Journal of Computing and Electronic Information Management
Journal of Computing and Electronic Information Management (JCEIM) 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 computing, electronics, information management across the world. All articles published are rigorously and fast reviewed meeting the Journal Quality standards.
Innovations in Machine Translation: The Role of Machine Learning in Enhancing Linguistic Accuracy and Efficiency
This essay explores Instance Induction, Analogy Induction, and Machine Learning, with a specific focus on the application of analogy-based machine learning in machine translation. This includes full instance translation, case pattern translation, and analogical reasoning. The study examines the underlying principles, advantages, and potential limitations of these methods to provide a theoretical foundation for further optimizing machine translation (MT). Moreover, an in-depth examination of Machine Learning Theory, particularly through the paradigms of Analogy Induction and Instance Induction, is conducted to unearth latent patterns and features, which are pivotal for the technological evolution of this field. The efficacy of these methodologies in augmenting the performance of machine translation is critically analyzed and discussed.
Copyright (c) 2024 https://creativecommons.org/licenses/by-nc/4.0
Review of underwater acoustic-optic hybrid routing algorithms
Since the 20th century, the demand for resources has increased geometrically. With the nearly complete exploitation of terrestrial resources, attention has shifted towards the oceans, leading to rapid advancements in underwater technologies. In underwater communication, the advantages and disadvantages of acoustic and optical signals are leveraged based on different scenarios, resulting in various hybrid acoustic-optical routing algorithms. This paper explores the research on underwater routing algorithms based on acoustic-optical signal integration, as well as some cutting-edge routing algorithms.
Multi-point Navigation Method for Intelligent Inspection Robots
A multi-point navigation method is introduced for intelligent inspection robots, aimed at enhancing efficiency and safety across various industries. The newly proposed WW algorithm improves the robots' fault tolerance and positioning accuracy. The study utilized tools such as the ROS operating system, Rviz, and Gazebo, as well as the Agilex Bunker MINI intelligent trolley and SCOUT MINI sensor platform. It discussed SLAM map construction with Cartographer and Gmapping algorithms and the AMCL positioning system. Additionally, the DWA algorithm for dynamic obstacle avoidance was introduced. The system design includes a Qt interface and Rviz interface for multi-point navigation and obstacle avoidance. The effectiveness of the WW algorithm was verified through simulations and experiments, which enhances navigation stability by setting a maximum standby time and alternative point strategy. The experimental results show that the WW algorithm can prevent the robot from entering a false dead state. The paper concludes with suggestions for further optimization of the algorithm and the integration of more complex intelligent functions using deep learning.
Rethinking Semantic Contrastive Learning and Content Fusion in Multimodal Retrieval
In the domain of image-text retrieval (ITR), recent advancements have enabled fine-grained (FG) instance-level retrieval through large-scale visual language pre-training (VLP). While these methods have achieved high accuracy, they have also led to an increase in computational complexity. primary challenges in cross-modal retrieval involve the induction of isomorphic knowledge and the association of heterogeneous knowledge. Homogeneous knowledge comprises elements with identical dimensions, whereas the interrelation of heterogeneous knowledge necessitates a prior unification of internal elements. Traditional cross-modal methods typically extract features from various modalities and engage in joint training. However, experimental results indicate that performance discrepancies among different modal networks can adversely affect overall generalization capability. Current state-of-the-art visual systems aim to minimize constrained supervisory signals to enhance the model's generalization performance. Although end-to-end models can simplify training, they often result in an exponential increase in data volume, which can be unmanageable for the average user. Our research demonstrates that pre-training with a singular focus can efficiently and scalably learn semantic features. This novel model is conceptually straightforward and can be implemented using existing, mature modules. In terms of performance, each module maintains a singular responsibility, significantly improving both the model's parameter count and training speed.
High-Performance Roaming Display Algorithm for Ultra-Large Screen Handwriting
With the rapid development of smart education, the application of smart chalkboard software in teaching is becoming increasingly widespread. This article proposes a high-performance roaming display algorithm aimed at improving the smoothness and response speed of handwriting input on large screens. This algorithm achieves efficient handwriting input and real-time display through techniques such as data collection and preprocessing, dynamic partitioning and caching mechanisms, parallel computing and multi-threaded processing, as well as intelligent prediction and optimization. The experimental results show that the algorithm performs excellently in terms of smoothness and response speed of handwriting input on ultra large screens, and can effectively solve the delay and lag problems of traditional handwriting input systems.
Study on France and EU Eastward Expansion
The large-scale eastward expansion of the EU has brought great opportunities and challenges to the EU's management. France, as a core country of the EU, is not enthusiastic about the eastward expansion, fearing that it will weaken its influence in the EU, but it voted in favor of the EU's eastward expansion. The reasons behind this include: the eastward expansion of the EU is an inevitable trend, it meets the strategic needs of Germany, and it is timely to partially realize the immediate interests.
Research on radio signal modulation mode identification algorithm based on transformer
Aiming at the deep learning-based radio signal modulation mode recognition algorithm, a Transformer network model of radio signal modulation mode recognition algorithm is proposed. Firstly, the Transformer network model is built, which contains 12 layers of Transformer encoder and 3 layers of fully connected layers; secondly, the original data is partitioned into sequences with a fixed window size, and the models with different window sizes are compared, and the model with the optimal window size is comprehensively selected for experimental comparisons; lastly, the optimal model is compared with the other benchmark models, and the modulation signal is used to recognize the modulation mode of radio signals using the RaidoML2016.10a international standard dataset. The sample is the RaidoML2016.10a international standard dataset to train the model. The experimental results show that the Transformer model algorithm can achieve recognition accuracy of 94.63% under the condition of signal-to-noise ratio of 16 dB, as well as an average recognition accuracy of 63.18%, and the maximum recognition accuracy and average recognition accuracy are higher than those of the current benchmark models by 2%~11% and 1%~7%, respectively. The model has superior recognition and fast convergence speed.
Mixer-NeRF: Research on 3D Reconstruction Methods of Neural Radiance Fields Based on Hybrid Spatial Feature Information
Compared to traditional 3D reconstruction techniques, Neural Radiance Fields (NeRF) have demonstrated significant performance advantages in implicit 3D reconstruction. However, simple multi-layer perceptron (MLP) models often result in blurry details in the reconstructed 3D scenes due to their lack of necessary local information capture during the sampling process. To address this issue, this paper proposes a joint learning method based on hybrid spatial feature information to enhance detail reconstruction. First, after the position encoding output, a hybrid spatial feature information learning module (MLCA) is added before entering the MLP network and before the input direction information, capturing spatial feature information and mixing the channels of these features to obtain the features of different channels at each spatial location. Then, a squeeze-and-excitation network module is introduced between the NeRF sampling and inference layers to filter out high-weight information. Experimental results show that the proposed 3D reconstruction method based on hybrid spatial feature information can effectively improve NeRF’s reconstruction performance. It is applicable to complex, real, multi-view scenarios, especially showing the best performance under high-texture and complex lighting conditions. Compared to NeRF, this method improves the learning of perceptual image block similarity by more than 30%.
CiteSpace-based Big Data Processing Technology Research Hot Spots and Frontier Analysis
The objective of this study is to present a visual representation of the literature on big data processing technology (2020-2024) in the China Knowledge Network (CNKI) and to conduct a detailed analysis thereof. The analysis is based on the community structure analysis software CiteSpace, which allows for a comprehensive examination of the institution that publishes the relevant literature, the authors of the literature, and the keywords. The results of the analysis demonstrate that the primary research foci in the field of big data processing technology are the analysis of data utilization in societal contexts and the development of techniques for processing and mitigating potential risks. In the future, the advancement of big data will witness a diversification of trends. As artificial intelligence, cloud computing, and other technologies continue to evolve, big data processing technology will increasingly prioritize intelligence and automation to enhance the efficiency and precision of data processing.
IoT Device Identification Based on IP Traffic
This study aims to address the challenge of IoT device classification by proposing a method based on the random forest classifier. By analyzing the network traffic characteristics of four common household IoT devices, we constructed a feature set and utilized the RF classifier for device identification. The experimental results demonstrate that the random forest classifier performs excellently in terms of precision, recall, and F1 score. By analyzing the network traffic characteristics of four common household IoT devices, we constructed a feature set that includes 20 important device feature information which can effectively represent device identity characteristics, and used the RF classifier for device identification. We conducted numerous experiments on a publicly available dataset and achieved an accuracy rate of 97.22%. The findings offer valuable references for the development of the IoT device identification field and point out potential directions for future research to further enhance the performance and adaptability of the classifier.
Research on Food Tourism Consumer Types and Characteristics
In recent years, food tourism has received increasing attention. This study conducts empirical exploration, targeting food tourism consumers in Guangdong Province, and collected 350 questionnaires using the questionnaire survey method. The data was analyzed using spss22.0 software for descriptive analysis, reliability and validity testing, cluster analysis, and difference analysis. Ultimately, food tourism consumers were divided into four types: general food tourism, food enthusiasts, food experience, and food exploration. Their characteristics were summarized. The study also identified the demand characteristics of tourists based on different genders, ages, and monthly incomes. Furthermore, suggestions were made for the development of differentiated food product designs, the development of characteristic foods that align with tourist destinations, improving the quality of food tourism products, and extending the food tourism industry chain.
Research Review on the Performance of Double Steel Plate Composite Shear Walls
Double steel plate composite shear walls are characterized by their lightweight, thin wall structure, high ductility, and construction convenience. These walls are widely used in the field of engineering. This paper focuses on double steel plate composite shear walls and summarizes the current state of research on their static load-bearing and seismic performance both domestically and internationally. The paper highlights the deficiencies in current research and points out the future direction for the study of double steel plate composite shear walls.
Design and implementation of the big data processing system based on cloud computing
As a product of the development of information technology, cloud computing technology has outstanding advantages in data processing. Therefore, cloud computing technology has been widely used in the field of big data processing. Building a big data processing system based on cloud computing technology can not only improve the efficiency of data processing, but also effectively reduce the cost of data processing. In this paper, the design and implementation of the big data processing system based on cloud computing are taken as the research object, which first introduces the relevant knowledge of cloud computing and big data processing technology, then analyzes the requirements of the system, and designs the overall architecture and functional module of the system, and finally the system is tested. The results show that the system can effectively handle large data with good stability and scalability.
From Voxels to Victory: Enhancing Racing AI with Fuzzy DQN and Imitation Learning
With the rapid development of the esports industry, racing games have become a key focus for both academic research and industrial applications. However, traditional racing game artificial intelligence (AI) struggles to adapt to increasingly complex track environments and meet evolving player demands. Deep Reinforcement Learning (DRL) has shown great promise in enhancing AI’s environmental perception and strategy optimization, yet its application in racing games remains in an exploratory phase, hindered by high data demands, slow convergence, and poor generalization. To address these challenges, this paper proposes a novel racing AI training method that combines voxel-based track feature extraction, fuzzy Deep Q-Network (DQN), and imitation learning. Voxelization enables the AI to automatically extract key track features, while fuzzy DQN refines speed adjustment strategies for better performance in complex environments. The inclusion of imitation learning reduces reliance on expert data, significantly accelerating training convergence. Experimental results demonstrate that the proposed method outperforms traditional algorithms like DQN, Q-Learning, and SARSA. Specifically, the custom algorithm achieved a 25% faster convergence rate, with a final total reward of around 400, compared to approximately 300 for DQN and 250 for SARSA. Additionally, the loss values were reduced by 60% in the final stages of training, indicating improved stability and learning efficiency. These results confirm that the proposed approach not only enhances AI training efficiency in racing games but also holds potential for real-world autonomous driving applications.
A comparative study of e-commerce review sentiment analysis models based on VADER and RoBERTa
The study compares the performance of two sentiment analysis models - VADER and RoBERTa - in analyzing Amazon product reviews. Using a dataset of reviews containing different star ratings, the study performed sentiment analysis on reviews using the VADER and RoBERTa models, respectively, and analyzed the performance of both on positive, neutral, and negative sentiment scores. The results show that the RoBERTa model performs well in capturing complex sentiment and contextual information, and especially has stronger recognition ability on extreme sentiments (e.g., 1-star and 5-star reviews.) The VADER model, although lightweight and fast, predicts weak correlation between sentiment scores and actual user ratings when dealing with complex semantics and long texts. This study provides reference value for sentiment analysis of user reviews on e-commerce platforms.
Improved small-world network --model definition and generative function derivation in infectious disease scenarios
In recent years, there has been growing interest in predicting and simulating the spread and prevention of infectious diseases. The most commonly used model is the SEIR model, which characterizes the spread process by introducing parameters such as the infection rate, contact numbers, incubation period, and recovery rate. Despite its widespread application, this model has some limitations. Firstly, it is a quantity-based model that only reflects changes in quantity and cannot capture the spatial spread of the disease. Secondly, in the transmission of infectious diseases, there is a noticeable threshold phenomenon in terms of quantity, which the SEIR model cannot depict. The use of SEIR in specific scenarios can partially address the aforementioned issues. Common approaches include considering individuals as organized within a small-world network, where the proximity of individuals on this network influences the spread of infectious diseases. The model constructed in this paper attempts to introduce geographical space on this basis, defining a small-world network in geographical space and providing a generating function for the percolation phenomenon of disease transmission within it.
Application of Reinforcement Learning in Complex Environmental Decision-making Problems
This paper provides an in-depth discussion on the application of reinforcement learning in decision-making problems in complex environments, focusing on the challenges it faces such as data sparsity and the balance between exploration and utilization, and proposes corresponding solutions. The study realizes an integrated process from data collection to decision support by developing a decision support system for sustainable enterprise management based on big data. Meanwhile, the exploration strategies and optimization algorithms in reinforcement learning are used to improve decision-making efficiency and accuracy. An application case in an intelligent transportation system demonstrates the potential of the multi-intelligence reinforcement learning framework in vehicle collaboration and optimization of traffic flow, effectively alleviating traffic congestion. Experiments show that the system significantly improves decision-making efficiency and accuracy, verifying the effectiveness of reinforcement learning in decision-making in complex environments.
Identification of User Preferences and Personalized Recommendation Strategy in E-commerce Platforms
With the rapid development of e-commerce platforms, accurately identifying user preferences and providing personalized recommendations has become a key strategy to enhance user satisfaction and platform market competitiveness. This article starts with the definition of user preference recognition, analyzes the core challenges faced by the e-commerce field, such as data complexity, differences in user behavior, privacy and data confidentiality issues, and potential biases in recommendation algorithms. It also strategically explores how to improve the quality of personalized recommendations. By using big data analysis technology to accurately capture user preferences, strengthen privacy and security protection, optimize the fairness and transparency of recommendation algorithms, and improve user interaction feedback mechanisms, specific improvement measures are aimed at providing practical suggestions for the optimization of personalized recommendation systems in future e-commerce.
Classification of Supply Chain Artificial Intelligence Application Scenarios
The purpose of this paper is to classify and analyze the application scenarios of artificial intelligence in supply chain based on case studies of multinational companies. The paper first introduces the background and development of AI and discusses its current applications in supply chain management. Three main large-scale application areas are proposed, namely supply chain demand forecasting, risk management, and transportation operations planning. Three representative cases from Walmart, HP and UPS are reviewed based on the applied technologies, implemented processes, and advantages and disadvantages. As can be seen, this study contains meaningful recommendations to enhance the use of AI models in other industries and to adapt them to their characteristics. In conclusion, it can be said that AI has great potential to improve supply chain performance and resilience to adversity if conditions are taken into account in practical applications.
This paper discusses the development history of artificial intelligence (AI) technology and its application in the medical field, including its progress in medical image analysis, intelligent diagnosis, drug development and genomics. It analyses how AI technology breaks through the imbalance between the supply and demand of medical resources, reduces the misdiagnosis rate, and promotes scientific medical decision-making. The application of AI technology in medical data processing and analysis is demonstrated through a case study of Mediadu Cloud, and its advantages and challenges are discussed. Finally, countermeasures such as data standardisation and technological innovation are proposed to promote the healthy development of AI technology in the medical field.
Research Overview of YOLO Series Object Detection Algorithms Based on Deep Learning
In the rapid development of deep learning, YOLO, as the first popular single-stage object detection model, has sparked an innovative storm in the computer vision community with its remarkable architecture and innovative concepts, marking a significant leap forward in object detection technology. Today, it is not only regarded as a milestone in this field but also sets an unparalleled example in the pursuit of the perfect fusion of detection speed and accuracy. YOLO has been widely applied in various fields such as agriculture, industry, pedestrian detection, and more. This research project will first introduce traditional object detection methods, then analyze object detection based on deep learning, and subsequently elaborate on the fundamental concepts of YOLO. It will systematically sort through the YOLO family and its significant improvements. Finally, based on different improvement strategies or application scenarios, the YOLO algorithm will be systematically classified and summarized.
Application of Large Language Models in Power System Operation and Control
The introduction of "carbon peak" and "carbon neutrality" targets and the vigorous advancement of national energy market construction, renewable energy sources like wind and solar power are experiencing rapid development. However, this also brings challenges in accounting for uncertainties in various operational scheduling and optimization control processes of the power system, especially for the theoretical control methods. Fortunately, Large language model (LLM) with rapid development has shown promising prospects in the power sector. This review summarizes the application of LLM technology in power system operation and control, outlining the new power system's demand for AI technology, the impact of LLM on system management, and the technological foundation including network architecture, training methods, and data configuration. Finally, it explores the applications of LLM in power system operation and control from the perspectives of generation, transmission, distribution, consumption, and equipment.
The impact of AI bias on social justice: challenges and solutions
This paper analyzes the wide-ranging impact of artificial intelligence (AI) bias on social equity, especially in key areas such as healthcare, education, and employment. With the rapid development of AI technology, its potential bias problems have been gradually exposed and attracted widespread attention. To this end, this paper not only explores the manifestations and causes of AI biases, but also proposes specific mitigation strategies, such as through improving data diversity, enhancing algorithm transparency, promoting interdisciplinary cooperation, and establishing a sound regulatory framework, with a view to reducing the negative impact of AI biases on social equity and promoting the fair application of AI technologies in various fields of society.
Research on the Application Status of Large Language Models in China's New Energy Vehicle Industry: Taking Huawei's NLP Large Model and XPENG 's XGPT as Examples
In the era of rapid development of artificial intelligence, large language models are widely used in various industries due to their powerful natural language processing and text generation capabilities, including intelligent manufacturing, intelligent services, voice interaction, and even music box text creation. In this article, I will use literature review and case analysis methods, using the Huawei NLP large model and XPENG XGPT model as examples, mainly focusing on two aspects: intelligent driving and intelligent cockpit. Deeply explore the advantages of big language models in the new energy vehicle industry, the feasibility and necessity of their application, and their future prospects. In my research, I found that previous studies on this topic mainly focused on the technical principles of intelligent driving and intelligent cockpit, model optimization solutions, and market and user feedback. In intelligent driving systems, large language models can achieve timely and accurate takeover of driving, precise navigation, and efficient scene learning through scene simulation and generation techniques. In terms of intelligent cockpit, I believe that the main approach is to utilize natural language recognition and text processing capabilities to timely complete user instructions and engage in efficient language interaction. Helping enterprises create intelligent cockpit systems that are personalized and emotional processing for users.
A Survey on Image Style Transfer Based on Deep Learning
With the rapid development of deep learning technology, style transfer has achieved significant breakthroughs at both algorithmic and application levels, greatly promoting innovative interactions between content and style. This paper comprehensively reviews the fundamental concepts, classifications, and widespread applications of style transfer within neural networks, with a particular focus on the principles, diverse variants, and synthesis techniques of neural network-based style transfer. Additionally, the article explores image style transfer as a research hotspot in the field of artificial intelligence and computer vision, highlighting that traditional methods primarily rely on physical and texture synthesis techniques, which often result in coarse outcomes and poor robustness. Through comparative analysis, this paper outlines the development trajectory and latest research advances in image style transfer and proposes future research directions.
Overview and optimization strategy of road crack detection based on YOLOv8 algorithm
This paper comprehensively reviews the YOLOv8 algorithm and its application in road crack detection, focusing on the advantages of the algorithm in real-time target detection and high accuracy. With the continuous expansion of road infrastructure around the world, road cracks have become a serious safety hazard, which not only poses a threat to drivers but also brings a huge economic burden. Traditional road crack detection methods, such as manual inspection and sensor technology, often face the problems of high labor intensity, low efficiency and high cost. With the development of computer vision and deep learning technology, YOLOv8, as an efficient target detection system, provides new possibilities for the automation and accuracy improvement of road crack detection. This paper reviews different application versions of YOLOv8 in road crack detection, evaluates their performance, and points out the challenges of small target detection, real-time processing and multi-scale feature fusion. This paper proposes a variety of optimization strategies, including model optimization, data enhancement, training strategy and hardware acceleration. In addition, this paper looks forward to the future development trends of deep learning in the field of road crack detection, such as multimodal fusion, cross-domain detection and automated system integration, to promote the improvement of road safety and maintenance efficiency.
Insurance Fraud Prediction Model Based on eXtreme Gradient Boosting
As the volume of insurance transactions continues to increase, combating insurance fraud has become increasingly important. One of the key challenges is how to predict fraud based on high-dimensional data. In the current research landscape, deep learning methods that perform well often require large amounts of data, but their training and deployment also pose significant computational challenges. This paper, based on the Alibaba Tianchi dataset, explores the feasibility of constructing a low-cost prediction system through feature engineering and parameter tuning. Additionally, this paper verifies the robustness of the eXtreme Gradient Boosting (XGBoost) model in handling high-dimensional sparse features and imbalanced data.
Research on energy consumption optimization of RetinaNet model based on lightweight improvement in edge intelligent terminals in power system
With the increasing application of edge computing in power systems, intelligent terminal devices are facing new challenges in processing capabilities and energy optimization. As a highly efficient target detection model, RetinaNet's potential application in edge intelligent terminals of power systems has not been fully explored. This study proposes a lightweight model based on the convolutional neural network RetinaNet. Multiple lightweight models are used to replace the original backbone network ResNet for comparison. The best model is selected while ensuring accuracy. Redundant connections in the model network are pruned through channel pruning to reduce model size and improve detection speed. The results show that compared with the original algorithm, the RetinaNet model proposed in this study reduces parameter count by 73%, decreases computational load by 41.8%, reduces model size by 72.7%, and only decreases the mean average precision (mAP) value by 1.8 percentage points.
Analysis of spatiotemporal changes in bicycle rental demand: Taking New York as an example
In order to optimize the rental patterns of public bicycles and the allocation plan, the temporal and spatial movement laws of public bicycles should be obtained. Propose a distribution model and time trend model for public bicycles at different time points and rental locations. Take New York city as an example, based on the public bicycle travel data in New York City, a reference plan for the allocation of rental points was proposed using the moving average time model, kernel density method, and decision tree regression model.
Using the Bert model and the attention mechanism to obtain an accurate sentiment analysis model
This study takes the network reviews captured by Ctrip as the object of investigation. 29,025 reviews are preprocessed first, and the training set, verification set and test set are divided according to proportion. The Bert model is used for training and data parameters are retained. Only the subject and affective adverbs were retained and the negative adverbs were artificially weighted. The data set was retrained. The research results supplemented the innovation and application value of the training results, and could prove whether there were commercial malicious competition and other phenomena in bad reviews in network evaluations.