Intelligent Information Research Articles

Ethical Considerations and Fundamental Principles of Large Language Models in Medical Education: Viewpoint.

This viewpoint article first explores the ethical challenges associated with the future application of large language models (LLMs) in the context of medical education. These challenges include not only ethical concerns related to the development of LLMs, such as artificial intelligence (AI) hallucinations, information bias, privacy and data risks, and deficiencies in terms of transparency and interpretability but also issues concerning the application of LLMs, including deficiencies in emotional intelligence, educational inequities, problems with academic integrity, and questions of responsibility and copyright ownership. This paper then analyzes existing AI-related legal and ethical frameworks and highlights their limitations with regard to the application of LLMs in the context of medical education. To ensure that LLMs are integrated in a responsible and safe manner, the authors recommend the development of a unified ethical framework that is specifically tailored for LLMs in this field. This framework should be based on 8 fundamental principles: quality control and supervision mechanisms; privacy and data protection; transparency and interpretability; fairness and equal treatment; academic integrity and moral norms; accountability and traceability; protection and respect for intellectual property; and the promotion of educational research and innovation. The authors further discuss specific measures that can be taken to implement these principles, thereby laying a solid foundation for the development of a comprehensive and actionable ethical framework. Such a unified ethical framework based on these 8 fundamental principles can provide clear guidance and support for the application of LLMs in the context of medical education. This approach can help establish a balance between technological advancement and ethical safeguards, thereby ensuring that medical education can progress without compromising the principles of fairness, justice, or patient safety and establishing a more equitable, safer, and more efficient environment for medical education.

IEEE Transactions on Emerging Topics in Computational Intelligence Information for Authors

IEEE Transactions on Emerging Topics in Computational Intelligence Information for Authors

Resilience of socio-ecological and energy systems: Intelligent information technologies for risk assessment of natural and technogenic threats

A method for studying the resilience of energy and socio-ecological systems is considered; it integrates approaches developed at the International Institute of Applied Systems Analysis and the Melentyev Institute of Energy Systems (MESI) of the Siberian Branch of the Russian Academy of Sciences. The article discusses in detail the methods of using intelligent information technologies, in particular semantic technologies and knowledge engineering (cognitive probabilistic modeling), which the authors propose to use in assessing the risks of natural and man-made threats to the resilience of the energy sector and social and ecological systems. More attention is paid to the study and adaptation of the integral indicator of quality of life, which makes it possible to combine these interdisciplinary studies.

Roles of artificial intelligence experience, information redundancy, and familiarity in shaping active learning: Insights from intelligent personal assistants

Roles of artificial intelligence experience, information redundancy, and familiarity in shaping active learning: Insights from intelligent personal assistants

Risk Challenges and Coping Strategies of Youth Value Guidance from the Perspective of Algorithm Recommendation

The deep integration of big data and artificial intelligence technology has given rise to the new information configuration paradigm of algorithm recommendation, which has not only profoundly changed the information-imparting model of the youth, but also exerted a far-reaching influence on the values of contemporary youth. At present, algorithm recommendation, with its intelligent information processing and precise information delivery mechanism, has expanded new space and provided new intermediaries for youth value guidance, but it has also aggravated the solidification of value cognition, eliminated the rationality of value judgment, eroded the subjectivity of value choice, and put forward unprecedented challenges to youth value guidance. Therefore, youth value guidance in the era of algorithm recommendation should be based on three aspects, namely, innovating guidance methods, improving media literacy, and activating value subjectivity, to give full play to the positive role of algorithm recommendation in positive value guidance, and effectively guide the youth to set up correct values.

How artificial intelligence can enable data classification for market sizing - Insights from applications in practice

Determining the size of the addressable market is a key aspect of market intelligence and requires identifying and delineating projected budget data from potential customers. The market intelligence arena is characterized by a wide range of disparate sources, many of which are unstructured, ranging across competitive, market, financial, and technology sources, and typically necessitating significant manual work to analyze, reconcile, and integrate. The authors present an approach for classification of data from one of these sources, facilitating aggregation and analysis of intelligence information. We describe a concept proof using machine learning that extends a model for automatic mapping of publicly available budget data to segments and subsegments of a market segmentation taxonomy. This approach automates the tagging of market and market segment for each program and cost element by training classification models on the manually labeled historical data. We describe the evaluation and use of multiple natural language processing (NLP) and classification modeling methods. This work's contribution is demonstrating how NLP and machine learning techniques can provide useful data classification and automatic classification even when source data diverges from its specified taxonomic description.

Optimal intelligent information retrieval and reliable storage scheme for cloud environment and E-learning big data analytics

Optimal intelligent information retrieval and reliable storage scheme for cloud environment and E-learning big data analytics

Intelligent Chinese Typesetting Model Based on Information Importance Can Enhance Text Readability

In today’s era of information overload, efficiently extracting valuable information from a large volume of textual data has become a crucial challenge in reading. This study aimed to explore the enhancement of Chinese readability in the digital domain through intelligent typesetting method. This study involved two experiments. The purpose of the first experiment was to achieve the machine learning-based assessment of the importance of individual words in Chinese articles and to automate typesetting based on the importance of words. For the second experiment, readability tests and eye-tracking reading tests were performed and the reading performance and reading attention between intelligent typesetting and general typesetting style was compared. This work proposed three Chinese typesetting methods that distinguished the importance of Chinese text information, based on font size and color. The results showed that first, when reading Chinese text, visual attention was more likely to be drawn to larger font sizes, darker brightness, or warmer-colored characters. Second, intelligent Chinese typography that distinguishes information importance through font size, color brightness, and color hue can enhance Chinese reading comprehension accuracy and subjective evaluation. It was concluded that using the TextRank model to distinguish importance of Chinese vocabulary and intelligent typesetting methods based on visual features of font could obviously improve text readability. Specifically, readability achieved through intelligent typesetting method distinguishing information importance through font color brightness surpasses that of general typesetting significantly. These intelligent typesetting methods can be widely applied in Chinese reading scenarios, such as web pages, e-books, information visualization, and other Chinese reading contexts.

SikuGPT: A Generative Pre-trained Model for Intelligent Information Processing of Ancient Texts from the Perspective of Digital Humanities

The rapid development of generative artificial intelligence has brought significant opportunities for the advancement of digital humanities research. Intelligent processing of ancient texts, as an essential part of digital humanities, is also undergoing a transformation in research methodologies in the wave of AIGC. The integration of generative pre-trained models with Chinese ancient texts, a vital carrier of Chinese culture, allows for deep mining of the content of these texts and provides services that make ancient texts more understandable and accessible to the general public. In this research, we propose a method that combines the most renowned Chinese anthology, the “Siku Quanshu,” with generative pre-trained models. We developed the SikuGPT model, a generative model for ancient text processing tasks, based on GPT-type language models by continued pretraining. This model was tested on two typical tasks of ancient text processing: translation between classical and modern Chinese, and classification of ancient texts. The findings reveal that our model achieves advantages in understanding and generating scenarios of ancient texts. The capability of SikuGPT in processing traditional Chinese texts helps to promote the organization of ancient information and knowledge services, and advances the international dissemination of traditional Chinese culture.

Study on the management effect of chronic disease intelligent information management platform in post percutaneous coronary intervention patients.

In 2019, approximately 330 million individuals in China were affected by cardiovascular diseases, with 11.4 million cases specifically attributed to coronary artery disease (CAD). A national public health report indicated that the mortality rate for CAD ranged from 121.59 to 130.14 per 100,000 individuals in 2019. The treatments for CAD include lifestyle changes, medications, percutaneous coronary intervention (PCI) and coronary artery bypass grafting. To investigate the management effect of a digital health program in patients with coronary artery disease (CAD) after percutaneous coronary intervention (PCI). This retrospective study compares blood pressure, blood glucose, low-density lipoprotein cholesterol (LDL-C), medication adherence, lifestyle modification, and readmission rate between digital health users and traditional follow-up in post-PCI CAD patients. In this study of 698 CAD patients, the 6-month readmission rate of all patients was 27.4%, with digital health users showing lower rates than those in traditional follow-up (22.6% vs. 32.1%, p= 0.005). Digital health users had significantly higher target achievements rates in blood pressure (79.7% vs. 54.7%, p< 0.001), blood glucose (98.9% vs. 82.5%, p< 0.001) and LDL-C level (71.3% vs. 52.7%, p< 0.001) at 6-month post-PCI. The digital health group had more patients adopting lifestyle changes, including quitting smoking, maintaining a healthy diet, and exercising regularly. In risk factor analysis, digital health utilization (OR = 0.60, 95%CI: 0.40-0.90, p= 0.014) and multivessel disease (double: OR = 1.72, 95%CI: 1.09-2.72, p= 0.02; triple: OR = 2.59, 95%CI: 1.61-4.17, p< 0.001) were independent predictors of CAD-related cardiovascular readmissions. Post-PCI patients using digital health platforms exhibited improved blood pressure, glucose, and LDL-C control, greater treatment adherence, enhanced lifestyle changes, and reduced six-month readmission rates versus those with traditional follow-up.

P-756 Neurodevelopmental outcomes at school-age after conception with hysterosalpingography using iodinated contrast media - follow-up of the nationwide multicentre H2Oil trial

Abstract Study question How does conception &amp;lt;6 months after hysterosalpingography (HSG) with iodinated contrast media affect the neurodevelopment of the offspring compared to naturally-conceived peers at a school-age? Summary answer Children conceived &amp;lt;6 months after HSG with iodinated contrast media perform lower on intelligence tests and information processing and attention control compared to naturally-conceived peers. What is known already Tubal patency in subfertile women is commonly assessed using hysterosalpingography (HSG), but the potential risks of exposure to high iodine content in contrast media used remains unknown. Exposure to iodine excess can cause transient reduction in thyroid hormone synthesis due to the Wolff-Chaikoff effect. Thyroid hormone sufficiency in early pregnancy is vital for children’s neurodevelopment. When conception occurs shortly after HSG with iodinated contrast, a temporary reduction of maternal and/or foetal thyroid hormone supply could jeopardize children’s neurodevelopment. Study design, size, duration We evaluated neurodevelopmental outcomes of 6–9-year-old children conceived &amp;lt;6 months after HSG with oil-based (iodine content: 480mg/ml) or water-based contrast (iodine content: 240-300mg/ml) in a nationwide randomised controlled trial (H2Oil trial; Netherlands; 2012-2014; NCT05168228). Naturally-conceived children (n = 44) with similar age, sex and parental education were used as a comparison group (NTR9574). We also compared children conceived &amp;lt;4w, 4-12w, &amp;gt;12w after HSG with those conceived naturally. The study took place January 2022 until November 2023. Participants/materials, setting, methods We contacted 140 mothers from the H2Oil trial, 69 children conceived &amp;lt;6m after HSG participated, and parallelly we recruited 44 naturally-conceived children. Primary outcomes were intelligence (Wechsler’s Intelligence Scale for Children V), behavioural functioning (SDQ and SWAN, administered to parents and teachers), and academic performance. Secondary outcomes were neurocognitive function assessed by a battery of computerized tests. We used linear regression adjusted for age, sex, and parental education and applied FDR correction. Main results and the role of chance Compared to naturally conceived children, children conceived &amp;lt;6m after HSG had lower performance on intelligence tests (-6.81; 95%CI-10.97 to -2.65; p = 0.02) and the neurocognitive domain Processing &amp; Control (-0.52; 95%CI-0.84 to -0.19; p = 0.02), with functions relating to information processing speed and consistency affected. No differences were found for other neurocognitive domains, behaviour or academic performance. Considering time between HSG and conception, intelligence scores were lower for children conceived &amp;lt;4w after HSG compared to naturally-conceived peers (-11.47; 95%CI-17.98 to -4.95; p = 0.03), while no significant differences were found for the groups of children conceived 4-12 weeks after HSG (-7.17; 95%CI -12.28 to -2.06; p = 0.10) or &amp;gt; 12 weeks after HSG (-4.20; 95%CI -9.33 to 0.94; p = 0.52). The findings of this study warrant replication in larger independent samples in order to evaluate the long-term neurodevelopment of children conceived after HSG with iodinated contrast media. Limitations, reasons for caution We aimed to recruit 64 children for each group, but our study was not sufficiently powered for small- and medium-sized differences. We did not collect information on the thyroid or iodine status of the mothers following HSG, and there is limited knowledge about pharmacokinetics of iodinated contrast media in humans. Wider implications of the findings Replication studies with are needed to further evaluate the safety of iodinated contrast media use periconception, including their effects on thyroid function and children’s neurodevelopment. Closer monitoring and treatment of thyroid dysfunction in the months following HSG may be warranted. Trial registration number NCT05168228

Application scenario design and prospect of generative artificial intelligence (AI) in intelligent manufacturing and supply chain of traditional Chinese medicine

Intelligent manufacturing technologies, including databases, mathematical modeling, and information systems have played a significant role in process control, production management, and supply chain management in traditional Chinese medicine(TCM) industry. However, their ability to process and utilize unstructured data, such as research and development reports, batch production records, quality inspection records, and supplier documents, is relatively weak. For text, images, language, and other unstructured data, generative artificial intelligence(AI) technology has shown strong potential for development in extracting information, extracting knowledge, semantic retrieval, and content generation. Generative AI is expected to provide a feasible set of tools for the utilization of unstructured data resources in the TCM industry. Based on years of research and industrial application experience in TCM intelligent manufacturing technology, this study reviewed the current situation of intelligent manufacturing in TCM and the utilization of unstructured data, analyzed the application value of generative AI in the TCM manufacturing process and supply chain, summarized four typical application scenarios, including intelligent pharmaceutical knowledge base/knowledge graph, intelligent on-the-job trai-ning, intelligent production quality control, and intelligent supply chain. Furthermore, this study also explained the data collection and processing, business process design, application potential, and value of each scenario based on industry demands. Finally, based on the integration of generative AI and TCM industrial models, the study proposed a preliminary concept of a smart industrial brain for TCM, aiming to provide a reference for the application of AI technology in the field of TCM manufacturing.

Intelligent analysis method of power grid monitoring signals based on affinity propagation clustering

Abstract As the scale of the power grid continues to expand, power supply companies centralize the management of grid monitoring signals at substations to adapt to the actual development of the power grid and improve the performance of grid operation. However, this leads to problems such as too many invalid signals, unidentified signal classification, excessive signal volume, and too many interfering signals at the monitoring site on a daily basis, which leads to difficulties in monitoring and certain security risks. Addressing this problem, this paper introduces a method of spatialization of text data using unique thermal coding and intelligent analysis of monitoring signals using AP clustering. The affinity propagation clustering algorithm can classify the monitoring signals faster and more accurately, count the number of signals, and deeply excavate the intelligent information implied therein. This greatly enhances the stability and safety of power grid operation. The proposed method is validated by the actual monitoring signal data.

Fuzzy Logic Controller-based Intelligent Irrigation System Using Solar Radiation Data

Solar radiation is a critical factor influencing agricultural productivity and water resource management. Irrigation systems play a pivotal role in maintaining crop health and yield, and optimizing their operation requires accurate solar radiation data. This abstract explores the significance of solar radiation data in enhancing the efficiency of irrigation systems. Irrigating agricultural fields using an intelligent information system plays a crucial role. This study introduces an irrigation control system employing closed-loop control to use the available water resources efficiently. Continuous data collection from field sensors was done and transmitted to a central station in wireless mode. The data was then retrieved and processed in a computer-based or microcontroller-based solution model, enhancing system autonomy, reliability, and cost-effectiveness. Weather conditions are translated into fuzzy set values. The pump, water outlet valves, and sprinklers are set into motion according to control commands. This research simplifies the need for manual labor and reduces water wastage.

Human Versus Machine Intelligence: Assessing Natural Language Generation Models Through Complex Systems Theory.

The introduction of Transformer architectures - with the self-attention mechanism - in automatic Natural Language Generation (NLG) is a breakthrough in solving general task-oriented problems, such as the simple production of long text excerpts that resemble ones written by humans. While the performance of GPT-X architectures is there for all to see, many efforts are underway to penetrate the secrets of these black-boxes in terms of intelligent information processing whose output statistical distributions resemble that of natural language. In this work, through the complexity science framework, a comparative study of the stochastic processes underlying the texts produced by the English version of GPT-2 with respect to texts produced by human beings, notably novels in English and programming codes, is offered. The investigation, of a methodological nature, consists first of all of an analysis phase in which the Multifractal Detrended Fluctuation Analysis and the Recurrence Quantification Analysis - together with Zipf's law and approximate entropy - are adopted to characterize long-term correlations, regularities and recurrences in human and machine-produced texts. Results show several peculiarities and trends in terms of long-range correlations and recurrences in the last case. The synthesis phase, on the other hand, uses the complexity measures to build synthetic text descriptors - hence a suitable text embedding - which serve to constitute the features for feeding a machine learning system designed to operate feature selection through an evolutionary technique. Using multivariate analysis, it is then shown the grouping tendency of the three analyzed text types, allowing to place GTP-2 texts in between natural language texts and computer codes. Similarly, the classification task demonstrates that, given the high accuracy obtained in the automatic discrimination of text classes, the proposed set of complexity measures is highly informative. These interesting results allow us to add another piece to the theoretical understanding of the surprising results obtained by NLG systems based on deep learning and let us to improve the design of new informetrics or text mining systems for text classification, fake news detection, or even plagiarism detection.

Digital Protection and Innovative Development Path of Red Culture Resources Based on Distributed Machine Learning Supported by Intelligent Information

As a product of the revolutionary war years, red culture, with its strong vitality, strong cohesion and extraordinary charm, with its incomparable positive energy, resists vulgar and flattering culture, promotes people to rebuild their faith, purify their minds, stimulate their motivation, and promote the process of cultural power. Yan’an, represented by red culture, is rich in resources. This is the holy land of Chinese revolution, the first batch of famous historical and cultural cities named by the State Council, and the three major education bases of patriotism, revolutionary tradition, and Yan’an spirit. The development and utilization of such resources have great political, cultural, educational and economic values. This research is based on the development of red culture, and uses the distributed machine learning system to realize in the system architecture of parameter server. In the distributed system set in this study, node downtime and network interruption are random. When the parameter server system adopts static scheduling, it leads to poor scalability and robustness. The experimental results show that under the intelligent simulation of machine learning system, the development of red culture resources meets the expected assumptions, and the accuracy of the model is relatively high.

Generative Artificial Intelligence and Personal Information Protection ― Constitutional and Personal Information Protection Legal Review ―

Generative Artificial Intelligence and Personal Information Protection ― Constitutional and Personal Information Protection Legal Review ―

ANALYSIS OF THE IMPACT OF SHARDING ON THE SCALABILITY AND EFFICIENCY OF BLOCKCHAIN TECHNOLOGIES FOR THE CREATION OF INFORMATION-ANALYTICAL SYSTEMS FOR ENVIRONMENTAL MONITORING OF EMISSIONS INTO THE ENVIRONMENT

This study examines the impact of sharding on the scalability and efficiency of blockchain systems, specifically in the development of a complex of intelligent information and communication systems for environmental monitoring of emissions into the environment for decision-making in the context of carbon neutrality. Utilizing the Ikarus Network infrastructure, sharding was implemented on masternodes as a key technology to optimize transaction processing. Sharding enables the blockchain to be divided into multiple parallel chains, significantly increasing throughput and reducing the load on individual nodes. The results demonstrate a 70% increase in transaction processing speed, allowing the system to handle up to 5000 transactions per second, compared to the previous 3000 transactions per second. Network throughput increased by 50%, ensuring more efficient load distribution and stable operation even with high data volumes. Statistical analysis using ANOVA confirmed significant improvements in transaction processing speed, confirmation time, and resource usage post-sharding implementation. The F-value for transaction processing speed was 4567 with a P-value of 0.0001, indicating substantial improvements. Visual data analysis further confirmed these results, showing noticeable performance enhancements in the blockchain system. Distribution charts and histograms of transaction processing speed and confirmation time revealed an increase in the average number of transactions per second and greater system stability post-sharding. Sharding not only increased throughput but also enhanced system security by decentralizing data among shards, complicating potential cyberattacks. The study aimed to determine how sharding can improve the scalability and efficiency of blockchain systems. These improvements position the Ikarus Network as a promising solution for scalable and secure blockchain-based applications, especially for tasks related to carbon emission monitoring and management. These findings can underpin further study and the development of more efficient blockchain technologies.

Preface: 3rd International Conference on Image Processing and Intelligent Information Systems (IPIIS 2024)

Preface: 3rd International Conference on Image Processing and Intelligent Information Systems (IPIIS 2024)

Communication security of intelligent information service platform combining AES and ECC algorithms

ABSTRACT The rapid progress of information technology has made the status of information service platforms increasingly prominent in social operations. However, their communication security is also facing unprecedented challenges. In response to the issues of insufficient security and efficiency in traditional encryption algorithms, this study combines advanced encryption standard algorithms with elliptic curve cryptography algorithms to generate encryption key pairs through elliptic curve cryptography algorithms. It utilizes advanced encryption standard algorithms to generate symmetric keys, and then encrypts the keys of the advanced encryption standard using the public key of the elliptic curve cryptography algorithm. Based on this, it implements advanced encryption standard encryption on data and designs a hybrid encryption algorithm. The results showed that the throughput of this method was 4000bit/s, with a delay of 550 m, which is higher than other algorithms, proving its high efficiency in processing data. The average bit change of the ciphertext in the hybrid encryption algorithm was 55.8 bits, with a range of bit changes of [69.3, 82.7], which is significantly higher than other algorithms, proving its high security. The designed algorithm has significant benefits in communication security of information service platforms and plays an important role in promoting the development of future network security defense technology.