Field Of Intelligence Research Articles

Intellectual Property in the Conditions of Digital Transformation of the Economy

A comparative analysis of the fulfillment of the necessary conditions for implementing the digital transformation of the economy using intellectual property in the world and in the Republic of Belarus was carried out. It has been established that in order to implement the digital transformation of the economy, it is necessary to fulfill two conditions directly related to intellectual property: the development of software and the availability of an instrument fleet, determined by patents for inventions in the field of information and communication technologies. It is concluded that Belarus has significant potential in the field of software creation. A noticeable lag in the Republic in terms of creating an instrument base for information and communication technologies has been revealed, which is confirmed by the small number of patents in the field of these technologies and in the field of artificial intelligence received by national applicants. The main directions for the development of digital transformation of the national economy in terms of intellectual property are formulated.

An improved SLAM algorithm for substation inspection robot based on the fusion of IMU and visual information

In the past, manual inspection was often used for equipment inspection in indoor environments such as substation rooms and chemical plant rooms. This way often accompanies high labor intensity, low inspection efficiency, and low safety, which is difficult to meet the increasingly stringent requirements of indoor equipment operation and maintenance management. For dealing with these issues, a VIORB-SLAM2 algorithm based on the integration of IMU and visual information, was proposed by this paper. Firstly, the IMU data and image data were integrated to restore scale information of cameras, and then an error function was established to enhance the algorithm’s robustness. Secondly, in order to improve the accuracy of the algorithm, the random sampling consensus method was used to eliminate the wrong matching points in feature point matching, and the normalized cross-correlation matching was employed to constrain key frame matching conditions. Finally, through the iterative closest point method to stitch the point clouds, a dense map for navigation was constructed. The experimental results show that the algorithm designed by this paper has solved the shortcomings of applying the ORB-SLAM2 algorithm to indoor inspection robots while achieving high positioning accuracy, which can be combined with other algorithms in the field of artificial intelligence for object detection and semantic map construction in the future.

The editorial for special issue “Russian Research in Artificial Intelligence for Cybersecurity” of Journal of Computer Virology and Hacking Techniques (JICV) highlights the studies of Russian researchers in the field of artificial intelligence to ensure cybersecurity

The editorial for special issue “Russian Research in Artificial Intelligence for Cybersecurity” of Journal of Computer Virology and Hacking Techniques (JICV) highlights the studies of Russian researchers in the field of artificial intelligence to ensure cybersecurity

A novel hybrid deep learning IChOA-CNN-LSTM model for modality-enriched and multilingual emotion recognition in social media.

In the rapidly evolving field of artificial intelligence, the importance of multimodal sentiment analysis has never been more evident, especially amid the ongoing COVID-19 pandemic. Our research addresses the critical need to understand public sentiment across various dimensions of this crisis by integrating data from multiple modalities, such as text, images, audio, and videos sourced from platforms like Twitter. Conventional methods, which primarily focus on text analysis, often fall short in capturing the nuanced intricacies of emotional states, necessitating a more comprehensive approach. To tackle this challenge, our proposed framework introduces a novel hybrid model, IChOA-CNN-LSTM, which leverages Convolutional Neural Networks (CNNs) for precise image feature extraction, Long Short-Term Memory (LSTM) networks for sequential data analysis, and an Improved Chimp Optimization Algorithm for effective feature fusion. Remarkably, our model achieves an impressive accuracy rate of 97.8%, outperforming existing approaches in the field. Additionally, by integrating the GeoCoV19 dataset, we facilitate a comprehensive analysis that spans linguistic and geographical boundaries, enriching our understanding of global pandemic discourse and providing critical insights for informed decision-making in public health crises. Through this holistic approach and innovative techniques, our research significantly advances multimodal sentiment analysis, offering a robust framework for deciphering the complex interplay of emotions during unprecedented global challenges like the COVID-19 pandemic.

Current problems of using artificial intelligence inHR management in Kazakhstan

The article provides an understanding of artificial intelligence in the field of HR, and it is said that the digitization of the industry is reflected in the widespread use of artificial intelligence. The theory and history of the field of artificial intelligence is systematized by reviewing the works of researchers. The possibilities of using artificial intelligence are clarified and its efficiency is considered. As a result of the study, it was determined that artificial intelligence is used in four main categories in the field of HR, and it was found that there is a high demand for automated resume search, analysis, and selection services. At the same time, in order to invite candidates for a vacant position, including the identification of qualified talented employees, chatbots, Skype interviews, the use of information on online recruiting platforms such as HeadHunter, notification of vacancies at fairs, and pilot projects introduced by the state will be analyzed. The features of artificial intelligence programs introduced in the Kazakhstan labor exchange, and changes brought to the HR system, the advantages and disadvantages will be discussed. Especially as the most effective programs in the field of artificial intelligence in recent years: IBM's Deep Blue program, Alvinn program for driving, and Microsoft Application and Services Group's talking robot program, the importance of research on the specificity of programs in the field of medicine and pharmaceuticals will be analyzed and considered as examples of regional enterprises.

Artificial Intelligence Applied to Civil Engineering: A Multidisciplinary Approach

Objective: Present the main applications of Artificial Intelligence (AI) in Civil Engineering, covering all phases of building a Smart Home, from conception to disposal. Theoretical Framework: Based on the multidisciplinary nature of artificial intelligence in Civil Engineering and the various methodologies and techniques to apply it, based on data collection and processing of the information obtained, a Smart Home is proposed that is planned from its conception to its disposal or demolition, covering the entire life cycle of the project. To manage the subsystems, a more extensive System called “Digital Home Management (DHM)” was developed, in which the data collected by the sensors is processed and transformed into information that will be stored and transmitted to the resident when necessary. Method: The methodology adopted for this study is qualitative and exploratory and is based on documentary bibliographic research in national and international databases. Results and Discussions: The results show the advantages of considering different fields of human knowledge when adopting Artificial Intelligence techniques, leading to better results in the products and services delivered by the Smart Home and Digital Home Management. Research Implications: AI applied to Civil Engineering is just beginning. This practice will increase considerably in the coming years. The integration between human beings (peopleware), computer programs (software), and machines (hardware) means that AI presents a wide range of options for services and products to be provided to improve the quality of life of society. Originality/Value: The relevance and value of this research are evidenced by proposing the implementation of a Digital Home Management System to mitigate the damage caused to the environment by human activities, whether routine or not, contributing to a healthier society and a more sustainable environment. This research on AI application to Civil Engineering is just beginning. This practice will increase considerably in the coming years. The integration between human beings (peopleware), computer programs (software), and machines (hardware) makes AI present a wide range of options for services and products to be provided to improve the quality of life of society. shows how much the field of Artificial Intelligence applied to Civil Engineering is quite promising and will increasingly require professionals who can interrelate with people from other areas.

Research Progress of Machine Learning in Extending and Regulating the Shelf Life of Fruits and Vegetables.

Fruits and vegetables are valued for their flavor and high nutritional content, but their perishability and seasonality present challenges for storage and marketing. To address these, it is essential to accurately monitor their quality and predict shelf life. Unlike traditional methods, machine learning efficiently handles large datasets, identifies complex patterns, and builds predictive models to estimate food shelf life. These models can be continuously refined with new data, improving accuracy and robustness over time. This article discusses key machine learning methods for predicting shelf life and quality control of fruits and vegetables, with a focus on storage conditions, physicochemical properties, and non-destructive testing. It emphasizes advances such as dataset expansion, model optimization, multi-model fusion, and integration of deep learning and non-destructive testing. These developments aim to reduce resource waste, provide theoretical basis and technical guidance for the formation of modern intelligent agricultural supply chains, promote sustainable green development of the food industry, and foster interdisciplinary integration in the field of artificial intelligence.

Improved GAN-based deep learning approach for strain field prediction and failure analysis of precast bridge slab joints

Nonlinear finite element analysis (FEA) is crucial for understanding complex stress-strain behaviors and assessing potential structural failure risks. Building on the recent success of deep learning (DL) methods in physical field predictions, there has been growing interest in forecasting internal force fields of structures beyond the existing data scope. In this study, a data-driven strain field analysis model based on an improved Generative Adversarial Network (GAN) is proposed. To enrich the input data, this novel approach incorporates the Signed Distance Field (SDF) derived from the characteristics of the original Finite Element (FE) models. The generator, based on U-Net, is enhanced with channel attention mechanisms and loss penalty terms, forming the core of the improved GAN. The effectiveness of the proposed method is demonstrated using two parallel datasets comprising 3-Headed bar tension models across two planes. Subsequently, the generated strain field is subjected to feature extraction using the 2D-Principal Component Analysis (2D-PCA) method, followed by structural failure determination through the K-Nearest Neighbor (KNN) algorithm. The results indicate that the proposed improved approach achieves a significant reduction in Root Mean Square Error (RMSE) of 20.71 % and 13.21 % across the two datasets, with the structural failure determination method reaching an F1-score of 0.971. Thus, the intelligent strain field analysis method introduced here effectively predicts strain distributions and promptly identifies failure states, offering valuable insights for further numerical analysis and structural design.

Artificial intelligence and machine learning in corporate governance: A bibliometric analysis

BACKGROUND: The study deeply explores the thriving domains of artificial intelligence (AI) and machine learning (ML) in corporate governance. OBJECTIVE: The study aims to thoroughly examine the rapidly developing fields of artificial intelligence (AI) and machine learning (ML) in corporate governance. METHODS: After completing an in-depth analysis of 229 research studies published between 2008 and 2023 (using software tools such as RStudio, VOSviewer, and Excel),), the study reveals a notable increase in publications since 2022. Corporate social responsibility (CSR), environmental, social, and governance (ESG) issues, executive remuneration, and sustainability are all considered as important key focal areas of focus. Scholars in this field are notably at the forefront from Taiwan, the United States, and China. IMPLICATIONS: However, the study stress the necessity for further researches to estimate the efficacy of different AI and ML methodologies. This may guide evidence-based governance practices various industries and geographical areas.

An Analysis of Research Trends for Using Artificial Intelligence in Cultural Heritage

Artificial intelligence (AI) techniques have been increasingly applied in assisting various cultural heritage (CH)-related tasks. The aim of this study is to examine the research trends and current applications of AI in this vast domain. After obtaining a dataset from the Web of Science and Scopus databases, a scientometric analysis of research publications from 2019 to 2023 related to the use of AI in CH was conducted. The trending topics based on the author’s keywords were identified by using the ScientoPy v2.1.3 software. Through this approach, five main topics were identified: classification, computer vision, 3D reconstruction, recommender systems, and intangible cultural heritage. The analysis highlights the upward trend in publications in this field since 2019, indicating a growing interest in the application of AI techniques in CH. By analyzing the latest research in the field, it is observed that AI techniques are mostly applied to assist CH in the discovery, description, classification, and preservation tasks. The report gives insights into the main research areas and developing trends in the field of artificial intelligence and machine learning. The study offers important information about the key research areas and emerging trends related to using AI techniques in the CH field. This helps to recognize the potential, development, and increasing influence of these technologies within the CH domain. The findings of this study contribute to the future development of AI applications in CH, enabling professionals to use the advantages of these technologies.

Artificial intelligence as a tool in drug discovery and development.

The rapidly advancing field of artificial intelligence (AI) has garnered substantial attention for its potential application in drug discovery and development. This opinion review critically examined the feasibility and prospects of integrating AI as a transformative tool in the pharmaceutical industry. AI, encompassing machine learning algorithms, deep learning, and data analytics, offers unprecedented opportunities to streamline and enhance various stages of drug development. This opinion review delved into the current landscape of AI-driven approaches, discussing their utilization in target identification, lead optimization, and predictive modeling of pharmacokinetics and toxicity. We aimed to scrutinize the integration of large-scale omics data, electronic health records, and chemical informatics, highlighting the power of AI in uncovering novel therapeutic targets and accelerating drug repurposing strategies. Despite the considerable potential of AI, the review also addressed inherent challenges, including data privacy concerns, interpretability of AI models, and the need for robust validation in real-world clinical settings. Additionally, we explored ethical considerations surrounding AI-driven decision-making in drug development. This opinion review provided a nuanced perspective on the transformative role of AI in drug discovery by discussing the existing literature and emerging trends, presenting critical insights and addressing potential hurdles. In conclusion, this study aimed to stimulate discourse within the scientific community and guide future endeavors to harness the full potential of AI in drug development.

NGHIÊN CỨU XÂY DỰNG HỆ THỐNG THEO DÕI, GIÁM SÁT TRẠNG THÁI BUỒN NGỦ DỰA TRÊN THỊ GIÁC MÁY TÍNH

This article focuses on the research and development of a model for a drowsiness detection and monitoring system while driving based on computer vision. The product can be applied to current vehicles (such as cars or vehicles with similar functions). The study utilizes several state-of-the-art sleep detection models to collect and process data and train the model to provide quantitative results. The model is evaluated through this process, and improvements are proposed to make it suitable for vehicles in Vietnam. Experimental results show that the system can capture images via a camera, detect human faces, predict sleep states, and send alerts to a server, displaying warning data on a website interface. Essential evaluations indicate that the system operates stably and relatively accurately according to the pre-established design and programming requirements. The results of this research can be applied in practical scenarios or serve as a practical model for students in artificial intelligence, robotics, and automation control fields to enhance their ability to apply theoretical knowledge to real-world problem-solving. Keywords: Drowsiness; traffic; smart robots; computer vision; artificial intelligence.

Leveraging foundation and large language models in medical artificial intelligence.

Recent advancements in the field of medical artificial intelligence (AI) have led to the widespread adoption of foundational and large language models. This review paper explores their applications within medical AI, introducing a novel classification framework that categorizes them as disease-specific, general-domain, and multi-modal models. The paper also addresses key challenges such as data acquisition and augmentation, including issues related to data volume, annotation, multi-modal fusion, and privacy concerns. Additionally, it discusses the evaluation, validation, limitations, and regulation of medical AI models, emphasizing their transformative potential in healthcare. The importance of continuous improvement, data security, standardized evaluations, and collaborative approaches is highlighted to ensure the responsible and effective integration of AI into clinical applications.

SURVEY OF ADVERSARIAL ATTACKS AND DEFENSE AGAINST ADVERSARIAL ATTACKS

In recent years, the fields of Artificial Intelligence (AI) and Deep learning (DL) techniques along with Neural Networks (NNs) have shown great progress and scope for future research. Along with all the developments comes the threats and security vulnerabilities to Neural Networks and AI models. A few fabricated inputs/samples can lead to deviations in the results of the models. Patch based Adversarial Attacks can change the output of a neural network to a completely different result just by making a few changes to the input of the neural network. These attacks employ a patch that is applied to the input image in order to cause the classifier to misclassify and make the incorrect prediction. The goal of this research is to develop effective defense strategies against these types of attacks and make the model/Neural Network more robust.

Holistic approach for selecting chatbot development tools: combining AHP and TOPSIS methodologies

ABSTRACT The proliferation of chatbot development tools presents a significant challenge in identifying the most suitable ones for specific needs. This study addresses this issue by integrating the Analytic Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to create a comprehensive methodology for selecting chatbot development tools. The study aims to provide a transparent, objective decision-making framework by evaluating tools across multiple dimensions including deployment characteristics, input processing capabilities, dialogue management, response generation, and economic considerations. Utilizing AHP for criteria weighting and TOPSIS for ranking alternatives, the methodology systematically evaluates various tools to identify the optimal choice. Key findings highlight the strengths and weaknesses of different tools, offering insights that guide developers and organizations in choosing the most suitable chatbot development tool aligned with their specific needs and strategic goals. The implications of this methodology are significant, enhancing decision-making processes within technological tool selection and contributing to the fields of artificial intelligence and automated systems development.

Thickness dependence and crystallization properties of amorphous GeTe thin films on silicon dioxide

Radio frequency magnetron sputtering was used to prepare the amorphous GeTe thin films on silicon dioxide and the thickness effects on the crystallization behavior were investigated. With the film thickness reducing, the crystallization temperature, crystallization activation energy, amorphous and crystalline resistance increase remarkably, indicating the great improvement in thermal stability and power consumption. Ozawa’s model was used to estimate the crystallization kinetics of GeTe thin films, it shows that nucleation and grain growth occur simultaneously, and grain growth dominates ultimately. XRD analysis demonstrated that the grain size can be reduced and the crystallization process of GeTe thin film can be inhibited with the film thickness decreasing. Furthermore, the thinner film has smaller resistance drift index and surface roughness, which are beneficial to improve the reliability of storage device. T-type phase change memory devices based on 25 nm GeTe thin film were fabricated by 0.13 μm CMOS technology, and the current–voltage and resistance-voltage characteristics demonstrate the excellent electrical performance, including the fast resistance switching between SET and RESET processes, low threshold current and voltage. All the results proved the strong dependency relationships between the crystallization properties and film thickness of GeTe thin film, which paves the way for developing high-density phase change memory in the fields of big data and artificial intelligence.

Improving deep learning-based automatic cranial defect reconstruction by heavy data augmentation: From image registration to latent diffusion models

Modeling and manufacturing of personalized cranial implants are important research areas that may decrease the waiting time for patients suffering from cranial damage. The modeling of personalized implants may be partially automated by the use of deep learning-based methods. However, this task suffers from difficulties with generalizability into data from previously unseen distributions that make it difficult to use the research outcomes in real clinical settings. Due to difficulties with acquiring ground-truth annotations, different techniques to improve the heterogeneity of datasets used for training the deep networks have to be considered and introduced. In this work, we present a large-scale study of several augmentation techniques, varying from classical geometric transformations, image registration, variational autoencoders, and generative adversarial networks, to the most recent advances in latent diffusion models. We show that the use of heavy data augmentation significantly increases both the quantitative and qualitative outcomes, resulting in an average Dice Score above 0.94 for the SkullBreak and above 0.96 for the SkullFix datasets. The results show that latent diffusion models combined with vector quantized variational autoencoder outperform other generative augmentation strategies. Moreover, we show that the synthetically augmented network successfully reconstructs real clinical defects, without the need to acquire costly and time-consuming annotations. The findings of the work will lead to easier, faster, and less expensive modeling of personalized cranial implants. This is beneficial to numerous people suffering from cranial injuries. The work constitutes a considerable contribution to the field of artificial intelligence in the automatic modeling of personalized cranial implants.

Governing with Intelligence: The Impact of Artificial Intelligence on Policy Development

As the field of artificial intelligence (AI) continues to evolve, its potential applications in various domains, including public policy development, have garnered significant interest. This research aims to investigate the role of AI in shaping public policies through a qualitative examination of secondary data and an extensive bibliographic review. By analyzing the existing literature, government reports, and relevant case studies, this study seeks to uncover the opportunities, challenges, and ethical considerations associated with leveraging AI in the formulation and implementation of public policies. This research will delve into the potential benefits of AI-driven policy analysis, such as enhanced decision-making processes, data-driven insights, and improved policy outcomes. Additionally, it will explore the risks and concerns surrounding AI’s influence on policy, including potential biases, privacy implications, and the need for transparency and accountability. The findings of this study will contribute to the ongoing discourse on the responsible and effective integration of AI in public policy development, fostering informed decision-making and promoting the ethical use of this transformative technology.

Exploring the power of KANs: Overcoming MLP limitations in complex data analysis

In the field of artificial intelligence, the requirements of models that can adeptly explore informative features of complex data sets has led to the emergence of advanced neural network architectures. Beyond the perceptron-based architectures that are currently in widespread use, a variety of innovative and cutting-edge designs have been proposed. This paper thoroughly explores the critical role of Kolmogorov-Arnold Networks (KAN) in overcoming the limitations of traditional Multilayer Perceptron (MLP) models, particularly highlighting KANs significant advantages in handling complex nonlinear and high-dimensional data. By analyzing the mathematical foundations and neural network architecture of KAN, and comparing it with MLP, the paper demonstrates KANs outstanding performance in time series analysis and image classification. The research indicates that KAN has distinct advantages in addressing high-dimensional nonlinear data. The paper also summarizes the current research progress on KAN and discusses its enormous potential in future machine learning and real-world applications, pointing out possible future research directions.

Application of Machine Learning in Education: Recent Trends Challenges and Future Perspective

In recent times, Machine learning (ML) is one of the most valuable fields of artificial intelligence (AI) that is transforming education. The application of ML in education provides a promising benefit both to the scientists and researchers and this is the focus of this study. This paper reviews recent trends and advancements of ML in education focusing on areas such as personalisation of learning, predictive analytics, plagiarism detection, intelligent tutoring systems, gamification of learning and recommendation systems. After conducting the literature review we found out the current benefits and challenges of ML in education. The paper also provides insight into the applications and provide the recommendations to address the challenges of ML in the field of education.