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.
Sentiment Analysis with LLMs for Predicting Trends in Bitcoin
This project uses LLMs to perform sentiment analysis on financial news headlines to predict Bitcoin price trends. First we replicated FinBERT’s performance and retrained it on GDELT subset to improve its accuracy from 64.8% to 73.8%. Next, three sentiment scores were extracted from GDELT news dataset using retranied FinBERT model and the results were aggregated to develop multiple sentiment signals. Then we calculate Bitcoin returns from Bitcoin price dataset and construct multiple return signals. By calculating Pearson correlation coefficient, we find that the continuous sum sigmoid sentiment signal demonstrates the strongest correlation with Bitcoin returns. Based on this finding, we develop several trading strategies. Quantitative analysis shows that the second sentiment based strategy has an average of 20 percentage points higher return than the buy-and- hold strategy for most of the time. Moreover, this strategy still generates positive returns given the overall downward trend and the high volatility of the Bitcoin price. This work contributes to both academic research and practical applications by demonstrating the effectiveness of Large Language Models in enhancing financial market analysis through sentiment based methods.
Copyright (c) 2026 Journal of Computer Science and Artificial Intelligence https://creativecommons.org/licenses/by-nc/4.0
Infrared and Visible Image Fusion Algorithm Based on Cross-Modal Attention Mechanism
As a critical branch of multi-modal image processing, infrared and visible image fusion boasts high application value in intelligent security and has drawn widespread global research attention. Enhancing model feature extraction is a core scientific challenge in this field. This paper presents a dual-branch infrared and visible image fusion algorithm based on feature decomposition. In fusion tasks, shared features characterize global information while private features focus on local details. To boost feature representation, we design a feature decomposition module that splits shallow features into shared and private components: a coarse-grained branch with medium-to-large receptive fields handles global shared features, and a fine-grained branch with small receptive fields extracts local private features. Parallel dual-branch processing of decomposed features enables precise data structure mining, reduces redundancy, and efficiently captures key information. Experiments on four mainstream public datasets validate that the proposed algorithm surpasses state-of-the-art methods in information extraction, detail preservation and fusion performance.
Drug-Target Interaction Prediction Based on Deep Feature Fusion
Drug-target interaction prediction plays an important role in drug discovery and drug repositioning. Traditional experimental screening methods are expensive and time-consuming, while existing computational approaches still have limitations in molecular representation and feature fusion. To address these problems, this paper proposes a drug-target interaction prediction model based on deep feature fusion. In the GCT-DTI, a Graph Isomorphism Network is used to capture the topological structure information of drug molecules, and Morgan fingerprints are introduced to supplement global chemical features. For target proteins, a Transformer encoder is employed to learn long-range dependency information from amino acid sequences. In addition, a cross-attention mechanism is designed to enhance the interaction between drug features and protein features. Experiments on the Human and C.elegans benchmark datasets demonstrate that the GCT-DTI achieves good performance in terms of AUC, Precision, and Recall. The results show that deep feature fusion can effectively improve the prediction accuracy of drug-target interactions and provide useful support for computer-aided drug discovery.
Research on Dialogue Generation of Emotion Enhanced Large Language Model Based on Deep Learning
As the scenarios of human-computer interaction in China's social development have gradually expanded and giant artificial intelligences (GIIs) have become more frequent, making users feel better through emotional identification and empathic responses has become increasingly significant. Although the current generation of models can achieve precise semantic encoding, they usually face more prominent issues such as emotion-recognition bias and rigid responses; In terms of multi-turn dialogues, there is also a lack of emotional consistency. Due to the above limitations, they are difficult to be adapted for use in emotion-sensitive scenarios such as psychological counselling and intelligent customer service, which affects their application in practice. Based on deep learning technology, this study combined affective computing theory to build an emotion-enhanced dialogue generation system that enhances LLM's ability of empathy and understanding through emotion recognition and adaptive response strategies. Emotion feature extraction, embedding fusion and reinforcement learning optimisation have been used to realise precise alignment of users' real-time emotions by a model. The evaluated deep neural network based on Qwen2.5-7B, daily dialogues and cped datasets has obtained better effects than before, an improvement is expected by increasing emotional adaptability to about 0.86 (from previous level 0.41); Emotion recognition precision increased by more than 25 percent to 0.9 (before) to improve the model performance of users' satisfaction above 79%, making the emotions expressed in the model much closer to those of real people, so this method can be used for improving the warmth and Humanization of Human-machine communication technologies.
Application and Implementation of Deep Learning-Based Road Pothole Detection and Segmentation Algorithms
Road pit detection is one of the important means to maintain road and traffic safety. Road pit detection includes target detection of pit holes in the video based on the driving recorder, and judging the location and size of road pit holes. This study aims to accurately judge the pit holes on the road, the size of the pits and the pit levels of the pits based on the driving recorder. Provide valuable pit repair advice for road inspectors, thereby reducing their workload and improving patrol efficiency. This study collected more than 2,000 images of real road pits containing complex conditions such as shadows and strong light in many places in Sichuan, and constructed a high-quality labeling data set. In the object detection task, compared YOLOv8 and Mask-R-CNN, select YOLOv8 as the basic network framework to identify potholes in on-board videos and capture images. Then, the three semantic segmentation models of YOLOv8, U-net and Mask-R-CNN were compared. Since Mask-R-CNN can better segment small potholes, it is selected as the segmentation model. The area is estimated and the inner diameter of the pothole is calculated using the mask to evaluate the warning level. After rigorous training and evaluation, the mAP50 of YOLOv8 for pits was 0.88 and the accuracy was 0.89. The frequency weight ratio of Mask-R-CNN in the semantic segmentation part was 0.55 and the ratio of 0.45. Experiments show that the system detection speed reaches 100FPS and the early warning accuracy rate exceeds 90%.
TriFNet: A Tri-Domain Frequency-Aware Network for 3D Brain Tumor Segmentation
Brain tumor segmentation from multimodal MRI is a challenging task due to large inter-patient heterogeneity, irregular lesion geometry, and ambiguous boundaries between pathological and normal tissues. Although existing 3D segmentation networks have achieved promising performance, most of them primarily operate in the spatial domain and often rely on coarse feature fusion, which limits their ability to explicitly disentangle global structural information from fine-grained boundary details. To address this issue, we propose TriFNet, a Tri-domain Frequency-aware network for 3D brain tumor segmentation. Specifically, a Radial Frequency Decomposition (RFD) module is introduced in the encoder to decompose intermediate features into low-frequency and high-frequency components for separate modeling of global structural cues and local detail information. At the bottleneck, a Cross-Stream Synergistic Fusion (CSF) module is designed to adaptively integrate the complementary responses of the two frequency streams. In the decoder, a Multi-scale Tri-domain Gating (MTG) module jointly exploits spatial-domain, Fourier-domain, and wavelet-domain representations to enhance multi-scale feature reconstruction and boundary refinement. Extensive experiments on the BraTS 2019, BraTS 2020, and BraTS 2021 datasets demonstrate that TriFNet achieves average Dice scores of 86.62%, 88.90%, and 91.88%, with corresponding average HD95 values of 3.32 mm, 2.98 mm, and 3.51 mm, respectively. Ablation studies further verify the effectiveness and complementarity of the proposed modules. These results indicate that TriFNet provides an accurate and robust solution for 3D brain tumor segmentation, especially for challenging tumor core and enhancing tumor regions.
Multimodal Brain Imaging for Brain Disorder Classification: A Review of Graph Neural Networks and Transformer-Based Methods
With the rapid advancement of neuroimaging technologies and deep learning methods, multimodal brain imaging analysis has emerged as an important research direction for the early diagnosis and progression assessment of neurological disorders. Different neuroimaging modalities, such as structural magnetic resonance imaging (sMRI), functional magnetic resonance imaging (fMRI), and diffusion tensor imaging (DTI), provide complementary information from anatomical, functional, and structural connectivity perspectives. Compared with single-modality analysis, multimodal learning can offer a more comprehensive characterization of pathological alterations in the brain. At the same time, the increasing use of connectome-based representations has introduced graph-structured data into neuroimaging analysis, making graph neural networks (GNNs) particularly suitable for this field. More recently, Transformer-based architectures have further enhanced the ability of deep models to capture long-range dependencies and complex interactions across temporal windows, graph nodes, and heterogeneous modalities. As a result, the integration of multimodal learning, graph neural networks, and Transformer models has become a promising paradigm for intelligent brain disorder classification. This review focuses on these three key aspects. First, the importance and major strategies of multimodal brain imaging fusion are discussed. Second, the development and applications of graph neural networks in connectome analysis and brain disease classification are summarized. Third, the role of Transformer-based models in multimodal fusion and spatiotemporal brain network modeling is analyzed. Finally, the current challenges and future research directions are discussed. This review aims to provide a structured and theoretically grounded overview of recent methodological progress in multimodal neuroimaging-based diagnosis.
A Feature Selection Method for High-Dimensional Medical Data Based on Adaptive Redundancy Penalty
High-dimensional medical data are often characterized by extremely high feature dimensionality, limited sample sizes, substantial redundant information, and strong noise interference. To address these challenges, this paper proposes a filter-based feature selection method, termed ARMR (Adaptive Redundancy-based Minimum Redundancy Maximum Relevance Feature Selection). The proposed method first employs mutual information to select initial features from the original feature set. It then uses joint mutual information to characterize the joint discriminative capability of candidate features and selected features with respect to class labels. Furthermore, an adaptive penalty factor, constructed from conditional mutual information and three-way interaction information, is introduced to dynamically weight the redundancy term, thereby enabling a more flexible balance between feature complementarity and redundancy suppression. To evaluate the effectiveness of the proposed method, experiments were conducted on six benchmark datasets and four medical datasets, with comparisons against several representative and recently developed methods, including CFR, DCSF, JMI, MRMD, and mRMR. Two classifiers, namely SVM and NB, were employed for performance assessment. The results show that the proposed method achieves superior overall performance. In particular, it attains the highest average classification accuracy of 84.58% with SVM and 76.36% with NB. Further analysis based on classification accuracy curves under different feature subset sizes, together with boxplots of F1-score and AUC, demonstrates that the proposed method exhibits strong performance in terms of classification accuracy, stability, and classifier adaptability.
Design and Implementation of Intelligent Infusion Monitoring System Based on Internet of Things
To address the limitations of traditional infusion management models, such as reliance on manual monitoring, low efficiency, high safety risks, and poor patient comfort, this paper designs and implements an intelligent infusion monitoring system based on the Internet of Things (IoT). The system uses an STC89C52 microcontroller as the core controller, integrates dual U-shaped photoelectric sensors for drip rate detection and infusion status recognition, and employs a DS18B20 temperature sensor with a graphene heating pad to establish a closed-loop temperature control system for the medication solution. An open push-type gear-rack mechanism driven by a stepper motor is used to achieve automatic cutoff of the infusion tube. The system uploads real-time monitoring data to the OneNet cloud platform via an ESP8266 Wi-Fi module, enabling visual remote monitoring from the nurse station. Experimental results demonstrate that the system has a drip rate detection error ≤±1 drop/min, temperature control error ≤±0.8°C, and no-liquid response time ≤3 seconds. This system integrates monitoring, control, communication, and temperature regulation, effectively enhancing infusion safety and nursing efficiency, and holds significant clinical application value.
Research and Mobile Implementation of Multi modal Medical Auxiliary Diagnosis Framework Based on Sparse Bayesian Learning
In recent years, a large amount of data resources has been accumulated in the fields of medical imaging, genetic information, and electronic medical records. Therefore, the effective integration of these different sources of data to improve disease diagnosis has received increasing attention. Therefore, this section proposes a sparse Bayesian learning method to achieve joint analysis between multiple medical data and apply it to assist doctors in judging patients' conditions. Due to its ability to simultaneously select variables and estimate their uncertainty, sparse Bayesian learning has good robustness. On the other hand, for clinical portable diagnostic applications, explore lightweight compression and mobile deployment methods for models, and develop real-time auxiliary diagnostic software on iOS/Android platforms. The experimental results show that the proposed framework performs well in various disease diagnosis tasks, and the efficiency of mobile operation meets real-time requirements, providing a new technological path for clinical auxiliary diagnosis.
Research on Game Character Modeling and User Experience Optimization Path Based on Emotional Design Theory
With the continuous development of the gaming industry, game characters are an important medium for users to interact with games, which to some extent determines the quality of the gaming user experience. This article refers to Donald Norman's three-level emotional design theory model and explores the relationship and optimization strategies between game character modeling and game user experience from three aspects: instinctive layer, behavioral layer, and reflective layer. This article explores the relationship between emotional design theory and game characters, and proposes a visual modeling method for characters from the instinctive level, a behavior model construction method for characters from the behavioral level, and a connotation construction method for characters from the reflective level. From this, it can be seen that a successful character design needs to take into account three aspects: perceived beauty, game enjoyment, and value belief, and use a three-level model to jointly create an immersive emotional experience for users.
YOLO-based Lightweight Drill Detection for Coal Mines
Aiming at the challenges of insufficient illumination, heavy dust interference, large scale variation of targets, and limited computational resources of edge devices in underground coal mine drilling scenarios, this paper proposes a lightweight object detection model named YOLO-Drill. The proposed model is developed based on the YOLO framework with several improvements. First, a Lightweight Drill Feature Block (LDFB) is designed by integrating depthwise separable convolution and directional perception mechanisms, which reduces computational complexity while enhancing the representation ability of slender targets. Second, a Drill-Aware Feature Pyramid Network (DAFPN) is constructed to achieve efficient multi-scale feature fusion through bidirectional cross-layer interaction and attention weighting, thereby improving the detection performance of small and occluded targets. In addition, a scene-adaptive data augmentation strategy is introduced to enhance the robustness of the model under low-light, high-dust, and low-contrast conditions. Finally, the CIoU loss is adopted to optimize bounding box regression and improve localization accuracy. Experimental results on the self-constructed Drill Scene Dataset (DSD) demonstrate that the proposed YOLO-Drill achieves 85.2% mAP@0.5 and 51.0% mAP@0.5:0.95, which outperform the baseline by 6.6% and 5.8%, re-spectively. Meanwhile, the model maintains a lightweight structure with only 6.0M parameters and 19.9 GFLOPs, achieving a real-time inference speed of 87.9 FPS. The results verify that the proposed method effectively balances detection accuracy and computational efficiency, showing strong applicability in underground coal mine drilling scenarios.
Prediction of Electric Load for Users Based on BP Neural Network
Rock masses in underground engineering are generally subjected to complex three-dimensional stress conditions rather than uniaxial loading. Therefore, triaxial compression testing has become a fundamental approach for investigating the strength, deformation, and failure behavior of rocks. In recent decades, extensive studies have been conducted on rock failure laws under triaxial compression, with particular attention to the effects of confining pressure, crack initiation and propagation, damage evolution, and macroscopic failure modes. Existing studies indicate that confining pressure not only increases the peak strength and residual bearing capacity of rocks, but also suppresses tensile crack growth, promotes shear localization, and drives the transition from brittle failure to ductile failure. In addition, the full failure process of rocks under triaxial compression is characterized by distinct stages, including crack closure, elastic deformation, stable crack propagation, unstable crack propagation, and post-peak failure. Acoustic emission monitoring, ultrasonic wave velocity analysis, and energy-based methods have further improved the understanding of progressive rock damage and failure mechanisms. This paper systematically reviews the current research on the strength evolution, deformation characteristics, failure modes, and major controlling factors of rocks under triaxial compression. On this basis, the limitations of existing studies are discussed, especially with respect to fractured rock masses, layered composite rocks, and complex stress paths. Future research directions are also proposed, including multi-method characterization, multi-field coupling analysis, and the integration of laboratory results with engineering-scale applications. This review may provide a useful reference for the study of rock mechanics and the stability evaluation of deep underground engineering.
Optimization of UAV Smoke Screen Cooperative Masking Using Improved PSO
Aiming at the scheduling problem of UAV smoke screen cooperative masking of incoming missiles, this paper proposes a hierarchical solution framework that combines geometric analysis and intelligent optimization. Firstly, based on kinematics and line of sight occlusion geometric analysis, the three-dimensional occlusion judgment is simplified to discrete point judgment, and the effective occlusion time of the benchmark strategy is calculated to be 1.3920 seconds. Then, an optimization model with the maximum occlusion time is established, and the particle swarm algorithm with dynamically adjusted inertial weight is used to solve the problem, and the optimal occlusion duration of 4.5900 seconds and the corresponding strategy are obtained. It is further extended to the scene of three smoke bombs being dropped consecutively on a single machine, and the effective occlusion time is increased to 6.4000 seconds by integrating Latin hypercube initialization, dynamic adjustment of learning factors and chaos perturbation strategies. The results show that the proposed improved particle swarm algorithm can effectively solve such high-dimensional nonlinear constraint optimization problems, and provide a theoretical basis and feasible scheme for the real-time planning of UAV active defense.
A Lightweight Multimodal Feature Alignment Framework for Depression Detection
With the rapid advancement of artificial intelligence, computer vision, speech recognition, and natural language processing, automatic depression detection based on multimodal data has attracted increasing research attention. Compared with unimodal approaches, multimodal fusion leverages complementary information from speech, text, facial expressions, and other behavioral cues, thereby improving the accuracy and robustness of depression assessment. However, existing multimodal models are often computationally intensive and parameter-heavy, which limits their deployment on resource-constrained devices. In addition, semantic and distributional discrepancies across modalities pose significant challenges for effective feature alignment, adversely affecting fusion performance. To address these issues, this paper proposes a lightweight multimodal feature alignment framework for depression severity estimation. The proposed method constructs lightweight feature extraction networks and introduces a cross-modal feature alignment mechanism to enable effective mapping and fusion across heterogeneous feature spaces. While significantly reducing model size and computational complexity, the framework maintains competitive predictive performance. Experimental results on multiple public depression datasets demonstrate that the proposed approach achieves a mean absolute error (MAE) of 4.44 and a root mean square error (RMSE) of 5.77 in PHQ-8 score estimation, indicating strong generalization capability and practical deployment potential.
An Integrated Deep Learning Framework for Road Distress Detection, Segmentation, and Quantitative Evaluation
Road distress inspection plays a critical role in pavement-condition assessment and maintenance planning. However, existing studies often address detection, segmentation, or measurement separately, which limits their practical applicability. This paper proposes an integrated deep learning–based framework for road distress detection, pixel-level segmentation, and quantitative evaluation. First, an improved YOLOv7 detector is developed by introducing SE attention, CARAFE-based content-aware upsampling, and a Dynamic Head to enhance multi-scale feature representation and robustness under complex road backgrounds. Second, a multi-scale encoder–decoder network termed MIResU-Net is designed to accurately extract crack and pothole regions with improved structural continuity and boundary precision. Finally, a calibration-based measurement strategy is employed to convert segmentation results into physically meaningful geometric parameters, such as crack length and pothole area. A real-world road-distress dataset collected by a vehicle-mounted system is constructed for comprehensive evaluation. Experimental results demonstrate that the proposed framework achieves superior detection and segmentation performance compared with mainstream methods and provides metrically reliable quantitative indicators for pavement-condition assessment. The proposed approach offers an effective and practical solution for intelligent road inspection.
Quantitative Study on the Effective Shielding Time Window of UAV Smoke Grenades Based on Geometric Determination and Kinematic Comparison
This study aims to quantify the effective duration of concealment provided by a single smoke grenade against an incoming missile, thereby optimizing the deployment strategy for drone smoke decoy grenades. Following a problem decomposition—model construction—precise solution framework, this work employs kinematic modeling and geometric detection. Within a unified coordinate system, it extracts the shielding time window and cumulative shielding duration through time-domain scanning with a fixed time step. To investigate aerodynamic effects, the model performs dynamic comparisons between drag-free flight at constant altitude and flight incorporating quadratic drag. The drag-inclusive model employs fourth-order Runge–Kutta numerical advancement. Results indicate: under no-drag conditions, the masking duration is 1.394 s with a minimum distance of 4.669 m; introducing quadratic drag increases masking duration to 1.809 s while reducing the minimum distance to 2.952 m, validating the positive gain from drag. The model converged through sensitivity analysis, demonstrating robust conclusions.
Research on Precise Determination of Dynamic Target Indicators and Optimal Resource Allocation Strategies Based on Binary Search and Genetic Algorithms
This study addresses the challenges of precisely determining dynamic target indicators and optimizing single-resource allocation strategies. For effective coverage of cylindrical targets by resources within a specific airspace, we first establish the kinematic equations governing the motion of missiles, resource carriers, and deployed resources. To accurately calculate effective engagement duration, we construct a cylindrical occlusion indicator function, which determines occlusion status based on the minimum distance between the line-of-sight and the center of the resource cloud. To enhance computational accuracy and overcome limitations of traditional time-scanning methods, this study innovatively employs a binary search algorithm to precisely locate discontinuity points where the occlusion indicator function value jumps, thereby accurately calculating the effective duration of resource-target occlusion. Building upon this foundation, a nonlinear single-objective optimization model was developed. This model uses the UAV's heading angle, velocity, deployment time, and fuse delay as decision variables, with the objective of maximizing the total effective coverage duration. The optimization model was solved using a genetic algorithm simulating biological evolution, yielding optimal resource deployment parameter combinations that significantly enhance resource allocation performance.
Optimization of Smoke Screen Interference Munition Deployment Strategy Based on Improved Particle Swarm and NSGA-II Algorithms
To address the threat posed by precision-guided missiles to ground targets in modern warfare, this study focuses on optimizing deployment strategies for smoke screen interference bombs using unmanned aerial vehicles . The research examines two typical scenarios: single-drone-single-bomb and multi-drone-multi-bomb operations. A geometric shielding model based on Newtonian kinematics is established to determine the intersection conditions between smoke cloud clusters and missile-target line-of-sight, enabling precise calculation of shielding duration. For single-drone scenarios, an improved Particle Swarm Optimization algorithm is employed to optimize flight direction, speed, smoke deployment timing, and detonation delay, with the primary goal of maximizing real target shielding duration. In multi-drone scenarios, a multi-objective optimization model is developed to maximize shielding time while minimizing ammunition consumption and enhancing uniformity of coverage, incorporating the military deception effect of decoy targets. The collaborative deployment strategy is solved using the Non-Stochastic Genetic Algorithm-Ⅱ. The proposed model and strategies provide valuable references for practical applications of UAV smoke screen interference technology.
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.
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.