the limitations and achieve expansion of the scope of AI&amp;ML applications.

Research on the Application of Animation Design Based on Machine Learning and Dynamic Image Index.

With the development of computer technology, animation is more and more used because of its simple, effective, and higher performance. Machine learning has become the core of artificial intelligence at present. Intelligent learning algorithms are widely used in practical problems such as evaluation. Knowledge-based automatic animation production system faces two challenges: (1) lack of learning ability and waste of data on the website; (2) the quality of animation produced that depends on the level of system designer and the inability of system users to participate in animation production.In order to solve these two problems, an active animation learning system enables the animation system to constantly learn experience and produce the most popular animation, for the first time, for animation production system design and implementation of applied research. Image retrieval technology is a research center in the field of image application. It is widely used in many fields, such as electronic commerce. Animation design will use dynamic image and machine learning to innovate.

3D Reconstruction Method of Virtual and Real Fusion Based on Machine Learning

With the continuous development of computer vision technology, people are paying more and more attention to the method of using computers to simulate actual 3D scenes, and the requirements for 3D reconstruction technology are getting higher and higher. Virtual and real fusion refers to combining the virtual environment generated by the computer with the actual scenes around the user through photoelectric display, sensors, computer graphics, multimedia, and other technologies. This is a technology that can obtain more convenient and direct expressions, and it is also a technique for expressing content more abundantly and accurately. The key to virtual and real fusion technology is the registration of virtual objects and real scenes. It means that the system should be able to correctly estimate the position and posture of the camera in the real world, and then place the virtual object where it should be. Machine learning is a multifield interdisciplinary subject that specializes in how computers simulate or realize human learning behaviors. It is the core of artificial intelligence and the fundamental way to make computers intelligent. Its applications are in all the fields of artificial intelligence. This article introduces the virtual-real fusion 3D reconstruction method based on machine learning, compares the performance of the method with other algorithms through experiments, and draws the following conclusion: the algorithm in this study is the fastest, with an average speed of 72.9% under different times. To evaluate the image acquisition indicators of each algorithm, the algorithm in this study has the lowest error rate. The matching accuracy of each algorithm is tested, and it is found that the average matching accuracy of the algorithm in this study is about 0.87, which is the highest.

Coal Mine Safety Evaluation Based on Machine Learning: A BP Neural Network Model.

As the core of artificial intelligence, machine learning has strong application advantages in multi-criteria intelligent evaluation and decision-making. The level of sustainable development is of great significance to the safety evaluation of coal mining enterprises. BP neural network is a classical algorithm model in machine learning. In this paper, the BP neural network is applied to the sustainable development level decision-making and safety evaluation of coal mining enterprises. Based on the analysis of the evaluation method for sustainable development of coal enterprises, the evaluation index system of sustainable development of coal enterprises is established, and a multi-layer forward neural network model based on error backpropagation algorithm is constructed. Based on the system theory of man, machine, environment, and management, and taking the four single elements and the whole system in a coal mine as the research object, this paper systematically analyzes and studies the evaluation and continuous improvement of coal mine intrinsic safety. The BP neural network evaluation model is used to analyze and study the intrinsic safety of coal mines, the shortcomings of the intrinsic safety construction of coal mines are found, and then improvement measures are put forward to effectively promote the safe production of coal mines and finally realize the intrinsic safety goal of the coal mine.

Machine learning-based approach: global trends, research directions, and regulatory standpoints

The field of machine learning (ML) is sufficiently young that it is still expanding at an accelerating pace, lying at the crossroads of computer science and statistics, and at the core of artificial intelligence (AI) and data science. Recent progress in ML has been driven both by the development of new learning algorithms theory, and by the ongoing explosion in the availability of vast amount of data (often referred to as "big data") and low-cost computation. The adoption of ML-based approaches can be found throughout science, technology and industry, leading to more evidence-based decision-making across many walks of life, including healthcare, biomedicine, manufacturing, education, financial modeling, data governance, policing, and marketing. Although the past decade has witnessed the increasing interest in these fields, we are just beginning to tap the potential of these ML algorithms for studying systems that improve with experience. In this paper, we present a comprehensive view on geo worldwide trends (taking into account China, the USA, Israel, Italy, the UK, and the Middle East) of ML-based approaches highlighting the rapid growth in the last 5 years attributable to the introduction of related national policies. Furthermore, based on the literature review, we also discuss the potential research directions in this field, summarizing some popular application areas of machine learning technology, such as healthcare, cyber-security systems, sustainable agriculture, data governance, and nanotechnology, and suggest that the "dissemination of research" in the ML scientific community has undergone the exceptional growth in the time range of 2018–2020, reaching a value of 16,339 publications. Finally, we report the challenges and the regulatory standpoints for managing ML technology. Overall, we hope that this work will help to explain the geo trends of ML approaches and their applicability in various real-world domains, as well as serve as a reference point for both academia and industry professionals, particularly from a technical, ethical and regulatory point of view.

Research on machine learning Algorithm optimization based on 0-1 coding

Machine learning algorithm is the core of artificial intelligence, is the fundamental way to make computer intelligent, its application in all fields of artificial intelligence. Aiming at the problems of the existing algorithms in the discrete manufacturing industry, this paper proposes a new 0-1 coding method to optimize the learning algorithm, and finally proposes a learning algorithm of “IG type learning only from the best”.

Hierarchical Fusion Process of Destination Image Formation: Targeting on Urban Tourism Destination

Image has been widely accepted as a combination of perceived elements that are commonly discrete and static ones. ‘Discrete’ means that the elements are treated as separate ones with each other, with no interactions among them. ‘Static’ means that the elements would not be changed into other forms in the process of destination image formation. This study, thinking outside the box, tries to explore destination image formation through perceived elements and take their interactions and corresponding changes into account. Machine learning, as the core of artificial intelligence, is applied for data analysis in this study. Urban tourism destinations are targeted because of their variety and abundance of perceived elements. Data are collected from both interview and questionnaire surveys of tourists. Through several phases of analysis, this study finally finds that perceived elements do interact with each other and change into new forms level by level in tourism destination image formation. Specifically, there are four levels from bottom to top in the whole process of destination image formation, i.e., the individual-landscape layer, compound-atmosphere layer, dual-factor layer, and overall-image layer. In the bottom stage, elements are commonly numerous, separate, and concrete. With the interactive effects of the elements, they integrate with each other and generate some new forms in higher levels, which would be more general and abstract. Based on the findings, the dynamic fusion process and pyramid hierarchy of destination image formation are disclosed. This study explores destination image formation from a new perspective, considering perceived elements within a dynamic, synthetic system, and therefore provides practical insights into destination image construction in a more comprehensive and targeted way.

A Multi-Core Controller for an Embedded AI System Supporting Parallel Recognition.

Recent advances in artificial intelligence (AI) technology encourage the adoption of AI systems for various applications. In most deployments, AI-based computing systems adopt the architecture in which the central server processes most of the data. This characteristic makes the system use a high amount of network bandwidth and can cause security issues. In order to overcome these issues, a new AI model called federated learning was presented. Federated learning adopts an architecture in which the clients take care of data training and transmit only the trained result to the central server. As the data training from the client abstracts and reduces the original data, the system operates with reduced network resources and reinforced data security. A system with federated learning supports a variety of client systems. To build an AI system with resource-limited client systems, composing the client system with multiple embedded AI processors is valid. For realizing the system with this architecture, introducing a controller to arbitrate and utilize the AI processors becomes a stringent requirement. In this paper, we propose an embedded AI system for federated learning that can be composed flexibly with the AI core depending on the application. In order to realize the proposed system, we designed a controller for multiple AI cores and implemented it on a field-programmable gate array (FPGA). The operation of the designed controller was verified through image and speech applications, and the performance was verified through a simulator.

Machine Learning, Deep Learning and Implementation Language in Geological Field

&lt;p class="15" align="justify"&gt;Geological big data is growing exponentially. Only by developing intelligent data processing methods can we catch up with the extraordinary growth of big data. Machine learning is the core of artificial intelligence and the fundamental way to make computers intelligent. Machine learning has become the frontier hotspot of geological big data research. It will make geological big data winged and change geology. Machine learning is a training process of model derived from data, and it eventually gives a decision oriented to a certain performance measurement. Deep learning is an important subclass of machine learning research. It learns more useful features by building machine learning models with many hidden layers and massive training data, so as to improve the accuracy of classification or prediction &lt;span style="font-family: 'Times New Roman';"&gt;at last.&lt;/span&gt; Convolutional neural network algorithm is one of the most commonly used deep learning algorithms. It is widely used in image recognition and speech analysis. Python language plays an increasingly important role in the field of science. Scikit-Learn is a bank related to machine learning, which provides algorithms such as data preprocessing, classification, regression, clustering, prediction and model analysis. Keras is a deep learning bank based on Theano/Tensorflow, which can be applied to build a simple artificial neural network.&lt;/p&gt;