bluuebunny/crossref_metadata_2025 huggingface dataset:
Cross-platform spam messages classification based on the multiple machine learning algorithms
Authors: Tan Mengliang
Published: October 2023

The proliferation of spam messages has had a detrimental impact on users' experience of emails and social media. Consequently, it is imperative to implement effective spam filtering mechanisms to enhance online experiences. Internet companies have leveraged machine learning algorithms to detect and thwart spam messages. Given the multitude of popular social media platforms, it is critical to evaluate the efficacy of prevalent machine learning algorithms across diverse online platforms. This study seeks to assess the performance of Support Vector Machine, Linear Regression, and Random Forest on social media. To this end, datasets containing spam and non-spam messages sourced from YouTube comment sections and Twitter will be procured. The text data will be transformed using a vectorizer to enable interpretation by machine learning models. Three models employing SVM, Linear Regression, and Random Forest will be trained and deployed to test their effectiveness. The models will be applied to detect spam messages in the test dataset, YouTube comment set, and Tweet set. The performance of the models will be evaluated based on accuracy, F1 score, and precision score. The findings indicate that the models' performance on various social media datasets is not satisfactory, as there is a significant reduction in accuracy.
Cross-data recognition on sign language letters based on convolutional neural networks
Authors: Zhu Zhixi
Published: October 2023

With an estimated 1.5 billion hearing-impaired people globally, sign language is a vital means of communication among them. Meanwhile, the complexity and diversity of sign languages across different regions bring challenges to researchers. As existing studies in this field usually lack the implementation of recognition models with multiple datasets, which limits their practical value in real-life scenarios, this study intends to get around this constraint. This work constructs a baseline American Sign Language Letter Recognition model using Convolutional Neural Network (CNN) and then optimizes it to enhance its ability. Finally, cross-data recognition is carried out. In order to train and evaluate the model, this study gathers data from several sources, including the MNIST sign language set and real-life photos. It also investigates how data augmentation affects recognition ability. Consequently, the CNN model is able to recognize hand gestures for different alphabetic letters in solid backgrounds. Its accurate rate of it reaches about 99.83%. For the extra real-life dataset, which contains 96 images, the model could still detect the majority of the images with sign language equivalents, despite some accuracy loss in more crowded backgrounds. This work, in general, concentrates on the potential of CNNs for sign language identification and highlights the significance of cross-data recognition in creating useful recognition models.
Target tracking and detection based on YOLOv5 algorithm
Authors: Ma Yizhou
Published: October 2023

The YOLOv5 algorithm has gained popularity in recent years as an effective solution for real-time object detection in images and videos. This paper explores its potential for solving the problem of target tracking detection by proposing a modified YOLOv5 architecture that integrates object detection and tracking capabilities.The proposed YOLOv5-based tracking system includes three major components: object detection, object tracking, and object association. The object detection component uses a YOLOv5 model to detect and localize the target object in each frame of the video. The object tracking component then tracks the target object across frames using a Kalman filter and a Hungarian algorithm for data association. Finally, the object association component uses a motion model to handle occlusions and re-identifies the target object when it reappears in the field of view.The performance of the proposed YOLOv5-based tracking system is evaluated on several benchmark datasets, and its results are compared to state-of-the-art tracking algorithms. The experimental results show that the system achieves competitive tracking accuracy and real-time processing speed. Additionally, the effectiveness of the proposed motion model for handling occlusions and re-identification of the target object is demonstrated. In conclusion, the YOLOv5 algorithm has promising potential for target tracking detection in real-world scenarios, and it could have various applications in surveillance, robotics, and autonomous driving.
Performance comparison of different convolutional neural network models and optimizers for dog bread classification
Authors: Mao Xinyu , Xie Mingrui , Zhu Ruochen
Published: October 2023

This study explores the performance of various deep learning models including ResNet152, VGG16, VGG19 and ResNet256 on the dog breed classification task. During training, observe the loss and accuracy trends. The loss gradually decreases, showing that the model is fitting the training data better. With the improvements of the capacity of the model, the accuracy trend shows a steady increase. These models converge after about 20 epochs and fluctuate little after that. The initial learning rate, adjustment factor and patience parameters play key roles in the convergence process. However, the achieved accuracy is below 90%, suggesting that further optimization or more complex architectures may be beneficial. Among all models, ResNet512 has the highest overall accuracy (83%), followed by ResNet256 (83%), VGG19 256 (79%) and VGG16 256 (78%). The ResNet model outperforms the VGG model in most cases, probably because its network structure reduces computational complexity while maintaining accuracy. Increasing the input size can improve the accuracy of the same network structure, such as ResNet 256 and ResNet 512, while modifying the network structure by adding more layers. Learning rate decay scheduling methods, such as ReduceLROnPlateau and CosineAnnealingLR, and optimizers such as SGD, Adam, and Adagrad, are explored as well.
AI education: The application of the AI system with childrens education
Authors: Li Zhuoxuan
Published: October 2023

Since the technology Since the Artificial intelligence is becoming a technology that is mature and efficient for people to use, it is efficient if using this technology at children s education. This paper concluded two experiments that have already done. In the first experiment, they test whether can children use the application to study, and what level of the app that they can use easily. More specifically, they provided both paper and electronic devices with a simple drawing app and a complicated drawing app to children. Then they let children to draw whatever they want. Based on children s reactions, comments, and final products, they concluded that children could do well with the application. In the second experiment, they want to test whether can children study well with AI system. For this experiment, they prepared three activities for students, which were both involved with three different AI system, and all of the AI system were presented to children as the form of robots. In these three activities, children learned about AI, trained their own AI, and finally using AI to draw. Through the whole experiment, children showed high interest in the AI, and they can learn with AI system efficiently. Both of the experiments show that children have the ability to use the new technology, and they should use it, since those technology are interesting to them, and are helpful to them.
The study and application of brain tumors classification based on artificial intelligence
Authors: Peng Rongzhao
Published: October 2023

The application of AI in the medical field has been increasingly popular due to its ability to enhance the accuracy and efficiency of diagnoses. A plurality of individuals employs diverse techniques and refine existing methods in order to achieve greater precision. This paper provides an overview of the methodology utilized in early research papers for brain tumors classification. This includes the input of datasets, preprocessing, model building, training and testing, evaluation, and application. In addition, this paper presents four models used in early research, including the Artificial neural network (ANN) which mimics the organization and operation of neural networks in living organisms by processing information through interconnected nodes or neurons. Another model is k-Nearest Neighbor (KNN), an instance-based learning algorithm that labels new data points by comparing them to the K closest labeled data points in the training dataset. The third model is Visual Geometry Group-16 (VGG-16), a 16-layer Convolutional Neural Network (CNN) that is highly regarded for its simplicity and effectiveness and is one of the top-performing CNN models used by VGG. Lastly, GN-AlexNet is a hybrid learning mode that combines the GoogleNet structure with the AlexNet model. The remainder of the article explains the utilization of this AI in three application scenarios, namely as an assistant for doctors, patients, and insurance companies. Moreover, the paper highlights potential challenges that may arise in both the present and future, such as patient mistrust and the emergence of new models like Vision Transformer (ViT) that could potentially outperform CNNs.
Revolutionizing law enforcement: The role of artificial intelligence in license plate recognition
Authors: Zhan Kewei
Published: October 2023

This paper aims to offer a comprehensive review of the current state of the art in artificial intelligence (AI) as applied to license plate recognition. With the rapidly evolving nature of AI technology, deep learning approaches have gained popularity in license plate recognition, as exemplified by the success of AlphaGo. The diversity of AI in license plate recognition is notable, with numerous studies proposing systems that have achieved high accuracy in segmentation and recognition. The process of reading license plates is complex and involves several stages, including image capture, pre-processing, license plate identification, character segmentation, and recognition. Law enforcement widely uses automatic license plate recognition (ALPR) technology for detecting and preventing criminal activities, tracking stolen vehicles, and identifying suspects. Additionally, ALPR technology can monitor travel time on significant roadways, which can provide the Department of Transportation with useful data for efficient traffic management. Overall, this paper highlights the importance of AI in license plate recognition and its potential to revolutionize the field.
Evaluation of histogram image defogging methods based on histogram equalization, dark channel prior, and convolutional neural network
Authors: Chen Maojia , Li Xiaoqing , Liu Yifan
Published: October 2023

Air pollution condition is getting worse with the advancement of society development, environmental pollution has gradually intensified, and smog frequently occurs in more and more cities. On foggy day, the saturation and contrast of an image could be low, and colors tend to drift and distortion. As a result, seeking a simple and effective image de-fogging technique is important for the subsequent research. In this study, three existing classical de-fogging algorithms are reproduced: histogram equalization, dark channel prior method, and convolutional neural network. The three de-fogging algorithms were compared respectively under the conditions of thin fog, thick fog, high brightness, and low brightness, so as to analyze their advantages and disadvantages. It is concluded that there is no obvious difference among the three algorithms in the de-fogging effect under the conditions of thick fog and high brightness, but relatively speaking, the de-fogging image generated by the dark channel prior is more real. When the fog is thin, the dark channel prior and convolutional neural network work better. Under the condition of low brightness, the histogram equalization has a better de-fogging effect.
Adaptive block level bilateral filtering algorithm
Authors: Zhang Yuhao
Published: October 2023

During the acquisition or transmission process, video images are subject to random signal interference and generate noise, which can hinder people's understanding of the image and subsequent processing work. Therefore, it is necessary to study video image denoising and filtering algorithms. Bilateral filter is one of many typical video image filtering algorithms. However, the traditional bilateral filter algorithm does not consider the differences in the contents of different regions of the image. It is difficult to obtain the optimal filtering effect by using a fixed filtering weight to filter the entire image, which leads to problems such as blurred image edges and inadequate details processing. This paper studies the influence of different filter block sizes on the bilateral filter effect, and proposes an algorithm to adaptively update the bilateral filter weight according to the block's variance. The experimental result shows that the performance of adaptive bilateral filter with different block sizes is expected to be better than that of traditional algorithms with fixed filter weights.
Applying machine learning models to breast cancer prediction problem
Authors: Mai Ziqi
Published: October 2023

Cancer has become the number one killer of human life and health. Therefore, a model that can predict cancer is able to help doctors to diagnose whether a patient has cancer or not, which can boost the accuracy of the diagnosis and enhance diagnostic efficiency, thus reducing the chance of misdiagnosis and other situations. This paper focuses on breast cancer prediction and adopted three machine learning based methods, including logistic regression, K-Nearest Neighbor, and decision tree models to build automatic solutions and investigate which model is more suitable for such a simple prediction problem. In this study, the detailed features, data collection and pre-processing approaches are presented to better understand such medical data. Then extensive experiments show that the accuracy scores of the three models are 97.08%, 94.89%, and 93.43%, respectively. Through comparison, it is concluded that the logistic regression model achieves the best performance for the breast cancer prediction task.
Digital signal processing techniques for image enhancement and restoration
Authors: Li Yiyang
Published: October 2023

Digital image processing has become a fundamental tool in modern image processing, including image enhancement and restoration. This paper reviews important image enhancement and restoration techniques in digital image processing. First, some important image enhancement techniques such as histogram equalization are introduced and compared in detail, including their advantages, disadvantages, and application scenarios. Secondly, for image restoration techniques, this paper introduces deblurring techniques such as deconvolution and blind deconvolution, explaining their working principles and application scenarios in detail. Finally, this paper introduces the development and applications of super-resolution technology, and explores their possible future development directions. This review provides comprehensive technical references for researchers in digital image processing.
Machine learning for privacy-preserving: Approaches, challenges and discussion
Authors: Pan Ziqi
Published: October 2023

Currently, advanced technologies such as big data, artificial intelligence and machine learning are undergoing rapid development. However, the emergence of cybersecurity and privacy leakage problems has resulted in serious implications. This paper discusses the current state of privacy security issues in the field of machine learning in a comprehensive manner. During machine training, training models often unconsciously extract and record private information from raw data, and in addition, third-party attackers are interested in maliciously extracting private information from raw data. This paper first provides a quick introduction to the validation criterion in privacy-preserving strategies, based on which algorithms can account for and validate the privacy leakage problem during machine learning. The paper then describes different privacy-preserving strategies based mainly on federation learning that focus on Differentially Private Federated Averaging and Privacy-Preserving Asynchronous Federated Learning Mechanism and provides an analysis and discussion of their advantages and disadvantages. By improving the original machine learning methods, such as improving the parameter values and limiting the range of features, the possibility of privacy leakage during machine learning is successfully reduced. However, the different privacy-preserving strategies are mainly limited to changing the parameters of the original model training method, which leads to limitations in the training method, such as reduced efficiency or difficulty in training under certain conditions.
Modelling methods of artificial intelligence in medical application
Authors: Sun Yixuan
Published: October 2023

Artificial intelligence is a branch of computer science, an intelligent system that can simulate human thinking, recognize complex situations, acquire learning abilities and knowledge, and solve problems. With the continuous development of information technology, artificial intelligence techniques are increasingly being improved and applied to large-scale genetics research, image detection and classification in medicine. Predictive models for medical data can be built using a wide range of machine learning algorithms: decision trees, multilayer perceptrons, plain Bayes, random forests, and support vector machines, etc., thus processing massive, high-dimensional data and conducting medical research. This paper addresses the specific applications of artificial intelligence in medical practice.
Detection of malicious websites across multiple classes using n-gram features and VGG based on URL analysis
Authors: Liu Qichen
Published: October 2023

Due to the ubiquity of the internet, cyber-attacks implemented through websites have become a severe issue with high frequency and appreciable overall financial damage. Detecting malicious URLs has become one of the most common solutions to tackle this threat, which is widely applied in the market and researched. Inspired by relevant work on URL classification using n-gram techniques and convolutional neural networks in other research areas, a method for detecting malicious websites using n-gram statistical features of URLs and a VGG-style neural network has been developed, which aims to provide classification for multiple website classes with arbitrary URL input lengths. Experimental results show that the method proposed in this paper provides an average accuracy of 96.60% on the 5-class ISCX-URL2016 dataset and 96.33% on the 4-class Malicious URLs dataset, which is 1.5 times larger. A further comparison reveals that the accuracies are competitive with similar methods for binary classifications that also use either n-gram features or a VGG-based network.
Survey of object detection in terahertz images
Authors: Li Hongshan
Published: October 2023

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.
Analysis on connection and translation of natural language to traditional computer language
Authors: Liu Hanxiang
Published: October 2023

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.
Lung cancer risk prediction and feature importance analysis with machine learning algorithm
Authors: Li Xinzhu
Published: October 2023

The morbidity and mortality of lung cancer are high and the detection is difficult, which poses a great threat to people's health. Early detection and accurate diagnosis can significantly improve survival rates. In recent years, machine learning models have been widely applied to classify and predict the risk of getting lung cancer based on clinical features and certain environmental factors. In the research, a decision tree model and a random forest model are developed and validated to predict lung cancer risk using a dataset that contains patients living environments and clinical symptoms. The research also compared the amount of key features which are used in both decision tree and random forest model during prediction. Additionally, several key features in the prediction period are identified and are applied to a winform application designed for patients to test their risk level of getting lung cancer. The accuracy of the model used in winform application shows that the application can effectively predict lung cancer according to the clinical symptoms and living environment of patients, which proves that it has good application value.
Analysis based on object detection algorithms
Authors: Lyu Puran
Published: October 2023

With the increasing demand for intelligent systems capable of comprehending visual information, the discipline of image object detection has experienced rapid expansion. Despite the fact that numerous methods have been proposed, the existing literature lacks exhaustive analyses and summaries of these methods. This paper seeks to address this deficiency by providing a thorough overview and analysis of image object detection techniques. This paper analyzes and discusses traditional methods and deep learning-based methods, with a focus on analyzing the current state and shortcomings of traditional methods. Further discussion is given to deep network-based object detection methods, mainly through a comparative analysis of two-stage and one-stage methods. The basic performance of the You Look Only Once (YOLO) series methods is highlighted. The contribution of large-scale datasets and evaluation metrics to the advancement of the state of the art is also examined. This comprehensive analysis is a useful reference for researchers who aim to contribute to the continual progress of image object detection.
Research on information construction of university management system app based on cognitive model
Authors: Shi-Shi Hao , Qin-Ling Dai
Published: October 2023

Based on the results of interviews conducted with university students in a certain university in Yunnan Province, this study identifies the problems of excessive information navigation, complex interface layout, and difficulty in locating the school system within the university management system APP. From the perspective of information architecture, this study designs a cognitive model that is suitable for the university management system APP of a certain university in Yunnan Province. By conducting tracking surveys and interviews with students from different majors, the study utilizes the affinity diagram method to construct a cognitive model for student users, which is further categorized by involving 10 participants. The data obtained after categorization is analyzed using hierarchical cluster analysis, and the information construction of the university management system APP is restructured accordingly. Through experiments, the characteristics and existing problems of the informationization construction of the university management system APP are determined, and the navigation label information of the management system is improved. The cognitive model of the university management system APP is reconstructed, resulting in the development of navigation names and classification methods for information construction that align with the cognitive models of student users. The research findings provide a basis and reference for other university management system APPs.
A virtual reality memory scene designed with memory reproduction as the concept
Authors: Li Jiaying
Published: October 2023

The use of VR (virtual reality) in memory recovery has considerable potential, and this is just beginning to materialize. Those exploratory studies conducted so far suggest that VR's participation will help improve the assessment of memory impairment and memory repair using recombinant techniques. Based on the current virtual reality technology is mostly used for game development, the relevant research on memory reproduction is relatively insufficient. In this paper, a memory gallery was built through 3D (three dimensional) modeling, and Metahuman technology was used to achieve character reproduction, combined with unreal5 development to achieve important memory reproduction. This work mainly uses 3D models to make a memory corridor, Metahuman digital virtual human technology and VR Chat VR social combination to bring users immersive precious memories reproduction, and can interact with it. Experiments have shown that recall reproduction is of great help to emotional repair. VR can help by making the presentation more immersive.
Galaxy recognition based on improved efficientNetV2S
Authors: Yan Xingchen
Published: October 2023

In order to identify effective metrics that can accurately duplicate the probability distributions resulting from human classifications, this paper analyzes an improved approach for galaxy morphologies classification. At the present stage, this field still faces the problem of insufficient quality and quantity of image data, low accuracy of computer recognition and weak generalization ability of the model. From the previous research, Convolution Neural Network (CNN) can be a valid technique to complete this task but usually spends a large time and space complexity. For the purpose of increasing effectiveness, this paper improves EfficientNetV2S to construct recognition models and characterize their performance in galaxy recognition. The procedure includes data preparation and augmentation, model structure creation, attention mechanism addition, fine-tuning, and result visualization. A Fused mobile inverted bottleneck convolution (Fuse-MBConv) structure was used to accelerate the model's convergence speed. Besides, the Convolutional block attention module (CBAM) was used to improve performance and feature representation capabilities. The model in this study can minimize complexity with the number of parameters and utilize less memory while maintaining excellent accuracy. This research is conducted on the Galaxy10 DECals dataset. Experimental results show that it achieves an 87% high precision with 20.6m parameters which is more efficient than models currently used in previous research.
Application and analysis of residual blocks in galaxy classification
Authors: Cheng Wenzheng
Published: October 2023

In astronomy, the automated galaxy classification method based on deep learning has significantly reduced the cost of manual annotation. The degradation problem in convolutional neural networks during galaxy classification tasks limits the accuracy improvement of deep models. Therefore, to address the issue of the model being too deep, which leads to a decrease in accuracy, the paper constructs the galaxy classification model using residual block structures. Specifically, this paper uses an improved ResNet as the backbone, stacking different numbers of residual blocks to extract input features. Meanwhile, smaller and deeper fully connected layers, regularization and activation functions, and Dropout layers are used to improve the model performance. For the best-performing ResNet152 model, the paper analyzes the classification report and confusion matrix and visualizes saliency maps and GradCAM heatmaps. Finally, the experimental results show that the introduction of residual blocks can increase the accuracy of the model by over 30%, and models with more residual blocks perform better, although the influence of the number of residual blocks on accuracy improvement is small. The visualization results show that the model can accurately segment the feature focus areas and points of interest in the original image. The model also focuses more on the central points with high planetary density by stacking multi-level residual blocks.
Improvement of data mining technology in the context of big data
Authors: Qin Zitong
Published: October 2023

As big data continues to permeate every facet of modern life, a growing number of academics are doing studies on the topic. If the potential of data mining is fully explored and researched, there is no doubt that the sector will grow economically. In other words, it is believed that the data information management work in these industries will go more smoothly if the data mining technology and its algorithm can be better applied to various industries and fields related to big data at this stage, handle these data well, and get the desired information. In order to accomplish the objective of increasing the sector's worth. Utilizing a literature review method, this research examines how different businesses have improved their data mining practices, demonstrating how this practice has advanced in the era of big data.
The information safety and ethical issues about ChatGPT
Authors: Li Linkun
Published: October 2023

With the development of artificial intelligence, ChatGPT technology has been widely used in daily life, bringing people a lot of convenience. However, the use of these technologies also brings some privacy and security risks. This paper focuses on the ethical and information security issues involved in ChatGPT technology, and propose corresponding preventive measures and solutions. In the initial stages of ChatGPT's development, we need to take full advantage of its benefits in terms of increased longevity and productivity, while developing stricter management regulations and usage specifications. Finally, this paper puts forward some measures to strengthen the information security and ethical protection of ChatGPT and other natural language generation models from multiple aspects of technology and ethics. These include establishing more secure data protection mechanisms, strengthening the security design of models, and establishing relevant ethical codes and regulations. At the same time, strengthening the research and supervision of natural language generation models such as ChatGPT can better promote their healthy development. Here are some useful references for organizations and individuals using ChatGPT technology.
Optimizing application and algorithm complexity of machine learning methods in traffic classification
Authors: Tan Qiyuan
Published: October 2023

As the Internet continues to evolve, it has become crucial for Internet Service Providers to analyze and classify their network flows. This enables them to identify suspicious activities and offer personalized services. Machine learning has been extensively deployed in network traffic classification, presenting a promising but challenging avenue. One of the primary challenges in applying machine learning to network traffic classification is reducing the computational resources used in training and implementing the model. By devising lightweight algorithms, traffic flow can be classified using fewer computational resources, effectively curtailing the escalating costs associated with the growing volume and transmission rate of traffic. In this study, we compare the performance of three classic machine learning algorithms - logistic regression, support vector machine, and shallow feedforward neural network - by employing them to classify mobile countries of origin, aiming to use as few features as possible. Remarkably, by utilizing only four features from the dataset, these three algorithms achieved an accuracy rate of 89%. This underscores the potential for computational and cost efficiency in network traffic classification with optimized machine learning methods.
Denoising convolutional autoencoder for improving the classification performance based on noisy galaxy images
Authors: Fu Wenda
Published: October 2023

Images of galaxy objects are of great importance to the work of astronomers. Nowadays, the task of galaxy classification has been aided by Neural-Network-based classification models, who are powerful yet vulnerable to the attack of noisy images. In this research, RGB noises and bright spots simulating stars were generated, and a Convolutional-Neural-Network (CNN) based lightweight denoising image autoencoder was proposed. Firstly, a benchmark CNN classifier using DenseNet structure was trained on the Galaxy 10 DECals dataset, which consists of over 17, 000 RGB color galaxy images. Then, noisy images were generated by adding bright spots of different size and color simulating stars and applying gaussian RGB noises over the original images. The CNN autoencoder that consists of Convolutional layer in its encoder and Convolution Transpose layers in decoder was trained on the raw and noisy training data to learn effective galaxy image denoising. Finally, the effect of the autoencoder was evaluated by contrasting the performance of the CNN classifier over the noisy and denoised images. In contrast with being evaluated on the raw testing set, the CNN classifiers accuracy dropped by 0.41 when tested on the generated noisy testing images, indicating the effectiveness of the attack of image noises. While after denoising with the proposed autoencoder, the classifiers accuracy increased significantly by 0.37. Output denoised images also suggest that the autoencoder can effectively remove the applied bright spot and gaussian RGB noise, recreating the original shape of the galaxy.
Research on COVID-19 X-ray image recognition algorithm
Authors: Chen Haolei
Published: October 2023

With the Covid-19 pandemic, early identification is considered an important measure to fight against this epidemic. Currently, the risk of secondary infection is high, so it is needed a method that can rapidly identify infected individuals. In this research, two algorithms are used, VGG16 and ResNet50, to classify the lung X-ray images from Kaggle and compare the performance of the two algorithms that ResNet50 has better accuracy than VGG16 on the Chest Radiography Database. Next, the CLAHE image enhancement algorithm is introduced in this research to pre-process the images in the dataset. Finally, the two algorithms, VGG16 and Resnet50, are retrained on the processed dataset and compared with their results. And the results show that the two algorithms are improved but ResNet50 still has better accuracy than VGG16. Thus, the CLAHE algorithm is good in this task, which not only significantly enhances the image quality, but also improves the performance on classification of the neural network model.
Review on the face recognition based on deep learning
Authors: Chen Runyi
Published: October 2023

Face recognition has emerged as a new trend in the era of intelligence due to the rapid growth of artificial intelligence and other cutting-edge technologies. This paper investigates the research and application of face recognition technology based on deep learning through a comprehensive literature review and analysis. The discussion encompasses the classification and processes of facial recognition technology, the incorporation of deep learning into face recognition, and the application of face recognition across a variety of domains. Particular emphasis is placed on the use of deep belief networks (DBN) and convolutional neural networks (CNN) in face recognition. Face recognition technology, facilitated by deep learning techniques, has become pervasive in our daily lives, vastly enhancing our quality of life and productivity.
A systematic review: Deep learning-based methods for pneumonia region detection
Authors: Xu Xinmei
Published: October 2023

Pneumonia disease is one of the leading causes of death among children and adults worldwide. In the last ten years, computer-aided pneumonia detection methods have been developed to improve the efficiency and accuracy of the diagnosis process. Among those methods, the effects of deep learning approaches surpassed that of other traditional machine learning methods. This review paper searched and examined existing mainstream deep-learning approaches in the detection of pneumonia regions. This paper focuses on key aspects of the collected research, including their datasets, data processing techniques, general workflow, outcomes, advantages, and limitations. This paper also discusses current challenges in the field and proposes future work that can be done to enhance research procedures and the overall performance of deep learning models in detecting, classifying, and localizing infected regions. This review aims to offer an insightful summary and analysis of current research, facilitating the development of deep learning approaches in addressing treatable diseases.
Encoder-decoder models in sequence-to-sequence learning: A survey of RNN and LSTM approaches
Authors: Zhang Yunong
Published: October 2023

In today's information age, from natural language processing to audio signal processing, from time series analysis to machine translation, the application of sequence data involves various fields. Encoder-decoder models, as a powerful approach to sequence modeling, have attracted extensive attention and research. This review paper aims to explore encoder-decoder models, focusing on the principles and operational steps of recurrent neural networks (RNN) and long short-term memory (LSTM), aiming to provide researchers and practitioners with a deep understanding of the fundamentals and applications of these models Condition. Through the analysis and summary of relevant literature, this study reveals the advantages of RNN and LSTM in sequence data processing; RNN structure is simple and effective, can process sequence input and output of any length, and can capture the time dependence in sequence data. But there is a problem of gradient disappearance, which makes it difficult to deal with long-term dependencies. To solve this problem, the LSTM model introduces different gating mechanisms, which effectively solve the gradient problem while better capturing long-term dependencies. Through the review of the principles and applications of the two, it provides a useful reference for further research and application.
Push mechanism and its applications in learning and entertainment
Authors: Lu Jinrun
Published: October 2023

Nowadays, people often use the push mechanism in daily life, especially on websites and the applications such as video applications. People often spend a lot of time using it because of the push mechanism. The appearance of the push mechanism deeply affects peoples daily life whether in socializing or entertainment, but most people dont know how the push mechanism works. This paper uses the literature analysis method, case study and review to focus on the development of the push mechanism, principle and its applications. The purpose of this paper is to increase public awareness of the application of the push mechanism and its principle. It can be found that the push mechanism is effective and helpful for all people whether what the identity of the people is.
A review on statistical language and neural network based code completion
Authors: Gao Ze
Published: October 2023

Code completion, also referred to as intellisense, is a prevalent feature of Integrated Development Environments (IDEs) and code editors. It aids developers by automatically recommending and inserting code segments, variable names, and method names, among other things. With the accelerated growth of the software industry and the process of digitalization in recent years, the demand for software engineers has reached a record-high level. Thus, the advancement of code completion is encouraged and has become a popular topic in software engineering. This paper examines and summarizes the development of a statistical language and neural network-based code completion system. The main contents consist of introducing the concepts of code completion system, summarizing the general process of code completion and the evaluation metrics used for performance benchmarking, reviewing and summarizing the existing work conducted on statistical language approach and neural network approach respectively, as well as the limitations and challenges of existing code completion method, and finally forecasting the future development of code completion techniques.
A survey of cross-modal pre-training
Authors: Sun Yitong
Published: October 2023

Pre-training allows autonomous learning of samples inherent semantics from unlabeled data in a large scale, and then obtains a task-independent general representation. Furthermore, the generalization of model encoding across different tasks can be improved. Inspired by early high-performance pre-trained models for a single modality of text or images, scholars began to explore image-text cross-modal pre-training methods, aiming to achieve the ability of understanding both visual information and textual semantics separately and achieve effective alignment between the two. This paper aims to summarize the development of image-text pre-training. Specifically, it summarizes the feature extraction and representation methods, pre-training tasks and extended downstream tasks used by various mainstream models. This paper also introduces and compares the mainstream models into two types of architectures: single-stream and dual-stream. Through the analysis, it can be concluded that the embedding representation determines the difficulty and performance of the subsequent fusion process, and the two kinds of architectures lead to different levels of fusion capabilities.
Research on the national income prediction based on Python
Authors: Sun Wanping
Published: October 2023

The national income level has always been a topic of concern, and there are many influences that affect the income. This paper focuses on the national work, age, education, marriage, gender, weekly working hours and other dimensions to explore the types of people with annual income above $50,000. In this paper, we select the data collected from the U.S. Census as the data set, divide the training set and the test set, and then construct logistic regression and decision tree models to predict the national income respectively. The experimental results show that the ACC of the logistic regression model is 0.773 and the AUC is 0.515, and the ACC of the decision tree model is 0.860 and the AUC is 0.900. It is verified that the decision tree has better performance in predicting national income.
Arthur Prior: A Calvinist route to logic
Authors: Grimshaw Mike
Published: October 2023

Arthur Prior is best known for tense logic and recent interest has also turned to his work in philosophical theology. It is also well known that Prior was deeply interested in Scottish moral philosophy up to 1949. Prior was a theology student before he turned to philosophy and even when a philosophy student he continued to think about and write on theology, stating in a letter from 1936: “I have hopes of ending up eventually as the editor of a religious periodical.” Prior’s theology was strongly influenced by the Swiss neo-orthodox theologian Karl Barth- and also by the nineteenth century theologian F.D. Maurice. What is far less well known is that Prior was also influenced by the work of John Calvin. This paper traces the influence of John Calvin on Prior’s thought via previously unknown (and recently published) letters and unpublished articles written by Prior. It argues that Logic and the Basis of Ethics (1948) is where Prior the theologian finally becomes Prior the logician, and does so because of the limits of Calvinist logic.
To write and think ‘in his own tongue’: Arthur Prior, Jack Bates & the New Zealand Journal of Theology
Authors: Grimshaw Mike
Published: October 2023

While the influence of John Findlay on Arthur Prior is well known, there are others who, in their own way, had a significant influence on the thought of the young Arthur Prior and the development of his intellectual career. In particular, both Lex Miller and Jack Bates provided support, ideas, and an example as to how to combine theological and political and philosophical interests that Prior followed for many years. This article discusses the influence of Jack Bates, and in particular the journal Bates cofounded with Jim Steele, the New Zealand Journal of Theology (hereafter NZJT), that Prior wrote for in the mid-1930s. It is argued that Bates can be seen to provide a model of combining theological and philosophical work and interests, as well as how to engage in the type of religious journalism that Prior noted, in 1936, was now his aim (Grimshaw 2018, p.93). Prior contined, via his roles as elder in the Presbyterian Church, to interact with Bates until Prior and his family finally left New Zealand for Manchester at the end of 1958.