Field Of Artificial Intelligence Research Articles

Comparative studies "Effect of educational policy on artificial intelligence in the countries of America, Singapore and Iran

Artificial intelligence under the paradigm of the fourth industrial revolution has become one of the fateful trends of societies in such a way that university education methods, as one of the most important social constructions, have influenced the advancement of technology. Undoubtedly, educational policy has changed the speed, depth and extent of artificial intelligence technology Artificial intelligence is one of the most important emerging technologies, which today has caused huge changes in all areas, including educational policymaking, at a high speed. Among the important applications of artificial intelligence in educational policymaking, we can mention the prioritization of issues based on the real needs and demands of the society and the diagnosis of problems based on the existing conditions, which will lead to the design and formulation of macro-educational policies. The use of methods such as neural networks, natural language processing, and machine learning algorithms makes the process of policy making more intelligent and dynamic, and as a result, the role of policymakers in the application of this technology becomes more efficient. Today, new technologies such as artificial intelligence, sensor networks, blockchain are changing aspects of everyday life. The study of artificial intelligence began in the 1940s and in recent years it has overshadowed the government departments of a large number of countries, of which e-government is one of the examples. One of the topics that is analyzed in the discussion of educational policy is the effect of content on artificial intelligence. In other words, understanding the complexities of decision-making processes as well as the importance of the actors involved in such processes, has undoubtedly influenced artificial intelligence and is influenced by it. This article is designed as a review and library method and it examines the educational policies of Iran, America and Singapore in the field of artificial intelligence with an exploratory view.

Robust visual question answering via polarity enhancement and contrast

The Visual Question Answering (VQA) task is an important research direction in the field of artificial intelligence, which requires a model that can simultaneously understand visual images and natural language questions, and answer questions related to images. Recent studies have shown that many Visual Question Answering models rely on statistically regular correlations between questions and answers, which in turn weakens the correlation between visual content and textual information. In this work, we propose an unbiased Visual Question Answering method to solve language priors from the perspective of strengthening the contrast between the correct answer and the positive and negative predictions. We design a new model consisting of two modules with different roles. We input the image and the question corresponding to it into the Answer Visual Attention Modules to generate positive prediction output, and then use a Dual Channels Joint Module to generate negative prediction output with great linguistic prior knowledge. Finally, we input the positive and negative predictions together with the correct answer to our newly designed loss function for training. Our method achieves high performance (61.24%) on the VQA-CP v2 dataset. In addition, most existing debiasing methods improve performance on VQA-CP v2 dataset at the cost of reducing performance on VQA v2 dataset, while our method not only does not reduce the accuracy on VQA v2 dataset. Instead, it improves performance on both datasets mentioned above.

A StarGAN and transformer-based hybrid classification-regression model for multi-institution VMAT patient-specific quality assurance.

The field of artificial intelligence (AI)-based patient-specific quality assurance (PSQA) for volumetric modulated arc therapy (VMAT) faces challenges in terms of developing general models across institutions due to the prevalence of multi-institution data collection and multivariate heterogeneity. Building a general model that is capable of handling diverse multi-institution data is critical for enabling large-scale integration and analysis. This study aims to develop a star generative adversarial network (StarGAN) and transformer-based hybrid classification-regression PSQA framework to address unification of heterogeneous data from different institutions. A StarGAN and transformer-based hybrid classification-regression model was developed as a general PSQA framework to predict gamma passing rates (GPRs) and classify quality assurance (QA) results as "Pass" or "Fail" at multiple institutions. A total of 1815 VMAT plans were collected from eight institutions to develop the general PSQA framework and perform clinical commissioning and implementation. Among them, 20 independent clinical plans from each of eight institutions, for a total of 160 plans, were used for the clinical commissioning, and 205 new clinical plans from eight institutions were used for clinical implementation. For the 3%/3, 3%/2, and 2%/2mm gamma criteria, the sensitivity of the proposed PSQA framework with pretraining was 90.13%, 92.03%, and 95.84%, respectively, while the specificity was 76.01%, 76.12%, and 85.34%, respectively. The mean absolute errors (MAEs) of the proposed PSQA framework with pretraining were 1.36%, 2.37%, and 3.96%, respectively, while the root-mean-square errors (RMSEs) were 2.31%, 3.89%, and 5.17%, respectively. The results demonstrated visible improvement at multiple institutions. For clinical commissioning, the deviations between the predicted and measured results were all within 3% for 3%/3 and 3%/2mm at eight institutions. For clinical implementation, all failure plans were correctly identified by the proposed PSQA framework. The general PSQA framework enables diverse clinical data sources to be handled to achieve enhanced model performance and generalizability, and provides a solution to the unification of heterogeneous data from different institutions to construct robust QA models. This approach can be clinically deployed for VMAT QA.

Exploring Generative Artificial Intelligence

This work reviews the concept of Generative Artificial Intelligence (GAI), its main characteristics, and its classification inside the Artificial Intelligence (AI) field. In this way, the main GAI foundational algorithmic architectures for data generation are presented; each considers a previously gathered data set and generates novel and synthetic data. Subsequently, use cases for generative models reported in the literature are presented, ranging from applications in healthcare, education, and customer experiences. The deployment of GAI models still needs to overcome a series of challenges inspected in this document, considering the presence of bias, lack of transparency, and hallucinations presented by the models. Finally, the risks and misuse of GAI are reviewed since widespread use of this type of technology is expected in society.

The trajectory of artificial intelligence for competency-based personalised learning: past, present and future

PurposeThe study aims to reflect on past research, uncover current trends and propose a future research agenda in the field of artificial intelligence (AI) for competency-based personalised learning.Design/methodology/approachThe study followed the SPAR-4-SLR protocol to retrieve 855 articles related to the field indexed in the Scopus database. Performance analysis, network analysis and science mapping were then performed using VOSviewer and the Biblioshiny app.FindingsThe analysis identified nine clusters of intellectual structure (healthcare, competencies, learning systems, digital transformation, AI literacy, computer-aided education, AI ethics, e-learning and active learning) and twelve themes (including motor, basic, emerging and niche).Originality/valueFollowing an extensive review of the literature, this would appear to be the first study to provide a panoramic view of AI for competency-based personalised learning based on the Scopus database. The core gaps in the current literature have been identified and the corresponding future agenda will be instrumental in shaping future research directions in the field.

Mastering Data Pipelines for Al: A Beginner's Guide to Building Efficient Workflows

This comprehensive article explores the critical role of data pipelines in Artificial Intelligence (AI) development, emphasizing their importance in ensuring high-quality, relevant data for machine learning models. It covers key stages of the data pipeline process, including data ingestion, cleaning and preprocessing, transformation and feature engineering, and storage and loading. The article discusses various tools, techniques, and best practices for each stage, addressing challenges in handling diverse data sources, scalability issues, and the need for efficient data management. It highlights the significance of robust data pipelines in enhancing AI model performance and reliability, while also considering the dynamic nature of the AI field and the necessity for continuous learning and adaptation.

Review of Convolutional Neural Network

Over the past decade, the domain of deep learning has emerged as a swiftly expanding area of interest among researchers globally. It offers substantial benefits over traditional shallow networks, particularly in the realms of feature extraction and model fitting. Deep learning excels at uncovering intricate, distributed features from raw data, which exhibit robust generalization capabilities. It has triumphantly addressed challenges that were once deemed intractable within the field of artificial intelligence. With the exponential growth in the volume of training data and a marked increase in computational power, deep learning has achieved remarkable strides and found applications across a spectrum of domains, including but not limited to object detection, computer vision, natural language processing, speech recognition, and semantic parsing, thereby accelerating the evolution of artificial intelligence. Deep learning encompasses a series of non-linear transformations organized hierarchically, with deep neural networks representing the predominant form of contemporary deep learning methodologies. Inspired by the neural connections found in the animal visual cortex, these networks are characterized by local connectivity, shared weights, and pooling operations. Such attributes not only simplify the network model and curtail the number of trainable parameters but also engender invariance to shifts, distortions, and scaling, thereby endowing the model with enhanced robustness and fault tolerance. This makes the training and optimization of the network structure more manageable. This paper commences with a retrospective on the evolution of convolutional neural networks (CNNs). Subsequently, it delineates the architectures of neuron models and multilayer perceptron. It proceeds to dissect the CNN architecture, which typically encompasses a sequence of convolutional and pooling layers, culminating in fully connected layers, each serving distinct functions. The paper also elaborates on various enhanced algorithms for CNNs, such as the “Network in Network” and “Spatial Transformer Networks,” while also introducing supervised and unsupervised learning methodologies pertinent to CNNs and highlighting several prevalent open-source tools. Further, the paper scrutinizes the application of CNNs in various fields, including image classification, facial recognition, audio retrieval, electrocardiogram analysis, and object detection. It posits the amalgamation of CNNs with recurrent neural networks as a potential alternative for training datasets. The research culminates in the design of CNN structures with varying parameters and depths, supported by experimental analysis that elucidates the interplay among these parameters and the ramifications of their configuration. Ultimately, the paper synthesizes the merits and lingering challenges associated with CNNs and their practical applications.

Artificial intelligence governance framework for healthcare.

Recent advancements in the field of Artificial Intelligence (AI) provide promising applications of this technology with the aim of solving complex healthcare challenges. These include optimizing operational efficiencies, supporting clinical administrative functions, and improving care outcomes. Numerous AI models are validated in research settings but few make their way into useful applications due to challenges associated with implementation and adoption. In this article, we describe some of these challenges, along with the need for a facilitating entity to safely translate AI systems into practical use. The authors propose a new AI governance framework to enable healthcare organizations with a mechanism to implement and adopt AI systems.

A Novel Natural Language Processing Model Transfer Strategy Tailored for Deep Learning Platforms

As artificial intelligence technology continues to evolve, numerous advancements enable smoother communication and collaboration between humans and computers. Natural language processing (NLP), as a key direction in the field of computer and artificial intelligence, has been attracting much attention. It focuses on the language used by people in daily life, devoting itself to improving the ability of language generation and understanding, thus making communication between people and computers more fluent and natural, effectively breaking through the problem of human-computer interaction. With the advancement of deep learning technology, the migration of NLP models onto deep learning platforms has emerged as a key trend. Model transfer, a crucial aspect of deep learning, holds significant value in enhancing the performance and efficiency of NLP models. This paper begins by outlining the fundamentals of deep learning platforms and NLP model transfer, followed by a comprehensive examination of current research progress and challenges in this field. Subsequently, we introduce a novel NLP model transfer strategy tailored for deep learning platforms and validate its effectiveness through rigorous experiments. In conclusion, the paper highlights our noteworthy advancements, pointing toward promising future developments.

Artificial intelligence and orthopaedic

Artificial intelligence is the next big thing in human history. It is set to affect large and small industries, entertainment, agriculture, literature, research, and healthcare. The field of Artificial intelligence includes machine learning and deep learning, which denote an increased level of specialization. These changes are also set to influence orthopedic trauma, from diagnostics, clinical assessment, surgical intervention, rehabilitation, and outcome prediction. Some of these fields will be affected more than others. We carried out a narrative review of artificial intelligence and orthopaedic surgery to provide an updated overview of the current and future applications. AI is set to revolutionize radiological diagnostics, outcome measurement, and rehabilitation in orthopedic trauma. However, concerns about clinical decision-making and intervention remain. Ethical concerns, regulation, and superiority over traditional methods of treatment also need to be assessed. Until then, the role of AI in orthopaedic trauma remains in the realm of possibilities.

Improved Ensemble Model for Insecticide Recognition by Incorporating Insect Toxicity Data.

Pesticide molecules, such as insecticides, play a critical role in modern agricultural production. Traditional pesticide development methods are often inefficient and expensive, while data-driven artificial intelligence (AI) techniques have emerged as a useful tool to facilitate drug discovery. However, currently available commercial pesticide data is limited, which makes the trained models unsatisfactory in terms of performance and generalization. From a domain knowledge perspective, insect toxicity data were incorporated to improve the insecticide recognition of AI models. Compared to the models trained with the original data set, the new models performed better in the external validation, and their generalization was more desirable. In addition, by integrating different types of individual models, we obtained an ensemble model with better performance. Based on this, an online platform was developed to provide researchers with free access to insecticide screening (https://dpai.ccnu.edu.cn/InsectiVS/). Finally, two potential insecticide molecules with insecticidal activity against Plutella xylostella were successfully identified in a real-world scenario. In conclusion, this idea connects the fields of AI and agricultural chemistry and is expected to have wide application in pesticide research.

The possibilities of big data in pharmacoepidemiology, problems of use, legal regulation

The growth of digitalization in medicine has significantly expanded the possibilities of using big data in pharmacoepidemiology. The use of big data makes it possible to reduce the cost of clinical research, increase the speed of recruitment and expand the sample, makes it possible to evaluate the effect of drugs in pregnant women and effectiveness in rare diseases. The databases PubMed, Scopus, Web of Science and Google Scholar for 12 years from 2012 to 2023, United Nations documents, World Health Organization, Federal laws of the Russian Federation in the field of artificial intelligence, protection of confidential information, and clinical research were analyzed. The search was carried out by keywords: «big data», «registers», «pharmacoepidemiology», «personal data», «legal regulation», «protection methods». The article provides examples of the use of big data in healthcare, including from 25 to 50 million people. The analysis of the literature data revealed the same type of problems – the lack of uniformity in the introduction of information, incomplete information, limited availability. The review identifies the problems of protecting the confidentiality of information. The mechanisms of information standardization, storage, and data processing are considered. The international and Russian legislative framework regulating the conduct of clinical trials using big data is presented.

Opportunities for the use of artificial intelligence in supply chain management

Aim. To evaluate the potential of generative artificial intelligence technology in the context of supply chain management.Objectives. To analyze how artificial intelligence is changing traditional management practices and structures in supply chain management; to identify fundamental shifts brought about by AI, including automation, data-driven decision making, and human empowerment; to examine current statistics on the effectiveness of artificial intelligence in management processes and conclude the potential, promise of this technology in the logistics industry; to provide strategic insight and practicalMethods. The research combines qualitative and quantitative methods to provide a deeper understanding of the problems considered. Based on these methods, an array of relevant statistical data and expert opinions in the field of generative artificial intelligence has been studied.Results. In today’s business environment, supply chains face a number of challenges: globalization, market volatility, changing customer expectations, and force majeure (natural disasters, pandemics, or wars). To meet these challenges and strengthen their market position, companies are increasingly turning to new technologies and innovations. These technologies have the potential to revolutionize the approach to supply chain management, increasing transparency, efficiency and responsiveness. All emerging technologies, from artificial intelligence and blockchain to the Internet of Things (IoT) and robotics, potentially offer opportunities to streamline operations, improve decision-making, and enhance supply chain collaboration.Conclusions. In this article, the authors have shown how artificial intelligence is changing traditional governance structures in supply chain management. Current statistical data on the effectiveness of artificial intelligence in management processes are analyzed, and a conclusion is made about the potential and prospects of this technology in the logistics sphere. Strategic understanding is formed and practical recommendations for organizations seeking to implement new tools of generative artificial intelligence are given.

Awareness About Artificial Intelligence Among Dental Practitioners And Dental Students

BACKGROUND Artificial intelligence (AI) is defined as acquisition of intelligence by computers or machines to perform complex tasks that generally require human intelligence. Nowadays usage of AI in various fields is appreciated because of its time effectiveness and less laborious.In dentistry, AI has been trending, specifically in diagnostic imaging and early detection of diseases,benefiting both dental graduates and practitioners. AIM The main aim is to assess the awareness of Artificial intelligence as an effective tool among dental trainees and dental practitioners. MATERIALS AND METHOD An online cross-sectional survey was conducted among dental trainees and dental practitioners in various institutions. A Google questionnaire form was developed and circulated to assess participant’s awareness about Artificial intelligence in dentistry. Data were collected in Microsoft excel and statistical analysis were performed. RESULTS More than 60% of the participants showed confidence about their familiarity towards AI in dentistry (p<0.01197). Among those, 60.5% answered that they are well aware of ChatGPT and 28% of them to Microsoft Bing (p<0.04395). 53% answered that machine learning is an important form of AI (p<0.00216). Over the advantages of AI, 43% believed AI can improve diagnostics, access to disease screening, cost effectiveness, reduced treatment time (p<0.0001) and 53% agreed that AI can be useful in day-to-day dental practices (p<0.0001). CONCLUSION The present study shows that awareness of AI among the dental fraternity is satisfactory. The field of AI is emerging rapidly and various other new applications in AI are being utilised. AI could act as a valuable tool in supporting clinicians delivering effective dental care and supplementing education for dental trainees. To enhance the future of AI in dentistry, the present curriculum needs to be approachable enough and various hands-on-training are essential. KEYWORDS:artificial intelligence, chatbots, dental caries, radiographic diagnosis, teledentistry.

Connecting the Wings of Dynamism: Bibliometric Analysis of Artificial Intelligence and Entrepreneurship Fields

This study aims to create a holistic viewpoint by concentrating on two dynamic areas of artificial intelligence and entrepreneurship with bibliometric analysis. The concept of artificial intelligence, which is constantly heard as the digital world gradually penetrates our lives, and entrepreneurship, which is referred to as the atomic element of the economic infrastructure, are addressed in the same pot with this research. The attitude of both areas against varying circumstances constitutes the essential basis of this examination. The view that the effectiveness in the areas can be increased with the synergy to be created between the two focuses is supported. With this intention, the study commences with an informative literature section, where the introductory elements of the areas are conveyed. Afterward, it tries to clarify why these zones need to be examined together. Following this, a bibliometric analysis study, frequently used to bring unfamiliar kinds of literature jointly, is conducted using data obtained from the Web of Science database and subjected to various analyses. In the last stage, the study is completed by examining these outputs and analyzes. As a result, conclusions support “the duo” can be investigated jointly. The study contributes to the idea that artificial intelligence and entrepreneurship are wings working in synchrony for the requirement of success.

Fabrication of carbon nanotube neuromorphic thin film transistor arrays and their applications for flexible olfactory-visual multisensory synergy recognition

Abstract Artificial multisensory devices play a key role for the human-computer interaction in the field of artificial intelligence (AI). In this work, we have designed and constructed a novel olfactory-visual bimodal neuromorphic carbon nanotube thin film transistor (TFT) arrays for artificial olfactory-visual multisensory synergy recognition with the ultralow single-pulse power consumption of 25 aJ, employing semiconducting single-walled carbon nanotubes (sc-SWCNTs) as channel materials and gas sensitive materials, and poly[[4,8-bis[5-(2-ethylhexyl)-2-thienyl]benzo[1,2-b:4,5-b0]dithiophene-2,6-diyl]-2,5-thiophenediyl-[5,7-bis(2-ethylhexyl)-4,8-dioxo-4H,8H-benzo[1,2-c:4,5-c0]dithio-phene-1,3-diyl]] (PBDB-T) as the photosensitive material. It is noted that it is the first time to realize the simulation of olfactory and visual senses (from 280 nm to 650 nm) with the wide operating temperature range (0-150 °C) in a single SWCNT TFT device and successfully simulate the recovery of olfactory senses after COVID-19 by olfactory-visual synergy. Furthermore, our SWCNT neuromorphic TFT devices with high Ion/Ioff ratio (up to 106) at a low operating voltage (-2-0.5 V) can mimic not only the basic biological synaptic functions of olfaction and vision (such as paired-pulse facilitation, short-term plasticity, and long-term plasticity), but also optical wireless communication by Morse code. The proposed multisensory, broadband light-responsive, low-power synaptic devices provide great potential for developing AI robots to face complex external environments.

A Systematic Review of Literature on Intellectual Property and the Creative Industry in Africa Using Scopus

The shift of the world to a knowledge economy, mainly driven by Intellectual Property (IP), calls for research dissemination through scholarly publishing to promote the protection and commercialisation of IP. In Africa, innovations such as mobile payment systems, drones, and development in the fields of renewable energy, biotechnology, and artificial intelligence have become key drivers of economic growth and development. However, many African countries have yet to know and appreciate the value of IP to their economies. The benefits of IP may be fully realised through proper awareness and understanding of IP using scholarly publications. The study sought to examine the published documents that focus on IP and the creative industry in Africa in order to create awareness and understanding of the breadth and scope of IP and IPRs. Content analysis was conducted on Scopus using descriptive informetrics. The Scopus database was used to ascertain the published documents that focus on IP and the creative industry in Africa. The researcher used the search string TITLE, ABSTRACT, KEYWORD and also applied Scopus “Analyse Search Results”, which facilitated in-depth analysis of the documents according to the key indicators (the year of publication, territory or country, source, author, institute/ affiliation, types of document, subject areas and sponsors). The analysis revealed that there were very few documents published each year on IP and the creative industry published in Africa. Although documents on IP and the creative industry in Africa were published through three document types, there was a diversity of the subject areas in which research on IP and the creative industry in Africa was found. Of the available research, most authors were affiliated with institutions in Africa, but only one of the journals belonged to an African country, and of all the sponsors, only four belonged to an African country. There was very little research on IP and the creative industry published in Africa, although IP is applicable to different disciplines. Africa needs to improve its science and technology capacity, research facilities and funding, and understanding and awareness of IP. Scholarly publishing on IP and the creative industries would be more pronounced if supported by local sponsors and journals instead of relying on scholars and institutions affiliated with countries outside Africa.

EFFICIENCY MEASUREMENT OF ARTIFICIAL INTELLIGENCE: A RESEARCH ON COMPANIES IN TÜRKİYE

The use of technology is increasing due to Industry 4.0. Both countries and organizations have had to invest in the field of artificial intelligence (AI) to compete with their rivals in global competitive conditions and to adapt to the ever-changing world. An organization or a country needs to evaluate its performance to ensure its sustainability constantly. The Data Envelopment Analysis (DEA) method is widely used in performance evaluation. This study aimed to evaluate Türkiye AI performance for the nine years between 2014 and 2022. In the research, years were included in the analysis as the decision-making unit. Two input and two output variables were used in the analyses. The study was carried out by using the input-oriented CCR DEA model and its super-efficiency model. According to the results of the analysis, efficient/inefficient decision-making units were determined. Several potential improvement suggestions have been put forward for inefficient decision-making units.

EEG-based emotional valence and emotion regulation classification: a data-centric and explainable approach

Emotion classification using electroencephalographic (EEG) data is a challenging task in the field of Artificial Intelligence. While many researchers have focused on finding the best model or feature extraction technique to achieve optimal results, few have attempted to select the best methodological steps for working with the dataset. In this study, we applied two different theoretical approaches based on the noise of the dataset: curriculum learning and confident learning. Curriculum learning involves presenting training examples to the model in a specific order, starting with easier examples and gradually increasing in difficulty. This approach has been shown to improve model performance. Confident learning is a method for identifying and correcting label errors in datasets. By identifying and correcting these errors, confident learning can improve the performance of machine learning models trained on noisy datasets. We then applied the Integrated Gradient technique in order to assess the explainability of each model. Our aim was to explore the impact of different models and methods on emotion classification performance using EEG data. We collected and used an EEG dataset in which participants rated the emotional valence of positive and negative pictures while performing an emotion regulation (ER) task, comparing a control condition (Look) with two ER strategies: cognitive reappraisal and expressive suppression. We performed a multilabel classification to identify emotional neutrality or polarization of emotional valence (both positive and negative) rated by participants and the emotion regulation strategy adopted during the task. We compared the performance of models trained on three datasets selected based on label noise and evaluated their suitability for this task. Our results suggest different patterns based on the architecture used for feature importance, highlighting both advantages and criticisms.

Classification of Infant Crying Sounds Using SE-ResNet-Transformer

Recently, emotion analysis has played an important role in the field of artificial intelligence, particularly in the study of speech emotion analysis, which can help understand one of the most direct ways of human emotional communication—speech. This study focuses on the emotion analysis of infant crying. Within cries lies a variety of information, including hunger, pain, and discomfort. This paper proposes an improved classification model using ResNet and transformer. It utilizes modified Mel-frequency cepstral coefficient Mel-frequency cepstral coefficient (MFCC) features obtained through feature engineering from infant cries and integrates SE attention mechanism modules into residual blocks to enhance the model’s ability to adjust channel weights. The proposed method achieved 93% accuracy rate in experiments, offering advantages of shorter training time and higher accuracy compared to other traditional models. It provides an efficient and stable solution for infant cry classification.