A conceptual model for technology intelligence

In order to cope with the rapidly changing technological environment, several companies have established the so-called Technology Intelligence (TI) Systems. The goal is to collect, analyse, disseminate and utilize information that is relevant to the company and will improve decisionmaking quality. In doing so, a company faces, in particular, two challenges: to what extent should the TI systems be organized and how should it be organized? Planning is a great concern of top management, thus top management are prime users of an intelligence system. The question is however, how is top management involved in the system?

Technology intelligence (TI) is a process that allows technology-based companies to identify technological opportunities and threats that can affect the future growth and survival of companies. Therefore, the primary operational function of TI is the collection, analysis and dissemination of information for the development of knowledge about threats and technological opportunities. New technology-based firms (NTBFs) should be aware of the latest technological innovations if they want to take advantage of new opportunities and be mindful of potential threats. Since the needs of NTBFs may diverse, there may be different ways to design and implement TI activities depending on the business environment, the level of uncertainty, the strategy used and the resources they can get. The challenge of this study is what NTBF’s capabilities should have to develop and how to implement TI processes for the firm. Therefore, this study considers a better understanding of how to develop and implement TI and its processes in practice for NTBFs in developing countries (Myanmar, China and India). Semi-structured interviews were conducted in this study with 19 technology-based organizations in the fields of Defense Avionics industry, ICT, pharmaceutical and electrical equipment located in Myanmar, China and India. 6 core questionnaires (60 detailed questions) were constructed for the interviews. Analysis and some managerial insights of the study are discussed. Finally, the general TI process cycle for NTBFs in developing countries is conceptualized with seven steps as a cycle for the continuation and integration of TI, and also comprehensively explained how it works.

Development of a maturity model for technology intelligence

In order to meet with the requirements of equipment network integration, flexible customized assembly, lean logistics management and control of assembly process, an intelligent manufacturing model of sophisticated equipment assembly workshop is proposed in this paper. By modeling the requirement of intelligent manufacturing mode in sophisticated equipment assembly workshop, an intelligent manufacturing technology architecture system and its operation mode are built, which include six layers as user layer, core functional layer, system integration layer, intelligent perception layer, resource layer and basic support layer. The key technologies such as the intelligent perception technology of assembly resources by using intelligent information interaction device based on IPv6 and the abnormal early warning method of critical assembly process based on BP neural network are studied. Finally, the analysis is illustrated in the case of a realistic application from a local manufacturing enterprise to highlight the proposal’s feasibility and applicability by applying the proposed intelligent manufacturing model.

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).

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.

In this study, the performance of two popular conceptual models, a classic artificial intelligence (AI) model, and a deep learning model is compared using simple and differential split-sample tests (SSST and DSST). Finally, the two conceptual models and the classic AI model are combined employing a model ensemble technique. The SSST and DSST schemes confirmed the ability of both types of models (conceptual and AI) to capture the hydrological response to rainfall events, although the conceptual models showed a better performance in the DSST scheme. The ensemble technique enhanced the modelling performance up to 22% and 35% in the calibration and verification steps. This study highlights the importance of applying DSST using existing data to build further confidence in model projections, and suggests that the use of model ensemble techniques combining conceptual and AI models can enhance the performance of rainfall-runoff modelling in the context of climate change.

In this paper we consider the use of agent-oriented models and technologies - in particular intelligent agent technologies - for the design and implementation of distributed interoperable software systems based on Service-Oriented Architecture (SOA) and Web Services (WS) standards (SOA/WS in brief). To this end, we introduce a platform called CArtAgO-WS which promotes the development of SOA/WS applications as open workspaces populated of possibly heterogeneous agents, including BDI agent platforms such as Jason and Jadex, and suitably designed artifacts as defined by the Agents and Artifacts (A & A) conceptual model. Such artifacts are used by agents as tools encapsulating basic functionalities to interact with existing Web Services, and to implement and manage Web Services. The platform is meant to be a computational environment for effectively supporting the agile experimentation of advanced agent-based SOA/WS applications, such as goal-oriented services and goal-oriented service orchestration.

This paper reviews the application of intelligent vision enhancement technology in the battlefield environment and explores new research directions. This paper mainly introduces three parts. First, we introduce a solution for enhancing the battlefield situation and using head-mounted displays for soldier combat. Second, we summarize three core technologies supporting intelligent vision enhancement technology: 3D environment reconstruction, tracking and registration, and situational awareness. Third, we summarize three application directions of intelligent vision enhancement technology on the battlefield. Finally, the problems and challenges in the research work are proposed, including current issues such as accurate battlefield situational awareness by augmented reality technology, multiperson collaborative management and data flow, as well as challenges in future battlefield situation enhancement and perception. The future development trend of intelligent vision enhancement technology on the battlefield has been prospected. Two meanings of this paper are as follows: the first is to review the research status of intelligent vision enhancement technology from the technical level and identify the key technical points that may restrict development in the future; the second is to analyze the advantages and disadvantages of intelligent vision enhancement technology from the level of battlefield application and the roles of users. In addition, this paper proposes how to take the lead and take initiative in future wars.

Development of industrial zones, districts and entire regions largely depends on the solution to the problem of the formation of the rational structure of territorial transport systems and their effective development. Peculiarities of the functioning of territorial transport systems are closely related to their structure which is characterized by some combination of pair proximity indexes. The functioning of such systems is closely linked with their structure. The structure of any transport system is multilevel. To clarify the number of structural levels of the system and their constituent elements, criteria and algorithms have been developed to determine the relative position of the sets in a plane taking into account their possible overlap. A generalized index of the proximity of several non-overlapping sets was developed based on the account of pair indexes of the proximity of individual sets and equal to their mean square value. The procedure of structural analysis of the transport system is connected with the necessity of preliminary determination of its structural index according to the results of calculations of values of pair proximity indexes. A method of establishing the number of structural levels of an interregional transport system was developed based on preliminary determination of the structural index of the system with subsequent decision-making on joining the overlapping sets if any. Practical problems related to the refinement of the structure, composition and modes of operation of transport systems should be solved on the basis of the predefined structural index. The study results make it possible to structure transport systems with singling out individual levels and differentiate costs for the development and operation of systems in order to optimize their properties

In the case of chronic management diseases like diabetes, cardiovascular disorders, and cancer, there is a significant challenge of predicting the reaction of patients to medical treatment. Patient health status, medical history, genetic and treatment arrangements create difficulty in the health practitioners determining the most effective treatment to be offered to each individual. The latest developments in Artificial Intelligence (AI) can open new possibilities in terms of using the large amounts of medical data and helping in the clinical decision-making. The proposed project aims at the creation of a smart system where Generative Artificial Intelligence and Large Language Models (LLMs) are applied to forecast patient treatment reactions and help in customized care. The suggested system will challenge structured and unstructured healthcare data, such as establishment health records, lab reports, treatment history, and clinical notes. Identifying treatment outcome patterns based on patient characteristics is done using machine learning and deep learning techniques. Large Language Models are also integrated to analyze and comprehend written medical data including doctor notes and patient reports so that clinical data could be interpreted more accurately. Python is used to implement the system along with deep learning systems such as PyTorch or TensorFlow, and healthcare datasets made publicly available are used in training and evaluation. Some of the standard metrics that are used to evaluate the performance of the system are accuracy, precision, recall, and F1-score in order to determine the effectiveness of prediction. The last system gives insights on whether a patient is likely to respond a given treatment in a positive way to assist medical practitioners give more effective and personalized treatment. The proposed system helps to reveal the potential of intelligent healthcare technologies by allowing them to enhance the planning of the treatment and supporting the personalized medicine through the combination of Generative AI, predictive analytics, and natural language processing.

Purpose – The aim of this paper is to promote the authors' understanding of technology intelligence by responding to two questions: What is technology intelligence? How is it accomplished? Design/methodology/approach – To respond to the two main questions about technology intelligence in this paper, the authors have studied relevant academic, peer-reviewed journals and books using the literature databases of Google Scholar, Sciencedirect, Inderscience, Wiley and Emerald Insight. They have used few selection criteria to choose papers and books for inclusion in their study. Findings – Enhancing the authors' understanding of the technology intelligence concept by responding to the two main questions (What is technology intelligence? How is it accomplished?), classifying the main studies in the field of technology intelligence and several practical and theoretical implications are the findings in this paper. Practical implications – A number of practical implications related to technology intelligence structure, process, methods, tools and players are suggested to managers of organizations and companies to improve their technology-related planning processes and decision-making. Originality/value – Despite the considerable level of consensus on the necessity of precise scanning and monitoring technological changes and trends, there is still limited understanding of the technology intelligence concept. This paper intends to enhance the authors' understanding of technology intelligence by responding to two questions: What is technology intelligence? And how is it accomplished?

With the deepening of the digital transformation of cities, the city-level digital operation platform, as an important infrastructure for the construction of smart cities, undertakes the task of collecting, processing, and analyzing huge amounts of data, and guarantees the efficient and coordinated operation of various systems in the city. In the face of the complex and changing urban operating environment, traditional monitoring and diagnostic means are difficult to meet the demand for real-time and intelligentization. Based on the topic of “Exploration and Research on the Development Mode of Deep Integration of Education and Industry under Digitalization Mode”, this paper designs and implements a set of intelligent diagnostic system for city-level digital platform by combining artificial intelligence and time series modeling technology. The system uses deep learning, machine learning and other artificial intelligence methods to model and analyze multi-source time series data, which can effectively identify abnormal states and potential failures in urban operation and realize accurate prediction and diagnosis functions. In the design of the system architecture, the concept of integration of industry and education is emphasized to promote the in-depth combination of the scientific research power of universities and industrial needs, and to promote the application transformation of digital technology and talent training. Through experimental verification and case analysis of real city operation data, the system shows good diagnostic accuracy and response speed, demonstrating strong practical value and promotion prospects. The research in this paper not only enriches the technical system in the field of intelligent diagnosis of urban digital operation platform, but also provides theoretical support and practical guidance for the innovation and development of digital technology in the context of the integration of industry and education, and helps to improve the level of urban intelligent management and sustainable development.

Present the Intelligent Leadership Multiple Model, According to the Organizations Management Systems in Iran

The article addresses the issue of diagnosing the power plant of unmanned aerial vehicles (UAVs) based on electric drives using intelligent data analysis technologies. In the context of modern challenges caused by military aggression against Ukraine, the relevance of this research is determined by the need to ensure the safe operation of autonomous aerial systems in regions with damaged infrastructure and limited telecommunication functionality. It has been established that the efficiency of UAV operation largely depends on the condition of its power unit, and therefore, on the timeliness and accuracy of diagnosing electric drives during flight. The main focus is placed on the development of conceptual foundations for constructing a monitoring system of the technical condition of the electric drive using artificial neural networks (ANN) within the TensorFlow environment. The study analyzes current trends in deep learning technologies that enable the recognition of nonlinear dependencies between the operating parameters of the power plant and its diagnostic indicators. The structure of a typical neural network, including input, hidden, and output layers that perform nonlinear transformations of input signals, is described. Optimization algorithms for the training process (including stochastic gradient descent, Adam, RMSProp) and loss functions applied for the classification of the technical condition of electric motors during operation are characterized. The research substantiates the feasibility of using the TensorFlow Keras API for the construction, training, and testing of diagnostic models for UAV power plants. It is shown that the proposed approach allows for high-accuracy determination of the operational state of electric motors in real time, detection of deviations from nominal operating modes, and prediction of potential failures. Simulation results confirm that the developed model can classify the power unit condition into three categories—operational, conditionally operational, and critical—with an accuracy exceeding 95%. The process of constructing the neural network model is described in detail, including the selection of hyperparameters, the number of hidden layers and neurons, activation functions (ReLU, Softmax), and training parameters. The importance of hardware acceleration (GPU, TPU) is emphasized for ensuring real-time processing of large volumes of diagnostic data. The results of the analysis of the influence of neural network architecture on the convergence rate of the training algorithm and resistance to overfitting are presented. It is concluded that the proposed diagnostic method for power plants based on electric drives using artificial neural networks is an effective tool for enhancing the reliability of unmanned aerial vehicle operation, particularly under conditions of limited telecommunication support. The practical implementation of such systems enables autonomous monitoring of the technical state of power elements, timely detection of faults, and prevention of emergency situations during flight. The obtained results have significant scientific and practical potential for further development of integrated diagnostic systems incorporated into UAV autopilot architectures.