bluuebunny/crossref_metadata_2025 huggingface dataset:
A New Consumer Behaviour and A New Frameworks for Challenges in E-paper Publishing
Authors: Dixit Monika , Ojha Mukesh , Diwakar Manoj , Singh Prabhishek , Shankar Achyut , E. Sathishkumar V.
Published: July 2023

This paper deals with reviews of the various publishing methods available. Due to emerging published technologies like electronic paper, newspaper publishers must now produce, print, and disseminate their information in new ways (e-paper). In addition to creating technical challenges for publishers, e-paper will change how people consume news. We address the impacts of this new channel in this article from the outside-in by assuming a starting point in prospective new consumption. We examine the impacts that the e-paper publication channel will have on distribution, media house systems including editorial, advertisement, and subscription, and procedures based on three empirically validated future consuming scenarios.
K-anonymous Mathematical Model Based on Greedy Algorithm
Authors: Chen Shuyang
Published: July 2023

This research propose a user-centered combinatorial data anonymization method. whereas a data matrix is said to be k-anonymous if each row occurs at least k times. Therefore, the authors propose PATTERN-GUIDED k-ANONYMITY, an improved k-anonymization problem. It allows users to designate the combinations in which suppressions may occur, building on prior work and addressing relevant shortcomings. Users of anonymous data can indicate that the aspects of the data are valued differently. The so-called K-anonymity is usually realized by Generalization and Suppression techniques. Generalization refers to Generalization and abstraction of data so that specific values cannot be distinguished, for example, the age data group can be generalized into an age group.
Stock Prediction using LSTM model
Authors: Xiao Junyan
Published: July 2023

With the development of the times, investors are increasingly in demand for stock price forecasting. However, stock price fluctuations are full of uncertainty, making traditional machine learning algorithms more erroneous in long-term forecasting. Based on the LSTM model, this paper uses Tushare to obtain the historical price of stocks, and the optimal structure and best training parameters of the LSTM model in stock price prediction are determined experimentally. The prediction accuracy of the LSTM model was evaluated by MAE, and the best result was 69.15, which achieved accurate prediction of stock prices. Compared with the traditional SVR model and the ARMA model, the prediction results of LSTM are more in line with the actual value, and the prediction accuracy of the algorithm is higher.
Identifying Potentially Habitable Exoplanets: A Study using the Transit Method and Kepler dataset
Authors: Gupta Richa , Muntaha Sidratul
Published: July 2023

An exoplanet is a planet that orbits a star outside of our solar system. The study of exoplanets is an active area of research in astronomy. In this research, we aim to utilize the Kepler dataset provided by NASA EXOPLANET ACRCHIEVE to identify and classify exoplanets that could potentially support life. The Kepler dataset, which comprises of observations of over 150,000 stars, has been instrumental in the discovery of thousands of exoplanets. We will analyse the dataset using machine learning techniques to classify exoplanets as potentially habitable based on their orbital period, size, distance from their host star, and other parameters. The findings of this research will greatly enhance our understanding of the frequency of life in the universe and the use of machine learning techniques on the Kepler dataset will be an essential tool in the quest for finding potentially habitable exoplanets. Emerging Machine Learning Algorithms aid in detecting habitability of exoplanet in different stellar system. For finding an Exoplanet we have used the transit method which is based on the principle that when an exoplanet passes in front of its host star, it causes a temporary dip in the star's brightness. By monitoring the brightness of a star over time, scientists can detect these periodic dips and use them to infer the presence of an exoplanet. The findings of this research have the potential to significantly advance our understanding of the prevalence of life in the universe.
Exploring the Advancements and Challenges of Automated Machine Learning
Authors: Jin Zhengyang
Published: July 2023

Automl,a rapidly growing field which is aiming to apply the machine to solve problems that human cant easily deal with . This includes tasks such as feature selection, model selection, and hyperparameter tuning. One of the many advantages about Auto Ml is that it can greatly shorten the cost of researchs and resources cost by applying machine learning to a problem. This makes it accessible to a wider range of users, including those without a background in computer science or statistics.In spite of some advantages of AutoML, many challenges are waiting to be addressed. The main challenge is that it is often challenging to ensure that the models generated by AutoML are of high quality and generalize well to new data. Another challenge is that AutoML can be computationally expensive, which can make it infeasible for some problems. Overall, AutoML has the potential to revolutionize the way we apply machine learning to real-world problems, but it is important to be aware of its limitations and challenges.
NA
Authors: NA NA
Published: July 2023

NA
Predictive Model of Psychoactive Drugs Consumption using Classification Machine Learning Algorithms
Authors: Almahmood Mothanna , Najadat Hassan , Alzubi Dalia , Abualigah Laith , Zitar Raed Abu , Abualigah Sayel , AL-Saqqar Faisal
Published: July 2023

It is difficult to predict the effect of drugs on the individuals, as its results are unpredictable and most often dangerous. For a police purpose that concerned with the protection of individuals, the problem of predicting drug abusing is highly important. A dataset was used from open-source website UCI, that includes specific attributes about using up of eighteen different psychoactive drugs. Our study aimed to use data mining classification techniques, in order to classify the individual into two categories: user or non-user. Eighteen classification models were built using different classification algorithms such as Gaussian Naive Bais, Logistic Regression, k-nearest neighbors, Random Forest, and Decision Tree. The accurate classifier was chosen by studying the accuracy, recall, precision, and f1-score measures for each one, and it was evaluated by the Holdout method. The results were obtained optimally, and we got 18 models, where each one had different high accurate outputs, that classify an individual to user and non-user. The final model is a combination of 18 models for 18 critical psychoactive drugs: Alcohol, Amphet, Amyl, Benzos, Caff, Cannabis, Choc, Coke, Crack, Ecstasy, Heroin, Ketamine, Legalh, LSD, Meth, Mushrooms, Nicotine and VSA. This study in turn may give a chance for the decision makers to reduce the risk of these drugs consumption, in order to avoid healthcare issues and keep the community in safe.
Predicting Death Risk of COVID-19 Patients Leveraging Machine Learning Algorithm
Authors: Yuan Jianming
Published: July 2023

The first instance of COVID-19 was found in Wuhan, China, which mainly caused damage to human body in the form of respiratory diseases. In this study, an XGBoost prediction model was put forward according to the analysis on age, pneumonia, diabetes, and other attributes in the dataset, which was employed to estimate the COVID-19 patients' risk of death. In this study, a lot of preprocessing was carried out on the dataset, such as deleting null values in the dataset. In addition, there are strong correlation between sex, pnueumonia and death probability. In this study, XGBoost, CatBoost, logistic regression and random forest were established by machine learning method to forecast the COVID-19 patients' chance of mortality. The findings revealed that XGBoost's prediction performance was the best, while the logistic regression model performed poorly in this reported dataset of COVID-19 patients when compared to other approaches. From the feature importance map of XGBoost, it is found that age and pneumonia have great influence on the prediction of death risk.
An Overview on On-chip Network Routing Optimisation
Authors: Tong Huyan
Published: July 2023

As the number of cores in multi-core systems increases, bus-based systems face significant challenges in terms of scalability, average transmission latency and power consumption. In this context, on-chip networks emerged as a suitable communication architecture for System On Chip (SoC) based entirely on the communication ideas in computer networks and taking into account the characteristics of the system on chip in SoCs. With the introduction of on-chip networks, related research has been developed, such as on-chip network topology, communication quality of service, on-chip network routing algorithms, and on-chip network fault tolerance. He then introduces on-chip network routing algorithms and fault-tolerant routing algorithms from different perspectives, and finally points out the directions of fault-tolerant routing algorithms worthy of research.
Inter-translation Between CT and MRI Brain Scans Based on Cycle Consistent Adversarial Networks
Authors: Li Haoran
Published: July 2023

Since Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) are often used together in diagnosing brain diseases, it is rather time-consuming and expensive for patients to obtain two scans simultaneously. In this paper, the author proposed a new idea for CT-MRI inter-translation, using dataset of human brain scans to achieve unpaired image-to-image translation between CT scans and MRI scans. More specifically, the author proposed using Cycle Consistent Adversarial Networks (CycleGAN) to realize the idea of style transfer between CT and MRI. Briefly, this paper had trained two sets of generator and discriminator to form a cycle. This model can retain the main characteristics of a scan while transferring the scans style. To keep this translation process cycle-consistent, the Loss_cyc is used to keep the main content. Furthermore, the Loss_GAN ensures that the generated images exhibit a close stylistic resemblance to the target domain and the Loss_identity guarantees that the hue of the generated images is retained during the translation process. In addition, the author has added weights to the Loss_cyc and the Loss_identity to help the model perform better. Finally, this paper visualize the training process by presenting the original images and generated images together. Experimental results indicates that CycleGAN achieves a competitive performance can hopefully be a good auxiliary in brain diseases diagnosis.
The Study and Implementation on Adversarial Attack Algorithm Based on Deep Convolutional Generative Adversarial Network
Authors: Ma Mingze
Published: July 2023

The field of machine learning has been growing rapidly in recent years, with significant advancements in computer vision. However, this progress has also led to increased concerns over the safety and security of machine learning systems. Despite previous research efforts in this area, traditional adversarial algorithms continue to fall short in defending against attacks in most circumstances. As a response to this challenge, this study seeks to develop an adversarial algorithm based on Deep Convolutional Generative Adversarial Network (DCGAN), which aims to reconstruct the data distribution of input data and generate new data to improve model robustness. To evaluate the efficacy of the proposed method, a Deep Neural Network (DNN) classifier is employed to perform classification on the generated dataset. The experimental results suggest the potential feasibility of the proposed hypothesis, but further improvement is required to strengthen the defence mechanism. Overall, this study contributes to the ongoing efforts to enhance machine learning safety and security in practical applications.
Comparison of Machine Learning Models for Employee Turnover Prediction
Authors: Yin Qing
Published: July 2023

In the era of big data, companies are able to more easily record, analyze, and utilize data, making it feasible to predict employee turnover. In the current economic situation, how to predict employee turnover has become particularly important. The core of this article is the establishment of a forecasting model based on real data, and then through the application to reflect the use and importance of the forecasting model. This paper uses XGBoost, LGBM, random forest and decision tree models, uses IBM's real data to construct prediction data, and evaluates the models to select the model that is most suitable for the lost data. Additionally, the role of Synthetic Minority Over-sampling Technique (SMOTE) in improving classification accuracy was explored. The results of this work indicate that: 1) balancing the data set using SMOTE oversampling technique is more effective than using the original data set; and 2) The XGBoost algorithm performs better, with higher precision and greater flexibility.
A Survey on Packet Capture: Tool Introduction and Security Vulnerability Description
Authors: Wu Yuxuan , Jiao Tuosheng
Published: July 2023

This work involves discovering network security vulnerabilities based on existing packet capture software. By learning the operation principle of packet capture software, we discuss the hidden network security problem among them. There are two basic packet capture software mentioned Wireshark and Sniffer. Analyse the SSL (Secure Socket Layer) protocol and HTTPS (Hypertext Transfer Protocol Secure) protocol to observe where causes a vulnerability in those protocols that allows the thief to obtain information through packet capture software. Finding an attack mode of the thief, we further study and discuss how to reduce the risk of information leakage caused by vulnerability existing. We hope the perspective proposed is not only to avoid workstation chaos caused by packet capture software as far as possible, but also to further encrypt the plaintext data to make it more difficult for packet capture tools to parse the packet.
The Incidence of Diabetes Prediction Based on the Logistic Regression Model
Authors: Yang Fengyuan
Published: July 2023

Diabetes is a rapidly growing global public health challenge that demands effective prevention and treatment strategies. This study aims to explore the relationship between diabetes incidence and relevant indicators such as BMI, blood pressure, and skin thickness by utilizing data from the Kaggle dataset. In this study, the logistic regression model was employed to identify risk factors associated with the incidence of diabetes. The logistic regression model allows this study to test the effect of correlation between predictor variables and the outcome variable, and develop a model to predict the likelihood of diabetes incidence. Using a Python implementation of the model, nearly 77% precision in predicting the incidence of diabetes was obtained. The analysis revealed a strong correlation between age and BMI and the incidence of diabetes, aligning with previous findings in the domain knowledge of diabetes. The results of the study may help individuals and healthcare providers to identify and manage the risk factors associated with diabetes, ultimately reducing the incidence of this disease. The proposed approach of utilizing the logistic regression model provides a valuable tool for predicting the onset of diabetes, contributing to the ongoing effort to combat this global public health issue.
Water Quality Monitoring Data Transmission Network Design
Authors: Zhang Zixu
Published: July 2023

With the continuous advancement of electronic technology and the introduction of new concepts, people's demands on life are increasing and the concept of intelligent automation is gaining popularity. With the continuous consumption of the source of life, people's concern about fresh water resources is also increasing. With the theme of intelligent detection, a water quality online monitoring system that integrates detection and monitoring is designed. The system can monitor temperature, humidity, water level and turbidity in real time, and can transmit monitoring values through wireless module. Users can use mobile applications to view the data detected at this time, which is very convenient. The system hardware design includes STM32 system processor circuit, temperature and humidity sensor module, water level monitoring module, WIFI module, and turbidity monitoring module design.
Research on the Impact of Recommendation Algorithms on User Stickiness Based on Data Analysis of TikTok
Authors: Lan Yifei
Published: July 2023

The rapid expansion of the Internet era provides these social media with an excellent potential. Due to its accurate location and intelligent algorithm, TikTok's daily active users distinguish it from other programs. TikTok ranked in the top ten most downloaded applications worldwide in 2022, becoming the most popular social software for short, creative music videos worldwide. TikTok's recommendation algorithm serves as a benchmark for other Internet applications in terms of customer retention. This paper, through methods of questionnaire and literature review, first analyzes the model of TikTok's recommendation algorithm and then explores the hypothesis that TikTok's recommendation algorithm has a positive effect on user stickiness in three dimensions: algorithm accuracy, perceived usefulness, and recommendation content satisfaction, based on the data from questionnaire.
Traffic Prediction Based on the GRU Neural Network
Authors: Ding Haichuan , Li Zitong , Su Nan
Published: July 2023

Traffic prediction is an important and challenging task in the last decade. In this project, we trained a GRU neural network to predict traffic at four junctions using a normalization and differencing transform to achieve a stationary time series. Given the varying trends and seasonality observed at each junction, we employed unique strategies to make each stationary, and evaluated model performance using the root mean squared error. Additionally, we plotted predicted values against original test data. Data analysis reveals that the number of vehicles at Junction 1 is increasing at a faster rate than at Junctions 2 and 3, while sparse data precludes conclusions regarding Junction 4. Moreover, Junction 1 exhibits stronger weekly and hourly seasonality, while the other junctions display a more linear trend. The results of this study can effectively inform vehicle flow rate prediction. However, GRU models may suffer from slow convergence rates and low learning efficiency, leading to prolonged training times and underfitting. Despite these limitations, the use of GRU models remains the most accurate, achievable, and straightforward method for calculating traffic predictions.
Petroleum Price Prediction Based on the Linear Regression and Random Forest
Authors: Bi Jiaming , Li Enhui , Luo Yongxing
Published: July 2023

Petroleum price prediction is a challenging task due to the strong volatility in the data. The present study details the development of a predictive model for petroleum prices utilizing two popular algorithms, linear regression and random forest. Based on the analysis of experimental results, it was observed that the model yields an acceptable level of error within the established parameters. Nevertheless, certain limitations were identified, such as the inadequate performance of linear regression in cases where the variation between data points is significant or where the relationship is non-linear. Similarly, random forest algorithms may suffer from reduced accuracy when handling data sets with small sample sizes or low-dimensional data. As a means to address these limitations, the study proposes the application of regularization techniques to mitigate overfitting in linear regression, as well as the handling of null and missing values. Additionally, improvements to random forest performance are proposed, including an increase in the number of trees and the application of hyperparameters to enhance accuracy.
Application Research of the Project Bid Evaluation Model Based on MATLAB
Authors: Yang Ming
Published: July 2023

Bid evaluation, an important part of bidding activities, is the most critical part to determine the quality of bidding. For large volume and complex technology projects, the comprehensive evaluation method is usually used in China. It is known that the selection of bid evaluation method has a great influence on the quality of bidding. In the face of the basic situation of China, although the comprehensive evaluation method is basically competent for the bidding work, it still has the defects of human factors in the evaluation of the bid. Therefore, it is essential to improve the bidding evaluation method. This paper mainly evaluates the bid evaluation activities of construction projects based on the comprehensive weighted evaluation method. Through the accumulation of relevant theoretical and practical research results, the author sorts out the advantages and disadvantages of various evaluation methods and analyzes the scientificity, fairness, and practicability of the established project bidding as well as the evaluation model in the evaluation of bidders through the actual project bidding and evaluation case. Combined with the mathematical model and case analysis, the bid evaluation activity is solved by the computer program, and the conventional bid evaluation results and the mathematical model evaluation results are compared to get more accurate bid evaluation results.
An Overview of the Application of Sentiment Analysis in Mental Well-being
Authors: Zhang Jiayi
Published: July 2023

Sentiment analysis is a field of study in natural language processing, usually implemented by machine learning and deep learning methods. Its purpose is to identify the information from pieces of text and categorize them based on their subjective opinion, such as positive, negative, and/or neutral. It has been used in a variety of fields, usually fields related to business, such as customer service and marketing, to analyze the opinions of customers to help businesses adjust their products to accommodate the customers needs. This work analyzes the use of sentiment analysis in mental health. Starting in 2020, the emergence of COVID-19 has disrupted the lives of billions around the world. The isolation brought by the pandemic has forced the public to spend a much greater amount of time on social media and very little time socializing. This has caused a significant increase in the numbers of mental diseases such as anxiety, depression, obsessive-compulsive disorder (OCD), schizophrenia, and bipolar disorder. This paper provides an overview of the recent research of machine learning and deep learning methods on sentiment analysis in the field of mental health since 2020, when the COVID-19 was confirmed and obligatory and non-obligatory quarantines were enforced within different countries. Then, this paper examines the potential applications of sentiment analysis in addressing the mental illness of the public, in aspects of diagnosing feedback from patients, social media monitoring, and mental health apps. Lastly, several challenges that sentiment analysis still faces today will be presented and briefly analyzed. More research on sentiment analysis needs to be done in order to achieve a higher level of accuracy when applying sentiment analysis models to psychiatric uses, but the benefits of a developed sentiment analysis model is highly substantial in mental health.
Analysis of Consensus Mechanisms: PoW and PoS
Authors: Wenxuan Jiang
Published: July 2023

Along with the trend of Bitcoin blockchain, the concept behind all virtual currency has be-come popular in the study of the Internet. This essay mainly researches two kinds of common consensus mechanisms for the current blockchain network and looks forward to the future development of the technologys usage in daily life. This research aims to overview the two most common consensus mechanisms in the construction of blockchain. By reviewing re-sources from other research, an explanation of the goal of the consensus method, the ad-vantages, and disadvantages of each approach and the future development of these two meth-ods are summarized and developed. The result of the review explains the shifts of mature vir-tual currencies from proof of work to proof of stake and advises what mechanism should be used at starting stage and why a shift is necessary for proof of stake currencies.
Deep Learning-Based Music Generation
Authors: Lu Gening
Published: July 2023

Music is one of the greatest inventions in human history. Traditionally, music composition is time-consuming and complex, requiring master sound knowledge based on music theory and musical intuition. In recent decades, deep learning have been applied in music generation, and it has experienced the process from simple sequence generation to multi-track generation considering musicality, multiple methods are implied in study to generate better music and combine existing music theory with deep learning technology, while the current technology already allow people composite easily even without domain knowledge and massive manpower. This paper offers an overview of automatic music generation task, covering majority of the currently popular deep learning-based music generation models. In addition, in latter section discusses how to unify objective criteria for music on subjective way, proposes existing deficiencies and expands possible directions. The research in this review has significant foreshadowing meaning and reference value for developing music generation in the future.
Tamil Sign Language Translation and Recognition System for Deaf-mute People Using Image Processing Techniques
Authors: Priya C. Bharathi , Ibrahim S.P. Siddique , Thangam D. Yamuna , Jency X. Francis , Parthasarathi P.
Published: July 2023

This work concentrates on the device, which helps as a translation system for translating sign gestures into text. The disabled people, in particular, hearing and speech impaired people, are facing difficulties in society. Communication of disabled people becomes worse as the majority of ordinary people do not understand it. These disabled people face difficulty communicating with others; some have many problems communicating with others in sign language. It causes the communication gap between them where impaired people cannot share their views and skills with others. We headed to facilitate communication between the disabled people and the "Tamil sign language translator to solve this problem." Here, gestures are translated to Tamil language to find a localized solution. It processes 31 Tamil alphabets, 12 Vowels, 18 Consonants, and 1 Aayudha Ezhuthu. It is 32 combinations with five fingers points either up or down and mapped to decimal numbers. Here in this process, we need edge detection, which is accurately done and Processed by canny edge detection. In addition to this process, we have used two gesture recognition methods and training the input system through our mainframe algorithm called scale-invariant feature detection transform. We have developed this system, which is useful for deaf and dumb people for essential communication.
Agri Food Supply Chain Using Blockchain
Authors: Ibrahim S.P.Siddique , Sumetha S.M. , Indumalini P.L. , Gupta B. Sujan
Published: July 2023

Supply chains are becoming into automated and extremely complicated networks, and they are becoming an increasingly important source of potential advantages in today's environment. In recent years, there has been a significant rise in customers' attention to concerns regarding food safety. However, tracking the source of all data and maintaining its traceability throughout the supply chain network is difficult. Traditional supply networks are centralized, and trading is done through a third party. It is a promising distribution system technology that has the potential to help agricultural supply chains by lowering transaction costs and speeding up processes while also boosting transparency and security. Blockchain for Agri-food supply chain has the potential to transform the agricultural industry by optimizing all stages of the supply chain and enabling for the tracking of products from the field to the retail shelf. The Agri-food supply chain also has the potential to be implemented digitally as a cost-cutting measure and yield accelerator for the food industry's supply networks.
Deep Learning Based Automated Image Captioning System
Authors: Ibrahim S.P. Siddique , Kumar S. Selva , Priya C. Bharathi , Kumar S. Vinoth , Parthasarathi P.
Published: July 2023

With the rise of deep learning in recent years, the merging of computer vision and natural language processing has inspired a huge interest. Image captioning is an unique deep learning application that has advanced rapidly in recent years. The ability to train a computer to properly describe the content of an image or an environment like a person has huge implications in computer vision, economics, and a variety of other fields. Across the year, this has been a difficult issue in the field of artificial intelligence, and many researchers have made tremendous progress. In this paper, we describe various deep neural network-based picture caption generation models, including RNN-based decoding and CNN-based accessing and Reinforcement-based framework. We also created captions for some of the images and compared the various feature extraction and encoder models to see which one provides the best accuracy and produces the desired outcome. Extensive experiments on the dataset reveal that the proposed framework outperforms the existing encoder based approach.
Research on the Internal Defect Detection Algorithm of Pre-baked Anode Based on Machine Learning
Authors: Mu Shouchen , Liang Yiwei
Published: July 2023

To improve the quality of the pre-baked anode products, it is necessary to check the internal cracks of the pre-baked anode. The developed automatic knocking device is used to determine whether there are cracks in the pre-baked anode by using the obtained sound signal of the pre-baked anode knocking and machine learning. In this paper, first, the Fast Fourier Transform (FFT) is used to transform the sound signal to obtain 10 features in the frequency domain. Then, the main features of the pre-baked anode are obtained by principal component analysis (PCA). Next, the binary classification of support vector machine (SVM) is used to determine whether the pre-baked anode contains cracks, and finally, satisfactory results are obtained.
A Prescriptive Framework for Selecting Optimal Machine Learning Algorithms for Enterprise-level Problems
Authors: Bhattacharya Prithvi
Published: July 2023

We are living in a world where data is touted to be the new oil and analytics is the combustion engine. Analytics or data science comprises a number of tools and techniques from the fields of statistics, machine learning and broader AI. While there is no dearth of such tools and techniques ranging from random forests to neural networks, problems most enterprises face is that of identify the most suitable tool (s) for a business problem at hand. This paper aims to address this problem by developing a novel, comprehensive framework that identifies the optimal data science tools given the nature and type of the business problem and the constraints on the underlying data used. This framework can be an effective device for enterprises to select the suitable data science tools and techniques to apply to their problems. The intention is to empirically test this framework in future research and develop a Natural language Processing (NLP) implementation of this prescriptive framework.
Studies Advanced in Artificial intelligence based Game
Authors: Zhou Jiayu
Published: July 2023

Thanks to the rapid development of high-performance computing equipment and pattern recognition technology, artificial intelligence technology has gradually matured in recent years and is widely used in many fields. Due to its potentially huge economic value, AI-based game production has attracted a lot of research attention from academia and industry in recent years. AI has progressed from using simple rules to playing a variety of games, and it has proven to be a formidable opponent, defeating human champions in some instances. This paper provides an overview of the algorithms used in different games and highlights the power of AI demonstrated in chess games. However, despite its successes, there are still some problems that exist. This paper concludes by discussing possible solutions to these issues.
Studies Advanced in License Plate Recognition
Authors: Yang Bintao
Published: July 2023

License plate recognition is a crucial mission in computer vision, and deep learning has significantly improved its performance. A representative license plate recognition system involves five components: license plate image preprocessing, image acquisition, license plate detection, character recognition, and character segmentation. This paper will explore the methods commonly used in each stage of the recognition process and analyze some of the current challenges and future trends of license plate recognition. This discussion will consider real-world factors such as lighting and weather conditions that can affect recognition accuracy. Ultimately, it is hoped that these insights will contribute to the development of intelligent transportation systems.
NeRFGeometry-aware 3D Scene Modelling
Authors: Fang Yao , Liu Yuzhao
Published: July 2023

NeRF (Neural radiation field) as one of the most popular research fields, the method has also been extended to the field of viewpoint synthesis, and the use of NeRF to represent three-dimensional scenes or models has been proposed, so that it has been successfully applied to the field of viewpoint synthesis and achieved high-quality synthesis results. This paper reviews the principle of NERF and its advantages and disadvantages, focusing on the areas where NeRF needs to be improved. The research directions of NERF in different fields were introduced and analysed. For example, the construction of Block-NeRF in Google Maps makes unmanned technology go to a higher level, and the digital protection and restoration of ancient cultural relics. And the research directions that can be optimized and expanded by NERF in the future are analysed and proposed.
Comparison of Six Machine Learning Algorithms for Stroke Risk Estimation
Authors: Li Yichen , Wang Zirui , Wu Tongyu , Zhou Tianrui
Published: July 2023

Every year there are millions of people suffer from stroke around the world. In order to timely prevent stroke and effectively reduce the damage caused by stroke, this paper uses a variety of machine learning algorithms to predict stroke. The dataset used in this work contains 12 variables that provide some demographic, health and lifestyle information such as age, gender, hypertension, heart disease, marital status. First of all, this work analyzes dataset of 5110 patients by visualizing the distributions of numerical features and investigating the correlation between these different features. Six different algorithms, including decision tree, KNN, logistic regression, SVM, Native Bayes and random forest) are used to build models. Finally, use accuracy and classification report to evaluate these models performance. Among these models, the SVM classifier had the highest accuracy at 99.97%. Therefore, the SVM classifier model could be chosen to predict whether a person have a stroke and quickly screen out possible patients from the population.
Prediction of MBTI Personality Leveraging Machine Learning Algorithms
Authors: Li Ze
Published: July 2023

In this study, the author attempted to implement a machine learning approach to determine users' corresponding MBTI personality types by relying only on the content of their online forum postings. Models based on different algorithms are built and trained, and the natural language of the collected data set is converted into machine language for machine learning and used in subsequent tests to determine the correctness of the predicting results. The data set is collected from the forum and divided into two parts, the training set is leveraged to train the model and the test data set is leveraged to make personality predictions and compare with the training data set to measure the correctness of the predicting outcomes. The results show that logistic regression algorithm and vectorized representation of text with TfidfVectorizer can best accomplish the prediction task. This study completed a preliminary comparison of algorithms for personality prediction from text, which became the basis for subsequent personality model predictions using other media.
User Interest Based Contextual Conversational Recommender System
Authors: Xia Yinghui
Published: July 2023

The Conversational recommender system is for item recommendation during human-machine dialogue, where items can be movies, music, and goods. Scenarios can be intelligent customer service and shopping assistants. The system's goal is to generate high-quality recommendations in short-round interactions. There are two main problems with the system. Firstly, recommending through dialogues is a hard problem as less contextual information is available, resulting in fewer signals for recommendation and less precision for user interest modeling. Secondly, the recommender system considers the absence of interaction between a user and an item as a negative sample. However, this approach can introduce bias in the objectives because non-interaction could potentially be a positive sample. To tackle the previously mentioned concerns, this paper suggests a new model. This model adds an extra knowledge graph to enrich contextual information. Then, this model introduces contrastive learning to separate the distribution between positive and negative samples and improve learning efficiency. This paper further tests the model on the ReDial dataset, and experiments have shown that the method is effective and performs better than previous work.
False Comment Detection Based on BERT and Long Short-Term Memory
Authors: Chen Qingyang
Published: July 2023

In the beginning and ending of an article of the rapid development of e-commerce, almost all e-commerce websites support consumers to rate and comment on the workmanship, express delivery, and price of products. Publishing many messages and comments on the network platform has become a popular form of the Internet. Nowadays, online paid Internet platforms are also in the process of continuous evolution. The comments written are more and more authentic and highly misleading. It is a waste of time and easy to be confused to distinguish by the naked eye alone. This article manually annotates a valid dataset containing 4000 snack product reviews and proposes a false review detection model based on Bidirectional Encoder Representation from Transformers (BERT) and Long Short-Term Memory (LSTM). The experimental results show that the accuracy of the BERT and LSTM false comment detection model and BERT false comment detection model is above 50% and effective. The BERT and LSTM model is slightly better than the BERT model regarding loss rate and accuracy. The experiments were scientific and effective, and the data provided by the two experimental models are of the reference value.
Wild Animals Detection Based on YOLOv5
Authors: Li Xudong
Published: July 2023

Wild animal monitoring is of great significance to population discovery and research on animal behavior and habits. Early wild animal monitoring mainly relied on human effort, which is time-consuming and contains safety risks. In recent years, with the continuous development of pattern recognition techniques, automated wildlife detection algorithms based on image content analysis have also made progress thanks to these achievements. However, due to the complexity of field scenes, the recognition accuracy and robustness of existing methods can not meet the practical application requirements. Based on considerations, we suggest a field animal detection method based on YOLOv5, which aims to localize and recognize the wild animal. We analyzed the change of recognition accuracy for different scenes in detail, especially for the scene containing multiple targets, small targets or occluded targets. We have used a large number of experiments to verify the feasibility of this method.
Breast Cancer Prediction Based on RF-SVM
Authors: Chen Sijun
Published: July 2023

Breast cancer prediction is crucial in identifying women who may be at risk for developing the disease. By doing the prediction, doctors can make the rapid diagnosis. Additionally, breast cancer prediction can also help guide research efforts and inform public health policies aimed at reducing the incidence and mortality of breast cancer. SVM (Support Vector Machine)is a classic method in machine learning, Random Forest is also widely used but they all have some shortcomings. Random Forest dont have high accuracy. So RF-SVM(Random Forest and Random Forest) is be chosen to do the prediction. The goal of this research is to train a model that can achieve high accuracy in a relatively short time. As for the result, it shows that RF-SVM has achieved a high accuracy(0.95), compared with other method although RF(Random Forest) has the highest accuracy(0.97), it has the lowest precision(0.95). Over all, RF-SVM has the best overall performance. After trial, traditional machine learning methods turns out to be more stable.
Análise do ethos tecnodiscursivo de @mellziland no ecossistema Twitter
Authors: Glück Eduardo Paré , Boaventura Luis Henrique
Published: July 2023

Este artigo objetiva verificar como o perfil @mellziland constrói seu ethos tecnodiscursivo na interação com seu interlocutor ao divulgar a ciência na temática da COVID-19 no ecossistema Twitter. Trata-se de tuítes sobre a aplicação da vacina CoronaVac em crianças de 3 a 5 anos no Brasil. Do ponto de vista teórico, valemo-nos de pressupostos da ADD, à luz de Paveau (2021), de conceitos como cenografia e ethos discursivo, de Maingueneau (2008, 2020), e de estereótipo, conforme Amossy (2019) e Amossy e Pierrot (2022). A análise dos tuítes gerados consistiu nas seguintes etapas, elaboradas com base na proposta de Glück (2021): (i) geração dos dados para análise, na temática da COVID-19; (ii) descrição dos tuítes dos dados gerados a partir do ecossistema Twitter; (iii) identificação das estratégias tecnolinguageiras de @mellziland, considerando as categorias da ADD (PAVEAU, 2021); e (iv) aplicação da teoria enunciativo-discursiva de Maingueneau (2008, 2020), para revelar como @mellziland constrói seu ethos tecnodiscursivo na interação com seu interlocutor nos tuítes de divulgação científica sobre a COVID-19. A análise revelou que as escolhas tecnoenunciativas de Mellanie corroboram para a construção de seu ethos tecnodiscursivo, o qual é revelado como um ethos de saber e de credibilidade.
Sleep Disturbance Trends in the Short-Term Postoperative Period for Patients Undergoing Total Joint Arthroplasty
Authors: Pitaro Nicholas L. , Herrera Michael M. , Alasadi Husni , Shah Kush C. , Kiani Sara N. , Stern Brocha Z. , Zubizarreta Nicole , Chen Darwin D. , Hayden Brett L. , Poeran Jashvant , Moucha Calin S.
Published: July 2023

Background:

Patients undergoing total joint arthroplasty (TJA) often experience preoperative/postoperative sleep disturbances. Although sleep quality generally improves > 6 months after surgery, patterns of sleep in the short-term postoperative period are poorly understood. This study sought to (1) characterize sleep disturbance patterns over the 3-month postoperative period and (2) investigate clinical and sociodemographic factors associated with 3-month changes in sleep.

Methods:

This retrospective analysis of prospectively collected data included 104 primary elective TJA patients. Patients were administered the PROMIS Sleep Disturbance questionnaire preoperatively and at 2 weeks, 6 weeks, and 3 months postoperatively. Median sleep scores were compared between time points using Wilcoxon signed-rank tests, stratified by preoperative sleep impairment. A multivariable logistic regression model identified factors associated with 3-month clinically improved sleep.

Results:

The percentage of patients reporting sleep within normal limits increased over time: 54.8% preoperatively and 58.0%, 62.5%, and 71.8% at 2 weeks, 6 weeks, and 3 months post-TJA, respectively. Patients with normal preoperative sleep experienced a transient 4.7-point worsening of sleep at 2 weeks (P = 0.003). For patients with moderate/severe preoperative sleep impairment, sleep significantly improved by 5.4 points at 2 weeks (P = 0.002), with improvement sustained at 3 months. In multivariable analysis, patients undergoing total hip arthroplasty (versus knee; OR: 3.47, 95% CI: 1.06 to 11.32, P = 0.039) and those with worse preoperative sleep scores (OR: 1.13, 95% CI: 1.04 to 1.23, P = 0.003) were more likely to achieve clinically improved sleep from preoperatively to 3 months postoperatively.

Discussion:

Patients experience differing patterns in postoperative sleep changes based on preoperative sleep disturbance. Hip arthroplasty patients are also more likely to experience clinically improved sleep by 3 months compared with knee arthroplasty patients. These results may be used to counsel patients on postoperative expectations and identify patients at greater risk of impaired postoperative sleep.

Study design:

Retrospective analysis of prospectively collected data.
THE INFLUENCE OF SERVICE QUALITY, PRICE PERCEPTIONS AND EXPERIENTIAL MARKETING ON WORLD OF MOUTH IN MANDIRI BINA PRESTASI UNIVERSITY (UMBP) STUDENTS
Authors: Christy Ardelia Tarigan  , Sugih Arto Pujangkoro  , Rulianda Purnomo Wibowo
Published: July 2023

This study examines the Quality Of Service, Perception Of Price And Experimental Marketing On The World Of Mouth In  Students Of Mandiri University Develop Achivements. As for this research method is quantitative, with data collection in the form of a questionnaire. while the respondents were students. The conclusions from this study are first that service quality has a positive and significant effect on Word of Mouth among Mandiri Bina Prestasi University (UMBP) students; Second, 2. That Perceived Price has a positive and significant effect on Word of Mouth for Mandiri Bina Prestasi University Students (UMBP); Third, that Experiential Marketing has a positive and significant influence on Word of Mouth for Mandiri Bina Prestasi University (UMBP) students; Fourth, 4. That Service Quality, Perceived Price and Experiential Marketing simultaneously have a positive and significant effect on Word of Mouth in Mandiri Bina Prestasi University Students (UMBP).
ANALYSIS OF FACTORS AFFECTING LABOR EFFICIENCY IN OIL PALM HARVESTING AT PT ASN KEBUN UJONG LAMIE, NAGAN RAYA DISTRICT
Authors: Darwin  , Teuku Athaillah
Published: July 2023

Labor efficiency is all the efforts made by each member in balancing the amount of labor with the production produced. The purpose of this study was to determine the labor efficiency of oil palm harvesters at PT ASN Kebun Ujong Lamie, Nagan Raya Regency. This study uses a quantitative research method with multiple linear regression analysis method. The sampling technique used was purposive sampling. The sample in this study amounted to 19 respondents. The results showed that partially with a 95% confidence level the premium variable had an effect on the labor efficiency of oil palm harvesters, and age and education level had no effect on the efficiency of the palm oil harvesters. Meanwhile, the age, education level, and premium variables simultaneously affect the labor efficiency of oil palm harvesters.