bluuebunny/crossref_metadata_2025 huggingface dataset:
A Review of Research on Image Classification Methods
Authors: Wu Jiarun
Published: November 2024

As one of the important research directions in the field of computer vision, image classification has a wide range of applications. Image classification is an important foundation for pattern recognition, machine learning, and artificial intelligence. Image classification generally includes three steps: region of interest selection, feature extraction, and classifier modeling. Among them, feature extraction of images is an important foundation for completing other tasks. In most pattern recognition scenarios, appropriate feature representation is a crucial step that directly affects the performance of the entire classification system. Among them, the most representative is the deep learning method that directly learns complex feature expressions from massive data. This article first reviews the research background and development history of image classification, then analyzes the application of image classification in different fields and lists case studies. It also outlines traditional image classification methods and mainstream image classification techniques in deep learning, including convolutional neural networks, model pre-training, and data augmentation. At the end of the article, the main challenges currently faced by image classification were analyzed, and future directions for improvement and technologies that can be combined were discussed. This review aims to provide researchers in image classification with certain research directions.
Wave-Based Sensors Utilized in Autonomous Driving Applications
Authors: Qi Haoxiang
Published: November 2024

This paper introduces several common sensors in automatic driving and their classification methods. It focuses on the comparison of sensors that use waves to sense environmental information, analyzes the influence of wave characteristics on their sensor performance from various angles, and analyzes their different advantages and disadvantages. Then, from the perspective of sensor application and scheduling, several common strategies for using sensors in automatic driving are introduced, and the principle of each strategy is interpreted and evaluated.
Achieving Dual Objectives of Maximizing Transmitted Solar Energy and Minimizing UV Transmission at Vertical Incidence
Authors: Li Jialin
Published: November 2024

Abstract. In northern China, exterior window glass significantly impacts heating energy consumption during winter by collecting heat and providing insulation. Additionally, it plays a crucial role in shading by attenuating ultraviolet rays. This paper presents an intelligent optimization algorithm based on genetic algorithms for optimizing the thickness of three-layer insulating glass to maximize solar radiation transmission and minimize ultraviolet radiation below 400nm wavelength, thereby enhancing the glass's heat collection and shading capabilities. The study utilizes MATLAB's genetic algorithm function, running 20 iterations to overcome local optima, achieving an optimal glass thickness configuration of 12mm for each layer. This configuration allows maximum solar energy transmission while substantially reducing UV radiation, balancing energy efficiency with health protection. Future research directions include examining different glass materials' effects on UV blocking, considering climate change impacts on glass performance, and scaling the optimization method for large-scale architectural applications to assess its practical and economic viability. The findings contribute to the development of energy-efficient and health-protective building materials.
Exploring the Role of Transformer Models in Vision, Multi-Modal, and Orbital Data for Autonomous Driving
Authors: Gong Yuxiang
Published: November 2024

Abstract. Autonomous driving aims to reduce human error in driving, improve traffic efficiency, and provide a more comfortable driving experience. The integration of computer vision, advanced sensors, and machine learning has been pivotal in this advancement. The introduction of Transformer models has particularly revolutionized the field by offering a novel approach to processing data through attention mechanisms, which is crucial for tasks involving complex relationships between data elements. The paper categorizes research into three main approaches based on input data types: camera-based perception, multi-modal data fusion, and orbital data integration. As autonomous driving technology progresses towards higher levels of autonomy, with L2+ systems becoming standard, challenges remain in accurately interpreting complex environments, handling edge cases, and navigating legal and regulatory landscapes. The paper concludes that while Artificial Intelligence (AI) and deep learning advancements have brought autonomous driving closer to full realization, further research is necessary to address current limitations and ensure safe and reliable autonomous vehicle operation.
Exploring the Impact of Word2Vec Embeddings Across Neural Network Architectures for Sentiment Analysis
Authors: Liu Ruoyu
Published: November 2024

Abstract. Sentiment analysis is crucial for understanding public opinion, gauging customer satisfaction, and making informed business decisions based on the emotional tone of textual data. This study investigates the performance of different Word2Vec-based embedding strategies static, non-static, and multichannel for sentiment analysis across various neural network architectures, including Convolution Neural Networks (CNNs), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRUs). Despite the rise of advanced contextual embedding methods such as Bidirectional Encoder Representations from Transformers (BERT), Word to Vector (Word2Vec) retains its importance due to its simplicity and lower computational demands, making it ideal for use in settings with limited resources. The goal is to evaluate the impact of fine-tuning Word2Vec embeddings on the accuracy of sentiment classification. Using the Internet Movie Database (IMDb), this work finds that multichannel embeddings, which combine static and non-static representations, provide the best performance across most architectures, while static embeddings continue to deliver strong results in specific sequential models. These findings highlight the balance between efficiency and accuracy in traditional word embeddings, particularly when advanced models are not feasible.
Comparison of Distributed and Parallel Machine Learning: Efficiency and Effectiveness in Large-Scale Data Processing
Authors: Peng Chengszu
Published: November 2024

Abstract. As a matter of fact, with the exponential growth of data, machine learning (ML) techniques have increasingly relied on distributed and parallel computing to handle large-scale problems. With this in mind, this paper provides a comparative analysis of distributed and parallel machine learning methodologies, focusing on their efficiency and effectiveness in processing large datasets. To be specific, this study will discuss as well as contrast key models and frameworks within both paradigms, assessing their performance based on computational cost, scalability, and accuracy. Through empirical evidence and case studies, this research will highlight the strengths and limitations of each approach. According to the analysis, the findings indicate that while distributed machine learning excels in scalability as well as fault tolerance, parallel machine learning offers superior computational speed for smaller-scale tasks. Overall, the insights from this study are crucial for researchers and practitioners seeking to optimize ML workflows for large-scale data environments.
Implementation of Distributed Machine Learning in Medicine Industry
Authors: Xia Yaoyang
Published: November 2024

Abstract. As a matter of fact, machine learning (ML) has become a cornerstone in modern science and engineering, evolving from early algorithms like the perceptron to sophisticated deep learning models. The rise of distributed machine learning (DML) has enabled scalable, privacy-preserving analysis of large healthcare datasets in recent years. With this in mind, this study summarizes the principles of DML, contrasting it with traditional as well as parallel computing approaches. At the same time, this research discusses its applications in medical imaging, predictive analytics, and real-time monitoring. According to the analysis, despite significant advancements, challenges related to data privacy, heterogeneity, and scalability persist. Based on the evaluations, future research is needed to overcome these limitations and further integrate DML into healthcare. Overall, these results underscore the transformative potential of DML in enhancing healthcare delivery and outcomes and pave a path as well as offer a guideline for implementations of machine leaning in medic industry.
Application and Performance Comparison of Compound Neural Network Model based on CNN Feature Extraction in House Price Forecast
Authors: Zhang Jiayuan
Published: November 2024

Abstract. This study used a total of eight machine learning algorithms to forecast property prices, it not only provides a robust comparison of the predictive power of different algorithms but also significantly advances our understanding of the factors that influence property prices. In this paper, four traditional machine learning algorithms and four neural network models are selected for comparative study and analysis, of which the neural network models include fully connected neural networks (FCNN), convolutional fully connected neural networks (FCNN+CNN), generative adversarial fully connected networks (FCNN+GANs) and generative adversarial convolutional fully connected neural networks (FCNN+GANs+CNN). This study applied to a Kaggle's sample. The results reveal that the models based on FCNN+CNN and FCNN+GANs+CNN perform relatively well in house price prediction, with both obtaining an explanatory power of R as high as 0. 96 and 0. 97, respectively and significantly outperforming traditional machine learning algorithms. It is worth mentioning that the FCNN+CNN model is slightly stronger in terms of error minimization, but both perform better in terms of stability and generalization capabilities. The conclusion is that neural network models generally have better results than traditional algorithms in house price prediction, and the neural network model of CNN composite has significantly better prediction performance.
Reviews on Transformer-based Models for Financial Time Series Forecasting
Authors: Lin Heyi
Published: November 2024

Abstracts. Relationship extraction is a key step in information extraction and aims to identify entities from text data and identify semantic links between them, which is the foundation of knowledge graph construction. Improving relationship extraction efficiency can effectively enhance the quality of information extraction, which has an impact on the construction of a knowledge graph and subsequent downstream tasks. The paper improves the efficiency and quality of relationship extraction by optimizing the model, choosing the way of relationship extraction and proposing new evaluation parameter criteria. The paper summarizes the current state-of-the-art research results, and briefly describes SIEF, an evaluation mechanism that focuses on important sentences, Coreference Aid, which captures the internal structure of entities as well as the external fine-grained information, and FILR, a framework that extracts global document information and performs multi-granular logical reasoning. Aggregate semi-automated data augmentation by integrating large model languages, remote supervision and construction of domain graphs integrating domain information of entities, cross-article entity-centered relationship extraction capable of capturing connections between different articles, and the introduction of mean-accuracy averaging capable of evaluating the comprehensibility of the model are presented. This paper presents a vision of new and better optimization algorithms by describing the latest approaches to relationship extraction optimization and finding that they all have their own limitations.
Research on the Risk Control Model Development Based on Scorecard
Authors: Wang Yunqi
Published: November 2024

Abstract. Effective risk management is vital for ensuring the stability and profitability of financial institutions. This study focuses on enhancing credit risk assessment by developing a scoring model that quantifies customer risk factors using logistic regression. Key features such as Weight of Evidence (WOE) and Information Value (IV) were employed to transform and select variables. The datasets used in this research, cs-training.csv and cs-test.csv, were preprocessed, including handling missing data and binning continuous variables to improve model interpretability and performance. The logistic regression model yielded an AUC value of 0.78, demonstrating strong predictive capabilities, though optimization is necessary to improve the F1 score. The final scorecard generated from the model provides actionable insights for financial institutions, enabling more accurate risk predictions and decision-making. This tool is particularly useful for loan approvals and credit issuance, offering a data-driven approach to managing credit risk in modern financial environments.
AI-Powered Voice Encryption: Securing the Future of Privacy and Safety
Authors: Ma Licheng
Published: November 2024

Abstract. In today's digital era, where waves of digitization sweep across the globe, voice, as one of the fundamental forms of human communication, is being captured, transmitted, and stored in unprecedented ways. From everyday phone conversations to remote conferences, from voice commands in smart homes to the digital distribution of musical compositions, voice data has become an indispensable part of the digital landscape. However, as these applications proliferate, concerns over the security and privacy of voice data have become increasingly prominent. This article delves into the innovative applications of ArtificialIntelligence (AI) in the realm of voice encryption, aiming to construct an efficient and secure shield for the transmission and storage of voice data.
Optimizing E-commerce Recommender Systems: A Comprehensive Review of Techniques and Future Directions
Authors: Wu Zhijing
Published: November 2024

Abstract. This paper examines recommender systems in e-commerce by reviewing technologies and real-world applications and identifying the importance of big data analytics in recommender systems. In the study, three kinds of recommendation algorithms are discussed: collaborative filtering, content-based recommendation, and hybrid models. Collaborative filtering methods did well in the case of large-scale user data, but had cold-start and sparsity problems. Based on the content, recommended methods have strong personalized suggestion functions, but the information cocoon phenomenon is a risk, which decreases the contents diversity. Hybrid models are a combination approach between the two techniques, providing a flexible and robustness solution, but only a more complex computationally. This article also looks at the technology trends that have emerged lately, for example, the use of deep learning models, as well as the privacy-preserving techniques utilized in recommender systems. By analyzing and summarizing the existing research, this paper provides a reference basis for future optimization and application of recommender systems and points out potential research directions.
Proposed Outlook based on Different Obstacle Avoidance and Pathfinding Algorithms
Authors: Bao Yizhan
Published: November 2024

Abstract. Currently, a variety of papers explore various directions in automated pathfinding and obstacle avoidance, with scholars developing diverse algorithmic models to address different theoretical frameworks. These models often enhance existing optimization algorithms or combine two distinct approaches to achieve improved results. In practical applications, it is common that no single paper can fully encompass the necessary background knowledge, which highlights the urgent need for a comprehensive article that integrates and enriches this knowledge base. Such a work would effectively address most path planning and obstacle avoidance challenges. This paper employs a literature review method to study automated pathfinding algorithms and obstacle avoidance systems. Its primary aim is to analyze cutting-edge research in these areas, providing a detailed examination and explanation of each relevant paper. Additionally, the paper critiques the current literature and the field as a whole, while also offering insights and prospects for future research directions in automated pathfinding and obstacle avoidance, ultimately contributing to advancements in the discipline.
Review on Multisensor SLAM Datasets for Advanced Perception and Mapping Technologies
Authors: Song Bochen
Published: November 2024

Abstract. SLAM (Simultaneous Localization and Mapping) is a technique in robotics and computer vision used to build a map of an unknown environment while simultaneously tracking the location of a robot or vehicle within that environment. The primary goal is to enable autonomous systems to navigate and understand their surroundings without prior knowledge of the environment. It has evolved significantly with the integration of diverse sensor modalities which initially used either a single LIDAR (light detection and ranging, or laser imaging, detection, and ranging) or visual sensor to perform the dual tasks of mapping an environment and localizing the device within it. These systems had limitations in accuracy and robustness due to their reliance on a single type of data input. Over time, the field has advanced to incorporate multiple sensor modalities, including LIDAR, visual cameras, Inertial Measurement Units (IMUs), ultrasonic sensors, and GPS. This multi-sensor fusion approach has dramatically enhanced the precision and reliability of SLAM systems. This paper reviews the state-of-the-art datasets that combine data from infrared cameras, depth cameras, LiDAR, and 4D millimeter-wave radar, focusing on their contributions to advancing SLAM technologies. The study analyzes the advantages and limitations of each sensor type, the challenges associated with data fusion, and the impact on perception and mapping accuracy. This review aims to provide a comprehensive understanding of how these multisensor datasets enhance SLAM systems and highlight areas for future research.
Application of Stable Diffusion and LoRA Models in AI Drawing
Authors: Gong Shunkai
Published: November 2024

Abstract. This paper explores the application of Stable Diffusion model and LoRA (Low-Rank Adaptation) model in AI-generated artwork. The authors introduce the foundational principles of Stable Diffusion model and LoRA, as well as their application in high-quality image generation. Using three popular datasets ImageNet, COCO, and CelebA we apply various image quality assessment metrics, including PSNR (Peak Signal-to-Noise Ratio), IS (Inception Score), and FID (Frchet Inception Distance), and further validate their potential in artistic creation through subjective evaluations. By comparing the performance of these two models across different datasets, we examine their strengths, weaknesses, areas for improvement in image generation tasks, along with user experience considerations. The experimental results show that the Stable Diffusion model excels in terms of image quality and diversity, while the LoRA model offers significant benefits in computational efficiency and resource usage. Through comprehensive experimental evaluations, this paper provides a scientific basis for model selection in AI art creation and offers insights for the future development of hybrid models.
A Review of Convolutional Neural Networks in Cancer Image Classification
Authors: Yi Haomeng
Published: November 2024

Abstract. Cancer image analysis is a crucial element in the field of modern oncology, offering insights into tumor characteristics that are vital for accurate diagnosis and treatment planning. Conventional diagnostic techniques are frequently constrained by discrepancies in interpretation and delays in processing. In recent years, deep learning techniques, in particular convolutional neural networks (CNNs), have demonstrated remarkable capabilities in processing large-scale medical images, enabling the rapid detection and classification of cancerous tissue with high accuracy and precision. This paper reviews the application of CNNs in cancer image classification, introduces common CNN models, and discusses advances such as transfer learning and multimodal data fusion. Furthermore, this paper examines the current challenges confronting convolutional neural networks (CNNs) in the domain of cancer classification. It proposes promising avenues for future research and underscores the potential of CNNs in enhancing cancer diagnosis and even revolutionizing the healthcare system.
The Role of Artificial Intelligence in Modern Software Engineering
Authors: Zhang Qinbo
Published: November 2024

Abstract. The rapid advancement of Artificial Intelligence (AI) has significantly influenced various industries, including software engineering. This paper explores the integration of AI into software engineering, focusing on its applications across different stages of the software development life cycle, including design, development, testing, project management, and maintenance. AI's ability to automate tasks, enhance efficiency, and improve code quality is revolutionizing how software is built and maintained. The paper also addresses the challenges and risks associated with AI-driven software engineering, such as dependency on AI tools, ethical concerns, and security vulnerabilities. Finally, the paper highlights future trends in AI-powered software engineering, including adaptive and self-healing systems, AI-enhanced collaboration, and full software automation. The role of AI in shaping the future of software engineering is both profound and transformative, making it a critical area of study.
Leveraging Distributed Processing for Enhanced Data Cleaning and Mining: Principles, Techniques, and Case Studies
Authors: Zhou Ke
Published: November 2024

Abstract. In the era of big data, the volume, variety, and velocity of data generated pose significant challenges for data cleaning and mining processes. Traditional approaches to data cleaning and mining often struggle to handle large datasets efficiently, leading to increased processing time and reduced accuracy. Leveraging distributed processing techniques can significantly enhance the efficiency and effectiveness of these processes. This paper explores the principles behind distributed processing, particularly in the context of data cleaning and mining. It delves into various techniques, including MapReduce, distributed databases, and parallel processing, highlighting their advantages in managing large datasets. Furthermore, the paper presents case studies that illustrate the application of distributed processing in real-world scenarios, demonstrating how these techniques can be employed to achieve cleaner, more accurate data and more insightful mining results. Through these case studies, the paper also discusses the challenges and considerations associated with implementing distributed processing systems, such as data distribution, fault tolerance, and the need for specialized hardware and software. The findings suggest that while distributed processing offers substantial benefits, careful planning and execution are required to fully realize its potential in data cleaning and mining.
Video Games Market Forecast Based on Linear Regression Model
Authors: Li Xiang , Liu Zhengyu , Xu Gaojie
Published: November 2024

Abstract. In recent years, the video game industry has been developing rapidly and the global market scale has been expanding. In this case, pre-study of the video game market has important theoretical and practical significance. Based on the Video Game Sales dataset, this study comprehensively analyses the development of the video game market over the last forty years through data analysis and mathematical modelling. This study uses a linear regression model to analyse the trend of the video game market over the past forty years. The paper finds that the scale of its tends to grow year by year, and the growth rate shows a linear trend. The video game market has shown a linear growth trend in the past forty years, and the market size has been expanding. The significance of this study is to provide a new idea and method for the prediction of the video game market, which helps to better understand the market dynamics and trends and provides a decision-making basis for related enterprises and policymakers.
Unleashing the Potential of Compact Language Models: A Context-Optimized Soft Prompting Approach
Authors: Jiang Zhanxu
Published: November 2024

Abstract. The field of Natural Language Processing (NLP) has seen remarkable advancements with the development of large pre-trained language models, which excel in various tasks, especially through in-context learning. However, the increasing size of these models presents significant challenges for widespread deployment, particularly in resource-constrained environments. This study introduces Context-Optimized Soft Prompts (COSP), a new approach that can improve the performance of smaller language models in few-shots learning scenarios. COSP uses information from the presentation to initialize soft prompts, effectively addressing the limitations of smaller models when performing contextual learning. COSP is evaluated on multiple tasks in the SuperGLUE benchmark and showed significant performance improvements. Results show that COSP not only enhances model performance but also generates more diverse and evenly distributed soft prompts, contributing to robust and generalizable model behavior. Additionally, COSP accelerates the training process, potentially reducing computational resources for model adaptation. By enabling smaller models to perform complex tasks competitively, COSP opens up new possibilities for deploying complex language understanding techniques in resource-constrained environments.
Deep Learning-Based Information Security
Authors: Yang Beijia
Published: November 2024

Abstract. With the rapid development of network technology, information security is facing increasingly complex challenges. Deep learning technology, due to its strong capabilities in data processing and pattern recognition, has become a key technology to improve the detection efficiency and accuracy in the field of information security. This paper delves into the application of deep learning in various aspects such as malware detection, network intrusion identification, User and Entity Behavior Analytics (UEBA), privacy protection technology, model explainability, and network security vulnerability detection, and proposes deep learning-based information security methods. Through experimental validation, our methods have outperformed traditional machine learning models in multiple evaluation metrics, providing new solutions for the field of information security. In the future, we will continue to explore new applications of deep learning in the field of information security to cope with the ever-changing network security threats.
Reinforcement Learning-Based Model for Enterprise Financial Asset Risk Assessment and Intelligent Decision-Making
Authors: Ju Chengru , Zhu Yida
Published: November 2024

Abstract. This study discusses the application of reinforcement learning (RL) in financial asset risk assessment, especially how to improve the ability of risk prediction and asset portfolio optimization through deep reinforcement learning (DRL) model. This paper experimentally verifies the advantages of DRL models in improving forecasting accuracy and risk management efficiency and discusses the potential impact of AI technology in the financial sector. The findings show that despite challenges such as data quality and model interpretation, the application of AI technology provides financial institutions with more precise and flexible risk management tools, driving further development of fintech.
Research on the Characteristics, Limitations, and Future Directions of Artificial Intelligence
Authors: Liu Shuhao
Published: November 2024

Abstract. The evolution of artificial intelligence (AI) has sparked widespread discussion regarding its potential to replicate or even surpass human intelligence. However, understanding the fundamental differences between AI and human intelligence is crucial for leveraging AI effectively and responsibly. This paper explores the fundamental differences between AI and human intelligence, focusing on structural, learning, and cognitive disparities. By reviewing the history and current state of AI, the paper highlights that despite significant advancements in specific domains, AI still faces limitations in causal reasoning, generalization, common-sense reasoning, and transparency. Based on these limitations, the paper defines AI's application scenarios, including handling unstructured data and automating repetitive tasks. Furthermore, the discussion extends to the prospects of AI in specific fields such as decision support, healthcare, autonomous driving, and finance, emphasizing collaborative work between humans and AI. Finally, the paper underscores that AI should serve as a tool to assist humans in solving complex problems, with a focus on fairness and safety in its societal applications.
Design Research on Smart Wearable Devices: A Case Study of Apple Watch
Authors: Yan Baining
Published: November 2024

Abstract. Smart wearable devices have rapidly evolved to become essential components of modern technology, transforming the way users interact with the digital world. This paper explores the design and development process of smart wearable devices, focusing on the Apple Watch as a case study. Key design principles such as sensor integration, power management, user interface, and connectivity are discussed in detail. Apple Watch, as one of the leading devices in this field, is examined through its hardware architecture, software environment, and communication protocols. Through this research, we delve into the technical challenges faced during its development and the solutions that make the Apple Watch a successful wearable product. Additionally, the paper highlights future trends in wearable device design, particularly in terms of energy efficiency and enhanced human-computer interaction. The findings indicate that the success of wearable devices, such as the Apple Watch, hinges on achieving a fine balance between performance, user comfort, and energy efficiency, while continuously innovating in the areas of sensor technology and machine learning integration. Ultimately, this study contributes to the understanding of how modern wearable devices are designed and how they can be further improved for future applications in health monitoring, entertainment, and productivity enhancement.
Advancements and Applications of Large Language Models in Natural Language Processing: A Comprehensive Review
Authors: Ren Mengchao
Published: November 2024

Abstract. Large language models (LLMs) have revolutionized the field of natural language processing (NLP), demonstrating remarkable capabilities in understanding, generating, and manipulating human language. This comprehensive review explores the development, applications, optimizations, and challenges of LLMs. This paper begin by tracing the evolution of these models and their foundational architectures, such as the Transformer, GPT, and BERT. We then delve into the applications of LLMs in natural language understanding tasks, including sentiment analysis, named entity recognition, question answering, and text summarization, highlighting real-world use cases. Next, we examine the role of LLMs in natural language generation, covering areas such as content creation, language translation, personalized recommendations, and automated responses. We further discuss LLM applications in other NLP tasks like text style transfer, text correction, and language model pre-training. Subsequently, we explore techniques for optimizing and improving LLMs, including model compression, explainability, robustness, and security. Finally, we address the challenges posed by the significant computational requirements, sample inefficiency, and ethical considerations surrounding LLMs. We conclude by discussing potential future research directions, such as efficient architectures, few-shot learning, bias mitigation, and privacy-preserving techniques, which will shape the ongoing development and responsible deployment of LLMs in NLP.
A Comparative Analysis of Convolutional Neural Networks for American Sign Language Recognition
Authors: Fang Hongyu
Published: November 2024

Abstract. Sign language recognition is an important technology that makes it possible for ordinary people to be able to communicate with deaf people, fostering inclusivity and accessibility. The came out of deep learning technology has completely changed the field by enabling the automatic extraction and learning of hierarchical features from raw data, leading to significant improvements in recognition accuracy. This paper presents a comprehensive comparative analysis of different Convolutional Neural Network (CNN) architectures for recognizing American Sign Language (ASL) signs. Utilizing the sign language dataset, which contains 24 classes of ASL letters represented by 28x28 grayscale images, the author evaluated the performance of a Basic CNN, a Modified Residual Network (ResNet)-50, and a LeNet-5 model. This study emphasizes the impact of architectural choices on recognition accuracy and computational efficiency. Results indicate that while ResNet-50 demonstrates superior accuracy, fluctuating significantly during initial training, the Basic CNN and LeNet-5 models offer greater stability with slightly lower accuracy. This work concludes that despite the initial challenges, deep learning models, particularly ResNet-50, show promise for ASL recognition, highlighting the need for diverse and enriched datasets to improve model reliability in real-world scenarios.
Reduce AI Illusion Based on Data Science Technology and Prompt Engineering
Authors: Li Yannan
Published: November 2024

Abstract. Since the advent of artificial intelligence (AI) systems such as ChatGPT, artificial intelligence has been widely used with its advantages of low cost and efficiency, but it has also brought negative effects such as academic plagiarism and fake news, especially the "AI illusion". Data science improves the performance of large language models and reduces the generation of error information through cleaning, preprocessing, and data enhancement techniques. The research direction of this paper starts from the data level and the algorithm level. At the data level, this paper discusses data cleaning and data preprocessing to demonstrate how to improve the accuracy, diversity and quality of data, optimize the performance of AI large models, and thus reduce the generation of AI illusions. At the algorithmic level, this paper introduces the role of prompt engineering, which guides the model to generate more accurate output by optimizing the problem design and prompt statements, and further reduces the risk of AI illusion. Finally, it is concluded that data science technology and prompt engineering play a key role in reducing the illusion of AI. Future research could delve deeper into data collection and model evaluation. At the same time, the algorithm level prompt engineering and alignment techniques also need to be further studied to enhance the robustness and reliability of the model. By multiple techniques, the performance of AI systems will be continuously optimized, effectively reducing the generation of illusions.
Effect and mechanism of Donepezil hydrochloride on Alzheimer’s disease
Authors: Song Weiling , Xue Jun , Wu Xuehui , He Jiali
Published: November 2024

Objective: To explore the effect and mechanism of donepezil hydrochloride on Alzheimer’s disease (AD).Methods: Thirty-six 3-month-old SD rats were selected as the research subjects and randomly divided into a normal control (NC) group and a model group. The model group was given continuous intraperitoneal injection of D-galactose solution (120 mg/kg/d) combined with aluminum trichloride (10 mg/kg/d) by gavage for 60 days. Morris water maze test was conducted to test the learning and memory abilities of the rats, and AD rats were selected. After modeling, AD rats were divided into a normal saline (NS) group and a drug treatment (DT) group. The DT group was given donepezil hydrochloride (1.0 mg/kg) by gavage intervention, and the NC group and the NS group were given equal volumes of physiological saline by gavage. After 4 weeks of intervention, Morris water maze test was conducted to detect the escape latency, the number of platform crossings and residence time in the target quadrant, the rats were euthanized, with serum and hippocampal tissues collected, and hippocampal tissue homogenate (10%) was prepared by using NS. Enzyme linked immunosorbent assay (ELISA) was used to detect the inflammatory and oxidative stress indicators in rat hippocampal tissues and serum, and hematoxylin eosin (HE) staining was used to observe pathological damage in rat hippocampal tissues.Results: Compared with the NC group, the NS group showed a significant increase in escape latency (p < .05), a significant decrease in the number of platform crossings and residence time in the target quadrant (p < .05), and a significant decrease in the activities of superoxide dismutase (SOD) and glutathione peroxidase (GSH-Px) in serum and hippocampal tissues (p < .05). The content of malondialdehyde (MDA) was significantly increased (p < .05), and the levels of interleukin-6 (IL-6) and tumor necrosis factor-α (TNF-α) were significantly increased (p < .05). Compared with the NS group, the DT group can significantly reduce the escape latency (p < .05), increase the number of platform crossings and residence time in the target quadrant (p < .05), significantly increase the activities of SOD and GSH-Px in serum and hippocampal tissues and reduce the content of MDA (p < .05) and the levels of IL-6 and TNF-α (p < .05). After intervention with donepezil hydrochloride, the number of neurons in the hippocampus were significantly increased.Conclusions: Donepezil hydrochloride can improve the learning and memory abilities of rats, reduce the levels of oxidative stress and inflammation in the brain and serum, and improve pathological damage in the hippocampus.
Optimization of Autosomal STR Markers for Equine Genotyping Using Multiplex PCR
Authors: Mustafa Usama , Zaroon . , Shoukat Sana , Juveria . , Hussain Manzoor
Published: November 2024

The investigation of horse lineage was of paramount importance in the registration of different breeds, trade, and formulation of studbooks. The pioneering technique of DNA fingerprinting emerged as the first highly responsive method reliant on DNA for individual identification and the examination of genetic affiliations. Microsatellites were a valuable tool for analyzing the genetic variations present among different horse breeds. The International Society for Animal Genetics (ISAG) has endorsed a set of 17 specific Short Tandem Repeats (STRs) for the equine identification, although these can be quite expensive to obtain through commercially available multiplex kits. Objective: To determine five autosomal STR markers (HMS6, HMS7, ASB23, VHL20, and LEX14) were optimized using multiplex PCR for equine genotyping. Methods: DNA was extracted from a Thoroughbred horse blood sample via an organic extraction method. Sensitivity analysis determined the optimal PCR concentration. Genotyping was performed on the ABI PRISM® 3100XL, and data were processed with Gene Mapper ID 3.2v software. Results: The optimal conditions for multiplex PCR of HMS6, HMS7, ASB23, VHL20, and LEX14 primers were 60°C annealing temperature, 3ng DNA concentration and 6μM primer concentration. A 12.5μL PCR reaction volume was recommended for cost efficiency. Conclusions: The results of this research have the potential to create a cost-effective, regionally produced multiplex PCR kit. This kit would be designed for analyzing parentage lineage within the Equine family in Pakistan, incorporating ISAG-recommended markers: VHL20, HMS6, HMS7, ASB23, and additionally LEX14. It could significantly streamline the import and export of horses in Pakistan.
UPAYA PENINGKATAN PENCAPAIAN VAKSIN COVID-19 PADA ANAK USIA 6-11 TAHUN DI WILAYAH KERJA PUSKESMAS SILOAM KECAMATAN TAMAKO
Authors: Surudani Conny Juliana , Makahaghi Yenny , Pangandaheng Nansy , Sawello Yesie B.
Published: November 2024

The increase in Covid 19 in Indonesia, the government is trying to reduce the death rate by preventing the transmission process. In addition to health promotion such as the 3M movement, namely washing hands, wearing masks, and maintaining distance, the provision of COVID-19 vaccination is expected to be one of the effective efforts in reducing the transmission rate. This PKMS aims to increase parental knowledge about the COVID 19 vaccine. The community service team visited the homes of children aged 6-11 years and had never been vaccinated against COVID-19 in the Siloam Tamako Health Center Work Area. There were 27 children who had been vaccinated against COVID 19. While 5 children were not vaccinated because they did not get permission from both parents. The community service team provided health education to parents who had less knowledge and refused their children to be vaccinated against COVID 19. The results of the evaluation showed that parents understood and were willing to take their children to the Siloam Health Center to get the COVID 19 vaccine.
Meningkatnya Covid 19 di Indonesia pemerintah berupaya untuk menurunkan angka kematian dengan cara mencegah proses penularan. Disamping promosi Kesehatan seperti upaya gerakan 3M yaitu mencuci tangan, memakai masker, dan menjaga jarak, pemberian vaksinasi COVID-19 diharapkan menjadi salah satu upaya efektif dalam menurunkan angka penularannya. PKMS ini bertujuan meningkatkan pengetahuan orang tua tentang vaksin COVID 19. Tim pengabdian melakukan kunjungan ke rumah-rumah anak usia 6-11 tahun dan belum pernah divaksin COVID-19 di Wilayah Kerja Puskesmas Siloam Tamako. Terdapat 27 anak yang sudah divaksin COVID 19. Sedangkan 5 anak yang tidak divaksin karena tidak mendapatkan ijin dari kedua orang tua. Tim pengabdi melakukan penyuluhan kesehatan pada orang tua yang memiliki pengetahuan kurang serta menolak anak mereka untuk divaksin COVID 19. Hasil evaluasi orang tua memahami dan bersedia untuk membawa anak-anak ke Puskesmas Siloam untuk mendapatkan vaksin COVID 19
PEMBINAAN KADER PHBS PADA ANAK SEKOLAH DASAR DI SDN INPRES ENEMAWIRA KECAMATAN TABUKAN UTARA
Authors: Kasaluhe Meityn , Pramardika Dhito Dwi , Sambeka Yana , Tanod Wendy Alexander , Cahyono Eko
Published: November 2024

Clean and Healthy Living Behavior (PHBS) in schools now feels even more crucial considering that children will soon return to school during the new normal of the corona virus pandemic. The benefit of PHBS in schools is to create a clean and healthy environment. Community service activities were carried out for students of SDN Inpres Enemawira, Tabukan Utara District, Sangihe Islands Regency. The purpose of implementing this activity is to increase students' knowledge about PHBS at school, especially during the adaptation period of new habits in preventing transmission of COVID-19. The flow of implementing community service activities consists of several steps, namely: (1) exploration and initial survey, (2) determination of implementation, (3) implementation, and (4) evaluation. The implementation of community service has a positive impact on students, namely increasing knowledge about PHBS and students being able to independently practice various indicators of PHBS at school.
Perilaku Hidup Bersih dan Sehat (PHBS) di sekolah kini terasa semakin krusial mengingat anak-anak akan segera kembali bersekolah di masa new normal pandemi virus corona. Manfaat PHBS di sekolah adalah menciptakan lingkungan yang bersih dan sehat. Kegiatan pengabdian masyarakat dilakukan pada siswa SDN Inpres Enemawira Kecamatan Tabukan Utara Kabupaten Kepulauan Sangihe. Tujuan pelaksanaan kegiatan ini adalah untuk meningkatkan pengetahuan siswa tentang PHBS di sekolah terutama pada masa adaptasi kebiasaan baru dalam mencegah penularan COVID-19. Alur pelaksanaan kegiatan pengabdian kepada masyarakat terdiri dari beberapa Langkah, yaitu: (1) penjajakan dan survei awal, (2) penetapan pelaksanaan, (3) pelaksanaan, dan (4) evaluasi. Pelaksanaan pengabdian masyarakat memberikan dampak positif bagi siswa yakni peningkatan pengetahuan tentang PHBS dan siswa mampu secara mandiri mempraktikkan berbagai indikator PHBS di sekolah.
PEMBERDAYAAN PROGRAM GERAKAN SATU RUMAH SATU JUMANTIK BAGI KADER DI DESA NAMPU KABUPATEN MADIUN
Authors: Pratiwi Hermiyanti  , . Setiawan , . Ngadino , Sulistio Irwan
Published: November 2024

The One House One Mosquito Larva Monitor Program (G1R1J) is a dengue mosquito control program in addition to the well-known PSN Plus. The environmental conditions in Nampu Village, which have the potential to become a breeding places for Aedes mosquitoes, as well as the larva-free rate (ABJ) data which is less than 95%, make the G1R1J program necessary to be promoted in Nampu Village. The purpose of this community service is to empower mosquito larva monitor in implementing G1R1J in an effort to control Aedes mosquitoes in Nampu Village. The implementation method is in the form of counseling on mosquito bionomics, mosquito nest eradication methods and ABJ calculation practices according to G1R1J guidelines. Counseling and ABJ calculation practices were enthusiastically followed by Nampu Village mosquito larva monitor. It is hoped that the G1R1J program can be implemented with the support of the Nampu village government and the guidance of the Gemarang Community Health Center.
Gerakan Satu Rumah Satu Jumantik (G1R1J) merupakan program pengendalian nyamuk DBD disamping PSN Plus yang sudah dulu lebih dikenal. Kondisi lingkungan di Desa Nampu yang berpotensi menjadi tempat perkembangbiakan nyamuk Aedes, juga data Angka Bebas Jentik (ABJ) yang kurang dari 95% menjadikan program G1R1J perlu digalakkan di Desa Nampu. Tujuan pengabdian masyarakat ini untuk memberdayakan jumantik dalam mengimplementasikan G1R1J dalam upaya pengendalian nyamuk Aedes di Desa Nampu. Metode pelaksanaan yang dilakukan berupa penyuluhan bionomik nyamuk, metode pemberantasan sarang nyamuk serta praktik penghitungan ABJ sesuai pedoman G1R1J. Penyuluhan dan praktik penghitungan ABJ diikuti secara antusias oleh Jumantik Desa Nampu. Harapan program G1R1J dapat diimplementasikan dengan dukungan pemerintah Desa Nampu dan bimbingan Puskesmas Gemarang