Journal of Computer Science and Artificial Intelligence
Journal of Computer Science and Artificial Intelligence (JCSAI) is a peer-reviewed journal published by Darcy & Roy Press. The mission of the journal is to provide an academic forum for discussing the issues of computer science and artificial intelligence across the world. All articles published are rigorously and fast reviewed meeting the Journal Quality standards.
The Design of The Main Class of Flying Saucer-Fighting Weapons
Globally, the computer game industry has a history of over three decades. After more than three decades of rapid development, the computer game industry has become one of the entertainment industries that are on par with film and television, music, and others. The emergence of the Internet has provided another fascinating carrier and tool for computer games, injecting fresh blood into the further development of the entire industry. In particular, online games, as an emerging industry, have developed rapidly in recent years. Online games, with their features such as real-time interaction, have become the preferred form of leisure entertainment for many young people. Here, we have developed a simple applet game. By placing the game on a server and utilizing the internet, players can start playing from different locations through a browser, enabling human-computer interaction.
Copyright (c) 2026 Journal of Computer Science and Artificial Intelligence https://creativecommons.org/licenses/by-nc/4.0
Research on the Design Principles of Intelligent Interactive Products from the Dual Perspectives of Human Factors Engineering and User Experience
In response to the problems of insufficient physiological adaptation and complex cognitive operations in current intelligent interactive products, covering common categories such as smart bracelets, car navigation, and smart home terminals, this article takes a dual perspective of ergonomics (physiological adaptation) and user experience (psychological perception) - the former focuses on the physiological laws of the human body, while the latter pays attention to the psychological needs of users. The two complement each other to solve the shortcomings of a single perspective design. Combining national standards, specific industry cases, and third-party research data, five design principles are extracted: human body size adaptation, operational force efficiency optimization, cognitive process simplification, instant feedback design, and emotional adaptation. Research has shown that dual perspective fusion can simultaneously improve product physiological comfort and user satisfaction, providing practical references for product design such as smart wearables and smart homes. All conclusions are based on publicly available standards and examples.
MS-VSSM: Multiscale Enhanced Visual State Space Model for Facial Expression Recognition
Facial Expression Recognition (FER) is a key technology in fields such as human-computer interaction and mental health assessment. However, its performance is constrained by the subtleties and dynamics of expressions, as well as the complexity arising from individual differences and environmental variations. To overcome the limitations of traditional Convolutional Neural Networks (CNNs), such as their restricted receptive fields, and the high computational complexity of Transformers, this paper proposes a Multiscale Enhanced Visual State Space Model (MS-VSSM) based on the Visual State Space Model (VSSM), aiming to improve the accuracy and robustness of FER. The model introduces three core improvements upon VSSM: (1) integrating a Path-aware Channel Attention mechanism (SE-SS2D) into the SS2D module to enhance the targeted capture of critical local facial features; (2) embedding a Dense Spatial Pyramid Pooling module (DSPP) at the beginning of each network stage to achieve multi-scale contextual information fusion; and (3) employing a Layer Scale mechanism to finely adjust the scale of deep features, thereby improving training stability and representational flexibility. In a four-category emotion recognition task constructed on the DEAP dataset, MS-VSSM achieved an accuracy of 97.31% and a weighted average F1-score of 97.56%, significantly outperforming the original VSSM and various mainstream visual backbone networks. These results validate the effectiveness and advancement of the proposed method. This study provides a new solution for efficient and accurate fine-grained facial expression recognition.
Review on Precise Segmentation Technology of colon polyp Integrating Convolutional Neural Networks and Pathological Features
Colon polyps are one of the common intestinal lesions in clinical practice and also the most typical type of precancerous lesions for colorectal cancer. Accurate segmentation is crucial for computer-aided diagnosis systems. Deep learning methods based on convolutional neural networks (CNNs) have been applied to the automatic segmentation of colorectal polyps, but they still face challenges such as inter-polyp differences, intra-polyp variations, and changes in imaging environments. These issues make it difficult to meet the clinical requirements for segmentation accuracy. Therefore, integrating pathological prior knowledge into deep learning models to extract colorectal polyp-related features has become a core approach to address the aforementioned problems. This review summarizes the current research status of colorectal polyp segmentation technologies that combine convolutional networks and pathological features, introduces the research background and significance of this study, and compares representative domestic and international research works. On this basis, the existing problems are summarized and the future development trends are analyzed, aiming to provide certain references for subsequent research.
Intelligent Traffic Signal Control Based on Reinforcement Learning with Edge Computing and Intelligent Reflecting Surface
With increasing traffic demand and growing environmental complexity, traditional signal control methods struggle with low efficiency and poor adaptability. This study proposes a reinforcement learning-based framework integrating edge computing and intelligent reflecting surfaces (IRS) to enhance traffic signal control. The framework is validated through simulation and optimized via an improved Proximal Policy Optimization (PPO) algorithm with multi-step returns and entropy-based adaptive regularization, ensuring better convergence and stability. Experiments on a 5×5 intersection network in SUMO, under varying traffic scenarios, including ablation and comparative studies, show that the proposed method significantly reduces average delay and improves throughput, demonstrating its effectiveness and reliability.
Agentic Commerce: A Unified Multi-Retrieval Framework for High-Fidelity E-Commerce Chatbots
E-commerce chatbots face critical limitations that undermine customer trust: hallucinations from ungrounded responses, poor multi-turn coherence, and an inability to execute real-world actions such as processing refunds or verifying live inventory. Existing retrieval-augmented generation (RAG) and chain-of-thought (CoT) approaches address knowledge grounding and reasoning, respectively, yet remain fundamentally passive—they inform but cannot act. We present a unified agentic framework that integrates RAG for factual grounding, CoT for structured multi-step reasoning, and multi-agent col- laboration for autonomous task execution. The modular architecture encompasses specialized agents for retrieval, reasoning, action generation, and safety enforcement, orchestrated through an LLM-based routing policy. This design enables the system to move beyond answering questions toward completing transactions, coordinating inventory checks, and resolving complex customer inquiries autonomously. Evaluated on a 10K-SKU e-commerce dataset spanning factual, comparative, and multi-turn query types, the framework achieves 96.2% response accuracy and 95.8% grounding reliability. The multi-agent ar- chitecture reduces errors in multi-turn interactions by 18% compared to single-agent baselines. The system operates with a median latency of 3.12 seconds—a deliberate safety-first design choice that pri- oritizes transactional reliability over conversational speed, ensuring business-critical accuracy in high- stakes operations where sub-second responses would compromise correctness.
Research on the Application of Human-Computer Interaction in the Digital Preservation of Cultural Heritage
The digital protection of cultural heritage is a key task in cultural construction as specified in the 14th Five Year Plan for Cultural Development, and human-computer interaction technology is the key support for breaking down barriers between digital resources and user needs, improving the efficiency and breadth of the utilization and dissemination of protection achievements. Based on the research of the real projects that have been launched by domestic authoritative institutions such as the Mogao Grottoes of Dunhuang "Digital Dunhuang", the Forbidden City "Digital Forbidden City", and the digital restoration of the Terra Cotta Warriors in the the Mausoleum of the First Qin Emperor, this paper analyzes the practical application forms of human-computer interaction in the digital protection of cultural heritage from the two dimensions of professional protection and mass communication, extracts three types of typical models that can be launched: immersive experience, collaborative restoration, and lightweight participation, and discusses the balance path between technology adaptability and cultural authenticity. Actual cases and data analysis show that human-computer interaction design that meets the needs of the scene can significantly improve the utilization rate of digital resources and effectively stimulate the public's enthusiasm for cultural heritage participation, providing practical technical references for the active inheritance of cultural heritage. Relevant practical experience can directly provide reference for similar digital protection projects.
digital preservation of cultural heritage
DIF-DETR: Dynamic Interactive Fusion Transformer with Adaptive Feature Enhancement for Efficient Aerial Small Object Detection
In recent years, object detection models based on Transformers have demonstrated outstanding performance in general scenarios due to their powerful global feature modeling capabilities. However, when directly applied to aerial image detection tasks, their performance often falls short of expectations. The root cause lies in the nature of aerial imagery, which typically contains numerous small objects. These objects occupy an extremely low proportion of pixels, resulting in weak feature representation. They are also susceptible to factors such as complex background noise and mutual interference from densely distributed targets, making it difficult for Transformer models to effectively capture and distinguish small object features. To address these challenges, this paper proposes an enhanced Transformer architecture for aerial small object detection: Dynamic Interactive Fusion DETR (DIF-DETR). Its core innovations comprise two aspects: First, introducing the DIENet backbone feature extraction network embedded with DIEBlocks. These DIEBlocks serve as feature enhancement units within the backbone network, leveraging dynamic Inception multi-branch deep convolutions and adaptive weight allocation mechanisms to efficiently capture multi-scale, long-range contextual information. Second, it introduces Context-Aware Bidirectional Fusion (CABF), which enables adaptive complementary fusion of high-level semantic features and low-level detail features within the FPN-PAN architecture of the neck network, effectively mitigating the issue of small target features being obscured by background interference. Experimental results demonstrate that on the highly challenging VisDrone and HIT-UAV aerial datasets, the proposed DIF-DETR network outperforms existing mainstream models with 30.5% mAP and 82.3% mAPtest, respectively. Simultaneously, it significantly reduces computational cost to 43.6 GFLOPs with only 13.4M parameters, achieving an optimal balance between detection accuracy and computational efficiency. This demonstrates that through the synergistic effects of three core innovations, DIF-DETR significantly enhances detection accuracy and robustness for small objects in aerial images, providing an effective solution for object detection tasks in aerial scenarios.
Copyright (c) 2025 Journal of Computer Science and Artificial Intelligence https://creativecommons.org/licenses/by-nc/4.0
Voiced and Unvoiced Sound Detection Based on Wavelet Transform
Voiced-unvoiced detection is a fundamental research topic in speech signal processing, holding significant importance for applications such as speech recognition. Addressing the differences in frequency-domain energy distribution between voiced and unvoiced sounds, this paper proposes a voiced-unvoiced detection method based on discrete wavelet transform. By performing multi-level wavelet decomposition on speech signals, the method extracts energy features from high-frequency wavelet coefficients at different scales. Combined with an energy threshold decision strategy, it achieves the distinction between voiceless and voiced consonants. Experimental results demonstrate the method's effective differentiation between voiceless and voiced consonants, showing feasibility under the selected experimental conditions.
Practical Research on Artificial Intelligence Technology in Transmission Optimization
Under the large-scale application of 5G-A and AI models, transmission networks face challenges such as bandwidth surges, latency sensitivity, and rising energy consumption, making traditional optimization methods difficult to adapt to dynamic environments. This paper focuses on the practice of AI in transmission optimization, analyzes the adaptation logic of machine learning, deep reinforcement learning and transmission network, and studies congestion control, resource scheduling, and collaborative reasoning from the perspective of congestion control, resource scheduling, and collaborative reasoning in combination with communication network, data center, and edge computing scenarios. Through case studies such as Singapore's M1 microwave transmission network and Shanghai Unicom's 5G-A intelligent scheduling system, verify the effectiveness of AI in improving bandwidth utilization, reducing latency, and optimizing energy consumption. Research has shown that AI dynamic optimization can increase bandwidth utilization by more than 40%, reduce end-to-end latency by about 30%, and save energy consumption by 18% -35%. It provides a feasible path for the intelligent upgrade of transmission networks and has reference value for promoting the deep integration of AI and communication transmission.
A Comparative Review of U-Net-Based Enhanced Polyp Segmentation Models: From Architectural Evolution to Multimodal Fusion
Colorectal cancer has been recognized as the third most prevalent and the third most lethal cancer worldwide. Accurate detection and segmentation of polyps in the colorectal region play a crucial role in facilitating the early diagnosis of cancer. The rapid advancement of deep learning has greatly accelerated the development of automated polyp detection and segmentation techniques. Although the well-established U-Net and its variants have long been regarded as classical architectures in medical image segmentation, the basic U-Net model still exhibits several limitations when applied to complex endoscopic scenarios. These include noise introduced by skip connections, restricted receptive fields for contextual information, and the absence of effective edge-awareness mechanisms. This review systematically elaborates on the recent research progress of polyp segmentation based on U-Net optimization models. According to different structural enhancement strategies, the existing methods can be broadly categorized into four major types: Depth and residual error optimization. for example, ResUNet++ [1] enhances gradient propagation through residual connections. Attention mechanism integration. such as PraNet [2], which employs reverse attention to focus on salient regions. Boundary and multi-scale feature fusion. exemplified by recent models like CaraNet [3] and BUNet [4], which improve the detection accuracy of small objects and enhance boundary precision. Semantic and cross-modal enhancement. represented by TGA-Net [5] and CLIP-Polyp [6], which leverage vision-language pretraining to achieve improved segmentation performance. Our proposed network, MSEANet, integrates the Edge Feature Extraction (EFE), Cross-layer Context Fusion (CCF), and Selective Edge Attention (SEA) modules into a framework and serves as a recent work representative of collaborative optimization. Experimental results indicate the effectiveness of MSEANet in achieving synergic multi-module fusion for polyp segmentation. This review finds that the research trajectory of the U-Net family has been developed from single-module optimization towards multidimensional fusion. Future research will tend to focus on light-weight framework, multimodal semantic guidance, and enhanced interpretability, designing to further improve the clinical applicability of deep segmentation models.
The Rise of Sparse Mixture-of-Experts: A Survey from Algorithmic Foundations to Decentralized Architectures and Vertical Domain Applications
The sparse Mixture of Experts (MoE) architecture has evolved as a powerful approach for scaling deep learning models to more parameters with comparable computation cost. As an important branch of large language model (LLM), MoE model only activate a subset of experts based on a routing network. This sparse conditional computation mechanism significantly improves computational efficiency, paving a promising path for greater scalability and cost-efficiency. It not only enhance downstream applications such as natural language processing, computer vision, and multimodal in various horizontal domains, but also exhibit broad applicability across vertical domains including medical diagnosis, autonomous driving, financial analysis, and business intelligence. Despite the growing popularity and application of MoE models across various domains, there lacks a systematic exploration of recent advancements of MoE in many important fields. Existing surveys on MoE suffer from limitations such as lack coverage or not extensively exploration of key areas. This survey seeks to fill these gaps. In this paper, Firstly, we examine the foundational principles of MoE, with an in-depth exploration of its core components—the routing network and expert network. Subsequently, we extend beyond the centralized paradigm to the decentralized paradigm, which unlocks the immense untapped potential of decentralized infrastructure, enables democratization of MoE development for broader communities, and delivers greater scalability and cost-efficiency. Furthermore we focus on exploring its vertical domain applications. Finally, we also identify key challenges and promising future research directions. To the best of our knowledge, this survey is currently the most comprehensive review in the field of MoE. We aim for this article to serve as a valuable resource for both researchers and practitioners, enabling them to navigate and stay up-to-date with the latest advancements.
Research on the Evolution and Classification of Artificial Intelligence Chip Technology: A Review of Architecture Characteristics, Algorithm Adaptation, and Application Scenarios
This paper systematically reviews the technological evolution, classification systems, and application scenarios of AI chips. Research shows AI chips have evolved from traditional general - purpose processors to a diverse ecosystem including GPUs, FPGAs, ASICs (e.g., TPUs, NPUs), and brain - inspired chips. By analyzing architectural features, algorithm adaptation, and performance limits of various chips, a multidimensional classification framework is constructed, categorizing AI chips from three dimensions: technical architecture, functional positioning, and application scenarios. The study finds general - purpose chips (e.g., GPUs) are suitable for cloud training but have low energy efficiency, specialized chips (e.g., TPUs) have excellent energy efficiency in inference but lack flexibility, and edge computing chips balance power consumption and performance. Future AI chip development will trend towards heterogeneous integration, memory - compute integration, and hardware - software co - design to tackle challenges like computational efficiency, memory wall issues, and algorithm diversity. This research offers theoretical guidance and practical references for chip selection, architectural optimization, and application deployment.
A Model for Detecting Fetal Chromosomal Abnormalities Based on Quality-Weighted Regression and K-Means Clustering
Non-invasive prenatal testing (NIPT) screens for abnormalities in chromosomes 21, 18, and 13 by analyzing fetal cell-free DNA in maternal blood. Conducted between 10 and 25 weeks of gestation, it aids in early assessment of fetal health, with accuracy contingent upon male fetuses exhibiting Y chromosome concentrations ≥4% and female fetuses displaying normal X chromosome concentrations. This paper focuses on constructing a data model for NIPT data preprocessing. A quality-weighted linear regression model was developed to establish associations between fetal Y concentration and gestational age/BMI, with statistical significance verified. Dual-breakpoint K-means clustering was used to initialize centers for BMI classification in male fetus pregnancies. Risk minimization was applied to determine the optimal NIPT timing while analyzing errors. Feature reconstruction fused LASSO regression with decision trees to establish a method for detecting female fetal chromosomal abnormalities. Final results demonstrate that this model can accurately quantify correlations between indicators, effectively capture the nonlinear relationship between gestational age and Y concentration as well as sequencing quality interference. The optimal timing for BMI groups is earlier, with high BMI groups requiring consideration of height and age. Simultaneously, it enables scientifically accurate detection of female fetal chromosomal abnormalities.
BMI-Stratified Mixed-Effects Modeling of Fetal DNA Fraction for NIPT Timing Optimization and Ensemble-Based Female Fetal
Non-invasive prenatal testing (NIPT) has gained widespread clinical adoption as an efficient and precise method for screening fetal chromosomal abnormalities. However, there remains room for optimization in balancing the timing of testing with diagnostic accuracy. This study employs multilevel modeling of clinical data from 267 pregnant women to propose a personalized testing timing optimization strategy based on multiple factors, including fetal cell-free DNA concentration and maternal body mass index (BMI). Using a mixed-effects model, we analyzed the relationship between BMI, gestational age, GC content, and fetal Y chromosome concentration, thereby establishing a predictive framework for optimal testing timing across different BMI groups. Experimental results indicate optimal testing timepoints of 10.0 weeks for low-BMI groups, 13.0 weeks for moderate-BMI groups, and 15.8 weeks for high-BMI groups. Compared to conventional methods, the model enables testing 3 to 6 weeks earlier. For determining chromosomal abnormalities in female fetuses, integrating multivariate Gaussian discriminant analysis with the RUSBoost ensemble learning algorithm successfully enhanced detection sensitivity and accuracy. Particularly when combining Z-scores with GC content, the approach effectively distinguished between normal and abnormal fetuses. Comprehensive analysis indicates this study provides scientific evidence for personalized optimization of NIPT testing timing and accurate diagnosis of chromosomal abnormalities in female fetuses.
Multivariate Gaussian discriminant analysis
Hierarchical Measurement Model of Semiconductor Silicon Carbide Epilayer Thickness Based on Infrared Interferometry
This paper proposes a hierarchical measurement model for silicon carbide (SiC) epilayer thickness based on infrared interferometry. Under a dual-beam interference framework, an analytical relationship between thickness and fringe spacing is established by integrating the Sellmeier dispersion model, resolving systematic errors caused by the constant refractive index assumption. For measured spectra, a five-step inversion algorithm is designed: Savitzky-Golay filtering suppresses noise, while robust spacing statistics combined with significant peak identification enable high-precision SiC thickness inversion at 10° and 15° incidence angles. Furthermore, four necessary conditions for multi-beam interference are derived, confirming significant Fabry-Pérot effects in silicon samples (Attachments 3–4). An Airy-function-based full-spectrum nonlinear fitting correction strategy is proposed, compressing systematic deviations from 5.2% to 1.5%. Experiments show: SiC measurements exhibit a coefficient of variation (CV) of 6.1% with multi-angle consistency δ < 10%; corrected silicon thickness is 6.6 μm; interference fringe spacing inversely correlates with thickness.
Research on Generative AI-Empowered Training Model for Shipping Information Professionals
The rapid advancement of the digital economy and smart shipping has precipitated a growing demand for interdisciplinary professionals who possess both information technology expertise and maritime business literacy. However, the current vocational education system exhibits a misalignment with industrial needs, particularly in curriculum content, pedagogical models, and faculty competency. The emergence of Generative Artificial Intelligence (GenAI) provides a novel avenue for the intelligent transformation of vocational education, enabling personalized learning, dynamic curriculum generation, and multi-modal simulation. Grounded in the specific requirements of the smart shipping industry, this study leverages big data technology and maritime information scenarios to systematically construct a GenAI-empowered training model for shipping information professionals. This framework aims to resolve existing educational disconnects and enhance the quality of talent cultivation in the maritime sector.
Logistics Network Sorting Center Cargo Volume Prediction and Personnel Scheduling Based on LSTM-ARIMA
With the rapid development of the e-commerce industry, the demand for logistics has increased sharply, and the management efficiency issues of sorting centers have become increasingly prominent. This article provides daily and hourly cargo volume predictions, transportation route optimization, and staff scheduling for 57 centers over the next 30 days to meet user demands and transmission speed requirements. This paper proposes a comprehensive framework that combines the LSTM-ARIMA model for cargo volume prediction, calculates the cargo volume change rate based on the actual sorting center data through route comparison for correction, and optimizes staff arrangement using the linear regression model. This framework effectively addresses issues such as insufficient accuracy in cargo volume prediction, cargo volume deviations caused by changes in transportation routes, as well as excessively high total person-days and unbalanced hourly person-efficiency in employee scheduling.
Resilience Analysis of Urban Traffic System Based on Extension AHP-CRITIC Comprehensive Weighting-Cloud Model: A Case Study of Guangzhou
Urban traffic congestion has become a common problem faced by major cities around the world. How to improve the resilience of urban transportation systems and alleviate urban traffic congestion issues urgently needs to be addressed. Therefore, taking the resilience of the urban transportation system as the topic, selecting the overall transportation system of Guangzhou as the research object, exploring the resilience of the entire Guangzhou transportation system, and proposing an evaluation model to handle uncertainty and ambiguity. Firstly, by analyzing the resilience of urban transportation and reviewing a large number of relevant literature, a series of indicators for the urban transportation system were selected based on scientific rigor, and an evaluation system for the resilience of the urban transportation system was constructed. Secondly, expert evaluations are collected through questionnaires, and subjective weights are obtained using extensible AHP. The collected objective data is then subjected to CRITIC method to obtain objective weights, which are then combined with game theory for weighting. Finally, a cloud model is introduced to simulate the uncertainty and fuzziness of the evaluation process, calculate the urban resilience index, classify the resilience of Guangzhou's transportation system as "strong resilience", and propose effective suggestions to improve the resilience of Guangzhou's transportation system: enhance social resource investment, create and promote employment, continuously optimize urban construction, develop diversified transportation networks, and finely manage urban cargo flow.
extensible AHP-CRITIC combination weighting
Construction and Application of AI-Based Intelligent Evaluation Model for DRP Level
As a crucial component of enterprise supply chain management, Distribution Requirements Planning (DRP) directly impacts product distribution efficiency, inventory control levels, and customer responsiveness. Traditional DRP rating evaluation methods, which predominantly rely on expert experience and static analysis, exhibit issues such as excessive subjectivity and poor dynamic adaptability, making them inadequate for modern complex supply network demands. To address these challenges, this study proposes an AI-powered intelligent DRP rating evaluation model. By integrating machine learning algorithms with multidimensional data features, the model establishes an evaluation index system and achieves dynamic rating determination. Through training and testing on multiple real-world corporate datasets, the results demonstrate that this evaluation system outperforms traditional methods in accuracy, stability, and practicality. In practical applications, the study explores the model's specific value in inventory optimization, distribution node configuration, and supply chain collaboration, providing theoretical foundations and practical pathways for enterprises to implement intelligent supply management. This research aims to drive the evolution of DRP system evaluation from static, subjective approaches toward intelligent, real-time methods, thereby enhancing the responsiveness and resource allocation efficiency of overall supply chain systems.
Research on Path Optimization of Urban Garbage Classification and Transportation Based on CVRP Model
With the acceleration of urbanization in China, the management of urban household waste is facing severe challenges. Optimizing and scheduling the path of garbage classification and transportation has become the key to improving efficiency and reducing costs. This article is based on the Vehicle Routing Problem (CVRP) model and combines multi-objective optimization methods to study complex scenarios involving single vehicles, multi vehicle collaboration, and intermediate stations. This article establishes a CVRP model with capacity constraints and uses greedy algorithm to generate paths, achieving the minimization of total driving distance. Through the verification of 30 collection points, the total driving distance was optimized to 1332.9 kilometers, and the time complexity and limitations of the algorithm were analyzed. The feasibility of introducing dynamic road network data to improve path planning was proposed. Then, the extended model is used for multi vehicle collaborative scheduling, taking into account the differences in load capacity, volume, and cost among the four types of garbage. Design a categorized path optimization algorithm to achieve the optimal solution for a total transportation cost of 1402.4 yuan, and explore the impact of time constraints on task splitting to verify the flexibility and robustness of the model.
Analysis of Digital Marketing Innovation Mode and Enterprise Transformation Path Driven by Artificial Intelligence
The rapid development of artificial intelligence technology is driving fundamental changes in the field of digital marketing, bringing innovation opportunities and transformation challenges to enterprises. This study focuses on the core innovation mode of digital marketing driven by artificial intelligence and its corresponding enterprise transformation path. Through in-depth analysis of key modes such as data-driven personalized access, predictive pre-layout and real-time closed-loop optimization, this paper reveals the internal logic of the evolution of marketing activities towards intellectualization, precision and self-adaptation. Furthermore, from the three dimensions of strategic guidance, technological infrastructure and compliance governance, this paper systematically constructs a feasible path framework for enterprises to realize the transformation of intelligent marketing, aiming at providing theoretical reference and practical guidance for enterprises to effectively deploy intelligent marketing strategies and enhance market competitiveness in the new competitive environment.
Graph Neural Networks and Multi-Objective Hybrid Optimization: A Review of Intelligent Decision-Making Frameworks for Waterflood Reservoir Injection–Production Regulation
Real-time regulation of water-flooding injection–production systems is a crucial factor in enhancing reservoir development efficiency. However, in practical water-flooding operations, reservoir heterogeneity causes the inter-well fluid connectivity to vary dynamically over time. As a result, conventional regulation methods often require a considerable amount of time to adjust control schemes, leading to significant lag effects. When oilfields enter the medium- to high-water-cut stage, the geological structures and flow patterns become increasingly complex, making it difficult for traditional approaches to maintain real-time management. In recent years, Graph Neural Networks (GNNs) and hybrid multi-objective optimization algorithms such as Genetic Algorithm–Multi-Objective Particle Swarm Optimization (GA-MOPSO) have emerged as promising technologies in the field of reservoir development, demonstrating frontier applications in dynamic optimization and intelligent regulation of water-flooding systems [1-3]. GNNs can effectively model the graph structure of dynamic inter-well connectivity, accurately capturing the spatial–temporal evolution characteristics of the reservoir, while GA-MOPSO achieves a robust trade-off between maximizing the Net Present Value (NPV) and minimizing the Injection–Production Difference (IPD) [4]. In addition, recent studies have shown that surrogate models exhibit strong performance in predicting unseen geological configurations during heterogeneous multiphase flow simulations [5]. This paper provides a comprehensive review of GNN- and GA-MOPSO-based hybrid approaches for water-flooding regulation, with particular emphasis on the advantages of the Heterogeneous Spatiotemporal Fusion Model (HSTMF) in water-cut prediction. Furthermore, it discusses the key challenges and potential directions for future research in terms of model generalization, algorithmic robustness, and engineering deployment [6].
Implementation Paths of Generative AI in Multimodal Learning
When AI is currently applied to complex scenarios, it only processes single information such as text and images, which cannot meet people's needs to "perceive things as they do." Instead, multimodal learning, which can integrate different information such as text, images, audio, and video, has become a key direction for the development of generative AI. However, the current multimodal applications of generative AI are still stuck in several hurdles: First, the different modalities are quite different. For example, text is segmented and images are pixelated, which can easily cause problems when put together; second, the generated content does not match the intended meaning of the multimodal representation; and third, the quality of cross-modal generation is also unstable, sometimes good and sometimes bad. The stakeholders outlined its implementation approach across five key aspects: data foundation, feature processing, model architecture, training strategy, and quality assessment. First, pre-process and standardize multimodal data to establish a solid data foundation; then, cross-modal feature alignment is used to address modality differences; then, the generative model architecture is adapted to support cross-modal generation; then, multimodal pre-training and incremental learning are used to adapt the model to a wider range of scenarios; and finally, a scientific quality assessment system is employed to optimize the generation results. This research aims to provide usable technical logic for the implementation of generative AI in multimodal scenarios such as intelligent interaction, content creation, and medical diagnosis, and to promote multimodal generation from "being able to generate" to "generating well"
From Conversation to Action: Opportunities and Challenges of Large Language Models as the Brain of Humanoid Robots
Humanoid robots, as the core carriers of embodied intelligence, are moving from specialized scenarios to general-purpose applications. The incorporation of large language models (LLMs) endows them with cognitive and decision-making capabilities similar to those of a human "brain," becoming a key enabler for technological breakthroughs. Based on the practical and technological achievements of the global humanoid robot industry from 2024 to 2025, this paper systematically analyzes the technical value and practical bottlenecks of LLMs as robot brains. The study finds that, through semantic parsing, task planning, and generalized learning capabilities, LLMs increase the success rate of task decomposition in complex scenarios to over 95%, reducing deployment costs by 60%. However, they also face core challenges such as data shortages, insufficient real-time performance, and poor architectural adaptability. Drawing on practical examples from Google's RT series, Tesla's Optimus, and domestic companies such as iFlytek and UBTECH, this paper proposes a three-step breakthrough path: "data accumulation - architecture optimization - ecosystem collaboration." Research indicates that the deep integration of LLM and robotics will accelerate the scale-up of the industry. The Chinese market is expected to reach 75 billion yuan in 2029, accounting for 32.7% of the global market.
Knowledge Graph-Based Multi-Objective Optimization Recommendation Model
The current recommendation application scenario is more complex, and the traditional recommendation algorithm can no longer meet the needs of user diversity. To solve this problem, this paper proposes an improved multi-objective optimization recommendation model based on knowledge graph to optimize four recommendation goals of novelty, diversity, accuracy and recall simultaneously. A user preference set is constructed based on user behavioral preferences and item-related characteristics before recommendation, which provides the basis for subsequent recommendations. Two improved algorithms are proposed in this paper: 1) at the bottom layer, a knowledge graph embedding algorithm with variable weighted scoring function is used to transform the association information between items, into the relationship between vectors; 2) for the top layer, a multi-objective evolutionary algorithm is used to optimize the recommendation list. Comprehensive experiments show that the model can effectively improve the evaluation metrics of the four recommendations. And it provides users with a recommendation list of items containing novel and diverse items in a more efficient way while maintaining accuracy.
Decision-Making on Product Testing and Swapping Based on Multi-Index Evaluation System
As an emerging product, electronic products have a large market demand, but related enterprises are also faced with the problem of high prices of raw materials and easy loss in the production process. This paper establishes a multi-index evaluation system model for quantitative analysis, which aims to help enterprises improve production steps, formulate reasonable product production process detection and exchange strategies, and improve enterprise revenue. The core idea of this paper is to establish a multi-index evaluation system by selecting appropriate evaluation indicators, calculate the scores of different production testing and exchange schemes in the system, and select the scheme with the highest score as the final production inspection and exchange scheme of the enterprise. This paper not only takes corporate profit as an indicator, but also introduces corporate reputation factor and resource utilization, and fully considers the long-term impact of different production product testing and exchange schemes on corporate reputation and environment. In addition to selecting a number of comprehensive evaluation indicators, this paper also borrows the 0-1 programming idea, entropy weight method and principal component analysis method to calculate the comprehensive score of each scheme, and realizes the calculation of the comprehensive score of different schemes under different defective rates. The analysis of different enterprise sizes and product quantities shows that the multi-index evaluation system has a wide applicability, and also reveals the influence of enterprise size on the decision to test and replace production products.
Electronic product production decision-making
Steam Game Data Collection and Visualization Based on Python Crawlers
With the booming development of digital game market, Steam platform, as the world's largest digital game distribution and sales platform, covers a huge amount and multi-dimensional game data. In this paper, based on Python crawler technology, we collect and organize the information of thousands of games on Steam platform, such as price, ratings, reviews, genres and tags, as well as release time. By visualizing and analyzing the game price distribution, the correlation of ratings and reviews, the characteristics of genres and popular tags, and the trend of release time and other dimensions, we reveal the price structure of the Steam game market, the pattern of user ratings, the popular genres and tags preferences, and the development dynamics of the game industry. The results of this paper not only help game developers to accurately grasp the market pricing strategy, understand the positioning and audience characteristics of games in different price ranges, but also provide consumers with more intuitive reference for their purchases, and at the same time, provide academics with empirical research cases for the digital game market.
HierDETR: Hierarchical Multi-Head Attention-based Pulmonary Nodule Detection in Medical Imaging
Lung cancer is one of the leading causes of morbidity and mortality worldwide, and early detection is crucial for improving patient survival rates. Medical imaging, particularly chest CT scans, plays a vital role in early lung cancer screening and pulmonary nodule detection. However, the detection of pulmonary nodules is challenging due to the variability in size, shape, density, and location of nodules, as well as their complex background, which often includes normal or benign structures such as blood vessels and bronchial walls. To address these challenges, this paper presents a novel pulmonary nodule detection framework based on Hierarchical Multi-Head Attention (HMHA) and Query-Key Cache Updating (QKCU) mechanisms. The proposed HierDETR model improves upon existing methods by reducing redundancy in attention heads and effectively utilizing multi-scale and hierarchical context information. Experimental results on the publicly available LUNA16 dataset demonstrate that the HierDETR model outperforms mainstream methods, achieving significant improvements in detection metrics such as F1 Score, Average Precision, and Average Recall. This work provides a promising approach for enhancing the robustness and accuracy of pulmonary nodule detection, with potential applications in clinical practice for early lung cancer diagnosis.
Large-scale Dynamic Scene Reconstruction Based on Neural Fields
Large-scale dynamic scene reconstruction methods based on spatiotemporal field models demonstrate significant potential in autonomous driving applications. However, existing neural radiance field (NeRF) and 3D Gaussian splatting (3DGS) techniques remain constrained by their dynamic element modeling capabilities and computational efficiency, failing to effectively address complex reconstruction tasks involving intertwined static and dynamic regions in driving scenarios. To address these challenges, this study proposes a novel framework integrating spatiotemporal attention mechanisms with sparse encoding strategies. The method employs a spatiotemporal attention module that captures dynamic motion patterns through self-supervised inter-frame prediction, addressing spatiotemporal inconsistencies caused by non-rigid deformations. Simultaneously, a KL divergence-guided hierarchical sparse encoding strategy achieves efficient multi-scale scene feature representation while preserving reconstruction accuracy. Furthermore, a mean-variance decoupled stochastic sampling mechanism enhances modeling robustness in dynamic regions. Experimental results demonstrate substantial improvements in reconstruction quality compared to state-of-the-art large-scale dynamic scene reconstruction methods, ultimately enabling more photorealistic 3D reconstruction outcomes."