Development Of Artificial Intelligence Technology Research Articles

Research on the Application Framework of Generative AI in Emergency Response Decision Support Systems for Emergencies

With the rapid development of artificial intelligence technology, generative AI has shown broad application prospects and potential in various fields. Frequent emergencies have put forward higher requirements for traditional Emergency Response Decision Support Systems (ERDSS). This paper proposes a theoretical framework of ERDSS based on generative AI (ERDSS-GAI), which deeply integrates generative AI with three stages of emergency response decision-making. The framework aims to leverage the advantages of generative AI in massive data processing, knowledge mining, strategy optimization, and other aspects, thereby enhancing the intelligence level and emergency response capability of ERDSS. The key components and implementation path of ERDSS-GAI are systematically explained from a theoretical perspective, and its application value is analyzed through the case of rainstorm and flood disaster in Shenzhen. This research demonstrates that generative AI can improve the scientific and refined level of emergency response decision-making, providing a theoretical framework and practical insights for its widespread adoption in emergency management.

Research on the Reform and Innovation of College Music Teaching Based on Collaborative Filtering Algorithm

With the rapid development of artificial intelligence technology, collaborative filtering (CF) algorithms are becoming increasingly widely used in the field of education. This paper aims to explore the application of CF algorithms in the reform and innovation of music teaching in universities, in order to improve teaching effectiveness and students’ learning experience. First, this paper analyzes the problems existing in current music teaching in universities, such as single teaching methods and low student participation. In order to complete the research on the extension of online music systems in music teaching, this paper reused the post filtering paradigm of contextual information and redesigned the two-stage process of the hybrid algorithm. After the initial screening of CF, the algorithm extracts recommendation results with contextual bias based on tag information. Then, based on the improved algorithm, an online music teaching system was implemented. Applying hybrid algorithm intelligent algorithms to university music teaching can not only improve the personalization and interactivity of teaching, but also promote the cultivation of students’ creativity and critical thinking abilities. The experimental results show that the music teaching system using hybrid intelligent algorithms significantly improves students’ learning participation, with an average participation rate increased by 30% compared to traditional teaching methods. At the same time, students’ learning outcomes have also significantly improved, with an average score increase of 15% in music theory exams. And in the assessment of creativity and critical thinking ability, students show stronger thinking activity and innovation ability. In addition, system feedback indicates that over 90% of students are satisfied with the teaching system, believing that it enhances the fun and interactivity of learning.

Landslide Displacement Prediction Stacking Deep Learning Algorithms: A Case Study of Shengjibao Landslide in the Three Gorges Reservoir Area of China

Computational models enable accurate, timely prediction of landslides based on the monitoring data on-site as the development of artificial intelligence technology. The most existing prediction methods focus on finding a single prediction algorithm with excellent performance or an integrated and efficient hyperparameter optimization algorithm with a highly accurate regression prediction algorithm. In order to break through the limitation of generalization of prediction models, this paper proposes an ensemble model that combines deep learning algorithms, with a stacking framework optimized with the sliding window method. Multiple deep learning algorithms are set as the first layer of the stacking framework, which is optimized with the sliding window method to avoid confusion in the time order of datasets based on time series analysis. The Shengjibao landslide in the Three Gorges Reservoir is used as a case study. First, the cumulative displacement is decomposed into a trend and a periodic term using a moving average method. A single-factor and a multi-factor superposition model based on multiple deep learning algorithms are used to predict the trend and periodic term of the displacement, respectively. Finally, the predicted values of the trend and periodic terms are added to obtain the total predicted landslide displacement. For monitoring point ZK2-3, the values of RMSE and MAPE of the total displacement prediction with the stacking model are 15.93 mm and 0.54%, and the values of RMSE and MAPE of the best-performing individual deep learning model are 20.00 mm and 0.64%. The results show that the stacking model outperforms other models by combining the advantages of each individual deep learning algorithm. This study provides a framework for integrating landslide displacement prediction models. It can serve as a reference for the geological disaster prediction and the establishment of an early warning system in the Three Gorges Reservoir Area.

Analysis of the Correlation of Topic Feature Changes Based on the LDA Model

Abstract. As is well known, with the continuous development of artificial intelligence technology and the increasing accessibility of data, various social platforms are committed to using intelligent recommendation algorithms to cater to user preferences. Some platforms even exaggerate facts and push sensational, valueless information to users, leading to the "Screaming Effect" and the "Echo Chamber Effect." The prolonged existence of these effects may result in "Information Cocoon," which is detrimental to the healthy development of individuals and society. To address this issue, different topics can have varying trajectories as online comments ferment, potentially reaching a neutral consensus or resulting in polarization. As a mainstream social platform in China, Weibo users serve as nodes for the dissemination of public opinion information, and the characteristics of information release, reception, forwarding, and commenting all influence the effectiveness of communication. First, we selected the topics "COVID-19" and "IG Electronic Sports Club" as our research subjects, identifying a range of topic popularity through Baidu Index, followed by data collection. Second, we used perplexity and coherence to determine the optimal number of topics for LDA, analyzing the changes in topic feature characteristics over different time periods. Through correlation analysis, we reached the following conclusions: Higher Weibo levels correlate with better information dissemination effectiveness; the quantity of information published on Weibo negatively impacts the forwarding and commenting behaviors; and as events progress, the enthusiasm for information dissemination on Weibo declines, with daily dissemination gradually decreasing.

Recognition of palaeo-glacial troughs with cirques on parts of Tibetan Plateau using multi-modal datasets with deep learning models

Recognition of landforms associated with past glaciation is crucial for understanding past ice dynamics and their relationship to climate. With the development of artificial intelligence technology, deep learning techniques have assisted in the automatic extraction of glacial landforms, but these methods still face problems of low precision and weak transferability. This study proposes a new method named geomorphology-attention DeeplabV3+ (GA-DeeplabV3+) model. This method adds spatial attention and channel attention modules based on the DeeplabV3+ network and utilizes a combination of multi-modal geographic data. Verification shows that the model proposed in this paper significantly enhances the recognition precision of glacial troughs with cirques compared to existing models, reaching a Mean Intersection over Union (MIoU) of 86.2% and a mean Pixel Accuracy (mPA) of 90.64% in the area of the Palaeo-Daocheng ice cap. In addition, validation experiments were conducted in the Peiku Gangri region and the Tenasserim mountains, achieving MIoU scores of 70.09% and 73.28% respectively. This accomplishment represents a vital stride towards automating the extraction of palaeo-glacial landforms, which holds great significance for analyzing the scale and evolution of ancient glaciers.

Deep learning based classification for alzheimer's disease detection using MRI images

Alzheimer's disease is a common type of dementia that can cause serious problems in cognitive functions and activities of daily living. Although there is no definitive cure for Alzheimer's disease today, early diagnosis is important to slow down the adverse conditions that may arise and to improve the quality of life. As a result of the development of artificial intelligence technologies and their consistent application in different fields, machine learning techniques have the potential to play an important role in the detection of Alzheimer's disease. In particular, deep learning-based methods, which have the ability to automatically extract patterns from complex patterns, are promising in this field. Recent studies show that the use of deep learning models for Alzheimer's detection on images is becoming widespread. In addition to contributing to the early diagnosis of the disease, these models also show potential in detecting different stages of the disease by analyzing the symptoms in magnetic resonance images. These developments enable the development of more effective treatment methods for patients. However, more studies are needed to evaluate the efficacy and safety of these technologies in clinical applications. In this study, classification studies were performed using MobileNetV2, InceptionV3, Xception, Vgg16 and Vgg19 models for the diagnosis of the disease on a publicly shared Alzheimer's dataset consisting of 6400 different samples and 4 different classes. An accuracy of 99.92% was calculated for the MobileNetV2 model. The performances of the models used in this study were compared with similar studies in the literature and their performances were reported in terms of different metrics. Among the five different models used, the highest accuracy value of 99.92% was obtained with MobileNetV2. It was concluded that the architectures used in the experimental studies produced generally better results than similar studies in the literature.

Thoughts on the Construction of Art Curriculum Using Generative AI

Curriculum construction is an important part of improving the quality of higher education. With the rapid development of artificial intelligence technology, generative AI is having a profound impact on the construction of first-class courses in art majors in colleges and universities. This study aims to explore how generative AI can enable the construction of first-class courses in art majors, and analyze its specific application and effect in curriculum construction through practical cases.

A Review of Deep Learning Based Object Detection Algorithms

With the rapid development of artificial intelligence technology, the traditional manual feature-based object detection method has been gradually replaced by deep learning-based object detection technology. Object detection is the basis and prerequisite for many computer vision tasks aimed at recognizing targets in an image and determining their class and location. The purpose of object detection algorithm research based on deep learning is to improve the detection accuracy and detection speed of the detection algorithm, so that the object detection algorithm can be more safe and convenient applied to People's Daily production and life. This article reviews the evolution of object detection algorithms, focusing on two-stage, one-stage and object detection algorithms based on the transformer architecture. In addition, the performance, advantages and disadvantages of different algorithms are compared, and the development trend of object detection algorithms based on deep learning is summarized in the end.

Comparison of transformer, LSTM and coupled algorithms for soil moisture prediction in shallow-groundwater-level areas with interpretability analysis

Accurate quantification of soil moisture is essential for understanding water and energy exchanges between the atmosphere and the Earth’s surface, as well as for agricultural applications. Predicting soil moisture content is vital for efficient water management, irrigation scheduling, and drought monitoring. Traditional forecasting methods, such as numerical regression models, often struggle due to various influencing factors and poor observation data quality. In contrast, deep learning algorithms, particularly recurrent and convolutional neural networks, show promise in predicting nonlinear data like soil moisture. This study focuses on shallow groundwater regions, using groundwater levels and meteorological data as features while coupling the Transformer model with other neural network structures. We investigate the potential of attention-based neural networks for soil moisture time series prediction. Our findings demonstrate that the Transformer model achieves an average R2 of 0.523 across different time lags, outperforming the LSTM model with an R2 of 0.485. The introduction of LSTM enhances the Transformer’s stability in handling temporal changes. Additionally, we verified the importance of groundwater for soil moisture prediction. This study introduces new methods for soil moisture prediction and offers new insights and recommendations for the development of artificial intelligence technology for soil moisture prediction.

Exploring the Practical Teaching Mode of Language Laboratory based on Artificial Intelligence

With the rapid development of artificial intelligence (AI) technology, its application in the field of education is becoming more and more extensive, especially in the teaching mode of language laboratory, AI technology shows great potential and innovation space. The purpose of this paper is to explore the practical teaching mode of language lab based on artificial intelligence, and to reveal its advantages and challenges in language learning by analysing the current status of the application of AI technology in teaching. This study aims to explore the in-depth analysis of the research on the practical teaching mode of language labs based on artificial intelligence. Firstly, it introduces the problems of language labs and analyses the limitations and problems of traditional labs. Secondly, the language laboratory solution measures are elaborated in detail to construct the practical teaching mode of language laboratory based on artificial intelligence. Then, the practical teaching mode of language laboratory based on artificial intelligence is explored. Finally, the application value of the language laboratory of artificial intelligence in the aspects of linguistic research and teaching practicability is discussed. As an innovative application of educational technology, the language laboratory of artificial intelligence has a positive role in promoting the transformation of the modern language teaching mode, and provides new perspectives and methods for foreign language teaching. This paper focuses on the design and construction of the practical language teaching mode of artificial intelligence. With the rapid development of artificial intelligence technology, its application in the field of education has become increasingly widespread. In language teaching, the construction of an AI-based practical teaching model can help to improve teaching efficiency and quality and stimulate students’ interest in learning. This paper elaborates the design principles, construction elements, implementation process and effect evaluation of this teaching mode from various aspects.

A review of load forecasting technology based on artificial intelligence

Abstract. Driven by the "dual carbon" goals and the construction of new-type power systems, the importance of power load forecasting technology has become increasingly prominent. Accurate power load forecasting can not only ensure the safe and stable operation of the power system, but also promote the efficient use of clean energy and reduce carbon emissions. Traditional power load forecasting methods have become insufficient in dealing with the complexity and nonlinear growth of power systems, and the development of artificial intelligence technology has brought new possibilities for power load forecasting. Power load forecasting methods based on machine learning and deep learning have gradually become a hot topic of research due to their powerful nonlinear mapping capabilities and advantages in processing big data. These methods not only perform well in short-term forecasting, but also show potential in medium- and long-term forecasting. This paper discusses the application of different artificial intelligence technologies in power load forecasting by sorting out the development status of related technologies, and provides a reference direction for future research.

A Systematic Review of Public Relations Research in the Context of Artificial Intelligence

In the context of the rapid development of artificial intelligence technology, this article provides a systematic review of public relations research in the context of artificial intelligence to provide valuable references for future public relations research and practice. Collect literature data from CNKI, use systematic review methods to select literature that meets the standards, and sort and summarize it from four dimensions: application background, application scenarios, challenges faced, and response measures. Research has found that currently, applying artificial intelligence technology to public relations practice has become a basic consensus in academia; The paradigm of public relations research in the era of artificial intelligence has changed; In the short term, artificial intelligence technology will not completely replace public relations personnel; The measures for responding to public relations in the era of artificial intelligence can be summarized and divided into two levels: theoretical and practical. In the future, it is necessary to further explore and improve public relations theories related to artificial intelligence, construct new analytical frameworks and research methods to adapt to the changes brought about by technology.

Substantive and Emotional: The Anthropomorphic Evolution of Generative AITaking GPT-4o as an Example

Under the background of the rapid development of artificial intelligence technology, "personal publishing" represented by blog in the past has transformed into "human-computer co-creation", showing a new human-computer interaction relationship, and generative artificial intelligence has become the "collaborator" of human content creation. The flagship multimodal large language model "anthropomorphism" represented by GPT-4o is evolving from "linguistic anthropomorphism" to "emotional anthropomorphism". Based on the anthropomorphic theory, this paper aims to reveal that GPT-4o, while demonstrating its linguistic intelligence in the human-machine co-created dialogue, simulates human emotional response, showing empathy and "resonance", that is, the "anthropomorphic" process at the two levels of "words have things" and "words have feelings". It also reflects on the potential ethical challenges of the anthropomorphic evolution of generative AI to future human society.

Deep-Learning-Based Amplitude Variation with Angle Inversion with Multi-Input Neural Networks

Deep-learning-based (DL-based) seismic inversion has emerged as one of the state-of-the-art research areas in exploration geophysics with the development of artificial intelligence technology. Due to its good portability and high computational efficiency, this method has emerged as a data-driven approach for estimating subsurface properties. However, most of the current DL-based methods rely solely on seismic data, lacking the incorporation of prior information. In addition, these methods are usually performed trace-by-trace, resulting in insufficient horizontal constraints. These limitations make traditional methods less robust, particularly when dealing with high noise levels or limited data. To address these challenges, we propose a multi-input deep learning network for pre-stack inversion, which combines data-driven and model-driven approaches for optimization. The proposed method separately extracts features from the model and data, merging them to improve feature utilization. Moreover, by adopting a 2-D training unit, rather than a trace-by-trace approach, the method improves the horizontal continuity of the results. Tests on synthetic and real seismic data confirmed the robustness and improved stability of the proposed method, even under challenging conditions. This dual-driven approach significantly enhances the reliability of seismic inversion.

Unmanned Ship Collision Avoidance Action Plan Deduction Method under Man–Machine Interactive Negotiation in Collision Avoidance Scenarios

With the development of artificial intelligence technology, the future water traffic environment will present a new pattern of coexistence of manned ships and unmanned ships, because unmanned ships are different from manned ships in situation understanding, collision avoidance decision-making, and so on. Therefore, the obstacle avoidance planning between unmanned ships and manned ships becomes extremely complex, and collision avoidance behavior scheme deduction becomes a key step in solving the problems related to situation understanding and collision avoidance decision-making in collision avoidance scenarios. In this paper, the situation understanding of the pilot for different collision avoidance situations is integrated into the dynamic obstacle avoidance model, and an intelligent navigation collision avoidance system is proposed to assist in deducing the collision avoidance action plan of the unmanned ship in the man–machine coexistence scenario. The intelligent navigation collision avoidance system is divided into two parts, namely a ship situation understanding part and a ship obstacle avoidance part, wherein ship situation understanding is used for realizing the transition of the collision state of the unmanned ship in the deduction process by constructing a collision-state set and a behavior decision set by using a finite state machine (FSM). Regarding ship obstacle avoidance, ship velocity obstacle is calculated based on the reciprocal velocity obstacle method (RVO), and the collision avoidance action is selected by using the behavior decision generated by the FSM to realize the dynamic collision avoidance deduction of the unmanned ship. In this study, the validity and effectiveness of the intelligent navigation collision avoidance system proposed in this paper are verified by case studies in a variety of collision avoidance scenarios. The system successfully solves the problem of intelligent collision avoidance planning, provides reliable support for the intelligent collision avoidance of unmanned ships, provides a feasible solution for safety and efficiency in sea navigation, and provides a valuable reference for the design and development of future intelligent navigation collision avoidance systems for ships.

Traffic Flow Prediction: A Method Using Bagging-Based Ensemble Learning Model

For the development of the national economy, transportation is strategically significant. As the increasing ownership of automobiles, traffic jams are a common occurrence. Accurate prediction of traffic flow contributes to diverting traffic effectively and improving the quality of urban traffic, in turn improving the operation of the overall transportation system. The rapid development of artificial intelligence technologies, especially machine learning and deep learning, has provided effective methods for accurate prediction of traffic flow. Based on the above, in order to improve the accuracy of the prediction and to extend the application of machine learning and deep learning in the prediction of traffic flow, this study proposed a bagging-based ensemble learning model. Firstly, normalization method is used to preprocess the data. Subsequently, base prediction models including decision tree, random forest, logistic regression, convolution neural network, long short-term memory and multilayer perceptron are selected for training the prediction model, respectively. Finally, bagging-based ensemble learning method is used to integrate these base prediction models to further predict traffic flow. The results of comparison between the single base prediction models and the bagging-based ensemble learning model on the five evaluation indicators show that, for predicting the traffic flow, the bagging-based ensemble learning model outperforms the base prediction models. Meanwhile, this study explores the potential in the application of machine learning, deep learning, and especially bagging-based ensemble learning to predict traffic flow.

The Construction of Personalized Language Learning System under the Guidance of ChatGPT

With the development of artificial intelligence technology, AI-powered chatbots are gradually being applied to language teaching and learning. Among them, ChatGPT, as a generative language intelligence model, plays its unique advantage in promoting personalized language learning. Hence, this article starts by elucidating recent research on the use of ChatGPT in language teaching and learning, giving some evidence about the possibility of personalized language learning under the guidance of ChatGPT. Then advantages associated with its usage in personalized language learning are discussed, including providing personalized language input, cultivating personalized language learning abilities and giving personalized language feedback for language learners. Based on these advantages, this article compares the traditional personalized language learning with the one under the guidance of ChatGPT and try to put forward a three-step personalized language learning system under the guidance of ChatGPT, hoping to improve the effectiveness of language learning and realize the truly personalized language learning in the future.

A facial and vocal emotion recognition system based on deep learning

The traditional teaching mode, mainly characterized by "cramming" instruction, often overlooks the absorption of knowledge by students during the teaching process. Even in recent years, the emerging "flipped classroom" model heavily relies on teachers' experience and expertise, making it challenging to quantify students' classroom learning states. Among young teachers lacking teaching experience, the replication of excellent teaching models faces difficulties. With the rapid development of artificial intelligence technology, deep learning and other AI techniques are increasingly applied to people's lives. Additionally, emotion computing technology is also becoming more sophisticated. In the field of education, the use of AI technology holds the potential to help teachers systematically and comprehensively assess teaching quality, promoting the sharing of high-quality educational resources nationally and globally. Existing emotion detection technologies often focus on single techniques such as image recognition or natural language processing, resulting in low accuracy in discerning human emotions and making it difficult to accurately identify emotional changes in complex scenarios such as classrooms. Furthermore, there is a lack of comprehensive emotional statistical analysis methods for audio and visual data. In light of these, we have designed and implemented a multi-modal emotion recognition and analysis system. Through testing in classroom settings, this system has the potential to assist classroom teaching and enhance teaching quality.

Information Extraction of Aviation Accident Causation Knowledge Graph: An LLM-Based Approach

Summarizing the causation of aviation accidents is conducive to enhancing aviation safety. The knowledge graph of aviation accident causation, constructed based on aviation accident reports, can assist in analyzing the causes of aviation accidents. With the continuous development of artificial intelligence technology, leveraging large language models for information extraction and knowledge graph construction has demonstrated significant advantages. This paper proposes an information extraction method for aviation accident causation based on Claude-prompt, which relies on the large-scale pre-trained language model Claude 3.5. Through prompt engineering, combined with a few-shot learning strategy and a self-judgment mechanism, this method achieves automatic extraction of accident-cause entities and their relationships. Experimental results indicate that this approach effectively improves the accuracy of information extraction, overcoming the limitations of traditional methods in terms of accuracy and efficiency in processing complex texts. It provides strong support for subsequently constructing a structured knowledge graph of aviation accident causation and conducting causation analysis of aviation accidents.

Cognitive aspects of interaction in the “Human — Artificial Intelligence” system

Abstract The article, based on empirical and theoretical research, reveals the phenomenology of transformations of the human cognitive sphere when interacting with artificial intelligence. The analysis of the indicated changes in the cognitive sphere is carried out on the basis of the “Concept of cognitive multi-channel Human-Computer interaction” developed by us. The essence of this concept is that the interaction of the cognitive sphere of human and artificial intelligence is implemented on the basis of the actualization and formation of typical cognitive phenomena. These phenomena are considered systemically and multifunctionally, namely as relatively independent cognitive: types of interactions, stages, strategies, channels, ontologies. Within the conceptual and substantive framework of this concept, we distinguish the following types of cognition (channels, strategies, etc.): I – orientational-cognitive; II – subject-cognitive; III – communicative and cognitive; IV – cognitive and analytical; V – cognitive and hermeneutic; VI-cognitive-ontological; VII – cognitive and creative. The identification of the indicated types of cognitive interactions is aimed at its representation as a complex, dynamic, multidimensional, multichannel intellectual system, the features of which are significant for educational and sociocultural practices, as well as for further development of artificial intelligence technologies, including its functional orientation and specificity, ergonomics, architecture, design and features of the interface. A study was conducted among students of higher education institutions aimed at determining the cognitive specificity (structure) of interaction in the “Human – Artificial Intelligence” system. Based on the analysis of the results of the distribution of answers for each of the test questions and the interpretation of the results of the cluster analysis (the Canopy algorithm was used), the dominance of the “I – orientational-cognitive” type of interactions was determined, which indicates a rather significant but initial interest in artificial intelligence technologies. There is also a relatively even distribution of all other types of cognitive interactions. The above reveals the novelty and innovation of artificial intelligence technology. This correlates with the respondents having developed different types of cognition, namely: orientational, analytical-synthetic, conceptual, interpretive, ontological, creative thinking, and corresponding intellectual intentions and motivation to use artificial intelligence tools in various spheres of activity.