bluuebunny/crossref_metadata_2025 huggingface dataset:
Mixed Text and Formula Recognition Using ResNet and Transformer
Authors: Yang Chenhe
Published: August 2024

Recent advancements in deep learning have enabled the recognition of images, objects, observations, texts, and other complex structures with increasing accuracy. In recent years, scene text recognition has stimulated a lot of researchers in the computer vision community. However, it still needs improvement due to the poor performance of existing scene recognition algorithms. This paper aims to combine deep learning research to recognize texts and formulas in images and convert them into LaTeX format. More accurately and efficiently recognizing and converting images containing mixed text and mathematical formulas can improve the quality of the generated LaTeX code. In terms of the model, this paper utilizes ResNet as an Encoder for feature extraction and Transformer as a Decoder for text generation, improving the accuracy of the generated text. Two types of datasets were used in the process: pure formulas and a mixture of text and formulas. This paper concludes that the effects on both datasets are good, with CERs of less than 0.05 and a loss of only 0.10. Besides, the accuracy of pure formula recognition is higher than that of mixed types, possibly because there is more data on pure formulas and it is difficult to accurately extract text features in mixed types. This method can automatically parse and generate content containing mathematical formulas, promoting the development of the education industry and improving the efficiency and quality of digital content generation.
Advancements and Challenges in Text-to-Image Synthesis: Exploring Deep Learning Techniques
Authors: Chen Ge
Published: August 2024

This paper presents an in-depth exploration of text-to-image synthesis, categorizing the field into three main areas based on deep learning methodologies. Each category undergoes meticulous analysis to chart its development and dissect its fundamental mechanisms. The emphasis is on the significant advancements in the field, with a special focus on major breakthroughs and the continual evolution of these technologies. Concurrently, the paper critically evaluates the challenges and limitations present in the current state of text-to-image synthesis, providing valuable insights into the obstacles hindering progress. The study also delves into potential applications, pondering the future impact of this technology across diverse industries. The implications of these advancements are examined, considering not only technological capabilities but also their wider societal and ethical consequences. This comprehensive review not only sheds light on the current landscape of text-to-image synthesis but also looks forward to future innovations, highlighting the dynamic and continuously evolving nature of this area. The fusion of deep learning with creative image generation is poised for groundbreaking applications, setting the stage for transformative shifts in the interaction and interpretation of visual and textual content.
Illegal wildlife trade measuring based on data-driven analyses
Authors: Dai Jingze
Published: August 2024

Illegal wildlife trade (IWT) stands as a silent yet devastating force. In this paper a 5-year project is proposed to have a measurable impact on reducing the illicit trade based on data-driven analyses. First, we develop the Client Performance Evaluation Model to identify the ideal client. Secondly, the AI-Powered Wildlife Guardian Project (AWGP) is designed to monitor and manage the IWT chain, from supply to demand. Additional powers and resources for the project’s success are identified. The C4ADS Air Seizure Database highlights China’s need for the authority for cross-border monitoring and enforcement. The Impact Prediction Model (IPM) is crafted using time-series analysis to predict the effects of the project on illegal wildlife trade. Initially, there were 41 seizures in 2023, with an expected rise to 104 by 2028 without intervention. A probability analysis is conducted to determine the likelihood of achieving the goal of reducing illegal wildlife trade by 50%. Finally, a probability analysis is conducted to determine the likelihood of achieving the goal of reducing illegal wildlife trade by 50%.
Enhancing Multimodal Emotion Analysis through Fusion with EMT Model Based on BBFN
Authors: Wang Wenshuo
Published: August 2024

Sentiment analysis, as one of the key technologies of natural language processing, has been widely used in medical, film and television fields. In order to increase sentiment analysis's precision, it is particularly important to integrate multi-modal data. This paper presents a pioneering fusion strategy that amalgamates the cutting-edge Efficient Multimodal Transformer (EMT) model with the innovative Bi-Bimodal Fusion Network (BBFN) to revolutionize emotion analysis. By synergistically integrating these two state-of-the-art models, the research endeavors to enhance the efficiency and precision of sentiment analysis in multimodal datasets by accentuating the intricate interplay of global-local cross-modal interactions. Through a rigorous process of meticulous experimentation and comprehensive analysis conducted on the challenging MOSI dataset, the integrated model unveils a plethora of groundbreaking advancements across pivotal metrics, including accuracy, correlation coefficient, and Mean Absolute Error (MAE). The innovative integration surpasses existing models and sets a new paradigm for multimodal sentiment analysis frameworks, highlighting the importance of holistic modal fusion in understanding human emotions.
Exploring the Application of K-means Machine Learning Algorithm in Fruit Classification
Authors: Chen Weidan
Published: August 2024

Fruits are very popular in people's lives because they contain vitamins and dietary fiber, making them an important source of human diet. According to fruit production statistics, global fruit production reaches millions of metric tons, so it is necessary for people to establish an advanced fruit recognition system. However, traditional methods, including physical inspect, are inefficient, labor-intensive, and have a high probability of errors. Based on these facts, developing more efficient classification algorithms is worth exploring, as they can help people classify fruits quickly and effectively. This paper describes an experiment using K-means machine learning algorithm in Python, aiming to classify different types of fruits using image datasets containing various types of fruit images. The K-means algorithm will cluster the fruit image data input from three apple images and three banana images samples as an efficient clustering algorithm. The conclusion that the success rate for apple is 33.3% and the same rate for banana is 66.7% can get from the experiment. Nevertheless, this does not mean K-means will classify banana better than apple absolutely, since many other factors like the fruit color ,image pixel, the unique feature of randomly assigning labels to clusters in the k-means algorithm can also lead to the success or failure of the experiments.
Short-Term Travel Volume Prediction and Delivery Volume Prediction of Shared Bicycles Built on Different Sites
Authors: Huang Xintong
Published: August 2024

With the progressive development of the “sharing economy”, sharing bicycles are becoming increasingly popular in cities, and citizens’ last-mile travel problems are greatly alleviated. In addition, bicycles are also a good solution for short-distance travel. However, sharing bicycle operators do not have a good approach to dealing with supply and demand relationships, and bicycles are distributed in a position where supply is greater than demand, making it convenient for considerable discounts. The study aimed to establish a predictive model for predicting the short-term travel of bicycles to be shared in a particular area of the city. Shared bicycle data from 10 to 19 May 2017 were collected from the online platform of the Moby Algorithm Challenge, selecting data from land ranging from 152Mx152M, with the number of bicycles used reaching 2484561. ADF testing is designed to ensure that the parameter input in the model maximizes the accuracy of the model. The ARIMA model is then used to match the data and obtain a highly matching parameter model. As a result, the fixed value of the model is relatively close to the distribution state of the true value. The model predicts data for the next 12 days and obtains relatively accurate predictions.
Exploiting DCGAN Generated Images for Improving Image Classification
Authors: Chen Jingqi
Published: August 2024

The power of Deep Neural Networks relies heavily on the quantity and quality of training data. Usually, it is expensive and time-consuming for people to collect and annotate data on a large scale. Traditional methods, including data augmentation, do not always have the effect, especially in some biomedical fields where some large-size anonymous datasets are generally not publicly available. The study investigates the effectiveness of Deep Convolutional Generative Adversarial Networks (DCGAN)-generated pseudo data compared to simple data augmentation techniques, such as geometric transformations and color enhancements. Various classifiers are trained and tested on both original and augmented datasets. Results indicate that while DCGAN-generated data visually resembles real images, it may not fully capture the statistical characteristics of the original data, leading to decreased classification accuracy compared to simple data augmentation, but the accuracy difference with the original dataset is less than 5% at worst. This shows that the fake data generated by DCGAN can indeed be used for training, but different datasets require network deepening, hyperparameter adjustment, etc.
Integrating AI with Financial Accounting Processes: Innovations and Challenges
Authors: Liang Yaxin , Jiang Gaozhe , He Yafeng
Published: August 2024

In the context of artificial intelligence (AI), the convergence of advanced technologies has profoundly reshaped financial accounting and management paradigms. This transformation is characterised by the emergence of intelligent finance tools, including integrated industry-financial software and financial robots, which facilitate automated, data-driven processes. These innovations significantly reduce human involvement in routine accounting tasks, leveraging AI capabilities to conduct comprehensive data analysis and enhance decision-making through more profound insights from extensive datasets. Despite these advancements, cybersecurity risks and the necessity for updated theoretical and regulatory frameworks persist. Nevertheless, integrating AI with financial practices holds promise for enhancing operational efficiencies and redefining financial management practices in the digital age.
Designing and Modeling of Saturn V base on Juno New Origins
Authors: Li Yilin
Published: August 2024

Saturn V, utilized by NASA during the 1960s Apollo program, stands as a pivotal heavy-lift rocket in space exploration history. Its design and fabrication mark a significant chapter in spaceflight history. Through an amalgamation of Saturn V's authentic design parameters and Juno New Origins' sophisticated modeling tools, we proficiently resurrected this emblematic rocket system. This investigation delves into design methodology, model validation, and the advantages of Juno New Origins for Saturn V spacecraft modeling, offering a fresh vantage point for spacecraft design and simulation research. The modeling process spans four sections: S-IC, S-II, S-IVB, and supplementary modifications, exclusively utilizing Juno New Origins. Empirical evidence from user studies underscores the model's commendable quality and efficiency, bolstering its credibility within aerospace engineering. Evaluation outcomes reveal overwhelmingly positive sentiments regarding the model's performance, highlighting its efficacy in simulating diverse rocket interactions and its practical indispensability within aerospace engineering.
Exploring the development trend of mechanical engineering intelligence
Authors: Zhu Ruitao , Wang Linjie , Feng Yunbo
Published: August 2024

This paper discusses the characteristics and development trend of intelligent mechanical engineering. Intelligent mechanical engineering is characterized by high quality, high efficiency, four-dimensional intersection, energy saving and environmental protection. The development trends include networking, informatization, integration, automation control, product intelligence and artificial intelligence, etc. These trends will push mechanical engineering towards a new stage of more intelligent, efficient and sustainable development.
Interactions between Emotions Detection and Drivers
Authors: Shen Boyu
Published: August 2024

Nowadays, intelligentized human-machine interactions technologies are developing in a incredible speed, more areas these technologies are applied in. In recent years, intelligent cabins become a main-stream topic which is a tech aims for serving drivers and improving the driving experience. As the trend towards increasingly intelligent vehicles, more scientific researchers put emphasis on researching practical applications which can improve driving experience and satisfaction of drivers and passengers. In this case, intelligent cabins is a main field of these technologies’ applications. In order to offer a better, safer driving experience, some human-computer interaction techniques are applied to cabins for the sake of ensuring the safety of drivers and passengers. As well as improve the driving experience, to enhance the safety and stability during the driving, stabilize the driver’s emotion is a crucial strategy. Emotions can significantly influence the status of drivers and lower the margin of safety. In this case the intelligent cabin can make some adjustments to prevent or eliminate any potential danger. This article wants to analyze some examples that focus on non-intrusive and broadly appealing aspects.
Progress and Impediments in Deep Learning-Driven Image Style Transfer
Authors: Jiang Yuqi
Published: August 2024

This detailed review explores key developments and ongoing challenges in image style transfer, emphasizing the transformative role of deep learning approaches, notably Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs). The paper examines the fundamental principles of this field, particularly the intricate process of blending 'style' and 'content' via advanced neural network designs. It chronicles critical breakthroughs, and progresses to contemporary solutions addressing real-time execution, diversity enhancement, and stability in results. Emerging techniques such as Deep Feature Interpolation and Multi-Scale Style Transfer are also scrutinized, offering insights into potential research directions. The review not only traces the technical evolution but also considers the wider impact of image style transfer, underscoring its significance in bridging art and technology. This intersection is demonstrated through applications that span from digital art creation to innovative adaptations in medical imaging.
A Quantitative Analysis of the Effect of Pitch Sports on Players' Potential Energy
Authors: Tang Weiyu , Yi Xuanzeng , Zhao Huimin
Published: August 2024

This paper introduces a research framework for sports science, particularly in competitive tennis, focusing on momentum as a crucial factor impacting match outcomes. It addresses controversies surrounding momentum's definition, quantification methods, and its influence on results, highlighting the lack of research on momentum strategies. The study proposes a paradigm for calculating potential energy and employs the XGBOOST model optimized with DA to predict players' potential energy values with high accuracy (R2=0.9836). Through SHAP analysis, it delves into potential energy fluctuations, identifying factors such as continuous court running, winning shot execution frequency, stroke distance, intensity, and ball speed as significant influencers, varying among players. Furthermore, the paper utilizes GA for potential energy optimization, offering tailored training strategies. It concludes by discussing the model's universality, emphasizing its research significance. This framework aids in pinpointing match shortcomings in individual players and devising personalized training plans accordingly.
Deep Learning for Temporal Stock Prediction: A Comparison
Authors: Xuan Bohan
Published: August 2024

Deep learning has emerging and numerous applications for most areas, including finance, physics and medical science, etc. The deep-based models achieve satisfying performance on those tasks. In this paper, we aim to provide a summary of deep learning-based temporal stock prediction. Specifically, we first categorize the models into three aspects, including CNN-based models, RNN-based models, and hybrid models, by combining CNN and RNN. Then, we detail the preliminary knowledge for deep-based models, including the components of CNN and RNN. Furthermore, we provide an in-depth review of those methods. Finally, we provide a perspective discussion on the stock prediction tasks for further research. We hope our method can be useful for future research and provide a brief introduction to beginners.
Research and Design of Chinese Dragon Based on OpenScad
Authors: Guo Zefei
Published: August 2024

OpenSCAD, an open-source 3D modeling software, has been widely favored by creators for its powerful functions and flexible scalability. Although OpenSCAD has a wide range of applications in the field of 3D modeling, there are still few studies on how to use it to build animated models with specific cultural characteristics. As one of the important symbols of traditional Chinese culture, the Chinese dragon has unique morphology and cultural connotation. Therefore, we hope to provide new ideas and methods for related fields by studying the application of OpenSCAD in building animated models of the Chinese dragon.This study mainly explored the process of using OpenSCAD, an open-source 3D modeling software, to construct animated models with Chinese cultural characteristics - the Chinese dragon. In the modeling process, we adopted a block-based modeling strategy, dividing the overall structure of the dragon into several main parts: head, body, claws, and tail. For each part, we fully utilized the basic primitive construction and transformation functions provided by OpenSCAD for fine shaping. Through this study, we successfully used OpenSCAD to construct an animated model of the Chinese dragon with distinct Chinese cultural characteristics. This achievement not only demonstrates the powerful functions and flexibility of OpenSCAD in modeling complex shapes, but also provides new ideas and methods for related fields.
Prediction of Ischemic Stroke in MRI Images based on Machine Learning: A Systematic Review
Authors: He Ziwei
Published: August 2024

Ischemic stroke is a dangerous disease that endangers human health and is one of the important causes of disability and death. Early detection and prediction of ischemic stroke are essential for timely intervention and prevention of serious consequences. Fortunately, deep learning-based medical image research has made great progress, which can assist doctors in diagnosing conditions. This paper analyzes the overall effectiveness of existing machine learning (ML) methods in predicting final infarction from baseline imaging. First, this paper searched 11 relevant studies and analyzed the impact of these methods on patients with acute ischemic stroke (AIS). Then, the performance, effect and characteristics of the model are analyzed. Although these methods are generally good at predicting infarction, they show great potential. Based on machine learning methods, reliance on abundant data is essential for effective learning. Insufficient data can lead to significant errors in model predictions. Therefore, future research should focus on collecting ample clinical data for model training to enhance both accuracy and robustness.
Light-Weight Semantic Segmentation Based on Mask RCNN
Authors: Liu Mingwei
Published: August 2024

Semantic segmentation is based on the image information to detect and identify various categories of objects and output these objects mask. Compared with object detection and image classification, semantic segmentation has a more accurate recognition effect, and has a wide range of applications in automatic driving and other fields. With the development of deep learning, semantic segmentation methods based on deep learning have achieved good results, such as mask rcnn. Nowadays, Mask RCNN already has a good ability to handle object segmentation and mask the object. However, the model needs to consume a lot of computational power so that raise request for the equipment. This paper proposes a lightweight semantic segmentation network based on Mask RCNN, which can achieve better semantic segmentation accuracy with less data. In particular, using deep convolution as the feature extraction operator can effectively reduce the calculation and parameter number of the model. Experiments show the effectiveness of the proposed method.
A study on the prediction of dandelion spread based on simulation analysis
Authors: Mao Jiayi , Qi Jiashi , He Pengcheng
Published: August 2024

In this paper, a dispersal prediction model was established to predict the dispersal of dandelion by taking dandelion as the research object and analyzing the climatic conditions of the key parameters of dandelion seeds. Firstly, the key parameters of dandelion seeds were introduced, including the number of crown hairs, the average length of crown hairs, and the diameter of crown hairs. Then a dispersal prediction model was established to predict the dispersal of dandelion in different regions by simulating the seed dispersal process and the influence of climatic conditions. The model is based on the movement characteristics of dandelion seeds in the air and the influence of climatic conditions on seed dispersal, and combines the methods of mathematical modeling and experimental data analysis to provide a comprehensive study and prediction of dandelion dispersal.
Research on the Prediction of the Price Change of the US Against the JPY Based on Neural Network Modeling
Authors: Xu Ze
Published: August 2024

With the global economy getting closer, the fluctuation of the foreign exchange market has an increasingly significant impact on the world economy. As one of the important foreign currencies, the research on the price change of the dollar against the yen is of great significance. In recent years, the emergence of neural network models has made remarkable achievements in the field of forecasting the price of USD against JPY. This paper discusses the feasibility of using the neural network model to predict the price change of the dollar against the yen, and the method of using the neural network model to predict the price change of the dollar against the yen. By collecting historical exchange rate data, we build a training neural network model, analyze the performance of the model in forecasting exchange rate changes, and explore its potential application value. Research results: The mean square absolute error of the prediction model constructed in this paper is less than 2.5%, and the accuracy of the model reaches 97.4%; The simulation analysis of multiple samples shows that the error of the calculation result of the exchange rate of USD and JPY based on BP neural network is very small, the prediction effect is relatively good, and the prediction performance is high, which indicates that the neural network model has certain accuracy and reliability in the exchange rate prediction, and provides a useful reference tool for investors and financial institutions.
Research and Application Analysis of Key Technologies of Zero-Knowledge Proof under the Background of Blockchain
Authors: Guo Keyi , Ren Haoyu , Wang Peiyu
Published: August 2024

In recent years, blockchain technology has evolved significantly, enabling a decentralized network application model that offers both user anonymity and transparency. This unique characteristic of blockchain has led to its adoption in various sectors, including healthcare, finance, and transportation. The advancement of modern zero-knowledge proof technology has further enhanced blockchain's applications across these fields, bolstering privacy protection. Zero-knowledge proofs have become a key mechanism in blockchain smart contracts, offering a balance between transparency and privacy. Moreover, the integration of zero-knowledge proof technology with blockchain is facilitating technical advancements in areas facing challenges, such as autonomous driving technology. It is also addressing security concerns in more established technologies like the Internet of Things. This synergy between zero-knowledge proof and blockchain technologies is paving the way for innovative solutions across a wide range of applications.
Research on Facial Expression Recognition Based on Improved VGG19
Authors: Liang Wei
Published: August 2024

Facial expression recognition is of great significance in the fields of computer vision and human-computer interaction. Given the problems of over-fitting, high computational complexity, and vanishing gradient in VGG19, this paper proposes an improved VGG19 network model. By introducing depth wise separable convolution, the number of parameters of the model is significantly reduced and the computational efficiency is improved. This paper applies Batch Normalization and Dropout technology to effectively alleviate the over-fitting problem and improve the model's ability for generalization. In addition, this paper introduces residual connections into the model, which solves the vanishing gradient problem and improves the training speed and model stability. This paper uses the FER2013 dataset to conduct comparative experiments on the improved VGG19 network model, the original model, and other network models. The results show that the parameter amount of the improved VGG19 decreased by approximately 45.26%, and the accuracy increased by 4.52%. In addition, the improved VGG19 model is also better than other network models implemented in this paper and has better facial expression recognition results.
Exploiting Deep Convolutional Generative Adversarial Network Generated Images for Enhanced Image Classification
Authors: Xing Jin
Published: August 2024

The power of deep neural networks relies heavily on the quantity and quality of training data. However, it is expensive and time consuming for people to collect and annotate data on a large scale. Traditional methods, including modifying the copies of existing data, do not always have the effect, especially in some biomedical fields where some large-size anonymous datasets are generally not publicly available. So, this paper tried to tackle this problem by generating specific data using Deep Convolutional Generative Adversarial Network (DCGAN). DCGAN structure combines convolution and traditional generative adversarial network, has the advantages of producing the clearer images than vanilla Generative adversarial network (GAN). The training dataset is from CIFAR-10 dataset, consist of 10 classes of natural item images. To measure whether it is useful, three classifiers, LeNet, AlexNet and InceptionNet, are trained by feeding original dataset and original dataset mixed with generated data. The final result is presented by comparing accuracy. It goes well by adding more generated data from DCGAN into the original data. The result proves that DCGAN is able to augment data.
NBA Player Comprehensive Score Prediction based on Linear Regression and Random Forest
Authors: Lyu Junliang , Wang Ouze , Zhang Yinghao
Published: August 2024

This paper introduces machine learning models for constructing a comprehensive quality prediction model to forecast National Basketball Association (NBA) players' specific scores. The objective is to facilitate the analysis, guidance, and evaluation of players' relevant value. Initially, data processing involves renaming the dataset and dividing it into an 80% training set and a 20% dataset through preprocessing, simultaneously addressing missing row. Subsequent steps include visualizing the data and conducting correlation analysis by group, producing a correlation heatmap to mitigate multicollinearity issues. Based on the visualization chart's summary, a tentative ability map of players across different positions is delineated, covering rebounds, assists, and other aspects. Employing random forest and linear regression methods, NBA player data is utilized to train the model, followed by comparison of different models' performances and analysis of their respective strengths. Histograms and linear graphs for the linear regression and random forest models are derived, with random forest exhibiting superior fitting to the data, indicating more accurate predictions compared to linear regression. For future projects, the aim is to employ a diverse range of models for comprehensive data analysis and utilize various evaluation methods for detailed assessments of the models.
Research on Surface Weathering Patterns of Glass Artifacts Based on Data Analysis and Machine Learning
Authors: Peng Yonghe , Yang Junhe , Liu Rui
Published: August 2024

With the deepening of cultural relics conservation and archaeological research, changes in the chemical and physical properties of ancient glass artifacts, especially the phenomenon of surface weathering, have become an important basis for the study of ancient civilizations and for guiding the authentication and preservation of cultural relics. To help reveal whether the glass surface is weathered or not and accurately classify the glass, this paper firstly preprocesses the sample point data of glass artifacts' surface weathering, establishes a multivariate linear model through the regression analysis method, and finally statistically identifies the weathering intervals of two types of glass. It provides a quantitative basis for the detection of the presence or absence of weathering on the glass surface. Further, using cluster analysis and similarity metrics, the glass samples were subclassified and quantitatively assessed for similarity. The research in this paper not only helps to deepen the understanding of the weathering phenomenon of ancient glass artifacts but also provides strong scientific support for the conservation and restoration of cultural relics).
Study of Application of Robotics in Spinal Surgery
Authors: Ong Zhi Inn
Published: August 2024

Pedicle fixation during spinal surgery done with robot assistance has sparked a wave of contemporary discourse within the surgical field. This contrasts with conventional methods such as freehand with fluoroscopy assistance. The comparison between the novel and conventional techniques, the gradual application of robotics into spine surgical field, prospects, and limitations of applying robotics in spinal surgery will be discussed in this review. Spine surgery with robot assistance was shown to have lowered the risk of surgical spine in multiple aspects including radiation exposure, neurologic/vascular damage due to its high precision. The first established of robotics into the system which had pioneered the field and which subsequently led to more innovative technologies that target the conveniency, accuracy and duration for incorporating robots into spinal surgery procedure. With the acceleration of inventions and reduced intervals between their emergences, had drawn some attention to the learning curves associated with the application of robotics in spinal surgery. Albeit the many flaws being refined with robot assistance during spinal surgery, however, the effect as compared to other techniques is still not as prominent and impactful thus, leading to the slow progressing of incorporation into the spinal surgical field.
The application and impact of artificial intelligence in the field of animation as well as the existing disadvantage
Authors: Jia Haoran
Published: August 2024

This paper provides an in-depth look at the application and impact of Artificial Intelligence (AI) in the animation industry, while also pointing out the existing limitations. In recent decades, AI has been seamlessly integrated into all aspects of life, and the field of animation creation is no exception.AI has not only automated tedious tasks, increased efficiency, and lowered costs, but also revolutionized the traditional animation ecosystem by providing animators with entirely new ways of expression. With the rapid advancement of digital technology, AI has become a key driver of innovation and change, especially in creative industries. In the animation industry, the introduction of AI technology has not only reinvigorated the traditional craft, but also opened up unprecedented creative possibilities. This paper explores the specific applications of AI in animation production, such as automatically rendering the texture of animation to help animators complete repetitive tasks. Or the use of algorithms to assist in the production of special effects and color modification. Through the animation creator's description to complete the modeling, or through deep learning, machine learning generated large models to assist the character's behavioral design and so on. While AI brings many opportunities to the field of animation, it also comes with the challenge of ethical considerations and the risk of possible substitution of traditional technologies. the application of AI in animation has pushed the industry in the direction of greater efficiency, innovation, and personalization, and has also prompted us to think deeply about the combination of creativity and technology.
Heart Disease Prediction and GUI Interaction based on Machine Learning
Authors: Yang Yimin
Published: August 2024

Machine learning is now being used to detect heart disease. Considering that failure to diagnose a heart disease patient can lead to serious consequences, including delayed treatment, worsening of the condition, and even life-threatening situations, it is crucial to ensure that as many true patients as possible are confirmed. Thus, it is important to consider recall rates while maintaining a focus on accuracy. This article compares the application of four machine learning models, including Decision Trees (DT), Random Forests (RF), K-Nearest Neighbors (KNN), and Support Vector Machines (SVM), in heart disease prediction, and measures the effectiveness of these models by using accuracy, recall rates, and F1 score. The outcomes of the experiment reveal that the SVM model performs the best with a recall rate of 0.97. The balance of the model ensures that it achieves high recall without affecting accuracy. In addition, the author combines it with a Graphical User Interface (GUI) to achieve interactive effects. The model and its interactive functions selected in this experiment can easily avoid missing patients in the first screening and improve the accuracy of disease diagnosis.
A Comparative Analysis of LSTM and Transformer-based Automatic Speech Recognition Techniques
Authors: Zhang Ruijing
Published: August 2024

Automatic Speech Recognition (ASR) is a technology that leverages artificial intelligence to convert spoken language into written text. It utilizes machine learning algorithms, specifically deep learning models, to analyze audio signals and extract linguistic features. This technology has revolutionized the way that people interact with voice-enabled devices, enabling efficient and accurate transcription of human speech in various applications, including voice assistants, captioning, and transcription services. Among previous works for ASR, Long Short-Term Memory (LSTM) networks and Transformer-based methods are typical solutions towards effective ASR. In this paper, the author focuses on an in-depth exploration of the progression and comparative analysis of deep learning innovations within the ASR domain. This work starts with a foundational historical perspective, mapping the evolution from pioneering ASR systems to the current benchmarks: LSTM networks and Transformer-based models. The study meticulously evaluates these technologies, dissecting their strengths, weaknesses, and the potential they hold for future advancements in ASR.
Study on the Dynamics and Ecological Impact of Lamprey Populations Based on Growth Indicators and Mathematical Models
Authors: Luo Kaiwen , Jiang Hengyi
Published: August 2024

This study explores the growth and population dynamics of lampreys, focusing on the relationship between growth rate, sex ratio, and interspecific interactions. Utilizing the Von Bertalanffy growth equation, we establish a non-monotonic growth rate function and an asymmetrical S-shaped weight gain curve for lampreys. Population distribution analysis reveals a normal distribution for mass and age, with a joint probability density function for coupled distributions. The study delves into the impact of sex ratio on growth rate and reproductive strategies, highlighting that slow-growing individuals are more likely to become male in resource-limited environments. The Lotka-Volterra Equation is employed to examine interspecific relationships, revealing distinct roles for males and females in predation, competition, and mutualistic symbiosis. Our findings suggest that sex ratios significantly influence population dynamics, with implications for understanding lamprey ecology and managing their populations in aquatic ecosystems.
Perception de la vaccination contre la COVID-19 par la population de la ville de Likasi
Authors: Mutanda Pierre A. , Ilunga Joseph K. , Mwape Esther M. , Kabundji Serge M. , Mudjat Patient N. , Mwadi Pegguy M. , Ilunga Marcelline K. , Nyundo Pierre K. , Kaswala Christophe N.
Published: August 2024

Introduction. L’hésitation à se faire vacciner, alimentée par les théories de conspiration et le déni, entre autres facteurs, reste un obstacle important à la réalisation des objectifs mondiaux et nationaux de couverture vaccinale contre la COVID-19. L’objectif de cette étude était de faire un état de lieu de la perception de la vaccination contre la COVID-19 par la population de la ville de Likasi Matériel et Méthodes. Il s’agissait d’une étude descriptive transversale. Elle couvre la période allant du 1er mars au 31 mai 2023. Notre échantillon a inclus 203 sujets adultes, sélectionnés par la méthode d’échantillonnage aléatoire simple. La collecte des données a été réalisée par la technique d’observation directe et indirecte suivie de l’interview. L’analyse des données a été effectuée par des calculs statistiques descriptifs au moyen du logiciel Microsoft Excel version 2007. Résultats. . Nos enquêtés sont en majorité de sexe féminin (62,6%) ; la plupart de ces sujets étaient dans la tranche d’âge de 36-45 ans (43%) ; la profession dominante est celle de vendeurs (31%) ; le niveau d’étude de nos enquêtés est plus primaire (69,9%) ; seuls 43,8% ont dit de se protéger contre la maladie à COVID-19 ; 54,7% sont d’accord de cette vaccination et 57,6% de l’environnement sont en désaccord. Conclusion. Il ressort que moins de la moitié de la population enquêtée perçoivent positivement la vaccination contre la COVID-19. Mots-clés : Perception, Vaccination, COVID-19, Likasi.
4 PILAR LITERASI DIGITAL YANG PERLU DIKETAHUI MASYARAKAT DESA TUGU UTARA
Authors: Alfiani Muhammad Raudan , Hariadi Muhaemin , Ismia Anwar Ivana , Aisah Adhetia Putri , Fardatun Nafisa Alma , Nabita Lidyanti Utomo Putri , Arzita Elfito Fidya , Sakina Siti Ainayya , Riskisiam Cahya , Ginaldo Kevin , Halim Maulana Hanan , Kusumadinata Ali A
Published: August 2024

Program 4 Pilar Literasi Digital merupakan program yang dicanangkan oleh kementrian komunikasi dan informasi agar masyakarat lebih cakap dalam digital. Pengelolaan informasi dimasyarakat masih lemah terutama dalam perlindungan data diri. Tujuan artikel pengabdian ini adalah untuk mendeskripsikan bagaimana caranya aman dalam dunia digital. Metode yang digunakan secara studi kasus dengan Teknik ceramah kepada remaja dan ibu-ibu di Desa Tugu Utara Kp. Ciburial. Hasil kegiatan tersebut masyarakat menjadi lebih paham mengenai budaya digital, bagaimana menyikapi budaya sosial yang ada pada zaman ini, selain itu masyarakat  dapat menerapkan tips-tips keamanan dalam dunia digital. Oleh karena itu masyarakat perlu terus di edukasi mengenai 4 pilar literasi digital.
Keyword : Digital, Data, Keamanan Cyber, Masyarakat Digital
Economic Resilience in the Face of Climate Change: Global Challenges and Impacts
Authors: Krishna Pasupuleti Murali
Published: August 2024

Abstract: This chapter explores the concept of economic resilience in the context of climate change, focusing on the global challenges and impacts that threaten economies worldwide. It delves into the various strategies for enhancing resilience, including adaptation and mitigation efforts, economic diversification, and the development of innovative financial instruments. The chapter also examines the significant barriers to building resilience, such as political and institutional challenges, economic inequality, and technological and financial constraints. Through detailed case studies and future-oriented discussions, the chapter provides a comprehensive analysis of how economies can adapt to and recover from climate-related disruptions, while highlighting the importance of global cooperation, innovation, and proactive policy-making in securing a resilient and sustainable future. Keywords: Economic Resilience, Climate Change, Global Challenges, Adaptation Strategies, Mitigation Efforts, Economic Diversification, Financial Instruments, Political Barriers, Technological Constraints, Global Cooperation, Innovation, Sustainable Development.
Innovative Nanotechnology: Designing Advanced Materials and Devices at the Nanoscale
Authors: Krishna Pasupuleti Murali
Published: August 2024

Abstract: This chapter delves into the transformative field of nanotechnology, focusing on the design and development of advanced materials and devices at the nanoscale. It explores the fundamental principles of nanotechnology, including quantum mechanics, surface area effects, and self-assembly, which govern the unique properties of nanomaterials. The chapter provides an in-depth analysis of the various techniques used for nanomaterial synthesis and fabrication, such as top-down and bottom-up approaches, nanolithography, and characterization methods. It also examines the wide-ranging applications of nanotechnology in creating nanocomposites, energy solutions, biomedical devices, and nanoscale systems like nanoelectronics, nanosensors, and nanorobotics. Additionally, the chapter addresses the challenges and ethical considerations associated with nanotechnology, including health and environmental risks, as well as the need for regulation and standardization to ensure the safe and responsible development of this cutting-edge technology. Through this comprehensive overview, the chapter highlights how innovative nanotechnology is paving the way for the next generation of materials and devices with unprecedented capabilities. Keywords: Nanotechnology, Nanomaterials, Quantum Mechanics, Surface Area Effects, Self-Assembly, Nanocomposites, Nanostructured Materials, Nanoelectronics, Nanosensors, Nanorobotics, Nanolithography, Synthesis Techniques, Fabrication Methods, Biomedical Nanotechnology, Energy Nanotechnology, Environmental Nanotechnology, Health and Environmental Risks, Ethical Considerations, Regulation and Standardization.
Machine Learning in Quantum Cryptography: Next-Gen Algorithms for Secure Computing
Authors: Krishna Pasupuleti Murali
Published: August 2024

Abstract: This chapter explores the intersection of machine learning (ML) and quantum cryptography, focusing on how next-generation algorithms are enhancing secure computing. It begins with an introduction to quantum cryptography, outlining its fundamental principles and importance in protecting data against classical and quantum attacks. The chapter then delves into the role of machine learning in cryptography, examining its applications in optimizing cryptographic protocols, detecting anomalies, and predicting vulnerabilities. The development of quantum-enhanced algorithms, particularly in Quantum Key Distribution (QKD) and quantum random number generation, is discussed, highlighting how ML is driving innovation in these areas. Practical case studies are presented to demonstrate real-world implementations, followed by an analysis of the challenges and risks associated with deploying ML-enhanced quantum cryptography, including computational complexity, security vulnerabilities, and ethical considerations. The chapter concludes with a discussion on future trends, such as quantum machine learning (QML) and the integration of AI, blockchain, and quantum cryptography, offering a vision for the future of secure computing in the quantum era. Keywords: Quantum Cryptography, Machine Learning, Quantum Key Distribution (QKD), Quantum Random Number Generation, Quantum Computing, Quantum Machine Learning (QML), Post-Quantum Cryptography, Secure Computing, AI in Cryptography, Blockchain Integration, Anomaly Detection, Cryptographic Algorithms, Adversarial Machine Learning, Data Security, Quantum Cryptographic Systems, Next-Generation Algorithms, Cybersecurity, Ethical Considerations in Cryptography, Computational Complexity.
Integrating GIS and Remote Sensing for Comprehensive Flood Risk Zonation in Tehsil Shah Alam (Peshawar)
Authors: Hina  , Gul Shehla , Ali Zahid , Ullah Ubaid
Published: August 2024

A comprehensive assessment of flood risk in tehsil Shah Alam, district Peshawar, Pakistan, was conducted using GIS and remote sensing techniques. Flooding in Pakistan is often exacerbated by human activities like deforestation and urbanization, posing significant socio-economic and environmental challenges. The study utilized 30-meter Landsat imagery, a 12.5-meter Digital Elevation Model (DEM), and rainfall data from the meteorology department to analyze critical factors influencing flood risk, including slope, river proximity, flow length, land use, and precipitation. Data processing included DEM generation, stream order classification, Euclidean distance calculation, flow length measurement, slope analysis, satellite imagery classification, and rainfall data interpolation. The Weighted Overlay Analysis (WOA) technique integrated these parameters to create a detailed flood risk zonation map. The findings highlighted areas with steep slopes, proximity to rivers, long flow lengths, high levels of built-up and barren land, and high rainfall as the most at risk. The study delineated the flood risk zones into five categories, identifying 23.28 square kilometers as very high-risk and 39.46 square kilometers as high-risk, with other areas classified into varying levels of risk. This GIS-based model offers essential insights for targeted interventions in high-risk regions, enhancing flood management and mitigation efforts.