bluuebunny/crossref_metadata_2025 huggingface dataset:
Fertilization with calcium nitrate and its influence on differen-tial growth of peach tree rootstocks in containers
Authors: Spinelli Victor Mouzinho , Souza Aline das Graças , Bianchi Valmor João , Barros Willian Silva , Panozzo Luís Eduardo , Magano Deivid Araújo , Silva José Antônio Gonzalez da , Pinto Luciana Barros , Oliveira Jaqueline Machado de
Published: April 2024

The objective was to evaluate whether the different levels of fertilization with Ca(NO3)2 influenced the growth of rootstocks cultivated in packaging. The plant material used in the experiment was obtained from seeds produced by mother plants of the rootstock cultivar Tsukuba 1, and from two intraspecific F1 hybrids of Prunus persica, derived from reciprocal crossing, selections being NR 0080412 ('Capdeboscq' x ' Flordaguard') and NR 0280401 ('Flordaguard' x 'Capdeboscq'), all with purple leaves. The experimental design was completely randomized, in a 3 x 5 factorial scheme, with three genotypes and five different doses of nitrogen, in the form of calcium nitrate. Five replications were used for each treatment, with the experimental unit consisting of one plant. It was verified that the highest dose of Ca(NO3)2, provided the best quality of the seedlings, which was expressed in the highest DQI, for the three genotypes evaluated, highlighting the seedlings of the 'NR 0080412' selection, which also proved to be more vigorous in relation to the other evaluated genotypes.
Intervenção terapêutica-educacional com crianças com síndrome da duplicação 22q11.2: revisão integrativa
Authors: Lumertz Fábia Daniela Schneider , Oliveira-Menegotto Lisiane Machado de
Published: April 2024

A Síndrome da Duplicação da Banda q11.2 do Cromossomo 22 ou SDup22q11.2 pode apresentar-se nos sujeitos acometidos de forma assintomática até quadros de graves comprometimentos ao desenvolvimento humano, como deficiência intelectual, dificuldades de aprendizagem, déficits de memória, déficits na organização perceptiva e na compreensão verbal e comprometimento de fala. Ainda estão descritos na literatura atrasos no desenvolvimento neuropsicomotor, atraso de crescimento e hipotonia muscular. Os dismorfismos mais comuns são hipertelorismo, nariz achatado, orelhas displásicas, pregas epicânticas, insuficiência velofaríngea, micrognatia e fissuras, assim como malformação cardíaca congênita, deficiência visual e auditiva, convulsões, microcefalia, anormalidades urogenitais e ptose. Assim, a apresentação fenotípica da síndrome pode variar desde a assintomática até quadros graves do neurodesenvolvimento, como transtorno do espectro autista, deficiência intelectual e epilepsia. Considerando a SDup22q11.2 quando presentes as manifestações clínicas e tendo em mente o impacto desses sintomas no desenvolvimento infantil e no núcleo familiar, é fundamental pesquisar técnicas de intervenções terapêuticas e educacionais que possam auxiliar na atenuação dos sintomas e otimizar o desenvolvimento do sujeito, auxiliando, também, na qualidade de vida da criança e de sua família. Assim, este trabalho de revisão da literatura tem por objetivo mapear as pesquisas dos últimos 10 anos que apontem tratamentos terapêuticos-educacionais, não medicamentosos, e que tenham por objetivo o desenvolvimento da criança com SDup22q11.2. Obteve-se como resultado dois trabalhos, um estudo de caso que apontou duas técnicas para o auxílio da comunicação expressiva de uma criança com a síndrome, e outro que apresentou um panorama de terapias e educação usado por pacientes com SDup22q11.2, em um estudo longitudinal. Percebeu-se a necessidade de mais estudos acerca de intervenções terapêuticas-educacionais que visem potencializar o desenvolvimento de sujeitos acometidos da síndrome.
Panorama do ensino da agronomia no estado de Mato Grosso do Sul, com ênfase aos cursos ofertados pelo IFMS
Authors: Aragão Samuel Carvalho de , Torsoni Guilherme Botega , Santos Elcio Ferreira , Oliveira Márcio Teixeira , Lorençone João Antonio , Lorençone Pedro Antonio , Araújo Renno Abreu
Published: April 2024

O objetivo com este trabalho foi de realizar um levantamento relacionado ao ensino do curso de agronomia no estado de Mato Grosso do Sul, enfatizando os cursos ofertados pelo Instituto Federal nos campi de Ponta Porã, Naviraí e Nova Andradina. A partir de um questionário pré-formulado foram coletados dados in loco em quinze instuições que ofertam o curso no estado, privadas e públicas (Federal e Estadual). Nove cursos são ofertados em instituições públicas e seis em instituições privadas. Somente uma universidade privada no município de Ponta Porã obteve nota máxima pelo Ministério da Educação e Cultura (MEC), ou seja, nota cinco, as demais todas tem nota quatro, ressaltando que os quatro cursos ofertados pelas universidades estaduais não são avaliados pelo MEC. Observou que 86,31% dos docentes são doutores, 12,98% são mestres e 0,7% são especialistas. A taxa de evasão nas instituições públicas foi em torno de 7% e nas privadas de 17%. Em todas as instituições existem três modalidades de seleção de estudantes. As instituições públicas ofertam cinco cursos stricto sensu e as privadas ofertam três. Observou ainda, que sete instituições ofertam o curso de agronomia em uma fazenda escola, seis possuem a área experimental. Sendo assim, pode-se levantar que no estado de Mato Grosso do Sul o curso ofertado supre a necessidade de profissionais desta área, uma vez que mostram que além dos cursos ofertados dentro do estado nas fronteiras e nas divisas do estado, também ofertam cursos de agronomia o que possibilita que a população do estado ter esta opção a mais para cursar agronomia.
O(a) professor(a) com deficiência da educação básica nos estudos da pós-graduação brasileira: uma análise integrativa
Authors: Taveira Valber de Resende , Ribeiro Luiz Paulo
Published: April 2024

Ao considerarmos que as preocupações a respeito da escola se dão em torno dos processos de ensino e aprendizagem, a figura do professor se torna um dos temas centrais em diferentes investigações e, nesse contexto, vai se inserir a pessoa com deficiência (PCD) que trabalha na função de professor. Ao longo da história da humanidade, a PCD vai quase sempre estar segregada e diferentes abordagens vão dar base a esse comportamento social, até que surja na atualidade o modelo social da deficiência (e da inclusão). Esse modelo preconiza que as limitações não estão nas pessoas com deficiência, mas sim na sociedade que se mostra ineficiente em eliminar as barreiras que impedem as PCDs de acessar direitos básicos como saúde, educação e locomoção. Este artigo se dedica a analisar os trabalhos de pós-graduação brasileiros a respeito dessa figura através de uma revisão integrativa de literatura, de forma a se conhecer a produção acadêmica nos programas de pós-graduação brasileiros sobre o tema. Foi realizada uma busca de trabalhos no banco de teses e dissertações da Capes, disponíveis através plataforma Sucupira, com os descritores “professor com deficiência” no período de 2002 a 2023 e que tivessem o professor com deficiência da educação básica como tema de estudo. Dessa busca, foram selecionadas 13 dissertações que atendiam aos parâmetros busca e nenhuma tese. As dissertações foram analisadas, tiveram seus elementos de concordância categorizados e apresentados nesta análise. Das conclusões destaca-se o fato que todos os trabalhos tratam em algum momento das dificuldades/desafios percebidos pelos professores com deficiência para a realização do seu trabalho, sendo esse enfoque predominante nas dissertações analisadas.
Menyongsong Masa Depan: Transformasi Karakter Siswa Generasi Alpha Melalui Pendidikan Islam yang Berbasis Al-Qur'an
Authors: Munawir Munawir , Alfiana Fina , Pambayun Sekar Putri
Published: April 2024

Technological advancements have an indirect impact on human character and behavior, particularly among the alpha generation. The purpose of this research is to examine the role of learning Islamic religious education based on the Qur'an in developing moral character among alpha-generation students. The method used in this research is descriptive qualitative using library research. The literature originates from books, journal articles, and theses written on Google Scholar. The findings of this study indicate that the Qur'an has a role in forming the moral character of the alpha generation. Technological advances indirectly affect human character and habits, especially in the alpha generation. The alpha generation is one of today's generations that has a high level of technology use because it was born after the internet generation existed. The purpose of this research is to find out the role of learning Islamic Religious Education (PAI), which is based on the Qur'an, as forming moral character in alpha generation students. The method used in this research is descriptive qualitative using literature study techniques. The literature used as a source of study comes from journal articles, books, and theses published on Google Scholar. The research results obtained show that the Qur'an has a role in shaping the moral character of the alpha generation through PAI learning. This can be proven by making the Koran a guide and foundation for all issues and knowledge, including character education.
Stock Price Prediction with Denoising Autoencoder and Transformers
Authors: Chen Zhiyang
Published: April 2024

Predicting stock market price movement has been a challenging problem for time series forecasting due to its inherent volatility. The introduction of machine learning techniques, such as the use of a recurrent neural network (RNN), have since dominated the field for stock trend forecasting and stock price prediction. RNNs have inherent model limitations that are solved with the introduction of the transformer model, which has since been used in many sequential classification and generative tasks. This study demonstrates the viability of a transformer-based model on the field of stock price prediction by comparing the predictions of a transformer-based model with some RNN-based baseline models on the hourly closing price of Apple (AAPL) stock prices. A dimension reducing simple autoencoder was also incorporated into the transformer model to increase performance. Although RNN-based models are shown to outperform the transformer model in accuracy, the transformer model leads in directional stock accuracy. This demonstrates that the transformer is applicable to compete with RNN based models within the field, along with the incorporation of an autoencoder to further integrate the transformer’s power to this field.
Design of an Encryption Chat Application Based on ChaCha20-Poly1305
Authors: Feng Yurui
Published: April 2024

This study introduces the meticulous design and development of a chat application fortified with the robust ChaCha20-Poly1305 encryption algorithm. The selected cryptographic algorithm is renowned for its efficacy in ensuring top-notch security. In our application, ChaCha20 serves as the backbone for stream encryption, ensuring that the message content remains confidential and impenetrable to eavesdroppers. Simultaneously, Poly1305, an esteemed message authentication code, guarantees the integrity and authenticity of each chat message. It ensures that the messages sent are genuine and have not been tampered with during transit. Together, the integration of these two components constructs a fortified wall against potential security breaches, safeguarding user communications. This not only instills confidence in users regarding the privacy of their conversations but also sets a benchmark in chat application security. The overarching aim is to offer an impeccable blend of user-friendly experience and top-tier security, revolutionizing the way secure communications are perceived and utilized.
Design of Multi-Time Scale Power Trading Platform in Regional Spot Market
Authors: Qin Jianrong , Qi Hongpeng , Qin Lijuan , Xie Tian , Mo Jiangzhi
Published: April 2024

In order to improve the reliability of power trading under the background of regional power spot market, a multi-time scale power trading platform is designed. The transaction status of foreign power spot markets, the general situation of China's power market construction and the particularity of China's market transactions are analyzed. A multi-time-scale trading platform in the spot market is constructed, the overall structure and technical architecture of the platform are established, the multi-time-scale transaction process is analyzed, and a clearing mechanism framework for cross-regional transactions is proposed. According to the needs of market connection, the basic plan for cross-regional transactions is proposed. Finally, the application effect analysis of the platform proposed in this paper is carried out, which shows the validity of the platform design.
Research On Laptop Price Predictive Model Based on Linear Regression, Random Forest and Xgboost
Authors: Tian Peiru
Published: April 2024

In today's rapidly evolving technological environment, the portability and versatility of laptops have led to a significant growth in their user base as well, making them essential tools for individuals and businesses alike. Particularly since the COVID-19 pandemic, the rate of Work From Home (WFH) partially or overall WFH has exceeded 30%. Global laptop shipments have also far surpassed desktop PCs so far in 2017. Accurately predicting laptop prices is beneficial for retailers to devise competitive pricing strategies and for consumers to effectively budget and select the most suitable laptops. In this study, we used a dataset of 1320 samples to investigate the significance of features in a laptop price prediction model using linear regression, random forest, and XGBoost methods. We incorporated 13 features in the modeling process, including laptop brand, type, screen size, RAM, GPU, operating system, and weight. Three mathematical models were established to predict the price of laptop. By comparing the regression model metrics RMSE and R2 under linear regression, random forest and XGBoost models, the RMSE under the XGBoost model is 294.11 with an R2 of 0.85. It is evident that the XGBoost model exhibits the smallest RMSE and the highest R2 value closest to 1. This suggests that the XGBoost model provides the highest accuracy and best fit for the predictive model.
The Investigation of Stock Price Prediction Based on Machine Learning
Authors: Huang Boming
Published: April 2024

As the global economy experiences unprecedented growth, the stock market is undergoing a period of expansion and prosperity. Driven by the allure of high risks and high returns, a significant number of individuals have committed themselves to stock trading. In the age of the Internet of Everything, the trajectory of the stock market is intertwined not only with the fortunes of individuals and businesses but also deeply influences the fate of nations and societies. However, stock market forecasting is quite a hard task owing to its volatile nature, where even seemingly unrelated factors can lead to dramatic changes. Therefore, the importance of constructing reliable stock prediction methods is self-evident. In recent years, scholars have mainly focused on machine learning and deep learning methods to complete this task. After literature review, this article summarizes and discussed several commonly used and effective methods, such as Linear Regression, Random Forest, Long Short-Term Memory (LSTM), etc. The advantages, disadvantages and applications of different methods are discussed, like the shortcoming of Linear Regression to handle non-linear models well and the relatively excellent ability of LSTM to predict long sequences. This article would be helpful for emerging scholars to have a grasp of the characteristics, advantages and scope of application of these methods, and facilitate the development of future research.
Image Segmentation Based On U-Net and Adjusted U-Nets
Authors: Pan Jixun
Published: April 2024

Image segmentation, the process of dividing an image into various areas is quite crucial recently. Numerous industries, including robotics, remote sensing, and medical imaging, have applications for this task. In recent years, deep learning techniques, especially U-shaped networks (U-nets), have shown remarkable success in solving image segmentation problems. This paper provides an overview of image segmentation using neural networks, introduces different types of adjusted U-nets used for this task, including the implementation of attention gates and the use of residual neural network as the encoder path based on the original encoder-decoder structure, and then uses U-net and adjusted U-nets to conduct image segmentation on the black sea sprat. The study uses dice similarity coefficient and binary cross entropy loss function to compare the model training results and further judges the functionality of the models by the predicted segmented images. According to the test results, the Res34-UNet with attention gates performs most efficiently in segmenting this image dataset, although it's more unstable compared to the basic U-Net.
Research and Application of VR in Training and Learning
Authors: Wang Kaidong
Published: April 2024

With the rapid development of VR technology in the past two years, VR education has broken the rigidity and single way of traditional teaching. This paper discusses and analyzes three aspects of VR in the field of training and learning. specifically, the convenience and efficiency of VR in the classroom; an overview of the savings and simplicity of VR skills learning. And a comparative analysis of innovation and breakthroughs in the field of VR art learning. So far, VR technology is still only in the newly developed stage, and there are various problems waiting to be solved, such as expensive, unfriendly to students who wear glasses, etc. But despite certain shortcomings, VR technology has great application prospects in the future. These skills training and learning based on VR are elaborated and analyzed. Finally, the paper summarizes and looks forward to the full text.
The Application of Machine Learning in Predicting the Results of Popular eSports Games: Win Rate Prediction in MOBA and FPS Games
Authors: Peng Yulin
Published: April 2024

Electronic Sports (eSports) are one of the most prevalent entertainments in modern society. As eSports is gaining more and more popularity over time, a proper evaluation system that can accurately analyze and predict players’ performance is indispensable. Therefore, machine learning is proposed as an effective prediction tool in the studying of data from eSports matches so that eSports teams can utilize it to ameliorate their future accomplishments. Two popular eSports game types (Multiplayer Online Battle Arena and First Person Shooter) are analyzed. Common features they share include communication, equipment, economy, and so on. Correct hero pick is the most predominant factor in MOBA games. Research shows that machine learning models like Logistic Regression, Support Vector Machine, and Neural Networks can effectively combine hero selection data to predict the outcome of a game. On the other hand, the most important factor in FPS games is the in-game equipment each round. Additional research proves that Logistic Regression, K-Nearest Neighbors, Random Forest, and Neural Networks are the most powerful models for predicting the team win rate. This paper can serve as an instruction for applying machine learning methods to making predictions in eSports games. Meanwhile, it can provide available techniques to help professional eSports teams improve their competitive level and future in-game understanding as well.
Research on Human Action Recognition Method Based on Machine Learning
Authors: Li Yushan
Published: April 2024

Human action recognition (HAR) is an important research direction in the field of computer vision, and human action recognition technology provides an important foundation for computers to understand and simulate human behavior. It has been widely used in many practical applications, including human-computer interaction, gesture recognition, motion analysis, motion capture, virtual reality and augmented reality. Early methods were mainly based on the characteristics of manual design and traditional machine learning methods, such as support vector machine (SVM) and Random Forest. These methods usually rely on manually extracted features, such as edge, texture and color information, but they do not perform well in complex scenes and changing environments. With the rise of deep learning, especially the successful application of Convolutional Neural Network (CNN), great breakthroughs have been made in human action recognition. Using CNN, we can learn the feature representation directly from the original image data, which avoids the trouble of manually designing features. This paper expounds the research progress and future development trend of human action recognition methods based on machine learning.
Research on Improved Design of School Shower Equipment Based on Ergonomics
Authors: Li Tianhui , Ma Guangtao
Published: April 2024

With the increasing demand of students for campus life quality, the improvement of campus public facilities has attracted much attention. In order to improve the experience of using the on-campus shower and enhance the comfort of the students in the process of using it, the design of the on-campus shower has been improved by applying ergonomic theory, taking into full consideration the physiological and psychological characteristics of the people using it, and optimizing the design of the ergonomic dimensions and operation methods of the on-campus shower equipment to make it more in line with the ergonomic dimension requirements. It provides users with higher efficiency and more comfortable use, and provides reference for the design research of campus shower public facilities.
Heart Disease Prediction Method Based On ANN
Authors: Chen Qizhong , Ma Ningyao
Published: April 2024

Heart disease is one of the main health problems worldwide, and heart disease prediction based on artificial neural networks (ANNs) has become a research hotspot, aiming to improve the accuracy of early diagnosis. At present, heart disease prediction still faces problems such as data complexity, feature selection, overfitting, etc., which need to be overcome in ANN models. The heart disease prediction based on ANN has potential importance, which can assist medical decision-making, reduce the incidence rate of heart disease, and provide support for personalized treatment and health management. This study proposes a heart disease diagnosis method relied on machine learning, which balances the dataset using the SMOTE (Synthetic Minority Over Sampling Technique) algorithm as well as ENN (Edited Nearest Neighbors) algorithm. Then, research was conducted based on a variety of machine learning models, such as LR (logistic regression), DT (decision tree), RF (random forest), GBDT (gradient enhancement decision tree), XGBoost (extreme gradient enhancement), SVM (support vector machine), and ANN (deep neural network), applied to predict heart disease datasets. Patients can measure the risk of heart disease through past medical history, household equipment, and personal habits. Research has shown that the auc and recall based on the ANN model are as high as 0.808 and 0.81, respectively.
House Price Prediction Based on Machine Learning Models
Authors: Ouyang Xiaoyan
Published: April 2024

This study aims to provide accurate house price predictions using machine learning algorithms. These predictions can assist decision-makers in making informed property investments and planning. Multiple linear regression and random forest were employed to achieve this goal. First, the acquired data underwent thorough analysis, including preprocessing and visualization. Subsequently, the study employed multiple linear regression and random forest models for house price prediction and evaluated their performance. The multiple linear regression model yielded promising results with an R² score of 0.73, explaining 73% of the target variable's variance. However, it exhibited prediction errors in specific cases, suggesting potential areas for improvement. In contrast, the Random Forest model achieved a slightly lower R² score of 0.69. Nonetheless, it excelled at capturing complex nonlinear relationships. Additionally, it identified the top five key features influencing house prices: house size, number of bathrooms, number of floors, parking spaces, and air conditioning. This study highlights the potential of machine learning models for house price prediction. Future research can further enhance these models and consider other influential factors to explain house price fluctuations comprehensively. The results offer valuable applications for investors, brokers, and government planners in the real estate market.
Age Estimation from Facial Photos: A CNN-Based Approach with Multi-Model Feature Fusion
Authors: Lyu Zixuan
Published: April 2024

This study was designed to improve the accuracy and efficiency of facial age recognition by training and tuning advanced deep learning models. This study evaluated the performance using the UTK Face dataset and multiple deep learning models for the facial age recognition task and found that a three-model fusion (EfficientNetB0, DenseNet121, and Inception V3) had the best accuracy and generalization ability. The study also tried adding a Transformer layer, but the results were not significantly improved. Triple Model Fusion (EfficientNetB0, DenseNet121, and Inception V3) with the addition of the Transformer layer had an overall accuracy of 64%, which was slightly lower than the version without the Transformer (65% accuracy). Triple Model Fusion performed in all tested configurations Best with 65% accuracy.
Utilizing Machine Learning Algorithms for Architectural Design
Authors: Sung Katrina
Published: April 2024

With the development of digital technology, artificial intelligence has brought a paradigm shift in numerous fields. Machine learning is used as a decision making tool to analyze large data and produce output data with similar characteristics. This paper aims to explore and analyze the influence of artificial intelligence on the architectural designing process. In the second section, traditional methods of architectural design are observed. The third section explains the methodology of machine learning and its current limitations. The fourth section investigates several real world applications of machine learning in designing architecture, and the last section speculates on the potential directions that the field of architectural design could take as machine learning continues to advance.
A Comprehensive Analysis of Game theory on Multi-Agent Reinforcement
Authors: Fan Wentao
Published: April 2024

In recent years, reinforcement learning has gradually emerged as a popular research field in artificial intelligence. However, for multi-agent systems, due to their complex environment and an abundance of agents, the learning target of maximizing the anticipated value of cumulative rewards for specific agents frequently fails to converge. Introducing game theory into reinforcement learning can effectively address the interactions among intelligent agents, provide a rationale for convergence points corresponding to strategies. To address the issue of non-existence of optimal solutions for certain tasks in multi-agent settings, this paper takes a game-theoretic perspective and summarizes classical reinforcement learning algorithms developed in recent years. The basic theory of multi-agent reinforcement learning, essential game theory, categorization of reinforcement learning using multiple agents game strategies, primary worries are addressed in the study. It also analyzes the challenges that game-theoretic multi-agent reinforcement learning algorithms may encounter in the future, along with the relevant optimization directions.
Traffic Accident Severity Prediction Based on Data Cleaning and Machine Learning (Random Forest / Xgboost)
Authors: Zhou Wenjing
Published: April 2024

Traffic accidents have increasingly become a global concern, significantly affecting lives and economic sustainability. The US Accidents dataset from 2016 to 2023 provides an extensive record of accidents across the United States, containing detailed data and environmental data of these accident. This study aims to harness the potential of this rich database to predict severity level of accidents. Our research predominantly revolved around meticulous data cleaning, ensuring that the dataset's integrity was uncompromised. After preprocessing, the cleaned data was subjected to sophisticated Machine Learning techniques, primarily focusing on the Random Forest and XGBoost algorithms. These models were chosen due to their renowned capability in handling complex datasets and rendering accurate predictions, especially in scenarios laden with multiple variables. Upon application, the models demonstrated impressive efficacy. To validate the reliability and performance of our models, we employed the confusion matrix. This tool provided a clear visualization of the models' accuracy, revealing true positives, false negatives, and other crucial metrics. Furthermore, to enhance prediction outcomes, the Voting Classifier was implemented, combining the strengths of our primary models and consequently elevating the overall accuracy. The Random Forest algorithm exhibited substantial precision, while XGBoost further enhanced prediction accuracy. These findings underline the significant role of advanced data analytics and Machine Learning in comprehending traffic accident dynamics. In conclusion, our study emphasizes that leveraging state-of-the-art Machine Learning techniques on well-curated datasets can substantially improve our understanding and prediction of traffic accident severity. Such insights pave the way for the development of more effective preventive measures and safety protocols, aiming for a safer traffic environment in the future.
Enhancing Children's Academic Learning Experience: Natural Language Interface Designs in Educational Application
Authors: Guo Zhicheng , Hao Tianxin , Wang Xinlinzi
Published: April 2024

User Interaction (UI) designs, a crucial part of the modern internet industry, have been envolving and becoming more and more sophisticated and advanced. Recently, with the development of Machine Learning in Computer Science, Natural Language Interface (NLI) designs, a new division of the UI designs, appears into public’s view. With its special focus on using Natural Language Processing (NLP) to enable applications respond and execute useful actions in response to users’ input, UI designers are able to implement an array of new functions into the application, allowing children (12 to 18 years old) to better interact with the applications. This research primarily focuses on how NLI designs in educational settings are able to enhance their learning experience and academic performance. In particular, with a deep literary analysis, this research incorporates several important findings about NLI designs’ positive effects on children’s academic learning experience and performance. Through thorough investigations, this research concludes that while there are several technical limitations on NLI designs due to the limited level of technology, they can still facilitate children’s learning experience and academic performance by satisfying children, letting them stay longer in the application, and providing them a better guide, which enables them to have a better navigation in the application’s learning environment.
Optimizing Intracranial Hemorrhage Detection: A Comparative Study of VGG Deep Learning Architectures with Transfer Learning on CT Brain scans
Authors: Jin Yancheng
Published: April 2024

Intracranial Hemorrhage (ICH) is a life-threatening condition that requires rapid and accurate diagnosis. This study applied Visual Geometry Group (VGG) deep learning model to binary classification of cerebral hemorrhage in Computerized Tomography (CT) brain scans, thereby improving the diagnostic process. Using a Kaggle dataset containing 6703 images, this study investigated the validity of VGG11, VGG13, VGG16, and VGG19 architectures, using transfer learning of ImageNet pre-trained weights to improve model performance. This study showed that VGG13 with transfer learning outperformed other models with 99.70% accuracy, indicating the profound impact of pre-trained weights on diagnostic accuracy. VGG models with untrained weights performed differently, with VGG11 achieving 98.61% accuracy, suggesting that less complex models were better suited to specific tasks. Comparative analysis shows that the depth of neural networks and the initialization of pre-training weights are the key factors to optimize performance. The discoveries present encouraging possibilities for integrating Artificial Intelligence (AI) into emergency medical services, with the potential to enhance the detection of cerebral hemorrhage and subsequently improve patient outcomes. While acknowledging limitations associated with dataset diversity and model generalization, the outcomes underscore the importance of tailoring models to specific task requirements. These findings also advocate for additional empirical research to fine-tune and advance the application of AI in healthcare.
Prediction of Milk Powder Raman Spectra and Ammonium Sulfate Adulteration Concentration Based on Neural Network Model
Authors: Zeng Sihan
Published: April 2024

This article aims to explore the changes in Raman spectra of milk powder when different concentrations of ammonium sulfate are adulterated. By conducting neural network tests on milk powder samples with different ammonium sulfate concentrations, we studied the impact of ammonium sulfate adulteration on milk powder. The research results show that moderate concentrations of ammonium sulfate adulteration can improve the solubility and flowability of milk powder, but high concentrations of adulteration may reduce the protein content of milk powder and have negative effects on its sensory quality and safety, causing significant adverse effects on human health. Therefore, the adulteration concentration of ammonium sulfate should be strictly controlled in the production and processing of milk powder to ensure its quality and safety.
The Happy and Sad Music Recognition Using Simple 2D CNN.
Authors: Liang Ziyi
Published: April 2024

At the present time is the rapid development of artificial intelligence, deep learning is an important part of it, the use of neural networks for deep learning in which has its own unique advantages. Music, as an important component of the human emotional world, has its related applications in various fields. This paper focuses on the classification of Happy and Sad in music emotion. The article constructs a simple VGG-like neural network model by using 2D convolutional neural network through Mel's spectrum processing. The data files in wav format are fed into the network for training and evaluated by observing the accuracy and loss function values. Eventually the model was able to perform some recognition but failed to achieve good recognition due to some of its limitations. By constructing such a neural network, the simple VGG-like convolutional neural network model is explored for simple music emotion recognition to try if a robust model can be built.
The Investigation of Multiple Optimization Methods on Convolutional Neural Network
Authors: Pan Hetian
Published: April 2024

In this study, the optimization of a Convolutional Neural Network (CNN) was conducted using the Fruits 360 dataset, with a specific emphasis on the impacts of Spatial Transformer Network (STN) and Stochastic Gradient Descent (SGD) optimization methods. Firstly, a baseline CNN model is built, which achieves 97.84% accuracy with a loss of 0.0999 after 50 epochs. Then, the impact of integrating STN and SGD into CNN models separately is investigated. The addition of STN slightly increased the accuracy to 97.92%, reduced the loss to 0.0994, and decreased the validation accuracy. This result suggests that while STN enhances the model's generalization ability, it may slightly reduce the maximum accuracy achievable on the validation set. After SGD optimization, the verification accuracy is increased to 98.19%, the loss is reduced to 0.0537, and the verification accuracy is increased to 98.40%. These results highlight the effectiveness of SGD in fine-tuning model parameters, resulting in more accurate models and improved generalization capabilities. A comparative analysis of these methods highlights their respective advantages. The effectiveness of the STN is rooted in its capacity to improve model generalization and mitigate overfitting, which is particularly beneficial in situations that demand robustness against varied data sets. In contrast, SGD stands out for its ability to significantly improve model accuracy and reduce loss, making it a balanced choice for comprehensive model optimization. Future research directions include exploring these optimization techniques on various datasets and investigating the potential of combining STN and SGD to achieve higher performance in CNN models.
Prediction Of the Impact of The Exchange Rate Between Euro and US Dollar Based on BP Neural Network
Authors: Huang Yikai , Jin Mohan , Yang Jiarui , Zhong Ziyue
Published: April 2024

In order to forecast the euro's future exchange rate against the US dollar while on the subsequent years., the writers use the self-programmed backpropagation neural network to import the opening and closing rates of the exchange rate for the latest 10 years from the US dollar to the euro and selects 1044 sets of data from them. Among them, 844 sets are used as the training set and the test set is comprised of the remaining 200 sets. A training model is established and then the nonlinear exchange rate changes are predicted using this training model. After repeated random simulation, conclusions are drawn, and results with a reasonable gap between the actual data are obtained to complete the training and finally achieve the purpose of prediction. Judging from the final experimental results, this training model has a high accuracy, and the backpropagation neural network is expected to achieve a rapid, relatively accurate and valuable prediction of the Euro to US dollar conversion rate. In the meantime, it can also provide some suggestions for the future data model from this experiment and put forward suggestions for correction.
Multi-Beam Line Measurement Model Based on Analytic Geometry
Authors: Xi Rui , Lv Lingfeng , Yan Xiaochen
Published: April 2024

The multi-beam sounding system has a wider coverage area and efficiency than the single-beam sounding system, and has been used in a variety of offshore operations. However, the design of flat sea area survey method can not meet the real seabed relief, so it is of great significance to study a multi-beam sounding system line planning model for different seabed topography to improve the measurement efficiency and accuracy in sea area exploration and other applications. In this paper, under the condition of real terrain, the linear model of the Angle of the fixed multi-beam transducer is studied with appropriate simplification. Based on the analytic geometric relationship, the functional expressions between the coverage area and overlap rate and the parameters of the transducer's open Angle, slope Angle and sea water depth are given, and the parameter model of the submarine landform linear model with a certain slope is established.
Designing And Implementing a Chat System with Enhanced Security Via AES Encryption Methods
Authors: Liu Heyang
Published: April 2024

As technology has advanced, we have ushered in the age of the Internet, where online communication tools have become the preferred method for information exchange. While this shift offers unprecedented convenience and efficiency, it also introduces a slew of security concerns. Messages traversing the vast expanse of the internet are vulnerable to interception, tampering, and even fabrication by malicious entities. Such actions can disrupt communications and lead to significant repercussions for all involved parties. Given these threats, the imperative for robust encryption becomes clear. Typically, to safeguard the contents of a message, the sender encrypts it using a specific key. In the absence of this key, adversaries find it challenging, if not impossible, to decipher the encrypted message. Upon receipt, the intended recipient, equipped with the necessary key, decrypts the message to access its original content. Beyond encryption, digital signatures are often employed to verify the authenticity of the sender and prevent message forgery. Such cryptographic measures are pivotal in maintaining the sanctity of digital communications. This article embarks on an exploration of prevalent encryption techniques employed by contemporary online communication tools. It delves deeper into the AES (Advanced Encryption Standard) method, spotlighting its mechanics and efficacy. Culminating in a tangible application, the article presents the design of a rudimentary chat system underpinned by AES encryption, showcasing its potential in real-world secure communication scenarios.
Federated Learning Security Threats and Defense Approaches
Authors: Feng Zecheng
Published: April 2024

Artificial intelligence technology has developed rapidly. As a new technology, Federated learning can keep all parties' data locally and train the global model together with all data parties. Therefore, it can solve the problem of "data islands" while protecting privacy, so Federated learning is widely used. However, the existing Federated learning system still has many loopholes. For example, when uploading a local model, an attacker may mix in models with incorrect data. This requires corresponding defensive measures. Before beginning this article, we learned about the previous work related to the security threats and defense measures of Federated learning. This paper first explains the concept, advantages, and disadvantages of Federated learning. Secondly, it summarizes five common security threats in Federated learning and explains and compares various threats. Then it summarizes four defense approaches commonly used in Federated learning and explains each approach in principle. Finally, this paper looks forward to the follow-up development of defense methods in Federated learning.
Studying Stock Prediction in the Context of Machine Learning Exemplified by Analyzing SVM and LSTM Models
Authors: Zheng Mingsheng
Published: April 2024

The impact of stocks on economic development is significant, with most companies employing quantitative finance methods to analyze and trade stocks. In the present day, there is a growing plethora of stock prediction methods, requiring individuals to carefully select the appropriate means of prediction. This study aims to present the highly regarded machine learning algorithms of recent years. Machine learning enables the extraction of trading strategies by learning patterns from historical stock data. Deep learning, a subset of machine learning, eliminates the need for researchers to manually extract features, instead utilizing neural network models that have proven effective for stock analysis. This paper also provides an extensive analysis of Support Vector Machine (SVM) and Long Short-Term Memory (LSTM), elucidating their algorithmic principles and assessing their strengths and weaknesses based on the findings of other researchers. It is observed that SVM is effective for predicting small quantities, but lacks suitability for non-stationary data prediction due to the non-informative nature of financial time series data, which significantly diminishes the accuracy of SVM predictions. In contrast, LSTM emerges as the most comprehensive single algorithm, exhibiting high accuracy and efficiency across all aspects. The paper also explores advanced hybrid models, which demonstrate that combining the advantages of multiple models can lead to higher accuracy and efficiency. The future of stock prediction algorithms holds promise for development, with the potential to construct an intelligent system that automatically selects data for analysis.
The Investigation of LSTM-Random Search with Various Standardization and Normalization Technologies
Authors: Ma Xinyu
Published: April 2024

The stock market, being a complex and chaotic system, presents a formidable challenge for prediction. This paper explores the intricacies of stock prediction by focusing on data preprocessing, specifically investigating the impact of different standardization and normalization techniques on LSTM structures. To enhance predictive accuracy, this study employed a stochastic search technique in this study. It is tested using 1520 data points of the closing prices of Bitcoin and Amazon that were obtained from Kaggle between May 1, 2013, and May 14, 2019. It is discovered that the prediction performance under the LSTM framework is significantly impacted by various data preprocessing techniques. The normalization techniques employing MinMaxScaler and MaxAbsScaler performed better for AMZN, a stock with stable prices, and the RobustScaler normalization method yielded the best results for Bitcoin stock prediction, suggesting that it is more useful for datasets with higher volatility. These results provide a useful guide for future research on stock price prediction by highlighting the significance of selecting the right preprocessing technique based on data characteristics and highlighting the benefits of dynamically modifying the lstm structure for stochastic searches.
Relationship Between Earth-Moon Distance and Tides
Authors: Bai Yanzhuo , Chang Shengwen , Wu Shangcheng
Published: April 2024

For residents and fishermen in coastal cities, tidal phenomena are a dangerous existence, and for tidal power stations, tidal phenomena will bring clean energy to them. For the formation of tidal phenomena, the most closely related is the distance between the Earth and the Moon. Therefore, this paper collects the tides and Earth-Moon records in New York in 2022, and performs data visualization operations on the data, and uses STL (Seasonal Decomposition of Time Series), MSTL (Multiple Seasonal-Trend Decomposition using Loess) models to predict, so as to explore the concern between the Earth-Moon distance and tides. Monthly seasonality is found from our analysis as well as the general trend which follows the research results regarding to earth moon distance variation. The results show that when the Earth is close enough in the Moon, the tides are more pronounced.