bluuebunny/crossref_metadata_2025 huggingface dataset:
Scheme Analysis for Enhancing Autonomous Driving Based on Computer Vision
Authors: Chen Xinyi , Luo Binbin , Xu Ziyue
Published: January 2025

This article explores the application and challenges of computer vision technology in autonomous driving, a critical component for the advancement of this field. Thesis adopted both literature review and technical analysis, focusing on recent developments in key technologies such as image processing, hybrid convolutional neural network (CNN)-transformer models, object detection, and multi-sensor fusion. The principles, benefits, limitations, and practical challenges of each technology were examined in detail. Thesis findings indicate that CNNs and their variants excel in tasks like object detection and semantic segmentation, significantly enhancing system perception and accuracy. Additionally, multi-sensor fusion technology boosts the reliability and the robustness of autonomous driving systems. However, challenges remain, including high computational demands, environmental perception accuracy, multi-sensor data fusion efficiency, and the high costs associated with implementation. Future research will prioritize developing highly effective deep learning models and optimizing cognitive computing visual systems to ameliorate the efficiency and ensure the safety of autonomous driving. The insights from this study offer valuable references for advancing autonomous driving technology and guide future research directions.
Network traffic monitoring based on CNN-SVM
Authors: Wu Qian
Published: January 2025

In a modern complex network, network monitoring and measurement have become increasingly important. The traditional network traffic monitoring methods face the challenge of efficiency and accuracy when dealing with massive data. The proposed hybrid model in this study uses convolutional neural networks (CNNs) and support vector machines (SVMs) to address these concerns and increase the effectiveness of network traffic monitoring. This paper uses CNN to extract features from network traffic data. CNN has the ability to recognize intricate patterns in the data and automatically extract valuable characteristics from the raw data. The SVM classifier receives the retrieved characteristics and uses them to further classify the data in order to distinguish between normal and abnormal traffic. By doing this, this paper may more successfully combine the benefits of SVM for classification with CNN’s advantages for feature learning, enhancing traffic monitoring’s precision and resilience. According to the experimental data, the hybrid model performs far better in network traffic categorization tasks than the standard techniques, with a reduced false positive rate and higher accuracy. This research shows that CNN-SVM model is an effective network traffic monitoring tool, which can provide high quality detection results while ensuring high efficiency.
A Comparative Analysis of Support Vector Machine and K-Nearest Neighbors Models for Network Attack Traffic Detection
Authors: Han Zhuoxi
Published: January 2025

With the continuous advancement of Internet technology, cybersecurity threats are growing more urgent as attack techniques become increasingly sophisticated. Conventional intrusion detection systems struggle to address these emerging threats because they depend heavily on predefined signatures and rules. This research centers on the use of advanced machine learning methods, particularly Support Vector Machines (SVM) and K-Nearest Neighbors (KNN), to improve the detection of network attack traffic. The UNSW-NB15 dataset, which includes various attack types and normal traffic patterns, is used to evaluate the performance of these two models. The results indicate that the SVM model excels in handling high-dimensional and intricate data, demonstrating its capability to tackle the complexity of modern cyber threats. On the other hand, KNN proves to be more efficient and straightforward when applied to less complex data structures. The outcomes of this study provide significant insights into enhancing cybersecurity systems, with recommendations for refining machine learning models to better address emerging threats. Moreover, the research highlights future directions to strengthen the resilience and precision of network intrusion detection systems, ensuring the development of more effective defenses against the ever-evolving landscape of cybersecurity risks.
Object Detection Techniques in Autonomous Driving
Authors: Yu Jianlei
Published: January 2025

This review comprehensively examines recent advancements in object detection (OD) methods for autonomous driving, highlighting their critical role in ensuring the safety and efficiency of autonomous vehicles in complex environments. It discusses various methodologies, including the application of machine learning (ML) techniques, and the integration of sensors like LiDAR and radar, which enhance the system’s ability to accurately identify and track nearby objects, such as pedestrians, vehicles, and obstacles, in real-time. The review synthesizes findings from multiple studies, showcasing innovations like adversarial learning techniques that improve detection performance, especially in adverse conditions. Furthermore, it addresses significant challenges, including environmental variability, computational efficiency, and the threat posed by adversarial attacks, which can compromise detection accuracy. The review emphasizes the importance of developing more robust and adaptive models, and it outlines future directions such as enhancing sensor fusion methods, optimizing model architectures, and employing open-world learning to prepare for unexpected scenarios, ultimately aiming to improve the reliability and safety of autonomous driving technologies.
Research Developments in Generative Adversarial Networks for Image Restoration and Communication
Authors: Zhou Peng
Published: January 2025

Generative Adversarial Networks (GAN) is always a popular study topic in artificial intelligence. This paper will analyze the principle of the GAN and introduce the development of the GAN and various derivative models. The improved Super-Resolution GAN (SRGAN) model and Cascading Residual Super-Resolution GAN (CR-SRGAN) model based on the GAN model achieve super-resolution of dark and old artifact images and solve the problem of color restoration and texture enrichment of dark and old artifacts. The GAN model is also widely used in the field of communication and information security. It proposes an End-to-End(E-to-E) communication encryption system based on Deep Convolutional GAN (DCGAN) to solve the secure transmission problem in wireless communication systems based on E-to-E learning. The system can realize encoding and decoding of input bits of arbitrary length with good generalization ability. Finally, the image restoration and communication encryption are summarized, along with an outlook on their development trends.
Research on the Application of Variational Autoencoder in Image Generation
Authors: Liu Jianing
Published: January 2025

The rapid development of artificial intelligence and deep learning has significantly influenced the domain of image creation, finding extensive applications in applications in fields like medical imaging, computer vision, and entertainment. Despite these advancements, challenges remain, especially in enhancing the quality and variety of produced images. This paper concentrates on applying Variational Autoencoders (VAEs) to image generation, a topic of increasing importance due to the model’s theoretical interpretability and stability. Through a detailed analysis of VAE principles, architecture, and applications, this research underscores the model’s capabilities in producing high-quality, varied images and its effectiveness in tasks such as image denoising and enhancement. The study also analysis the limitations of VAEs, like the inclination to generate blurry images, and discusses potential improvements, including hybrid models and enhanced loss functions. The results of this research enhance the comprehension of VAE’s capabilities and provide a foundation for future research aimed at advancing image generation technologies.
Robustness of Big Language Modeling in Finance
Authors: Yao Mohan
Published: January 2025

With the gradual entry of artificial intelligence into all aspects of people’s lives, people begin to use big language models to solve problems in various fields. In the financial field, people use financial big prediction models to solve problems such as stock prediction, risk assessment, etc., but the big language models can be incorrect due to model hallucination and adversarial attacks. Therefore, investigating the robustness of large language models in finance is the main topic of this article, and searches the literature using the keywords “large language model”, “adversarial attack”, “model illusion”, etc. in recent years. We searched the literature in recent years. The existing literature explains the causes of adversarial attacks and model illusion, and methods that enhance the robustness of large language models are come up. It is shown that an attacker can trigger the model illusion of a large language model through an adversarial attack to reduce the reliability of the large language model. There is a lack of specific datasets of big language models in the financial domain to get a solution to improve the big language models in the financial domain in a better way. Future research should be specific in the financial domain for further adversarial training and robustness optimization of big language models in the financial domain.
Overview of Sign Language Translation Based on Natural Language Processing
Authors: Wang Hanmo
Published: January 2025

This paper explores the progress, challenges, and future directions in Sign Language Translation (SLT) within the broader field of Sign Language Processing (SLP), which combines Computer Vision (CV) and Natural Language Processing (NLP) to translate sign language videos into spoken language texts. The study begins by examining various sign language representation methods, such as video, gesture, symbol systems, and annotation, analyzing their strengths and weaknesses. It highlights the critical need for high-quality, large-scale datasets to advance SLT research, while acknowledging challenges like data scarcity, annotation inconsistencies, and ethical concerns. The paper then reviews recent SLT research, identifying key challenges and proposing solutions, such as expanding datasets through collaboration with the deaf and hard-of-hearing community, and employing advanced data collection techniques. Additionally, it suggests applying NLP methods like transfer learning and large language models to address specific challenges. Finally, the paper advocates for stronger interdisciplinary collaboration between CV and NLP to develop models and algorithms that are better suited to the unique aspects of sign languages.
Enhancing Predictive Models in E-Commerce: A Comparative Study Using XGBoost Across Diverse Scenarios
Authors: Zhang Jianhao
Published: January 2025

With the growth of the internet, online shopping has become increasingly popular. However, sudden demand spikes during holidays or special events can disrupt market equilibrium, causing stock shortages and logistical challenges. To address these sudden surges in demand, this study utilizes existing online sales data, transforming it into actionable insights. Our strategy involves continuously feeding historical data into selected models to predict future sales volumes. By identifying patterns in the data, we aim to make the predictions more tangible and assess the validity of our approach through a statistical linear regression model. We employed three different models—Gradient Boosting Decision Tree (GBDT), Light Gradient Boosting MachineLight (GBM), and Extreme Gradient Boosting (XGBoost)—to determine which one is the most efficient. After comparing the performance of these models, the results indicate that XGBoost is the optimal choice. The findings suggest that accurate sales predictions enable e-commerce platforms to increase inventory during peak periods, maximize the utility of goods, ensure customer satisfaction, and stimulate transaction activity. This study underscores the importance of accurately forecasting sales and revenue in e-commerce, helping platforms to stay ahead of demand, optimize resource allocation, and maintain market competitiveess.
A Study on the performance of Four Regression Models in Predicting Weather Temperature Based on Python
Authors: Li Taobei
Published: January 2025

For industries like agriculture and disaster management, weather forecasting is essential. This study assesses how well four regression models—linear regression, random forest regression, support vector regression (SVR), and K-Nearest Neighbors (KNN)—predict weather temperatures using a dataset from England. Standardizing and expanding features were part of the data preprocessing process to capture non-linear interactions. Performance metrics were used to evaluate the models' predictive capacity. With the highest R2 value and the lowest error metrics, Random Forest Regression fared better than the other models, suggesting higher predictive accuracy, according to the data. KNN exhibited greater sensitivity to local fluctuations compared to SVR, which performed slightly better overall. linear Regression was the least effective, struggling with non-linear data and exhibiting higher error metrics. This study offers a thorough comparison of weather prediction regression models, emphasizing the performance of the Random Forest regression.
Research on House Price Prediction based on Machine Learning
Authors: Yang Xiangjun
Published: January 2025

Accurately predicting house prices is of vital importance to individual home buyers and investment groups, which not only profoundly affects the formulation of home-buying strategies, but also is closely related to the smooth operation of the economy and the overall development of the society. In recent years, machine learning techniques have shown remarkable potential in house price prediction, as these models can mine the complex nonlinear correlations in large amounts of historical data to produce more detailed and accurate predictions. This study aims to evaluate and compare the performance of various machine learning models on the task of house price prediction. For the house price prediction task, Random Forests generally perform better than Linear Regression and Single Decision Tree because they can better capture complex patterns in the data and reduce the risk of overfitting. Linear regression models are simple and easy to interpret, but may not be accurate enough when dealing with nonlinear relationships and outliers. The advantages of random forests are reflected in higher predictive accuracy, robustness to outliers, and the ability to handle interactions between variables automatically.
Forecasting Stock Prices with Artificial Intelligence
Authors: Zhao Danxuan
Published: January 2025

The purpose of this study is to investigate the closing prices of stocks in Artificial intelligence. The objective is to enhance the accuracy of future stock price Prediction to support investment or trading decisions. The models used in this paper include Simple Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), LSTM with peephole connectivity, and Gated Recurrent Unit (GRU). To conduct the study, Wal-Mart stock data is utilized to accurately predict future stock prices. The results show that the MSE for the SimpleRNN test is higher, indicating weaker generalization. The MSE of the basic LSTM test is lower than that of the RNN, indicating stronger generalization. The validated and tested MSEs of LSTM with peephole connectivity are higher than the basic LSTM and GRU. GRU performs as well as the basic LSTM but has the lowest LSTM training MSE. This stock prediction task requires the GRU model, which is the most suitable choice based on the training time.
Enhancing Rehabilitation Assessment with Artificial Intelligence: A Comprehensive Investigation of Posture Quality Prediction Using Machine Learning
Authors: Zhang Wenxi
Published: January 2025

This paper comprehensively reviews the application of Artificial Intelligence (AI) in rehabilitation exercise assessment, with a particular focus on posture quality prediction. AI techniques, including Support Vector Machines (SVM), decision trees, random forests, Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN), show great potential in improving the accuracy and personalization of rehabilitation assessment. Various supervised and unsupervised learning methods are analyzed and their effectiveness in classifying rehabilitation movements and providing real-time feedback to improve rehabilitation outcomes is demonstrated. Despite some progress in the application of AI techniques in rehabilitation exercises, some challenges remain, especially in terms of model interpretability, generalizability to different patient populations, and handling differences in data distribution between clinical and home settings. Techniques such as Explainable Artificial Intelligence (XAI), transfer learning, and privacy-preserving machine learning can be a way to unlock the limitations of adopting AI techniques in a wider range of rehabilitation settings. This paper concludes by highlighting the need for more adaptable and interpretable AI systems that can be seamlessly integrated into different rehabilitation scenarios while maintaining patient data privacy and ethical standards.
Application of federated learning in predicting breast cancer
Authors: Chai Jiarui
Published: January 2025

The prediction and diagnosis of breast cancer relies on multimodal data, such as imaging, genetic information, and patient lifestyle habits. Federated learning provides a framework to protect data privacy, allowing multiple institutions to share model training without sharing the original data. This paper proposes a breast cancer prediction model combined with federated learning, where each participant trains the model locally using multimodal data such as imaging, genes, and treatment history. During the local training process, the data is normalized and feature extracted, initially classified using support vector machines (SVM) or penalized logistic regression and optimized using stochastic gradient descent (SGD). Subsequently, each participant then sends the updated model parameters to the central server, where the FedAvg algorithm combines them to produce a global model. The model achieves data protection, also accurately predicts the progression and recurrence risk of breast cancer. Although federated learning effectively solves the privacy protection problem, the issues of data heterogeneity and model interpretability still need to be addressed. In the future, interpretability technologies (such as SHAP and LIME) and transfer learning can be combined to improve the transparency and adaptability of the model.
Perspectives on Image Aesthetic Evaluation Techniques
Authors: Yang Kaiwei
Published: January 2025

With the advancement of technology, support of datasets, cross-fertilization of disciplines, and increasing demand for reference scenarios, image aesthetics has become an active research field and has been widely applied in real life. This paper discusses the application of deep learning in image aesthetics evaluation, including the selection of datasets and the process of image aesthetics evaluation (including aesthetics feature extraction and aesthetics evaluation decision-making), analyzes the application of aesthetics evaluation techniques, and looks forward to the technological prospect. Although deep learning technology can improve the accuracy of evaluation and has achieved some results in aesthetic evaluation tasks, the technology still faces challenges such as high subjectivity and poor diversity, and it is believed that with the progress of science and technology, these problems will be solved. This paper also provides basic information on image aesthetic evaluation, analyzes the research trends in image aesthetic evaluation, and provides insights into future research directions.
Research and Analysis of Facial Recognition Based on FaceNet, DeepFace, and OpenFace
Authors: Li Minghan
Published: January 2025

This study provides a comprehensive review of recent advancements in face recognition technology, focusing on deep learning models such as FaceNet, DeepFace, and OpenFace. The primary evaluation criterion is these models' ability to produce accurate facial embeddings, which are essential for reliable identification and verification. The findings demonstrate that these models significantly enhance recognition performance, particularly under challenging conditions such as varying lighting and occlusions. However, the study also identifies ongoing issues, including the need for efficient processing and reliance on large, annotated datasets. Future research should address these challenges by improving the efficiency and scalability of deep learning models. Additionally, expanding datasets to include a broader range of facial features will enhance model robustness in real-world applications. Exploring the integration of advanced technologies, such as sophisticated data augmentation techniques, will further boost the accuracy and adaptability of face recognition systems. These efforts are expected to advance the development of more versatile and reliable face recognition technologies.
Handwriting Digital Image Generation based on GAN: A Comparative Study of Basic GAN and CGAN Models
Authors: Zeng Hongzhi
Published: January 2025

The vast application of artificial intelligence in numerous fields—image generation being one of them—has been made possible by the quick development of deep learning. Generative Adversarial Networks (GAN) can generate high-quality images through an adversarial training mechanism. The use and performance of GAN and its conditional variation, CGAN, in the field of handwritten digital image generation, are thoroughly examined in this research. The basic GAN and CGAN models, based on the PyTorch deep learning framework and the Modified National Institute of Standards and Technology (MNIST) dataset, are applied to generate handwritten digital images respectively. To assess and compare the variations between the two models concerning the fineness of image generation, the loss changes, and other relevant factors, the generation outcomes and the loss changes that occur during the training phase are documented. The experimental results demonstrate that, compared with the basic GAN, CGAN exhibits notable advantages in terms of image quality stability, the avoidance of model collapse, and the control of image categories. Furthermore, an investigation of other cutting-edge generating models indicates that there is still room for optimization in the CGAN network structure to improve its performance for increasingly intricate generative tasks.
Image Inpainting of Portraits Artwork Design and Implementation
Authors: Zhang Hongting
Published: January 2025

In modern society, the restoration of artwork has become increasingly important. Generative models can provide reference images for the damaged or blurred core areas of these artworks. This paper simulates artificial damage to classic portrait paintings in the Art Portraits dataset by adding center masks during data preprocessing and then implements the image inpainting task. During the training phase, the Denoising Diffusion Probabilistic Model (DDPM) is fine-tuned by progressively adding noise to the center-masked images in the noising stage, followed by denoising in the denoising stage to generate images. The generated images are compared with the original undamaged images through loss calculations to optimize the model. Additionally, a Generative Adversarial Network (GAN), which has shown promising results on other datasets, is used as a baseline for comparison. The damaged images are used as inputs, and the generated images are compared to the ground truth to evaluate the performance of both models. In the testing phase, two widely used metrics in image evaluation, Mean Squared Error (MSE) and Fréchet Inception Distance (FID), are introduced to assess the performance. The fine-tuned DDPM achieves an MSE of 0.2622 and an FID of 16.85, while the GAN scores 0.2835 and 22.78, respectively. Since lower values indicate higher fidelity in reproducing the original image, which is crucial for art restoration, the conclusion drawn from this paper is that the fine-tuned DDPM demonstrates higher accuracy and is more suitable for restoration projects related to Art Portraits.
The effect of using Naive Bayes to detect spam email
Authors: Sun Zehui
Published: January 2025

The rapid growth of the Internet has made email a huge help in business, life, and education. However, it has also become a channel for spreading undesirable information, such as content from hackers, viruses, violence, pornography, and superstition. Spam, which is unsolicited commercial email, often carries such undesirable information. It wastes network bandwidth, consumes users’ precious time, and interferes with normal life. Therefore, spam detection and filtering have become especially urgent and of great practical importance. This paper focuses on the spam detection method based on the plain Bayesian algorithm. The plain Bayesian algorithm is particularly suitable for spam detection due to its high detection accuracy and its wide application in text classification tasks. The results and analysis of the experimental dataset demonstrate that the accuracy of Park’s Bayesian algorithm in spam detection reaches an impressive 99.193%. This high level of accuracy underscores the effectiveness of the Bayesian approach in identifying and filtering out spam, thereby enhancing the overall efficiency and security of email communication.
A Comprehensive Investigation of Fraud Detection Behavior in Federated Learning
Authors: Sun Rui
Published: January 2025

This research delves into the application of Federated Learning (FL) models for detecting fraud across different financial bodies. FL facilitates decentralized training of models using local data, ensuring privacy, crucial for handling sensitive financial data. The comparison involves three machine learning models - Artificial Neural Networks (ANN), Random Forest (RF), and Convolutional Neural Networks (CNN) - to assess their efficacy in the FL context. While ANN and CNN demonstrate strong capacity in identifying complex fraud patterns, their communication efficiency and overfitting challenges are significant. In contrast, RF offers more robustness to Non-independent and Identically Distributed (non-IID) data and is less prone to overfitting, though it poses communication overhead issues. This paper also highlights the challenges of FL in fraud detection, including data heterogeneity, communication costs, and security risks. This paper proposed future research directions, emphasizing model personalization, communication optimization, and advanced privacy-preserving techniques. By addressing these challenges, FL can offer scalable, secure solutions for real-time fraud detection, ensuring the protection of sensitive financial data while enhancing detection accuracy across diverse data sources.
Inescapable Media: The Role of Social Media Data Mining in Understanding American Adolescent Media Engagement
Authors: Peng Cheryl Siyuan
Published: January 2025

With social media as the frontier of many American adolescent lives, there seems to be a shift in experiences of the new generation. This paper delves into the impacts of American adolescent media engagement, with a priority of the role of social media data mining in understanding behaviour and trends. This study utilizes innovative data mining techniques such as sentiment analysis, trend detection, and network analysis; it allows insights into how social interactions impact psychological and emotional growth. The discovery of the twofold nature of the media provides opportunities for affirmative individualism and likewise, perils such as cyberbullying. By applying social media data mining, it contributes to an innate understanding of the effects of the media on youth development. The paper highlights the progress of tactics to mitigate the undesirable effects of social media whilst enriching its progressive influences on growth and welfare.
Enhancing Spam Filtering: A Comparative Study of Modern Advanced Machine Learning Techniques
Authors: Zhang Chenwei
Published: January 2025

Spam remains a persistent issue that not only consumes time and bandwidth but also poses significant cybersecurity threats. As a result, effective spam filtering has become essential. With an emphasis on Naïve Bayes (NB), Decision Trees (DT), and Support Vector Machines (SVM), this study offers a thorough analysis of the major machine learning techniques utilized in contemporary spam filtering. This paper investigates underlying principles of these methods, compares their performance through extensive experiments conducted on the Kaggle dataset, and discusses the cunent challenges and future directions for spam filtering technology. The study reveals that SVM is particularly effective for handling high-dimensional data. DT offers superior interpretability, and NB simplifies probabilistic classification. Experimental results demonstrate that while each method has its strengths and weaknesses, combining SVM with NB notably enhances classification accuracy. Despite these advances, spam filters still face challenges due to evolving spamming tactics. In order to address these persistent problems, the conclusion part highlights the need for more reliable and flexible spam filtering teclmologies and makes recommendations for future research directions.
Machine Learning Based Engagement Prediction for Online Courses
Authors: Wang Wanning
Published: January 2025

Within the constraints of the epidemic, the demand for distance learning in education is growing rapidly, and technological advances are opening up new possibilities for online education. This study investigates the performance of three machine learning models (decision trees. SVMs, and random forests) in predicting online course participation. To ensure the accuracy and generalizability of the results, the paper evaluated the models using k-fold cross-validation. Performance metrics such as accuracy, precision, recall and F1 score were used for comparison. The results show that the Random Forest model outperforms the other models on all metrics while the SVM model performs the weakest among the three models. Therefore, this study conducted a feature importance analysis specifically for the decision tree and random forest models to gain insight into the predictive power of individual features. This helps educators and course designers to develop strategies to improve engagement and retention. In summary, this study emphasizes the effectiveness of random forests in predicting engagement in online courses and highlights the potential of machine learning in improving the quality of e-learning environments. The findings can help optimize ongoing online education discussions and can guide future research in the field of e-learning.
Application of machine learning in diabetes prediction based on electronic health record data analysis
Authors: Yang Zihan
Published: January 2025

With the application of electronic health records (EHRs) in the medical field, the use of machine learning to predict disease has become one of the important research hotspots in the healthcare industry. This study introduces an improved machine learning model specifically designed to predict diabetes risk, with the aim of improving the accuracy of predictions. The purpose of the study is not only to refine the model, but also to evaluate the performance of the model according to the experimental results. The integrated model was used in this experiment, and the prediction accuracy of diabetes reached 77.7%, showing strong generalization ability on the test data set. These results show that the model performs well at predicting diabetes, but there is still room for further improvement. While presenting the current research results, this study also Outlines future research directions, focusing on further improving the accuracy and reliability of the model. Th is research contributes to the development of machine learning in healthcare, specifically improving disease prediction models through advanced data analysis techniques.
Exploring A Better Way to Constraint Propagation Using Naked Pair
Authors: Chen Kaiqi
Published: January 2025

Sudoku is an NP-complete problem therefore developing various efficient algorithms is crucial. This paper presents an enhanced approach to solving sudoku puzzles by improving on recursive backtracking with constraint propagation and bitmask, focusing on the implementation of the naked pair technique. This research aims to add constraints to reduce the backtracking steps in solving sudoku using naked pairs, which is a technique that eliminates candidates of a cell from other cells in the same row, column or sub-grid when two cells share the same candidate sets. This research uses a dataset of 1 million sudoku puzzlers from Kaggle to evaluate the proposed solver’s performance. The solver first processes the puzzle into bitmask vectors then uses the singles and naked pairs technique to minimize the candidates, and then uses depth-first search to backtrack and solve the puzzle. As a result, the naked pair strategy slightly increases the total steps, and it significantly reduces the computation of depth-first search steps, making the solver potentially more efficient in solving difficult puzzles.
Deep Learning in Music Generation: A Comprehensive Investigation of Models, Challenges and Future Directions
Authors: Kong Xiangchen
Published: January 2025

Deep learning has made a lot of progress in the field of music generation. It now has powerful tools for both preserving traditional music and creating new, innovative compositions. This review explores various and recent deep learning models, such as Long Short-Term Memory (LSTM) networks, Transformer-based models, Reinforcement Learning (RL), and Diffusion-based architectures, and how they are applied to music generation. LSTMs effectively capture temporal dependencies, which are vital for producing coherent melodies and chord progressions. Transformer models, like MUSICGEN and STEMGEN, handle large amounts of data and dependencies efficiently, but they need a lot of computational resources. Reinforcement Learning models, such as MusicRL, combine human feedback to fine-tune AI-generated compositions based on the individual's preferences. Diffusion-based models, like MusicLDM, enhance audio fidelity, though real-time application remains a challenge. The objective of emotion-conditioned models, such as ECMusicLM, is to combine music with emotional cues so that the output has a stronger emotional resonance. However, each model faces its own set of limitations, such as computational inefficiency, data dependency, and challenges in capturing complex emotional nuances. Future research should focus on improving the computational efficiency of these models, expanding training datasets, and integrating more interactive, real-time systems.
Spam Email Detection using Naïve Bayes classifier
Authors: Wang Liansong
Published: January 2025

Spam email detection is still a considerable and ongoing challenge in today’s online environment, as the number of unsolicited emails keeps growing exponentially. Various algorithms such as the tree-based model, support vector machine Algorithm, and Convolutional Neural Network have been explored in prior research to tackle this challenge. This research specifically examines the effectiveness of the Naïve Bayes classifier for identifying and filtering spam emails. By delving into the fundamental principles of this classifier, its practical implementation, and the comprehensive evaluation of its performance on a combined dataset, its strengths and limitations in distinguishing spam from ham messages are revealed. The result of the study demonstrates an overall accuracy of 97.82%, showcasing the Naïve Bayes classifier's high efficiency and stability in identifying spam. With consistently high metrics score throughout both classes, the Naïve Bayes classifier has proven to be an exceptionally reliable tool for spam email detection, underscoring its suitability for numerous real-world applications.
Discussion on Artificial Intelligence Safety and Ethical Issues
Authors: Chen Xinyu , Hui Tianfang , Li Yanlin , Yang Haoyuan
Published: January 2025

As artificial intelligence (AI) is increasingly integrated into society, people are relying on it more and more, and higher requirements are put forward for the safety and ethical standards of AI. This article explores the development of artificial intelligence technology and its potential safety and ethical challenges in various fields. In terms of security, the risk of adversarial attacks is analyzed in depth, and the robustness of the model is enhanced through adversarial training and data enhancement techniques. In addition, it is recommended to adopt measures such as data encryption and differential privacy to address data privacy and security issues. Regarding ethical considerations, this paper identifies the origins of algorithmic bias and argues for mitigating it through rigorous testing, validation, and regulatory frameworks. It also highlights the importance of increasing the transparency and explainability of AI to enhance public trust. Finally, the paper emphasizes the importance of defining accountability for AI behavior and suggests establishing laws and regulations that effectively govern AI applications. In conclusion, the study argues that the development of AI should emphasize safety and ethical considerations. Through the combination of technical intervention, legal supervision and social responsibility, the sustainable development of artificial intelligence is effectively promoted.
Machine Learning Optimization and Challenges in Used Car Price Prediction
Authors: Zheng Yufan
Published: January 2025

With the rapid expansion of the second-hand vehicle market, correctly forecasting car prices is essential for both researchers and industry experts. The paper initially reviews existing machine learning models and their performance in predicting luxury car prices, emphasizing both their strengths and limitations. To begin with, models like XGBoost and Random Forest excel at processing large-scale data and identifying complex feature patterns, thanks to their ability to use an ensemble of decision trees to reduce bias and variance. However, these models struggle to accurately capture the unique characteristics of luxury vehicles, such as brand reputation, rarity, and personalized configurations. Because these complex factors cannot be easily represented by simple numerical features, the result is often suboptimal predictions for high-value vehicle prices. The paper found that feature engineering could enhance model performance by introducing more representative attributes specific to luxury vehicles, such as brand reputation, rarity, and customization options. Additionally, stratified modeling, which segments data based on price tiers, may provide more accurate predictions by targeting different price levels, especially in the high-value vehicle segment. Despite these theoretical benefits, the paper acknowledges that while these strategies were discussed, they were not empirically tested in detail. Consequently, their practical effectiveness still requires further investigation.
Outcomes of endoscopic submucosal dissection for high-risk colorectal colitis-associated neoplasia in inflammatory bowel disease
Authors: Maselli Roberta , de Sire Roberto , Barbaro Federico , Cecinato Paolo , Andrisani Gianluca , Rosa-Rizzotto Erik , Sferrazza Sandro , Fiori Giancarla , Azzolini Francesco , Pugliese Francesco , Facciorusso Antonio , Spadaccini Marco , Capogreco Antonio , Massimi Davide , Alfarone Ludovico , Chiappetta Michele Francesco , Gubbiotti Alessandro , Menini Maddalena , Khalaf Kareem , Sassatelli Romano , Di Matteo Francesco Maria , Spada Cristiano , Hassan Cesare , Repici Alessandro , Armuzzi Alessandro ,   on behalf of Endoscopic Resection Italian Network (ERIN) Group
Published: January 2025

Background Patients with inflammatory bowel disease (IBD) have an increased risk of colorectal cancer. High-risk colorectal colitis-associated neoplasia (HR-CAN) can be difficult to treat using traditional endoscopic resection methods. This study evaluated the outcomes of endoscopic submucosal dissection (ESD) in patients with IBD and HR-CANs.

Methods This retrospective multicenter study consecutively included patients with IBD who were referred to expert Italian endoscopy centers for ESD or hybrid ESD (hESD) of HR-CANs. The main outcomes were rates of en bloc, R0, and curative resections, adverse events, local recurrence, metachronous lesions, and post-resection surgery. Kaplan–Meier method was used to analyze survival rates. Risk factors associated with the main outcomes were investigated by univariable analysis.

Results 91 patients with colonic IBD (disease duration 15.3 [SD 8.7] years, 82.4 % with ulcerative colitis) with 96 HR-CANs (mean size 34.8 [SD 16.2] mm, 53.1 % high grade dysplasia/adenocarcinoma) were included. ESD and hESD were performed in 82.3 % and 17.7 %, respectively. En bloc, R0, and curative resections were achieved in 95.8 % (95 %CI 89.6–98.8), 85.4 % (95 %CI 76.7–91.7), and 83.3 % (95 %CI 74.3–90.1). Adverse events occurred in 12.5 % (95 %CI 6.6–20.8), which were all conservatively managed. After a mean follow-up of 23.4 (SD 16.1) months, local recurrence and metachronous lesions each occurred in 3.1 %. Post-resection surgery was required in 11.5 %.

Conclusions ESD of HR-CANs showed favorable outcomes on the medium- and long-term course in patients with IBD.
On possible explanation of temperature hysteresis of the superconducting transition temperature
Authors: Saĭko A. P. , Gusakov V. E.
Published: January 2025

It is shown that in HTS models in which the main contribution to BCS pairing comes from the bistable apical oxygen sublattice, the superconducting transition temperature under sample cooling can differ from the critical temperature corresponding to the destruction of the superconducting state under sample heating.
The seepage characteristics of rough fractures under cyclic shear loading
Authors: Zhu Qiang , Yin Qian , Tao Zhigang , Ren Shulin , Pu Hai , Wu Jiangyu
Published: January 2025

The continuous wear and degradation of rough surfaces induced by cyclic shear significantly affects fluid flow patterns within rock fractures. This study explored the seepage behavior of rough fractures during cyclic shear processes using a numerical simulation method that couples ANSYS/LS-DYNA with ANSYS/FLUENT. Initially, cyclic shear tests were performed on rough fracture specimen, and these tests were subsequently replicated in LS-DYNA to generate fracture models at various shear stages. The pore size distribution and contact ratio evolution of the fractures were analyzed, and based on these findings, flow simulations were conducted using FLUENT to solve the Navier–Stokes equation. The simulation results indicate that as shear displacement u increases, the dilatancy effect leads to an increase in fracture aperture, a decrease in vortex distribution, a weakening of fluid flow nonlinearity, and an increase in fracture transmissivity. However, with an increase in the number of cyclic shear cycles N, the fracture aperture decreases, the contact ratio increases, vortex distribution increases, fluid nonlinearity intensifies, and fracture transmissivity decreases. Additionally, during forward and backward cyclic shear processes, fluid flow within the fractures exhibits anisotropy. By calculating the equivalent hydraulic aperture of the fractures and comparing it with results obtained using existing equations, it became evident that considering the contact correction term is essential when evaluating the hydraulic characteristics of rough fractures. This study not only clarifies the impact of cyclic shear on fluid flow behavior in rock fractures but also showcases the potential of numerical simulation in predicting fracture hydraulic characteristics.
Mental health risk for wildland firefighters: a review and future directions
Authors: Wagner Shannon L. , White Nicole , Krutop Elyssa
Published: January 2025

Wildland fire is increasingly a consequence of the climate crisis, with growing impacts on communities and individuals. Wildland firefighters are critical to the successful management of wildland fire, yet very limited research has considered mental health in this population. Although a wealth of research in mental health risk and associated risk and protective factors exists for structural firefighters, unique demands of wildland firefighting such as the seasonal nature of work, the length and intensity of shifts, and the often geographically isolated working conditions, among other factors, require special consideration. The present review considers available literature on mental health in wildland firefighters, highlighting the importance of distinguishing occupation-related risks for firefighters from occupation-specific risks of wildland fire service work, and offers concrete evidence-based recommendations for future work in this high-priority research area.
ANKRD11 binding to cohesin suggests a connection between KBG syndrome and Cornelia de Lange syndrome
Authors: Liu Haiyang , Li Hao , Cai Qixu , Zhang Jie , Zhong Hongxin , Hu Gongcheng , Zhao Shuaizhu , Lu Yuli , Mao Yudi , Lu Youming , Yao Hongjie , Zhang Mingjie
Published: January 2025

Ankyrin Repeat Domain-containing Protein 11 (
ANKRD11
) is a causative gene for KBG syndrome, a significant risk factor for Cornelia de Lange syndrome (CdLS), and a highly confident autism spectrum disorder gene. Mutations of
ANKRD11
lead to developmental abnormalities in multiple organs/tissues including the brain, craniofacial and skeletal bones, and tooth structures with unknown mechanism(s). Here, we find that ANKRD11, via a short peptide fragment in its N-terminal region, binds to the cohesin complex with a high affinity, implicating why
ANKRD11
mutation can cause CdLS. The crystal structure of the ANKRD11 peptide in complex with cohesin, together with biochemical experiments, revealed that ANKRD11 competes with CCCTC-binding factor in binding to the cohesin complex. Importantly, a single point mutation in ANKRD11 (Tyr347 to Ala) specifically disrupted the interaction between ANKRD11 and cohesin and perturbed gene expressions in a mouse embryonic stem cell model. Mice carrying the ANKRD11 Y347A mutation display neural and craniofacial anomalies, which mirror clinical phenotypes observed in KBG syndrome patients. Thus, our study reveals how ANKRD11 functions together with cohesin to regulate gene expression and also provides insights into the molecular mechanisms underpinning developmental disorders caused by
ANKRD11
mutations.
940 nm laser diode pumped ultrashort pulsed laser with a Yb:YAG single-crystal rod
Authors: Dong Jiahao , Dai Yun , Zhao Yuanhao , Li Yanan , Liu Jingjing , Liu Jie , Peng Qianqian , Wu Anhua , Su Liangbi
Published: January 2025

A Yb:YAG single-crystal rod (SCR) with great optical properties was successfully grown based on the laser-heated pedestal growth (LHPG) method, and its laser characteristics were comprehensively studied. Pumped by a 940 nm laser diode, the output power of the continuous-wave (CW) laser was 3.72 W, corresponding to a slope efficiency of 44.3%. The continuous tuning range of the Yb:YAG SCR was 22 nm. In addition, a semiconductor saturable absorber mirror (SESAM) was used to realize the passively mode-locked Yb:YAG SCR laser. Operating at 1049.5 nm, the laser oscillator had a 1.6 ps pulse width with a repetition rate of 54.5 MHz.