bluuebunny/crossref_metadata_2025 huggingface dataset:
A comparison of feature extraction methods in image stitching
Authors: Zheng Junxin
Published: October 2023

The usage of feature detectors for image stitching has become a popular research area in computer vision. Various feature extraction algorithms can be used in the process of image stitching process, but they perform varyingly when handling different images and no single algorithm could outperform all others. This paper focuses on the comparison of feature extraction algorithms used for panoramic image stitching. The research utilizes the SIFT, ORB, AKAZE, and BRISK to conduct feature points and match feature points on a group of image sets. The RANSAC algorithm is then used to filter out the outliers and calculate the homograph matrix. Completes the panoramic with image splicing and smoothing through the matrix transformation. Derived from the comparison of the matching and stitching results, the AKAZE detector is found to be the fastest feature point detection and extraction algorithm, while the SIFT detector will provide more feature points to make more accurate matches possible. These findings have implications for the development of efficient and effective computer vision technologies for various applications.
How AI evolved with game and implementation of modern AI in game
Authors: Shen Bohan
Published: October 2023

For a long time, game was a relatively unrecognized area by academic community, which lacks detailed and sufficient discussion. But with the growth of game industry, game AI has become a heated topic in recent years. As an important and evolving application of AI, there is a need to better discuss the application and future improvement of game AI technologies. This paper introduced history and breakthrough of AI made in game area. And made discussion centered on current implementation of some popular approaches for game AI, followed by the possible future of these technologies. Some new implementations like procedural content generation were then covered to further discuss future implementations of AI in game area. All in all, hot spots and development prospects of this research topic were prospected to enlighten future development of game AI.
Semantic information based solution for visual SLAM in dynamic environment
Authors: Shao Chuqi
Published: October 2023

In recent years, the utilization of visual SLAM with a camera as a sensor has become increasingly widespread, particularly in the context of rapidly developing artificial intelligence such as mobile robots, VR, and AR. This approach is favored due to its affordability, lightweight design, ability to capture comprehensive information, and other advantages. The traditional slam technology has achieved a very mature effect with the static scene as the assumption condition, and the classic representatives are LSD-SLAM and ORB-SLAM which will be introduced in the following. However, since dynamic scenarios are unavoidable in the real world, overcoming the influence of dynamic objects becomes a challenge if researchers want to move forward with more applications. At present, under the dynamic environment of the visual slam algorithm faces positioning problems of low accuracy and poor robustness. To address the challenges posed by dynamic objects in a scene, many researchers are incorporating deep learning techniques and exploring the use of reference semantic information to collaboratively resolve the issue. This paper reviews this and summarizes the development process and important algorithms.
Image classification based on CNN with three different networks
Authors: Sun Wenyan
Published: October 2023

Image classification refers to classifying images based on the different features reflected by the information in each image. Image classification is a fundamental issue in computer vision and has important significance. It has a wide range of applications, such as autonomous driving, face recognition, image retrieval, and other fields. This article first gives a birds-eye view of the development of image classification and briefly introduces the factors that affect the accuracy of convolutional neural networks. Experiments and comparative analysis are conducted on the effects of convolutional layer numbers and optimizers on the accuracy of convolutional neural networks. Three convolutional neural networks with different convolutional layer numbers are built, their structural design is introduced in detail, and their structural diagrams were given. Different models are used to classify images from the CIFAR 10 dataset. The experimental results show that with the continuous increase of the convolutional layer, the accuracy rate is continually improving, but the program running time is continually increasing. In addition, using different optimizers can lead to changes in accuracy.
Prediction of carbon dioxide emissions based on machine learning algorithms
Authors: Chen Zhihuang
Published: October 2023

The escalating emergence of environmental issues, including the greenhouse effect and coral bleaching, has raised global awareness of the significance of sustainable development and preservation of the earth's resources. Although reducing emissions is essential to mitigate the adverse effects of greenhouse gases, it remains challenging to detect without specialized equipment. This constraint is particularly burdensome for small organizations and individual groups due to the high associated costs. Therefore, this study proposes using machine learning algorithms and common vehicle attributes to predict greenhouse gas emissions accurately. Specifically, the research employs the random forest algorithm, incorporating vehicle power parameters to predict carbon dioxide emissions. The study employs Mean Square Error (MSE), Root Mean Square Error (RMSE), and R-squared metrics to analyze the model's effectiveness, accuracy, and feasibility in predicting greenhouse gas emissions. This approach will enable small groups to participate in environmental protection efforts, democratizing the process for all who desire to safeguard the environment.
Automated classification of brain tumors based on the convolutional neural network
Authors: Jin Zhekai
Published: October 2023

The treatment of brain tumors, utilizing conventional methods such as surgery, radiotherapy, and chemotherapy, is limited in terms of accuracy and effectiveness. Furthermore, there exists a possibility of missing the diagnosis for small lesions and certain benign tumors with comparable density to normal tissue. To improve the precision and efficiency of brain tumor diagnosis, recent developments in artificial intelligence have been explored, including the use of Convolutional Neural Networks (CNNs). This research investigates the potential of a four-class CNN-based deep learning algorithm for the diagnosis of brain tumors. A dataset of MRI images, including various forms of brain tumors, underwent preprocessing and cleansing, and was subsequently classified into four categories. The CNN model trained to identify and diagnose MRI images achieved an 85.4% accuracy on the validation set. This study underscores the potential of CNNs to enhance the detection and precision of brain tumors, in addition to improving the consistency and dependability of diagnosis, thereby providing new leads for the discovery of novel therapies and medications. However, the study recognizes that limitations and areas of improvement exist in terms of dataset size, model architecture, and evaluation metrics.
Stock price prediction on Australian companies under Chinas trade restriction based on the LSTM model
Authors: Lin Xinrui
Published: October 2023

The volatility of Australian companies' stock prices in 2020, caused by China's trade restrictions, poses a significant challenge for predicting financial gain or loss. This research contributes to future scholarship in predicting stock prices under specific circumstances or during special time periods. The study proposes a novel approach to stock price prediction, incorporating news sentiment analysis into a deep learning model. The research collected news items potentially affecting the stock price, incorporating them into an analysis model to generate a new feature for the Long Short-Term Memory (LSTM) model. The LSTM model used in this study was bidirectional, with two sets of gates per layer, and a three-layer model with different units. Each layer employed a dropout layer and a dense layer in the final stage. The study also utilized the feature engineering of lookback, selecting a window of time in the past to predict the next day's stock prices. Following multiple hyperparameter tunings and feature engineering adjustments, the results and graphs demonstrate a successful prediction for all three of the chosen companies, even during an unstable stock market. The overall trend lines achieve optimal predictions for the stock prices, illustrating both upward and downward trends.
Taxi fare prediction based on multiple machine learning models
Authors: Huang Hao
Published: October 2023

The use of taxis as a fundamental mode of transportation in everyday life has led to the increased popularity of various ride-hailing applications such as Uber and Lyft, enabling users to conveniently request and view the predicted fare for their desired destination. Accurately predicting the fare is thus of significant importance. In this study, machine learning models were employed to forecast taxi fares based on factors such as distance and passenger count. As the initial data only contained latitude and longitude values, the Haversine formula was utilized to calculate the distance between two locations. Moreover, the raw data was plagued with inconsistencies such as negative fares and grossly exaggerated distances, which were resolved by implementing four data cleaning criteria. Following the preprocessing stage, three distinct models (i.e., linear regression, decision tree, and random forest) were trained and evaluated using the root mean square error metric. The results indicated that the random forest model produced the smallest error (1.264), followed by the decision tree model with a similar error rate (1.277), and lastly, the linear regression model with the highest error (1.718). Thus, the random forest model demonstrated superior performance and is recommended for accurate fare predictions.
A secure system for electric vehicle battery swap stations using blockchain
Authors: Qiu Ke
Published: October 2023

As electric vehicles become more popular, battery swap stations are gaining attention as a new type of charging facility. However, the charging process for electric vehicles involves privacy information such as user location and charging mode, which can be easily stolen or leaked, posing security risks and personal privacy concerns for users. Therefore, protecting the privacy of electric vehicle battery swap station users has become an important issue. This paper aims to study a privacy protection system for electric vehicle battery swap stations using blockchain technology. First, the basic principles and application scenarios of blockchain technology are introduced. Second, potential privacy leaks in electric vehicle battery swap stations are analysed, and a privacy protection scheme based on blockchain is proposed, including anonymous identity authentication, zero-knowledge proof, and encrypted communication. Third, a blockchain-based privacy protection system for electric vehicle battery swap stations is designed and implemented, and its performance is experimentally evaluated and compared with traditional privacy protection schemes in terms of security and efficiency. This paper demonstrates that the blockchain-based privacy protection scheme for electric vehicle battery swap stations possesses high levels of security and reliability, effectively safeguarding users' privacy information. Furthermore, this scheme exhibits promising application prospects and potential for widespread adoption. With the continuous development and utilization of blockchain technology, the privacy protection scheme for electric vehicle battery swap stations using blockchain is expected to provide users with more secure, reliable, and convenient charging services.
A research on PDF protection mechanisms
Authors: Jiang Linfeng
Published: October 2023

Portable Document Format, also named PDF, can integrate images, text, tables, or other elements into a single file, making it an easy-to-store and transfer document format. Due to the biggest feature of PDF being its portability, the modification of such files is very difficult and can ensure the security of the text to a certain extent. Therefore, it has been welcomed by a large number of researchers, office workers, and students. But as the demand for PDF files continues to rise, its security vulnerabilities are gradually being exposed. Consequently, a plethora of security measures for PDFs has come into view. This article primarily summarizes a portion of the overarching principles governing PDF security. Subsequently, this paper will focus on introducing the principles of steganography and cryptography in PDF security protocols while also selecting meaningful and representative research papers to summarize the advantages and disadvantages of the proposed methods.
Comparison of models of deep convolutional neural networks
Authors: Wang Zihan
Published: October 2023

Recently, deep learning has gained considerable success and acceptance in a variety of fields, attracting an increasing number of researchers who are delving deeper and gaining a broader perspective on the subject. It provides more sustainability and opportunities to advance the development of society and transform the lives of individuals. Consequently, it is crucial for individuals to understand the neural network development path. This paper provides a concise overview of the structure and components of Convolutional Neural Network, as well as some of the most well-known and influential learning models in the history of its development. Through an analysis of various models of convolutional neural network, the workings of convolutional neural networks were investigated. The paper discovered that the structure of neural networks is becoming deeper and more complex in order to achieve greater efficacy and avoid the overfitting issue. For researchers to enhance and advance neural network performance, there are still numerous parameters and perspectives to improve and advance.
Research on the optimum path Taking the hospital delivery robot as an example
Authors: Liu Guodong
Published: October 2023

Medical delivery robot refers to the delivery robot used in the medical field. Compared with ordinary delivery robots, the medical delivery robot needs to work in an environment with many people, which means that it needs to deal with many random obstacles at any time. This article will discuss automatically avoiding obstacles, and formulate and compare algorithms to analyze the advantages and disadvantages of automatic road exploration algorithms of different algorithms. In order to accomplish this goal, this paper will use Matlab as the main development tool and use the A-star algorithm and the Euclid algorithm as the main heuristics in the main mathematical model of the program. This program needs to be able to complete the obstacle avoidance task in a map with random size, random position, random shape, and random number of obstacles, and be able to reach the end point from the starting point. In addition, this paper will also discuss the efficiency of the A-star algorithm and the Dijkstra algorithm in obstacle avoidance and route planning, and demonstrate why the A-star algorithm is more efficient by taking time and congestion indicators.
Visualization analysis and logistic regression based heart disease risk prediction
Authors: Zhao Haoyu
Published: October 2023

Heart-related illness is the major cause of global death. The optimal solution to tackle this problem and to improve public health is early detection and prevention. Manually diagnosis is tedious and time consuming, which is difficult to be applied for large scale medical inspections, and hence machine learning, computer-based automatic algorithms could be adopted. Logistic regression is a commonly used statistical method for predicting the risk of binary outcomes, such as the presence or absence of heart problems. In this study, logistic regression is leveraged to a dataset of medical records. It is not only developed as an effective model for the early detection of heart disease, but also leveraged for identifying the crucial risk factors of the disease. The results showed that the logistic regression model achieved a high level of accuracy for heart risk prediction, which overall accuracy is 85%. Factors including sex, cholesterol level, age, and blood pressure are observed possessing highest correlations with heart disease.
The study and analysis related to the evolution of the knowledge tracing research
Authors: Bai Yu
Published: October 2023

In the field of education, the level of awareness of students' knowledge status largely affects students' learning efficiency. Knowledge Tracing, as a method of modeling students' learning status, obtaining their knowledge status to predict their future learning can provide more targeted learning solutions, which is extremely useful for improving students' learning efficiency and implementing tailor-made education. This paper introduces the related research methods and their core ideas in the order of probability-based models, logistic function-based models, and deep learning-based models by investigating the development of this field since its introduction. Among the probability-based models, the mainstream methods include BKT and DBKT, which are based on Hidden Markov Models with certain assumptions and are not ideal in practical application scenarios. In contrast, logistic-based models consider a variety of factors in the student learning process and have achieved good results. After that, deep learning-based model has achieved excellent assessment results through its powerful feature extraction ability, and many variants have been derived. This paper introduces DKT in detail and briefly introduces its variant models. At last, this paper summarizes some of the mainstream evaluation criteria in this field and gives the widely used datasets and their categories. Finally, this paper proposes some suggestions for the future development of the field in view of the shortcomings of the current research status for reference.
GAN and art: Facilitation of artistic production and expression based on artificial intelligence
Authors: Ran Yihan
Published: October 2023

The advancement of Artificial Intelligence has resulted in the investigation of its applications in a variety of sectors. The creation and depiction of artistic images as a tool for demonstrating human creativity and intellect has been a significant focus of Artificial Intelligence (AI) content generation. This study discusses picture generation approaches and highlights image creation principles as well as numerous varieties of Generative Adversarial Network (GAN). GAN models for image creation can inspire designers and artists, generate images that assist people grasp historical elements by imitating old artistic styles and cultural forms, and generate images of humans. GAN models for image generation can help designers and artists inspire their creativity, produce images that help people understand historical features by mimicking classic artistic styles and cultural forms, and produce images of people that provide intuitive access to information for communication and learning purposes. The application of GAN models in the field of art production is becoming increasingly sophisticated, and many novel applications are yet to be described and popularised. The article summarises the applications of artworks generated by GAN models, for one it can be applied as a thinking aid in art design and the creation of stylised artworks, while GAN-generated images can also help people GAN-generated images can also help people understand history, culture and other related knowledge, making profound art forms more common and popular. The article also analyses the potential hazards of GAN model-generated art images that are related to the passion and creative rights of the creators.
The application of machine learning algorithms in speech recognition error detection
Authors: Yang Jiayi
Published: October 2023

The field of speech recognition technology has experienced a significant progress in recent years, with error detection being a crucial research domain. The present study offers a comprehensive overview of this area. Prior to the emergence of neural networks, Hidden Markov models (HMM) have been widely employed as a primary framework for speech recognition. However, this model only achieves local optimality, leading to its gradual replacement by neural network models, which have attracted considerable attention from researchers aiming to enhance their recognition performance. Various models, such as variant RNNs, bidirectional recurrent neural networks, and Fastcorrect2 have been developed. This paper introduces HMM, followed by a presentation of several neural network models, which entail a detailed description of their respective framework, principle, and idea. The variant RNN model is designed to enhance the recursive connection between input and output layers, while the deep bidirectional recurrent neural network model simulates the nonlinear relationship between input feature vectors and output labels using two models, namely bidirectional and deep. Additionally, the FastCorrect2 model enhances the voting effect of candidate words and the alignment algorithm. Finally, the study highlights the application of speech recognition error detection in everyday life, emphasizing the importance of speech recognition.
Educational data mining: Methods and applications
Authors: Wang Dong
Published: October 2023

Educational data mining is a rapidly growing field that applies various statistical and data mining techniques to analyze educational data. This paper provides a general review of the literature on educational data mining, focusing on the methods and applications. Methods used in education data mining include classification and clustering. A classification algorithm is a supervised learning technique that seeks to categorize a given set of data objects into specified categories, build a classification model using the input data that already exists, and then apply the model to categorize new data items. The Naive Bayes, Decision Tree, Neural Network, and K-Nearest Neighbors have commonly employed classification algorithms in educational data mining. Clustering is unsupervised learning, whose objective is to divide a collection of data objects into various groups, where samples within a cluster exhibit a high level of resemblance and those between clusters are dissimilar. In educational data mining, the K-means Clustering Algorithm, Grid-Based Clustering, and Hierarchical Clustering are common clustering techniques. Those data mining algorithms are used in education such as student behavior prediction, student bad behavior detection, and student grouping. Overall, this research demonstrates that education data mining has a significant potential to improve educational programmers and student results. To solve the legal and privacy issues associated with the collecting and use of educational data, however, more research and solutions are required.
Research on the applications of natural language processing
Authors: Wen Bo
Published: October 2023

In recent years, there has been a rapid advancement in natural language processing (NLP), leading to notable improvements in areas like sentiment analysis, machine translation, and text recognition. However, belonging to the same field under AI, the research materials and academic topics of NLP are not so adequate. Therefore, this paper introduces and shows the reader a general introduction to NLP for the current research environment, so that the reader can clearly understand the history of NLP, its uses, and the current and future research directions. It also analyzes the current mainstream applications of NLP, including the most basic Rule Based method and the more popular deep learning, development of pre-trained language models such as GPT. Besides, this article also analyzes the current mainstream application scenarios of NLP and its related use of technology, so that readers can have a clearer picture of the research direction of NLP. At the end, this paper also summarizes the general development of NLP, research methods, and application scenarios to provides an outlook on the future development of NLP with respect to these points.
Virtual reality technology in the field of education
Authors: Gao Ruohan
Published: October 2023

Virtual Reality (VR), the technology has been around for years, are now developing rapidly since 2010s, VR game facilities can often be seen in shopping malls and the conception. Metaverse are now mentioned and are put into practise. Moreover, from word-of-mouth through books, blackboards, and ultimately multimedia, the educational approach has consistently developed. But in the process of practice, it has been proved that conventional education is predicated on teacher-cantered instruction, and students frequently wind up being passive knowledge carriers who are unable to fully acquire, interpret, and apply knowledge. In this paper, the new form of education which combined with VR technology will be discussed, and the analysis of positive effect of applying VR on education will be find out. The education of VR is divided into three parts to discuss, school safety education, subject education and vocational education. This paper will conduct a targeted analysis based on the respective attributes of these three types of education. With the knowledge of VR on education, people can carry out more in-depth cooperation and development on future VR and education based on these advantages.
Applications of transformers in computer vision
Authors: Jin Yanxiu , Ma Rulin
Published: October 2023

Recently, research based on transformers has become a hot topic. Owing to their ability to capture long-range dependencies, transformers have been rapidly adopted in the field of computer vision for processing image and video data. Despite their widespread adoption, the application of transformer in computer vision such as semantic segmentation, image generation and image repair are still lacking. To address this gap, this paper provides a thorough review and summary of the latest research findings on the applications of transformers in these areas, with a focus on the mechanism of transformers and using ViT (Vision Transformer) as an example. The paper further highlights recent or popular discoveries of transformers in medical scenarios, image generation, and image inpainting. Based on the research, this work also provides insights on future developments and expectations.
Comparative study of feature extraction algorithms for panorama stitching
Authors: Ding Tianxingjian
Published: October 2023

Panorama stitching is a fascinating and rapidly advancing research field. By integrating many photographs that were taken from various angles and viewpoints, with various exposure and color settings, a seamless image is primarily the aim of panorama stitching. This paper investigates the performance of three widely used feature extraction algorithms Speeded-Up Robust Features (SURF), Scale-Invariant Feature Transform (SIFT), and Oriented FAST and Rotated BRIEF (ORB) for panorama stitching. The study compares these algorithms in terms of accuracy, robustness, and speed. Results indicate that while SURF and SIFT produce more accurate and robust results than ORB, they require longer processing time. The study evaluates the approach on a real-world dataset and demonstrates its effectiveness in creating seamless and visually appealing panoramas. This study provides valuable insights into the trade-offs between different feature extraction algorithms and presents a practical solution for panorama stitching applications.
Research and analysis of browser games
Authors: Chen Yibing , Qin Haoyu
Published: October 2023

In 1987, the first video game was published. People entered a new world to explore gaming. After ten years, browser games were invented. In order to deeply discuss browser games future, this article summarizes the features and current situation of browser games. This article analyzes the strengthens of browser games in three aspects, which are convenient setup for players to start the game, social interaction for players to interact with other people, and free-to-play experience provided for players. The analysis of browser games weaknesses is included in this article as well, which were the potential factors that lead browser games to current situation. The weaknesses are browser games low quality of display and the dependencies of the internet. Also, this article analyzes the reasons why the revenue of browser games reduced, which are related to the presence of mobile games, the reduction of funding, and the stopped service of Adobe Flash player. In conclusion, the full article and the future development of browser games were concluded.
A study of the strategies of the combinatorial game
Authors: Wu Qi
Published: October 2023

The combinatorial game theory is not only a new branch of modern mathematics but also an important subject of operations research. One basic combinatorial game can develop many different rules and different strategies to solve the problems, and both the rules and strategies follow the fundamental game theory to give the answer for the new version of the combinatorial game, such as the Nim game, one of the most basic combinatorial games, which has been researched for many years by a large number of researchers. This paper shows and extends the strategies of two combinatorial games, Tic-Tac-Toe and Green Hackenbush, based on the regular version which is connected with the combinatorial game theory.
Performacnes comparison of CNN-based models for fine-grained image classification
Authors: Lu Yuxin , Pan Yang
Published: October 2023

Fine-grained visual classification is regarded as a more refined classification that can identify specific types of objects. It has been widely used in commodity sales, vehicle recognition and person recognition, etc., and has played a great value in many fields. For example, it requires an algorithm to identify different species of birds or dogs to facilitate more practical applications. This task is difficult since the objects has similar appearance and there exist obvious intra-class variance and limited inter-class differences, where different kinds of birds could share very similar appearance. Deep learning techniques has been applied to image recognition, natural language processing, and many other fields. Several approaches tackling the fine-grained classification problem are proposed. To further demonstrate the different designs of these solutions, in this paper, fine-grained identification methods are compared and analyzed, among which WS-DAN achieves better results and it is preferred to be an effective method, which is expected to be more widely used in this field.
Medical image recognition based on VGGNet19
Authors: Liu Yanke , Wang Honglin , Zhao Yizhuo
Published: October 2023

Gastrointestinal diseases are one of the common clinical diseases, which often require medical imaging for diagnosis and treatment. Recently, the development of deep learning technology has promoted the development of medical image recognition, which provides new ideas and methods for the automatic recognition and analysis of medical images. VGGNet19 is a convolutional neural network model that has attracted much attention because of its simple structure, easy training and better recognition effect. For this reason, this paper proposes an improved VGGNet19 model for medical image recognition of gastrointestinal diseases. Specifically, the project adds an additional fully connected layer and Dropout layer on top of the built VGGNet19 to achieve the recognition of medical images of stomach diseases. Extensive experiments on standard medical stomach images show that the proposed method improves the recognition performance to a certain extent.
Comparison of machine learning methods for estimating customer churn in the telecommunication industry
Authors: Tang Thomas
Published: October 2023

With the rising competition in business, particularly in the telecommunications industry, there has been a growing emphasis on churn prediction. This is attributed to the higher cost involved in attracting new customers than retaining the remaining ones. In telecom churn analysis, the primary goal is to accurately estimate churn behavior by identifying customers who are at risk of leaving. Another objective is to determine the primary reasons for customer churn. Manually predicting the churn in telecommunications is expensive, tedious, and time-consuming. To relieve the burden, machine learning algorithms are introduced to tackle this problem. This article examines several machine learning algorithms for predicting customer churn, including Random Forest, Decision Tree, and Naive Bayes. By constructing and comparing these classification models, the effectiveness of these algorithms is demonstrated. The aim of this work is to examine and compare the effectiveness of various models according to accuracy, which can be applied in real-life scenarios. The result shows that Naive Bayes outperforms the other models.
The comparison of three MST algorithms
Authors: Zhang Rener
Published: October 2023

The minimum spanning tree, connecting all the nodes of a connected, weighted graph with the minimum possible total edge weight, is a fundamental concept of graph theory. Thus, how to find the minimum spanning tree (MST) with higher efficiency appears to be crucial. Three kinds of algorithms for this problem have already been developed: Kruskal, Prim, Boruvka. In order to make readers have a better understanding of the three algorithms in the process of studying and teaching, this paper uses the method of comparing to indicate the differences and commonalities between these three algorithms, draws a conclusion afterwards, and points out the most suitable situations to apply them in the end: Kruskals algorithm is suitable in sparse graphs while Prims algorithm and Boruvkas algorithm are suitable in dense graphs.
Exploring the potential of brain signals beyond traditional medical applications
Authors: Chen Y.
Published: October 2023

This article proposes a new method for utilizing brain signals by connecting them to the fields of electrical devices and phased arrays. The innovative modulation technique presented in this study goes beyond traditional medical applications of brain waves and has the potential to revolutionize the way we interact with technology. The article highlights the implications of this new approach for various fields, including telecommunication, artificial intelligence, and military applications. The paper also explains the BCI processing algorithm, brain signal extraction and analysis techniques, and electrical device arrangement used in the study. Overall, this paper presents a significant contribution to the field of brain signal research and would be a valuable addition to the IEEE community.
The advance of consensus algorithm in blockchain
Authors: Wei Runze
Published: October 2023

As a distributed ledger technology, blockchain has found widespread use in a variety of industries, including finance, the Internet of Things (IoT), healthcare, and manufacturing. This technology addresses the trust issue by converting a low-trust centralized ledger into a highly trusted distributed ledger maintained by various entities. Consensus algorithms are one of the fundamental building blocks of the blockchain, controlling how nodes cooperate and synchronize data to perform secure and reliable activities in a decentralized setting. This paper examines the extant mainstream consensus algorithms, introduces six representative consensus algorithms, analyses their benefits and drawbacks, and discusses the application scenarios and suitability of each consensus algorithm in various blockchain platforms.
Applying multi-layer perceptron and ResNet for handwritten digits recognition
Authors: He Zehui
Published: October 2023

The task of handwritten digit recognition is to recognizing the handwritten digits from pictures. Applying machine learning based models to automatically perform handwritten digit recognition task can significantly improve efficiency. This paper applies two machine learning based models, including multi-layer perceptron and residual neural network, for such a task. Firstly, this paper introduces the basic concept of the simple multi-layer perceptron model and then presents the structure of the residual neural network model. Subsequently, such two models are trained on the MNIST corpus, one of the classical dataset for the handwritten digit recognition task. The data pre-processing, like the splitting of training and test set, is described. Also, the processes of testing and training of the two models are presented. According to the experiments on the test set of MNIST, it is observed that the residual neural network can achieve better performance where the accuracy score is 99.240%, while the accuracy score of the multi-layer perceptron model is 97.260%.
The prediction of stroke and feature importance analysis based on multiple machine learning algorithms
Authors: Li Songhan
Published: October 2023

Stroke is a leading cause of death and disability worldwide, which requires the accurate and timely diagnosis for effective stroke management. Based on the Kaggle dataset, data preprocessing, which included addressing missing values, encoding categorical variables, and normalising numerical features, was done first in the study. Next, this paper implemented three commonly used machine learning models: logistic regression, decision tree, and random forest. To assess the performance of the models, the paper applied accuracy as the evaluation metric, which measures the proportion of correct predictions out of all predictions. This study also identified the most important features affecting stroke risk using feature importance analysis provided by the machine learning. All three models achieved accuracy rates, according to the experimental findings, albeit random forest outperformed the other two models. The reliability of the models for random forest, decision tree, and logistic regression were 0.963, 0.925, and 0.961, respectively. Feature importance analysis revealed that age, average glucose level, and work type were the most important predictors of stroke risk. Findings in this study suggest that machine learning algorithms, particularly the Logistic Regression model, can effectively predict the likelihood of stroke using the Stroke Prediction Dataset. These findings are in line with other research that also showed how machine learning has the potential to enhance stroke diagnosis. The identification of important features affecting stroke risk can provide valuable insights for clinicians and researchers in developing targeted interventions for stroke prevention and management.
Visualizing the role of analytical diffusion models in AR--Focused analysis of the application of stable diffusion
Authors: Zhang Yizhi
Published: October 2023

The article focuses on the application and development of the Stable Diffusion module in the field of artificial intelligence image generation. The article presents a comprehensive description, analysis and discussion of the module's overview, operating environment, usage methods and its instructions, and points out the corresponding advantages and disadvantages.
DDos attack detection method based on machine learning
Authors: Wu Yeefong
Published: October 2023

This paper introduces a novel machine learning-based approach for the detection of Distributed Denial of Service (DDoS) attacks. The proposed method employs three different classifiers, namely Support Vector Machine (SVM), Random Forest, and Multilayer Perceptron (MLP), to accurately classify network traffic as normal or malicious. The approach incorporates various features extracted from network traffic, such as packet length, packet inter-arrival time, and destination port number, to train the classifiers. The results demonstrate high accuracy rates, with Random Forest outperforming the other classifiers in terms of detection accuracy. This proposed method offers a promising solution for detecting DDoS attacks in real-time and has the potential to be integrated into existing network security systems. The issue of DDoS attacks is becoming increasingly critical, with the proliferation of connected devices and the growing dependence on the Internet. There is an urgent need for advanced techniques to detect and mitigate such attacks. Machine learning approaches have shown great potential in identifying anomalous behavior and detecting DDoS attacks in real-time. The proposed method is a promising step towards achieving this goal. Furthermore, the proposed approach has practical applications in the context of network security. By integrating this method into existing security systems, it will enhance the system's ability to detect and prevent DDoS attacks. The approach can be implemented in different network environments, making it versatile and applicable to a variety of settings. In conclusion, the proposed machine learning-based approach provides a robust and effective solution for the detection of DDoS attacks. It is capable of accurately classifying network traffic as normal or malicious in real-time, and has the potential to enhance the overall security of networking systems.
Research on the sharing platform of college instructional resources based on blockchain technology
Authors: Mu Tong
Published: October 2023

The integration of instructional resources in colleges is gradually entering the innovation era from electronic and information technology. A new issue that has emerged is the storage and reading of electronic instructional resources in colleges. Additionally, with the rapid development of blockchain technology, its use in numerous fields has burgeoned. In the information management field, confidential and reliable technologies are still needed to help manage resource storage. Blockchain, which is immutable and untrusted, can be well applied in the establishment of an educational resource sharing platform in universities. The main focus of this paper is as follows: first, to analyze and compare the three educational resource sharing systems proposed by Chinese scholars, enumerate the main contents and functions of each system, compare and analyze the differences of the three systems, and secondly, to put forward the construction framework of a new educational resource sharing platform according to the disadvantages still existing in the three systems. The paper also explores the research on college instructional resource sharing platforms using blockchain. It uses blockchain techniques to build an educational resource sharing system, provides a reliable platform with high security and traceability through intelligent contract technology, and connects user groups through "alliance chain + private chain". Its aim is to facilitate efficient use by users of different levels. The advantages of using blockchain technology to construct a sharing platform for university educational resources have practical significance in further promoting educational informatization.
The fundamental theory of quantum computing, applications in practical fields, and future challenges
Authors: Hung Shengyu
Published: October 2023

Building on the fundamental principles of quantum computing, this paper sequentially presents the development of quantum computing and its associated application domains. We also analyze and compare the differences between quantum and classical computing, highlighting the benefits of quantum computing for specific problems. To comprehend the advantages of quantum computing in particular fields, extensive research has been conducted in three areas: cryptography, chemistry, and artificial intelligence. We also discuss the wider applications of this emerging technology for future investigation, considering the current state of quantum computing development and anticipated trends.
Edge Impulse-based automated machine learning for comedy performance evaluation
Authors: Xing Shibo
Published: October 2023

The evaluation of comedic performances presents a challenge due to the subjectivity of humor and various performance factors. This paper proposes a potential solution to this problem by utilizing artificial intelligence (AI), particularly automated machine learning (AutoML), to develop an automatic comedy show scoring system using a diverse Chinese acoustic dataset and the Edge Impulse platform. The proposed method involves analyzing complete video recordings of performances by the renowned Chinese comedy troupe, Deyunshe, to assess audience responses and critical acoustic elements that contribute to a comedic performance's success. The study collected five comedic clips, totaling approximately 50 minutes, segmented and labeled them for training purposes. This study systematically explored various configurations using Edge Impulse to optimize accuracy and minimize loss, yielding encouraging results that demonstrate the potential of AI-driven scoring systems. The study identifies several avenues for future research, including enhancing the quality and quantity of training data, refining classification algorithms, and exploring alternative machine learning techniques to further improve accuracy and loss rates. Moreover, the research highlights the potential benefits of integrating real-time scoring systems, which could facilitate audience engagement and refining comedic material. This innovative approach demonstrates the potential to develop a more sophisticated, accurate, and reliable method of evaluating comedic performances, offering valuable insights into audience preferences, enabling the production of high-quality comedic content, and fostering a more objective evaluation of talent in the competitive world of comedy, which could significantly benefit the entertainment industry.
1-D convolutional neural network-based crosstalk quality assessment
Authors: Yang Shuxin
Published: October 2023

Due to the current state of the declining field of crosstalk, many studies are engaged in investigating causes and devising strategies for its improvement. Nonetheless, there are often no quantitative measures available for assessing the quality of crosstalk. This study employed recordings made during traditional crosstalk performances to assess crosstalk quality based on the percentage of positive audience feedback sounds such as applause and laughter throughout the performance. This study compares Mel Frequency Cepstral Coefficients (MFCC) and Mel Filterbank Energies (MFE) audio feature extraction methods and compares different classification models by training a one-dimensional convolutional neural network model to explore the model that performs better in audio classification under low audio quality conditions, such as low sampling rate and small signal-to-noise ratio. In this study, the training data set has been divided into two different schemes. One is the sound of laughter, applause, singing and speaking, the other is the sound of speaking, singing and laughter&applause. In this study, the final performance of the different models, including accuracy and loss, is counted. The experimental results demonstrated that the models obtained when the MFE method is used and the audio classification is labeled as singing, applause and laughter, and performance speech training have better performance when the comic audio is classified.