bluuebunny/crossref_metadata_2025 huggingface dataset:
Application of Data Mining Algorithm in Network Security Detections
Authors: Yang Yukun
Published: April 2024

In today's world, it is important to recognize online risks. The article is written to let more and more people able to recognize the threats on the network while surfing the Internet. This article is about the definition of data mining and some basic properties. In this paper, the use of data mining technology to prevent network security is examined along with current Internet network security risks. This paper describes several algorithms, including C4.5 and KNN. In addition, this paper describes the network intrusion detection system and explains how data mining methods have been applied to this system. The purpose of this paper is to let more people fully understand and recognize data mining and to be able to use data mining as a method in other fields. In addition, this paper introduces several network security detection methods to describe how data mining can maintain network security.
Performance Evaluation of M|M|1 Queueing Models in Cloud Computing Environments
Authors: Li Xinzichen
Published: April 2024

Queues are ubiquitous, occurring at any given moment. Traffic congestion, healthcare facilities, dining establishments, and even the digital realm. With the continuous advancement of global society, there has been a significant surge in the utilization of the internet, leading to a substantial increase in the volume of incoming service requests occurring on a constant basis. The emergence of cloud computing can be attributed to its inherent advantages in terms of user comfort and cost-effectiveness. The utilization of cloud computing enables users to do computational tasks without the need for physical server ownership. However, it is important to note that the presence of queues is not eliminated as a result. Queueing theory was established as a means to address the issue of lengthy and sluggish lines. This research focuses on the analysis and optimization of queueing systems in order to develop efficient and expedient queues, while also considering cost-effectiveness. In the context of cloud computing, particular attention is given to the goal of energy conservation. This study aims to examine the performance of M|M| queues within the context of cloud computing. It will include both numerical and textual analyses, as well as proofs, while primarily focusing on comparing the similarities and differences of two M|M| queues.
Brain-Computer Interface for Emotion Recognition Based on Electroencephalography
Authors: Fan Ziyan
Published: April 2024

Human emotions are erratic, and so are their brain signals throughout the day. Brain-computer interface devices must handle high signal throughputs in the form of electroencephalography from different areas of the human brain for us to learn more about these emotions. Researchers can recognize emotions from these signals after data acquisition, preprocessing, feature extraction, and feature selection to the classifier. Efficient machine learning algorithms for the process are imperative to quickly provide emotional feedback to a device. Models of such devices are already applicable in medical fields, gaming, and more. In the meantime, ethical considerations arise. Most people are concerned about their privacy infringements. This paper aims to assist researchers in swiftly comprehending the basic theory of emotion recognition while also offering insights into the future development of this highly interdisciplinary technology. Furthermore, it underscores the need for a delicate equilibrium between technological progress and human values.
Simulation Analysis of Fire-Fighting Path Planning Based On SLAM
Authors: Jing Jingliang
Published: April 2024

Reasonable and optimal planning of fire fighting paths is crucial to improving the efficiency of fire fighting, and has attracted increasing research attention in recent years. The traditional fire path selection method is mainly to locate the hot spot of the fire scene, and at the same time, use the hot spot sensor to locate the path correction algorithm to realize the planning of the fire path. Thanks to the rapid development of artificial intelligence algorithms, the fire extinguishing path planning and disaster relief resource scheduling methods based on artificial intelligence algorithms have continuously made breakthroughs in accuracy and speed. Based on simultaneous localization and mapping (SLAM) technology, this paper solves the positioning and mapping problems simultaneously to determine the optimal path. Specifically, this paper mainly uses the map construction and navigation functions of SLAM, and uses Gazebo to simulate an open shopping mall environment and a factory environment with multiple obstacles. Through simulation experiments, the results verify the feasibility of this proposed method in fire evacuation path planning, and can realize the evacuation path planning in case of fire.
Improve Unsupervised Machine Learning Model on Fruits by Using VGG-16
Authors: Geng Zhui
Published: April 2024

The demand for efficient fruit classification has risen with supply chain complexities. For the sake of saving the labor force and improving efficiency, applying machine learning algorithms on fruit categorization to multiple stages in the factory is a feasible solution. The paper briefly delivers the rationale of algorithm VGG-16 and validates its advantage in accuracy by comparing it with other convolutional neural network models. Traditional convolutional network reaches its peak accuracy which is around 40% after 45 training epochs. VGG-16 results with a 97% percent accuracy on the classification of more than 20 kinds of fruits only after 6 epochs and can be improved further by enriching and augmenting the dataset. However, although VGG-16 only requires a small number of epochs, the weighted parameter for each layer increases dramatically and increases the running time. Future researchers should focus on optimizing the algorithm to make it more feasible in the industry. Also, remote computing might be a solution for large computational requirements.
Optimizing Stock Price Prediction: Exploring LSTM Architectural Parameters in Financial Forecasting
Authors: Li Haoran
Published: April 2024

Accurate stock price prediction is a crucial concern in the field of finance, impacting investors, traders, and the broader economic landscape. Traditional time series analysis and statistical models often fall short in capturing the dynamic and nonlinear nature of financial markets. Financial market forecasting has advanced significantly with the advent of deep learning technology, especially Long Short-Term Memory (LSTM) networks. This study conducts an in-depth exploration of LSTM networks' critical architectural parameters and their impact on stock price prediction. The performance of the LSTM-based model is assessed using historical stock price data from Amazon, with particular attention paid to hyperparameters like the number of neurons, dropout rates, and LSTM layers. The research reveals that a well-balanced model architecture with moderate units per layer (e.g., 64), a single layer, and a dropout rate of 0.7 is key to achieving accurate predictions in financial markets. The outcomes of this research hold practical significance for both investors and financial analysts. Furthermore, they emphasize the promising potential of LSTM networks in the realm of financial forecasting, opening avenues for future studies in finance. This investigation deepens the comprehension of LSTM network architecture and its applicability in predicting stock prices, providing valuable insights that extend beyond the finance sector.
A CNN-based implementation of fruit recognition
Authors: Huang Zixuan
Published: April 2024

Image recognition technology is now widely used in various industries, and CNN has played an indispensable role in it over the past decade. The paper discusses the use of Convolutional Neural Networks (CNN) for fruit image recognition, aiming to verify the impact of different CNN designs on model training time, test accuracy, and test accuracy. The experiment uses data from the Kaggle Fruits 360 project and focuses on ten different categories of fruit. The CNN model is built using 3*3 convolutional kernels and features four combinations of convolutional and relu layers. The final test accuracy is recorded as 98.1714%. The paper also discusses potential reasons for lower-than-expected accuracy and attempts to address these issues, including overfitting, image resolution, and the simplicity of the training set. The impact of regularization and different image resolutions on model accuracy is observed. The paper concludes by highlighting the practicality of CNN in image recognition, but also acknowledges limitations such as training time, computational resources, and the abstract nature of extracted features. It also emphasizes the importance of choosing an appropriate training set for model accuracy and suggests that other AI models may offer solutions to the shortcomings of CNN. Overall, the paper provides insights and experiences for those working with CNN in image recognition and acknowledges the rapid development of artificial intelligence in recent years.
Analysis of the State-of-art Implementations for Artificial Intelligence in Gaming
Authors: Huang Jipeng
Published: April 2024

As a matter of fact, Artificial Intelligence (AI) stands out as a crucial area of interest, seeing significant integration across diverse sectors. The gaming industry's evolution has further escalated the integration of AI, leading to an increased necessity for sophisticated game AI systems. These systems, crafted to emulate the cognitive actions and decision-making abilities of humans, enable the automation of game creation and dynamic game management. In order to give a better account of the application of AI in the game field, this study will introduce the following contents. First of all, this study will introduce what is AI, the second is to introduce the three kinds of application scenarios of AI in the game field; the third is to make a prediction on the future fusion of AI and the game industry. According to the analysis, the current features as well as limitations and prospect will be demonstrated at the same time. Overall, these results shed light on guiding further exploration of AI in gaming.
Analysis Of Different Types of Digital Audio Workstations
Authors: Yang Yutong
Published: April 2024

With the development of technology, digital audio workstation (DAW) has become a must-have tool for modern music production, which allows music producers to edit, record, mix and add effects on the computer to create music works of various styles and qualities. This paper mainly introduces the definition, purpose, function, type and development of DAW, and how to choose the appropriate DAW according to individual needs. This article compares and analyzes three commonly used DAWs (Apple Logic Pro X, Cubase 12, Ableton Live Lite 11) from the aspects of personal budget, user experience, software operating system compatibility, long-term development, and whether live performances are needed. Their advantages, disadvantages and application range are pointed out. This paper also points out some limitations of DAW, (e.g., the requirements of hardware and software conditions, the difficulty of learning and mastering, the problems of music creation), and looks forward to the future development direction of DAW (the improvement of user experience and personalization, the utilization of cloud computing and artificial intelligence, and the integration of other fields and media). These results can help independent music producers to understand the basic situation and selection criteria of DAW, so as to better use DAW to create and express their own music ideas, and provide some reference and inspiration for further improvement and innovation of DAW.
Synthetic Music Random Generation Based on Nyquist
Authors: Li Wenhao , Ma Zhengmo , Zou Zijian
Published: April 2024

The idea of composing music based on programs has a long history, from some pioneers who first tried to fulfill this idea, to now, the variety of software and workshops that allow people to generate music in different approaches. On this basis, this study will briefly discuss the main idea and some methods used to compose the music in this paper. We aimed to randomly generate a piece of music using only Nyquist. Thus, each time when listen to this, there is a slight difference. With this in mind, this paper has used two ways: the first one is to use several functions in Nyquist, which would randomly choose numbers in a given list or range. The second way is a method based on the functions mentioned earlier, called the random walk, which lets the program stochastically choose one item in a given list of a range of numbers every time to add or subtract to the origin number given by programmers. By using these two techniques, one can generate random sounds and random rhythmic patterns, and when we put these sounds and patterns into scores to generate various melodies, we can stochastically increase or decrease the overall pitch of that score. Finally, the scores are combined to create the whole piece of music. Although there are limitations, this study has provided some methods that can be useful for generating stochastic music.
An Effective Sentimental Analysis Model Based on spaCy
Authors: Shen Yuanhao
Published: April 2024

As machine learning and the Internet continue to advance, the role of sentiment analysis in discerning positive and negative emotions within text has become increasingly pervasive. Its applications extend to enhancing human-computer interactions, monitoring mental health, and conducting business analyses. Numerous efforts have been dedicated to sentiment analysis and prediction. This article utilizes a dataset of 50,000 movie reviews, sourced from natural language processing (NLP) and included in the spaCy library, to contribute to this ongoing body of work. Two feature extractions, Count Vectorizer and TF-IDF are used, and three Machine Learning (ML) algorithms, Logistic Regression (LR), Decision Tree (DT) and the Multilayer Perceptron (MLP) are used to make predictions on the IMDB review data set with Python programming language. Comparing the experimental results, it can be found that different models have significant differences under different feature extractions, and using TF-IDF feature extraction combined with logistic regression model achieves the best accuracy (88.53%). It proves that LR, among three tested models, performs best in sentimental analysis.
Enhancing Image Classification Accuracy Based on AlexNet
Authors: Zeng Yu
Published: April 2024

This study explores the potential of deep learning for small-scale image classification tasks through the utilization of the classical AlexNet model on the CIFAR-10 dataset. The methodology involves meticulous architectural adjustments, comprehensive data preprocessing, and strategic optimization of the learning rate decay, resulting in substantial improvements in model performance. In addition, innovative data augmentation techniques are introduced to enhance the model's robustness and generalization capabilities. The experimental outcomes unequivocally underscore the efficacy of deep learning in addressing small-scale image classification challenges, offering versatile applications across diverse domains such as image recognition, autonomous driving, and medical diagnostics. Acknowledging the dynamic nature of deep learning, ongoing research endeavors are warranted. Future directions may encompass the exploration of more intricate neural network architectures, advanced data augmentation methodologies, and the implementation of interpretability tools to augment both performance and comprehensibility. This study provides compelling evidence of deep learning's aptitude for small-scale image classification tasks, offering valuable insights and guidance for future research and practical implementations. As the field of deep learning continues to evolve, this work aims to serve as a valuable reference and catalyst for further advancements in the domain.
Prediction Of Raman Spectra and Adulteration Concentration of Wheat Flour Based on Neural Network Modelling
Authors: Li Zongze , Xiao Jingru
Published: April 2024

In order to quickly detect the adulteration of flour containing wheat flour quality testing, the Raman spectrum of wheat flour as the object of study, based on a number of data-based neural network to identify and determine the concentration of wheat adulteration. Firstly, the neural network system in this study borrowed the Raman wave number and Raman intensity curve from "Non-contact Detection of Benzoyl Peroxide in Flour Based on Raman Spectroscopy" as the training and testing set. Next, we created the neural network, set the training parameters, trained the network and simulated the test. Finally, the supervised learning process resulted in error analysis and comparison of results, and the Raman spectra of wheat flour and adulteration concentration were plotted visually.
Market Requirements Document on the Use of AI to Facilitate and Manage Stock Price Prediction
Authors: He Xuan , He Ying
Published: April 2024

This market requirements document explores the potential of artificial intelligence (AI) in stock price prediction, an area integral to the financial market's profit-maximization aspirations. Historically, the prediction of stock prices, with its intricate patterns and influences, has posed significant challenges. Traditional techniques like PCA and LSTM have limitations in adapting to the dynamic nature of stock markets. AI, on the other hand, offers transformative possibilities, with its capabilities to process vast data sets, identify patterns, and continuously learn. Drawing parallels from AI's success in other sectors, like healthcare, the essay suggests that AI can revolutionize stock price prediction by offering higher accuracy and adaptability. It underscores the need to combine various AI methodologies for optimized results while also emphasizing the importance of regulating AI applications. In essence, this paper envisions a future where AI becomes the fulcrum of stock price prediction, potentially overhauling the entire financial sector's operations and delivering unparalleled benefits to all stakeholders.
A Comparative Study of Random Forest Regression for Predicting House Prices Using
Authors: Mao Mohan
Published: April 2024

Based on the rapid development of the real estate market, real estate prices in various regions of the world fluctuate greatly and are unstable, and we need to make some predictions for real estate prices. However, in reality, we pay too much attention to the relationship between past property prices and current property prices and often ignore the prediction of future house prices. Research on predictive models is lacking. Therefore, studying real estate forecasting models is one of the best solutions to solve the problems faced by the real estate market based on the thinking of the current situation. In response to this problem, I propose to use a random forest model, gradient boosting, and optional to build a reasonable predictive model. The final results prove that this predictive model can be used to some extent to predict changing real estate prices in the future market. It is hoped that the method in this paper can provide a reference for subsequent research on predictive models.
Future Development Trend and Sustainability Analysis of Electric Vehicles
Authors: Xiao Jiyuan
Published: April 2024

Technology is the important driving force to improve the society. Due to the Industrial Revolution and World War II, the automobile industry develops rapidly and becoming increasingly popular and safe for daily transportation. However, due to concerns about environmental issues and fuel fossil shortage, the invention and promotion of electric vehicles gradually alleviated these issues. This paper aimed to explore the future development trend for electric vehicles. At the same time, it will analyze the issues and potential solutions for electric vehicles and the possibilities that gasoline vehicles might be replaced by electric vehicles by comparing both advantages and disadvantages of gasoline vehicles and electric vehicles. As the increase of development trend for electric vehicles, the sales volume continues increase. The reason why the sale volume keeps increasing is because of high gas price, governmental policies, and the increase awareness of environmental issues. This paper could help people to understand the reason why electric vehicles are more sustainable than gasoline vehicles.
Movie Recommendation System Using KNN, Cosine Similarity and Collaborative Filtering
Authors: Wu Yuetong
Published: April 2024

The movie recommendation system is becoming increasingly popular in the digital era. With the continuous emergence of a vast amount of movie resources, users are facing more and more choices. Therefore, an intelligent movie recommendation system can assist users in quickly finding movies that match their personal preferences, thereby enhancing user satisfaction and movie-watching experience . Our movie recommendation system recommends high-rated and well-reviewed films and TV shows to the general audience based on Netflix viewers’ ratings for them. These are the movies and shows that are considered worth watching. We utilize the Netflix open dataset, which consists of a large number of user ratings and movie information [1]. By merging the movie and rating datasets and performing data cleansing and preprocessing, we obtain a dataset suitable for models. Subsequently, we represent the movie rating data using sparse matrices and train a k-nearest neighbours (k-NN) algorithm to achieve personalized movie recommendations [2].Our approach considers both user rating preferences and movie similarities, enabling accurate and personalized recommendations for users. Through evaluation experiments and user surveys, we validate the effectiveness of our recommendation system in terms of accuracy and user satisfaction [3]. This research is significant for improving the effectiveness of movie recommendation systems and enhancing user experiences.
Face Mask Recognition based on YOLOV3
Authors: Chen Yansong
Published: April 2024

Wearing masks is considered one of the effective ways to prevent epidemics and protect physical health. In densely populated public places, detecting whether the mask is correctly worn has important application value, which can help managers reduce the potential risk of disease transmission. Previous works mainly rely on manual features, which are limited to fully expressing complex scene information. Thanks to the rapid development of artificial intelligence technology, automatic mask detection methods based on deep learning have achieved breakthroughs in speed and accuracy, while mask detection in complex scenes is still an open issue. To alleviate this issue, this paper proposes a mask detection method based on YOLOV3. Specifically, this study introduces regularization methods such as Drop Block and Label Smoothing, as well as data augmentation strategies such as Gridmask and Mosaic. This study further explores the recognition performance for refined scenarios including no mask, standard mask-wearing and incorrect mask-wearing. Extensive experimental results show the effectiveness of our method. By introducing the improved YOLOV5 algorithm to realize the mask detection function, the data structure is optimized through pruning, the data volume is reduced, the calculation speed is accelerated, and the mask detection is well realized.
Comparative Assessment of Attack Strategies and Their Efficacy on the RSA Encryption Algorithm
Authors: Hao Zhengli
Published: April 2024

The RSA encryption system, standing as the preeminent public key cryptographic mechanism, has managed to sustain its robust security profile despite the manifold attack algorithms that have emerged over time. In fact, to this day, no attack strategy has genuinely undermined or imperiled the inherent security of the RSA algorithm. This manuscript embarks on a meticulous exploration of the contemporary, predominant RSA attack algorithms. It elucidates the foundational principles underpinning each algorithm, offering readers an in-depth understanding. Furthermore, to lend a practical dimension to this theoretical exposition, simulated attack experiments are designed. These are tailored to suit specific environments, thereby facilitating a robust comparative analysis. Drawing upon empirical evidence harvested from these experimentations, this document offers critical appraisals of each algorithm. This assessment juxtaposes their theoretical prowess against tangible deployability, illuminating their strengths and potential vulnerabilities. A pivotal aspect underscored in this treatise is the necessity of judiciously architecting RSA algorithms. Only by acknowledging and circumventing potential pitfalls can one truly ensure the algorithm's resilience. The insights and evaluations furnished herein aim not only to serve as a beacon for those navigating the vast sea of cryptographic research but also to inspire and guide future endeavors aimed at developing potent attack algorithms against the RSA framework.
Research on Multi-agent Sparse Reward Problem
Authors: Meng Fanxiao
Published: April 2024

Sparse reward poses a significant challenge in deep reinforcement learning, leading to issues such as low sample utilization, slow agent convergence, and subpar performance of optimal policies. Overcoming these challenges requires tackling the complexity of sparse reward algorithms and addressing the lack of unified understanding. This paper aims to address these issues by introducing the concepts of reinforcement learning and sparse reward, as well as presenting three categories of sparse reward algorithms. Furthermore, the paper conducts an analysis and summary of three key aspects: manual labeling, hierarchical reinforcement learning, and the incorporation of intrinsic rewards. Hierarchical reinforcement learning is further divided into option-based and subgoal-based methods. The implementation principles, advantages, and disadvantages of all algorithms are thoroughly examined. In conclusion, this paper provides a comprehensive review and offers future directions for research in this field.
Stock Price Prediction Using Machine Learning Techniques
Authors: Li Zeyu
Published: April 2024

Large variations in stock price and unstable fluctuations will not only bring huge losses but also might influence the decisions made by investors. Machine learning in financial areas has been widely used in the past few years, and they are especially superior in forecasting tasks. This article will talk about how machine learning techniques could be used to predict stock prices and some possible ways of improvements. The main method used in this article is the LSTM structure, and the building blocks are constructed into a full Recurrent Neural Network to predict the price for several famous technology companies from NASDAQ. According to the results in this article, a forecasting algorithm based on the LSTM model can minor the error down to a few dollars. It is overall not accurate enough and cannot be used to draw a financial conclusion about investment, but still possible to explain some personal confusion and give solutions to individual difficulties. In addition, this study can be further upgraded in terms of structure and logic. This article hopes to provide a broader research perspective for the application of machine learning in the financial field.
Aspect-Based Sentiment Analysis of Specific Targets
Authors: Hong Lei
Published: April 2024

Different from traditional sentiment analysis tasks, Aspect-Based Sentiment Analysis (ABSA) aims to automatically identify the emotional polarity of different aspects in the same sentence, which helps to mine users' more delicate emotional expressions for different targets and has become a research hotspot in the field of natural language processing in recent years. Thanks to the rapid development of attention-based deep neural network models, the accuracy of aspect sentiment analysis has continuously made breakthroughs. However, existing works pay little attention to the performance bounds of different application scenarios, such as different text topics or text lengths. In response to the above issues, this paper conducts a comparative analysis of several classic end-to-end neural network models to explore topic-level sentiment polarity analysis. By quantitatively evaluating the accuracy and F1 score of each model at different fixed text lengths, this paper summarizes the strengths, weaknesses, and different aspects of each model's efficiency in use. In addition, this paper also discusses the existing problems with these models and puts forward suggestions for improvement, which can provide some new insights into the research of ABSA.
Application research of 5G/6G cellular network in intelligent Transportation Internet of Things
Authors: Hu Cunhui
Published: April 2024

This study paper's primary goal is to examine the developments and use cases of 5G/6G wireless communication technologies and the Internet of Things (IoT) in intelligent transportation. This paper will delve into the foundational aspects of IoT technology and its integration into intelligent transportation systems (ITS). It will conduct in-depth research on 5G and 6G wireless communication technologies and the critical IoT technologies that play a pivotal role in intelligent transportation. By analyzing these cutting-edge technologies, the paper highlights their significance and potential impact on transportation systems' efficiency, safety, and sustainability. This research will identify and elucidate the major applications of 5G and 6G wireless communication technologies in intelligent transportation. By comprehensively addressing these objectives, the essay will provide readers with an insightful understanding of the fundamental IoT technology, important 5G/6G wireless communication technologies, and their diverse application domains in intelligent transportation and smart cities. Through this exploration, the study aims to shed light on the transformative potential of these technologies in shaping the future of transportation systems and urban living.
This study addresses the challenge of intelligent diagnosis in Alzheimer's Disease by employing machine learning to classify MRI images depicting various disease stages. The author conducted a comparative analysis to assess the efficacy of diverse models
Authors: Li Ziqi
Published: April 2024

This study addresses the challenge of intelligent diagnosis in Alzheimer's Disease by employing machine learning to classify MRI images depicting various disease stages. The author conducted a comparative analysis to assess the efficacy of diverse models for this purpose, yielding several key findings. The primary model utilized in the research is founded on ResNet152V2, encompassing 152 pre-trained layers, 1 GlobalAveragePooling2D layer, 3 dropout layers, 3 dense layers, and 1 output layer. The research aims to achieve precise classification of MRI images corresponding to distinct Alzheimer's Disease stages, a pivotal step for early detection and intervention. To achieve this, this research adjusted the structure of the original model and changed some of the parameters and compared their results. Based on the results obtained, the author drew several conclusions. The author found that setting a larger value for the "patience" parameter in the Early Stopping callback helps avoid stopping the training process too early. Additionally, the author observed that the original model with only 2 fully connected layers was not stable enough in terms of gradient descent, indicating the need for additional fully connected layers. In conclusion, this research paper addressed the problem of classifying MRI images of different stages of Alzheimer's Disease using machine learning techniques. The comparative study of different models provided insights into the performance and potential improvements of the classification taskt.
Designs of Energy Harvester for Wireless Sensor Networks
Authors: Zhong Hao
Published: April 2024

An essential part of the IoT architecture are wireless sensor networks. There has been extensive study done to identify alternative methods of powering wireless sensor networks because the nodes of wireless sensor networks are mostly power constrained. This study mainly introduces three energy collector solutions that can power wireless sensors. They are an electromagnetic vibration energy harvesting system that powers the light source of the fiber Bragg grating sensor, a solar energy collection system that uses ambient photovoltaic energy, and a wind energy harvester that uses gas elastic vibration to convert wind energy into electrical energy. The solar energy collection system is based on maximum power point tracking, in which the solar panels can provide 3W of power with a high efficiency of up to 96%; the wind energy harvester is composed of a wind generator and a power management unit, capable of operating at wind speeds of 2-9m/s, providing 70mW of power. Besides, the electromagnetic vibration energy harvesting system provides an average power output of approximately 40mW when subjected to acceleration levels greater than 0.05g. These results can serve as solutions for sustainable power output.
Python-based Population Forecasting with Standard Deviation Analysis
Authors: Zheng Jiewen
Published: April 2024

Changes in the world's population are a constant problem, such as the aging crescent, declining fertility and so on. Having an accurate forecast can chart a better and more useful plan for future development. The accuracy of the python prediction is obtained by computing the standard deviation of the python prediction and the raw data. The code runs within an extremely basic set of read, judge, and compute rules. This data set has been obtained from more than 10, 000 data sets of populations between 1950 and 2021 in different age conditions and countries. Each individual age segment gets a different shift during this period, especially for segments with large historical events, so it will be a challenge to see if Python can or cannot produce accurate predictions. In the experiments, python was used to predict the future population and the standard deviation was obtained by comparing the predicted results with the source data.
Netflix Stock Price Prediction Based on Machine Learning
Authors: Shi Jiahao
Published: April 2024

As big data and Artificial Intelligence (AI) are more and more getting into society, people are beginning to ask for predictions in all fields to make prevention. With the technological improvements, the stock market has become more and more predictable as compared to before. The finance field, especially the stock market field, was being put on for making predictions. This paper evaluates the pros and cons, accuracy, and precision when making predictions using the ARIMA model and the Linear Regression model on Netflix stock price trends. The result shows that both models perform moderately. From that, this paper also found that the Linear Regression Model seems to be performing better in the short term and the ARIMA model predicted that it might perform better in the term of long term. This paper proved that these models could play a role in the field of finance but are still restricted to the dataset’s size and the application of the model. Hopefully, this paper will find well to finance related members, give and inspire some advice to them.
Software engineering implementation and e-commerce model design based on neural digestive system monitoring and rapid prediction of amylase
Authors: Fan Pei , Yuan Yezhi , Zhang Jie , Huang Yuting , Wu Xiaobo , Li Liangyu , Qin Yi
Published: April 2024

Convolutional neural networks can be used to predict amylase levels in patients with acute abdomen. Based on this algorithm, we can reduce the error rate in hospital emergency work. As a biomedical engineering study, this study processed a series of continuous data such as patient age, symptoms, pain index, and life risk exposure factors. This software project has the ability to perform regression analysis based on the above characteristics, it can be used to determine whether the blood amylase level is normal, which provides assistance in the clinical diagnosis of pancreatitis, a disease that directly affects the 24-hour survival of patients. We believe that this software engineering can serve as a purchasing option for e-commerce systems and propose a solution for this situation.
Research Advanced in Multimodal Emotion Recognition Based on Deep Learning
Authors: Kong Weiqi
Published: April 2024

In summary, the field of computer science has long been intrigued by emotion recognition, which seeks to decode the emotional content hidden within data. Initial approaches to sentiment analysis were predominantly based on single-mode data sources like textual sentiment analysis, speech-based emotion detection, or the study of facial expressions. In recent years, with the increasingly abundant data representations, Many people has gradually pay attention on the multimodal emotion recognition. Multimodal emotion recognition involves not only text, but also audio, image, and video, which is of great significance for enhancing human-computer interaction, improving user experience, and improving emotion-aware applications. This paper thoroughly discusses the research advancements and primary techniques of multimodal emotion recognition tasks, with an emphasis on the aforementioned tasks. Specifically, this paper first introduces representative methods for single-modal emotion recognition based on graphic data, including its basic process, advantages and disadvantages, etc. Secondly, this article introduces pertinent studies on multimodal emotion recognition and offers a quantitative comparison of how different approaches perform on standard multimodal data sets. Lastly, it addresses the complexities inherent in multimodal emotion recognition research and suggests potential areas for future study.
Capitalized Comparison of Three Machine Learning Models: Linear Model, Decision Tree, Neural Network
Authors: Zhao Xiantong
Published: April 2024

As a matter of fact, with the rapid development of computation ability, machine learning has boosted rapidly with much faster training speed in recent years. In reality, machine learning is transforming industries with its ability to derive insights and patterns within massive datasets. Among numerous algorithms available, certain foundational models stand out due to their efficiency and capability. With this in mind, this study compares three typical models among various machine learning scenarios, i.e., linear models, decision trees, and neural network models. According to the analysis, the basic principle, concepts as well as parameters will be demonstrated. While all three has its unique pros and cons, this study aims to guide readers in choosing most fitting model with their tasks, by clarify the difference and future outlooks of these models. At the same time, the future development trends for the machine learning models will be proposed based on the analysis. Overall, these results shed light on guiding further exploration of machine learning.
Overview of Judicial Text Summarization Method
Authors: Zhao Hongtao
Published: April 2024

This article delves deep into the core aspects of the task of generating judicial text summaries. Through a systematic review and distillation of existing relevant literature, the article primarily focuses on extractive text summarization techniques in both unsupervised and supervised learning contexts, conducting a multidimensional and comprehensive analysis. To begin with, the article traces the evolution of text summarization techniques and dissects the differences between extractive and generative text summarization methods, along with a comparison of various algorithms. Furthermore, it provides a detailed introduction to a pipeline judicial summary generation model that combines both extractive and generative approaches. The article also conducts an in-depth analysis of the impact of transfer learning, using three different models, on judicial text summary generation. Lastly, while acknowledging significant progress in the field, the article points out the main issues and challenges in current judicial text summarization research. It also suggests potential solutions and outlines future development trends.
Neural Network Model for Surface Raman Spectroscopy and Pesticide Concentration of Spinach
Authors: Wang Yi
Published: April 2024

At present, there are over 1000 chemical pesticides. The widespread use of these pesticides not only causes serious environmental pollution, but also poses a threat to human health. Currently, there are over 1000 types of chemical pesticides. The widespread use of these pesticides has caused serious environmental pollution. Meanwhile, due to some workers not effectively removing pesticides from food during the picking and processing stages of the farm plants, so the long-term consumption of such food will also pose a threat to human health. An analytical technique called Raman spectroscopy is based on the Raman scattering effect, which was discovered by Indian scientist C.V. Raman. It is used in molecular structure research to get data on molecule vibration and rotation by analyzing scattering spectra at various frequencies from incident light. As a newborn way, the Raman spectroscopy analysis method has higher sensitivity. In this thesis, I put it and the BP neural network together and set up the model, to predict pesticide residues in spinach.
The Price Prediction of Vegetables by Using Machine Learning
Authors: Chen Qiaochu , Yu Zhihua , Zheng Kaixin
Published: April 2024

Vegetable prices are great important in ensuring people's livelihood. With the progress of science and technology, machine learning algorithms have been applied in the field of vegetable price prediction. This paper analyzed the model that has been used in vegetable price prediction and its results and compared the accuracy of each model. The study in this paper concluded that LSTM is suitable for handling sequence data with long-term dependence but this model requires a large amount of data for training. ARIMA is suitable for short-term predictions but may not fit well with non-stationary and nonlinear data. In addition, this paper introduces other algorithms. For example, the paper shows How to use Lasso regression to compress the input variables and how to reduce model complexity and improve model generalization ability. The BP neural network was used to fit the output variables to improve the model fitting ability. This paper further studies and summarizes the combination of each model and the accuracy of the combined model for predicting vegetable prices. It can be seen that the prediction error of the L-LBNN model is very small, which can be effectively used in the price prediction of vegetables. The purpose of this study is to summarize the current situation of predicting vegetable prices with machine learning algorithms to provide guidance and inspiration for researchers and to promote the development of the use of machine learning in the field of price prediction of vegetables.
Forecasting Stock Price: A Deep Learning Approach with LSTM And Hyperparameter Optimization
Authors: You Yifan
Published: April 2024

This research embarks on the development and implementation of a comprehensive exploration of stock price forecasting, harnessing the potential of Long Short-Term Memory (LSTM) neural networks—a renowned tool in time series analysis and deep learning. Its overarching aim is to construct a robust model for precise stock price predictions, leveraging the intrinsic capabilities of LSTM and deep learning techniques. In the intricate landscape of financial markets, the ability to forecast stock prices accurately holds paramount significance, facilitating well-informed decision-making and effective risk management. This comprehensive study encompasses the essential phases of the research process, including meticulous data collection, preprocessing, architectural configuration centered around LSTM, and rigorous model evaluation. The automated optimization of hyperparameters is executed to fine-tune model performance, ensuring that it attains optimal precision. The outcomes of this study underscore the remarkable efficacy of the LSTM-based deep learning model in capturing intricate patterns inherent in stock market data. These findings not only offer promising insights for the application of LSTM and deep learning in finance but also advance the field by elevating the accuracy and reliability of stock price predictions.
Early Heart Disease Diagnosis Method Based On KNN
Authors: Su Yuxuan , Yang Zhen
Published: April 2024

Heart disease is a health problem of wide concern around the world. This paper aims to realize an early prediction of heart disease based on k-nearest neighbors algorithm to warn the users of their potential risks of heart disease in an early stage so that measures can be taken to minimize the danger. The dataset introduced contains basic information of body that is possibly related to heart disease, with 14 feature dimensions and 303 samples in total. To realize the proposition of early prediction, three different strategies for feature dimension choosing are introduced to evaluate the predictions based on all the data, data that can be measured at home with household devices and data that do not require any extra measurements and devices. Several indexes are introduced to evaluate and compare the performance of the models that are trained and the changes caused by the decrease of feature dimensions are analyzed. Finally, possible future work for improving the models is discussed.
Time Series Data Forecasting: Alcohol Sales Prediction Based on Prophet
Authors: Kuang Yuhan
Published: April 2024

Sales forecasting plays an important role in a business’s operation. It points out the outline of future development and customer targets for companies. Due to the fact that sales forecasting is mostly based on large amount of past data, machine learning has a successful implementation in this field. This paper employed Prophet, a package specialized in dealing time series, to forecast the alcohol sales amount across the USA. The data showed significant trends and holiday effects that fit with the algorithm behind Prophet. By adjusting the parameter functions, the model generated by Prophet demonstrated impressive ability on forecasting the third year’s data based on the previous two. A variety of performance indicators, including Mean squared error (MSE), Mean absolute percent error (MAPE), and Median absolute percentage error (MDAP) and so on, were used to evaluate the predicted results The result of this study implies that machine learning has a huge potential in the field of sales forecasting.
Research On Adaptation and Generalization Issues in Image Processing
Authors: Liu Yufan
Published: April 2024

Image processing has been a fundamental research problem in the computer vision community, which aims to construct models to recognize the content in a given image. With the accuracy and speed of image recognition have been making breakthroughs in recent years, yet the problems of model adaptation and generalization are still challenging topics. Adaptation problems involve the effective transfer of knowledge learned in one domain to another domain, despite distribution differences between them, to enhance the model's performance in the target domain. On the other hand, generalization problems refer to how well the model adapts the knowledge acquired from the training dataset to previously unseen, new data samples, i.e., its performance on the test dataset. This review focuses on mainstream methods and their effectiveness in addressing adaptation and generalization issues in the current image processing domain. It analyzes the existing challenges and proposes potential solutions, further exploring how to improve the model's expressive power while maintaining its generalization performance.