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.
Cross-Lingual Sponsored Search via Dual-Encoder and Graph Neural Networks for Context-Aware Query Translation in Advertising Platforms
Cross-lingual sponsored search is crucial for global advertising platforms, where users from different language backgrounds interact with multilingual ads. Traditional machine translation methods often fail to capture query-specific contextual cues, leading to semantic ambiguities that negatively impact click-through rates (CTR) and conversion rates (CVR). To address this challenge, we propose AdGraphTrans, a novel dual-encoder framework enhanced with graph neural networks (GNNs) for context-aware query translation in advertising. Specifically, user queries and ad contents are independently encoded using multilingual Transformer-based encoders (mBERT/XLM-R), and contextual relations-such as co-clicked ads, user search sessions, and query-ad co-occurrence-are modeled as a heterogeneous graph. A graph attention network (GAT) is then applied to refine embeddings by leveraging semantic and behavioral context. These embeddings are aligned via contrastive learning to reduce translation ambiguity. Experiments conducted on a cross-lingual sponsored search dataset collected from Google Ads and Amazon Ads (EN-ZH, EN-ES, EN-FR pairs) demonstrate that AdGraphTrans significantly improves query translation quality, achieving a BLEU score of 38.9 and semantic similarity (cosine score) of 0.83, outperforming strong baselines such as mBERT and M2M-100. Moreover, in downstream ad retrieval tasks, AdGraphTrans yields +4.67% CTR and +1.72% CVR improvements over baseline methods. These results confirm that incorporating graph-based contextual signals with dual-encoder translation provides a robust solution for enhancing cross-lingual sponsored search in advertising platforms.
Copyright (c) 2025 Ziyang Gao, Yuanliang Qu, Yi Han (Author) https://creativecommons.org/licenses/by/4.0
Research on Collaborative Development Mode of C# And Python in Medical Device Software Development
C# and Python are two widely used programming languages, each with unique advantages, and they play a crucial role in the development of medical device software. C# is particularly effective in designing and implementing graphical user interfaces (GUIs) and managing core business logic, providing a stable and high-performance environment for software front-end and system integration. Python, on the other hand, excels in data analysis, scientific computing, algorithm development, and machine learning, offering flexibility and rapid prototyping capabilities that are essential for advanced data-driven functionalities in medical devices. This paper presents a comprehensive study on the joint development model of C# and Python, focusing on the design of an effective cooperative framework that leverages the strengths of both languages in the field of medical device software development. The proposed framework provides a structured approach for integrating front-end GUI design, core process management, and advanced computational modules, and details the interaction and cooperation between C# and Python during the development lifecycle with specific task-oriented guidance. To validate the proposed approach, integration testing and performance evaluation were conducted to ensure the reliability, efficiency, and robustness of the system. The results demonstrate that the combined use of C# and Python not only improves software performance and maintainability but also enhances the flexibility and scalability of medical device applications. Finally, the practical implementation of this joint development model in real-world projects confirms its effectiveness, providing a valuable reference for future development of high-quality, data-driven, and user-friendly medical device software.
Copyright (c) 2025 Minkang Zhang (Author) https://creativecommons.org/licenses/by/4.0
Intervention Research and Optimization Strategies for Neuromuscular Function Degeneration in the Context of Aging
With the aging of the population in China, neuromuscular function degradation has become one of the most important problems affecting the health level and quality of life of the elderly. The degradation process of this neuromuscular function involves a decrease in nerve conduction velocity, muscle strength, coordination, and balance, which seriously affects the daily activity and self-care abilities of the elderly. This article is based on the perspectives of sports science and physical therapy, and elaborates on the physiological mechanisms of neuromuscular degeneration. It summarizes the mainstream intervention paths of aerobic exercise, resistance training, and proprioceptive training, and proposes suggestions and improvement plans for functional evaluation, personalized training, multidisciplinary integration, and information-based assistance to improve neuromuscular degeneration. The aim is to provide theoretical basis and practical reference for the development of a scientific and effective neuromuscular function intervention system for the elderly.
Copyright (c) 2025 Yihao Wang (Author) https://creativecommons.org/licenses/by/4.0
Construction And Optimization Of AI-Based Real-Time Clinical Decision Support System
With the rapid development of AI technology, its role in clinical decision support systems (CDSS) has become increasingly prominent. This paper focuses on the construction ideas of real-time clinical decision support systems, elaborates in detail the key elements of multi-source data fusion, high-precision prediction and real-time reasoning, and proposes improvement paths to accelerate system response, strengthen model iteration and optimize interaction structure, aiming to provide reliable, efficient and sustainable technical support for smart medical practice.
Copyright (c) 2025 Danyating Shen (Author) https://creativecommons.org/licenses/by/4.0
Application of Multi-Source Remote Sensing Data and Lidar Data Fusion Technology in Agricultural Monitoring
The fusion technology of multiple and LiDAR data has important value in agricultural monitoring process, which can contribute to the accuracy of crop identification, real-time monitoring of crop growth status, and evaluation of advanced land resource characteristics. Due to the large-scale coverage and complex spectral features of remote sensing images, they have the potential to identify vegetation and soil characteristics; And LiDAR images have precise spatial structural characteristics, such as tree crown height, ground morphology, etc., which can complement the latter in structural and informational spaces, filling the limitations of single source data acquisition in representation. Of course, having great technological potential does not necessarily mean that it can be fully realized. In fact, it also faces some challenges, such as mismatched temporal spatial resolution, cumbersome data acquisition and processing processes, high development costs for hardware facilities and algorithm software, etc., all of which hinder the effectiveness and depth of the development of this technology. This article mainly starts from the agricultural and forestry scenarios, explores suitable integration development paths and strategies based on their existing problems, in order to enhance the practical value and intelligence level of agricultural remote sensing data integration, promote the intelligent and refined development of agriculture, and provide technical support for its development.
Copyright (c) 2025 Chuying Lu (Author) https://creativecommons.org/licenses/by/4.0
Constructing a Dynamic Causal Inference Framework for Digital
In digital scenarios, traditional linear causal inference models struggle to accurately depict complex causal relationships due to the breadth of data sources and intricate environmental dynamics. This study establishes a dynamic causal inference model suited to digital contexts based on dynamic systems theory. The model comprises multiple layers: data input, generation and evaluation of causal relationships, feedback and learning of causal relationships, and visualization of outcomes. This model automatically adjusts and refines causal structures, exhibiting adaptability and stability. Through time-varying modelling and machine learning optimization strategies, it achieves greater flexibility and interpretability in high-dimensional, non-stationary data scenarios. The research serves as a tool to address practical challenges in digital governance, intelligent decision-making, and social science experimentation.
Copyright (c) 2025 Jing Xie (Author) https://creativecommons.org/licenses/by/4.0
Enhancing Small Business Customer Engagement through Sentiment Analysis and Predictive Modeling
In today's highly competitive business environment, small enterprises face multiple challenges, including limited resources, high customer acquisition costs, and insufficient customer engagement. Customer engagement not only impacts sales and brand loyalty but also directly affects the long-term growth of the business. This paper explores the practical value and strategies of applying sentiment analysis and predictive modeling to enhance customer engagement in small enterprises. First, the study analyzes the current state and challenges of customer engagement for small businesses, including fragmented data, difficulties in personalized marketing, delayed response to customer feedback, and intense market competition. It then discusses how sentiment analysis can identify customer emotions, satisfaction levels, and potential pain points by analyzing feedback, social media comments, and survey data, thereby helping businesses optimize their service and interaction strategies. Predictive modeling, by combining historical behavioral data with emotional information, forecasts future customer behaviors, purchase tendencies, and churn risks, providing data-driven support for personalized marketing and proactive customer care. Furthermore, the paper examines the integration of sentiment analysis and predictive modeling, creating a closed-loop system from emotion perception to behavior prediction. This approach enhances the accuracy and timeliness of customer interactions, enables early detection of potential issues, and improves customer satisfaction and loyalty. Implementation steps are also outlined, including data collection, cleaning, analysis, modeling, and strategy execution, while potential challenges and mitigation strategies are discussed, such as insufficient data, technical costs, employee capability limitations, and privacy compliance concerns. Finally, the paper highlights future development directions, including multi-channel data integration, advanced emotion recognition (e.g., voice and image analysis), AI-driven automation in customer interactions, and continuous optimization of data-driven strategies. The study demonstrates that the combination of sentiment analysis and predictive modeling can significantly enhance customer engagement in small enterprises while providing data-driven decision support, enabling small businesses to achieve growth and sustainable competitive advantage despite limited resources.
Copyright (c) 2025 Zongze Li (Author) https://creativecommons.org/licenses/by/4.0
Optimization of Large Models for Efficient Inference: Algorithm, Compiler, and System Co-Design
Large language models have achieved remarkable capabilities in natural language processing, yet their inference cost remains a significant challenge due to high computation, memory usage, and latency. This study presents a cross-layer co-optimization framework that integrates algorithmic, compiler, and system-level strategies to enhance inference efficiency. Algorithmic techniques, including structured pruning, sparsity, quantization, and dynamic inference, reduce computational workload and memory footprint. Compiler optimizations, such as operator fusion, graph rewriting, and layout specialization, translate algorithmic improvements into hardware-efficient execution. System-level strategies, encompassing parallel execution, memory management, and KV cache optimization, further improve resource utilization and reduce latency. The framework synergistically coordinates these layers, providing a theoretically grounded approach for reducing FLOPs, memory consumption, and inference latency. Its adaptability extends to cloud, edge, and interactive deployment scenarios, offering a unified methodology for efficient and scalable large-model inference. This work contributes a systematic and extensible pathway for accelerating model inference without relying on empirical performance measurements.
Copyright (c) 2025 Shengyi Gao (Author) https://creativecommons.org/licenses/by/4.0
A Context-Aware Personalized Recommendation Framework Integrating User Clustering and BERT-Based Sentiment Analysis
With the rapid growth of e-commerce platforms, there is an increasing demand for highly accurate and personalized recommendation systems. Traditional recommendation algorithms often struggle to capture the complex and dynamic nature of user preferences, especially when dealing with heterogeneous data sources. This study introduces a novel recommendation framework that integrates user clustering, BERT-based sentiment analysis, contextual encoding, and deep learning techniques. Utilizing a real-world dataset from Kaggle, the proposed model incorporates user behavior records, review texts, and contextual information to construct detailed user and item representations. Dimensionality reduction and clustering methods are employed to identify latent user groups, while BERT is used to extract deep semantic features from user-generated reviews. The resulting feature vectors are then fed into a multi-layer perceptron to generate personalized recommendations. Extensive experiments demonstrate that the K-means + BERT + MLP model consistently outperforms a variety of traditional and hybrid baselines across multiple evaluation metrics, including accuracy, precision, recall, F1-score, and AUC. The results validate the effectiveness and robustness of the proposed approach, showcasing the potential of multi-source feature fusion and advanced modeling techniques in next-generation recommender systems.
Copyright (c) 2025 Siyu Li, Kuangcong Liu, Xuanjing Chen (Author) https://creativecommons.org/licenses/by/4.0
Edge-Intelligence-Based Dynamic Spectrum Allocation for 6G Wireless Networks
The rapid proliferation of ultra-dense 6G networks has intensified the challenges of real-time spectrum management, as traditional centralized or static allocation methods struggle to achieve a balance among responsiveness, energy efficiency, and scalability. Existing approaches that rely on global coordination incur considerable signaling overhead and fail to adapt effectively to non-stationary wireless environments. To address these limitations, this study introduces an Edge-Intelligence-Based Dynamic Spectrum Allocation (EI-DSA) framework that integrates deep reinforcement learning (DRL) with federated learning (FL) for localized spectrum prediction and distributed decision-making. Utilizing empirical parameters derived from the NTT Docomo Tokyo 6G Testbed and Huawei Futian CBD Field Trials, the proposed framework achieves notable improvements-8.8% higher spectrum utilization, 26% lower latency, and 43% better energy efficiency-compared with centralized RL and proportional fairness baselines. The findings validate that embedding edge intelligence within radio access networks enables real-time, energy-aware, and privacy-preserving control. Theoretically, this research bridges communication engineering and intelligent optimization, presenting a scalable paradigm for AI-native 6G systems. Practically, it provides design guidance for developing green, autonomous, and adaptive wireless infrastructures that align with next-generation communication and electronic engineering advancements.
Copyright (c) 2025 Ruoxi Yu (Author) https://creativecommons.org/licenses/by/4.0
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