International Scientific Technical and Economic Research
type: channel
description: Call for Papers International Scientific Technical and Economic Research Dear Authors. We cordially invite you to submit your manuscripts to the journal International Scientific Technical and Economic Research. The journal is an international academic publication with the international issue number: ISSN 2959-1309. It is dedicated to the publication of high quality research in the fields of science, technology and economics. We welcome original research papers in all fields, including but not limited to the following topics: 1. scientific research: research results in the fields of physics, chemistry, biology, earth sciences, mathematics, etc; 2. technological developments: technological innovations in the fields of engineering, computer science, information technology, biotechnology, etc; 3. economic research: economic theory and empirical research in the fields of macroeconomics, microeconomics, international economics, finance, etc; 4. interdisciplinary: interdisciplinary research in multiple fields, such as the relationship between science and technology innovation and economic development, the impact of technology applications on society and the environment, etc. Please follow the following requirements for the call for papers: 1. Originality: Your submitted paper must be original, with a repetition rate of less than 20%, and not published or submitted in other journals or conferences. 2. Academic quality: We value academic rigor and quality, so please ensure that your research methods, data analysis, and paper structure meet academic standards. 3. Article format: Please write and format your paper according to the journal's author guidelines. We accept submissions in English language only. 4. Submission method: Submit your paper via our email address. email (istaer@126.com). 5. Collaboration and number of authors: We encourage collaborative research, but please ensure that all authors have substantial contributions and are clearly listed in the paper. 6. Revie…
publisher: Sichuan Knowledgeable Intelligent Sciences
language: en
publication name: International Scientific Technical and Economic Research
issn: 2959-1309
copyright: This work is licensed under the Creative Commons Attribution International License (CC BY 4.0).
items: Application of Smart Wearable Device Interaction Design Based on User Experience Map
items: Framework for the Application of Digital Twin Technology in Intelligent Production Line Condition Monitoring and Predictive Maintenance
items: An Exploration of the Application of Bionic Design Methods in the Design of Miniaturized Personal Vehicles
items: The practical value, implementation approaches, and effectiveness of integrating Chengdu's red culture into ideological and political education courses at local universities
items: Applications of MEMS sensors in industrial equipment condition sensing and the Internet of Things
items: Construction of Self-Optimization and Quality Prediction Model for Intelligent Die Casting Unit Process Parameters
items: Design of an AR-based assembly guidance and remote maintenance system for complex electromechanical equipment
items: Research and Development of Robotic Grinding and Polishing Process Parameter Adaptive Control System
items: Research on Lightweight Design and Performance Optimization of Lattice Structures Based on Additive Manufacturing
items: MAPFusion: Enhancing RoBERTa for News Classification with Multi-Head Attention Pooling and Feature Fusion
items: Research on the Application Logic and Ethical Boundaries of Artificial Intelligence in Systemic Financial Risk Early Warning
items: Research on Business Models and Risk Governance Frameworks for Fintech-Enabled Financing of Micro and Small Enterprises
items: Solving the NIPT optimal timing problem based on NSGA-II
items: Research on MRI Object Detection Using an Enhanced YOLOv8 Framework
items: Machine Learning Approaches for Detecting Abnormalities in Female Fetuses
items: Study on the Agricultural System in a Certain Region Based on the LSTM Model and Genetic Algorithms
items: Research on regulated biological regularity LED light source based on normality test algorithm
items: Design and evaluation system construction of VR cultural heritage products from the perspective of user experience
items: Online music personalized recommendation algorithm based on KKBox large-scale real-world dataset and integrated behavior sequence transformer
items: Trust building mechanism of multimodal interaction in autonomous driving system
items: Application of modular design strategy in the life cycle of household appliances under the goal of carbon neutrality
items: The impact of social media live streaming on fashion brands’ sales performance: A case study of TikTok
items: Bridging the digital divide for seniors: Research on design principles for age-friendly touch interfaces
items: Human Failure Analysis and Fault-Tolerant Interaction Design of Deep-Sea Exploration Robots
items: Research on iterative design of human-machine interface of intelligent agricultural machinery based on digital twin
items: Privacy-Utility Tradeoff: Studying the Boundaries of Anonymization in Health Data Visualization Design
items: Design and verification of an AI-based interactive tool for early screening of autism in children
items: Transformation Design and Human-Machine Scale Adaptability of Office Furniture in the Post-epidemic Era
items: Cultural Gene Translation: Semiotic Practice of Regional Elements in High-Speed Rail Interior Design
items: Research on the Design of Intelligent Voice Interaction System Based on Affective Computing

Application of Smart Wearable Device Interaction Design Based on User Experience Map
type: item
description: This paper addresses the interaction design challenges faced by smart wearable devices in limited screen space and diverse usage scenarios, proposing and systematically demonstrating an interaction design method based on user experience maps. By constructing a closed-loop design framework of "user research - journey description - pain point analysis - solution generation - prototype verification," the user experience map is elevated from an analysis tool to a core navigator throughout the entire design process, effectively solving the problems of experience breakpoints and scenario disconnects in traditional design methods. In a case study of a smartwatch's sports and health monitoring function, this framework successfully guided the design process from identifying pain points such as inconvenient data viewing and cumbersome operation to implementing specific solutions such as interface simplification, gesture optimization, and voice feedback. Empirical data shows that after optimization, task operation time was reduced by 45%, the completion rate reached 100%, and the system usability scale improved to an excellent level. This research innovatively establishes a systematic experience design method applicable to smart wearable devices, not only providing an operable process guide for design practice but also laying a theoretical foundation for exploring the integration of user experience maps with new technologies such as artificial intelligence and promoting the development of adaptive interaction.
subject: Research Article
  topic: section
subject: User experience map; Smart wearable devices; Interaction design; User research; Touchpoint optimization
  topic: keywords
creator: Jiahao Yang*
rights: Copyright (c) 2025 International Scientific Technical and Economic Research https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-11-28
publication date: 2025-11-28
starting page: 147
ending page: 155
doi: 10.71451/ISTAER2573

Framework for the Application of Digital Twin Technology in Intelligent Production Line Condition Monitoring and Predictive Maintenance
type: item
description: This paper addresses the urgent needs of intelligent manufacturing for highly reliable equipment operation and precise maintenance and delves into the innovative application of digital twin technology in production line condition monitoring and predictive maintenance. By systematically reviewing the core theories of digital twins, multi-source heterogeneous data acquisition and processing technologies, and predictive maintenance methodologies, a five-dimensional integrated framework comprising a physical layer, data layer, model layer, functional layer, and application layer is constructed. This framework innovatively achieves real-time dynamic mapping and bidirectional interaction between physical and virtual spaces, establishes a data-model hybrid-driven mechanism for equipment health status assessment and remaining life prediction, and forms a closed-loop optimization system from condition perception and fault early warning to maintenance decision-making. To verify the effectiveness of the framework, this study conducts a case study using a precision CNC gear machining production line. The results show that the framework can control the latency of critical equipment condition monitoring within 200 milliseconds, improve the accuracy of remaining life prediction by approximately 15% compared to purely data-driven methods, successfully achieve early fault warning, reduce unplanned downtime by 65%, and save maintenance costs by 28%. The research findings provide theoretical guidance and practical examples for achieving precise and forward-looking equipment health management in the context of intelligent manufacturing and have important reference value for promoting the digital transformation of the manufacturing industry.
subject: Research Article
  topic: section
subject: Digital twin; Intelligent production line; Condition monitoring; Predictive maintenance; Framework construction
  topic: keywords
creator: Yu Zhang*
creator: Mengxi Wang
creator: Jiayi Zhou
rights: Copyright (c) 2025 International Scientific Technical and Economic Research https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-11-25
publication date: 2025-11-25
starting page: 138
ending page: 146
doi: 10.71451/ISTAER2572

An Exploration of the Application of Bionic Design Methods in the Design of Miniaturized Personal Vehicles
type: item
description: With the acceleration of urbanization and the deepening of the concept of sustainable development, miniaturized personal transportation vehicles have become a key carrier for solving the "last mile" travel demand. However, current market products generally face the dilemma of homogenized styling and a lack of emotional value. Meanwhile, biomimetic design, as an interdisciplinary innovation method, provides a new perspective for breaking through this bottleneck. This study aims to systematically explore the application value and practical path of biomimetic design methods in the styling of miniaturized personal transportation vehicles. Through literature review, case analysis, and morphological deconstruction, it deeply analyzes the internal logic and manifestations of three application modes: morphological biomimetic, functional biomimetic, and imagery biomimetic. The study finds that biomimetic design can endow products with natural beauty and vitality through organic forms, optimize aerodynamic performance and lightweight structure with biological prototypes, and evoke emotional resonance in users through abstract imagery. The study further proposes an integrated strategy of "form-function-emotion," emphasizing that the selection of biological prototypes should be deeply aligned with user cognition and usage scenarios. Despite the challenges of engineering implementation and over-mimicry, biomimetic design combining intelligent interaction and sustainable technologies will become an important evolutionary direction for future personalized mobility experiences. This study provides a theoretical basis and practical inspiration for constructing miniaturized transportation vehicles that combine aesthetic value, functional rationality, and emotional warmth.
subject: Research Article
  topic: section
subject: Swinburne College of Shandong University of Science and Technology, Jinan, China
  topic: keywords
creator: Jiahao Yang*
rights: Copyright (c) 2025 International Scientific Technical and Economic Research https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-11-23
publication date: 2025-11-23
starting page: 129
ending page: 137
doi: 10.71451/ISTAER2561

The practical value, implementation approaches, and effectiveness of integrating Chengdu's red culture into ideological and political education courses at local universities
type: item
description: This paper, grounded in the contemporary imperative of "cultivating virtue and nurturing talent," explores strategies to integrate Chengdu's rich revolutionary cultural heritage into practical teaching of ideological and political theory courses at local universities. The study first analyzes the distinctive connotations and educational value of Chengdu's revolutionary culture. It then systematically examines three primary implementation models: the "classroom narrative + resource immersion" approach, the "on-site teaching + emotional resonance" model, and the "project-driven + knowledge-action integration" framework. Through reflective analysis of practical outcomes, the paper identifies challenges in resource integration, content adaptation, and evaluation mechanisms. It proposes optimization recommendations including establishing a collaborative "university-local-library" education mechanism, developing digital teaching resources, and refining formative assessment systems. These insights aim to provide actionable strategies for enhancing the appeal, engagement, and effectiveness of ideological and political education in regional universities.
subject: Research Article
  topic: section
subject: Chengdu Red Culture; ideological and political course
  topic: keywords
creator: Nanjun He
creator: Guoqing Chen
rights: Copyright (c) 2025 International Scientific Technical and Economic Research https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-11-23
publication date: 2025-11-23
starting page: 185
ending page: 195
doi: 10.71451/ISTAER2562

Applications of MEMS sensors in industrial equipment condition sensing and the Internet of Things
type: item
description: This paper systematically studies the integrated application of microelectromechanical systems (MEMS) sensors in industrial equipment condition sensing and the Internet of Things (IoT). The research shows that MEMS sensors, with their advantages of miniaturization, low cost, and low power consumption, effectively overcome the limitations of traditional monitoring technologies in terms of deployment density and economy, laying a solid foundation for building large-scale equipment condition sensing networks. By analyzing multi-dimensional parameters such as vibration, attitude, and pressure, and combining edge computing and data fusion technologies, accurate diagnosis and early warning of the operating status of industrial equipment are achieved. Further research indicates that when MEMS sensors are deeply integrated into the industrial IoT architecture, not only is real-time monitoring of equipment status realized, but the operation and maintenance model is also promoted from reactive maintenance to predictive maintenance, significantly improving equipment reliability, production efficiency, and safety management levels. Although challenges remain in measurement accuracy, environmental adaptability, and standardization, with the deep integration of new materials, new processes, and artificial intelligence technologies, MEMS sensors will play an increasingly important role in building intelligent and adaptive industrial ecosystems, providing key technological support for industrial digital transformation.
subject: Research Article
  topic: section
subject: MEMS; MEMS sensors; Condition monitoring; Predictive maintenance; Industrial Internet of Things (IIoT)
  topic: keywords
creator: Wenjing Zhou*
rights: Copyright (c) 2025 International Scientific Technical and Economic Research https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-11-22
publication date: 2025-11-22
starting page: 121
ending page: 128
doi: 10.71451/ISTAER2560

Construction of Self-Optimization and Quality Prediction Model for Intelligent Die Casting Unit Process Parameters
type: item
description: With the deepening of intelligent transformation in the manufacturing industry, the die-casting industry faces an urgent need to improve product quality, production efficiency, and stability. Traditional trial-and-error process debugging methods relying on manual experience are no longer adequate to meet this challenge. Therefore, this research aims to construct an intelligent die-casting system integrating quality prediction and parameter self-optimization to achieve closed-loop optimization of the production process. First, the system collects multi-source process parameters such as injections and thermal parameters in the die-casting unit through an industrial Internet of Things (IoT) architecture. Based on this, machine learning algorithms such as XGBoost, random forest, and artificial neural networks are compared and screened, successfully constructing a high-precision quality prediction model with internal porosity as the core indicator. Then, using this prediction model as a surrogate model, a process parameter self-optimization strategy is developed, combining a multi-objective genetic algorithm and a Bayesian optimization strategy, to automatically seek multiple objectives such as quality, efficiency, and energy consumption while meeting equipment and process constraints. Experimental results show that this system can not only effectively predict key quality characteristics, but the process parameter combinations recommended by its self-optimization module significantly improve the product qualification rate and reduce the single-piece production cycle compared to traditional methods in actual production. This study successfully combines data-driven methods with the die-casting process mechanism, forming a complete technical solution from perception and prediction to decision-making, providing a theoretical model and system framework with practical value for the digital and intelligent upgrading of die-casting production.
subject: Research Article
  topic: section
subject: Intelligent die casting; Process parameter optimization; Quality prediction; Machine learning; Digital twin
  topic: keywords
creator: Hongfang Shan*
creator: Yuwen Su
creator: Nuo Xu
rights: Copyright (c) 2025 International Scientific Technical and Economic Research https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-11-21
publication date: 2025-11-21
starting page: 111
ending page: 120
doi: 10.71451/ISTAER2559

Design of an AR-based assembly guidance and remote maintenance system for complex electromechanical equipment
type: item
description: This paper addresses the challenges of high precision requirements, complex operations, and scarce expert resources in the assembly and maintenance of complex electromechanical equipment. It designs and implements an integrated guidance and remote maintenance system based on augmented reality (AR) technology. The research first constructs a core technical solution integrating hybrid tracking and registration, dynamic information visualization, and real-time remote collaboration. Then, it elaborates on the system's layered architecture and modular design and completes the development of a prototype system using a precision reducer for industrial robots. Test results show that the system effectively improves the accuracy and efficiency of assembly operations and significantly enhances the sense of presence and effectiveness of remote collaborative maintenance, providing valuable theoretical reference and practical examples for the in-depth application of AR technology in the field of industrial precision operation.
subject: Research Article
  topic: section
subject: Swinburne College of Shandong University of Science and Technology, Jinan, China
  topic: keywords
creator: Yu Zhang*
creator: Yongzhuang Li
creator: Jiayi Zhou
rights: Copyright (c) 2025 International Scientific Technical and Economic Research https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-11-20
publication date: 2025-11-20
starting page: 102
ending page: 110
doi: 10.71451/ISTAER2558

Research and Development of Robotic Grinding and Polishing Process Parameter Adaptive Control System
type: item
description: With the increasing demands for surface quality in intelligent manufacturing, robotic grinding and polishing technology has become a key solution due to its flexibility. However, traditional robotic grinding and polishing based on fixed parameters struggles to cope with process disturbances such as workpiece geometric errors and tool wear, leading to unstable processing quality. Therefore, this study focuses on the research and development of an adaptive control system for robotic grinding and polishing process parameters. This paper first analyzes the grinding and polishing process mechanism and adaptive control theory, constructing a robot-workpiece-tool interactive dynamics model and a process model based on contact force. Then, a core algorithm based on high-precision force or position hybrid control and incorporating a fuzzy adaptive strategy is designed, realizing the dynamic optimization of key parameter control for feed rate and spindle speed based on real-time force feedback. At the system implementation level, a hardware platform is constructed by integrating an industrial robot, a six-dimensional force sensor, and a grinding and polishing spindle, and a software system with real-time control and human-machine interaction functions is developed. Experimental results show that, compared with fixed parameter grinding and polishing, this system can reduce the range of surface roughness (Ra) by about 40%, and exhibits excellent material removal uniformity and strong robustness to changes in working conditions in the processing of complex curved surfaces, effectively verifying the advanced nature and engineering application potential of the proposed method and system.
subject: Research Article
  topic: section
subject: Robotic grinding and polishing; Process parameters; Adaptive control; Force control; Intelligent regulation; System development
  topic: keywords
creator: Xiaolin Wang*
creator: Yu Zhang
creator: Xinyu Bu
creator: Zhenglin Chen
rights: Copyright (c) 2025 International Scientific Technical and Economic Research https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-11-19
publication date: 2025-11-19
starting page: 94
ending page: 101
doi: 10.71451/ISTAER2557

Research on Lightweight Design and Performance Optimization of Lattice Structures Based on Additive Manufacturing
type: item
description: Addressing the urgent need for lightweight and functionally integrated structures in aerospace, transportation, and other fields, this paper focuses on the systematic design and performance optimization of additive manufacturing-based lattice structures. To overcome the core challenge of the disconnect between theoretical models and manufacturing practice in lattice structure design, this study constructs a comprehensive research system encompassing theoretical modeling, process adaptation, performance optimization, and experimental verification. First, the equivalent mechanical properties of lattice structures are explored in depth, and process adaptability design criteria for additive manufacturing are established to address manufacturability issues. Then, a multi-objective optimization method is used to synergistically improve the static and dynamic mechanical properties and energy absorption characteristics of the lattice structures. Finally, experimental prototypes are prepared by carefully selecting additive manufacturing process parameters, and systematic mechanical performance tests are conducted to verify the accuracy of the numerical model and the effectiveness of the optimized design. The research results not only demonstrate the enormous potential of additively manufactured lattice structures in terms of lightweighting and multifunctional load-bearing capacity but also provide a closed-loop methodology covering design, manufacturing, and performance evaluation for their engineering applications, possessing significant theoretical and engineering reference value.
subject: Research Article
  topic: section
subject: Lattice structure; Additive manufacturing; Lightweight design; Performance optimization; Process adaptability
  topic: keywords
creator: Hongfang Shan*
creator: Xiaolin Wang
creator: Yunfei Jiang
creator: Nuo Xu
rights: Copyright (c) 2025 International Scientific Technical and Economic Research https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-11-17
publication date: 2025-11-17
starting page: 85
ending page: 93
doi: 10.71451/ISTAER2556

MAPFusion: Enhancing RoBERTa for News Classification with Multi-Head Attention Pooling and Feature Fusion
type: item
description: This paper proposes MAPFusion, a novel approach to enhance RoBERTa for news text classification using multi-head attention pooling. Traditional methods often rely on static pooling or simple averaging of [CLS] tokens, which can overlook subtle contextual information across token positions. The proposed method addresses this limitation with multi-head attention pooling (MAP), which captures context-aware representations by aggregating token-level embeddings with learned attention weights across multiple representation subspaces. The outputs from multiple attention heads are then integrated through a feature fusion layer to create a comprehensive sentence representation. This component is seamlessly integrated into the output layer of RoBERTa, preserving its pre-trained weights and requiring minimal additional parameters during fine-tuning. Experiments on benchmark datasets demonstrate that MAP-Fusion consistently outperforms baseline models, achieving significant improvements in classification accuracy. The framework is computationally efficient, broadly applicable to various text classification tasks, and provides a principled approach to more effectively utilize the latent representations of RoBERTa. Our work highlights the importance of adaptive feature aggregation in Transformer-based models, providing insights for future representation learning research. The code for this paper is available by contacting the respective authors.
subject: Research Article
  topic: section
subject: MAPFusion, News text classification, Multi-head attention pooling, RoberTa, Attention mechanism
  topic: keywords
creator: Xinran Bu
rights: Copyright (c) 2025 International Scientific Technical and Economic Research https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-11-16
publication date: 2025-11-16
starting page: 71
ending page: 84
doi: 10.71451/ISTAER2555

Research on the Application Logic and Ethical Boundaries of Artificial Intelligence in Systemic Financial Risk Early Warning
type: item
description: With the deep penetration of artificial intelligence (AI) technology into the field of financial risk control, its application in systemic risk early warning is attracting widespread attention from academia and regulatory agencies. This study focuses on the dual characteristics of AI technology in financial risk early warning: on the one hand, through multi-source data fusion, complex algorithm models, and intelligent decision support, AI technology has significantly improved its ability to identify high-dimensional nonlinear risks, realizing a paradigm shift from static assessment to dynamic early warning; on the other hand, ethical issues such as algorithmic bias, black-box decision-making, and data privacy violations are becoming increasingly prominent, and may even give rise to new systemic risks due to model homogeneity. This paper constructs a three-dimensional analysis system of "technology application-ethical boundaries-governance framework," and uses case analysis and comparative research methods to demonstrate the necessity of establishing a responsible AI early warning mechanism. The study finds that effective risk governance requires the organic combination of technological empowerment and ethical regulation. By developing explainable artificial intelligence, constructing an algorithm audit system, and improving cross-departmental collaborative supervision, while leveraging the early warning advantages of AI technology, it is essential to ensure that its application complies with the fundamental requirements of financial stability and fairness and justice. This provides an important reference for building a new paradigm of intelligent financial supervision in the digital age.
subject: Research Article
  topic: section
subject: Artificial intelligence; Systemic financial risk; Risk warning; Machine learning; Ethical boundaries
  topic: keywords
creator: Zhenglin Chen*
creator: Zhendong Ma
creator: Chengkai Kang
rights: Copyright (c) 2025 International Scientific Technical and Economic Research https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-11-16
publication date: 2025-11-16
starting page: 64
ending page: 70
doi: 10.71451/ISTAER2554

Research on Business Models and Risk Governance Frameworks for Fintech-Enabled Financing of Micro and Small Enterprises
type: item
description: This study focuses on the core proposition of "financial technology empowering the financing of micro and small enterprises," systematically exploring its business model innovation and risk governance framework construction. The paper first analyzes the theoretical logic and practical motivations for financial technology to solve the financing difficulties of micro and small enterprises, pointing out that it effectively alleviates the two core obstacles of information asymmetry and excessively high transaction costs by reconstructing risk pricing models and service processes. The study further deconstructs the value creation mechanisms and operational characteristics of typical business models such as digital lending, supply chain finance, and big data risk control, finding that while these models improve the accessibility and efficiency of financial services, they also generate complex challenges such as technological risks, new business risks, compliance risks, and systemic risks. Based on an in-depth analysis of risk characteristics, this paper innovatively proposes a multi-layered, dynamic risk governance framework. This framework emphasizes the organic unity of internal governance, external supervision, and ecosystem collaboration: at the micro level, it requires fintech companies to establish a full-process risk control system and compliant technology applications; at the meso level, it advocates adaptive regulatory models such as regulatory sandboxes and penetrating supervision; and at the macro level, it requires improving infrastructure such as credit information sharing and multi-party co-governance mechanisms. The case study of MYbank validated the practical value of this governance framework. The study ultimately concluded that the sustainable development of fintech must be built upon the simultaneous improvement of business model innovation and risk governance capabilities, requiring the construction of a dynamic governance ecosystem that balances innov…
subject: Research Article
  topic: section
subject: Fintech; SME financing; Business model; Risk governance; Inclusive finance; Regulatory technology
  topic: keywords
creator: Zhenglin Chen*
creator: Zhendong Ma
creator: Chengkai Kang
rights: Copyright (c) 2025 International Scientific Technical and Economic Research https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-11-12
publication date: 2025-11-12
starting page: 54
ending page: 63
doi: 10.71451/ISTAER2553

Solving the NIPT optimal timing problem based on NSGA-II
type: item
description: This study addresses the optimal timing of noninvasive prenatal testing (NIPT) testing. A multi-objective optimization model centered on minimizing testing error and maternal and fetal risk was established and solved using the NSGA-II algorithm. Results demonstrated that the model effectively reflects the optimal gestational age distribution across populations with varying BMIs (BMIs). The optimal timing for NIPT testing was 18.63 weeks for those with a BMI ≥ 38.3, 23.74 weeks for those with a BMI 28.6–34.5, and 24.72 weeks for those with a BMI 20.0-28.6. With increasing iterations, the uniformity and diversity of the Pareto front significantly improved, and the HV index continued to rise. Monte Carlo perturbation and sensitivity analyses validated the model's stability and robustness, with BMI having the greatest impact on the results. Overall, the NSGA-II-based model effectively addresses the optimal timing of NIPT testing, providing accurate and reliable results and providing an effective quantitative basis for stratified and personalized testing.
subject: Research Article
  topic: section
subject: Non-invasive prenatal testing; Multi-objective optimization; NSGA-II algorithm; Optimal testing time; body mass index
  topic: keywords
creator: Yiran Chen
creator: Yueyuan Chen
creator: Keyu Yao
creator: Junwen Luo
rights: Copyright (c) 2025 International Scientific Technical and Economic Research https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-10-20
publication date: 2025-10-20
starting page: 43
ending page: 53
doi: 10.71451/ISTAER2552

Research on MRI Object Detection Using an Enhanced YOLOv8 Framework
type: item
description: Early diagnosis and accurate localization of brain tumors are crucial for improving patient survival rates. Among these, automated detection methods based on deep learning have become a research hotspot. However, tumor detection, especially for complex shapes and small targets, remains challenging. To address this, this study proposes an improved YOLO model—YOLO-CDF—aimed at enhancing the detection accuracy for complex and small targets in brain tumor MRI images. The model builds upon YOLO by incorporating the BRASPPF module (A combination of bi-level routing attention mechanism and spatial pyramid pooling), dilated convolution, and small object detection layers. Experimental results show that the YOLOv8-CDF model achieves a good balance between precision and recall, with an overall mAP@0.5 of 0.929 and an F1 score reaching 0.90, demonstrating excellent detection performance. When detecting tumors, the model's precision values are 0.974, 0.964, and 0.851, respectively. Validation results show that the model can provide accurate predictions at both high and low confidence levels, with strong detection capabilities and good generalization ability, making a significant contribution to the identification of brain tumors.
subject: Research Article
  topic: section
subject: YOLO; MRI; Deep learning; Comparative experiment; Image processing
  topic: keywords
creator: Nan Gao
creator: Fuguang Han*
creator: Yao Yao
creator: Zhijie Gao
creator: Yihan Zhang
rights: Copyright (c) 2025 International Scientific Technical and Economic Research https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-10-12
publication date: 2025-10-12
starting page: 34
ending page: 42
doi: 10.71451/ISTAER2551

Machine Learning Approaches for Detecting Abnormalities in Female Fetuses
type: item
description: Non-invasive prenatal testing (NIPT) plays a vital role in the early detection of female fetal abnormalities, which is essential for birth defect prevention. In this study, clinical data containing Z-scores of chromosomes 21, 18, and 13, GC content, X chromosome concentration, read count ratio, and maternal BMI were analyzed. To address the class imbalance caused by the limited number of abnormal cases, the Synthetic Minority Over-sampling Technique (SMOTE) was applied, and stratified sampling was used to divide the dataset into training, validation, and testing sets (7:2:1). Multiple machine learning models, including XGBoost, Decision Tree, CNN, MLP, SVM, and Random Forest, were developed and evaluated with accuracy, precision, recall, F1-score, and AUC-ROC metrics. Results demonstrated that Random Forest outperformed other models, achieving an AUC of 0.997 with strong stability and generalization. These findings highlight the effectiveness of machine learning combined with proper data preprocessing in enhancing female fetal abnormality detection.
subject: Research Article
  topic: section
subject: Female fetal abnormality; NIPT; Machine learning; Random Forest; SMOTE
  topic: keywords
creator: Yuxuan Cai
rights: Copyright (c) 2025 International Scientific Technical and Economic Research https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-10-11
publication date: 2025-10-11
starting page: 25
ending page: 33
doi: 10.71451/ISTAER2550

Study on the Agricultural System in a Certain Region Based on the LSTM Model and Genetic Algorithms
type: item
description: In response to the two core demands in agricultural production, namely precision irrigation and sustainable transformation, this study conducts two key research tasks. First, it predicts the crop irrigation demand based on meteorological data. Initially, the 3σ principle is adopted to identify and label outliers isolated outliers are filled using linear interpolation, while consecutive outliers are filled with average value. Subsequently, two types of models are constructed: on the one hand, three evapotranspiration formulas (Hargreaves, Priestley-Taylor, and Makkink) are integrated, and the evapotranspiration (ET) amount is calculated through weighted fusion to establish the ET formula; on the other hand, a Long Short-Term Memory (LSTM) model is built via a two-layer architecture. These two models are then compared. Second, it carries out research on the transformation of organic agriculture based on farm economic and environmental data. First, farms are classified into geographical types (Plain, hilly, and mountainous). Then, a multi-objective optimization model is constructed with the goals of maximizing economic benefits and maximizing environmental benefits. Combined with constraints on transformation ratio and annual transformation, the genetic algorithm is used to solve the model.
subject: Research Article
  topic: section
subject: 3σ Principle; LSTM Model; ET Formula; Multi-Objective Optimization; Genetic Algorithm
  topic: keywords
creator: Yangyun Wang
creator: Yan Qian
creator: Shangyang Zou
creator: Dongping Sheng*
rights: Copyright (c) 2025 International Scientific Technical and Economic Research https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-10-03
publication date: 2025-10-03
starting page: 1
ending page: 10
doi: 10.71451/ISTAER2548

Research on regulated biological regularity LED light source based on normality test algorithm
type: item
description: In this paper, the optimal channel weight combination and the key parameters of the synthesized spectrum are obtained respectively for the two scenes of daytime illumination mode and night sleep aid mode. The question of whether optimal lighting has a beneficial effect on sleep quality compared to normal light and dark environment, we first used genetic algorithms (GA), a multichannel spectral synthesis model is established, and then five key performance indexes are solved and optimized. Then, the overnight sleep data of 11 subjects in three environments were processed. Six groups of data including total sleep time, sleep efficiency, sleep latency, deep sleep proportion, rem sleep proportion and wake times were extracted. Next, using the normality test model, we pass the KS test and SW test, concluded that optimal light does not improve sleep quality compared with normal light and dark environment.
subject: Research Article
  topic: section
subject: Intelligent optimization algorithm; Genetic algorithms (GA); Normality test model; KS test; SW test
  topic: keywords
creator: Wenxing Kong
creator: Keyan Huang
creator: Xiaowei He
creator: Donping Sheng*
rights: Copyright (c) 2025 International Scientific Technical and Economic Research https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-10-04
publication date: 2025-10-04
starting page: 11
ending page: 24
doi: 10.71451/ISTAER2549

Design and evaluation system construction of VR cultural heritage products from the perspective of user experience
type: item
description: In the digital age, virtual reality (VR) technology offers innovative avenues for cultural heritage preservation, but its development still faces a disconnect between user experience and the transmission of cultural values. This paper systematically constructs a design and evaluation system for VR cultural heritage products from a user experience perspective. First, by analyzing the connotations and types of VR cultural heritage products and integrating user experience theory with cultural communication theory, a design framework is proposed with the core goal of "integrating cultural value transmission with deep user experience." This multi-layered design strategy encompasses strategic, content, interaction, and sensory layers. Second, a comprehensive evaluation model is established encompassing four dimensions: system usability, experience quality, emotion and cognition, and behavioral intention. This model integrates subjective and objective evaluation methods to form a closed-loop optimization mechanism. Finally, implementation recommendations are proposed for typical application scenarios such as museum guided tours, intangible cultural heritage teaching, and historical restoration. This study aims to provide theoretical guidance and practical tools for the development of VR cultural heritage products, promoting their transformation from a technology-oriented to a user-value-oriented approach.
subject: Research Article
  topic: section
subject: User experience (UX); Virtual reality; Cultural heritage; Design framework; Evaluation model
  topic: keywords
creator: Yuyuan Liu
rights: Copyright (c) 2025 International Scientific Technical and Economic Research https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-09-12
publication date: 2025-09-12
starting page: 135
ending page: 141
doi: 10.71451/ISTAER2547

Online music personalized recommendation algorithm based on KKBox large-scale real-world dataset and integrated behavior sequence transformer
type: item
description: To address the user selection overload and decision making difficulties caused by the explosive growth of online music platforms, this paper proposes and validates a deep learning recommendation model that incorporates the Behavior Sequence Transformer, aiming to significantly improve music recommendation accuracy, diversity, and novelty. We use the BST Behavior Sequence Transformer to perform multi head self attention modeling on user listening sequences and compare it with a LightGBM boosted tree and logistic regression baseline model. Experiments use AUC, accuracy, and RMSE as core metrics, and use an 80/20 train-test split and early stopping to control overfitting. The BST model achieved an AUC of 0.7662 and an accuracy of 0.7551 on the test set, significantly outperforming LightGBM and logistic regression . Feature importance analysis shows that user genre cross features, contextual entry points, and sequence position encoding contribute most to the model. The recommendation algorithm based on the Behavior Sequence Transformer effectively captures the dynamic evolution of user interests, alleviates information silos, and possesses good scalability and engineering potential. Future research into multimodal fusion and reinforcement learning can further enhance diversity and interpretability. **************** ACKNOWLEDGEMENTS**************** This work was supported by Jiangxi Provincial College Students' Innovation and Entrepreneurship Program (S202510421047).
subject: Research Article
  topic: section
subject: Music recommendation system; Behavior Sequence Transformer; Attention Mechanism; LightGBM; KKBox dataset; Deep Learning
  topic: keywords
creator: Mingchuan Zhong
creator: Xinyi Lan
rights: Copyright (c) 2025 International Scientific Technical and Economic Research https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-08-29
publication date: 2025-08-29
starting page: 116
ending page: 134
doi: 10.71451/ISTAER2546

Trust building mechanism of multimodal interaction in autonomous driving system
type: item
description: Multimodal interaction technology, as a key means of enhancing trust in autonomous driving systems, integrates multi-channel information such as vision, voice, and touch to build a transparent and explainable human-machine collaborative mechanism. This study systematically explored the role of multimodal interaction in building trust, analyzed interaction design strategies for different driving scenarios, and experimentally verified the significant effect of multimodal feedback on improving user trust. The results show that reasonable multimodal interaction design can enhance users' understanding and prediction of system behavior, reduce uncertainty anxiety, and accelerate trust restoration in abnormal situations. At the same time, the study also revealed issues such as sensor fusion accuracy and ethical privacy faced by current technologies and envisioned future development directions such as anthropomorphic interaction and contextual awareness. The results of this study provide a theoretical basis and practical guidance for the interaction design of autonomous driving systems, which is of great value in promoting the commercialization of the technology.
subject: Research Article
  topic: section
subject: Multimodal interaction; Autonomous driving; Trust building; Human-computer interaction; Explainability; User experience
  topic: keywords
creator: Jialin Sun
rights: Copyright (c) 2025 International Scientific Technical and Economic Research https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-08-21
publication date: 2025-08-21
starting page: 106
ending page: 115
doi: 10.71451/ISTAER2545

Application of modular design strategy in the life cycle of household appliances under the goal of carbon neutrality
type: item
description: Modular design, a key strategy for achieving carbon neutrality in home appliances, provides a systematic solution for the industry's green transformation by restructuring carbon footprint management across all stages of the product lifecycle. This study systematically analyzes the application mechanisms of modular design in the design, production, use, and recycling stages of home appliances, revealing its carbon reduction benefits through three pathways: extending product lifespan, improving resource utilization efficiency, and promoting a circular economy. The study shows that home appliances employing modular design can reduce carbon emissions over their entire lifecycle by over 30%, with reductions of 15% in production, 10% in use, and 5% in recycling. The study further explores challenges facing modular design, including technical standards, market acceptance, and policy support, and proposes strategies such as establishing an open modular platform, innovating service models, and strengthening the standard system. Finally, the study explores the potential of modular design, enabled by digital technology, intelligent carbon management, business model innovation, and the construction of cross-industry circular systems, providing theoretical basis and practical guidance for the home appliance industry's path to carbon neutrality.
subject: Research Article
  topic: section
subject: Modular design; Carbon neutrality; Home appliances; Life cycle assessment; Circular economy; Carbon emission reduction
  topic: keywords
creator: Jialin Sun
rights: Copyright (c) 2025 International Scientific Technical and Economic Research https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-08-20
publication date: 2025-08-20
starting page: 98
ending page: 105
doi: 10.71451/ISTAER2544

The impact of social media live streaming on fashion brands’ sales performance: A case study of TikTok
type: item
description: With the rapid development of mobile internet and social media, livestreaming sales have become a crucial channel for fashion brands to expand their markets and boost sales performance. Douyin and TikTok, with their vast user bases and sophisticated content distribution mechanisms, have become core platforms for livestreaming e-commerce. This paper, focusing on the impact of livestreaming sales on the sales performance of fashion brands, systematically analyzes the operational models, impact mechanisms, and pathways of livestreaming sales, drawing on case studies of fast fashion and luxury brands for comparative study. The study finds that livestreaming sales forms a complete conversion cycle through "traffic acquisition, trust building, and consumer stimulation," effectively driving brand exposure and sales. Furthermore, livestreaming strategies vary significantly across fashion brands: fast fashion brands prioritize traffic volume and price advantages, luxury brands emphasize brand storytelling and high-end experiences, and emerging brands rely on differentiation and emotional resonance to achieve breakthroughs. The paper also points out that while livestreaming sales presents significant opportunities, it also carries potential risks, such as reliance on low prices, brand image dilution, and operational pressure. Based on the research findings, this paper proposes management recommendations, including optimizing brand strategies, improving platform mechanisms, and forecasting future trends. Overall, live streaming sales on social media is not only a new e-commerce model, but also a strategic location for fashion brands in the process of digital transformation. It is of great significance to both short-term sales and long-term brand value.
subject: Research Article
  topic: section
subject: Social media; Livestreaming; Fashion management; TikTok; Sales performance
  topic: keywords
creator: Meng Yuan
rights: Copyright (c) 2025 International Scientific Technical and Economic Research https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-08-19
publication date: 2025-08-19
starting page: 87
ending page: 97
doi: 10.71451/ISTAER2543

Bridging the digital divide for seniors: Research on design principles for age-friendly touch interfaces
type: item
description: This study addresses the digital divide faced by the elderly and systematically explores the design principles and practical paths of aging-friendly touch interfaces. By integrating human-computer interaction theory, universal design principles, and cognitive psychology, an aging-friendly design framework with four dimensions, namely vision, interaction, function, and emotion, was constructed. The study adopted a mixed method, combining case analysis and user testing, to verify the effectiveness of innovative designs such as dynamic font size adjustment, multimodal feedback, and progressive learning guidance. The study found that successful aging-friendly design not only needs to solve usability issues at the operational level but should also pay attention to the psychological needs and trust building of elderly users. The "capability-centered" design concept and quantitative evaluation tools proposed in the study provide a systematic solution for the aging-friendly transformation of digital products and have important theoretical and practical value for promoting digital inclusion.
subject: Research Article
  topic: section
creator: Jialin Sun
rights: Copyright (c) 2025 International Scientific Technical and Economic Research https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-08-17
publication date: 2025-08-17
starting page: 78
ending page: 86
doi: 10.71451/ISTAER2542

Human Failure Analysis and Fault-Tolerant Interaction Design of Deep-Sea Exploration Robots
type: item
description: Human-induced failures in deep-sea exploration robots are a key factor affecting the safety and reliability of deep-sea operations. This study systematically analyzes the mechanisms and influencing factors of human-induced failures in the extreme deep-sea environment and proposes a multi-layered fault-tolerant interaction design approach. By constructing a three-dimensional human-induced failure classification model, the authors reveal the coupled relationship between environmental pressure, task complexity, and operator cognitive characteristics. The developed layered fault-tolerant architecture provides comprehensive protection, from hardware redundancy to cognitive collaboration. Experimental results show that it can reduce the incidence of typical human-induced failures by 65%-78%. The study innovatively proposes the concept of "predictive fault tolerance," combining digital twins with operational model learning to enable the system to anticipate risks. Dynamic interface optimization and multimodal feedback strategies significantly enhance the operator's situational awareness. The research findings provide a systematic theoretical framework and practical guidance for the design of deep-sea exploration robots, promoting a paradigm shift from a "machine-centric" approach to a "human-machine collaborative" approach, and are of significant significance for improving the safety and efficiency of deep-sea operations.
subject: Research Article
  topic: section
subject: Deep-sea exploration robots; Human error; Fault-tolerant design; Human-computer interaction; Digital twin; Multimodal feedback
  topic: keywords
creator: Jialin Sun
rights: Copyright (c) 2025 International Scientific Technical and Economic Research https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-08-16
publication date: 2025-08-16
starting page: 68
ending page: 77
doi: 10.71451/ISTAER2541

Research on iterative design of human-machine interface of intelligent agricultural machinery based on digital twin
type: item
description: With the continuous development of digital twin technology, its application in the field of intelligent agricultural machinery has gradually attracted attention. This paper studies the iterative design of human-machine interfaces for intelligent agricultural machinery driven by digital twins. By analyzing the role of digital twins in intelligent agricultural machinery, key elements of iterative design, and interface optimization strategies, it explores how digital twin technology can promote interface design optimization and improve operational efficiency of intelligent agricultural machinery. The study shows that digital twin technology, through real-time data feedback and virtual simulation technology, can effectively support continuous interface optimization and significantly enhance the operator experience and agricultural machinery operation efficiency. Furthermore, experiments and case studies validate the application value of digital twin technology in interface design, providing theoretical basis and practical experience for the intelligent upgrade of intelligent agricultural machinery. However, the study also points out the challenges and limitations of digital twin technology in practical applications and envisions the promising future of digital twin technology in intelligent agricultural machinery interface design.
subject: Research Article
  topic: section
subject: Digital twin; Smart agricultural machinery; Human-machine interface; Design iteration; Interface optimization
  topic: keywords
creator: Jialin Sun
rights: Copyright (c) 2025 International Scientific Technical and Economic Research https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-08-15
publication date: 2025-08-15
starting page: 58
ending page: 67
doi: 10.71451/ISTAER2540

Privacy-Utility Tradeoff: Studying the Boundaries of Anonymization in Health Data Visualization Design
type: item
description: With the increasing prevalence of health data, how to maximize the utility of data while ensuring privacy protection has become a core issue in health data visualization design. This paper explores the issue of anonymization boundaries in health data visualization design, focusing on the trade-off between privacy and utility. Through theoretical analysis and experimental verification, the application effects of different anonymization techniques in visualization are studied, and the optimal balance between privacy protection and visualization utility is proposed. Experimental results show that under moderate anonymization boundary settings, privacy protection and data utility can be effectively balanced, preventing privacy leaks while retaining sufficient analytical value. This study provides theoretical support for the selection of privacy protection technologies and the setting of anonymization boundaries in health data visualization and offers new perspectives and directions for future research.
subject: Research Article
  topic: section
subject: Health data; Privacy protection; Utility trade-off; Anonymization techniques; Visualization design; Differential privacy
  topic: keywords
creator: Jialin Sun
rights: Copyright (c) 2025 International Scientific Technical and Economic Research https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-08-05
publication date: 2025-08-05
starting page: 49
ending page: 57
doi: 10.71451/ISTAER2539

Design and verification of an AI-based interactive tool for early screening of autism in children
type: item
description: With the increasing prevalence of autism spectrum disorder (ASD) among children, early screening has become an important tool for improving autism intervention outcomes. This paper proposes and designs an interactive tool for early screening of autism in children based on artificial intelligence (AI) technology. This tool uses an AI model to analyze children's behavioral data, such as facial expressions, body movements, and eye movements, to automatically identify potential features of autism. Through the design, development, and validation of this tool, this paper demonstrates its advantages in improving screening accuracy, reducing human error, and enhancing screening efficiency. Experimental results demonstrate that the tool has high accuracy and good user adaptability, providing effective early screening support for parents and physicians. Finally, this paper analyzes the limitations of the tool and proposes future optimization directions to provide more precise technical support for the early diagnosis of autism.
subject: Research Article
  topic: section
subject: Artificial Intelligence; Childhood autism; Early screening; Behavioral analysis; Interactive tools; Deep Learning
  topic: keywords
creator: Jialin Sun
rights: Copyright (c) 2025 International Scientific Technical and Economic Research https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-08-04
publication date: 2025-08-04
starting page: 38
ending page: 48
doi: 10.71451/ISTAER2538

Transformation Design and Human-Machine Scale Adaptability of Office Furniture in the Post-epidemic Era
type: item
description: In the post-pandemic era, the office environment has undergone profound changes, with flexible and remote working becoming mainstream work styles. With the diversification of working methods and changes in office space, the fixed design of traditional office furniture can no longer meet the needs of modern offices. This article explores the transformable design and ergonomic adaptability of office furniture in the post-pandemic era, focusing on how transformable design can improve furniture flexibility and space utilization, and the importance of ergonomics in furniture design. By analyzing outstanding office furniture cases both domestically and internationally, this article showcases innovative applications of transformable design and ergonomics, and based on user feedback, evaluates the effectiveness of these designs in actual use. The study found that the combination of transformable design and ergonomics can help improve the comfort, functionality, and adaptability of office furniture, but in practical application, it still faces challenges such as design complexity, stability, and personalized needs. Finally, the article looks forward to future trends in office furniture design, proposes development directions in terms of intelligence, personalization, and environmental protection, and provides theoretical basis and practical guidance for subsequent research.
subject: Research Article
  topic: section
subject: Affective computing; Intelligent voice interaction; Emotion recognition; Speech synthesis; User experience; Human-computer interaction
  topic: keywords
creator: Jialin Sun
rights: Copyright (c) 2025 International Scientific Technical and Economic Research https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-08-02
publication date: 2025-08-02
starting page: 28
ending page: 37
doi: 10.71451/ISTAER2537

Cultural Gene Translation: Semiotic Practice of Regional Elements in High-Speed Rail Interior Design
type: item
description: This paper explores the semiotic application of regional cultural elements in high-speed rail interior design, focusing on the application and challenges of cultural gene translation in design. As a modern means of transportation, high-speed rail interior design not only fulfills functional requirements but also serves as a crucial vehicle for cultural expression and emotional transmission. Drawing on a semiotic theoretical framework, this paper explores the process of cultural gene translation and reconstruction in high-speed rail interior design, emphasizing the selection, deconstruction, and innovation of regional cultural elements. By analyzing regional cultural symbols in high-speed rail design both domestically and internationally, this paper proposes modern strategies for the translation of cultural symbols and explores the balance between a sense of place and a global perspective in design. Furthermore, this paper explores challenges facing high-speed rail interior design, such as the misunderstanding and misuse of cultural symbols, the conflict between regional elements and modern design, and how to achieve the transmission of regional cultural identity in a globalized context. Finally, this paper explores future directions for cultural gene translation in high-speed rail interior design, particularly the integration of diverse cultures, technological innovation, and cultural heritage.
subject: Research Article
  topic: section
subject: Cultural DNA translation; Regional elements; High-speed rail interior design; Semiotics; Cultural symbols; Design innovation; Globalization
  topic: keywords
creator: Jialin Sun
rights: Copyright (c) 2025 International Scientific Technical and Economic Research https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-07-31
publication date: 2025-07-31
starting page: 16
ending page: 27
doi: 10.71451/ISTAER2536

Research on the Design of Intelligent Voice Interaction System Based on Affective Computing
type: item
description: With the rapid development of intelligent voice technology, traditional voice interaction systems have gradually been unable to meet users' needs for emotional interaction. To improve the naturalness and humanity of human-computer interaction, this study proposed and designed an intelligent voice interaction system based on emotional computing. The system accurately analyzes the user's emotional state through emotion recognition technology and combines speech synthesis technology to generate voice feedback that meets the user's emotional needs. The study first discussed the relevant technologies of emotion computing and voice interaction in detail, designed the system architecture, and implemented emotion recognition and speech generation algorithms. Through experimental evaluation and user feedback analysis, the superiority of the system in emotion recognition accuracy, voice interaction effect, and user experience were verified. Compared with traditional voice interaction systems, the system of this study significantly improved the user's emotional resonance and interaction satisfaction, reflecting the practical application value of emotion computing in voice interaction. Finally, the study discussed the limitations and future development directions of the system and proposed the possibilities and challenges for the further development of emotion computing and voice interaction.
subject: Research Article
  topic: section
subject: Affective computing; Intelligent voice interaction; Emotion recognition; Speech synthesis; User experience; Human-computer interaction
  topic: keywords
creator: Jialin Sun
rights: Copyright (c) 2025 International Scientific Technical and Economic Research https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-07-30
publication date: 2025-07-30
starting page: 1
ending page: 15
doi: 10.71451/ISTAER2535