Quality of Knowledge in Virtual Entities

Well-established design patterns offer the possibility of standardized construction of software systems and can be used in various ways. The systematic use of design patterns in the field of Artificial Intelligence (AI) Systems however, has received little attention so far, despite AI being a popular research area in recent years. AI systems can be used for a wide variety of applications and play an increasingly important role in business and everyday life. AI systems are becoming more complex however, the actual machine learning (ML) task comprises only a small part of the total source code of a system. In order to maintain a clear and structured architecture for such systems and to allow easy maintenance, standardized elements should be reused in the design. This paper describes possible applications of well-known design patterns in AI systems to improve traceability of the system design.

Biochemical origin of the schizophrenia and of the behavioral pathologies of schizoid type

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.

Inconsistency and its handling are very important in the real world and in the fields of computer science and artificial intelligence. When dealing with inconsistency in a knowledge base (KB), there is a whole host of deeper issues we need to contend with in order to develop rational and robust intelligent systems. In this paper, we focus our attention on one of the issues in handling KB inconsistency: how to measure the information content and the significance of inconsistency in a KB. Our approach is based on a fixpoint semantics for KB. The approach reflects each inconsistent set of rules in the least fixpoint of a KB and then measures the inconsistency in the context of the least fixpoint for the KB. Compared with the existing results, our approach has some unique benefits.

This book is a platform for anyone who wishes to explore Artificial Intelligence in the field of agriculture from scratch or broaden their understanding and its uses. This book offers a practical, hands-on exploration of Artificial Intelligence, machine learning, deep Learning, computer vision and Expert system with proper examples to understand. This book also covers the basics of python with example so that any anyone can easily understand and utilize artificial intelligence in agriculture field. This book is divided into two parts wherein first part talks about the artificial intelligence and its impact in the agriculture with all its branches and their basics. The second part of the book is purely implementation of algorithms and use of different libraries of machine learning, deep learning and computer vision to build useful and sightful projects in real time which can be very useful for you to have better understanding of artificial intelligence. After reading this book, the reader will an understanding of what Artificial Intelligence is, where it is applicable, and what are its different branches, which can be useful in different scenarios. The reader will be familiar with the standard workflow for approaching and solving machine-learning problems, and how to address commonly encountered issues. The reader will be able to use Artificial Intelligence to tackle real-world problems ranging from crop health prediction to field surveillance analytics, classification to recognition of species of plants etc.

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.

As the field of Artificial Intelligence (AI) matures, methods relying less on ad hoc procedures and more on well defined operations in the context of formal structures are emerging. Evidence for this can be found, for example, in the increasingly sophisticated approaches to inexact reasoning in knowledge-based systems and in the mathematical framework developed for semantic net representation. This tutorial and brief survey presents a facet of formal language theory which provides a formalism for naturally representing and operating on strings of symbols and which also admits obvious means of handling issues such as specialization and generalization, pervasive in AI. The broad subject area covered is part of semigroup theory which has a natural relationship to both finite state automata theory and regular languages. This paper aims to introduce some of the underlying theory relevant to representation issues in AI in this context.

HRMS tools are the application that helps any organization to set up an environment that helps them to easily view their employees, their activities, and other work-related jobs. This application is available to everyone where a user can register into the application with the required details and access to the credentials. The admin then can add the employees of their organization and give them roles and responsibilities. The admin can also give updated responsibilities to HR who can then make the required changes. The HR is responsible for recruiting the employees and also he can add upon the employees into the organization. The employees can look up their inbox, dashboard, recruit, personal details on the application. They can also get a help and support system which they can contact if there are any requirements. They can edit their profile anytime, can search for any requirements, and can get news feed from the organization. These are some of the functionalities of the application. Apart from that they can also view their salary slips, can apply for a leave application, and so on. With the advancement in the development of computers, HRMS tools are developed in such a way that humans can interact with it. The recent developments in the field of computer science especially in the field of Artificial Intelligence(AI) have opened wide doors for the development of efficient HRMS tools thereby reducing human efforts. In this paper, an analysis has been made between the HRMS tools developed with AI and HRMS tools without AI and based on the suitability the applications of these HRMS tools are drawn. KeywordsArtificial intelligenceFeature engineeringMongoDBSybaseNavigation

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

Artificial intelligence (AI) systems are increasingly being used in consumer as well and business applications. Companies are using them, Governments are using them, public security, medical researchers, you and I – everybody seems to be somehow touched. As it becomes more advanced and ubiquitous in our lives, it is critical that AI developers and users understand the ethical implications and challenges posed by this powerful technology. This paper explores the key ethical skills and considerations that are essential for navigating the new world of AI. Artificial Intelligence (AI) presents significant ethical dimensions across various sectors, including research, business, and daily life. It is important to appreciate the major ethical dimensions of AI and how they impact AI systems. Policies and guidelines needs to be laid out for development and usage of such systems. Automated Decisions/Responsibility: No matter how intelligent a AI system might perceived to be, it is always prone to making errors. So, AI system that make automated decisions will always be dangerous. Therefore, it is important to ensure that there is always "human intervention" or "human review" for outcomes/decisions made by AI systems. Determining accountability for AI-generated actions or content remains a significant challenge, emphasizing the need for ethical governance frameworks. There has to be human accountability.Fairness/Bias: AI systems can perpetuate existing biases present in training data, leading to discriminatory outcomes.Transparency: The opaque nature of many AI algorithms complicates accountability, making it difficult to understand decision-making processes.Privacy: AI's capability to process vast datasets can infringe on individual privacy rights, necessitating stringent data protection measuresHuman Dignity: The risk of AI-generated content resembling existing works raises ethical issues regarding originality and authorship.

The paper addresses the issue of identification the social and humanitarian grounds for constructing criteria for assessing the impact of various factors on the development of innovations using artificial intelligence (AI) technologies. The analysis of basic concepts, standards and criteria for evaluating innovations has been carried out; an analytical review of foreign scientific publications was made. It is shown that modern systems for assessing innovations related to digital technologies and AI place the main emphasis on technological factors in the development of innovations. Social value orientations, including defining the ethical dimensions and social consequences of the malicious use of AI was identified; the analysis of innovation models in the context of quality management and differentiation of semantics of AI models is carried out. As a result, a classification of various types of AI was constructed, taking into account the social and humanitarian grounds for constructing criteria for assessing the impact of various factors on the development of innovations in the field of AI. It allows to increase the reputation potential of companies developing AI systems and AI systems themselves, to counter negative trends in the field of ethics associated with the use of digital technologies and AI.

Clinical physiology involves a complete, comprehensive, multilateral study of the functions of both affected and healthy organs, which allows us to assess the compensatory capabilities of the body.
 Artificial intelligence is increasingly being used in medicine, including in clinical physiology. This is facilitated by the increase in computing processing power, development of cloud services and datasets, and numerous scientific articles demonstrating the effectiveness and viability of such intelligent solutions.
 Although the approach to medical dataset development is generally similar, there are a number of key features and significant differences in clinical physiology. Artificial intelligence systems in clinical physiology may be effectively trained and applied in practice by following the recommendations in this study.
 The national standard of the Russian Federation GOST R 59921.9-2022, which has entered into force, is included in the set of standards Artificial Intelligence systems in clinical medicine and establishes additional requirements for data analysis algorithms and test methods of artificial intelligence systems used in the field of clinical physiology. A crucial feature of the created standard is its qualimetric type (i.e., it has a mandatory set of demonstration data).
 Russia is one of the first countries to start developing quasi-metric standards worldwide, and 15 industry standards in the field of artificial intelligence (2 of them in medicine) will come into force this year.

Although the categorization of ultrasound using the Breast Imaging Reporting and Data System (BI-RADS) has become widespread worldwide, the problem of inter-observer variability remains. To maintain uniformity in diagnostic accuracy, we have developed a novel artificial intelligence (AI) system in which AI can distinguish whether a static image obtained using a breast ultrasound represents BI-RADS3 or lower, or BI-RADS4a or higher, to determine the medical management that should be performed on a patient whose breast ultrasound shows abnormalities. To establish and validate the AI system, a training dataset consisting of 4,028 images containing 5,014 lesions and a test dataset consisting of 3,166 images containing 3,656 lesions were collected and annotated. We selected a setting that maximized the area under the curve (AUC) and minimized the difference in sensitivity and specificity by adjusting the internal parameters of the AI system, achieving an AUC, sensitivity, and specificity of 0.95, 90.0%, and 88.5%, respectively. Furthermore, based on 30 images extracted from the test data, the diagnostic accuracy of 20 clinicians and the AI system was compared, and the AI system was found to be significantly superior to the clinicians (McNemar test, p < 0.001). Then, we conducted a trial to introduce the system for use in clinical practice. Physicians reviewed the images and determined whether they were BI-RADS3 or lower, or BI-RADS4a or higher. Next, the classification was performed again for the same images concerning the AI diagnosis. At this time, the initial judgment was allowed to be overturned. We checked whether there was any difference in the diagnostic accuracy, sensitivity, and specificity before and after reviewing to the AI diagnosis. Reviews by 24 physicians were evaluated: 4 Japanese Breast Cancer Society breast specialists, 5 non-specialists and physicians with experience treating more than 40 cases of breast cancer, and 15 non-specialists and physicians with no experience treating more than 40 cases of breast cancer. The average rate of accuracy before confirming the AI diagnosis increased to 73.1% after confirming the AI diagnosis (p=0.00548), compared to 69.3% on average before the AI diagnosis. Compared to practice experience, the accuracy increased from an average of 77.1% to 79.6% for the 9 physicians who were breast specialists or who had treated 40 or more cases of breast cancer. For the 15 physicians with less than 40 breast cancer cases, the average rate of accuracy increased from 64.7% to 69.2%. Furthermore, sensitivity increased significantly to an average of 99.7% after reviewing of the AI diagnosis from an average of 88.8% prior to reviewing the AI-diagnosis.(p< 0.01). Specificity increased from an average of 62.4% to 63.8% (p=0.433) after reviewing AI diagnosis. We showed that our AI system, when applied to clinical practice and used by physicians, contributes to the improvement of diagnostic accuracy. Our results indicated that our AI diagnostic system was sufficiently accurate to be used in the clinical practice. Citation Format: Erina Odani, Tetsu Hayashida, masayuki kikuchi, Aiko Nagayama, tomoko seki, maiko takahashi, Akiko Matsumoto, Takeshi Murata, Rurina Watanuki, Takamichi Yokoe, Ayako Nakashoji, Hinako Maeda, Tatsuya Onishi, Sota Asaga, Takashi Hojo, Hiromitsu Jinno, Keiichi Sotome, Akira Matsui, Akihiko Suto, Shigeru Imoto, Yuko Kitagawa. Establishment of the breast ultrasound support system using deep-learning system [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P1-05-06.

We discuss how over the last 30 to 50 years, Artificial Intelligence (AI)\nsystems that focused only on data have been handicapped, and how knowledge has\nbeen critical in developing smarter, intelligent, and more effective systems.\nIn fact, the vast progress in AI can be viewed in terms of the three waves of\nAI as identified by DARPA. During the first wave, handcrafted knowledge has\nbeen at the center-piece, while during the second wave, the data-driven\napproaches supplanted knowledge. Now we see a strong role and resurgence of\nknowledge fueling major breakthroughs in the third wave of AI underpinning\nfuture intelligent systems as they attempt human-like decision making, and seek\nto become trusted assistants and companions for humans. We find a wider\navailability of knowledge created from diverse sources, using manual to\nautomated means both by repurposing as well as by extraction. Using knowledge\nwith statistical learning is becoming increasingly indispensable to help make\nAI systems more transparent and auditable. We will draw a parallel with the\nrole of knowledge and experience in human intelligence based on cognitive\nscience, and discuss emerging neuro-symbolic or hybrid AI systems in which\nknowledge is the critical enabler for combining capabilities of the\ndata-intensive statistical AI systems with those of symbolic AI systems,\nresulting in more capable AI systems that support more human-like intelligence.\n

The international scientific and practical journal “Software products and systems” was organized in 1988 on the initiative of the General Director of the NGO “Centerprogramsystem” prof. V. P. Tikhomirov (Tver) and V. M. Silina (Moscow), head of the Main editorial office of the international journal “Problems of management theory and practice”. In the period from 1988 to 2018, the editor-in-chief of the journal was academician S. V. Yemelyanov. Since 2020, the international editorial Board, which includes well-known scientists in the field of information technologies of the Russian Federation, Belarus, Ukraine, Azerbaijan, Germany, Finland, Mexico, Vietnam and other countries, is headed by academician G. I. Savin. Associate members of the editorial Board are the national research University “MEI” (Moscow), the technological Institute of the southern Federal University (Taganrog), TvSTU and the research Institute “Centerprogramsystem”. The journal is reviewed in the bibliographic databases of RSCI, CrossRef, and is included in the List of leading peerreviewed journals and publications recommended by the higher attestation Commission of the Russian Federation for publication of research results in the defense of candidate and doctoral theses in the fields of computer Science, computer engineering and management (05.13.XX). The journal publishes the results of original research in the field of information technologies, applied artificial intelligence systems, including the development of databases and knowledge bases, expert and training systems, decision support systems and image recognition, multiagent systems, data mining systems, neural networks, robots, and telecommunications systems. However, the subject area of research is not limited. The magazine “Software products and systems” regularly acts as an information sponsor for international and national scientific and technical events in the field of information technology and artificial intelligence.