Journal of Artificial Intelligence and Information
Journal of Artificial Intelligence and Information, ISSN: 3064-8033 is an open access, peer-reviewed journal that will consider any original scientific article that expands the field of information technology and various other related applied computer sciences themes. It publishes annually and continuous publication schedule is kept so that articles are published online as soon as the final proofs are approved by the authors. The journal publishes articles of interest to information technology, researchers and practitioners. Manuscripts must be original and educationally interesting to the audience in the field. The goal is to promote concepts and ideas developed in this area of study by publishing relevant, peer-reviewed scientific information and discussion. This will help information technology practitioners advance their knowledge for greater benefit and output in their professional contexts. The focus and scope are Software Engineering, Network and Communication Technology, Geospatial Cloud Computing, Computer Vision, Data Science, Machine Learning, Bioinformatics, Fuzzy Systems, Natural Language Processing , and Multimedia. Authors can submit papers to jaii@woodyinternational.com
Computer Big Data Analysis and Cloud Computing Network Technology Application
Computer big data analysis and cloud computing network technology are highly significant information technologies in todays society. Computer big data analysis involves a series of technical methods that utilize the powerful computing capabilities of computers to process, analyze, and visualize large datasets. Cloud computing network technology, on the other hand, is an internet-based computing model that separates data and applications from hardware devices, allowing access and usage through the internet. We extensively discuss the applications of computer big data analysis and cloud computing network technology in areas such as visualization, predictive analysis, data mining algorithms, ensuring data storage security, supervising data resource sharing, and modeling data processing systems.
Copyright (c) 2025 Journal of Artificial Intelligence and Information
Cross-Domain Adaptation and Anti-Interference Performance of Autonomous Driving Perception Models under Extreme Conditions
Severe weather and lighting changes often cause significant degradation in the recognition performance of autonomous driving perception systems. This study proposes a visual perception model that integrates adversarial training with a domain adaptation mechanism. By combining Generative Adversarial Networks (GANs) and a self-supervised pretraining strategy, the model aims to enhance feature consistency and recognition stability across different environmental domains. At the model structure level, a feature alignment loss and a style transfer module are introduced to improve the model's adaptability in extreme conditions such as rain, nighttime, and strong light. Evaluations are conducted on the Oxford RobotCar and DAWN multi-domain datasets. The results show that the proposed method achieves a 14.2% improvement in average recognition accuracy and maintains a low false detection rate during environmental transitions. These outcomes demonstrate excellent cross-domain adaptability and strong anti-interference performance.
Research on the Present Situation and Development of Anti-Harsh Environment Computer Technology
Resilient computer technology is a technology developed to meet the computational requirements under harsh environmental conditions. Harsh environments present numerous challenges to computer technology, including high temperatures, low temperatures, humidity, vibrations, radiation, and electromagnetic interference. To address these challenges, various techniques have been applied to resilient computer technology, such as enclosed and sealed designs, as well as dust and waterproofing technologies. Looking ahead, the development trends of resilient computer technology encompass the application of new materials, improvements in chip and hardware technology, and the enhancement of intelligence and autonomy. These technological advancements will offer better solutions for computing and data processing in challenging environmental conditions.
Computer Application Technology and Practice in the Big Data Environment
In recent years, China has undergone numerous reforms and innovations in the field of technology, successfully entering the era of big data. In this era, there has been significant progress in computer application technology. Within the context of the big data era, computer application technology faces numerous challenges, but it also presents boundless opportunities. Continuous reform, innovation, and breakthroughs are essential to construct application thinking, approaches, and patterns that align with the characteristics of big data. Therefore, its imperative that we fully understand and comprehend big data and actively embrace the impact it brings. This paper delves into a thorough exploration of various types of computer application technologies within the big data environment, as well as future development trends and practical implementations. Furthermore, it provides corresponding recommendations with the aim of offering valuable insights for related endeavors.
The Application and Discussion of Virtual Network Technology in Computer Network Security
The development of information technology has led to an improvement in people's living standards and quality of life. With the widespread adoption of current computer technology, concerns about computer network security are increasingly shared among users. The application of virtual network technology in computer networks can significantly enhance network security and reduce the losses caused by computer vulnerabilities. This paper primarily investigates the utilization of virtual network technology in computer networks and presents the application scenarios of virtual network technology in computer network security. It aims to provide effective references for the application of relevant technologies.
Thinking on PHP Dynamic Website Development Technology in the Internet Background
In the current societal context, the connection between peoples lives and the internet has become increasingly close. Internet technology has become an integral part of social production and development. The use of dynamic networks in the context of the internet has further expanded the applications of the internet. In the development of dynamic websites, PHP dynamic web development technology has become the primary approach due to its unique advantages. This paper starts by analyzing the characteristics of PHP technology and dynamic networks. It presents scenarios for development and applications utilizing PHP dynamic web technology. Furthermore, considering the current state of internet technology, the paper outlines the primary development directions for future PHP dynamic websites. The aim is to provide references for the implementation and research of related endeavors.
Application of Key Technologies of Cloud Computing Energy Saving in IT Support System
This article introduces cloud computing technology, analyzes the application principles of energy-saving key technologies in cloud computing for IT support systems, and dissects the practical energy efficiency of these key technologies. By examining cloud-based business scenarios and analyzing the basis and algorithms for resource scheduling, intelligent power management contributes to reducing host power consumption during data center operation. The computational demands of business operations are positively correlated with energy consumption, and these demands can vary due to business requirements. Creating an energy-saving scheduling model and implementing it within the IT support cloud platform helps address energy-saving and emission reduction issues in cloud computing. Furthermore, the key energy-saving technologies in cloud computing enable flexible implementation of resource scheduling.
Mobile Phone Behavior Recognition Based on Convolutional Neural Network
With the continuous enhancement of computational capabilities in smartphones and the increasing integration of various sensors into them, smartphones have transcended their conventional role in communication and gained significant application value in areas like human-computer interaction, fall detection, and information security. Utilizing smartphone sensor data for human activity recognition has also become a prominent research focus. Based on mobile phone behavior recognition data from Baidu Feizi, this paper constructs convolutional neural network and uses batch normalization method to accelerate the convergence speed, and the final accuracy rate reaches 96.7. The algorithms proposed in this paper are effective in recognizing six distinct behaviors. The study concludes with an analysis of various models, discussing their strengths and weaknesses in the context of behavior recognition applications.
Risk Identification and Network Optimization of Regional Supply Chains Based on Multi-Source Data Fusion
In response to increasingly complex global economic dynamics, enterprises are encountering heightened supply chain challenges, including transportation disruptions, frequent shifts in international trade regulations, and the demand for greater supply chain autonomy. This study proposes a network optimization framework based on multi-source risk data fusion and the multi-objective evolutionary algorithm NSGA-III. Four core indicators—logistics efficiency, regulatory adaptability, historical disruption frequency, and substitution costs—are integrated, and node risk features are extracted through graph convolutional networks (GCNs) to provide quantitative assessments of supply chain elements. A simulation was conducted within a multinational component group with an annual procurement volume exceeding USD 800 million, systematically evaluating centralized, zoned, and regionalized collaborative supply chain layouts. The results demonstrate that the "3 Centers + 6 Radiations" regionalized collaborative model achieves a 34.5% reduction in average delivery cycle fluctuations and a 26% decrease in annual plan adjustments, compared to traditional structures. Furthermore, the dynamic elasticity adjustment mechanism developed in this study enables scenario-based simulations under varying geopolitical and economic disruptions, providing robust decision support for resilient supply chain optimization.
Analysis of Computer Information Security and Protection Strategies in the Era of Artificial Intelligence
Currently, there are still some problems and shortcomings in the operation of computer systems, such as illegal attacks, malicious destruction, and information leakage, which affect the stability of computer system development.In response to this, industry related technical personnel need to investigate and analyze current information security issues, realize the importance of information security protection, and develop scientific and specific solutions to the exposed problems to ensure the stability of computer system operation.This article explores how to maintain computer information security, aiming to promote the development of science and technology.
Macro Financial Prediction of Cross Border Real Estate Returns Using XGBoost LSTM Models
This study develops a combined prediction model for international real estate returns using gradient boosting and long short-term memory networks. The model integrates macro-financial and property-specific indicators with a lagged input structure to capture both contemporaneous and delayed effects. Quarterly data from twelve countries between 2010 and 2023 are used to train and evaluate the model. Empirical results show that the proposed method consistently outperforms conventional approaches, including linear regression, random forest and standalone recurrent networks. The average root mean squared error (RMSE) is reduced by over 14%, with particularly notable gains in high-volatility markets. Variable attribution using SHAP values confirms the substantial influence of interest rate spread, inflation and capital account openness on return variation. Interaction effects between key indicators and sensitivity to variable exclusion further enhance model transparency. The study suggest that the integration of nonlinear feature modeling and sequence learning offers measurable improvements in short-term return forecasting. The approach provides a viable tool for international real estate investment analysis under changing macroeconomic conditions.
Application Exploration of Virtual Network Technology in Computer Network Security
To safeguard the security of computer network information systems and effectively address network information risks, such as Trojan viruses and hacker intrusions, it is crucial to actively employ advanced technologies. This article takes virtual network technology as its subject of study and provides a brief overview of virtual network technology and the significance of computer network security. It delves into its applications in computer network security from four aspects: technology types, application scope, real-world impact, and effective measures. The aim is to reinforce the stability of computer networks and protect user information security, providing valuable insights for relevant individuals.
Key Points of OXC Technology and Application Strategy
This article elaborates on the technical characteristics and engineering design principles of Optical Cross-Connect (OXC) technology from various perspectives, including its advantages, application scenarios, and design essentials. It highlights that in the era of algorithmic networks, to meet the requirements of ubiquitous access, high-speed interconnection, and flexible scheduling in an all-optical infrastructure system, the OXC/ROADM+OTN optical-electronic integrated system will become the next-generation transmission network.
Research on Intelligent Shelves Based on RFID Technology
Intelligent shelves are the foundation of intelligent warehouse system management. As an intelligent storage management technology application, RFID technology can achieve efficient and precise management of material shipment, logistics, warehousing, stacking, inventory and outbound through wireless radio frequency identification communication.With the continuous maturity of technology, at present, it is the opportunity for traditional warehousing enterprises to transform into intelligent warehousing enterprises. The application and development of intelligent shelves will also be a hot spot in the fierce competition of the warehousing industry.With the development of industrial automation and intelligence, the research and application of intelligent shelf technology is urgently needed.This article analyzes the communication principles and advantages of RFID systems, as well as the technological research in intelligent shelves, with the hope of further improving the application level of RFID technology and promoting the intelligent development of warehousing systems.
Trends and Bottlenecks in Virtual Reality and Tactile Interaction Technologies
The main form of artistic expression of virtual reality technology is not visual expression. In the course of human history development, visual art plays an extremely significant role, such as ancient mural art, Western Han shadows and modern cinema and television are all visual arts, and these are vivid reflections of economic life and scientific and technological standards during the stage of human social development, becoming the essence of human civilization development. Art comes from everyday life and is above life. It not only records all aspects of human life, but also reflects people's aspiration for a better life and their boundless vision for the future. Visual expression not only allows the plot of the story to be more vivid, but also brings people a sense of enjoyment. This article explores the trends and bottlenecks of virtual reality and tactile interaction technology.
Analysis of the Application of AI Technology for Radio and Television Broadcasting under Computer Networks
The times are constantly progressing and science and technology are developing rapidly. Among them, computers and networks have become an important outcome of scientific and technological development, bringing new directions for the reform of radio and television multimedia technology. Compared with traditional media communication methods, radio and television multimedia communication under computer network has a new direction, which can abandon the geographical and time limitations of traditional media transmission methods and greatly broaden the transmission channels of radio and television media technology. People nowadays prefer the new computer network for radio and television multimedia technology. This greatly promotes the innovation and development of radio and television multimedia technology, can attract more people to interest in radio and television media, and further promotes improvement and improvement of radio and TV multimedia technology.
Application of Cloud Computing Technology in Computer Data Processing
In recent years, cloud computing in China has developed rapidly. In the process of continuous progress, it provides a good platform for computer data processing work, plays a role in promoting the progress of computer technology, and has a wide range of applications, which can enhance the level of computer data processing, which is of great significance. Based on this, this paper studies and analyzes the application value of cloud computing in computer data processing, and puts forward some suggestions to enhance the effect of data processing.
Development and Application of Cloud Storage Technology in Video Surveillance
Through the cloud-based data storage deployment mode, the application of distributed computing methods, a large number of different types of data storage devices in the network through the application software set up, effectively and reasonably unified computing and data processing, End users have centralized access to cloud-stored data resources and business systems through remote or similar software application and program interfaces to enable efficient and fast resource analysis and data processing in large-scale data storage and access environments. Therefore, this article makes a specific analysis on the development and application of cloud storage technology in video surveillance.
A Comparative Analysis of Traditional GARCH Models and Modern Machine Learning Approaches
In this work, I will provide a general comparison of the conventional financial economic approaches and the contemporary machine learning techniques in forecasting stock market volatility. On a sample of 505 S&P 500 stocks between 2013 and 2018, we apply several GARCH models, as well as a random forest and LSTM neural networks. According to our analysis, 1/1/ Student-t GARCH(1,1) is the best model in comparison with other traditional models due to its performance on a variety of volatility regimes. The machine learning exploration shows the strong limitations of data leakage and autocorrelation in financial time series, and it has very important methodological implications. Findings indicate that GARCH models attained realistic out of sample RMSE values of 0.96-4.82 whereas optimally implemented ML models result in more modest but candid performance indicators. The study will add to the knowledge about the shortcomings of volatility models and offer a strict framework of comparing econometric and machine learning methods in financial forecasting.
Application of Artificial Intelligence Technology in Coal Mine Mechanical and Electrical Equipment
This article explores the application of artificial intelligence technology in coal mine electromechanical equipment. Firstly, the definition and development history of artificial intelligence were outlined. Then, the advantages of its application in the coal mining industry were elaborated, including improving equipment operating efficiency, enhancing fault diagnosis capabilities, improving safety assurance levels, and optimizing equipment maintenance management. Specific applications include unmanned coal mining, coal mine safety monitoring, intelligent power supply, ventilation and belt conveyor systems, as well as mechanical and electrical equipment fault diagnosis and maintenance. Although there are still areas for improvement in the application, it is expected to promote the intelligent, efficient, and safe production of the coal mining industry in the future, which will contribute to energy security and economic development.
Design and Implementation of Web Crawler Based on C++
With the booming development and continuous progress of the design and artificial intelligence industries, the demand for high-quality and diverse images is showing an explosive growth trend. This trend is not only reflected in creative design and product development, but also deeply rooted in multiple cutting-edge fields such as machine learning and computer vision. However, in the face of massive image resources, manually accessing websites and downloading images one by one appears time-consuming and laborious, greatly limiting work efficiency and the scale of data acquisition. Therefore, developing efficient and automated tools to collect images has become a top priority. In this context, this article elaborates on the design and implementation of a web crawler system based on the C++programming language. This web crawling system focuses on processing websites that use the HTTP protocol, and can efficiently penetrate webpage structures, accurately locate and extract image resources. Users only need to provide the URL of the target website as a starting point, and the crawler will automatically start working by parsing the webpage content, identifying and downloading all available image files. This process not only greatly reduces the manual burden, but also significantly improves the speed and breadth of image collection, providing a solid foundation for subsequent image processing, analysis, and model training. In addition, the design of the crawler system considers flexibility and scalability, making it easy to adjust and optimize according to specific needs to adapt to the constantly changing network environment and image acquisition requirements.
Implementation of An Automated Intelligence Collection Framework Based on Go Language
At present, search engine hacking is still a highly threatening attack method to security. Through search engines, victims' website backend or source code, and even users' personal privacy information can be directly obtained from the network. In order to minimize this security threat as much as possible and improve the efficiency of maintaining network security, an automated intelligence collection framework based on Go language is proposed, mainly through Bing and Google search engines for intelligence collection. This framework utilizes the concurrency feature of Go language, greatly improving the performance of crawlers and accelerating the speed of intelligence collection.
Research on Access Layer Communication Line Design Based on PON Technology
Since the network structure becomes more and more perfect, the public has put forward many requirements regarding the communication quality of the access layer. However, if the access layer communication still uses the traditional techniques to design the line, the communication quality cannot be guaranteed to meet the public requirements. Therefore, PON technology should be flexibly integrated into the line design, and the technical advantages should be used to strengthen the communication quality fundamentally. To further analyze the design of access layer communication line based on PON technology, this paper first introduces the basic concepts of PON technology and access layer, then puts forward the key points of design of access layer communication line based on PON technology, and finally points out the specific design method of access layer communication line with a case as the core.
Application of K-Means Algorithm in Image Compression
In the Internet era, image information is widely used in all walks of life. The amount of data in images is very large, and it is necessary to compress images in order to effectively transmit and store them. The K-Means algorithm is one of the most commonly used clustering algorithms. This article applies the clustering algorithm to image compression applications and uses the K-Means clustering algorithm to compress images through Python programming. From the experimental results, it can be concluded that the K-Means clustering method can indeed compress images.
Threat Factors and Prevention Strategies for Computer Network Security
With the rapid development of Internet technology, computer network has become an indispensable part of modern society. Computer networks play a crucial role in personal life, business operations, and national management. However, with the popularization of network applications, network security issues have become increasingly prominent and have become an important factor restricting the development of computer networks. Therefore, strengthening research on network security technology during the operation of computer systems, analyzing influencing factors, and timely preventing and controlling related risks are of great significance for ensuring the effective application of computer network technology.
Research on the Application of Python in Big Data Analysis
Python based development tools are of great significance for improving the analysis and processing of big data. Integrating the advantages of Python with its own strengths, optimizing and improving it from a "fast" perspective, and enhancing its efficiency in practice through visualization and data analysis. Under data programming, Python language can perform data processing in areas such as information acquisition and storage. Based on big data, it can enhance the ability of data processing and analysis.
Research on the Application of Big Data Technology in Artificial Intelligence
With the rapid development of network information technology, human society is entering an era of information explosion, which has created unprecedented convenience for the public to access diverse and in-depth information resources. Behind this transformation, it is inseparable from the deep integration and mutual promotion of big data technology and artificial intelligence, which together constitute the key technological guarantee to support people's efficient and accurate access to information resources. This article delves into the unique advantages of big data and artificial intelligence technology, revealing how their combination can reshape the landscape of the information age. Analyzed its core types, including but not limited to distributed storage technology, data mining algorithms, and real-time data processing capabilities, which provide a solid foundation for the collection, organization, and analysis of massive data. The widespread application of big data in the field of artificial intelligence, such as optimizing prediction models through machine learning algorithms and improving image recognition accuracy through deep learning techniques, not only greatly enriches the functional boundaries of artificial intelligence, but also promotes its innovative practices in multiple fields such as healthcare, smart cities, and financial technology. The work of this article is not only a comprehensive examination of the current status of big data and artificial intelligence technology, but also a forward-looking exploration of future development trends. With the continuous advancement of technology, the deep integration of big data and artificial intelligence will continue to deepen, opening up broader space for the future development of artificial intelligence and having immeasurable value in promoting social progress and improving the quality of life.
Research on the Application of Artificial Intelligence in Computer Network Technology in the Era of Big Data
With the steady development of society, the development effect of computer network technology has been extremely good, and its prospects for development are also relatively broad. The integration of computer network technology into the production and development process of modern society has already had an impact on people's daily lives, providing convenient conditions for their clothing, food, housing, and transportation. With the emergence of modern big data and artificial intelligence technology, the direction for the intelligence of computer network technology has been pointed out. Applying this modern technology to computer network technology can better improve the overall level of computer technology and provide high-quality services for the development of society. This article briefly analyzes the relevant concepts of big data era and artificial intelligence, introduces the advantages and necessity of applying artificial intelligence in computer network technology in the big data era, and elaborates on the practical application of artificial intelligence in computer network technology in the big data era.
Graph Neural Network-Based Forecasting of Nutritional Trends and Consumer Behavior in Health-Oriented Food Markets
This study presents a trend analysis framework based on Graph Neural Networks (GNNs) and Reinforcement Learning (RL), designed to process data from multiple sources, including social media, online shopping records, and nutrition databases. A BERT-GNN model is used to extract food-related topics and sentiment from online text, while a Transformer-based time series model analyzes changes in demand over time. The outputs are combined within an RL structure to support adaptive decision-making under varying market conditions. Compared with conventional approaches such as RNN and ARIMA, the proposed method improves prediction accuracy by 21.7%. The framework also proves effective in detecting rising interest in health-related food products, such as plant-based and probiotic items. These results suggest that the system can serve as a practical tool for anticipating consumption shifts and informing policy or supply chain responses in the food sector.
Research on the Application of AI Functional Programming in JavaScript
Traditional JavaScript development mainly uses object-oriented programming patterns, interspersed with some functional programming ideas. In the latest ES6 standard, functional programming is increasingly used in front-end development. This article introduces several main features, applications, and advantages and disadvantages of functional programming in JavaScript.
Research on the Integration and Innovative Development Path of Cross border Cultural Communication of English Tour Guides Driven by the Dual Wheel of "AI+Big Data"
With the rapid development of technology, "AI+big data" has become an important force driving changes in various industries and plays a key role in the tourism industry. For English tour guides, who bear the responsibility of cross-border cultural dissemination, "AI+big data" brings new opportunities and challenges. This article deeply analyzes the current application status of "AI+big data" in cross-border cultural communication of English tour guides, explores the advantages brought by its integration and innovation, such as precise communication and diversified content, and analyzes the problems faced by insufficient technological application capabilities. On this basis, an integrated and innovative development path has been proposed, including enhancing the digital literacy of tour guides, optimizing cultural communication content creation, and strengthening cross-cultural communication and interaction, aiming to help English tour guides better utilize "AI+big data" to carry out cross-border cultural communication in the new era and enhance the international influence of Chinese culture.
Graph Application of Social Network AI Language Understanding and Reasoning
With the booming development of social networks, the value contained in social big data is increasingly prominent. This article focuses on the analysis of social network language understanding and reasoning techniques based on knowledge graphs. It systematically explores the construction methods of knowledge graphs, the semantic understanding mechanism of social texts, and the key tasks of reasoning decisions. It proposes a social language understanding framework that integrates knowledge graphs and deep learning, and provides technical implementation paths for specific tasks. The research aims to fully tap into the value of social data, enhance the intelligence level of social networks, and provide new ideas and methods for related research.
On the Administrative Law Protection of Personal Information in the Era of Big Data
In the era of big data, the security and privacy of personal information face new challenges, and the diversity of data sources and large-scale collection methods increase the risk of information leakage. Administrative law demonstrates higher efficiency and flexibility in protecting personal information compared to civil law and criminal law. By regulating the information processing procedures of administrative agencies, strengthening regulatory systems, and improving relief mechanisms, it helps to more effectively protect personal information. At the same time, the transformation of government information disclosure and data sharing has prompted administrative law to play an important role in balancing personal privacy and public interest. This article explores the necessity, current issues, and optimization paths of personal information protection in the era of big data from the perspective of administrative law.
Risk Assessment and Legal Countermeasures for Copyright Infringement in the Cultural Industry under the Background of Digital Age
Digital technology has promoted the innovative development of the cultural industry, and new forms of copyright carriers have emerged, giving rise to the new concept of digital copyright. However, it has also increased the risk of copyright infringement. This article analyzes the main impact of digitization on copyright, explores the current problems faced in legal norms and technical management regulations, and proposes targeted legal countermeasures and management suggestions to address the complex situation of copyright protection in the digital age.
A Generative Adversarial Network-Based System for Food Appearance Enhancement and Automatic Grading
The study presents an automated system based on Generative Adversarial Networks (GANs) for food appearance refinement and defect classification. A modified StyleGAN model was trained to generate 20,000 high-resolution food images, thereby expanding the dataset and improving the classification accuracy of rare defect categories by 22%. Using this extended dataset, a two-stage inspection framework was developed. YOLOv8 is applied to detect candidate defect regions, followed by EfficientNet—integrated with an attention mechanism—for classifying defect types with improved sensitivity, particularly for subtle and small-scale flaws. Experiments conducted on a public food image dataset demonstrated that the proposed method achieved a classification accuracy of 94.3%, which is 15.8% higher than conventional CNN-based models. The system also maintained a processing speed of 40 frames per second (FPS), supporting real-time industrial applications. Compared with existing approaches, the method provides more reliable data augmentation, improved model integration, and better adaptability to diverse inspection scenarios, indicating its potential for practical deployment in automated food quality assessment.
Digital Competence and Sustainable Development of Museums: A Literature Review
This systematic review investigates how digital capabilities influence sustainability in museums, drawing on 273 articles from Web of Science (2000-2024). The findings highlight that digital tools boost economic efficiency, minimize environmental impact, and enrich social engagement. Critical mechanisms driving these outcomes include managerial innovations and resource integration, underpinned by theories such as firm capability, resource-based view, and organizational innovation. Despite these insights, research gaps remain, particularly regarding smaller, county-level museums, long-term sustainability effects, and visitor experiences. The review underscores the importance of strategic digital adoption, especially for resource-limited institutions, to enhance sustainability across economic, environmental, and social dimensions. It recommends that future studies prioritize these underexplored areas to deepen understanding and support the museum sector’s evolution.
Autonomous UAV Navigation in Unfamiliar Indoor Environments Using Deep Reinforcement Learning
Efficient and collision-free navigation in GPS-denied indoor environments remains a fundamental challenge in the development of intelligent Unmanned Aerial Vehicles (UAVs). This study introduces DRAL-UAV, a Deep Reinforcement Adaptive Learning framework tailored for path planning in partially observable environments. The proposed model features a hierarchical policy network that integrates a visual encoder, a recurrent memory module, and an adaptive reward adjustment mechanism. Training and evaluation are conducted using the AirSim 3D simulation platform, incorporating complex conditions such as dynamic obstacles and narrow corridors. Experimental results demonstrate that DRAL-UAV achieves an average navigation success rate of 92.6% over 5,000 trials across 100 scenarios, outperforming traditional A* and DDPG methods. Furthermore, the model reduces the average path length by 28.4% and maintains a low failure rate of 5% in challenging narrow corridor scenarios (corridor width ratio = 1.2). The finding highlight DRAL-UAV’s robust decision-making ability, improved generalization in unstructured environments and high applicability in real-world UAV tasks, including search and rescue, warehouse automation and industrial inspections.
Reinforcement Learning-Based Food Recommendation System for Dietary Optimization and Health Management
This study presents a reinforcement learning-based food recommendation model aimed at improving personalized dietary planning and long-term health outcomes. The proposed system applies the Deep Deterministic Policy Gradient (DDPG) algorithm to generate food suggestions by incorporating users’ dietary records, nutritional targets, and historical behavior patterns. Compared with conventional collaborative filtering and content-based techniques, the model adjusts recommendations over time to reflect users’ changing dietary needs. Experimental evaluations based on real-world data indicate that the system improves users’ compliance with healthy eating guidelines by 19.8% and lowers the intake of high-calorie foods by 16.3%. The results demonstrate the model’s practical value for personalized nutrition and health management.