Overview of Autonomous Unmanned Systems

The article examines the essence, classifies and highlights the historical aspects of the development of artificial intelligence technologies, identifies the trends and outlines the prospects for using the potential of modern information technologies in various spheres of public life. It is substantiated that the application of the capabilities of artificial intelligence-based systems in accounting practice makes it possible to automate certain accounting operations, in particular, processing and analysis of large amounts of accounting information, verification of accounting data and detection of errors, prevention of fraud, and also saves material and human resources for accounting, and increases the efficiency of the accounting process in general. Despite the significant advantages of integrating artificial intelligence into the accounting process, it is established that the use of the intelligent information technologies has risks for the accounting industry, which are associated with the release of a significant number of jobs, insufficient confidentiality and security of accounting data, the complexity of implementing an intelligent accounting system, and the lack of regulatory regulation of the procedure for using artificial intelligence systems in the accounting process. It is determined that the potential of artificial intelligence-enabled systems is gradually being used in accounting and this process will only intensify due to the rapid development of information technology and the trend towards automation of business processes, but there is still no talk of full integration of artificial intelligence into accounting practice. In order to ensure widespread implementation of artificial intelligence in the field of accounting, the author proposes a set of measures which include cultivation of a positive attitude towards intelligent information technologies, development of a national policy and strategy for implementation of artificial intelligence systems, professional development of practitioners, and development of information systems for automation of accounting based on artificial intelligence technologies.

This paper presents a technology for simple and non-iterative improvements of Multilayer and Deep Learning neural networks and Artificial Intelligence (AI) systems. The improvements are, in essence, shallow networks constructed on top of the existing Deep Learning architecture. Theoretical foundation of the technology is based on Stochastic Separation Theorems and the ideas of measure concentration. We show that, subject to mild technical assumptions on statistical properties of internal signals in Deep Learning AI, with probability close to one the technology enables instantaneous “learning away” of spurious and systematic errors. The method is illustrated with numerical examples.

Intravenous fluid therapy is one of the important treatment methods for hospitalized patients, and it is also an important part of nursing work. Traditional intravenous infusion therapy has many problems in the aspects of medicine preparation, operation, inspection and quality control. The application of intelligent information technology in the field of medical care continues to develop and expand. The construction of nursing information is an inevitable trend for nursing care to adapt to social development. Applying intelligent information technology to intravenous therapy can innovate intravenous infusion nursing operations, optimize nursing service procedures, improve the efficiency of intravenous therapy, ease the imbalance between supply and demand for nursing staff, and ultimately improve patient satisfaction. This article mainly reviews the three aspects of the application of information technology in the intravenous infusion management system, including the IPAD-based mobile intravenous infusion quality management system, the intravenous medication intelligent error management system, and the intelligent equipment to optimize the intravenous infusion treatment process. It is proposed to empower the nursing staff with the application of modern intelligent nursing information technology, and build an overall intravenous therapy collaborative nursing quality management information system, in order to provide a reference for comprehensively strengthening the application of information management in intravenous therapy.

In this era of rapid development of global science and technology, artificial intelligence and industrial technology are converging at an unprecedented speed, driving social progress and industrial upgrading. In order to deeply discuss the latest development in these two and relevant fields, share research results, and promote academic exchanges and cooperation, we are honored to have organized the 2024 4th International Conference on Artificial Intelligence and Industrial Technology Applications (AIITA 2024). This proceedings is the crystallization of this event, which brings together the wisdom and insights of experts, scholars, engineers and technicians from all over the world. AIITA 2024 was grandly held in Guangzhou, China from April 12th to 14th, 2024 via hybrid form, attracting over 200 participants from all over the world. The conference aimed to provide a platform for experts and scholars, engineers and technicians, researchers and developers in the fields of artificial intelligence and industrial engineering to share scientific research results and cutting-edge technologies, understand academic development trends, broaden research ideas, and strengthen academic research and discussion. During the conference, the participants had an in-depth discussion on a number of topics related to the application of artificial intelligence and industrial technology, and jointly explored the new direction of future development. In the keynote speech part, latest research results and cutting-edge technologies in related fields including Graph Neural Networks: Models and Applications in Pattern Recognition (by Prof. Bo Jiang, Anhui University, China), Distributed GPU-accelerated Evolutionary Computation (by Dr. Ran Cheng, Southern University of Science and Technology, China), Quantum Model Learning (by Prof. Daowen Qiu, Sun Yat-sen University, China), Face Image Editing from the Perspective of Conditional Generative (by Prof. Si Wu, South China University of Technology, China), Cooperative Global Robust Output Regulation for Nonlinear Multi-Agent Systems with Switching Networks (by Prof. Wei Liu, South China University of Technology, China) were discussed. At the same time, we have set up a number of presentations and panel discussion sessions, in which participants gained more opportunities to have in-depth discussions and exchanges on specific issues. This conference calls for papers on a wide range of topics, covering all aspects of artificial intelligence and industrial technology applications. We received hundreds of submissions from all over the world, and after strict evaluation and screening, we finally included some excellent papers in this proceedings. These papers not only have high academic value and practical significance, but also represent the latest research dynamics and trends in the fields of artificial intelligence and industrial technology applications. Finally, we would like to thank all the experts, scholars, engineers and staff who participated in the AIITA 2024 for their support and contribution. We believe that with the joint efforts of all of us, the research and application in the fields of artificial intelligence and industrial technology applications will achieve more brilliant achievements. The Committee of AIITA 2024 List of Committee Member is available in this Pdf.

Introduction: the article deals with the issues concerning the protection of the rights to digital content created with the use of artificial intelligence technology and neural networks. This topic is becoming increasingly important with the development of the technologies and the expansion of their application in various areas of life. The problems of protecting the rights and legitimate interests of developers have come to the fore in intellectual property law. With the help of intelligent systems, there can be created not only legally protectable content but also other data, relations about which are also subject to protection. In this regard, of particular importance are the issues concerning the standardization of requirements for procedures and means of storing big data used in the development, testing and operation of artificial intelligence systems, as well as the use of blockchain technology. Purpose: based on an analysis of Russian and foreign scientific sources, to form an idea of the areas of legal regulation and the prospects for the application of artificial intelligence technology from a legal perspective. Methods: empirical methods of comparison, description, interpretation; theoretical methods of formal and dialectical logic; special scientific methods (legal-dogmatic and the method of interpretation of legal norms). Results: analysis of the practice of using artificial intelligence systems has shown that today intelligent algorithms include a variety of technologies that are based on or related to intelligent systems, but not always fall under the concept of classical artificial intelligence. Strictly speaking, classic artificial intelligence is only one of the intelligent system technologies. The results created by autonomous artificial intelligence have features of works. At the same time, there are some issues of a public law nature that require resolution: obtaining consent to data processing from the subjects of this data, determining the legal personality of these persons, establishing legal liability in connection with the unfair use of data obtained for decision-making. Standardization in the sphere and application of blockchain technology could help in resolving these issues. Conclusions: in connection with the identified and constantly changing composition of high technologies that fall under the definition of artificial intelligence, there arise various issues, which can be divided into some groups. A number of issues of legal regulation in this area have already been resolved and are no longer of relevance for advanced legal science (legal personality of artificial intelligence technology); some issues can be resolved using existing legal mechanisms (analysis of personal data and other information in course of applying computational intelligence technology for decision-making); some other issues require new approaches from legal science (development of a sui generis legal regime for the results of artificial intelligence technology, provided that the original result is obtained).

The article is devoted to the study of the application of artificial intelligence technologies in law enforcement activities in Ukraine and the world. The purpose of the article is to analyze the international and domestic experience of using digital technologies in law enforcement activities, to identify the shortcomings of the legal regulation of the use of artificial intelligence in this area, to provide proposals for its improvement in Ukraine. It was found that one of the countries that is actively expanding the use of artificial intelligence and successfully introducing relevant technologies into public administration is China. The artificial intelligence system which is used in China called «Zero Trust». The purpose of using this system is to prevent the commission of corruption offenses. It is emphasized, that the disadvantage of the «Zero Trust» system is that artificial intelligence, identifying the cause of corruption, does not indicate the algorithms and principles of this process. At the same time, the experience of using artificial intelligence in the field of preventing and combating corruption in Ukraine is considered when carrying out automated verification of persons declarations of authorized to perform state and local government functions. Ways of improving legal regulation when conducting financial control of declarations using artificial intelligence technologies are proposed. It has been found that artificial intelligence is widely used in the USA to predict the prevalence and state of crime. It was emphasized that Ukraine also has experience in the application of artificial intelligence technologies in law enforcement activities. Artificial intelligence is used in the development and implementation of departmental specialized intelligent software, the implementation of operative and investigative actions (for the recognition of video objects observation by comparing images in social networks, etc). It was concluded that artificial intelligence and digital technologies help to quickly process large volumes of information in the investigation and prevention of criminal activity. Modern developments in the field of artificial intelligence and advanced digital technologies, which are implemented in foreign countries to prevent and fight crime, are of great importance for the improvement of legal regulation in this field in Ukraine.

Intelligent control of a rotary kiln fired with producer gas generated from biomass

We were glad to introduce you that the 2020 International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT 2020), hosted by AEIC Academic Exchange Information, Centre, was successfully held on September 18-20, 2020. In light of worldwide travel restriction and the impact of COVID-19, AINIT 2020 was carried out in the form of virtual conference to avoid personnel gatherings. Because most participants were still highly enthusiastic about participating in this conference, we chose to carry out AINIT 2020 via online platform according to the original schedule instead of postponing it. AINIT 2020 is to bring together innovative academics and industrial experts in the field of artificial intelligence, networking and information technology to a common forum. The primary goal of the conference is to promote research and developmental activities in artificial intelligence, networking and informational technology, another goal is to promote scientific information interchange between researchers, developers, engineers, students, and practitioners working all around the world. The conference will be held every year to make it an ideal platform for people to share views and experiences.We were greatly honored to have invited Prof. Junhao Chu to serve as our Conference Chairman. He is an infrared physicist, expert in semiconductor physics and devices, Chinese Academy of Sciences, researcher of Shanghai Institute of technology and physics, Chinese Academy of Science and dean of faculty of science, Donghua University. The rest of the committee was composed of nearly 130 privileged scholars from different renowned universities and institutes around the globe.During the conference, the conference model was divided into three sessions, including oral presentations, keynote speeches, and online Q&A discussion. In the first part, some scholars, whose submissions were selected as the excellent papers, were given about 5-10 minutes to perform their oral presentations one by one. Then in the second part, keynote speakers were each allocated 30-45 minutes to hold their speeches.List of Committee member are available in this pdf.

In this paper, we consider the results of research on some approaches to decision-making in irrigated agriculture - Intelligent Systems. The traditional resources for increasing the efficiency of agriculture have practically dried up. Achieving a qualitatively new level of agricultural development is possible only with the use of modern digital and intelligent technologies, because of traditional resources for increasing efficiency do not lead to a result. Global climate change and the need for agriculture in risky farming areas necessitate decision support systems for effective governance and sustainable agricultural development. The rapid development of data science and artificial intelligence has led to a new stage in the development of decision support systems - intellectualization. We have proposed to put the Kohonen neural network (Self-organizing map) as the basis for an intelligent decision support system for agro-climatic resources. At the same time, the use of modern software makes it possible to implement complex mathematical models, while new tools for creating intelligent systems such as remote sensing, cloud technologies, big data processing systems and machine learning allow more accurate management decisions at all levels of agricultural production.KeywordsIntellectualizationDecision supportIrrigated agriculture

The National Conference on “Emerging Trends in Artificial Intelligence and Digital Systems (ET-AIDS-2026)” is a focused academic and industry oriented forum designed to explore how intelligent technologies are reshaping digital ecosystems across sectors. The conference aims to bring together researchers, academicians, industry professionals, innovators, and students to exchange ideas, share research findings, and discuss practical challenges in the rapidly evolving fields of artificial intelligence and digital systems..

Since the birth of the subject of artificial intelligence, the long-term goal of this subject is to realize the intelligent system of human level. However, after nearly 60 years of development, no general intelligent system has been able to approach the level of human beings: it has the ability to cooperate with a variety of different cognitive abilities; it has a strong adaptive ability to complex environments; it has the ability of self-learning to new things and new environments. With the development of Brain and Neuroscience and Cognitive Science, it is possible to observe some activities of Brain Neural Networks under various cognitive tasks at different scales and obtain relevant data. Therefore, inspired by brain working mechanism, the development of Brain-like Intelligence has become a hotspot in the field of Artificial Intelligence and Computational Science in recent years. Machine Intelligence Brain-like Intelligence System (MIBS) inspired by cognitive behavior mechanism and realized by hardware and software cooperation is similar to human beings in information processing mechanism, cognitive behavior and intelligence level. The goal is to enable machines to realize various human cognitive abilities and their coordination mechanism, and ultimately reach or surpass the level of human intelligence. In this paper, we will study from the perspective of brain science, cognitive science and artificial intelligence. This paper reviews the history, current situation and research focus of brain-like intelligence research, and looks forward to its development direction, potential application fields and potential far-reaching impact.

The current stage of integration of artificial intelligence (AI) technologies into Educa- tion is characterized by a gradual transition to the triad “teacher – student – artificial intelligence”. AI is gradually beginning to take on many functions previously associated with the teacher, and this brings changes to the traditional learning process, transferring it to a new, more complex level in terms of solving cognitive problems. In turn, it creates a need for teachers and lecturers to solve new didactic objectives, which requires a revision of some of the teacher’s functions and requirements for his competence in the field of AI. The purpose of the study is to develop the structure and content of a teacher’s competence in the field of AI and to determine which of the structural components of this type of competence higher education teachers are able to implement at the present stage. Based on the analysis of academic literature, the following structural components of a teacher’s competence in the field of AI were proposed: 1) motivational; 2) normative and legal; 3) information security; 4) ethical; 5) prompt engineering; 6) teaching and assessment; 7) management of the edu- cational process; 8) professional development. As part of the empirical component of the study, an online survey was conducted to determine the structural components of competence in the field of AI of higher education teachers, which they are able to implement. The respondents were 219 teach- ers of specialized disciplines from 17 universities of the Russian Federation. The results of the study showed that among the substantive components of competence in the field of AI, teachers are more proficient in such aspects as teaching and assessment (x̄ = 3,35–3,71, Мо = 4), information security (x̄ = 3,56–3,88, Мо = 4), management of the educational process (x̄ = 3,41–3,84, Мо = 4). The most difficulties for teachers at the present stage are caused by the normative and legal component (x̄ = 3,35–3,47, Мо = 3) and prompt engineering (x̄ = 2,97–3,21, Мо = 3). The structure and content of the teacher’s competence in the field of AI proposed in the paper are of a recommendatory and framework nature. Based on them, depending on the specifics of the subject area and the availability of AI technical solutions, it is possible to develop the content of the competence in the field of using AI by teachers of specific academic disciplines or specialties.

This article analyzes technologies and modern means of military use with intelligent computer systems (platforms). Such means include "smart" bullets, rifles with augmented reality glasses, as well as ballistic computers, "smart" weapons (mortars), high-precision ammunition, robotic combat vehicles and other equipment, laser air defense systems for ground, air, as well as naval forces, unmanned aerial vehicles, etc. Examples of intellectual means of military use, their tactical and technical characteristics and fields of application are given. The advantages and disadvantages of using technologies and means with intelligent systems (platforms) are highlighted, as well as the justified expediency of their use for military purposes. The examples of tools with intelligent systems (platforms) listed above are effectively used in the leading countries of the world, namely: the USA, Great Britain, Japan, France, Germany, Israel, the Netherlands, etc. The experience of use by the military of these countries has shown that intelligent systems make it possible to simplify the performance of relevant tasks, measures to protect against accidents or excessive aggression of criminals (enemies), and also increase the safety of the civilian population. It was determined that the introduction of world achievements, as well as the development of domestic systems and means with intelligent systems, is one of the priority directions of the development of the military-industrial complex of Ukraine. This, in turn, requires close cooperation between scientists and the military, which prompts the study of the possibilities of using intelligent systems in various fields, in particular in the field of national security development of Ukraine. Further prospective studies in this direction are highlighted.

Rapid development in computer and sensor technology not only used for highly specialised applications but widespread and pervasive across a wide range of business and industry has facilitated easy capture and storage of immense amounts of data. Examples of such data collection include medical history data in health care, financial data in banking, point of sale data in retail, plant monitoring data based on instant availability of various sensor readings in various industries, or airborne hyperspectral imaging data in natural resources identification to mention only a few. However, with an increasing computer power available at affordable prices and the availability of vast amount of data there is an increasing need for robust methods and systems, which can take advantage of all available information. In essence there is a need for intelligent and smart adaptive methods but do they really exist? Are there any existing intelligent techniques which are more suitable for certain type of problems than others? How do we select those methods and can we be sure that the method of choice is the best for solving our problem? Do we need a combination of methods and if so then how to best combine them for different purposes? Are there any generic frameworks and requirements which would be highly desirable for solving data intensive and unstationary problems? All these questions and many others have been the focus of research vigorously pursued in many disciplines and some of them will be discussed in the talk and have been addressed in greater detail in our recently compiled book with the same title: ”Do Smart Adaptive Systems Exist?”. One of the more promising approaches to constructing smart adaptive systems is based on intelligent technologies including artificial neural networks, fuzzy systems, methods from machine learning, parts of learning theory and evolutionary computing which have been especially successful in applications where input-output data can be collected but the underlying physical model is unknown. The incorporation of intelligent technologies has been used in the conception and design of complex systems in which analytical and expert systems techniques are used in combination. Viewed from a much broader perspective, the above mentioned intelligent technologies are constituents of a very active research area known under the names of soft computing, computational intelligence or hybrid intelligent systems. However, hybrid soft computing frameworks are relatively young, even comparing to the individual constituent technologies, and a lot of research is required to understand their strengths and weaknesses. Nevertheless hybridization and combination of intelligent technologies within a flexible open framework seem to be the most promising direction in achieving the truly smart and adaptive systems today. Despite all the challenges it is unquestionable that smart adaptive intelligent systems and intelligent technology have started to have a huge impact on our everyday life and many applications can already be found in various commercially available products as illustrated in the recent report compiled by one of the world’s leading think tank advanced technology organisations and very suggestively titled: ”Get smart: How intelligent technology will enhance our world”.

As the popularity and accessibility of artificial intelligence (AI) in learning continue to rise, online education has increasingly incorporated AI. It is imperative to investigate how AI in the scholarly community supports the learning of autonomous students. This scoping review investigated articles that employed AI in discussion forums. The objective of this study is to develop a summary that denotes the effective integration of AI in discussion forums, thereby characterizing the overall learning experience. In accordance with the search, appraisal, synthesis, and analysis (SALSA) framework, the preferred reporting items for systematic reviews and meta-analyses (PRISMA) flow diagram was employed to design the article screening and selection process. Scopus was utilized for the literature search, resulting in the identification of twelve articles that met the predetermined inclusion and exclusion criteria. The extracted articles covered the following six applications: intelligent tutoring systems, AI-assisted apps, chatbots, automatic formative assessment, AI teaching assistants, and AI-assisted lifelong learning assistants. Three types of man-machine partnerships related to the activities are discussed: student-AI partnerships, student-AI-student partnerships, and student-AI-teacher partnerships, the latter being the most commonly observed. The generated content was frequently processed through teacher adjustments and checks. Components of community of inquiry theory were adapted as indicators. Cognitive presence was observed to be more predominant than social presence, with a particular emphasis on individual learning achievements. Three distinct perspectives regarding the role of AI in learning were identified. The first perspective posited that AI functioned as a virtual teacher, supplementing the teaching functions of human teachers and delivering information to students. In the second perspective, AI facilitated students in collecting information by enabling interactions with messages or peers. In the third perspective, the significance of the automatic evaluation function, which assessed learning activities and provided feedback to enhance the system while simultaneously implementing immediate changes in the learning process, was acknowledged. Regarding the role of communication, teachers clearly intended for students to acquire knowledge through it. Despite the focus on assisting individual learners, each article encompassed references from a wide range of fields and exhibited substantial diversity overall. Consequently, the domain of discussion forums with AI was depicted through the complex interplay between technology, pedagogy, and the learning environment. This context is known as entangled pedagogies, which emphasize understanding the interconnections among these elements and their reciprocal influences. In summary, AI was predominantly utilized in the form of chatbots and GenAI in discussion forums. Emerging AI roles included virtual teachers, peer/message matching, and automated evaluation. Aggregated information was presented in a manner that supported student learning. AI also served as a conduit between students and teachers.