bluuebunny/crossref_metadata_2025 huggingface dataset:
Heart failure prediction based on multiple machine learning algorithms
Authors: Peng Lin
Published: October 2023

Heart failure is a complex medical condition that arises due to the heart's inability to adequately circulate blood throughout the body, which is challenging to predict. This research aims to investigate three distinct models, namely logistic regression, random forest and decision tree generation algorithms. Logistic regression is essentially a logistic function applied to linear regression, and the loss function associated with linear regression is similar to the mean square error-like loss. In contrast, the loss function for logistic regression follows cross-entropy loss. while cross-entropy loss is often used in practice, it differs from mean square error loss. The derivative of cross-entropy loss is a difference that updates rapidly when the error is significant and slowly when the error is small, which is a desirable trait for the purposes. Decision tree generation algorithms utilize tree structures in which internal nodes represent judgments on attributes, branches represent outputs of judgments, and leaf nodes represent classification results. Random forest is an integrated learning algorithm that employs decision trees as the base learner. In classification models, multiple decision trees are processed for voting, while multiple decision tree results are processed for averaging in regression models. Experimental results indicate that random forest outperforms the other two models, albeit with a marginal difference. Further studies should incorporate additional models to identify a more suitable model for predicting heart failure.
Research on methods and applications of question answering system in the context of ChatGPT
Authors: Li Xiangyu
Published: October 2023

In the 21st century, there has been a growing importance placed on the "body" of artificial intelligence, particularly as it relates to language processing. Researchers have developed various machine learning models with a focus on language understanding, including Large Language Model (LLM), Bidirectional Encoder Representation from Transformers (BERT), and Natural Language Processing (NLP). These models have led to the development of numerous applications, such as ChatGPT-3.5, which has recently gained widespread attention. In addition to ChatGPT, other applications have also benefited from these language processing models, including Question Answering Systems (QAS). This paper will examine three QAS that have been enhanced by the context of ChatGPT, discuss the relevant applications, and analyze these different applications in order to predict future trends in this field. One notable QAS is OpenAI's GPT-3-powered AI that can answer questions about any topic. This application leverages the capabilities of GPT-3 to provide accurate and informative responses to a wide range of questions. Another QAS is IBM's Watson, which utilizes natural language processing and machine learning algorithms to understand and respond to user queries. Watson has been used in various industries, including healthcare, finance, and retail. A third QAS is Google's BERT-based system, which uses pre-trained language models to improve its responses to user queries. This system has been integrated into Google Search and other products, allowing users to receive more precise and relevant search results. Overall, the development of these QAS and other language processing applications marks an exciting period of progress in the field of artificial intelligence. As researchers continue to refine these models and explore new applications, we can expect to see even more advanced and sophisticated language processing systems emerge in the future.
Detection of IOT botnet attack based on machine learning
Authors: Cheng Chi , Zhang Kaiyue , Zhang Yuhang
Published: October 2023

The paper discussed the process of data processing and algorithm selection for three different scenarios in order to improve accuracy in detecting DDOS attacks, SPAM emails, and malware. It provided detailed descriptions of each process involved in the simulation. For the DDOS attack detection simulation, three different datasets were used, and missing data was removed to ensure the quality of the data. In addition, features were processed to make sure they could be applied to specific algorithms. Both decision tree and random forest algorithms were selected and tuned to obtain maximum accuracy. Similarly, for the SPAM email detection simulation, binary was used to represent whether an email was spam or not, and Count Vectorizer function was applied to convert mail contents into feature vectors. The KNN and decision tree algorithms were chosen, and emphasis was given on parameter adjustment to eliminate overfitting and ensure optimal model accuracy. The paper also discussed the importance of considering multiple factors when selecting and tuning algorithms, such as accuracy, complexity, and computational efficiency. These factors must be balanced to achieve the best overall performance. Overall, the paper provided a comprehensive overview of the methods and processes involved in data processing and algorithm selection to improve detection accuracy for DDOS attacks, SPAM emails, and malware. This research can greatly benefit organizations that are looking to enhance their security measures and minimize the risks associated with these cyber threats.
Research on botnet detection technology in network security
Authors: Yu Haodong
Published: October 2023

In the current world of consistent cybersecurity threats, the priority of protecting precious data from malicious activities has never come this high. A network of infected computers that are under the control of bad actors is known as a botnet. These networks may be used for a variety of things, including spam distribution, distributed denial of service (DDoS) assaults, identity theft, and malware distribution. A botnet's constituent computers are frequently referred to as "bots" or "zombies.". And there have been appalling statistics of a 100% increase in DDoS attacks from 2021 to 2022, and attackers have been consistently evolving, implementing smaller, yet more persistent attacks. Fortunately, the measures for protecting computers from botnet attacks have also been evolving. The very first step to defending against botnet attacks is to spot suspicious requests, and in this paper, the machine learning method is utilized to help pinpoint the potential attacks. First, a comprehensive dataset is found and used to train the model. This is a dataset consisting of source IPs, protocols, bidirectional flows, packets and a total of 33 features of internet flows with a mix of normal and malicious internet flow. As for the models performed, random forest and logistic regression were chosen and run with 80 percent of the data from the dataset as a training set and 20 percent as a testing set. Overall, the two models perform greatly with the given dataset. It is a very basic study on the prevention of botnet detection, yet certainly, it provides insights and contributions into further developments in cybersecurity.
Revolutionizing student course selection: Exploring the application prospects and challenges of blockchain token voting technology
Authors: Hu Tiansu , Song Yuzhao , Zhang Linjing , Zhou Xiaoya
Published: October 2023

This paper explores the utilization of blockchain token voting technology in student course selection systems. The current course selection systems face various issues, which can be mitigated through the implementation of blockchain technology. The advantages of blockchain technology, including consensus mechanisms and smart contracts, are discussed in detail. The token voting mechanism, encompassing concepts, token issuance and distribution, and voting rules and procedures, is also explained. The system design takes into account the system architecture, user roles and permissions, course information on the blockchain, student course selection voting process, and course selection result statistics and public display. The technology offers advantages such as transparency, fairness, data security and privacy protection, and system efficiency improvement. However, it also poses several challenges, such as technological and regulatory hurdles. The prospects for the application of blockchain token voting technology in student course selection systems and its potential impact on other fields are summarized. Overall, the utilization of blockchain token voting technology in student course selection systems holds promising future implications, which could revolutionize the education sector.
Problem analysis and future vision for GameFi: A transition from GameFi to DeFi gaming
Authors: Pei Shaoshuai
Published: October 2023

The increasing popularity of GameFi, a combination of gaming and financial models, has led to the emergence of certain problems with current financial models that can only be overcome through a transition towards de-game, or full-on-chain games. This paper provides an overview of the history and financial models of GameFi, identifies pain points on the path of transition from GameFi to de-game, such as capacity and TPS, and presents a concept for a de-game with achievable performance levels. Both single and double currency models have limitations that prevent them from reaching their full potential, demonstrating the need for a transition to de-game, capable of offering unique gaming experiences and financial rewards. Overcoming pain points such as capacity and TPS will be essential in achieving this transition. Proposed solutions include using sharding technology and optimizing smart contract architecture to enhance system capacity and TPS. Ultimately, this paper highlights both the challenges and opportunities presented by GameFi, proposing the transition to de-game to overcome the limitations of current financial models. Addressing critical pain points such as capacity and TPS, our proposed solutions offer feasible approaches to building reliable on-chain gaming systems. The future vision of GameFi in the form of de-game offers a promising outlook for the gaming industry, where players can engage in immersive games with financial incentives.
The investigation of transferability related to the ImageNet-based transfer learning model in galaxy classification
Authors: Xu Harry
Published: October 2023

The phenomenon of transfer learning, specifically the transferability of ImageNet, in the context of galaxy datasets has been relatively under-researched. This study seeks to address this gap by employing the Galaxy10 DECals dataset, which is a 10-classification dataset. Three classic models, namely MobileNet, VGG, and ResNet, were developed and customized to the current dataset by modifying the number of neurons in the last layer. The experimental phase is divided into three parts, including the comparison between the use of ImageNet and non-ImageNet, model performance comparison, and confusion matrix analysis. The results demonstrate that the utilization of ImageNet produces better outcomes, with the MobileNet model exhibiting the highest performance. Further analysis revealed that the inclusion of ImageNet weights can enhance the classification accuracy of some data. Although the present study was successful in achieving its objectives, future research should focus on exploring and elucidating the underlying mechanisms driving these findings.
AutoDL-based convolutional neural networks for wildfire detection
Authors: Chien Ching-Syuan
Published: October 2023

Wildfires have emerged as a pressing issue in many regions of the world due to the ongoing impact of global warming on the planet. However, a reliable and high-performance detection system is currently lacking. This study strives to introduce a wildfire detection system that is based on neural networks and image recognition. This study utilized Edge Impulse to train a neural network to identify wildfire occurrences in the given pictures. To optimize the performance and adaptability of the model, an extensive dataset was compiled by collating images from two Kaggle projects, resulting in a final dataset of over 3000 images. The core technological advancements that Edge Impulse is based on are Auto Deep Learning (AutoDL) and Convolutional Neural Networks (CNN). By applying technologies like Neural Architecture Search (NAS), hyperparameters optimization, and transfer learning, AutoDL enables people interested in machine learning to approach the technology without an extensive understanding of math or programming that machine learning was built upon. CNN is a highly effective and efficient form of neural network popular for image classification. It consists of three different layers: the convolutional layer, the pooling layer, and the fully connected layer. The result of this study consists of a fully functional model for wildfire detection that is ready to be deployed, with a final testing accuracy of over 99%.
The transferability of transfer learning model based on ImageNet for medical image classification tasks
Authors: Zhang Zhanhao
Published: October 2023

Transfer learning with pretrained weights is commonly based on the ImageNet dataset. However, ImageNet does not contain medical images, leaving the transferability of these pretrained weights for medical image classification an open question. The core purpose of this study is to investigate the impact of transfer learning on the accuracy of medical image classification, utilizing ResNet18, VGG11, AlexNet, and MobileNet, which are four of the most widely used neural network models. Specifically, this study aims to determine whether the incorporation of transfer learning techniques leads to significant improvements in the performance of image classification tasks, as compared to traditional methods that do not utilize transfer learning. The dataset consists of approximately 4,000 chest X-ray images with labels of healthy, COVID, or Viral Pneumonia. The final layer's output neurons of the networks architecture were revised to three to accommodate the ternary classification task. Preprocessing techniques include downsampling and normalization of the pixel values. By maintaining the same dataset and preprocessing methods, this study compares the performance of the models with and without pretrained weights. The results demonstrate that, compared to not using transfer learning, all four network models converge more quickly and achieve higher validation accuracy in the initial epochs when transfer learning is employed. Furthermore, the models exhibit higher prediction accuracy in the final test set. This study suggests that using transfer learning with pretrained weights based on ImageNet can boost the efficiency of medical image classification tasks.
Advancements in precision agriculture: Edge Impulse-based convolutional neural networks for robust apple disease diagnosis
Authors: Liao Yijie
Published: October 2023

Apple cultivation is essential to the global economy, but it is vulnerable to diseases that can have severe impacts on crop yields and cause economic losses. Accurate and timely detection of these diseases is crucial for effective prevention and management. This study addresses the limitations of traditional machine learning methods, such as manual feature extraction and high computational costs, by employing convolutional neural networks (CNNs) and transfer learning using the MobileNetV2 architecture for the detection and categorization of four common apple ailments: Apple Scab, Black Rot, Cedar Apple Rust, and Healthy apples. The model produced excellent accuracy of 96.7% on the testing dataset while working with an enriched dataset of 12,000 photos in various dimensions. The model's performance on the validation set was 89.7% with a loss of 0.60. The transfer learning model demonstrates superior on-device performance, with faster inferencing time and lower RAM usage, making it suitable for real-world applications in apple orchards. In conclusion, this study successfully demonstrates the potential of using convolutional neural networks and transfer learning with the MobileNetV2 architecture for apple disease detection. The model's high accuracy and performance make it a promising tool for assisting apple growers in monitoring and managing apple diseases, ultimately leading to more efficient and sustainable apple production.
Comparison of Deep Convolutional GAN and Progressive GAN for facial image generation
Authors: Zhu Hanjing
Published: October 2023

Generative Adversarial Networks (GANs) have generated realistic and diverse facial images with promising results. This work demonstrates a technique for creating facial pictures using GANs and evaluates the effectiveness of several GAN designs and training approaches. The CelebA dataset is leveraged for training and evaluating the GAN model, and employ a variety of evaluation metrics, such as the Structural Similarity Index (SSIM) to assess the quality and diversity of the generated images. Progressive GAN outperforms Deep Convolutional GAN in terms of image quality and diversity, and conditional GAN training is more effective than standard GAN training for generating facial images with specific attributes. The combination of Progressive GAN and conditional GAN training produces facial images of the utmost quality and diversity. The findings contribute to a broader comprehension of the use of GANs for generating facial images and have ramifications for a variety of applications, from facial recognition to virtual reality.
Diagnosing brain tumors based on Edge-Impulse platform
Authors: Xie Tianyou
Published: October 2023

Brain tumors pose a serious threat to human health, and timely diagnosis is crucial for effective treatment. Conventional methods for diagnosing brain tumors, such as manual diagnosis, are often inaccurate and comparatively expensive. In this paper, a new method of diagnosis that can replace classic methods in the future is proposed. Ideas of machine learning is used to train a model so that it can classify images of brains into 2 categories: with tumors and with no tumors. Process of training is based on Edge Impulse, an AI platform on which model can be created conveniently. Technology used in the training is Convolutional Neural Network, which uses convolution as a mathematical operation instead of traditional matrix multiplication. It comprises convolutional layers, pooling layers and fully connected layers. Convolutional layers and pooling layers are used to operate feature maps, transforming them into a 1-dimensional matrix, followed by being multiplied by matrices of weights in fully connected layers. Cross entropy loss function is utilised to assess accuracy and modify weights. Transfer learning is also used to improve effectiveness and efficiency of training. Additionally, experiments are taken to determine the best image size suiting the model. This is accomplished by changing sizes of input image while keeping other parameters constant. Results of the experiments indicate that the model has a fantastic performance on brain tumor diagnosis.
Lung diseases classification based on auto-machine learning
Authors: Zhou Changhao
Published: October 2023

The escalating incidence of lung diseases and the substantial costs associated with their diagnosis underscore the significance of developing approaches that can enhance the effectiveness and precision of detection. In this regard, this study employs an auto-machine learning platform called Edge Impulse to train a greyscale dataset of lung diseases. Specifically, the study compares two classification models and two transfer learning models generated by edge impulse and BrainChip, respectively, and employs accuracy as a metric to assess the efficacy of these models. The research analyzes the potential causes behind the differing performances of the two authors and the two types of models and selects the optimal model for implementation in real-world devices. Additionally, the study takes into account the accuracy in detecting individual types of diseases to evaluate the model's performance in detailed classification. The findings reveal that the most effective model, the transfer learning model developed by Edge Impulse, performs exceptionally well in detecting abnormal lungs, particularly in diagnosing COVID-19. However, its classification capabilities with respect to identifying the specific type of disease remain limited.
Edge impulse-based convolutional neural network for brain tumor classification
Authors: Huang Dingyuan
Published: October 2023

Brain tumor, a type of intracranial tumor, poses significant threats to human health. In an effort to improve the accuracy and efficiency of tumor recognition and classification, this paper used convolutional neural network to train the model through the Edge Impulse platform. In this paper, BrainChip's Keras-based classification model was selected as the learning block. The initial data set was broken down into training set and test set categories for data processing. Data in the training set made up 88% of the overall data set, whereas data in the test set made up 12% of the total data set. In this paper, the number of training epochs was set as 10. In addition, under the condition that other conditions remain unchanged, this study conducted the experiment with the learning rates of 0.0005, 0.001, 0.002 and 0.003 respectively. After training models with different learning rates through convolutional neural network, four groups of results with different accuracy were obtained in this study. Finally, this study's experiment produced the most accurate results (87.0%) when the learning rate was 0.002. The findings of this study suggest that training a CNN model can effectively and accurately identify different types of brain tumors in a large dataset.
Implementing NLP techniques and data for Europe financial market forecasting
Authors: Su Yihong , Xiang Yuanhong , Lai Yiming , Liu Rui
Published: October 2023

The large military conflict between Russia and Ukraine has consequences for the global financial market. The European region is one of the hardest hits, which experienced a high energy cost and devaluation of the currency. This paper investigates the Employment Expectations Indicator (EEI) and Economic Sentiment Indicator (ESI) from the business consumer survey index to reflect the reaction of the consumers of the European market by implementing Natural Language Processing (NLP) techniques and machine learning. In the object, the monetary policy decisions from European Central Bank are considered the data set for forecasting the Index. In the result, all nine models from scikit-learn contributed a great job in both categories of Accuracy, Precision, and F-score and Recall with a level of 90% as average. It represents that the NLP techniques effectively forecast the future value of two European business and consumer indexes. However, the limit of the dataset and model mostly related to the performance of NLP techniques is working for forecasting but less related to the expected values of the EEI and ESI indices. Future research would focus more on forecasting the expected value of the two indexes and reflect the consumers' reactions in the markets.
Involvement of the variance in the UCB algorithm regarding risk aversion and the regret bound
Authors: Zeng Yijia
Published: October 2023

The classical Upper Confidence Bound (UCB) algorithm implemented overestimates of the true mean of reward distributions based on the sample mean and the number of times such arms were chosen to decide the best arm. In this case, the variances, the essential components of any distributions, of reward distributions are dismissed. Moreover, in real-world applications, arms with relatively high means and small variances sometimes are preferable to arms with the highest mean and large variance. Such concerns are considered risk-aversion in the Multi-Armed Bandits algorithm. Additionally, since the combination of the variance and mean could help estimate the range of the distribution, proper utilization of the sample variance might help people to construct a tighter upper confidence bound to perform the UCB algorithm, leading to a smaller regret. In this paper, the author investigates and summarizes the current algorithm regarding risk aversion based on the estimations and implementations of variances in constructing the upper confidence bound in the UCB algorithm.
Personalized movie rating prediction based on the data of short video users and videos information
Authors: Wang Yizhe
Published: October 2023

This study presents a sophisticated personalized movie rating prediction model that incorporates user-generated content from Douyin, a prominent video-based social media platform. The proposed model effectively leverages user demographic data, movie metadata, historical rating records, and sentiment analysis of user comments to precisely predict a user's rating score for films. This study employs lexicon-integrated two-channel CNN-LSTM family models for conducting sentiment analysis. Furthermore, the study explores a variety of regression models, such as linear regression, ridge regression, LASSO regression, and Elastic Net regression, to determine the most suitable model for the dataset being analyzed. This analysis aims to enhance the accuracy and effectiveness of the sentiment analysis performed on the given dataset. Furthermore, we contemplate the application of collaborative filtering algorithms, such as Alternating Least Squares (ALS), in developing our personalized movie rating prediction model. By incorporating user-generated content from Douyin, our methodology enhances the accuracy of movie rating predictions and underscores the significance of personalized recommendations in the era of social media.
Cloud computing technology applied in 5G mobile communication network
Authors: Gao Weiting , Xiao Yuxiang , Yin Haoxu
Published: October 2023

5G mobile communication network and cloud computing are the technological products and focus of today's era. Compared to 5G, 5G has seen a huge increase in peak speeds to 10-20Gbit/s, air interface latency as low as 1ms and much more. Cloud computing uploads data to the cloud so that users can access it more easily. They bring great convenience and high working efficiency to people's life. The use of cloud computing in 5G could make more efficient.5G, as a combination of new technology and cloud computing, will become a much larger market. This paper mainly describes the theoretical basis of 5G mobile communication network and cloud computing, the application of cloud computing in 5G (including automatic driving technology, surgery mobile communication network) and the current dilemma and the improvement needed. It aims to further promote the combination of 5G mobile communication network and cloud computing.
Optimized federated learning based on Adagrad algorithm and algorithm optimization
Authors: Chu Enming , Li Dengbo , Tong Yangfan
Published: October 2023

Federated learning allows you to train machine learning models without sharing your local data. Due to the No-iid problem, this paper is based on the Moon algorithm, which can have excellent performance in datasets of images with models that use deep learning and outperforms FedAvg, FedProx, and other algorithms, with the goal to decrease communication costs while enhancing efficiency more effectively. This study optimizes its gradient descent technique based on Moon's algorithm by utilizing Adaptive Gradient (AdaGrad) optimizer and combining with knowledge distillation to improve Moon's algorithm in order to better reduce communication costs and improve efficiency. That is, it reduces the loss and improves the accuracy faster and better in local training. In this paper, we experimentally show that the optimized moon can better solve the communication cost and improve the accuracy rate.
Approaches on improving privacy and communication efficiency of FedFTG
Authors: Geng Dongjun , Wang Dingxiang
Published: October 2023

The FedFTG plug-in can effectively solve the problem of knowledge forgetting caused by the server-side direct aggregation model in Federated Learning. But FedFTG runs the risk of compromising customer privacy, as well as additional transmission costs. Therefore, this paper introduces methods to enhance the privacy and communication efficiency of FedFTG, they are: Mixing Neural Network Layers method which can avoid various kinds of inference attack, Practical Secure Aggregation strategy which uses cryptography to encrypt transmitted data; The Federated Dropout model which focuses on reducing the downward communication pressure, and the Deep Gradient Compression method that can substantially compress the gradient. Experimental results show that, MixNN can ensure the privacy protection without affecting the accuracy of the model; Practical Secure Aggregation saves the communication cost when dealing with large data vector size while protecting the privacy; Federated Dropout reduces communication consumption by up to 28; DGC can compress the gradient by 600 while maintaining the same accuracy. Therefore, if these methods are used in FedFTG, its privacy and communication efficiency will be greatly improved, which will make distributed training more secure and convenient for users, and also make it easier to realize joint learning training on mobile devices.
Learning rate adjustment and optimization of RepVGG network based on warmup strategy
Authors: Lin Ying
Published: October 2023

Artificial neural networks have developed rapidly in recent years and play an important role in the academic field. In this paper, the RepVGG artificial neural network model is adjusted by the learning rate algorithm, so as to realize the optimization of the model including but not limited to accuracy. The main optimization strategy is to add the warmup strategy based on the learning rate algorithm of the original model so that the model can obtain good prior information on the data early in the training process, so as to converge quickly in the later training. Through a series of tests and simulations, the RepVGG-A0 model improves the Top1 accuracy by about 2.6% to 68.56% and the Top5 accuracy by about 0.38% to 94.32% on imagesetter dataset within 25 training epochs. The precision and recall are improved to 68.43% and 68.63%, respectively.
Intelligent optical transceiver technology based on federated learning traffic prediction
Authors: Zhang Yirui
Published: October 2023

With the development of optical network, modern optical network needs better performance. Because the traditional optical transceiver technology has a delay according to the flow switching transmission configuration, the delay optical network service still adopts the original configuration transmission, so a certain degree of frequency spectrum resources waste and high blocking rate will be caused. The above situation can be improved if the transmission configuration can be deployed in advance based on the predicted traffic. Federated learning is a scheme of distributed training model, which can train the traffic prediction model in distributed way under the premise of ensuring the privacy of client data, which is very suitable for the traffic prediction of optical network terminals. This paper proposes an intelligent optical transceiver technology based on federal learning traffic prediction, applies the federal learning on the traffic prediction of optical communication network terminal, distributed training traffic prediction model, and deploy the optical transceiver early transmission configuration such as modulation format and baud rate parameters, thus to weaken the delay of optical transceiver technology, reduce the network blocking rate and improve the transmission performance of optical network.
A simplified U-net model for Covid-19 CT image segmentation
Authors: Zhan Haitao
Published: October 2023

The ongoing COVID-19 pandemic has highlighted the importance of accurate and efficient medical image analysis to aid in the diagnosis and treatment of patients. In particular, the segmentation of COVID-19 medical images has become a critical task to identify regions of interest, such as the infected lung areas, and to track disease progression. Traditional image segmentation methods have been widely used in medical image analysis. However, these methods are often challenged by the complex and diverse nature of COVID-19 images, as well as the limited availability of data. In this paper, we propose a simplified version of the U-Net that eliminates redundant crop operations. This simplification reduces computational complexity and memory usage, and enables the model to learn from larger input images, resulting in better performance. We evaluate the performance of our simplified U-Net model on a public COVID-19 dataset and demonstrate that our model achieves state-of-the-art results while using fewer computational resources.
The analysis and application of Prim algorithm, Kruskal algorithm, Boruvka algorithm
Authors: Chen Jiayu
Published: October 2023

Minimum spanning tree has many applications in real life. For example, the government needs to build roads between many cities. Therefore, it is necessary to find the plan with the shortest path to save the cost. The problem is essentially generating a minimal spanning tree, and it require a suitable algorithm to find the minimum spanning tree. In this paper, the author analyzes the structure and time complexity of the Prim algorithm, the Kruskal algorithm and the Boruvka algorithm. Through this research, the author finds Prim algorithm is suitable for dense graphs. The Kruskal algorithm can generate the minimum spanning tree in sparse tree. And the Boruvka algorithm is suitable for graphs that have some special characters. Based on the above conclusions, the author gives some suggestions for urban highway network planning.
DBSCAN-based line density clustering algorithm for CAD architectural drawings
Authors: Li Rui
Published: October 2023

In the traditional architecture industry, architects often need to convert CAD drawings into BIM in order to express the final effect of the building more concretely and to understand whether the design of each profession is reasonable after the drawings are completed. The whole modeling process is boring and tedious, and due to human fatigue, the final result is prone to problems, which eventually leads to failure to meet expectations. In order to free architects from the tedious task of model transformation, companies have designed software for automatic model transformation using computers. The core of the software design is related to computational geometry. Line segment clustering is one of the elements of computational geometry and a frequent problem in programming. Appropriate clustering of line segments can often bring convenience to the problem. This paper combines the basic idea of the DBSCAN algorithm, improves the shortcomings of DBSCAN algorithm in dealing with line segment clustering, and proposes a density-based line clustering algorithm with shapely library as a tool. The algorithm is highly interpretable, and the method performs well and efficiently in the test of actual drawings, whether to group the line segments or to find the target line segments that meet the conditions, which provides convenience for the subsequent calculation.
Current perspective on artificial intelligence, machine learning and deep learning
Authors: Yuan Hang
Published: October 2023

Artificial intelligence has exploded in the past few years, especially after 2015. Much of it is due to the widespread use of GPUs, which has made parallel computing faster, cheaper, and more efficient. Of course, the combination of infinite expansion of storage capacity and sudden explosion of data torrent (big data) also makes image data, text data, transaction data, mapping data comprehensive and massive explosion. The wave of artificial intelligence has swept the world, and many words still plague us: artificial intelligence, machine learning, and deep learning. Many people do not have a deep understanding of the meaning of these high-frequency words and the relationship behind them. In order to better understand artificial intelligence, this article explains the meaning of these words in the simplest language to clarify the relationship between them, hoping to be helpful to the beginners. Deep learning expands the scope of artificial intelligence by enabling a wide range of applications for machine learning. Deep learning can overwhelmingly accomplish a variety of tasks, making all machine access capabilities available. For more complex applications, many implementations do not have to rely on supercomputing environments and big data. Data is indispensable, but too much data will also lead to overfitting. Algorithms are the key to solving learning problems. Efficient algorithms make artificial intelligence and machine learning less dependent on big data and supercomputer environment. Data, algorithms and computing power (computing speed, space) maintain a dynamic triangle in the implementation of artificial intelligence.
Exploring and envisioning the application of blockchain technology for privacy data protection
Authors: Gao Yuhan , Guo Liyuan , Zhang Tao
Published: October 2023

Addressing the issue of data leakage in social networks, this paper presents a classification of users' privacy information and introduces various personal data protection schemes utilizing blockchain technology. These schemes employ timestamp data storage within the blockchain, hash function anonymization techniques, and the Rivest-Shamir-Adleman (RSA) asymmetric encryption algorithm for encrypting and digitally signing transmitted data files. This innovative blockchain-based approach effectively tackles privacy leakage concerns in social networks and sets a benchmark for research in data security and social network safety. In this article, we propose a unique blockchain-based data protection scheme, specifically designed for different types of privacy leaks. This innovative solution addresses the pervasive problem of privacy leaks in social networks. However, current methods require more computational power during data interaction, which may hinder performance. Future research will concentrate on optimizing blockchain computational efficiency, aiming to develop a more robust data privacy protection system for blockchain-based social networks. By enhancing the efficiency and effectiveness of these privacy protection schemes, we hope to create a more secure environment for users to interact and share information on social platforms, ultimately fostering trust and confidence in digital social networks.
Autoencoder combined with the multilayer perceptron for Alzheimers disease classification
Authors: Yu Xinran
Published: October 2023

Alzheimer's disease (AD) is a prevalent neurodegenerative disorder that poses significant challenges for accurate diagnosis and treatment. The classification of AD Neurofibrillary Changes (ADNC) levels is crucial for understanding disease progression and developing effective interventions. In this paper, a method was proposed for classifying ADNC levels based on single-cell RNA sequencing (scRNA-seq) data obtained from the SEA-AD dataset. An autoencoder was employed to reduce the dimensionality of the scRNA-seq data, followed by a Multilayer Perceptron (MLP) for classification based on the autoencoder's embedding. The autoencoder effectively reduces the dimension of the scRNA-seq data from 4344 to 30 features. However, the embedding does not exhibit clear boundaries between different ADNC levels. The MLP model achieves a classification accuracy of 39% on the ADNC levels, indicating the complexity of the task and the need for more advanced classification methods. Additionally, the overfitting in both models was observed, and dropout regularization is applied to mitigate this issue. While the results indicate the potential of feature extraction and dimensionality reduction using autoencoders, the accuracy of ADNC level classification remains limited. Combining multiple approaches and aspects in AD diagnosis is necessary, as RNA-seq data alone may not be sufficient for accurate prediction. Future work could explore more sophisticated classification algorithms to improve the accuracy of ADNC level classification and consider integrating other data modalities to enhance disease diagnosis and understanding.
Research on pruning optimization techniques for neural networks
Authors: Wang Jiajun
Published: October 2023

Large deep neural networks have been deploying in more and more application scenarios due to their success in multiple application scenarios. However, deep neural networks are difficult to apply to devices with fewer resources, as the large models and huge demand for computing resources make this difficult. Pruning optimization, as a critical model compression method, has become an essential part of the deployment process of deep neural networks and has extreme significance. This article summarizes the methods of deep neural network pruning optimization technology, sorts out the current research status of pruning optimization technology, analyzes different fine-grained pruning optimization technologies based on the different fine-grained levels of pruning optimization technology, and comparing the characteristics of different fine-grained pruning optimization techniques. This article also introduces the development process and current development direction of different fine-grained pruning optimization technologies, compares the effectiveness differences between different fine-grained pruning optimization technologies and looks forward to the combination of pruning optimization technology and model quantification technology. The end of the paper summarizes pruning optimization techniques and provides prospects.
Exploring challenges and approaches for ChatGPT in multilingual and multisectoral contexts
Authors: Tan Shuaiyu
Published: October 2023

ChatGPT, a transformer-based chatbot model, has gained significant attention for its ability to generate natural and coherent responses. However, the model still faces several challenges that limit its performance and applicability. This essay explores the current challenges of ChatGPT and proposes solutions to overcome them. The challenges identified include the scarcity of diverse and high-quality training data, coherence and topic transition issues in long conversations, and the risk of overfitting during training. To address these challenges, the essay proposes several solutions. Transfer learning techniques are suggested to improve model generalization by pre-training on a large corpus and fine-tuning on specific chatbot tasks. Regularization methods such as dropout and weight decay are recommended to prevent overfitting and improve generalization. The design of more effective evaluation metrics, including F1 score and human evaluation, is proposed to accurately assess the model's performance. Additionally, incorporating contextual information from previous conversation turns is explored to enhance coherence.The proposed solutions are evaluated through experiments and benchmarking. The results demonstrate promising improvements in the performance of ChatGPT, including enhanced coherence, better topic transition, reduced overfitting, and higher-quality generated responses. This research contributes to the advancement of chatbot models by addressing the challenges faced by ChatGPT. The proposed solutions offer practical strategies to improve the model's performance and applicability in real-world scenarios. The findings have implications for various industries that rely on chatbot technology, enabling them to provide more natural and coherent interactions with users.
Revolutionizing social media: The applications and implications of big data analytics
Authors: Sun Qiyao , Liu Lu
Published: October 2023

The increasing deployment of online social channels has generated an unprecedented volume of data that can be analyzed to provide valuable insights into human behavior, preferences, and trends. The progression of extensive data analysis is clear. As a crucial technology for processing and making sense of this vast amount of data, enabling businesses and organizations to better understand their customers and improve their marketing strategies. Big data analytics have the potential to revolutionize social media by unlocking valuable insights and enhancing user experiences. However, the collection and analysis of user data raise ethical questions regarding consent and user control over their personal information. Moreover, algorithmic biases can lead to discriminatory outcomes and echo chambers, impacting society at large. It is crucial to address these issues and establish appropriate regulations and safeguards to mitigate potential harm. This paper explores the applications and implications of big data analytics in social media, highlighting its benefits and challenges and discussing its impact on society as a whole.
Review of Adversarial Attacks in Object Detection
Authors: Wang Li
Published: October 2023

Object detection, a fundamental element of computer vision and artificial intelligence, has experienced considerable advancements through the incorporation of deep learning-based techniques. Yet, despite the impressive strides in both accuracy and efficiency, object detection algorithms harbor inherent vulnerabilities to adversarial attacks. These well-crafted disruptions pose significant risks, especially considering the broad application of object detection across an array of safety-critical sectors such as autonomous transportation, medical imaging, and security systems. This comprehensive paper offers a thorough review of adversarial attacks against object detection systems, dissecting the methods employed, and scrutinizing the implications of their exploits. It dives deep into the mechanics and consequences of both white-box and black-box attacks on prevalent object detection networks, including but not limited to Faster R-CNN, YOLO, and SSD. Furthermore, this paper underscores an assortment of defense strategies developed to mitigate the effects of adversarial attacks. These include adversarial training, gradient masking, input transformations, and randomized defenses. While these strategies hold promise, it is acknowledged that they have their limitations and do not offer a universal solution against all adversarial attacks. As such, this paper underscores the urgent necessity for robust defense mechanisms and stimulates further discourse and investigation into developing truly resilient object detection systems, capable of withstanding the growing threat of adversarial attacks.
Design and implementation of 4-bit absolute value detector based on verilog HDL
Authors: Li Zhen
Published: October 2023

Absolute value detectors are widely utilized in many disciplines, such as image processing, speech recognition, and control systems, due to the advancement of digital signal processing and computer vision technologies. This paper presents a 4-bit absolute value detector based on the Verilog HDL programming language. Absolute value detectors are extensively used in signal processing, computer vision, and other fields to determine if the absolute value of the input signal meets specific conditions. This paper describes in detail the design process, including module analysis, the drawing of state transfer diagrams, the writing of Verilog HDL code, and simulation verification with a focus on practical applications. The results demonstrate that the absolute value detector is accurate and reliable in determining whether the absolute value of the input signal is greater than or equal to the threshold value.