Journal of Computer, Signal, and System Research
The Journal of Computer, Signal, and System Research(JCSSR) is a peer-reviewed journal focusing on cutting-edge research in computer science, signal processing, and systems engineering. It covers a wide range of topics, including algorithms, signal analysis, system design, and computational methods, aiming to promote innovation and interdisciplinary collaboration in these fields.
Optimization of Financial Fraud Risk Identification System Based on Machine Learning
With the ongoing digital transformation of the financial sector, the landscape of financial fraud has become increasingly complex and sophisticated, presenting significant challenges to traditional fraud detection systems, which often suffer from low accuracy and delayed response times. Financial fraud risk assessment systems powered by machine learning technology can automatically detect anomalous patterns by analyzing large volumes of historical transaction data, significantly enhancing the precision and timeliness of detection. Currently, machine learning algorithms such as deep learning, decision trees, and support vector machines serve as the core tools for improving detection efficiency and predictive capability. Despite these advancements, challenges such as inconsistent or poor-quality data, model overfitting, and the selection of relevant features continue to constrain system performance and generalizability. Addressing these issues requires comprehensive optimization strategies, including advanced data preprocessing, robust feature engineering, algorithmic fine-tuning, and model validation techniques, to strengthen the system's ability to identify and anticipate fraudulent behaviors. This article proposes a series of targeted improvement measures to tackle the current limitations in financial fraud detection systems and explores the future potential of machine learning technologies to enable proactive, intelligent, and adaptive approaches to financial fraud prevention.
Copyright (c) 2025 Xuanrui Zhang (Author) https://creativecommons.org/licenses/by/4.0
Application and Practice of Machine Learning Infrastructure Optimization in Advertising Systems
The explosive growth of advertising data has increasingly challenged the performance and predictive accuracy of traditional advertising platforms. Optimizing and upgrading machine learning infrastructure has emerged as a critical solution to address these challenges. This optimization encompasses the enhancement of computing hardware, the development of scalable and efficient distributed processing architectures, and the refinement of model training, tuning, and deployment strategies. Such improvements enable advertising platforms to handle massive volumes of data more efficiently while delivering more precise insights. This article explores the practical applications of machine learning infrastructure optimization in advertising systems, including personalized ad targeting, accurate estimation of advertising effectiveness, proactive fraud detection and risk management, and intelligent allocation of advertising resources. Empirical evidence suggests that these optimizations not only improve the efficiency and quality of ad placement but also play a pivotal role in maintaining system stability, reducing operational costs, and supporting more informed, data-driven decision-making in dynamic advertising environments.
Copyright (c) 2025 Yixian Jiang (Author) https://creativecommons.org/licenses/by/4.0
Optimization and Innovation of AI-Based E-Commerce Platform Recommendation System
With the rapid advancement of artificial intelligence (AI) technology, recommendation systems on e-commerce platforms have become essential tools for enhancing user experience and optimizing sales strategies. This study systematically investigates the optimization and innovation of AI-based recommendation systems, identifying key challenges in data quality, algorithmic accuracy, and system adaptability in current platforms. To address these challenges, we propose integrated strategies involving big data analytics, deep learning models, and data fusion techniques to improve the precision and personalization of recommendations. Additionally, the exploration of emerging technologies, such as natural language processing and graph computing, highlights the potential for more context-aware, interpretable, and scalable recommendation frameworks. By leveraging these approaches, intelligent recommendation systems can provide more diversified, user-centric services, thereby promoting higher engagement and supporting the sustainable development of e-commerce ecosystems.
Copyright (c) 2025 Jiangnan Huang (Author) https://creativecommons.org/licenses/by/4.0
Study on Efficiency Improvement of Data Analysis in Customer Asset Allocatior
With the continuous development of the financial market, customers' investment preferences and purchasing behaviors are evolving rapidly. Leveraging data analysis to enhance the efficiency and accuracy of customers' asset allocation decisions has become a crucial technical approach in modern finance. This paper primarily focuses on the practical application and existing challenges of data analysis in customer asset management. Key issues, including fragmented data integration, inconsistent data quality, limited real-time processing capabilities, and suboptimal algorithm models, are thoroughly analyzed. In response, optimization strategies are proposed, such as establishing a unified data integration platform, strengthening data quality management mechanisms, and improving real-time data processing capabilities. By addressing these challenges, financial institutions can significantly enhance decision-making effectiveness, increase investment returns, and provide more reliable guidance for customers' asset allocation. This study offers valuable insights for the development and refinement of data-driven strategies in the financial industry.
Copyright (c) 2025 Chenyang An (Author) https://creativecommons.org/licenses/by/4.0
Design and Implementation of Code Completion System Based on LLM and CodeBERT Hybrid Subsystem
In the rapidly evolving industry of software development, coding efficiency and accuracy play significant roles in delivering high-quality software. Various code suggestion and completion tools, such as CodeBERT from Microsoft and GPT-3.5 from OpenAI, have been developed using deep learning techniques and integrated into IDEs to assist software engineers' development. Researches have shown that CodeBERT has outstanding performance in code summarization and capturing code semantics, while GPT-3.5 demonstrated its adept capability at code generation. This study focuses on implementing a hybrid model that integrates CodeBERT and GPT-3.5 models to accomplish code suggestion and autocomplete tasks, leveraging the context-aware effectiveness of CodeBERT and taking advantage of advanced code generation abilities of GPT-3.5. Evaluated in three main metrics: accuracy, quality of generated code and performance efficiency with various software and hardware, the hybrid model outperforms benchmarks, demonstrating its feasibility and effectiveness. Robustness testing further confirms the reliability and stability of the hybrid model. This study not only further emphasizes the importance of deep learning in the software development industry, but also reveals the potential of synthesizing complementary deep learning models to fully exploit strengths of each model.
Copyright (c) 2025 Bingbing Zhang, Ziyu Lin, Yingxin Su (Author) https://creativecommons.org/licenses/by/4.0
Innovative Application and Effect Evaluation of Big Data in Cross-Border Tax Compliance Management
In the era of global economic integration and growing demands for tax transparency, cross-border tax compliance management faces significant challenges, including the complexity of multidimensional data and pervasive information gaps. This study explores the innovative application of big data technologies in cross-border tax compliance and evaluates their effectiveness in improving governance outcomes. A comprehensive application framework is proposed, integrating data collection and organization, intelligent risk management, and collaborative supervision and information sharing. Specifically, the development of a citizen identity verification system, a model-driven tax risk warning system, and a collaborative monitoring platform has markedly enhanced the accuracy, timeliness, and reliability of tax management processes. Furthermore, real-time assessment of system performance, recognition efficiency, and platform interoperability demonstrates practical pathways for leveraging advanced technologies to build a more intelligent, efficient, and equitable international tax governance system. This research provides both methodological insights and technical support for policymakers and practitioners aiming to optimize cross-border tax compliance in increasingly complex global economic environments.
Copyright (c) 2025 Cheng Sheng (Author) https://creativecommons.org/licenses/by/4.0
Optimization Strategy for Personalized Recommendation System Based on Data Analysis
Personalized recommendation systems utilize comprehensive user information analysis to deliver customized content and services across various domains, including e-commerce, online media, and social networking. These systems have significantly improved user engagement and experience by aligning recommendations with individual preferences and behaviors. However, their practical implementation still faces challenges such as data imbalance, algorithmic homogenization, and concerns over privacy and security. This study explores the essential role of data analysis in enhancing personalized recommendation mechanisms, emphasizing its applications in data mining, pattern recognition, and real-time data processing. To address existing limitations, several optimization strategies are proposed, including the integration of multi-source heterogeneous data, the adoption of hybrid recommendation models, the implementation of robust privacy-preserving technologies, and the incorporation of user feedback mechanisms for continuous correction. By adopting these optimization approaches, recommendation systems can achieve greater accuracy, robustness, and adaptability. The enhanced systems not only improve the precision and personalization of recommendations but also contribute to higher user satisfaction and platform profitability, providing a more intelligent and secure user experience.
Copyright (c) 2025 Xinran Tu (Author) https://creativecommons.org/licenses/by/4.0
Financial News Sentiment Analysis and Market Sentiment Prediction Based on Large Language Models
This study investigates the application of large language models (LLMs) to financial news sentiment analysis and market sentiment prediction. By integrating textual signals from financial news with structured market data, the research constructs a methodological framework combining sentiment classification, multimodal learning, and time-series forecasting. Experiments demonstrate that LLMs, particularly domain-specific models such as FinBERT, outperform traditional machine learning and deep learning approaches in capturing nuanced financial sentiment. Moreover, incorporating sentiment variables into market prediction models enhances forecasting accuracy, as illustrated by improvements in RMSE, MAE, and R² metrics. Although the analysis relies on simulated data for demonstration, the results align with existing empirical studies, underscoring the potential of LLMs to bridge qualitative sentiment extraction and quantitative market forecasting. This study highlights opportunities for improving investment decision-making, risk monitoring, and policy evaluation while also addressing challenges related to data quality, interpretability, scalability, and ethical concerns.
Copyright (c) 2025 Ruoyu Qi (Author) https://creativecommons.org/licenses/by/4.0
A Comparative Study of LSTM, GRU, and Transformer Models for AI Music Generation
This study compares the performance of three deep learning models-LSTM, GRU, and Transformer-on single-voice and multi-voice melodies across different musical styles. The LSTM model demonstrates strong capabilities in generating melodies with simplicity and temporal continuity. For smaller datasets, the GRU model is particularly effective, as it offers similar performance to LSTM while initiating computations more quickly, resulting in lower computational costs. When the self-attention mechanism is incorporated in the Transformer model, it can handle sequences of unprecedented length, enabling the generation of complex rhythms that can be rendered and performed by synthesized instruments. The BLEU scores of these generated musical pieces provide quantitative insights into the efficiency of longer compositions compared to shorter ones. While longer pieces can offer richness and depth, their contribution to musical quality warrants careful evaluation, as they may become overly repetitive or simply serve as an experimental demonstration of the model's capacity. This study provides valuable insights into the impact of model architecture on music generation and emphasizes the importance of aligning model choice with dataset characteristics. Researchers in AI-driven music generation can benefit from the findings of Slevinsky and colleagues, guiding future work toward more effective and contextually aware music generation approaches.
Copyright (c) 2025 Ava Zihan Gao, Cynthia Bailey (Author) https://creativecommons.org/licenses/by/4.0
Index Weight Prediction and Capital Liquidity Analysis Based on Data Science
This study explores the intrinsic logical relationship between index construction methodologies and capital liquidity by leveraging advanced data science and computational technologies. We propose a multi-model hybrid framework for predicting index weight changes, incorporating diverse model sets to capture complex market dynamics. Key variables are systematically identified and screened through an integrated data platform and rigorous feature engineering, enabling the construction of a forward-looking index weight fluctuation pattern. This model serves as a foundational case study for examining the impact of index rebalancing behaviors on capital liquidity. Furthermore, we design early-warning mechanisms and clustering-based response strategies to anticipate liquidity risks, simulate stress scenarios, and develop a dynamic network transmission model that maps the propagation of market shocks. The results provide a comprehensive theoretical and practical reference for index management, risk mitigation, and the maintenance of financial market stability, offering valuable insights for both regulators and institutional investors.
Copyright (c) 2025 Minghao Chi (Author) https://creativecommons.org/licenses/by/4.0
From Data to Dollars: Quantifying the ROI of AI Visibility Tools Among U.S. SME Importers
As small and medium-sized enterprises (SMEs) play an increasingly important role in supply chains, AI visibility tools have emerged as a key means to enhance logistics efficiency and financial performance. This study analyzes a panel of 156 early-adopter SMEs from 2022 to 2024, employing a two-stage Difference-in-Differences approach combined with Propensity Score Weighting (PSW) to assess the impact of AI visibility tools on logistics costs, inventory turnover, and gross margin. Robustness checks include staggered DiD and Callaway-Sant'Anna dynamic treatment effect estimations. Empirical results indicate that the adoption of AI visibility tools significantly reduces logistics costs (by approximately 8.5%), increases inventory turnover (by approximately 12.3%), and improves gross margin (by approximately 3.6 percentage points), demonstrating the tools' economic return on investment (ROI). This study provides quantitative evidence for SME digital transformation and offers managerial insights for technology investment decisions.
Copyright (c) 2025 Xiangying Chen (Author) https://creativecommons.org/licenses/by/4.0
Research on Optimization of M&A Financial Due Diligence Process Based on Data Analysis
With the continuous development of data technology, enterprises have put forward higher requirements for the efficiency and accuracy of financial due diligence in merger and acquisition activities. The traditional due diligence process, due to many problems such as scattered data sources, lagging analysis, and insufficient security, can no longer meet the increasingly complex needs of mergers and acquisitions. This paper focuses on data analysis, conducts in-depth research from aspects such as the acquisition, processing and risk identification mechanism of financial data, proposes data-driven optimization strategies, builds a unified interface, real-time monitoring platform and data security system, which can enhance the intelligence, standardization and systematization level of financial due diligence in mergers and acquisitions, and provide more efficient and reliable support for enterprises' merger and acquisition decisions.
Copyright (c) 2025 Wei Li (Author) https://creativecommons.org/licenses/by/4.0
User-Centered Conversational AI for Small Business Customer Service: A Cost-Effective and Accessible Framework
The rapid digitalization of small and medium-sized enterprises (SMEs) has highlighted the need for cost-effective and accessible customer service solutions. Traditional commercial conversational AI platforms, such as Dialog flow and AWS Lex, often present high costs and steep learning curves, which pose significant barriers for SMEs. This paper proposes a user-centered conversational AI framework based on an open-source technology stack, including the Microsoft Bot Framework (MBF), OpenStreetMap, Azure Cognitive Services, and MongoDB. The framework is designed to support essential customer service functions—automated order processing, FAQ management, and voice-enabled interactions—while maintaining accessibility compliance (ADA). We demonstrate that this approach reduces operational costs, simplifies deployment, and enhances usability for SMEs. Experimental evaluation comparing our system with commercial alternatives shows competitive accuracy and latency, alongside improved accessibility satisfaction. Finally, we discuss the practical implications, limitations, and future directions for expanding knowledge automation and sentiment analysis integration.
Copyright (c) 2025 Yuxin Liu (Author) https://creativecommons.org/licenses/by/4.0
Supply Chain Digital Transformation and Standardized Processes Enhance Operational Efficiency
With the integration of digital transformation and standardized processes, this has become a key path to enhancing business efficiency for enterprises. With the advancement of digital technology, real-time data collection and analysis have been achieved, which promotes efficient integration of information, logistics, and capital flows, and enhances the adaptability and collaboration capabilities of the supply chain. The role of standardized processes is to unify operational norms, optimize resource allocation, ensure smooth integration, and quality supervision of various links in the supply chain. This article focuses on the exploration of digitization and standardization, with a particular emphasis on analyzing their interactions in information visualization, risk management, automation implementation, and other aspects. It discusses measures such as establishing digital infrastructure, improving transparency and traceability, and building feedback mechanisms, which are important for improving supply chain efficiency and enhancing its resilience. The aim is to provide theoretical support and practical direction for enterprises in optimizing supply chain management.
Copyright (c) 2025 Hao Xu (Author) https://creativecommons.org/licenses/by/4.0
Research on Recommendation Algorithms in Short Video Platforms: From Traditional Methods to Deep Learning and Multimodal Fusion
With the rapid advancement of mobile internet technologies, short video platforms have swiftly risen to prominence, attracting hundreds of millions of users worldwide. At the heart of these platforms, recommendation algorithms serve as essential tools for mitigating information overload and enhancing user engagement and satisfaction. This paper provides a systematic and comprehensive review of the development of recommendation algorithms tailored for short video platforms, tracing their evolution from traditional recommendation methods to deep learning techniques and, more recently, to multimodal fusion strategies. A detailed analysis is conducted on the strengths, limitations, and application scenarios of different approaches. First, the fundamental concepts and evaluation metrics related to short video recommendation are introduced. Next, the applications and shortcomings of traditional recommendation algorithms in this domain are examined thoroughly. Subsequently, deep learning–based methods, including deep neural networks and sequence modeling, are explored extensively, followed by an in-depth investigation of the most recent advances in multimodal fusion for short video recommendation. Finally, the paper discusses current challenges and outlines potential future research directions. By presenting these in-depth discussions and critical comparisons, this work aims to provide researchers and practitioners with a panoramic perspective on short video recommendation algorithms and to foster further academic progress and practical innovation in this rapidly evolving field.
Copyright (c) 2025 Yeqiang Zheng (Author) https://creativecommons.org/licenses/by/4.0
Road Defect Detection System Based on Deep Learning
Road defect detection plays a vital role in the development of smart cities, enhancing the efficiency of road maintenance and providing reliable perception information for automated repair and inspection technologies. This article presents a deep learning-based road defect detection model, built upon advanced frameworks such as YOLOv8 and YOLOv5. Trained on a large-scale dataset of road images, the model is capable of accurately identifying common defect types, including cracks, potholes, and pavement deterioration. In addition, we developed a comprehensive road defect detection system featuring a user-friendly graphical interface, which supports real-time detection and visualization of road defects. Implemented using Python and PyQt5, the system enables intuitive display of detection results and provides detailed information for road maintenance planning. The proposed approach demonstrates promising performance in both static image recognition and continuous video monitoring, offering potential applications in intelligent transportation systems, urban road management, and automated infrastructure maintenance.
Copyright (c) 2025 Shijie Lin (Author) https://creativecommons.org/licenses/by/4.0
Reliability Assessment of Cu-Al Wire Bonds under Thermal Aging: An Investigation of Interfacial Degradation and Mechanical Failure
As the microelectronics industry increasingly adopts copper (Cu) wire for cost-effective interconnection, understanding its long-term reliability is paramount. The primary failure site in Cu wire bonding is the interface with the aluminum (Al) pad, where the formation of brittle intermetallic compounds (IMCs) under thermal stress poses a significant threat. This study provides a systematic investigation into the impact of isothermal aging at 175°C on the reliability of Cu-Al wire bonds over 1000 hours. A research framework was established to test the hypotheses linking thermal exposure, interfacial microstructure, and mechanical performance. Advanced sample preparation was performed using Focused Ion Beam (FIB) milling to obtain pristine cross-sections for analysis. Subsequent Scanning Electron Microscopy (SEM) revealed a progressive and detrimental growth of the Cu-Al IMC layer, leading to the formation of voids and micro-cracks. This microstructural degradation directly correlated with a severe decline in mechanical integrity, as measured by wire pull testing. The bond strength, after a slight initial increase, dropped by over 48% after 1000 hours of aging. This weakening was accompanied by a definitive transition in failure mechanism from robust ductile fracture to brittle interfacial failure. The findings conclusively demonstrate that uncontrolled IMC growth is the primary driver of Cu-Al bond degradation, highlighting the critical need for robust thermal management in modern electronic devices.
Copyright (c) 2025 Zihan Yang, Xuguo Jia (Author) https://creativecommons.org/licenses/by/4.0
The Practical Application of Traffic Flow Forecasting and Capacity Analysis
With the rapid advancement of urbanization and the continuous increase in vehicle ownership, urban traffic systems are facing unprecedented pressure, leading to frequent congestion, increased travel times, and reduced overall transportation efficiency. In this context, traffic flow prediction and road capacity analysis have emerged as essential tools for modern traffic management. These technologies enable urban planners and transportation authorities to anticipate traffic trends, allocate resources more effectively, and design targeted congestion mitigation strategies. This paper conducts a comprehensive analysis of the core methodologies and key technologies involved in traffic flow forecasting and capacity evaluation, including statistical models, machine learning algorithms, and simulation-based approaches. Furthermore, the study illustrates the practical application of these technologies through real-world case studies, highlighting their role in enhancing urban mobility, reducing operational bottlenecks, and supporting the development of intelligent transportation systems (ITS). The findings suggest that scientifically grounded traffic prediction and capacity assessment not only contribute to improved traffic system performance but also lay the groundwork for data-driven urban planning and sustainable city development.
Copyright (c) 2025 Naizhong Cui (Author) https://creativecommons.org/licenses/by/4.0
An Empirical Study on Credit Risk Assessment Using Machine Learning: Evidence from the Kaggle Credit Card Fraud Detection Dataset
This paper investigates machine learning approaches for assessing credit risk, with an emphasis on detecting fraud in credit card usage. Based on the Kaggle dataset, we compare models such as Decision Tree, Random Forest, SVM, Neural Networks, and XGBoost using metrics like Accuracy, Precision, Recall, F1-Score, and AUC. Results show that Random Forest and Neural Networks achieve high accuracy, while XGBoost and Neural Networks are more effective in identifying fraud, with better Recall and AUC. The study underlines challenges from imbalanced data and points out that methods like resampling and ensemble techniques are vital for improving detection. Future work should further enhance fraud detection by integrating deep learning and reinforcement learning methods.
Optimization and Application of Gesture Classification Algorithm Based on EMG
Electromyographic signals (EMG) have a wide range of applications in biomedicine, intelligent human-computer interaction, and rehabilitation assistance. However, due to noise interference, individual differences, computational complexity, and other issues, traditional classification strategies still face challenges in terms of accuracy, stability, and real-time performance. In this paper, the traditional EMG gesture classification methods are comprehensively investigated, and data enhancement, feature mining and lightweight deep learning models are used to improve their accuracy and real-time performance. Moreover, multiple modes are combined to optimize boundary processing to improve real-time performance and robustness. Based on various published databases and practical application environments, the proposed optimization algorithm has demonstrated significant improvements across multiple evaluation metrics, showing both high feasibility and practicality. Meanwhile, the possibility of applying the algorithm to intelligent prosthetics, intelligent human-computer interaction, rehabilitation assistance and other fields is discussed, so as to promote the progress of EMG gesture recognition technology.
Copyright (c) 2025 Jun Ye (Author) https://creativecommons.org/licenses/by/4.0
Immersive Digital Modeling and Interactive Manufacturing Systems in the Textile Industry
The textile industry is undergoing a profound digital transformation driven by the convergence of immersive technologies and interactive manufacturing systems. This review explores the evolution from traditional textile processes to digitally enabled workflows, focusing on immersive digital modeling tools—such as VR, AR, and AI-assisted 3D simulation—and their integration with intelligent, sensor-based manufacturing environments. By examining core technologies, real-world applications, and current limitations, the paper highlights how immersive modeling and interactive systems collaboratively enhance design precision, manufacturing flexibility, and sustainability. Furthermore, it discusses emerging trends, including data-driven feedback loops, digital twin integration, and the strategic application of AI, blockchain, and edge computing in textile production. The review concludes by identifying key challenges in technical integration, workforce readiness, and policy development, while outlining future research directions and strategic implications for the global textile industry.
Copyright (c) 2025 Xiaoxun Luo (Author) https://creativecommons.org/licenses/by/4.0
The Influence and Countermeasures of Generative Artificial Intelligence on Academic Achievement of College Students in Hubei Province
With the rapid advancement of generative artificial intelligence (AI) technology, its application in education has become increasingly widespread, significantly impacting college students' learning and daily lives. This study focuses on college students in Hubei Province to explore how generative AI affects their academic performance and proposes strategies to address these impacts. Through literature review and empirical research, it is confirmed that generative AI has a dual impact on college students. On one hand, it can enhance learning efficiency, assist in writing papers, and solve problems; on the other hand, it may also lead to over-reliance and issues of academic integrity. Based on educational theories, this study systematically analyzes the pathways through which generative AI influences college students' learning methods, thinking abilities, and academic evaluations. The findings indicate that if used appropriately, this technology can optimize the learning process, but it is crucial to be wary of potential negative effects. Based on the study's conclusions, several strategies are proposed — such as enhancing AI literacy education, improving academic standards, and optimizing the teaching evaluation system — to help universities adapt to educational changes in the AI era. This study is highly relevant for promoting positive interactions between technology and education and enhancing college students' self-directed learning capabilities.
Copyright (c) 2025 Hai Guo, Wei Hui Suan (Author) https://creativecommons.org/licenses/by/4.0
Blockchain Future in Cloud Computing: The Challenges to Implement Blockchain Technology in Cloud Computing
With the rapid development of cloud computing technology, more and more industries are starting to utilize data to make meaningful business decisions. Digital innovation has profoundly enhanced business performance. As a result, a more powerful data solution is required to enhance the data management process. Blockchain technology is one of the cutting-edge technologies that has recently been deployed in the cloud computing industry. There are difficulties in implementing blockchain technology that need to be understood by both cloud providers and cloud users.
Research on the Application of Multilingual Natural Language Processing Technology in Smart Home Systems
With the popularization of smart home systems, users' demand for multilingual voice interaction is increasing day by day. This paper proposes a multilingual natural language processing system architecture for the smart home environment, designs core modules such as speech recognition, semantic understanding and instruction mapping, and conducts system deployment and experimental tests in the multilingual home environment. The experimental results show that the system has a high language recognition accuracy rate and good human-computer adaptability. It can support the realization of multi-language intelligent interaction.
Copyright (c) 2025 Xiang Chen (Author) https://creativecommons.org/licenses/by/4.0
Resource Demand Prediction and Optimization Based on Time Series Analysis in Cloud Computing Platform
In the cloud computing environment, dynamic load changes pose higher requirements for the predictive ability of resource scheduling. To improve resource utilization and reduce the risk of SLA default, this paper proposes a multi-model integrated resource demand prediction framework, combining ARIMA and LSTM to capture linear and nonlinear features in time series data. The framework uses Alibaba Cloud Tianchi load data, including CPU, memory, and network metrics, as learning samples for model training and evaluation; Experiments show that the ARIMA-LSTM hybrid model has better performance in both RMSE and MAE indicators than the single model, with the minimum RMSE being 7.32. To further improve the efficiency of resource allocation driven by prediction, the study introduces the ensemble learning method based on LightGBM, the hierarchical time series analysis mechanism, and the ridge regression dynamic allocation strategy combined with L1 regularization. In the deployment environment of the hybrid cloud platform, this framework has increased resource utilization by 19.6% and reduced service Level Agreement (SLA) default events by more than 50%, mainly due to improved prediction accuracy that minimizes excessive reservations and prediction deviations. The research results provide practical and effective methods and examples for the accurate and secure resource scheduling of cloud service platforms and the implementation of cloud services.
Copyright (c) 2025 Jiaying Huang (Author) https://creativecommons.org/licenses/by/4.0
Research Progress of Content Generation Model Based on EEG Signals
The EEG-based content generation model holds great promise in areas such as emotion recognition, thought decoding, and multimodal interaction. EEG signals can monitor the state of brain activity in real time, thereby enabling the decoding of information related to brain activity, such as emotional states or thought patterns. However, there exist problems such as noise interference, low recognition accuracy, difficulty in signal synchronization, and time delay with action signals. To address these issues, this paper proposes using Independent Component Analysis (ICA) for noise reduction, deep convolutional neural networks for spatial feature extraction, and Dynamic Time Warping (DTW) and Long Short-Term Memory (LSTM) networks for signal alignment. These methods aim to improve signal processing accuracy and alignment efficiency, thereby advancing brain-computer interface technologies.
Copyright (c) 2025 Bukun Ren (Author) https://creativecommons.org/licenses/by/4.0
Enhancing User Experience in Enterprise-Level Web Applications through Technological Innovation
With the rapid development of information technology, various industries are increasingly relying on enterprise level web applications in their operations. However, traditional applications often encounter problems such as complex interface design, slow response, and poor compatibility between different platforms in terms of user experience, which have a negative impact on the work efficiency and market competitiveness of enterprises. This article explores user experience strategies for enhancing enterprise-level web applications through technological innovation, including improvements in interface interactivity, performance, cross-platform consistency, and personalized content recommendations. By analyzing the challenges faced by enterprise level web applications at present, this article proposes practical and feasible improvement paths, with the aim of assisting enterprises in finding efficient solutions, promoting their digital transformation, and enhancing business value.
Copyright (c) 2025 Qianru Xu (Author) https://creativecommons.org/licenses/by/4.0
Gateway Architecture and Security Design
In modern network architecture, gateways play a crucial role, and their architecture and security design play a decisive role in ensuring overall network performance and reliability. A reasonable gateway architecture requires modular design, flexible expansion, high stability operation, and efficient resource configuration to meet various needs under changing network conditions. In terms of security, the design needs to focus on strengthening identity verification and access permission management, enhancing security measures for encrypted data storage, and implementing real-time monitoring and threat defense mechanisms to ensure the stable operation of the network. This article will delve into the core concepts of gateway design and its security enhancement strategies, and analyze specific measures and their actual effects to improve system stability and reliability, optimize data exchange efficiency, and enhance overall network security based on practical application scenarios. The aim is to provide practical guidance for building efficient and secure network environments.
Copyright (c) 2025 Yixin Zhou (Author) https://creativecommons.org/licenses/by/4.0
Analysis of Dynamic Capacity Management Technology in Cloud Computing Infrastructure
This paper discusses the method of dynamic capacity management under cloud computing architecture, focusing on the application of key technologies such as virtualization, automatic scheduling, data analysis and edge computing. Through real-time data monitoring and intelligent decision-making, cloud computing systems can analyze workload patterns and anticipate resource demands, enabling them to flexibly adjust resource allocation. This improves the efficiency of computing and storage resource configuration and meets constantly changing work needs. The combination of technologies such as elastic scaling and containerization management has improved the efficiency of resource utilization and further enhanced the flexibility and response efficiency of cloud computing to adapt to changes. This paper explores the application details of these advanced technologies in practical scenarios, with the goal of providing a theoretical basis and operational guidance for scale control of cloud computing systems, as well as supporting efficient operation and sustainable development in cloud computing environments.
Copyright (c) 2025 Jingtian Zhang (Author) https://creativecommons.org/licenses/by/4.0
Application of Efficient Load Test Strategies in Infrastructure
Load testing is an important means of evaluating the performance of infrastructure under different load conditions. This article discusses the application of efficient load testing strategies in infrastructure, with a focus on analyzing automatic scalability testing, high concurrency load simulation, and virtual machine load testing. It reveals the practice of building diverse load scenarios to test the reliability and stability of infrastructure. Further elaborated on measures to ensure efficient load testing, including utilizing automation and intelligent tools to improve testing efficiency, optimizing resource allocation through elastic resource management and intelligent scheduling, and adopting layered and continuous testing to ensure comprehensive and accurate testing coverage, providing theoretical basis and practical guidance for load testing in practical operations.
Copyright (c) 2025 Buqin Wang (Author) https://creativecommons.org/licenses/by/4.0