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.

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.

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.

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.

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.

Brain tumors have a high-risk factor and are extremely harmful to the human body. With the development of science and technology in recent years, automatic segmentation has become popular in medical diagnosis because it provides higher accuracy than traditional hand segmentation. At present, more and more people start to study and improve it. Due to the non-invasive nature of MRI, MR images are often used to segment and classify brain tumors. However, limited by the inaccuracy and inoperability of manual segmentation, it is very necessary to have a complete and comprehensive automatic brain tumor segmentation and classification algorithm technology. This article discusses the benefits, drawbacks, and areas of application of several traditional algorithms as well as more modern, improved, and more advanced algorithms. Segmentation methods and classification methods can be used to classify these techniques. Convolutional neural networks (CNN), Support vector machines, and Transformers are examples of classification methods. Random forests, decision trees, and improved U-Net algorithms are examples of segmentation methods. To discuss the capability of classification and segmentation, there are three sections in the area used for segmenting brain tumors with three types, including Tumor Core, Enhance Tumor, and Whole Tumor, which could be abbreviated as TC, ET, and WT. Through the comparative analysis of these methods, useful insights for future research are provided.

The topic of Intrusion Detection System (IDS) has become a highly debated issue in cybersecurity, generating intense discussions among experts in the field. IDS can be broadly categorized into two types: signature-based and anomaly-based. Signature-based IDS employ a collection of known network attacks to identify the precise attack the network is experiencing, while anomaly-based IDS employ machine learning models to detect anomalies present in the network traffic that could indicate a potential attack. In this study, we concentrate on anomaly-based IDS, evaluating the effectiveness of three supervised learning algorithms - Decision Tree (DT), Naive Bayes (NB), and K-Nearest Neighbor (KNN) - to determine the most suitable algorithm for each dataset based on its source. We conducted tests to evaluate each algorithm's performance and choose the best one for each dataset. Our findings show that anomaly-based IDS is highly effective in enhancing network security, providing valuable insights for organizations looking to improve their security measures.

The technique of restoring sections of a picture that have been lost or damaged is known as "image inpainting." In light of recent developments in machine learning, academics have begun investigating the possibility of using deep learning methods to the process of picture inpainting. However, the current body of research does not include a comprehensive review of the many different inpainting methods that are based on machine learning, nor does it compare and contrast these methods. This article provides an overview of some of the most advanced and common machine learning based image restoration techniques that are currently available. These techniques include Multivariate inpainting technology and Unit inpainting technology, such as Context-Encoder Network, Generative Adversarial Network (GAN), and U-Net Network. We examine not just the benefits and drawbacks of each method, but also the ways in which it might be used in a variety of settings. At the conclusion of the piece, we predict that machine learning-based inpainting will continue to gain popularity and application in the years to come.

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.

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.

This paper discusses the combinatorial interpretation of the H_n numbers, where each Hn denotes the number of ways to tile a 2 × 2 × n block with 2 × 2 × 1 “plates” and 6-block “L” shapes. It then investigates a closely related tiling sequence, which is tiling a 2×2×n bracelet with the same two building blocks, and discusses its relation with Hn. The recursive equation for both integer sequences are found using one to one correspondence, induction and Newton’s Sum. Additionally, in the case of H_n numbers, its related “Lucas Style” sequence P_n is found. The relationship between the P_n numbers, the Bn numbers and the Hn numbers is similar to that of the Lucas numbers and the Fibonacci numbers. Finally, identities concerning H_n’s generating function and its relations with other existing {2,1,2} sequences are discussed, and a theorem that generalizes the generating function of all tiling sequences is proposed and proved.

In recent years, object detection algorithms have undergone a further development and improvement, resulting in a wider range of application scenarios. As one of the most fundamental and challenging issues in the field of computer vision, the application of object detection in the field of security has also received considerable attention. Terahertz (THz) imaging which is widely used in this area because of the ability to detect hidden objects, as a type of electromagnetic wave imaging with poor imaging performance and low resolution, traditional target detection methods cannot achieve high robustness and effectiveness simultaneously. However, anyway in this possible application scenario, many possible ideas and algorithms have been proposed. This paper analyzes the possibility of applying different object detection methods to terahertz images and analyzes the existing problems in order to give the learner some basic idea and future direction. Several detectors are covered, including the traditional object detection methods and the algorithms based on Convolutional Neural Network(CNN) framework.

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.

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.

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.

As calculated by the Ministry of Culture and Tourism, there were 308 million domestic tourist trips in China during the Spring Festival in 2023, witnessing a year-on-year increase of 23.1%. And the satisfaction of tourists with certain spots can be partly reflected in the comments and scores they made on social media. Therefore, this research was aimed at mining useful information from the comment and scores of Canton Tower. After collecting detailed comment information from the web, this research used the plot module of Python to make data visualization to observe the distribution of users’ location, comment time, and comment label as well as the word cloud of remarks. Then the research used the data set to train three different sentiment analysis models including Naïve Beyas, SnowNLP, and Bert, then compared their accuracy in predicting. This research shows that over half of the comments came from Guangdong Province, most of the tourists were content with Canton Tower, and the number of comments has increased obviously since 2023. In addition, the research found that the model having the highest accuracy of sentiment analysis is the Bert model, about 90%.

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.

Object detection technology is a hot research direction in computer vision field technology, which is broadly used in face recognition, vehicle navigation, aviation and other important fields, with broad development prospects. In recent years, object detection algorithms based on deep learning have also appeared as computer science technology has advanced quickly. Comparing modern object detecting algorithms to conventional ones, this algorithm has gradually highlighted the advantages of high precision and good real-time performance. This article reviews traditional object detection algorithms, and focuses on the HOG algorithm of traditional object detection, reviews deep learning-based two-stage and one-stage object identification systems and weighs the benefits and drawbacks of each. A summary and outlook for the future object detection algorithm development is also provided.

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.

From Siri to ChatGPT, natural language processing has been applied to human-computer conversations with remarkable success. Natural language processing (NLP) is an intimate connection between humans and computers, allowing machine learning to directly translate human language into computer language. They appear to be able to communicate directly with humans. Using a literature review methodology, this paper examines the connection and translation of natural language to traditional computer language. In addition to describing the fundamental principles, primary tasks, and implementation steps of NLP, it analyzes the problems that may arise during the development process in the future. In addition to comparing, citing, and classifying human language and computer language, the paper employs appropriate computer language processing tools. Through a side-by-side comparison of natural language and traditional computer language, the fundamental principles of natural language processing are explained and analyzed concisely, and some helpful suggestions for the detailed processing of natural language are provided.