bluuebunny/crossref_metadata_2025 huggingface dataset:
A research on different digital signature schemes
Authors: Fang Yuchen
Published: October 2023

In the past years, digital signature development has rapidly with new products combined with blockchain, named distribute networks, and quantum computers, while there plays a vitally important role in file authorization and verification. In combination with various new technologies, digital signatures present a vigorous vitality, and new algorithms are widely used in varieties of scenarios including banking, financial services, and insurance (BFSI), education, E-government, healthcare, and the military. In this case, there is no paper illustrating a summary of those new digital signature applications, which is the aim of this paper working on. This paper indicates the technology details of digital signatures and blockchain. And the paper discusses which digital signature algorithms are used in different fields to give an overview of the relationship between algorithms and scenarios. Furthermore, the paper demonstrates the comparison in the most commonly used digital signature algorithm containing RivestShamirAdleman (RSA) algorithms, Lamport algorithms, Elliptic Curve Digital Signature Algorithm (ECDSA), and Edwards-curve Digital Signature Algorithm (EdDSA) algorithms on their difference in performance.
Design and implementation of secure student score system
Authors: Su Yiming
Published: October 2023

The purpose of designing this system is to provide schools with a new way to make score certifications of a student and a safer way to search for a students previous academic scores. When a student is applying for a new school to further his study, the school always uses his previous academic scores to decide whether to enroll him. The current way for a school to mark a student and to search for a new students score record is depend on the student himself, which faces the problem of illegal modification and data leakage. The system mentioned in this paper can add a certification that includes the time information and score information. It can also use the digital signatures of teachers and headmasters to ensure that the scores are given by the corresponding teacher. This system does not include the direct Grade Point Average (GPA), but just includes the raw scores of each subject. Because different schools have different ways to turn the raw scores into a GPA and they also have different GPA upper limitations.
Research and design of Chinese cookie model with chun character based on OpenSCAD
Authors: Chen Zhuohao
Published: October 2023

In todays society, the 3D modelling field has become an indispensable part of peoples lives. Whether it is the metaverse, games, industry, or medicine, 3D modelling cannot be separated from them. The models used in various fields are also diverse. In the models of various fields, there are not many models for reference in the food fields biscuit modelling and there are no special views. This article creates a Chinese New Year biscuit with the Chinese handwritten character Spring on it based on the different styles and types of biscuit models in the food industry. OpenSCAD software was used to create this model. This article first introduces the methods and usage of OpenSCAD software that are encapsulated for modelling. Then this paper describes in detail how to layer and create biscuits layer by layer. How to use code to create a handwritten Chinese character and a cream biscuit completely in OpenSCAD is detailed by this article. For the result, this article implements a Chinese New Year biscuit with a reference to the Spring character written by the ancient Chinese calligrapher Yan Zhenqing. This work fully implements every stroke of handwritten Chinese characters in the cookie model, and these details have been recognized by the majority of people in the final evaluation.
Application of artificial intelligence technology in the digital transformation of small and medium-sized enterprises in China
Authors: Wang Ting
Published: October 2023

With the development of a new round of scientific and technological revolution, big data, cloud computing, and other information technologies have injected new momentum into the research of artificial intelligence. The continuous improvement of artificial intelligence technology has had an impact on social life that cannot be ignored. In the wave of digital transformation of global enterprises, the low level of digital construction and blocked digital transformation of small and medium-sized enterprises (SMEs) in China are still prominent. How to seize the opportunities brought by the development of artificial intelligence and effectively apply artificial intelligence to promote the transformation and upgrading of SMEs? To clarify this issue, this paper first uses crawling technology to get the annual reports of SMEs from 2017 to 2021 and analyses how much emphasis SMEs place on AI through Python. Then, this paper analyses the transformation difficulties faced by Chinese SMEs and systematically sorts out the technical means of artificial intelligence that could embed into the main functions of enterprises. Finally, this paper puts forward specific measures for Chinese SMEs to actively apply artificial intelligence for digital transformation and provides suggestions for SMEs to better integrate into the era of the digital economy.
Stroke risk early prediction leveraging machine learning algorithms
Authors: Zhang Chi
Published: October 2023

When the blood supply is suddenly blocked in a part of the brain, stroke could be triggered. With blood supply faulted, brain cells would die bit by bit, and handicap happens according to the affected region of the brain. It could cause paralysis or even death to stroke patients. Early symptom recognition can be very helpful in predicting stroke and fostering a healthy lifestyle. Considering conducting large-scale manually early diagnosis is almost impossible, machine learning is always regarded as the first choice for automatic risk estimation. In this research, multiple models of machine learning are developed and trained to predict long-term of the risk of suffering from stroke. This studys main contribution is a randomized forest which presents a high yield which is verified by different kinds of parameters, for instance, F-measure, recall, AUC, accuracy and precision. The outcome of the paper presents that other models are surpassed by random forest model, with 97.6% AUC.
Diagnosis of breast cancer based on logical regression and random forest algorithms
Authors: Chen Junhao
Published: October 2023

Currently, breast cancer possesses a dominate position among all kinds of cancers. Besides, it is also the major cause of the cancer-related death in China. Considering the existence of various cancer types, it is hard to be accurately diagnosed. After early diagnosis and treatment, the 5-year survival rate of female breast cancer reached 97%. Although early diagnosis is curable, about one third of female breast cancer patients still die of the disease. However, despite early detection and selection of new treatment methods, as many as 50% of women will still have metastasis. At present, as the cause of breast cancer has not been determined, accurate early detection is crucial to reduce mortality. This paper aims to leverage machine learning methods for achieving accurate cancer diagnosis. This paper explains the principal knowledge of logistic regression in detail, and classifies the data set by combining the logistic regression and random forest. It is a binary classification problem, which annotations are made up of malignant and benign. It can be well applied to the logical regression model. The classification results are more than 90% accurate, so machine learning-based solutions have broad application prospects and can produce certain value.
A study of stock price prediction models based on time series analysis: LSTM and CNN
Authors: Zhang Luoyuan
Published: October 2023

The stock market is one of the most dynamic and complex systems and stock price prediction is essential for investors to make informed investment decisions and minimize risks. Over the years, various techniques have been developed for this topic. This paper studied two machine learning models for predicting the price of stock, leveraging time series analysis, namely Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN). This essay use time index as labels, and the data was standardized before training the two models with a step-size. A circulation was created for predicting the daily price of stock with the data of the last three days. RMSE and MAE were two indicators used to assess the models in this task. The results of this study indicated that both of the two models performed well in this topic, and the CNN model showed a better performance than LSTM. As a suggestion, investors should consider other factors such as market trends and risk management strategies when relying on these models to ensure a higher accurate result.
Indicators and predictions of heart disease based on machine learning scenarios
Authors: Fan Jiaqi
Published: October 2023

Nowadays, various patients are suffering from heart disease and even die owing to the disease. According to common knowledge, many health problems can cause heart disease directly or indirectly, e.g., overweight, stroke, high blood pressure, and so on. This study uses Heart Disease Health Indicators Dataset from Kaggle to find out significant indicators of heart disease or heart attack, and predicts heart disease by logistic regression, random forest and LightGBM. Based on the analysis, 10 response variables, including health conditions, living habitats and age are significantly relevant to heart disease. In addition, the comparison among the model shows random forest is the most suitable model to predict heart disease with multicollinearity. This paper selects out important factors of heart disease and provides a fitting model for heart disease prediction. Based on the evaluation models, logistic regression and random forest, this paper finds random forest is the fittest model in prediction. Overall, these results shed light on guiding further exploration of indicators of heart disease.
Behavior recognition technology research and application analysis
Authors: Wei Kaixuan
Published: October 2023

This paper provides a comprehensive overview of the current state of behavior recognition technology research and its applications in computer vision. Firstly, it discusses the fundamental concepts and categorization methods employed in behavior recognition technology, such as multimodality, double-sided depth photos, bone key points, and RGB data. These techniques enable the recognition and analysis of various human behaviors with high accuracy and precision. Furthermore, this paper highlights the vast potential of behavior recognition technology in several fields, including safety and education. In safety settings, behavior recognition technology can assist managers in identifying abnormal behaviors and enhancing safety precautions. In educational settings, behavior recognition technology can help teachers gain insight into student learning levels and enhance their teaching efficiency. Additionally, this technology can be used to identify patterns of behaviors that might indicate a student is struggling or needs extra support. The paper concludes with a summary of the current state of behavior recognition research and suggests areas for further investigation. One potential area for future research is the development of more accurate and efficient recognition models. Additionally, exploring the ethical implications and privacy concerns of behavior recognition technology is also essential. Overall, this paper emphasizes the immense potential of behavior recognition technology in various fields and encourages further research to realize its full potential. By leveraging the power of computer vision, we can gain valuable insights into human behavior that could have far-reaching implications for safety, education, and other areas of our lives
Artificial intelligence technology in license plate recognition
Authors: Huang Gang
Published: October 2023

Due to the importance of license plate recognition in many parts in human society, several methods are concluded and proposed in this article. In this work, artificial intelligence (AI) technology, deep learning, and neural networks are put into use in different aspects or modules of license plate recognition methods. Current methods can be divided into two categories, one is the traditional methods and the other is method using AI technology. Two-stage method is a typical example for traditional methods. In this work, other methods such as RT-LPDRnet method, which takes YOLO v5 into use, is an example for AI technology in license plate recognition. Now commercial license plate recognition generally adopts the method of end-to-end deep learning, while some methods of character segmentation first and then recognition are still worthy of study. In this work, three evaluation metrics are concluded to help evaluate the performance of previous methods. In addition, application of license plate recognition is proposed such as recognition in the parking lots, entrances and exits of expressway and electronic police system.
Investigation of Artificial Intelligence algorithms in education
Authors: Lin Yangqi , Luo Qian , Qian Yuzhao
Published: October 2023

The application of Artificial Intelligence (AI) in education has garnered increasing attention in recent years. This study investigates the primary methods of AI in education, including The Teaching Machine System, Intelligent Tutoring Systems, Intelligent Educational Systems, and the Intelligent College Student Comprehensive Quality Evaluation Model. The Teaching Machine System employs pre-defined rules and algorithms to deliver personalized instruction to students. Intelligent Tutoring Systems use AI algorithms to provide real-time feedback and guidance to students, tailoring the learning experience to address their learning gaps. Intelligent Educational Systems use AI to assist with instructional design, assessment, and delivery, while the Intelligent College Student Comprehensive Quality Evaluation Model is an AI-based system designed to evaluate students' comprehensive quality. Furthermore, this paper provides relevant descriptions and insights into the applications of AI in language and mathematics learning, highlighting the limitations of current AI models in these areas. Despite the advances in AI, challenges still exist, including limitations in processing unstructured data and the need for more effective human-AI interactions. Nonetheless, the potential of AI to enhance the quality and accessibility of education remains promising, and further research is needed to explore its full potential in the field of education.
Methods and applications of artificial intelligence in education
Authors: He Cindy , Wu Tianyu , Zhou Letian
Published: October 2023

Artificial Intelligence (AI) has undergone significant development over the past few decades. This review paper gives an introduction of the study of AI and some of its applications in the realm of education. Specifically, this paper discusses three methods used by AI education. The first is learning decomposition which is a way to evaluate learning efficiency., The second is Randomized Controlled Trials (RCT) analysis which is a general idea for conducting experiments, we also introduce a method to analyze data produced by RCT. The third is iNotitium, an online platform using AI algorithms to provide useful educational information and prevent people from misleading by incorrect messages. Besides, the paper also introduces three specific fields in that AI is applied in education, including navigating visually impaired people, tutoring English learning, and helping with early childhood education (ECE). Finally, the paper discusses the prospect of AI and its application in education field, along with the difficulties that we may face in the future when developing AI method in this field.
Characteristics, prospects and limitations of ChatGPT
Authors: Li Jinyue
Published: October 2023

In November 2022, Chat Generative Pre-trained Transformer (ChatGPT) released by OpenAI attracted worldwide attention as soon as it was released. Both artificial intelligence practitioners and the public are scrambling to try this latest chatbot. This paper mainly studies what ChatGPT is, and the remarkable characteristics of ChatGPT compared with other chatbots, as well as the prospect and limitations of ChatGPT. The main purpose of writing this paper is to provide some references for students or practitioners in the direction of artificial intelligence, and to make people who do not know ChatGPT and other large-scale language chat models more systematic understanding. The latest large-scale language model ChatGPT is studied by literature analysis and summary. This paper finds that although ChatGPT has indeed brought shock to the global people in the field of machine learning in the field of artificial intelligence, the hidden dangers and defects behind it (academic integrity, privacy, etc.) are gradually exposed, and relevant practitioners still need to propose measures to improve and develop new uses of chatbots.
Research on 3D scene graph rendering based on neural radiance fields
Authors: Cui Hongyang
Published: October 2023

Since the release of Neural Radiance Fields (NeRF), it has become the research focus of computer graphics in only three years and has become a representative method of using deep learning to deal with computer graphics tasks. The application scenarios of NeRF are very extensive, including virtual reality, augmented reality, computer graphics, 3D scene graph rendering, and other fields. However, the literatures on these aspects are not sufficient. To this end, this article introduces the basic principles of NeRF and compares and analyzes the advantages and disadvantages of NeRF and traditional graphics methods in 3D rendering tasks. According to different improvement directions, from the perspective of improving training speed, improving network generalization, and expanding to dynamic scenes, etc. Representative follow-up work based on NeRF is summarized. And the future application scenarios and development direction of NeRF are prospected.
A detection research of spams based on machine learning algorithms
Authors: Liu Zhe
Published: October 2023

The wide spread of spam has brought a lot of inconvenience and trouble to peoples work and lives. Therefore, it is of great practical significance to constantly update the methods of spam classification and filtering to improve the current situation of email use. In this paper, linear regression and logistic regression are examined to test whether a random email is spam or a normal email. The logistic regression model is based on a public data set that is estimated by calculating the number of entries in the entire set and then the probability of spam. The linear regression model is based on the data from the logistic regression model and is estimated to give a line representing the probability of spam in a given range of emails. Finally, the results of these two models clearly indicate the rampant and widespread nature of spam, which can enhance the publics overall awareness of carefully examining unknown emails.
The application and challenges of artificial intelligence in brain tumor recognition
Authors: Han Zhihao
Published: October 2023

There is no denying the fact that Artificial intelligence technology has developed rapidly in recent years. In medicine, especially in brain tumor detection field, it has attracted great attention. By combining artificial intelligence with physiological imaging, the classification of brain tumors and the selection of the best treatment options can become more accurate and precise. AI brain tumor detection can reduce the rate of misdiagnosis and improve the speed of diagnosis. The article researches the method of AI in brain tumor detection. The process of the general method can be divided into four phases: Data collection, Preprocessing, Feature extraction, and Classification. At the same time, this article also analyzes the application of AI in brain tumor detection and treatment, including the advantages and challenges of AI application, development direction, and conclusions. As a review paper, this paper provides a relatively complete overview of this field. Even though the growing presence of AI technology in the brain tumor medical field is already bringing greater assistance, there is still a lot of room to improve. Indeed, a highly accurate, explainable system is needed in the future. With the rapid development of AI methods, so do the corresponding high-performance hardware become increasingly essential.
Procedural content generation for VR educational applications: The investigation of AI-based approaches for improving learning experience
Authors: Wang Yifei
Published: October 2023

In recent years, artificial intelligence (AI) has witnessed significant advancements in the field of education, with its ability to personalize and adapt content to individual student needs. In parallel, virtual reality (VR) has emerged as a powerful tutorial tool, providing immersive and interactive experiential learning experiences which has the advantages of improving students' motivation and engagement. Previous researchers have demonstrated the potential of ML algorithms, particularly RL, for generating educational content and VR environments. To create high-quality content, researchers have started exploring the integration of Machine Learning (ML) and Reinforcement Learning (RL) algorithms into Procedural Content Generation (PCG) methods for automatically generating both textual and non-textual content such as practice questions, quizzes, VR learning environments, etc., which have the potential to increase the efficiency and effectiveness of educational interventions. Nonetheless, the development of these techniques requires addressing several challenges. Significant advancements are yet to be made in developing and refining these algorithms to produce high-quality and effective educational content for VR applications. This article provides a comprehensive overview of the current state of research in reinforcement AI learning content generation for VR educational applications. For each area, it discusses the state-of-the-art techniques, applications, limitations, and challenges faced in development, covering the use of natural language processing, reinforcement learning, and machine learning algorithms. The review concludes by highlighting some of the key opportunities for future research in this field, including the development of more sophisticated models and the exploration of new applications of machine learning in educational technology.
The application and challenges of artificial intelligence in speech recognition
Authors: Wang Bohan
Published: October 2023

This paper provides an overview of artificial intelligence (AI) and speech recognition technology, including its history, applications, challenges, and future prospects. AI-powered speech recognition technology has significantly improved over the years, and it is used in various applications, such as virtual assistants, voice-activated devices, and dictation software. The technology leverages machine learning algorithms that are trained on vast amounts of speech data to recognize and interpret human speech with accuracy levels that are comparable to those of humans. However, the technology still faces many challenges, such as speech variability and background noise, which make it challenging to develop speech recognition algorithms that can accurately recognize all types of speech. The article provides a comprehensive review of the technical aspects of automatic speech recognition, including the process involved, the algorithms used, and the challenges and opportunities for future research in this area. The paper also discusses the architecture of automatic speech recognition (ASR) systems and the main components that make up the system. The authors explain that ASR systems consist of three main components: the acoustic model, the language model, and the decoder. They also discuss the challenges that ASR systems face, such as speaker variability, noise, and limited vocabulary. Overall, this paper provides a detailed introduction to AI and speech recognition technology and its potential for various industries.
The application of WeChat navigation assistant
Authors: Zhang Yiming
Published: October 2023

The field of artificial intelligence and deep learning has made significant progress in recent years, with the development of innovative techniques and technologies that have the potential to improve various aspects of human life. However, the elderly population faces significant challenges when it comes to utilizing electronic devices, as the demand for convenient and easy-to-use products in their daily lives remains unmet. To address this challenge, this paper proposes the design of a software solution, consisting of four key components. Firstly, the software application is simulated, and the process of software operation when the elderly use WeChat is modeled. Secondly, automatic speech recognition technology is utilized to enhance the usability of WeChat for the elderly. Thirdly, a dataset is created for data collection, processing, and analysis, with the aim of constantly improving the software. Finally, evaluation methods such as formative and summative evaluations are utilized to assess and enhance the effectiveness of the software. The proposed software solution has the potential to significantly improve the quality of life of the elderly population by enabling them to better access and utilize electronic devices. Moreover, the incorporation of automatic speech recognition technology and data analysis for continuous improvement has the potential to contribute to the advancement of the field of artificial intelligence and deep learning. Further research should focus on refining the software solution to better cater to the specific needs of the elderly population.
Research analysis of blockchain IOT domain according to knowledge graph
Authors: Yu Sihan
Published: October 2023

The purpose of this paper is to analyze and organize the research in the field of Blockchain Internet of Things (BIoT) using knowledge mapping technology, in order to get a more comprehensive understanding of the research status and development trend in this field. By organizing and statistically analyzing the relevant literature in the field of blockchain IoT, information on keywords, topics, and hot issues of research in this field is obtained and presented in a visual way. At the same time, this paper also provides an in-depth analysis of the research in the field of blockchain IoT, including technical applications, development trends and other aspects. The research results show that the research in the field of blockchain IoT shows a rapid development trend and has received wide attention from academia and industry sectors. In terms of technical applications, the combination of blockchain technology and IoT technology has brought new ideas and technical means for the development of BIoT field. Meanwhile, with the application of knowledge graph technology, the research in the BIoT field has gradually developed in a more refined and in-depth direction. This study has certain reference value and practical significance for the research in the field of blockchain IoT, and can provide more scientific and accurate guidance for the development and application of this field.
Optical character recognition with different languages
Authors: Wu Yifan , Zhang Yuxi
Published: October 2023

Optical character recognition is the combination of optical technology and computer technology to identify text in an image and then recognize the text content in the image, providing individuals with a great deal of ease in their daily lives. Document text recognition, natural scene text recognition, bill text recognition, and ID card recognition have been used in daily life, but there are still many factors that lead to inaccurate identification and detection. Therefore, different texts, patterns or characters are suitable for different types of Optical character recognition. In this paper, we can learn about the Optical character recognition operation methods and find the similarities and differences through researching the technical routes and four different types of Optical character recognition. In addition, by comparing the Optical character recognition of several commonly used languages, the advantages and disadvantages of each method can be analysed.
Research on identification of floating garbage using improved YOLO v7 algorithm
Authors: Li Weixuan , Zhu Haotian
Published: October 2023

The floating garbage is becoming more and more serious, but little research has addressed recognition of these floating garbage. Intelligent target recognition of floating garbage using deep learning techniques is therefore essential. The YOLOv7 algorithm has strong ability of extracting target features and is significantly faster than its previous version at the same accuracy. The technique is provide based on the YOLOv7 algorithm for identifying floating garbage in this paper, as a result, develop and implement a target monitoring function for floating garbage identification. Specifically, the combination of YOLO v7 and SE attention mechanism was used to improve ability of target sensing. The training process was optimized by using EIOU loss, resulting in a significant improvement in the efficiency of the final model compared to the normal YOLOV7 algorithm model, significant improvements of 20% and 25% in the model metrics mAP_0.5:0.95 and mAP_0.5, respectively.
Prediction of chronic kidney disease from 25 clinical features using machine learning
Authors: Xing Yifan
Published: October 2023

A growing number of people worldwide suffer from chronic kidney disease, with many individuals in developing countries lacking the necessary resources for treatment. Medical records often contain valuable information that can be utilized to predict the development of CKD, with machine learning algorithms proving particularly effective. In this study, the author analyzes a dataset of 250 participants with CKD and 150 participants without from 2015, utilizing various machine learning classifiers to determine the most significant characteristics and predict CKD development. The analysis reveals that serum creatinine, specific gravity, red blood cell count, and potassium are the four most relevant risk factors for CKD prediction. Based on these four factors, the author builds machine-learning models that can accurately predict CKD development from medical records. The results show that a combination of all the features in the original dataset achieves a similar level of accuracy as the four-feature models. This research has significant implications for clinical practice, providing doctors with a new tool to predict CKD in patients. By focusing on the most relevant features, such as serum creatinine, red blood cell count, specific gravity and potassium, physicians can make more informed decisions when treating patients with CKD.
Determination of basketball interference ball violation based on visual recognition technology electronic referee artificial intelligence algorithm
Authors: Xun Zijian
Published: October 2023

This article set up an electronic referee system based on monocular vision recognition. The system requires only two cameras to operate. By establishing a multi-camera real-time video monitoring system, the system cooperates with artificial intelligence automatic recognition judgment, overcomes many blind spots of human eye judgment, completes the judgment efficiently and accurately, improves the accuracy of the referee's ruling on basketball interference ball violation, and enhances the fairness and spectacle of the game. This project overcomes many blind spots of human eye judgement, efficiently and accurately completes the penalty, improves the accuracy of the referee's ruling on basketball interference ball violation, and enhances the fairness and spectacle of the game by establishing a multi-camera real-time video monitoring system with artificial intelligence automatic identification judgement.
Comparative analysis of machine learning and deep learning techniques for prediction of the stock market
Authors: Zheng Xuyang
Published: October 2023

In recent years, there has been a continuing search for reliable instruments that can predict trends in financial markets and activities related to investments. In the past, academics have used traditional methods to forecast the investment worth of equities by analyzing metrics such as the financial records of companies from both a fundamental and technical point of view. The effectiveness of these strategies could decrease as market information asymmetry continues to rise and high-frequency trading becomes increasingly prevalent. Researchers have developed novel methodologies as a result of the progress that has been made in the field of artificial intelligence technology. One of these methodologies is the application of neural networks for forecasting. In the meantime, data visualization is becoming increasingly common, which could make it easier to conduct an in-depth analysis of the advantages and disadvantages presented by various models. The purpose of this research is to evaluate the performance of machine learning and deep learning strategies, including logistic regression, support vector machine, multi-layer perceptron and convolution neural networks, in forecasting stock market prices where various data visualization techniques are utilized for investigation. The findings from error analysis demonstrate that convolutional neural networks operate superbly.
A series of machine learning methods for user rating prediction
Authors: Wang Jingbo
Published: October 2023

Users feedback online for services or products reflects their satisfaction. For example, higher rating implies better user satisfaction and experience. It is crucial to provide users satisfying services or products to ensure user experience and thus improve the long-term value of the platform. Manually reviewing all candidate services and products to select potentially user-preferred ones it time-costuming due to large amount of candidates. Therefore, this paper studies a series of machine learning based algorithms to automatically predict the user feedback for an online shopping website, i.e., user ratings in this scenario. To be specific, three machine learning models are investigated, including logistic regression, multi-layer perceptron, and convolution neural network. Starting with the pre-processing of raw data crawled from Amazon, an international online shopping website such three models are first trained over the training set and then evaluated on the testing set. The experiments demonstrate the convolutional neural networks give the highest accuracy for the unseen data.
Breast cancer prediction and feature importance estimation leveraging logistic regression model
Authors: Han Ruining
Published: October 2023

In 2022, breast cancer become the largest cancer among the world. It is necessary to help people know how to prevent breast cancer. Machine learning use algorithms and statistic models to help the computer find some rules based on mass data. It can rapidly analyse, predict, and process the data in a timely enough manner. By significantly reducing errors caused by data duplication and other inaccuracies, it can simplify time-intensive documentation in data entry. In medicine, it can facilitate accurate medical prediction and diagnosis by quickly identifying high-risk patients, recommending a range of drugs, and predicting readmission rates. This paper will use Logistic Regression to build a model to predict the whether a patient will develop cancer and find the dominant factors from the characteristic values. The research found that Bare Nuclei, Clump Thickness, and Single Epithelial Cell Size are the key factors that will create a large impact to the result of the predication.
Energy optimization using virtual machine migration for power aware
Authors: Kothari Sukhesh , Parsad Chandan , Mahajan Shubham , Abualigah Laith
Published: October 2023

Using cloud computing, the cloud service can be delivered to the user via an internet connection. A major concept in cloud computing is virtualization, which allows for the dynamic sharing of physical resources. The deployment of virtual machines (VMs) in the cloud is a difficult problem since they are placed on top of real machines in the data center. A good VM placement policy should increase resource utilization and also provide energy optimization, as saving energy has become crucial due to the high demand for the cloud and its data centers consuming high power. In this work, an approach is made to optimize the energy consumption during VM migration called, Power-Aware Energy Optimized VM Migration (PAEOVMM). The approach uses the maxPower of the host to allocate VM to the host. Our proposed approach is analyzed using CloudSim. As per the simulation results, PAEOVVM performs better in energy consumption than existing baseline CloudSim algorithm. PAEOVVM performs improvement in energy consumption on an average of 27-40%.
Effectiveness of principal component analysis in breast cancer diagnosis: An empirical and visualization analysis
Authors: Lu Kangjie
Published: October 2023

With the development of computer science technology, it is increasingly common to apply computer science technology to the medical field, and one of the typical examples is to predict the probability of cancer cells recurrence in cancer patients. However, there is still a lack of unified explanation for the impact of the principal component analysis (PCA) dimensionality reduction method used in prediction on the final experimental results. Therefore, the research topic of this paper is the effect of PCA dimensionality reduction method on the accuracy of cancer prediction. The methodology in this paper is as follows: First, patient data were collected, based on a usable machine learning repositor. It has 569 instances, which contains both malignant and benign tumors. Second, exploratory data analysis is performed on the data, detecting outliers and discovering existing correlated data. Then, the comparative method is used to explore the effectiveness of PCA dimensionality reduction method on the prediction accuracy by observing and analyzing tables and images. It is found that the PCA dimensionality reduction method plays a positive role for boosting the prediction accuracy, but the PCA method does not significantly improve the prediction accuracy for the data that has been processed and the dimension is not high.
Predicting and visualization analysis of customer churn in telecommunications leveraging decision tree model
Authors: Luo Ruiyang
Published: October 2023

Nowadays, how to keep more customers is one of the most crucial problems to be solved facing communication companies in the increasingly competitive market. In these companies, one of their most important business indicators is customer churn for it is significantly less expensive to keep a current client than to find a new one. In order to carry out truly effective methods to minimize customer loss, build a prediction model is a reasonable way to analyze the characteristics and reasons of possible lost customers. In this research, after creating a Decision Tree Model and visualize it, it could be concluded that there was little difference between male and female, but the aged, unmarried and customers who are economic dependent have a great possibility to lose. Moreover, signing duration matters in the prediction. The longer the contract term is, the less likely the users to be lost. According to mentioned and even more analysis below, certain targeted suggestions are introduced to improve the situation. This research would like to provide research thought and analytical method for the prediction of customer churn.
EEG signals of depression diagnosis in recognition using deep learning methods
Authors: Chen Xianyang
Published: October 2023

Depression is a mental disease which symptom is people feel negative about life during long period. With the fast-paced social lifestyle, increasingly stress make people tired toward their life and work. The prevalence rate of depression become higher worldwide. However, the frequent way to detect the depression is depended on the Self-Rating Depression Scale (SDS) and the diagnosis given by the Professional psychologist. Those methods performance is always unstable and inefficiency. Electroencephalogram (EEG) as a functional neuron signal have been widely used in neurology diagnosis. It has been testified as a great tool to diagnose the depression. Deep learning (DL) can extract some latent features from complex data which tradition way cant analyze efficiently. For this reason DL intensely utilized in Medical field.This paper aims to analyze the general and feasible solution of applying deep learning to diagnose depression through EEG signals. It reviews the relevant literature in this field in recent years, summarizes the corresponding methods and breakthroughs used in this paper, and systematically constructs this solution. Finally, based on the shortcomings and deficiencies found in these papers, the main problems that need to be addressed in the future are proposed, and the future potential of this field is discussed.
Theoretical analysis of the network structure of two mainstream object detection methods: YOLO and Fast RCNN
Authors: Hou Bodong
Published: October 2023

Object detection technology has a wide range of practical applications, and it is a very challenging field. Countless researchers have developed many important ideas in this area. This article reviews the important milestones of object detection in the first part. In the second and third parts, the first-order detection, such as the YOLO series, and the second-order detection, including RCNN and pyramid structure, are comprehensively analyzed. This paper describes the development process of these algorithms in detail and systematically analyzes the network structure, training effect, loss function, advantages, and disadvantages, among other factors.
Stock market analysis using ensemble learning
Authors: Madhavi Katamaneni , Abualigah Laith
Published: October 2023

A stock market or share market is the combination of shoppers and sellers of shares. Prediction of the stock market is a method for calculating the future value of a company's stock. Stock market can be regarded as a specific records of data mining as well as machine learning problem. The daily changes within the stock depends on the profits and losses and many people think that stock market is irregular and uncertain. Based on daily changes we can predict some movements in the stock. In the previous years, researchers have used many machine learning models to know the development of the stock market to enhance the accuracy. However researchers have implemented these machine learning models independently and compared the results of models. It is observed that the group of models produce quite much less noisy compared to independent models. Our vision is to study investment strategies to predict and analyses the stocks using machine learning models such as Random Forest, K Nearest Neighbor, Gaussian Naive bayes and Decision Tree. And these models are ensemble by using different techniques such as Voting Classifier, Adaboost classifier and Bagging Classifier to estimate their accuracies. From the results it is observed that ensemble approach gives maximum accuracy compared to individual machine learning models.
Predict students performance based on machine learning algorithms
Authors: Han Yuqi
Published: October 2023

Several studies have used models for students academic performance prediction to improve teaching quality. The aim of this study is to use machine learning algorithms to forecast students performances using their daily study behaviors and the extent of parents concerns about their childrens studies to generate synchronous predictions with daily teaching activities. The data includes study attitudes, behaviors, demographic features, and parents concerns about the students. The preprocessed dataset after feature engineering was used to train the models (i.e Support Vector Machine, Decision Tree, Random Forest, And K Nearest Neighbor). Random Forest has the best performance among the algorithms applied. The impact of students daily study behavior is highly related to academic achievement, and parents impact is also an influencing factor on childrens performances. This study could encourage and motivate parents to care more about their childrens studies with their favorable actions and behaviors. Besides, this study would help students realize the importance of their daily performance and realize it is essential to their final exam grades.
Application of neural network in object motion tracking in the context of big data
Authors: Shao Ruiheng
Published: October 2023

In the context of big data, the advancements in computer analytics and computational capability have promoted transformative opportunities. At the forefront of this progress, neural networks have emerged as an essential instrument across diverse domains, catalyzing enhancements in operational efficiency. This paper embarks on an exploration of the applications of neural networks within the realm of object motion prediction and tracking, with the primary objective of improving the efficacy and precision of tracking mechanisms. Amidst the escalating complexities of data proliferation, the imperative for streamlined tracking methodologies has intensified. In response, this study investigates the amalgamation of neural networks with object motion prediction and tracking processes. By using the powerful abilities of neural networks, the research aims to bring a significant change to the field. This change will be seen in improved accuracy and faster tracking efficiency. Combining neural networks with motion prediction and tracking creates a cooperative partnership that develops a new system based on data-driven insights. By merging computational power and advanced algorithms, the paper anticipates a fresh direction for tracking technology.
Overcoming data silos in healthcare: The potential of blockchain and federated learning on the Hedera platform
Authors: He Jiashao
Published: October 2023

The lack of access to extensive and varied datasets remains one of the major issues facing the field of machine learning, despite recent advancements. This is especially true in the healthcare sector, where it can be challenging to gather and use patient data for research because it is frequently compartmentalized across many healthcare providers. By enabling secure and privacy-preserving access to distributed data, blockchain technology, and federated learning have the potential to overcome these difficulties. In this article, we'll look at how federated learning and blockchain are used in the healthcare industry and talk about their benefits and drawbacks. We will also examine the Hedera platform, which makes use of blockchain technology and a new algorithm called Gossip Degree to provide a revolutionary method of federated learning. We will also go over the potential effects of federated learning on the healthcare sector and what it means for future research.