European Journal of AI, Computing & Informatics
European Journal of AI, Computing & Informatics (EJACI) is an international, refereed, double-blind peer-reviewed, open-access journal dedicated to the advancement of research and development in the fields of artificial intelligence, computing, and informatics. The journal publishes original research articles, reviews, and technical papers that address the challenges, methodologies, and innovations in these rapidly evolving areas. It provides a platform for interdisciplinary collaboration and knowledge sharing among researchers, practitioners, and educators from diverse domains, contributing to the global scientific community's understanding of AI and its applications in computing and informatics.
Application of Network Security Vulnerability Detection and Repair Process Optimization in Software Development
Ensuring system safety and maintaining high-quality software development critically depend on the timely identification and rapid remediation of network security vulnerabilities. In this study, an efficient operating framework is established based on the optimization of the vulnerability management process. From the dual perspectives of vulnerability detection and repair, a comprehensive process improvement is implemented, forming a technical framework that integrates both detection and repair optimization modules. This framework emphasizes security robustness while offering flexible scalability, enabling it to adapt to varying software environments and evolving threat landscapes. During the software development lifecycle, the framework leverages the coordinated application of multiple tools, including automated repair technologies, intelligent detection mechanisms, and cross-department collaboration strategies. Such integration significantly enhances both the efficiency and accuracy of handling security vulnerabilities. Furthermore, the proposed approach supports continuous monitoring and iterative refinement, ensuring that potential security risks are proactively addressed, and that the overall reliability and stability of software systems are improved. By systematically combining detection, repair, and collaborative optimization, the framework provides a practical and scalable solution for strengthening software security and supporting sustainable software development.
Copyright (c) 2025 Shuang Yuan (Author) https://creativecommons.org/licenses/by/4.0
Innovations in Machine Translation: The Role of Machine Learning in Enhancing Linguistic Accuracy and Efficiency
This essay explores Instance Induction, Analogy Induction, and Machine Learning, with a particular focus on the application of analogy-based machine learning in machine translation. It examines techniques such as full instance translation, case pattern translation, and analogical reasoning. The study investigates the underlying principles, advantages, and potential limitations of these methods to provide a theoretical foundation for further optimization of machine translation (MT). Furthermore, an in-depth analysis of Machine Learning Theory, especially through the paradigms of Analogy Induction and Instance Induction, is conducted to uncover latent patterns and features that are pivotal for the technological advancement of this field. The efficacy of these methodologies in enhancing machine translation performance is critically evaluated and discussed.
application of machine learning technology
Copyright (c) 2025 Kaiwen Xin, Bingchen Liu, Lihao Fan (Author) https://creativecommons.org/licenses/by/4.0
High-Performance Cloud-Based System Design and Performance Optimization Based on Microservice Architecture
With the rapid advancement of cloud computing and microservice architecture, designing cloud systems based on microservices has demonstrated significant potential for delivering efficient, flexible, and highly scalable services. As cloud systems expand in scale and service complexity increases, however, microservice architectures face substantial challenges in maintaining optimal performance. This study explores strategies for constructing high-performance cloud systems within a microservice framework, focusing on three critical aspects: module partitioning, inter-service communication mechanisms, and distributed data management. Specifically, it examines methods to optimize the granularity of microservice modules to balance system maintainability with performance efficiency, and investigates communication patterns that minimize latency and reduce inter-service overhead. The study further addresses strategies for distributed data handling, ensuring consistency, fault tolerance, and scalability across heterogeneous service nodes. To enhance overall system performance, measures such as efficient service interaction protocols, automated scaling and load balancing, and comprehensive performance monitoring coupled with proactive fault detection are proposed. Collectively, these strategies provide a structured approach to designing microservice-based cloud systems that are resilient, adaptive, and capable of sustaining high throughput under dynamic workloads.
Copyright (c) 2025 Jin Li (Author) https://creativecommons.org/licenses/by/4.0
Leveraging Large Language Models for Anomaly Event Early Warning in Financial Systems
Given the strong semantic concealment, complex data structures, and high responsiveness requirements of anomaly events in financial systems, this study investigates advanced anomaly early warning methods that integrate large language models (LLMs) to enhance detection and response capabilities. The research systematically outlines semantic processing approaches for multi-source heterogeneous data, including structured transaction records, unstructured textual reports, and real-time market indicators, to ensure comprehensive analysis of potential anomalies. It further details the construction path for risk identification models, emphasizing feature extraction, multi-dimensional correlation analysis, and adaptive learning mechanisms that allow the system to dynamically adjust to evolving financial patterns. In addition, the study presents the generation and push mechanism for early warning information, ensuring that alerts are delivered promptly to relevant stakeholders while maintaining high interpretability and actionable insight. The system's response efficiency and recognition performance are rigorously evaluated in real trading environments, demonstrating the framework's robustness under high-frequency and high-volume transaction conditions. Comparative experiments with various baseline models show that the proposed approach exhibits strong adaptability and practical value, achieving superior performance in both recognition accuracy and response timeliness. These results indicate the potential of LLM-enhanced anomaly early warning systems to support more reliable and intelligent risk management in complex financial environments.
Copyright (c) 2025 Luqing Ren (Author) https://creativecommons.org/licenses/by/4.0
Research on Cross-Modal Semantic Alignment Methods for Low-Resource Languages
This study addresses the challenge of cross-modal semantic alignment in low-resource languages, a critical problem for enabling inclusive and equitable AI-driven multimodal applications. We propose a novel framework that synergistically integrates multi-level textual embeddings, visual Transformer modeling, and the construction of a unified cross-modal projection space. To enhance alignment quality, the approach incorporates advanced mechanisms including contrastive learning, distributed semantic constraints, and fine-grained local alignment strategies. Furthermore, to mitigate data scarcity inherent in low-resource settings, we leverage transfer enhancement techniques such as cross-lingual knowledge distillation, pseudo-pair augmentation, and multi-task training. Comprehensive experiments on the FLORES-200 dataset demonstrate that our method consistently surpasses state-of-the-art models such as CLIP and ALIGN across multiple metrics. Specifically, significant gains are observed in Recall@1 and Mean Rank for languages including Swahili and Sinhala, underscoring the method's effectiveness, robustness, and generalizability in low-resource scenarios. These findings highlight the potential of the proposed approach for advancing cross-lingual multimodal understanding and bridging the performance gap for underrepresented languages.
Copyright (c) 2025 Zhizhi Yu (Author) https://creativecommons.org/licenses/by/4.0
Bridging the AI Adoption Gap: What Drives U.S. SME Owners' Willingness-to-Pay for Supply-Chain Risk Software?
The application of artificial intelligence (AI) in supply chain risk management offers small and medium-sized enterprises (SMEs) opportunities to enhance early warning capabilities, improve compliance, and strengthen operational resilience. However, SMEs often face resource constraints and cognitive differences during technology adoption, and their willingness-to-pay (WTP) remains unclear. This study employs an online discrete choice experiment (N = 512) conducted in August–September 2024 to examine SME owners' decision-making regarding AI-enabled risk management software. The experiment incorporates attributes such as monthly fee, early-warning lead time, compliance module, and data localization option. A mixed logit model and latent class analysis are applied, followed by Bayesian post-estimation to derive optimal price ranges. Results indicate that risk sensitivity, technology adoption willingness, compliance awareness, and understanding of supply chain complexity are significant drivers of WTP. Distinct customer segments are identified: price-sensitive firms focus on subscription cost, whereas function-oriented firms value early-warning and compliance features. Analysis further shows that lower-revenue SMEs exhibit lower maximum acceptable prices compared to larger firms, yet their subscription appeal can be increased through tailored feature bundles. Based on these findings, the study proposes a tiered pricing strategy: an entry-level plan emphasizing core early-warning, a mid-tier plan adding compliance functions, and a premium plan including data localization. The study contributes by highlighting the role of behavioral and cognitive factors in SME pricing decisions and provides empirical guidance for the design and pricing of AI-based supply chain risk management software.
small and medium-sized enterprises (SMEs)
Copyright (c) 2025 Xiangying Chen (Author) https://creativecommons.org/licenses/by/4.0
Research on AI-Based Multilingual Natural Language Processing Technology and Intelligent Voice Interaction System
It has shown broad development prospects in intelligent application fields such as cross-lingual voice interaction, machine translation, and voice assistants. Faced with challenges such as complex speech features, diverse semantic structures, and limited terminal deployment, technical systems need to achieve effective collaboration between recognition accuracy, semantic consistency, and operational efficiency. The application of training language models, context-aware mechanisms, and end-cloud collaborative structures provides a new path for optimizing system performance. This article focuses on key aspects such as speech recognition, semantic understanding, and deployment mechanisms, exploring technical bottlenecks and feasible improvement solutions in multilingual environments, with the aim of providing a theoretical basis and practical guidance for cross-language applications of intelligent speech systems.
Copyright (c) 2025 Xiang Chen (Author) https://creativecommons.org/licenses/by/4.0
Data Consistency and Performance Scalability Design in High-Concurrency Payment Systems
In the blowout development of mobile payment and Internet financial payment systems, highly concurrent payment systems face dual challenges of data consistency assurance and system performance expansion. To address these issues, design strategies including hierarchical consistency control, data distribution optimization, and consistency scalability collaborative regulation are proposed to achieve a system architecture that balances transaction accuracy and throughput. The analysis of three typical cases-aggregated payment, high-speed sales system, and financial remittance channel-demonstrates that this strategy can be applied to complex operation scenarios, enhancing efficiency and providing a theoretical foundation and practical guidelines for future development of high-reliability payment systems.
Copyright (c) 2025 Yue Qi (Author) https://creativecommons.org/licenses/by/4.0
The Influence of Financial Due Diligence in M&A on Investment Decision Based on Financial Data Analysis
With the gradual growth of enterprise mergers and acquisitions activities, the role of financial due diligence in investment decisions has become increasingly prominent. In the traditional financial due diligence process, due to the constraints of information granularity and analytical tools, potential financial risks cannot be fully revealed. Starting from the analysis of financial data, this article expounds its application value and practical effectiveness in the due diligence process. The research builds an assessment system based on four core dimensions: profitability, debt-paying ability, operational efficiency, and risk identification, and analyzes its role and function in valuation correction, risk early warning, and decision support. The research results show that the data-driven due diligence approach can improve the accuracy of investment decisions, is of great significance for merger and acquisition integration and performance prediction, and promotes the realization of high-quality merger and acquisition transactions.
Copyright (c) 2025 Wei Li (Author) https://creativecommons.org/licenses/by/4.0
Lightweight Strategic Path for Digital Transformation in Small and Medium Enterprises — A Solution Based on Management Consulting Practice
As global economic digitalization accelerates, small and medium-sized enterprises (SMEs) play a critical role in the U.S. economy. However, due to limited budgets, insufficient technical capabilities, and a lack of management experience, SMEs face significant challenges in digital transformation. This paper focuses on the digital pain points of SMEs and proposes a "lightweight" strategic approach, which emphasizes low-cost, easy-to-implement, and stepwise digital solutions to enhance operational efficiency. Drawing on management consulting practices, the study designs actionable digital transformation solutions encompassing tool selection, phased implementation, budget and resource allocation, and risk management. Case analyses demonstrate the effectiveness of the lightweight strategy in improving operational efficiency, reducing transformation risks, and optimizing resource utilization. Furthermore, the paper highlights the value of management consultants in facilitating SME digitalization. The findings provide practical guidance for SMEs in the U.S. and offer strategic insights for future digital development.
Copyright (c) 2025 Chongguang Zhu (Author) https://creativecommons.org/licenses/by/4.0
Designing Accessible AI Systems for SMEs: Compliance with ADA and Section 508 through Conversational Interfaces
With lawsuits related to the Americans with Disabilities Act (ADA) resulting in approximately $4 billion in annual economic losses for businesses, small and medium-sized enterprises (SMEs) face significant cost pressures when developing accessible applications. This paper proposes an accessibility-focused conversational system based on an open-source natural language processing (NLP) framework, aiming to reduce legal risks for SMEs while enhancing digital accessibility for users with disabilities. The system integrates text-to-speech (TTS) and speech-to-text (STT) modules with a simple user interface, leveraging technologies such as Azure Cognitive Services, Play Framework, and MongoDB. Through example applications in online retail and restaurant ordering systems, the paper demonstrates the system's usability, ADA and Section 508 compliance, and seamless integration with existing content management systems. Experimental results indicate that the proposed approach significantly reduces development costs and improves the user experience for individuals with disabilities, providing a viable path for SMEs to implement digital accessibility and laying the foundation for future integration with large language models (LLMs) to enable more natural human-computer interactions.
Copyright (c) 2025 Yuxin Liu (Author) https://creativecommons.org/licenses/by/4.0
Analysis of Efficiency Improvement Path Scheme in Biomedical Industry Driven by AI
The biomedical industry faces persistent efficiency challenges, including prolonged R&D cycles, high development costs, complex clinical trials, and fragmented data management. Driven by advances in artificial intelligence (AI), novel solutions are emerging to address these bottlenecks across the drug discovery, clinical, manufacturing, and knowledge management domains. This review systematically analyzes AI-driven efficiency improvement pathways, highlighting accelerated drug discovery, optimized clinical trials, intelligent manufacturing and supply chain, and data-driven decision support. Key challenges, such as data quality, regulatory constraints, system integration, and talent gaps, are discussed, alongside potential future developments in self-supervised learning and generative models. The study emphasizes the transformative potential of AI to enhance productivity, reduce costs, and support informed decision-making, offering strategic insights for enterprises seeking sustainable innovation in the biomedical sector.
Copyright (c) 2025 Dajiang Guo (Author) https://creativecommons.org/licenses/by/4.0
Stock Price Prediction of Apple Inc. Based on LSTM Model: An Application of Artificial Intelligence in Individual Stock Analysis
This paper explores the potential of artificial intelligence in financial forecasting by applying a Long Short-Term Memory (LSTM) neural network to the task of predicting Apple Inc. (AAPL) stock prices. Using historical daily closing prices from August 2020 to August 2023, the model was trained and tested under a structured framework of data preprocessing, normalization, and sequential input construction. The forecasting performance was evaluated with widely used error measures, including Mean Squared Error (MSE), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE). Experimental results indicate that the LSTM model effectively captures sequential dependencies and nonlinear dynamics within the time series, generating predictions that closely align with observed prices. While the model demonstrates high accuracy in relatively stable market conditions, its reliance on univariate input limits adaptability to abrupt market fluctuations and external influences such as macroeconomic shifts. These findings suggest that LSTM-based models can serve as valuable tools for supporting individual stock analysis, but their effectiveness depends on stock-specific data availability and market characteristics. Future work may extend this framework by incorporating multi-source financial indicators and enhancing model interpretability to achieve broader practical relevance.
Copyright (c) 2025 Ruoyu Qi, Liao Hu (Author) https://creativecommons.org/licenses/by/4.0
Artificial Intelligence Assisted Dual-Teacher Model Constructing Practices
This study explores the development and practical application of an innovative AI-assisted dual-teacher model for foreign language education. By introducing a dynamic framework that combines human educators with AI-driven teaching assistants, the research establishes a three-phase collaborative approach designed to meet the cognitive, linguistic, and cultural demands of language learners. The approach capitalizes on the complementary strengths of both human teachers and AI tools, offering a personalized and responsive learning experience. The findings of the study underscore the effectiveness of this model in leveraging AI's advanced capabilities, such as semantic generation and data analytics, to streamline and automate routine instructional tasks. This automation allows human instructors to focus their expertise on higher-level educational objectives, including fostering intercultural communication, nurturing emotional engagement, and cultivating critical thinking skills among students. As a result, the dual-teacher model significantly enhances the quality of foreign language instruction by diversifying teaching methods and creating opportunities for individualized learning trajectories. Furthermore, this approach promotes the development of digital literacy in both students and educators. The integration of AI technology empowers learners to navigate a variety of digital tools, while teachers are better equipped to manage and interpret the data generated by these systems. By blending the strengths of both human instruction and AI support, this model is positioned to transform traditional language education, providing a richer, more inclusive, and adaptive learning environment.
Copyright (c) 2025 Min Wang (Author) https://creativecommons.org/licenses/by/4.0
Innovative Exploration and Practice of Archives Collection Automation
As a fundamental part of archives management, the efficiency and quality of archives collection directly affect the utilization of archival resources. Currently, China's archives collection work is in a critical period of transition from traditional models to digitalization, facing multiple challenges such as technical bottlenecks, outdated concepts, and institutional obstacles. This paper systematically analyzes the current situation and existing problems of archives collection work from four dimensions: technical dilemmas in collection methods, backwardness in management concepts, innovative explorations in grassroots practice, and prospective development paths. It also proposes practical automation solutions for archives collection, providing references for promoting high-quality development of archival undertakings.
Copyright (c) 2025 Zhe Liu (Author) https://creativecommons.org/licenses/by/4.0
AI-Based Enterprise Notification Systems and Optimization Strategies for User Interaction
In modern enterprises, notification systems play an important role as key tools for information exchange and user interaction. However, the current notification system faces various challenges such as data confidentiality, security protection, message quality, development costs, and technical difficulties. The introduction of artificial intelligence (AI) technology has brought new solutions to these problems. For example, AI can enhance data confidentiality, use deep reinforcement learning to improve content distribution, utilize cloud computing to reduce development costs, and incorporate fairness principles into model training, thereby improving the performance and user satisfaction of notification systems. This study explores the problems existing in current notification systems and proposes targeted improvement solutions based on AI technology, providing theoretical support and practical guidance for enterprises to create efficient, secure, and highly intelligent notification systems.
Copyright (c) 2025 Qianru Xu (Author) https://creativecommons.org/licenses/by/4.0
Vision-Based AI Solutions for Human Life and Social Science: From Image Processing to Human Behavior Modeling
Artificial Intelligence (AI) has become a transformative force in social science research, enabling the analysis of large-scale, heterogeneous data to uncover latent patterns and predict complex human behaviors. Among AI’s core methodologies, computer vision has evolved far beyond its early role in data acquisition to now encompass sophisticated systems capable of interpreting, analyzing, and synthesizing visual information across a range of socially relevant contexts. By integrating advanced image processing, machine learning, and computer graphics, computer vision empowers interdisciplinary investigations in psychology, sociology, and economics, modeling phenomena such as decision-making, emotional expression, and social interaction at unprecedented scales and resolutions. This paper presents a comprehensive survey of the state of the art in computer vision applications within the social sciences, with particular emphasis on recent breakthroughs in algorithms and enabling technologies that facilitate automated visual understanding. Key topics include object detection, facial recognition, scene understanding, and predictive modeling, which collectively underpin impactful applications in healthcare, autonomous systems, surveillance, and digital media. To structure this rapidly expanding domain, we propose a conceptual framework that organizes the field into four foundational pillars: image processing, object recognition, adaptive machine learning, and computer graphics. Each pillar contributes critical capabilities such as feature extraction, quality enhancement, semantic interpretation, and photorealistic rendering — functions that are increasingly pivotal in addressing contemporary social challenges. By critically evaluating current methodologies, benchmarking performance across domains, and identifying emergent trends, this work not only synthesizes existing knowledge but also outlines promising directions for future research at the intersection of AI and social science. Finally, we highlight how these advancements are reshaping societal norms and enabling AI-driven solutions to pressing global issues, from autonomous navigation to public health and beyond.
Copyright (c) 2025 W. A. Jarvis (Author) https://creativecommons.org/licenses/by/4.0
Research on Positioning Technology of Smart Home Devices Based on Internet of Things
With the rapid progress of Internet of Things technology, intelligent home systems have gradually become an integral part of contemporary family life. For the smart home system, the high-precision positioning function of the device is the core of ensuring the efficiency of the intelligent control and management of the device. This paper mainly discusses the positioning technology of smart home devices under the environment of Internet of Things, and analyzes the wireless communication technology, sensor technology and accurate positioning optimization methods in detail. Through the construction of an efficient positioning system framework and the innovative introduction of multi-path effect compensation technology, the system significantly improves positioning accuracy. The experimental data prove that the positioning system has outstanding performance in improving the positioning accuracy of equipment and the comprehensive performance of the system.
Copyright (c) 2025 Yajing Cai (Author) https://creativecommons.org/licenses/by/4.0
Development and Optimization of Social Network Systems on Machine Learning
The intelligent and personalized development of social network system cannot be separated from the support of machine learning technology. This paper discusses the application of machine learning in social network system, focusing on the implementation of data flow design, machine learning model integration, personalized recommendation, social graph analysis, emotion recognition and security protection. At the same time, the strategy of optimizing system throughput, computing efficiency, data transmission and resource allocation through machine learning is studied to improve system performance.
Copyright (c) 2025 Huijie Pan (Author) https://creativecommons.org/licenses/by/4.0
Discussion on Using RNN Model to Optimize the Accuracy and Efficiency of Medical Image Recognition
With the continuous development of artificial intelligence technology, especially represented by deep learning, recurrent neural networks have made revolutionary breakthroughs in medical image recognition. This paper first introduces the concept of RNN pattern and its application in medical image recognition. By analyzing various applications of RNN in medical image classification, we explore how to improve the accuracy and computational efficiency of medical image recognition by optimizing the RNN model. Specifically, the gating mechanism, convolutional neural network (CNN) construction, lightweight technology and other optimization strategies, multi-modal learning and attention mechanism input are discussed. Finally, the prospects and challenges of the RNN model in medical image recognition are summarized, and the future research directions in this field are also discussed.
Copyright (c) 2025 Minkang Zhang (Author) https://creativecommons.org/licenses/by/4.0
Cross-Border Integration of Chinese Traditional Opera and Animation under Digital Technology
The advancement of digital technology has provided unprecedented opportunities for the cross-border integration of traditional Chinese opera and animation, promoting the innovative development and inheritance of traditional culture. However, there is still a lack of systematic research on the specific paths and impacts of this integration. This article aims to explore how digital technology can promote the combination of traditional Chinese opera and animation, in order to enhance the artistic expressiveness and the effectiveness of its dissemination. In this research, the article adopts qualitative research methods, including text analysis and case studies, to deeply analyze successful works that have integrated opera and animation in recent years, aiming to summarize their artistic characteristics and innovative models. The research finds that the flexibility and creativity of animation can enhance the expressiveness of opera, making stylized movements, character images and storylines more dynamic and visually impactful, thereby attracting a wider audience. In addition, digital technology gives traditional opera elements a new visual context, enabling them to regain new vitality. However, the research also shows that in the process of animating traditional opera, how to find a balance among audience perception, artistic expression and business models remains a challenge that needs to be addressed urgently. Through case analysis and integration with traditional cultural elements, the rational application of digital technology and animation techniques can not only promote the innovative expression of traditional Chinese opera, but also broaden its dissemination channels and enhance its appeal among younger audiences. In the future, the practice of opera animationization needs to explore more diversified forms of expression while maintaining the core value of opera culture, in order to achieve the modernized inheritance of traditional culture.
Copyright (c) 2025 Jinling Dai, Husaini Yaacob (Author) https://creativecommons.org/licenses/by/4.0
Cutting-Edge Challenges and Solutions for the Integration of Vector Database and AI Technology
Vector databases, based on semantic queries and high-dimensional vectors, have become an important supporting environment for artificial intelligence applications such as intelligent question answering, multimodal fusion, and knowledge extraction. In the in-depth integration and application of artificial intelligence technology, it has been found that low indexing efficiency, semantic matching errors, and insufficient system adaptability in vector databases can affect collaboration efficiency and intelligence effectiveness. To ensure efficient execution and stable operation of intelligent applications, improvements are needed in retrieval structure, semantic processing mechanism, and system interfaces. This article provides an overview of the main technical issues around typical application backgrounds, forming targeted solutions to promote the steady development of the integration of vector databases and artificial intelligence technology towards standardization and high quality.
Copyright (c) 2025 Zhongqi Zhu (Author) https://creativecommons.org/licenses/by/4.0
Design and Implementation of AI-Based Multi-Modal Video Content Processing
Multimodal information interaction is gradually becoming an important direction for intelligent video content understanding. In videos, image, voice, and text collaboratively form a semantic system, which goes beyond the capabilities of single-modal information analysis. Efficient extraction and fusion of multi-source information has become a key challenge in artificial intelligence applications for various tasks such as classification, summarization, and content monitoring. Current research tends to focus on single-task or single-modal processing, and there is still a lack of universal fusion frameworks. In this context, establishing a universal, highly integrated, and well scalable AI multimodal video processing framework not only conforms to the trend of technological development, but also provides reliable technical support for intelligent communication, social services, educational innovation, and more.
Copyright (c) 2025 Da Xu (Author) https://creativecommons.org/licenses/by/4.0
Optimizing Game Conversion Rates and Market Response Strategies Based on Data Analysis
With the rapid development of mobile Internet technologies, the gaming market has become a significant sector within the digital entertainment industry. In such a challenging market environment, game developers increasingly focus on data analysis to optimize conversion rates and reduce market response time. Game conversion rate refers to the proportion of users who engage with a game and complete specific actions such as registration and payment. It is a key metric for measuring game success. Market response includes factors such as advertising effectiveness and user feedback. By analyzing game conversion rates and market response through data, game developers can continuously adjust user experience and market response strategies to maximize overall revenue. This paper discusses the main issues in applying data analysis to optimize game conversion rates and market responses and offers solutions.
Copyright (c) 2025 Xia Hua (Author) https://creativecommons.org/licenses/by/4.0
Research on Enterprise Data Security Protection Technology on Cloud Platforms
With the popularization of cloud computing technology, enterprises are increasingly relying on cloud platforms for data storage and processing. Although cloud platforms bring flexibility and scalability to enterprises, data security concerns have become increasingly prominent. Due to the openness and resource sharing characteristics of cloud platforms, enterprises face multiple security challenges in data storage, transmission, and access permission management. This article elaborates on the basic definition of data security and provides an in-depth analysis of the data protection management model in cloud computing platforms. It further discusses the technical security challenges in multiple aspects such as data storage, transmission, and identity verification, and introduces various protective measures such as data encryption, data backup, and strengthened access permission management. By adopting these technological solutions, enterprises can effectively defend against the risks of data leakage and unauthorized access, ensuring the confidentiality, integrity, and availability of cloud platform data.
Copyright (c) 2025 Yu Pan (Author) https://creativecommons.org/licenses/by/4.0
Research on Data Analysis Application Based on Spark Computing
With the development of the big data era, how to efficiently process and analyze massive data has become an urgent problem that needs to be solved in various industries. Spark, as an open-source distributed computing framework, quickly emerged as a mainstream tool in data processing due to its excellent in memory data processing capabilities and user-friendly interface. Not only does Spark have efficient computing performance, a rich ecosystem, and comprehensive support for data analysis tasks, but it is also widely used in multiple fields such as data management, machine learning, and real-time analysis. This article will delve into the application of Spark in data analysis platforms, reveal the current challenges faced, and explore corresponding optimization approaches, with the aim of significantly improving the efficiency and overall performance of data analysis.
Copyright (c) 2025 Jialu Yan (Author) https://creativecommons.org/licenses/by/4.0
Uncovering Hidden Data a Comparative Study of Statistical Detection Models for Grayscale Image Steganography
With the widespread use of digital images in communication and storage, detecting hidden information embedded through steganography has become increasingly important for information security. This review presents a comparative analysis of three classical statistical steganalysis techniques — Histogram-based method, RS (Regular-Singular) analysis, and Chi-square test — applied to grayscale images. Each method's underlying principles, sensitivity to embedding rates, computational complexity, and robustness are systematically discussed. Experimental results on BMP image datasets with varying embedding rates highlight their respective strengths and limitations, including detection accuracy, processing efficiency, and error characteristics. The review also explores the adaptability of these methods to different embedding scenarios and potential improvements through multi-scale analysis and hybrid approaches. This work aims to provide researchers and practitioners with a comprehensive understanding of foundational statistical steganalysis methods and to guide future developments in image steganography detection.
Copyright (c) 2025 Chengping Ye, Mogilevskaya Nadezhda S. (Author) https://creativecommons.org/licenses/by/4.0
Decision Analysis of System Architecture in Artificial Intelligence Cloud Service Model
The artificial intelligence cloud service model is gradually becoming the core carrier to support the implementation of intelligent applications, and the rationality of its system architecture decision directly affects service efficiency and resource utilization. This paper focuses on the architecture design challenges in the integration scenario of artificial intelligence and cloud services, and explores the multi-dimensional balance mechanism of technology selection, cost control, and performance optimization. The research points out that system architecture decisions need to take into account algorithm complexity, data dynamics, and business flexibility requirements, and traditional architecture evaluation methods have limitations in the cloud-native environment. By introducing a multi-dimensional decision-making framework and combining cost-benefit analysis with simulation modeling tools, the comprehensive value of different architecture solutions can be quantitatively evaluated. Practical cases show that the dynamically scalable microservice architecture and serverless computing model have significant advantages in real-time inference scenarios, but one needs to be vigilant against the security risks caused by fragmented deployment. Future research should focus on the collaborative architecture design between edge intelligence and the cloud to meet the industrial needs of low latency and high concurrency.
Copyright (c) 2025 Changming Li (Author) https://creativecommons.org/licenses/by/4.0
Optimization of Valuation Models for AI Investment Projects and Decision Support
With the rapid development of artificial intelligence (AI) technology, investment in the field of AI has gradually become a capital hotspot. This paper explores the integration of AI project valuation and decision support systems, analyzes the core challenges of investment in AI, and proposes optimization paths, including building a flexible valuation framework, optimizing valuation methods, designing a comprehensive risk assessment system, and promoting the improvement of policies and regulations. It aims to improve the level of decision support, reduce investment risks, and promote the healthy development of the AI industry.
Copyright (c) 2025 Lingyun Lai (Author) https://creativecommons.org/licenses/by/4.0
Internet of Things Applied on Assistive Devices and Rehabilitation Robotics
This paper reviews the latest studies of assistive devices and rehabilitation robotics applied in healthcare using Internet of Things (IoT) as a base technology. The utilization of IoT in assistive devices and robots empowers elderly people and individuals with disabilities through advanced assistive technology solutions, highlighting its potential benefits and applications. The review explores the intersection of IoT sensors, assistive devices and rehabilitation robots showcasing the transformative potential of IoT in enhancing accessibility, independence, and quality of life for people with diverse abilities. Various types of smart sensors are discussed and illustrated by IoT applications in healthcare. By comparing different types of IoT sensors, we conclude the data they can measure, algorithms, principle, and applications. The other contribution of this review is to present summary research of assistive devices and rehabilitation robotics with IoT sensors. Additionally, improving the quality of life for the elderly and people with disabilities requires enhanced support through IoT technologies. Key areas that need further improvement include security, reliability, and data privacy. In the future, this work will serve as the basis for further research work in the related area. Further advancements are necessary to improve assistive devices and robotics through IoT applications.
Copyright (c) 2025 Jason Liu, Wei Shi, Ahmet Turkmen, Jeff Leismer (Author) https://creativecommons.org/licenses/by/4.0