bluuebunny/crossref_metadata_2025 huggingface dataset:
Optimizing Intelligent Parking Decisions using Q-Learning
Authors: Lin Zhili
Published: August 2024

With urbanization progressing rapidly, the availability of transportation resources in large cities has become increasingly strained. Among these resources, parking spaces are crucial for maintaining the smooth functioning of cities and ensuring the efficient operation of vehicles. However, due to spatial limitations, it is not always feasible to continuously increase the number of parking spaces. Hence, accurate prediction of future parking space demand has become imperative. The purpose of this paper is to predict the future parking space demand by using a mathematical model, to provide scientific decision support for the transportation department. Specifically, the author chose a Q-learning model in reinforcement learning, using a dataset from the year beginning 2016 combined with an algorithm to make predictions about future data. The accuracy of the model is 81.31% and the mean square error is 2200.30. In addition, the author also combined the weather and holiday conditions to analyze the data box line. Through the analysis and modeling of urban parking data, the author will discuss the feasibility and effectiveness of the Q-learning model in future parking demand prediction. Through this study, it is concluded that the Q-learning model performs well in the prediction of future available parking Spaces.
Research on Extreme Weather Prediction Based on ARIMA Modeling
Authors: Lu Yi , Luo Dezhi , Yang Xi
Published: August 2024

The greatness of nature is often astounding, but few people realize that behind its greatness lies a host of impacts from extreme weather, and with global climate change continuing to intensify, it poses unprecedented challenges to the property insurance industry. In this paper, we develop a broadly applicable risk scoring model that provides the property insurance industry with critical insights for more accurate risk assessment and adjustment of underwriting strategies. In this paper, we use an ARIMA model to predict the expected annual losses from extreme weather hazards in different regions, and based on this, we develop a premium calculation model to ensure that insurers can cover compensation costs and realize expected profits.
Research on Abnormal Driver Behaviors Monitoring based on Bone Key Points
Authors: Fang Yiwen
Published: August 2024

With the development of social economy, the number of road vehicles is increasing, and road traffic safety has become one of the major factors threatening people's life and property safety. Abnormal driving behavior of drivers represented by mobile phone operation is one of the important causes of road traffic safety accidents. Real-time monitoring of drivers' mobile phone use and implementing appropriate measures when abnormal behavior occurs are of great significance to protect the life and property safety of drivers and passengers. Due to the differences in the driver's own appearance, the various ways of operating the mobile phone, and the chaotic background in the car, the routine abnormal behavior monitoring is prone to wrong detection and missing detection. Based on the existing human pose estimation model, this paper proposes to classify the driver's mobile phone use by using the method of calculating the Angle between the arm and the bone key points: Did not use the mobile phone, answer the phone, edit the information, to prepare for the subsequent use of the model classification. Based on this model, it can be extended to monitor other abnormal behaviors of drivers. It has practical application value to promote civilized driving and improve vehicle safety.
Spinal Cord Stimulation Control Modalities: Open-loop versus Closed-loop and Perspectives
Authors: Zheng Zhide , Cai Jiacheng
Published: August 2024

Spinal cord stimulation is currently widely used in the treatment of chronic pain, which has a prominent effect on reducing pain in the trunk and limbs. With the development of related technology, spinal cord stimulation has shown great advantages in the field of pain management and a lot of stimulation methods have been born. This paper will first introduce the working principle of spinal cord stimulation and introduce several typical open-loop stimulation methods, including conventional tonic SCS, High Frequency SCS and Burst SCS. Then it will analyze the factors that affect the stimulation effect. The concept of electrically evoked compound action potentials (ECAPs) is explained, and then the closed-loop SCS controlled by ECAPs are introduced and their working principle is explained. Finally, the future development direction of this field is prospected, and it is believed that this field needs a convincing quantitative index. Finally, the possible development direction of technology is given.
Quantitative modelling of momentum in tennis based on factor analysis
Authors: Ge Yiwei
Published: August 2024

Tennis, with its playful and elegant nature, has perennially captivated individuals of all ages. Beyond being a mere game, it represents a clash of momentum. In modern sports, the term "momentum" is used to describe a player's drive and momentum during a game. In the tennis match, it can affect the player's rhythm of the match and the result, is the key factor to determine the result of the match. But the quantification of momentum has no definitive reference in sport. This study attempts to propose a model that can be used to quantify the momentum of tennis players based on factor analysis. In this paper, data from the men's singles match at Wimbledon was first taken and subjected to missing and outlier detection and processing to ensure the reliability of this data. Secondly, the paper selected all the indicators that may be related to a player's momentum for factor analysis. In this paper, the proportion of variance contribution is used as the weight of each factor to calculate the composite score as the quantitative value of momentum. The analysis and modelling process in this paper could provide new insights into the understanding and development of the sport of tennis, offering a perspective and direction to quantization. The results of the factor analysis may provide valuable perceptions for coaches and athletes to help them better understand the game situation and develop strategies for future games.
Research on the Influencing Factors of Movie Popularity based on Random Forest
Authors: Wei Zhuwen , Zhang Yifan
Published: August 2024

Movie, also known as moving or moving pictures, or "reflections," is a form of visual art that communicates ideas, stories, cognitions, emotions, values, or various atmospheric simulated experiences through the use of moving images. As the mainstay of cultural industry, film has brought huge economic and social benefits, and the box office is the most important index to measure the economic benefits of film. In this study, the prediction results of the random forest analysis model are acceptable, it is well in line with the structure of the data, which is conducive to the analysis of the data, and the data is displayed with charts and graphs at the same time, so as to show the readers the indicators and data used in this topic more clearly and clearly, which increases the scientific nature of this paper. This study provides more scientific and accurate strategies for decision-makers and creators, and contributes to the high-quality dissemination of short videos.
Research on the Volume Prediction of Sorting Center Based on ARIMA-Random Forest
Authors: Zhao Yuxin , Wang Zhaoyang , Chen Yucheng , Li Xin
Published: August 2024

In terms of sorting center volume forecasting, the traditional ARIMA model is difficult to cope with the rapid changes in the market and environment, so an innovative new method based on machine learning is proposed. Then features such as trend, period and lag are constructed for each sorting center to accurately capture the temporal features and improve the prediction accuracy. By comparing various models such as random forest, neural network, support vector machine and linear regression, and comprehensively considering the evaluation indexes such as MSE, MAE and RMSE, the random forest model with optimal performance is finally selected for cargo volume prediction. In the forecasting process, rolling forecasts are used to ensure that the model can be updated in real time and adapt to new market changes. In addition, considering the impact of changes in transport routes on cargo volume prediction, the model is further optimized by introducing transport network information to improve prediction accuracy. This comprehensiveness and flexibility make this method more applicable and effective in practical applications.
Prediction of winning and losing in tennis match based on entropy weight -TOPSIS and machine learning model
Authors: Zang Quankun , Li Zetong , Hu Jiren , Zhang Tingxu , Zhang Weiqi
Published: August 2024

Tennis is a beloved sport worldwide, and spectators often eagerly predict the winners of both sides based on various playing conditions. Research has shown that there is a significant correlation between players' performance in the game and their momentum. To explore the impact of players' momentum on match results, this paper establishes a TOPSIS evaluation model based on entropy weight method to measure players' momentum value at every moment. Additionally, this paper establishes a decision tree model to predict real-time trends in match results according to match data. Ultimately, this paper findings suggest that players with higher momentum perform better at any given time, and predictions about game outcomes can be made based on differences in momentum. This study sheds light on various indicators and psychological states of athletes during competition and training while providing scientific recommendations for developing strategies, adjusting mentality as needed, and achieving optimal results.
Application of GANs-based virtual environment generation in automatic driving simulation training
Authors: Wang Haiyi
Published: August 2024

The safety and efficiency of autonomous driving technology rely heavily on precise simulation training, which is greatly limited by the quality and diversity of the training environments. Generative Adversarial Networks (GANs), as a powerful generative tool, can create highly realistic virtual images and environments, providing complex and variable training data for autonomous vehicles, thus enhancing the algorithms' generalization ability and adaptability. This study first reviews the major challenges faced by autonomous driving technology and the shortcomings of current simulation training methods, such as the monotony of existing training data and lack of scenario variation.Further, this paper provides a detailed introduction to the working mechanism of GANs, including the interaction process between its core components—the generator and the discriminator. Through case analysis, this paper demonstrates how GANs effectively generate new training scenarios without actual data, and these scenarios closely align with the real world in visual and physical properties. Particularly in simulating complex traffic scenes, different weather conditions, and various emergency events, GANs showcase their powerful capabilities.Additionally, this paper explores the practical operations involved in integrating GAN-generated virtual environments into the autonomous driving simulation training framework, including the technical details of data integration and the requirements for adjusting training algorithms. Through several specific application cases, this paper validates the effectiveness of GAN-enhanced simulation training in improving the performance of autonomous driving algorithms.Despite the great potential of GANs in autonomous driving training, there are still some challenges in practical applications, including the validation of the realism of generated data, high computational resource demands, and potential ethical issues. The article concludes by discussing possible strategies to address these challenges and anticipates future directions in technology development, such as improving the stability and efficiency of GAN algorithms and developing new evaluation standards to ensure the reliability and effectiveness of generated environments.
Research on Product Demand Forecasting Based on Random Forest and ARIMA Time Series: Precision Forecasting Method for Data-Scarce Environments
Authors: Wu Shuo , Zhang Zeshuo , Ru Yixuan
Published: August 2024

In the field of e-commerce, accurately predicting the future demand for specific products is crucial for efficient inventory management and supply chain optimization. Although traditional time series forecasting methods have shown reliability in handling rich data, they are significantly affected by seasonal fluctuations and sudden market events. This is especially true for newly launched products or long-tail products with insufficient sales data, which often results in poor performance, limiting the accuracy and feasibility of traditional models in practical applications. To address this issue, this paper proposes an innovative forecasting framework based on extensive historical sales data from e-commerce platforms. This framework employs random forests to deeply mine and learn from historical data, and uses the K-Means clustering algorithm and Euclidean distance to identify the most matching sales feature trends for target products with limited data. It further provides accurate predictions for these products through the ARIMA time series model, offering robust support for further optimization of the forecasting model. Ultimately, by comparing with traditional models, the innovative forecasting framework presented in this paper demonstrates a significant improvement in accuracy when predicting sales of products with limited data, overcoming the challenges of forecasting under data constraints, and fully proving its effectiveness and innovativeness.
Optimization of Super-Resolution Features via Deep Learning Techniques
Authors: Hu Yongkang , Jing Chuyue , Li Ziyuan , Liu Xiao
Published: August 2024

This manuscript delves into the pivotal role of feature optimization within the realm of deep learning-driven super-resolution techniques, a topic of considerable importance across diverse domains such as medical imaging and electron microscopy. The paper confronts the inherent challenges posed by deep learning in super-resolution tasks, including issues related to complexity, computational burden, and the paucity of model generalizability. It underscores the significance of feature optimization strategies, particularly the application of time-to-frequency transformation techniques such as wavelet and Fourier transforms, as well as attention mechanisms, in enhancing model efficiency and curtailing the demand on computational resources. A central theme of the discourse is the efficacy of amalgamating time-frequency separation methods — notably wavelet and Fourier transforms — with attention mechanisms in super-resolution endeavors. This integrated approach, termed multi-scale feature fusion, is lauded for its capacity to augment the model's attentiveness to high-frequency details, which are crucial in super-resolution. This fusion strategy not only optimizes the allocation of computational resources but also elevates learning performance.
Fatigue driving detection technology based on EEG signal
Authors: Yang Zhe
Published: August 2024

Fatigue, significantly impacting daily work and life, is a common state triggered by overexertion. Given these statistics, the development of reliable fatigue driving detection technology becomes critical to enhancing road safety. Therefore, fatigue driving detection technology is essential to reduce accidents and improve traffic safety. Effectively detecting fatigue has become a significant topic of interest among researchers. Methods for detecting fatigue that rely on physiological data are viewed as the most objective and precise. Current methods mainly rely on facial expression observation and physiological signal monitoring, such as yawning and blinking. Although these methods are effective, lighting and environmental changes may affect the results of facial expression-based detection. In contrast, electroencephalography (EEG) signals, as physiological signals that directly reflect human mental states, can reduce environmental interference with detection results. This paper reviews recent advancements in classification algorithms for EEG-based fatigue detection. In the majority of studies, traditional machine learning algorithms and manual feature extraction methods are commonly employed as the primary techniques for fatigue detection. In addition, deep learning algorithms, which have demonstrated significant results on large-scale datasets, have also been applied to fatigue detection. However, training for long-term calibration against subjects remains challenging in practical applications. Given the increasing demands in this field, researchers must continually explore efficient decoding algorithms and design rational experimental paradigms. Additionally, they need to gather and accumulate standardized data to enable the widespread application of fast and accurate fatigue detection methods or systems.
Deep Learning Technique for MRI-Based Brain Tumor Classification
Authors: Zhang Zicheng
Published: August 2024

Brain tumors represent a severe threat to human health due to their potential to disrupt vital functions controlled by the brain, such as movement, sensation, and cognition. This study aims to classify a comprehensive dataset of 7,023 magnetic resonance imaging (MRI) images from Figshare, SARTAJ, and BR35H into four distinct classes (Glioma, Meningioma, Pituitary, and No-Tumor). After evaluating the state-of-the-art models including VGG16, DenseNet201, ResNet50, and V3 Inception, the study proposes a novel Convolutional Neural Network (CNN) model with multiple inception layers. As a result, the model achieved an accuracy of 98.71% and high AUC scores for all four categories. These results suggest the potential of this model in enhancing diagnostic accuracy and reducing radiologist workload in clinical applications. Further improvements could focus on advanced feature extraction and integration of multi-parametric MRI data. The model's robustness and effectiveness underscore its potential utility in real-world clinical settings for accurate tumor classification.
Construction and Measurement of Comprehensive Evaluation System of Life Quality of the Elderly in China
Authors: Zhang Shiyu , Zhu Yueye , Lin Yuxin , Deng Shiying
Published: August 2024

Population aging is one of the basic national conditions in China. The implementation of the strategy of actively coping with the aging of the population is of great significance to the construction of the working pattern of the aging which is compatible with the modernization of the Chinese style and the promotion of the modernization of the Chinese style population. Conducting a national survey on the quality of life of the elderly and constructing a comprehensive evaluation system to measure the quality of life of the elderly can understand the real situation of the life of the elderly in our country, and is conducive to improving people's livelihood and improving people's quality of life. However, at present, the comprehensive evaluation system of the quality of life of the elderly in China lacks a unified standard, and some indexes in the past research are no longer applicable under the background of Chinese-style modernization. Therefore, the evaluation of the quality of life of the elderly urgently needs a set of contemporary, scientific, and systematic system standards. Based on the data of the fourth sample survey on the living conditions of the elderly in urban and rural China, this paper first clarified the existing achievements and standardization status of the assessment of the quality of life of the elderly, then combined with the requirements of the working pattern of the elderly under the background of Chinese modernization, selected 26 secondary indicators, and used DHNN for data specification and objective empowerment. An evaluation index system composed of six first-level indicators, namely family life, health care, economic status, social participation, cultural life, and subjective feelings, was formed to obtain the score distribution of the life quality of the elderly in the country and the specific scores of the six first-level indicators. On this basis, reasonable development suggestions were given for the cause of active aging.
Predicting and Analysing Road Accident Severity with Machine learning Models and Resampling
Authors: Wang Xin
Published: August 2024

Road accident threaten people’s life safety as well as wealth seriously, therefore predicting and analysing car accidents have great significance. This research first compared the performance of four machine learning models in analysing the severity of traffic accidents, including Random Forest, Naïve Bayes, Logistic Regression and Multi-Layer Perceptron. Naïve Bayes performs badly with a low accuracy while other three models have equal level of performance. Multi-Layer Perceptron performs better in minority classes than Random Forest and Logistic Regression. Among all features, Random Forest focus on geographical and time features, Logistic Regression and Multi-Layer Perceptron focus on driving features, including lighting and road surface.  Then resampling method is applied to imbalanced data. After trained in resampled data, Random Forest and Logistic Regression perform better in minority classes with higher precision and recall. In summary, this research compares the performance of machine learning models in road accident severity predicting and analysing, and addresses the challenge of imbalanced data with resampling method.
Research on the Impact of Sex Ratio of Lamprey on Ecosystem Stability Based on Dynamic System Model
Authors: Peng Runze
Published: August 2024

In the Great Lakes of the United States, lampreys, as an invasive species, have seriously affected the survival of native fish and destroyed the ecological environment. To find natural ways to control its population, it is necessary to study the relationship between lamprey and other species. The sex ratio of lampreys has an enormous impact on their population size. To study the impact of lampreys on ecosystem, it’s important to investigate the relationship between the sex ratio of lampreys and their population size. Based on Logistic model, Lotka-Volterra model, Competition model and Independence model, a Dynamic System model of simplified ecosystem is established. The effects of lampreys on other species under different interspecific relationships were explored. A population quality assessment model was established, and the population of lampreys was quantitatively assessed from eight aspects including Predation Control, Food Chain Role, Ecosystem Provider, et al.
Research on 3D Reconstruction Methods Based on Deep Learning
Authors: Ge Lu
Published: August 2024

Deep learning applications have been applied extensively and have made tremendous strides in the 3D reconstruction field in recent years. This paper offers a methodical review of deep learning-based techniques for single-view images, multi-view images, and video-based sequence approaches. For single-view methods, we focus on depth estimation using Convolutional Neural Networks (CNNs) and image-to-depth mapping using Generative Adversarial Networks (GANs). For multi-view methods, we explore 3D reconstruction based on multi-view stereo matching method, 3D points cloud reconstruction method and stereo flow estimation method. For video-based methods, we introduce depth estimation method based on optical flow and video sequence modeling using Recurrent Neural Networks (RNNs). Generally, the multi-view image method is more accurate and sophisticated than the single-view image method, while the method based on video sequences is more challenging and complex. Different 3D reconstruction methods depend on specific application scenarios and requirements. This review provides a considerable insight in research for 3D reconstruction and also make the conclusion as well as future prospect for this field.
Research on the Prediction of Metropolitan Traffic Density in China using ARIMA Model
Authors: Hu Shuai , Yu Zhongluo
Published: August 2024

This investigation targets to research on construction of prediction model of traffic density and discuss suitable methods to decrease heavy traffic. The data is obtained from the public website of Government of Shenzhen which provides updates of traffic monitoring, time sequence analysis is put in practical manipulation, and it is verified that traffic undergoes regular alternations responsible for specific time periods every day. Data sets of traffic density of routes are analysed using ACF and PACF testing procedures and ARIMA, to obtain an output of the prediction model of tendency of traffic congestion of related area. The prediction model does not have apparent invalidity in statistical perspectives, and illustrate approximate results about probable time portions that produces proliferating heavy-traffic phenomena. Relevant analysis does encounter limitation that the model may become unpredictable to sudden situations like temporary route construction, and unpleasant weather conditions which may result in chaotic circumstances to fluctuation of traffic for citizens.
Progress of Object Detection Based on Deep Learning
Authors: Zhang Aohua
Published: August 2024

Object detection is commonly utilized in fundamental computer vision research fields: medical, autonomous driving, sensing monitoring, and other fields. With the advancement of high-performance computing technology, the computing speed of hardware has made a significant advancement. Meanwhile, machine learning-related algorithms, especially deep learning-based algorithms, greatly boosted the detection speed and accuracy. In practical applications, engineers have little understanding of the principles, effects, and performance of these algorithms, which seriously hinders the industrial application of object detection. However, the advancement has fueled challenges and competition for better modules and industrialization. Accordingly, the main purpose of this study is to present the algorithms of object detection and summarize it from three aspects: data set, algorithm, and performance, so as to provide a reference for researchers in related fields. In terms of algorithms, the paper mainly introduces anchor-free, single-stage, and two-stage algorithms. Finally, the existing problems and future research directions for target detection are discussed.
Research on the Urban Bike-sharing Usage based on ARIMA Model
Authors: Cheng Hao , Li Muze , Zhang Haoting
Published: August 2024

This study forecasts the demand for shared bicycles using the ARIMA model, incorporating insights from extensive literature on urban bike-sharing usage. Through in-depth analysis, first-order differencing was identified as crucial for achieving stationarity, leading to the recommendation of the ARIMA (312) model. This model effectively encapsulates the dynamics of shared bicycle usage, evident from the significant ADF test results and the careful selection of model parameters based on ACF and PACF plots. The research reveals the complex relationship between urban mobility and shared bicycle systems, providing a comprehensive framework for predicting usage trends. These findings make a significant contribution to the discussion on sustainable urban transportation and offer practical guidance for city planners and bike-sharing operators to enhance service efficiency and meet user demands effectively. The precise prediction of shared bicycle demand by the ARIMA (3, 1, 2) model highlights the effectiveness of advanced time series analysis in understanding and predicting bike-sharing usage patterns. According to the predictions, there are clear seasonal fluctuations, with a cycle of four quarters during which the predicted values gradually increase. This study emphasizes the potential of shared bicycles to enhance urban mobility and points out the need for more regulated development and management strategies to address challenges posed by the rapid growth of the bike-sharing industry. By providing a detailed understanding of the factors influencing shared bicycle usage, this research contributes to optimizing bike-sharing systems, thereby aiding in the sustainability of urban transportation networks.
Research on Constructing a Departmental Guidance Model Using TextCNN, BiLSTM, and BERT
Authors: Li Jieqi
Published: August 2024

Current departmental guidance platforms in intelligent systems predominantly rely on selective options, necessitating patients to select pertinent medical information before scheduling an appointment. However, most patients' lack of specialized medical knowledge frequently results in misregistration incidents. Moreover, the focus of existing research on medical guidance systems does not emphasize or distinguish among specialized departments, leading to reduced consultation efficiency, overstretched resources in specific departments, and potential delays in patient treatment. This study aims to develop a departmental guidance model utilizing TextCNN, BiLSTM, and BERT. Initially, it involves training the base models of TextCNN, BiLSTM, and BERT on departmental guidance under uniform hyperparameters and environmental settings, comparing the outcomes to ascertain each model's strengths and weaknesses. Subsequently, an innovative hybrid model is proposed incorporating an attention mechanism, capitalizing on their synergistic features to address the challenge of low accuracy in guiding specialized departments. This hybrid model achieved an accuracy rate of 96.0%, precision of 95.7%, recall of 95.5%, and an F1 score of 95.6%, all demonstrating superior performance and significantly surpassing the experimental results of the standalone foundational models. Therefore, this study concludes that the innovative model can accurately direct patients to the appropriate specialized department based on their symptom descriptions.
An Overview of the Chinese SM9 Algorithm: A Cutting-Edge Cryptographic Breakthrough
Authors: Ma Wenbo , Ni Zejun
Published: August 2024

The SM9 algorithm represents a sophisticated advancement in cryptographic protocols, characterized by its unique implementation of bilinear pairing techniques and identity-based encryption mechanisms. Unlike conventional approaches, SM9 leverages the user's identity directly in the generation of cryptographic keys, streamlining the process and enhancing security. This method stands in contrast to traditional schemes like SM2, which typically rely on external certificate management for public key association. Central to its innovation is the seamless integration of identity information into the public key itself, thereby simplifying the encryption process and reducing the overhead associated with certificate handling. SM9's versatility extends to various applications, including digital signature creation, robust data encryption, secure key exchange, and reliable identity verification. This algorithm has garnered significant attention for its ability to maintain a high level of security while offering greater efficiency and user convenience. By directly binding keys to user identities, it eliminates several complexities and potential vulnerabilities inherent in certificate-based systems. As a result, SM9 has demonstrated remarkable performance and utility across a wide range of research and practical applications, making it a noteworthy subject of study in the field of modern cryptography.
Advancements and Challenges in Visual Perception for Autonomous Driving
Authors: Wang Haotang
Published: August 2024

As autonomous driving has become more popular, research on the technology has proliferated. There are many schemes to realize autonomous driving, including visual perception, lidar, and radar. Among them, visual perception implements advanced neural networks and transfer learning to achieve excellent performance. This article mainly discusses the advantages of visual perception, the challenges it faces, and possible solutions, while comparing it with other schemes from various aspects at the same time. The approaches to the study mainly involve literature review and comparative analysis. The article concludes that visual perception will thrive and dominate the autonomous driving industry in the future. The prospect of development is also investigated, and some potential solutions that can improve the performance of autonomous driving are provided in the passage. This comprehensive analysis underscores the pivotal role of the visual perception in advancing autonomous driving technology, highlighting its cost-effectiveness, scalability, and future potential in transforming transportation.
Gaussian process regression model based on stacking algorithm and application to stock price prediction
Authors: Wang Fan , Wang Tong , Fan Jiayu
Published: August 2024

Gaussian process regression, as a nonparametric statistical method capable of fitting nonlinear functions, holds an important place in the realm of quantitative finance. However, when applied to the prediction of noisy stock price time series, a single Gaussian process regression is prone to overfitting, and its computational efficiency diminishes with an increase in data volume. Considering this, we propose an ensemble learning model for Gaussian processes based on Bootstrap and stacking algorithms. The proposed approach balances the training and testing errors of individual predictive models through the concept of model averaging. Additionally, it enhances the computational efficiency of Gaussian process regression by employing subsamples instead of the full sample. Furthermore, by utilizing the stacking model, it can surpass the predictive limits of Gaussian processes, thus enhancing the predictive performance of the method. Simulated data analysis indicates that under the assumption of a Gaussian process model, the proposed method exhibits smaller mean squared error, absolute error, and relative error compared to some classical methods. Ultimately, when applied to the task of predicting stock price movements, it demonstrates higher accuracy and stability.
Particle swarm optimization for the Capacitated Helicopter Routing Problem
Authors: Liu Shaoxukang
Published: August 2024

To improve the ability to prevent potential natural disasters, countries around the world are actively formulating emergency plans to protect people's lives and property. In this study, Sichuan Province was used as a case study to explore the allocation of medical supplies in a single distribution center using data from 21 cities and a storage base of medical supplies in Sichuan province. By constructing integer mixed nonlinear programming models-Planar helicopter Problem (HRP) and capacity-constrained Helicopter Routing Problem (CHRP), and using Particle Swarm Optimization (PSO) algorithm to solve the model, effective scheduling results and solutions are successfully obtained. It is found that the restriction of flight distance has a significant impact on the helicopter scheduling strategy, which in turn affects the consumption of human and material resources. The results of this study are valuable for practical applications.
A Comparative Analysis Study of Deep Learning Methods in Cat And Dog Classification
Authors: Lin Meng
Published: August 2024

In recent years, deep learning has significantly advanced image classification. However, as models grow more complex, their computational demands increase, prompting a shift toward lightweight models. The MobileNet series, known for its efficiency in resource-constrained environments, is a prime example. Despite its popularity, performance comparisons among different MobileNet versions for specific tasks like cat and dog image classification remain underexplored. This study addresses this gap by evaluating MobileNetV1, MobileNetV2, MobileNetV3Large, and MobileNetV3Small on a Kaggle dataset containing over 1000 images. The dataset underwent preprocessing before training and testing. The paper assessed the models' classification accuracy and convergence speed through comparative analysis. Results indicate that MobileNetV2 outperforms the others, with superior accuracy and faster convergence, making it the preferred choice. MobileNetV1 also showed stable performance, while MobileNetV3Large's larger size led to overfitting issues. In conclusion, MobileNetV2's exceptional performance in cat and dog image classification suggests its broad applicability in resource-limited scenarios. This study provides valuable insights for deploying image classification models on mobile devices and other constrained environments. Future research should focus on further optimizing these models to enhance their performance and generalization capabilities.
Optimizing Commercial Site Selection Using the Thompson Sampling Algorithm
Authors: Liao Hanyue
Published: August 2024

In the era of big data, businesses are inundated with vast amounts of information across a wide spectrum of types, creating both opportunities and challenges in data utilization. Among the various algorithms adapted for this environment, the Thompson Sampling algorithm stands out due to its exceptional capability to handle dynamic data and complex problems. This algorithm leverages probabilistic models to continuously update and optimize decision-making processes based on incoming data streams. Thompson Sampling excels particularly in determining the optimal locations for businesses by analyzing extensive databases integrated with sophisticated business location models. By assessing and adjusting to real-time data, it allows for a more nuanced understanding of geographical and demographic trends that are crucial for strategic business placement. This method not only enhances the accuracy of business decisions but also significantly improves adaptability in rapidly changing market conditions.
Exploring Object Detection: Datasets, Metrics and Algorithms
Authors: Zhao Yifei
Published: August 2024

Object detection, as one of the basic issues in the computer vision (CV) field, has huge application value in realm such as autonomous driving and face recognition. Since the object differences in images in different scenes, angles, and lighting environments are too great, the number and size of objects are also different, those greatly increase the difficulty of detection. Therefore, how to detect objects quickly and accurately has attracted widespread attention from researchers. This article provides a review from the perspectives of data and algorithms in accordance with the order of technological development. It mainly introduces commonly used data sets, evaluation indicators, traditional object detection algorithms, and object detection algorithms based on deep learning. Finally, this paper will discuss future research directions, explore what difficulties still exist in the field of target detection, and how researchers should further improve the performance and generalization capabilities of target detection to cope with more complex and changeable scenarios and needs.
PENDAMPINGAN KEMAMPUAN KOMUNIKASI MAHASISWA DALAM MENINGKATKAN KESADARAN MASYARAKAT TERHADAP KEBERSIHAN RAWA DESA BURAI
Authors: Sumah Astrid Sri Wahyuni , Genisa Marlina Ummas , Saputri Wulandari , Astriani Meli , Nuraini Nita , Angraini Erni , Hidayat Saleh , Wardhani Sri , Sidik Bagas Rasid , Helida Asvic
Published: August 2024

Kemampuan berbicara merupakan salah satu dari empat keterampilan berbahasa. Berbicara merupakan keterampilan produktif dalam artian pembicara menghasilkan bunyi bahasa dan komunikasi dalam dua arah. Pembicara dan pendengar dapat menegosiasikan arti dari apa yang mereka katakan. Era industri 4.0, kemampuan berbicara dan mengemukakan sebuah masalah di depan umum sangat penting karena informasi yang didapatkan tidak lagi terbatas. Oleh karena itu, untuk menghindari kesalahpahaman terhadap isi informasi, diperlukan kemampuan berbicara di depan umum. Kemampuan tersebut membantu seseorang untuk mencari dan memahami informasi dengan benar. Selain itu, kemampuan berbicara di depan umum juga mengajarkan cara meyakinkan orang lain untuk mendapatkan informasi yang benar dan berguna untuk yang membutuhkan. Tulisan ini membahas tentang keterampilan berbicara mahasiswa di depan masyarakat Desa Burai mengenai pengelolaan sampah di daerah rawa. Para mahasiswa didorong untuk melatih kemampuan berbicara mereka dengan topik permasalahan mengenai pembuangan sampah masyarakat di daerah rawa melalui teknik wawancara. Metode kegiatan dimulai dari kegiatan pendampingan oleh dosen pendamping dimulai dengan pendahuluan, pemberian materi, dan evaluasi kegiatan. Hasil yang didapatkan bahwa mahasiswa dapat meningkatkan kemampuan berbicara di depan umum dengan rasa percaya diri dan termotivasi, tanpa harus takut mengemukakan pendapat atau butir pertanyaan yang dapat menyinggung masyarakat. Kata kunci: Desa Burai, kemampuan berbicara, rawa, wawancara
Technological Advances and the Quest for Sustainable Energy
Authors: Krishna Pasupuleti Murali
Published: August 2024

Abstract: This chapter explores the cutting-edge technological advancements and global efforts in the pursuit of nuclear fusion as a sustainable energy source. It delves into the scientific principles of fusion, the significant technological challenges that must be overcome, and the current and emerging technologies being developed to achieve controlled fusion reactions. The chapter also examines the environmental and economic implications of fusion energy, highlighting its potential to provide clean, safe, and abundant energy, as well as the challenges associated with its commercialization. Additionally, the chapter emphasizes the crucial role of international collaboration in advancing fusion research, addressing both the challenges and opportunities that arise from global partnerships. Ultimately, this chapter offers a comprehensive overview of the quest to harness fusion energy and its potential to revolutionize the global energy landscape. Keywords: Nuclear Fusion, Sustainable Energy, Fusion Technology, Plasma Containment, Magnetic Confinement, Inertial Confinement Fusion (ICF), Tokamaks, Stellarators, Environmental Impact, Economic Viability, Global Collaboration, Energy Security, Fusion Research.
Innovations in Cancer Immunotherapy: Harnessing the Immune System to Combat Cancer
Authors: Krishna Pasupuleti Murali
Published: August 2024

Abstract: This chapter explores the transformative advancements in cancer immunotherapy, focusing on how the immune system is harnessed to combat various forms of cancer. It provides a comprehensive overview of the different types of immunotherapies, including immune checkpoint inhibitors, CAR T-cell therapy, cancer vaccines, and oncolytic virus therapy. The chapter delves into the underlying mechanisms of these therapies, such as the modulation of the tumor microenvironment, targeting of tumor-specific antigens, and the induction of immune memory for long-term protection. Clinical applications and success stories are highlighted, showcasing the impact of immunotherapy on patient outcomes. Additionally, the chapter addresses the challenges and limitations of immunotherapy, including immune-related adverse events, resistance, and access issues. The future of cancer treatment is discussed, with a focus on next-generation immunotherapies, the role of AI and big data, and the importance of global collaboration. This chapter underscores the potential of immunotherapy to revolutionize oncology and the ongoing efforts to overcome the challenges that remain. Keywords: Cancer Immunotherapy, Immune Checkpoint Inhibitors, CAR T-Cell Therapy, Cancer Vaccines, Oncolytic Virus Therapy, Tumor Microenvironment, Tumor Antigens, Immune Memory, Personalized Medicine, Artificial Intelligence (AI), Big Data, Global Collaboration, Immunotherapy Resistance, Immune-Related Adverse Events (irAEs), Next-Generation Immunotherapies.
Precision Modeling: Advanced Mathematical Approaches to Complex Systems in Science and Economics
Authors: Krishna Pasupuleti Murali
Published: August 2024

Abstract: This chapter delves into the advanced mathematical approaches used in precision modeling to analyze and understand complex systems in both science and economics. It explores the foundational principles of nonlinear dynamics, chaos theory, stochastic processes, and agent-based modeling, which are essential for capturing the intricate behaviors of these systems. The chapter further examines the application of these models in various scientific fields, including biology, ecology, physics, and climate science, as well as in economic contexts such as macroeconomic modeling, financial markets, and behavioral economics. It also addresses the challenges associated with precision modeling, including model uncertainty, data quality, and ethical considerations, while highlighting the emerging trends and innovations, such as artificial intelligence integration and hybrid models. The chapter concludes by emphasizing the importance of interdisciplinary collaboration and the transformative potential of precision modeling in driving scientific discovery, informing policy decisions, and addressing global challenges. Keywords: Keywords: Precision Modeling, Complex Systems, Nonlinear Dynamics, Chaos Theory, Stochastic Processes, Agent-Based Modeling, Macroeconomic Modeling, Financial Markets, Behavioral Economics, Interdisciplinary Collaboration, Artificial Intelligence, Hybrid Models, Scientific Discovery, Policy Decision-Making, Global Challenges.
Confronting Biodiversity Loss: Strategies to Mitigate Climate Impact on Global Ecosystems
Authors: Krishna Pasupuleti Murali
Published: August 2024

Abstract: This chapter addresses the critical issue of biodiversity loss in the context of climate change, exploring the interconnected drivers and impacts that threaten global ecosystems. It provides an in-depth analysis of the factors contributing to biodiversity loss, such as habitat destruction, species distribution changes, phenological shifts, and ocean acidification. The chapter outlines effective strategies for mitigating these impacts, including conservation efforts, ecosystem restoration, sustainable land and water management, and the development of climate-resilient agricultural systems. It also examines the role of international agreements, national policies, and local initiatives in supporting biodiversity conservation, highlighting the importance of public awareness, education, and community involvement. The chapter concludes with a discussion on the challenges and opportunities in biodiversity conservation, emphasizing the need for technological innovation, interdisciplinary collaboration, and global cooperation to protect ecosystems and ensure the resilience of biodiversity in the face of ongoing climate change. Keywords: Biodiversity Loss, Climate Change, Ecosystem Resilience, Habitat Destruction, Conservation Strategies, Ecosystem Restoration, Sustainable Management, International Agreements, Public Awareness, Phenological Shifts, Species Distribution, Ocean Acidification, Technological Innovation, Global Cooperation, Environmental Education and Policy and Governance.