Intelligent Information Research Articles

IEEE Transactions on Emerging Topics in Computational Intelligence Information for Authors

IEEE Transactions on Emerging Topics in Computational Intelligence Information for Authors

A theoretical framework for human-centered intelligent information services: A systematic review

Intelligent Information Services (IIS) employ Artificial Intelligence (AI)-based systems to provide information that matches the user's needs in diverse and evolving environments. Acknowledging the importance of users in AI-empowered IIS success, a growing number of researchers are investigating AI-empowered IIS from a user-centric perspective, establishing the foundation for a new research domain called “Human-Centered Intelligent Information Services” (HCIIS). Nonetheless, a review of user studies in AI-empowered IIS is still lacking, impeding the development of a clear definition and research framework for the HCIIS field. To fill this gap, this study conducts a systematic review of 116 user studies in AI-empowered IIS. Results reveal two primary research themes in user studies in AI-empowered IIS: human-IIS interaction (including user experience, system quality, user attitude, intention and behavior, information quality, and individual task performance) and IIS ethics (e.g., explainability and interpretability, privacy and safety, and inclusivity). Analyzing research gaps within these topics, this study formulates an HCIIS research framework consisting of three interconnected elements: human values and needs, environment, and service. The interconnections between each pair of elements identify three key research domains in HCIIS: interaction, ethics, and evolution. Interaction pertains to the facilitation of human-IIS interaction to meet human needs, encompassing topics including human-centered theory, evaluation, and the design of AI-empowered IIS interaction. Ethics emphasize ensuring AI-empowered IIS alignment with human values and norms within specific environments, covering topics like general and context-specific AI-empowered IIS ethical principles, risk assessment, and governance strategies. Evolution focuses on addressing the fulfillment of human needs in diverse and dynamic environments by continually evolving intelligence, involving the enhancement of AI-empowered IIS environmental sensitivity and adaptability within an intelligent ecosystem driven by technology integration. Central to HCIIS is co-creation, situated at the intersection of interaction, evolution, and ethics, emphasizing collaborative information creation between IIS and humans through hybrid intelligence. In conclusion, HCIIS is defined as a field centered on information co-creation between IIS and humans, distinguishing it from IIS, which focuses on providing information to humans.

The Other Side of the Hill – Endeavouring to Define Intelligence in the 21st Century: A Perspective

This paper discusses not a definition but defining characteristics of intelligence, those it has inherited from the Cold War, and those which new conditions are forcing it to adapt to. Intelligence cannot be reduced to a simple sentence-length definition. Intelligence is iterative – its nature varies depending on the context in which it is produced. Its unconventional methods combined with its exclusivity of method and content makes intelligence information different from other sorts. These essential characteristics allow us to understand the relationship between different intelligence’s and between them and related concepts of ‘intelligence-adjacency’ as well as good old fashioned, but ever reliable, basic ‘research’. Intelligence has a wide ambit; it is not a discipline in itself, but something combining varying methods of information gathering and analysis to create ephemeral knowledge of genuine, but ephemeral, value – within the varying contexts in which it is used.

Application of Fuzzy Reasoning with Fusion Rules in Intelligent Information Processing

The core of artificial intelligence (AI) research is intelligent information processing, and fuzzy reasoning and fuzzy neural networks are significant research areas in models of intelligent information processing. The study examines the robustness of fuzzy reasoning by using the property characteristics of triangular modal operators and implication operators, which are used to analyse the Lipschitz property of fuzzy operators and its corresponding property characteristics of triangular modal operators and implication operators. Inference operators are used to investigate the robustness and error-control capabilities of fuzzy operators for fuzzy reasoning. The results showed that the robustness of quasi-copula fuzzy reasoning with fusion rules is better, and the corresponding maximum output perturbation is lowest at 0.36 when the fuzzy operator is T[Formula: see text]&R[Formula: see text] and the corresponding maximum output error is lowest at 0.41 when the fuzzy operators are T[Formula: see text]&R[Formula: see text] and T[Formula: see text]&R[Formula: see text]. This shows the optimal performance of the fuzzy associative memory model with fuzzy rule for quasi-copula fuzzy operators and can improve the theoretical technology. The best-performance fuzzy copula operator of the fuzzy associative memory model can enhance the theoretical foundation and technical support for the processing of intelligent information.

Опыт внедрения информационной системы с технологиями искусственного интеллекта в работу поликлиники для оптимизации организации периодических медицинских осмотров.

Organizing medical examinations for patients with minimal time losses is the most difficult task for an outpatient clinic when creating a New model of a medical organization providing primary health care. Since during medical examinations it is necessary to perform a whole range of works associated with certain sets of actions, the authors developed an information system with technologies of artificial intelligence, which was tested during periodic medical examinations. The purpose of the study is to develop and test an information system with technologies of artificial intelligence that allows balancing the capabilities of a medical organization and the needs of the patient when undergoing periodic medical examinations. Materials and methods. The tool for solving this problem was an artificial intelligence platform based on the principle of decision making by controlling the positions of attractors in the state space of the Hopfield neural network, which was built into the medical information system using HL7 FHIR. Results. The result of the work was the “Smart Clinic” project, which provides for the creation of an information system with technologies of artificial intelligence for routing patients during periodic medical examinations. The information system provided dynamic routing of patients in real time with the construction of the shortest trajectory and minimal movement across the floors of the building, based on current information about the workload of specialists and the duration of each stage of the examination. Conclusions. The use of an artificial intelligence information system in the outpatient clinic made it possible to reduce the average duration of a patient’s trajectory during a periodic medical examination by 45%, the average waiting time for an appointment – from 7 minutes to 3 minutes, and the total time for undergoing a preventive examination – from 200 to 150 minutes. This made it possible to increase the number of periodic medical examinations per year by 1.5 times, and the level of patient satisfaction reached 95%.

Low-altitude intelligent transportation: System architecture, infrastructure, and key technologies

In the context of the burgeoning low-altitude economy, low-altitude intelligent transportation (LAIT) has emerged as the focal point of research. This study comprehensively explores the current state, challenges, and future development prospects of LAIT from three key aspects: system architecture, infrastructure, and critical technologies. First, we propose a future LAIT system framework based on a cyber-physical system (CPS) layered architecture to provide a potential solution for urban air transport. Second, we introduce a framework for the entire lifecycle and management chain of the LAIT system, offering an in-depth analysis of each stage from design, construction, and operation to management, with the aim of realizing intelligent management and operation of low-altitude transportation. Finally, this study discusses the technical, security, and social challenges that future LAIT will face in the context of Industry 4.0, while envisioning the pathways of technological innovation. It summarizes the need for advanced infrastructure and breakthroughs in key technologies such as the Internet of Things (IoT), 6G, artificial intelligence (AI), and low-altitude geographic information system. This study provides a systematic framework and technical guidelines for the future development of low-altitude intelligent transportation, supporting continuous innovation, and upgrading the low-altitude economy.

Principles of constructing mental models of a solution for external users in the task of generating explanations in an intelligent system

The subject of the research is the process of constructing user mental models in intelligent information systems. The aim is to improve the principles of building mental models of external users regarding the decisions of an intelligent information system to form explanations in accordance with both the needs of these users and the limitations on the use of solutions in a specific subject area. Objectives: improvement of existing principles of mental model construction based on structuring the description of the intelligent system's solution; development of a principle for constructing mental models of the intelligent system user, considering the limitations when using the obtained solution. The scientific novelty of the obtained results is as follows. The existing principles of human mental model construction have been adapted, considering the differences in constructing explanations for a solution in an intelligent system represented as a «black box». The adapted principles consider the correspondence between the structure of the mental model and the solution, the multiplicity of mental models regarding the solution, and the incompleteness of input data for decision-making. The principle of input data supplementation is proposed, which consists in using the values of solution properties that are irrelevant from the user's point of view to limit the set of relevant solutions. The use of this principle in constructing an explanation makes it possible to consider the negative aspects of using the obtained result and thereby simplify the user's choice and application of this solution.

Artificial intelligence, machine learning and GIS in environmental engineering: current trends

The use of computational tools, such as Artificial Intelligence, Machine Learning or Geographic Information Systems, has had a significant impact on the knowledge generated on environmental issues in recent years. The number of publications shown in this preliminary review of the IEEE Xplore Digital Library database shows its broad applicability and scientific relevance. Search filters and keywords such as water, air, soil, climate change, energy and waste were used. The data obtained was processed to visualize the proportion of use in key topics of environmental interest, giving scientific guidance towards the sites of greatest applicability, as well as towards areas that deserve to be reinforced. In this way, this work aims to promote the development of collaborative work solutions in the related areas of environmental and computational engineering.

Leveraging Large-scale AI Models for Transforming China's Marine Industry: A TOE Framework Analysis

As an important driving force leading a new round of scientific and technological revolution and industrial change, artificial intelligence is becoming a hot spot. Promoting the deep integration of "artificial intelligence + ocean" will help build a world-class Marine artificial intelligence industry cluster with global competitiveness. How to effectively apply artificial intelligence to the Marine industry to solve the problems of resource redundancy and information island caused by industrial agglomeration is urgent for innovation and breakthrough. In this paper, TOE and modified T-TOE framework are used to analyze the above problems, and the following conclusions are drawn: 1) From the perspective of technical factors, intelligent information technology plays an important role in promoting the construction of large-scale models at the level of hardware and software; From the perspective of organizational factors, enterprise digital transformation and open sharing platform play an important role in supporting organization construction. From the perspective of environmental factors, policy support and market orientation play an important role in encouraging the construction of large models. 2) From the perspective of T-T, the breakthrough and application of technology need to balance the contradiction between development and conflict; From the perspective of T-O, intelligent supply-demand matching and transition resistance have both positive and negative effects; From the perspective of T-E, there is a problem of actively promoting multidimensional interactive innovation and achieving a balance. Relevant studies provide new ideas for the comprehensive development scenario of Marine industry and inject new impetus into the development of Marine industry.

Optimizing Ship Pilotage with Intelligent Information Services: Integrating GIS-Based Big Data Positioning and Neural Network Approaches

Optimizing Ship Pilotage with Intelligent Information Services: Integrating GIS-Based Big Data Positioning and Neural Network Approaches

Intelligent Tourism Information Search Behavior based on Data Mining

Intelligent Tourism Information Search Behavior based on Data Mining

Dynamic near-field and far-field radiation manipulation using a reprogrammable guided-wave-excited metasurface

The dynamic and integrated control of near- and far-field electromagnetic waves is essential for advancing emerging intelligent information technology. Metasurfaces, distinguished by their low-profile design, cost-effectiveness, and ease of fabrication, have successfully revolutionized various electromagnetic functions. However, current research on the dynamic integrated manipulation of near-field and far-field electromagnetic waves using a single metasurface remains relatively constrained, due to the complexity of element-level control, restricted dynamic tuning range, and tuning speed. Herein, we propose an element-level controlled, versatile, compact, and broadband platform allowing for the real-time electronic reconstruction of desired near/far-field electromagnetic wavefronts. This concept is achieved by precisely regulating the 1-bit amplitude coding pattern across a guided-wave-excited metasurface aperture loaded with PIN diodes, following our binary-amplitude holographic theory and modified Gerchberg–Saxton (G–S) algorithm. Consistent findings across calculations, simulations, and experiments highlight the metasurface’s robust performance in 2D beam scanning, frequency scanning, dynamic focusing lens, dynamic holography display, and 3D multiplexing holography, even under 1-bit control. This simplified and innovative metasurface architecture holds the promise of substantially propelling forthcoming investigations and applications of highly integrated, multifunctional, and intelligent platforms.

A web intelligence information system to support the production of EuroGroups Register (EGR) statistics

There is a growing demand for statistics to better understand the globalisation that is accelerating due to the removal of barriers in international trade and to technological progress. Key players in globalisation are the multinational enterprise (MNE) groups that have increased in number and complexity and need to be properly represented by macroeconomic and business statistics. To deal with this need, the European Union Member States, the European Free Trade Association (EFTA) countries and Eurostat have collaborated to create the EuroGroups Register (EGR). This paper explores the use of web intelligence to improve the accuracy and completeness of the EGR, which makes use of tools for extracting and exploring information from the World Wide Web. Additionally, it presents a methodology to assess the quality of the information retrieved from the web, based on an ex-post comparison with the official information contained in the EGR. The results provide indications about the possibility of using web intelligence tools to support MNE group monitoring and to complete the EGR data in cases where this information is missing. Finally, it dives deeply into the approach to harness Wikipedia as a data source and into the techniques and methodology used to extract data from this source.

Correction: an intelligent information recommender system (IIRS) for next generation sustainable social tourism industry

Correction: an intelligent information recommender system (IIRS) for next generation sustainable social tourism industry

Assessment of state policy and legal framework related to enhancing the well-being of the elderly in Thailand

The ageing of society significantly influences the economic system and social stability and is a global challenge of the 21st century. The study aimed to explore ways to improve the quality of life of older people in the Lower Central Region of Thailand. The study was based on a qualitative approach and was conducted by collecting information from 8 key informants through structured interviews. The study substantiated the expediency of creating a centralised database to improve communication between all actors in the system of ensuring the welfare of the elderly, which improves the quality of services provided, contributes to a better basis for political decisions and promotes a more rational use of resources. The study noted that key respondents identify expanding access to healthcare, increasing financial assistance and strengthening social support systems to ensure the holistic well-being of older people as urgent problems in the region. The need for wider use of the latest technologies and the creation of an intelligent information management system in the area of elderly people’s welfare is emphasised in the study. Given the negative impact of social isolation on the quality of life of older people, particular attention is devoted to the effective management of senior clubs, namely the priority of various activities, intergenerational interaction and meaningful leisure to strengthen social ties and belonging to society. The integration of Buddhist principles and psychological theories is emphasised as an important tool for developing compassion, mindfulness and positive outcomes. Overall, the findings highlight the importance of integrated approaches and comprehensive measures to optimise care for older people and improve their well-being. The results of the study can be used to determine the content of information to be included in the centralised database and to develop an algorithm for exchanging information between its users

DKA-RG: Disease-Knowledge-Enhanced Fine-Grained Image–Text Alignment for Automatic Radiology Report Generation

Automatic radiology report generation is a task that combines artificial intelligence and medical information processing, and it fully relies on computer vision and natural language processing techniques. Nowadays, automatic radiology report generation is still a very challenging task because it requires semantically adequate alignment of data from two modalities: radiology images and text. Existing approaches tend to focus on coarse-grained alignment at the global level and do not take into account the disease characteristics of radiology images at fine-grained semantics, which results in the generated reports potentially omitting key disease diagnostic descriptions. In this work, we propose a new approach, disease-knowledge-enhanced fine-grained image–text alignment for automatic radiology report generation (DKA-RG). The method combines global and disease-level alignment, thus facilitating the extraction of fine-grained disease features by the model. Our approach also introduces a knowledge graph to inject medical domain expertise into the model. Our proposed DKA-RG consists of two training steps: the image–report alignment stage and the image-to-report generation stage. In the alignment stage, we use global contrastive learning to align images and texts from a high level and also augment disease contrastive learning with medical knowledge to enhance the disease detection capability. In the report generation stage, the report text generated from the images is more accurate in describing the disease information thanks to sufficient alignment. Through extensive quantitative and qualitative experiments on two widely used datasets, we validate the effectiveness of our DKA-RG on the task of radiology report generation. Our DKA-RG achieves superior performance on multiple types of metrics (natural language generation and clinical efficacy metrics) compared to existing methods, demonstrating that the method can improve the reliability and accuracy of automatic radiology report generation systems.

Application of artificial intelligence sensor and visual image technology in the analysis of hydrophilic space landscape characteristics

With the continuous development of intelligent sensor technology and artificial intelligence, the application of artificial intelligence feature recognition at the level of intelligent sensor information acquisition to urban landscape analysis has become a current research trend and hot spot, but also brings us new research directions and challenges. In this paper, the multi-sensor collaborative filtering algorithm is optimized, and a method based on evidence theory is proposed to deal with the fuzzy and unclear information generated in the process of multi-sensor collaboration. Then, the analysis and recognition of multi-sensor fusion information using AI technology are analyzed in detail. This paper discusses the problems in multi-sensor information fusion and related solutions. Combined with the key nodes of 3D object AI feature recognition, multi-sensor collaborative Dempster Shafer evidence theory and 3D convolutional neural network waterfront space landscape feature recognition sub-model are constructed, and the waterfront space landscape recognition analysis model is tested and analyzed. The results show that the multi-sensor information collection Kalman filtering fusion algorithm effectively realizes the recognition of landscape feature information and the filtering of irrelevant interference information combined with an artificial intelligence algorithm to build a landscape feature recognition model. Among all kinds of landscape recognition, the recognition effect of beach inflow is the best, and the recognition accuracy, recall, and F1 value are all above 95. However, the identification effect of hydrophilic plank roads and underwater breakwater is relatively poor. The recognition accuracy of the hydrophilic platform is the lowest at 83.16 % among the eight landscape types, and the recall rate of underwater breakwater is the lowest at 79.96 % among the eight landscape types. In general, the multi-sensor Kalman filtering algorithm information fusion model designed in this paper has higher recognition accuracy and better classification performance for the collection and classification of the entire landscape dataset. The work of this paper provides a new idea and direction for the design and application of waterfront landscape identification and analysis systems based on intelligent sensors and artificial intelligence technology and lays a certain foundation for the intelligent design of urban waterfront landscapes.

A Bayesian hypothesis testing model for quality control of mass customized cabinet manufacturing process

The demand for customization of cabinets and homes has increased, and customized product components are cumbersome in size and time-consuming. In order to realize the mass production of customized cabinet furniture, research from the custom design, optimization of factory machine production process and quality inspection, as well as the flow of the way, is to realize the fundamental method of large-scale production of cabinet furniture. Based on modern intelligent information processing functions instead of manual ordering, order splitting, and unit parts for reasonable division of classification, optimize the topology of the production line, to achieve modern intelligent production of panels. However, in the production process, there is a lack of on-line inspection of some links, and the yield of the product is affected. This thesis establishes a Bayesian hypothesis testing model based on the topological model of the production line process, and through the actual production data, the defective nodes of the manufacturing process are retraced to achieve the purpose of optimizing the customized production.

The quantitative and qualitative analysis of dye in fentanyl tablets via ultraviolet-visible spectroscopy-A forensic approach.

In the United States, illicit fentanyl is often trafficked as blue tablets mimicking the legitimate M-30 oxycodone tablet produced by Mallinckrodt. The analysis of dyes extracted from seized fentanyl tablets could provide a useful tool for law enforcement to establish linkages between cases and could prove useful for attributing a seizure to a given trafficking organization. Fentanyl tablet seizures associated with a particular drug trafficking organization (DTO), either through investigative or intelligence information, were used as the sample set for this study. The blue dye from the tablets was isolated by solid phase extraction and then qualitatively and quantitatively analyzed via ultraviolet-visible spectroscopy. This research revealed that the illicit tableting facilities use a different dye than several known pharmaceutical companies. The concentration of dye in individual tablets within a seizure proved to be very minimal, and the small sample size made it difficult to draw linkages from case to case. Analysis of the dyes could not effectively differentiate between the drug trafficking organizations in the tested population due to each DTO using the same dye; however, it is important to note that the dye found was consistent between illicit tablets.

Preface

It is a pleasure to welcome you to the 2024 International Symposium on New Energy Technologies and Power Systems (NETPS 2024). At this dynamic and innovative time, we were honored to have invite academic elites from all over the world to discuss the latest advances and cutting-edge research in new energy technologies and power systems. NETPS 2024 was held in Nanjing, China from March 22nd to 24th 2024 (virtual event). It focused on cutting-edge issues in new energy technologies and power systems, and provided a platform for academics to share knowledge, exchange views and promote collaboration. To make the program more informative and colorful, the Conference invited renowned scholars and experts from around the world, who brought us a series of fascinating thematic presentations. These reports covered multiple aspects of new energy technologies and power systems, including but not limited to hot topics such as New Energy Power Generation, Power System Communication and Control, Intelligent Energy Information Support Technology, Computational Intelligence in Electrical Engineering, Smart Grid, Big Data in Power Systems, etc. Experts and scholars including Prof. Ting Yang (Tianjin University, China), Prof. Pierluigi Siano (University of Salerno, Italy), Prof. Mohan Lal Kolhe, University of Agder (Norway), Norway, etc. performed dramatic presentations on their own researches. We believe that these reports have provided participants with both insight and inspiration, and promoted research and development in related fields. In addition, it featured oral presentations and poster presentations, providing an opportunity for graduate students and young researchers to present their research findings. It was an important platform for participants to exchange and interact with each other, helping to share experiences, promote cooperation and build new partnerships. We believe that through the interaction and cooperation in this Conference, we have deepened our understanding of relevant fields, promoted interdisciplinary integration, and provided new solutions to the complex problems facing the world today. Finally, we believe that the Proceedings of this Conference will be of great significance to the academic community. This Proceedings features outstanding papers from NETPS 2024, which represents the latest research results and innovative ideas in the fields. By publishing and sharing these papers, we are able to further expand the impact of our research, promote communication and collaboration among the academic community, and provide valuable references for future research. We would like to acknowledge all those who supported and participated in NETPS 2024. We look forward to have your active participation in the future series conference of NETPS! The Committee of NETPS 2024 List of Committee Member is available in this Pdf.