bluuebunny/crossref_metadata_2025 huggingface dataset:
Dance Movement Analysis Based on Big Data Somatosensory Interaction Technology
Authors: Chenlu Wang
Published: January 2025

Traditional dance evaluation is mostly carried out through the combination of manual and machine recognition, which can't capture dance movements properly, and can't realize self-evaluation for users who lack manual evaluation conditions. In order to improve the analysis effect of dance movement fluency, this paper proposes a dance movement fluency evaluation system combined with somatosensory interaction technology, and in order to correct the trainer's dance posture, this paper establishes a standard dance movement comparison template. In addition, this paper uses Kinect to collect standard dance movement data, and repairs the occluded joint information to form a standardized movement number. Through the analysis, it can be seen that the dance movement fluency analysis system combined with somatosensory interaction technology proposed in this paper can effectively improve the dance movement evaluation effect. Therefore, applying interactive movements to dance movement analysis is the development direction of dance movement evaluation in the future, and it is also the basis of independent dance learning.
Exploration of Dance Choreography Strategies Based on Big Data Information Analysis Technology
Authors: Qin Wang
Published: January 2025

Intelligent choreography is the inevitable direction of the intelligent development of dance art. At present, there is the problem of insufficient accuracy of choreography feature extraction. This paper proposes an automatic music choreography algorithm combined with big data. Based on a large number of existing music and dance data, this algorithm uses machine learning algorithm to train the model, which can automatically and intelligently generate desired dance movements in combination with screening conditions. Moreover, combined with the idea of data mining, this paper puts forward a data generation strategy, which randomly adds irregular disturbances at the time series level and spatial level to the professional dance sequences in the data set, synthesizes non-professional dances, and constructs a training set that meets the requirements in scale and choreography complexity. In addition, this paper designs a two-stage framework: dance-music alignment stage. Through the analysis of the results, it can be seen that the choreography method combined with feature extraction proposed in this paper can effectively identify various dance features. In particular, it can learn from a large number of dance features, effectively improve the quality of dance choreography, and play a certain role in promoting the further development of subsequent dance choreography.
Dynamic Trust Computational Model with Secure Data Transmission in IoT and WSN
Authors: Bhawana Atul Ahire
Published: January 2025

As the integration of the Internet of Things (IoT) and Wireless Sensor Networks (WSN) continues to grow and its security and trust becomes a major issue. Due to transmission of sensitive between various networks, it is necessary to provide higher security such networks as well as nodes. Direct trust and indirect trust calculation models which are successful in a comparatively smaller network, but cannot deliver the requirements of adaptability of trust level in each instant for this smart environment such as IoT and complex WSN. Therefore, the aim of this paper is to design and develop a dynamic trust computational model to enhance data security in IoT and WSN. The framework describes the calculation of trust values assigned to different nodes depending on the values of data credibility, node’s behavior, the frequency of communicating with other nodes. It also calculated the reliability coefficients to determine the current overall trust values for the nodes. The model incorporates machine learning algorithms to monitor and identify abnormal node activities and learn from the network’s changes and identify possible security threats. In extensive experimental analysis we built IoT and WSN environments, the work showed a marked improvement over state-of-art approaches and decreased the loss of data and reduces the compromised node activity. Compared to the traditional static trust models, this proposed model ensured a more effective secure data routing with up to 99.40% accuracy in identifying the malicious nodes. In contrast to static and dynamic procedures for calculating trust, this model proposes an innovative, context-based system for estimating trust with reference to genuine-time node action. The use of the proposed framework rather than static models produces better security outcomes by enhancing work with 30% due to real-time metrics dependent on the context and adaptive threat identification.
Exploring Braj Bhasha for Text Analytics, A Comprehensive Preprocessing Approach
Authors: Mukta M. Deshpande
Published: January 2025

India's linguistic landscape is incredibly diverse, characterized by numerous languages and dialects, often with significant variations. This research addresses the challenges of Natural Language Processing (NLP) in low-resource languages by focusing on data pre-processing techniques for Braj Bhasha. This study significantly contributes to Braj Bhasha by creating a new dataset due to the limited resources available for the language. There were a total 17, 356 words present in the dataset, a curated stop word list with 44 words is created and utilized on the dataset, improving its applicability for further NLP operations. The pre-processed dataset is a significant resource with reduction in 7.5% words that enables and facilitates further study and development of NLP applications for Braj Bhasha. This initiative seeks to address the technological gap and enhance the representation of under-resourced languages in the expanding domain of natural language processing.
The Interplay of Self-Actualization, Habitus, and Capital in the Interpretation of Tiktok Video Content and the Appeal of Gen-Z
Authors: Joko Utomo Hadibroto
Published: January 2025

Individuals born between 1995 and 2012 are known as Gen-Z. A significant portion of this generation has ventured into TikTok to self-actualize as content creators. Among these Gen-Z TikTokers, some have amassed a substantial following of hundreds of thousands to millions. Notable accounts include dddkembar, nayyarafeeza09, hanamileniaa, and keykoaurlia. Their large followings have led to endorsements from various brands, particularly in the cosmetics and food industries. This research aims to provide insights into the phenomenon of Gen-Z individuals who have successfully self-actualized and achieved financial success through TikTok. A mixed-methods approach (both qualitative and quantitative) was employed to explore this phenomenon. Qualitatively, the study utilized Pierre Bourdieu's concepts of habitus and capital as a unified framework. Additionally, Maslow's hierarchy of needs, specifically the need for self-actualization, was incorporated. To uncover the meaning of TikTok video content, semiotic analysis was applied. Quantitatively, the study employed Melvin Defleur's theory of social categories. This research focuses on the interplay between self-actualization, habitus, and capital, and the allure of Gen-Z in TikTok video content, as perceived by audiences based on social categories (age, gender, education, religion, and residence). The findings reveal a reciprocal relationship between self-actualization, habitus, and capital in the interpretation of TikTok video content, and the appeal of TikTokers to audiences based on their social categories.
Research on the Innovative Model and Guarantee Mechanism of Industry-Education Integration Based on Big Data: A Case Study of Wuhan Technical College of Communication
Authors: Beibei Li, Dong Zhang,
Published: January 2025

With the rapid development of China’s socioeconomic landscape and tourism market from 2010 to 2019, the hotel industry entered a fast track of transformation characterized by groupization, scale expansion, digitalization, and intelligence, leading to a surge in demand for highly qualified talents in digital operations and service management. With industrial upgrading and economic structural adjustments, the deep integration of vocational education and industrial development has become a key factor in enhancing national competitiveness. As an important bridge connecting education and industry, research on the innovation and guarantee mechanisms of industry-education integration models holds significant theoretical and practical value. Using the “Qingmiao” Reserve Store Manager Order-Based Class at Wuhan Technical College of Communication in collaboration with Dossen Hotel Group as a starting point, this paper aims to explore innovative paths and guarantee mechanisms for industry-education integration models based on big data, providing practical references for cultivating high-quality, market-oriented skilled talents.
Stereo Matching Algorithm Combining Image Segmentation and Improved Belief Propagation
Authors: Guangming Lu
Published: January 2025

In this paper, a stereo matching algorithm combining image segmentation and improved belief propagation is presented to address the problems of poor real-time performance, low matching accuracy of weak texture regions, error matches and disparity holes. The mean shift algorithm is applied to segment the reference image and define the disparity template, which improves the real-time performance of the algorithm and the matching accuracy of the disparity flat regions. The cost value is calculated by the Sum of absolute differences algorithm to obtain the initial disparity map, then the occlusion points are detected by the left-right consistency detection and the invalid values are filled, in the disparity map acquisition. The weighted least squares method is used to calculate the parameters of disparity template, and the template is allocated by the improved belief propagation algorithm to obtain the regional optimal disparity plane. Finally,the dense disparity map is acquired by this algorithm. The result shows that the presented algorithm can greatly reduce the error match rate in weak texture regions and occlusion regions which meets the requirements of stereo matching.
Traditional Chinese Medicine Symptom Text Classification Based on Label Mask
Authors: Pengtao Jia
Published: January 2025

Traditional Chinese Medicine (TCM) symptom text classification refers to the use of computer technology and TCM texts to analyze content and identify different symptom categories, thereby automatically predicting patients’ descriptions of their feelings and physical examination results. However, TCM clinical texts are often lengthy and complex, with valuable insights often obscured by noise or redundancy. Additionally, TCM texts contain many obscure words and specialized terms, hindering traditional models from accurately interpreting TCM terminology. To resolve these challenges, this paper first creates a template using label masking, which is incorporated into the input sequence fed into the BERT model. A KAN linear layer is then applied to classify the contextual features extracted by BERT, with the KAN layer adjusting the activation function to better align with the data and enhance classification accuracy. After the original label prediction, a CorNet module is introduced to effectively mine the correlations between labels. F Lastly, the MLM task is included in the training phase, helping the model not only estimate label distributions but also predict labels from masked input positions. This enhances the training process and further improves the model’s robustness. Experiments on various datasets show that the proposed model is highly effective and generalizable for real-world clinical data symptom text classification.
Explore the Integration of Low and Medium Code Platforms in Data Center Development in Colleges and Universities
Authors: Kewen Wu
Published: January 2025

The digital transformation within China's higher education institutions is accelerating, unveiling challenges such as data silos and impediments to efficient cross-departmental collaboration. Low-code development platforms, renowned for their agility and efficiency, have emerged as pivotal solutions to these challenges. This study, grounded in a survey of five diverse universities, delves into the deployment of low-code platforms in university data center construction, with a particular focus on their impact on the rapid deployment of tailored systems. Employing questionnaires and a behavioral science analysis framework, we assess the efficacy of low-code platforms in university management systems, examining metrics such as deployment velocity, user-friendliness, and satisfaction. Our findings indicate that low-code platforms markedly enhance the swift deployment of customized management systems, streamline inter-departmental collaboration, mitigate the cognitive burden on developers, and bolster productivity. The streamlined development environment also positively influences user experience and psychological comfort. This study posits that low-code platforms hold substantial potential for broader application in university digital transformation, facilitating data center construction and catering to bespoke management demands. Future research is encouraged to probe the long-term benefits and expanded applications of low-code platforms in educational administration.
Exploring the Integration of Digital Twins and Artificial Intelligence in Enhancing Multinational Fashion Design and Manufacturing
Authors: Hongcai Chen
Published: January 2025

In the whirlwind of globalization's relentless advancement, the realm of cross-border fashion design and production is increasingly besieged by multifaceted challenges. These include navigating the intricate maze of cultural differences, unwinding the tangled complexities of supply chain management, and keeping pace with the mercurial whims of market demands. Recently, a beacon of hope has emerged through the synergistic fusion of digital twin technology and Artificial Intelligence (AI), heralding potential solutions to these formidable issues. This study embarks on an exploratory journey to illuminate the application of AI-enhanced digital twin technology within the intricate tapestry of cross-border fashion design and production, casting a spotlight on the endeavors of fashion enterprises in China and South Africa. Employing a case study methodology, this paper delves deep into the labyrinth of challenges and strategies associated with the practical deployment of this technology, encompassing AI-assisted cross-cultural collaborative design systems, machine learning-driven cultural sensitivity analysis, and AI-powered real-time collaborative design systems. The findings reveal that these technological interventions significantly amplify design innovation, streamline production processes, and fortify the bonds of cross-border collaboration. Despite the shimmer of positive outcomes, shadows of challenges linger in the realms of data acquisition, the labyrinth of technical implementation, the dance of cultural differences, and the horizon of long-term benefit assessment. Future research is poised to navigate these challenges, endeavoring to unlock the full potential of digital twin technology and AI in the vibrant world of the global fashion industry.
Reform Thinking of Medical Education on the Use of Online Learning Platform in Comprehensive Universities
Authors: Amin Li
Published: January 2025

With the rapid development of information technology, medical education has ushered in the era of digitalization and intelligence. However, it seems that comprehensive universities do not integrate the advantages of computers into their medical education, which leads to many problems in the medical education. For these problems, this paper first conducted questionnaires with285 clinical medicine students and interviewed 9 medicine teachers from two comprehensive universitiesto better understand the present situation of medical education. This paper proposes the teaching mode of "organ system as the main line, disease as the center, clinical thinking path as the guide" on the use of online learning platform and augmented reality (AR)/virtual reality (VR) technology. Meanwhile, the research gives advice to use the professional advantages of informatization and digitalization of online learning platform in comprehensive universities to expand teachers' interdisciplinary and cross-professional knowledge system. In this way, the quality of medical education in comprehensive universities can be significantly improved.
Research on Automatic Composition and safety of Chinese Guzheng Based on Joint Cross Attention
Authors: Sisi Zhu
Published: January 2025

Computer music automatic composition not only allows the general public to experience the joy of music creation, but also provides creative inspiration for professionals, greatly reducing the time for music creation. However, there is relatively little research on the use of technological means to create Guzheng music. This paper will conduct research from a new perspective by introducing joint cross attention and automating composition. The first step is to collect Guzheng music data, the second step is to process Guzheng music data, the third step is to extract Guzheng music features, the fourth step is to combine cross attention data processing, and the last step is to automatically predict the work. Through experimental data analysis, it was found that the model quickly converged and reached its peak, meeting the requirements for model migration implementation. After migration, the model can automatically compose music, meeting the needs of ordinary users.
Improved Ant Colony Algorithm in Optimizing Evacuation Path Planning
Authors: YuGuo
Published: January 2025

The traditional ant colony algorithm has played an important role in the evacuation path planning of ship personnel. By simulating the process of ants searching for food, it effectively solves the optimization problem of evacuation paths, improves evacuation efficiency and safety.However, traditional ant colony algorithm in path planning applicationsalsohas some shortcomings, such as the heuristic function does not introduce the distance of the target point, insufficient utilization of obstacle information, and insufficient difference in the probability of each node. Therefore, this paper proposes an improved ant colony algorithmto solve these problems.In view of  that the traditional ant colony algorithm converges slowly and easily falls into local optimum, the distance correction function is introduced into the heuristic function by combining the basic concept of the evaluation function of A* algorithm; the obstacle avoidance function is introduced to make the heuristic function guide the ants forward more reasonably; the smoothness function is introduced to reduce the number of turns and control the turning angle of the planned path.The effectiveness and applicability of the improved algorithm are verified through two sets of experiments, and the improved ant colony algorithm meets the needs of emergency evacuation of actual ship personnel.Simulation and experimental results show that the method proposed can solve the randomness of turning path selection at the early stage of the algorithm, has better global search ability and convergence, and performs better in the number of iterations and computation time, and can provide efficient and safe evacuation plan in the shortest time, which meets the demand of emergency evacuation of actual cruise ship personnel.
A Study on Internet Use, Gender Concepts, and Full Employment of Women Based on the Ologit Model
Authors: Qiaoling Xu
Published: January 2025

Full employment is not only related to the healthy development of the labor market but also to the optimization of educational resource allocation and the normal functioning of the education system. Therefor, identifying the factors that affect females' full employment is particularly necessary. This study examines the state of full employment from the perspective of females' internet use. Based on data from the China Family Panel Studies (CFPS), this research constructs indicators for internet use and full employment, employing ordinal logistic regression (Ologit) to analyze their relationship. The findings reveal that internet use among females increases their likelihood of achieving full employment. Females who use internet more are less likely to experience educational mismatches, insufficient working hours, and lower labor compensation. Particularly, females with low levels of education, those living in rural areas, and married females are more significantly affected. Internet use influences full employment by steering females toward equal gender perception. The article emphasizes the importance of considering internet use and gender perception in females' full employment and advocates for the promotion of females' legal and equal labor rights.
Deep Learning Prediction Model of Drought and Flood in Summer Based on Random Forest and Attention Mechanism
Authors: Jie Li
Published: January 2025

The traditional linear statistical forecasting approach is frequently utilized to tackle the significant interannual variability and nonlinear traits of summer drought and flood patterns in Guangxi. However, its forecasting precision tends to be inadequate. In this research, the year-to-year change in the average precipitation for the month of August was employed as a means to predict trends in droughts and floods. The correlation factor between the forecast and the previous 500 hPa monthly average height field was calculated, yielding 81 preliminary forecast factors for the early monthly average circulation field. First, the random forest algorithm was utilized to assess and prioritize the significance of the 81 predictive elements and indicators. Subsequently, the top six crucial variable components were chosen to feed into the deep learning LSTM network's forecasting framework. Second, an attention mechanism was employed to provide different attention values to the input variables of the model. Third, a forecasting model for the year-to-year change in average summer rainfall in Guangxi was constructed, incorporating a random forest and an attention mechanism-enhanced LSTM network (RF-LSTM-Attention). Upon applying this predictive model to forecast the average summer rainfall for the eight-year period in Guangxi (June–August) from 2013 to 2020, the model exhibited an average absolute percentage error of 9.49%. The relative error of prediction in the six years, especially in the years with maximum and minimum precipitation in the eight-year return sample, did not exceed 15%. The RF-LSTM-Attention model was approximately doubled and showed better forecast accuracy in qualitative and quantitative forecasting.
Research on Coal and Gas Outburst Warning from Missing of Samples
Authors: Qinghua Chen
Published: January 2025

In order to improve the prediction accuracy of coal and gas outburst in the case of missing data, a coal and gas outburst prediction model based on MICE_NN interpolation algorithm and improved Pathfinder Algorithm (IPFA) optimized Extreme Learning Machine (ELM) is proposed. Firstly, the correlation analysis of various indicators affected by coal and gas outburst is carried out, and the MICE_NN algorithm is used to interpolate the missing values, which is easy to obtain more sufficient information from incomplete data sets and improve the prediction effect and accuracy of the model. Secondly, the Pathfinder algorithm is jointly improved by introducing the evolutionary boundary constraint processing scheme, Levy flight strategy and group fitness variance strategy to improve its global optimization ability, so as to optimize the relevant parameters of ELM and construct the coal and gas outburst prediction model. Finally, the measured data interpolated by MICE_NN are used as samples for experimental verification, and the proposed algorithm is compared with single machine learning and ensemble algorithms. The results show that the data quality based on MICE_NN interpolation is significantly better than the data without interpolation. The classification accuracy, recall rate and   of IPFA_ELM model based on MICE_NN interpolation are significantly higher than those of other comparison models. It provides a new idea and method for coal and gas outburst prediction, and provides a strong reference basis for the next step of gas outburst prevention and control.
Prediction of Soil Moisture in Grassland Based on Information Gain and Model Modification
Authors: Lizhu Yue
Published: January 2025

Accurate prediction of grassland soil moisture is the key to understanding and responding to the destruction of grassland ecosystems. In order to improve the prediction effect of grassland soil moisture, this study proposed a grassland soil moisture prediction model based on information gain and model modification based on multi-dimensional and small sample size data from a testing station in Xilingol Grassland. First, we conduct correlation analysis on the 22-dimensional feature data of Xilin Gol grassland based on the Pearson correlation coefficient, and screen out the 15-dimensional feature data that can represent the whole; then calculate the information gain of the features on soil moisture, and screen out the 6-dimensional high information gain features; Secondly, by introducing the evolutionary boundary constraint processing scheme, Levy flight strategy and group fitness variance strategy to jointly improve the pathfinder algorithm (CLPPFA), it improves its global optimization capability, thereby optimizing extreme learning machine (ELM) related parameters and constructing a grassland soil moisture prediction model, and use 6-dimensional high information gain feature data to predict the preliminary results of grassland soil moisture; Finally, by establishing an ARIMA error correction model, the error prediction value and the preliminary prediction value are superimposed to obtain the final prediction result. The results show that the improved extreme learning machine's fitting degree R2 for grassland soil moisture prediction is 0.937, which is better than the PFA_ELM model and the ELM model; the ARIMA model is introduced to analyze the error sequence of the preliminary prediction results of CLPPFA_ELM, and ARIMA (2,0,2) The model is error corrected. The prediction fit R2 of the CLPPFA_ELM-ARIMA model is 0.988, which is significantly improved compared to the prediction effects of the CLPPFA_ELM, SVR, RF, BP and ridge regression models. In summary, it is shown that the model has good fitting effect and generalization ability in grassland soil moisture prediction. This model provides model reference and technical support for formulating effective grassland management and protection strategies.
Research on Collaborative Filtering Recommendation Method Based on User Behavior Analysis
Authors: Xuewei Li
Published: January 2025

In online resource search systems, users who lack clear query or viewing objectives often have a certain degree of blindness. Users hope to obtain recommended items from other users with similar behaviors, because the filtered items are more likely to be accepted by new users. Based on this, the paper constructs a knowledge graph using the ontology concept architecture of the project as the knowledge representation foundation. It conducts in-depth research on user behavior around user behavior ontology, project resource ontology, user relationship graph instances, user similarity, and rating matrix completion, in order to find the target user's similar neighbor set. Based on the user's collaborative filtering recommendation algorithm, the predicted rating of the recommended project is calculated and Top-n projects are recommended to the target user.  The experimental evaluation results indicate that the collaborative filtering recommendation method based on user behavior analysis proposed in this study has good recommendation quality in online resource recommendation systems.
Investigating the Role of Lexical Bundles in Enhancing Argumentation Skills in EAP Writing
Authors: Nisar Ahmad Koka
Published: January 2025

This paper seeks to explore the research question: What is the critical aspect of lexical bundles that will improve the quality of arguments within written text in EAP? By centering on the rhetorical roles of stance, discourse-organizing, and referential bundles. Drawing on the SEM technique, the research investigates the intersections between lexical bundle knowledge with clarity, coherence and structural features of academic argumentation based on data from 150 post-advanced EAP learners. The study establishes that discourse-organizing bundles enhance contrast ratios and argument structures leading to enhancement of coherence of an argument, and stance bundles lead to an increase in the learning of clarity. While creating arguments, advanced proficiency learners demonstrated a better understanding of when to use the lexical bundles more effectively and, therefore, developed a better lexical argument. The study also showed that LRs mediate the conversion of linguistic competence into argumentation, which illustrates that lexical bundles are not only formulaic phrases; they are major organisational elements that define entry into the academic language domain. From a pedagogical perspective, the findings imply that, while the teaching of lexical bundles should move beyond rote instructions, more focus should be placed on the reflective use of the bundles for rhetorical purposes. This study adds to the knowledge base of language, power and academic writing, and can inform more beneficial approaches to EAP instruction.
Impact on Gamification on Learning and Teaching Languages: A Study among Ug Students
Authors: Aayesha Sagir Khan
Published: January 2025

Purpose: The study mainly focuses on conducting an analysis of the impact of gamification on the language learning process among UG students.
Methodology: The researcher has applied a mixed method of data analysis consisting of both primary and secondary quantitative data. With a sample of randomly selected 91 undergraduate students from 10 different universities in India, the researcher has focused on surveying with 16 close-ended questions. Their answers are gathered in an Excel sheet, and through the usage of IBM SPSS the researcher has conducted the primary study. On the other hand, depending on two themes regarding the aspects of different online gamifying platforms, the researcher has chosen two articles already published in 2021 and 2022, for analysing the necessary secondary data.
Results: The results show that almost 91.1% of participants are aware of Gamification in language learning. Most students have agreed that their grammar skills have developed based on gamification. Moreover, a high dependency is seen on the role of gamifying simulative platforms in enhancing the lingual problem-solving skills of the students, with a value of 0.843.
Extended TAM Model Analysis of Continuous Use Factors and Psychological Well-Being on University Learning Platforms
Authors: Jinlei Li, Meilin Jin, Xiaowei Chen
Published: January 2025

This study investigates the elements inspiring the continuous usage of intelligent learning systems in advanced educational environments emphasizing technological and psychological aspects. The present work investigates how perceived trust, perceived enjoyment, and ideological alignment affect user involvement and retention using an updated Technology Acceptance Model (TAM). Moreover, taken under more importance as a better understanding of user behavior are psychological elements such as user contentment, perceived fear, and expectation confirmation. Structural equation modeling (SEM) was used to evaluate data taken from a structured questionnaire. The study underscores the relevance of user experience and shows that, with a path coefficient = 0.52, p < 0.001, subjective satisfaction is the most important factor of continuous usage intention. Path coefficient = -0.13, p = 0.05 indicates that perceived trust negatively affects contentment mostly because of unmet expectations generating discontent. Though expected confirmation increases perceived usefulness (path coefficient = 0.31, p < 0.01) system quality does not affect on satisfaction (path coefficient = 0.05, p > 0.05). The outcomes highlight the requirement of platform developers to give realistic management of customer expectations top priority, matching platform features with user requests, and creating interesting and engaging user experiences a top priority. These pragmatic findings provide fast fixes to boost user participation and improve the utilization of intelligent learning technology in learning settings.
Cryptographic Techniques for Secure IoT Data Transmission
Authors: Sanjivani Hemant Kulkarni
Published: January 2025

The rapid expansion of the Internet of Things (IoT) has revolutionized industries by enabling interconnected devices to communicate and exchange data seamlessly. However, the transmission of data within IoT networks faces significant security challenges, including vulnerabilities to eavesdropping, man-in-the-middle attacks, and data tampering. This paper explores the role of cryptographic techniques in securing IoT data transmission. It provides an overview of IoT architecture, common communication protocols, and the various security threats that compromise IoT data. The research focuses on cryptographic methods such as symmetric and asymmetric encryption, as well as lightweight cryptography, which are tailored to the resource constraints of IoT devices. Additionally, the paper examines key management protocols and post-quantum cryptography as emerging solutions to future IoT security challenges. Through case studies and performance analysis, the paper highlights the importance of cryptographic techniques in ensuring confidentiality, integrity, and authenticity in IoT data transmission. This research contributes to the understanding of how secure cryptographic solutions can safeguard the evolving landscape of IoT ecosystems.
Blockchain-Based Supply Chain Security: Mitigating Fraud and Cyber Attacks
Authors: Vivian Brian Lobo
Published: January 2025

The increasing complexity of global supply chains has made them susceptible to fraud, cyber-attacks, and other security vulnerabilities. Traditional supply chain systems often rely on centralized structures that are prone to data breaches, counterfeit goods, and manipulation of records. Blockchain technology, with its decentralized, transparent, and immutable ledger system, offers a potential solution to these security challenges. This paper explores the role of blockchain in enhancing supply chain security by mitigating fraud and cyber-attacks. Through an analysis of case studies and real-world implementations, the paper highlights how blockchain can ensure product authenticity, improve traceability, and secure data integrity. Additionally, the study examines the limitations of adopting blockchain in supply chains, including technological, financial, and regulatory challenges. The findings suggest that while blockchain offers significant advantages for supply chain security, widespread adoption requires overcoming several barriers. The paper concludes with recommendations for policy changes and technological advancements to facilitate blockchain integration in supply chains, ultimately enhancing their resilience against security threats.
Cloud Security in Edge Computing: Addressing Data Privacy Concerns
Authors: Lakhwinder Kaur
Published: January 2025

The integration of cloud and edge computing has revolutionized data processing by enabling low-latency and efficient handling of data at the network's edge. However, this shift has introduced significant data privacy challenges, particularly due to the decentralized nature of edge computing. This paper explores the core security concerns related to data privacy in cloud-edge ecosystems, focusing on the vulnerabilities introduced by the distribution of data across edge nodes. Current cloud security practices, such as encryption and access control, are assessed for their effectiveness in securing edge environments. The paper also examines existing privacy-preserving technologies, such as federated learning and lightweight encryption, and identifies gaps in their application to edge computing. In response, the study proposes decentralized security models and enhanced data privacy mechanisms tailored to the unique requirements of edge networks. Through a review of case studies in IoT, healthcare, and autonomous systems, this paper offers practical insights into improving data privacy in edge computing environments.
Cyber Security Policy for the Protection of Critical National Infrastructure
Authors: Anjali Shrivastav
Published: January 2025

The increasing reliance on digital technologies across critical national infrastructure (CNI) sectors has exposed these vital systems to a growing range of cyber threats. Effective cybersecurity policies are essential to ensure the protection and resilience of infrastructure that supports national security, economic stability, and public well-being. This paper examines the evolving threat landscape facing CNI and assesses the current cybersecurity frameworks and policies in place. It explores the vulnerabilities inherent in interconnected systems and discusses key strategies for mitigating these risks. Emphasis is placed on risk assessment, public-private partnerships, and the role of emerging technologies like artificial intelligence and blockchain in enhancing cybersecurity. The paper also addresses legal and ethical challenges, particularly regarding privacy and surveillance concerns. Based on a comprehensive review, recommendations are proposed for strengthening cybersecurity policies to safeguard CNI, including improved incident response mechanisms, workforce development, and international cooperation. This study underscores the need for a robust, adaptive, and forward-looking approach to cybersecurity in an increasingly digital world.
Mitigating Cyber Fraud in E-Commerce: Emerging Technologies and Trends
Authors: T.K.Shaik Shavali
Published: January 2025

The rapid growth of e-commerce has made it a prime target for cyber fraud, posing significant threats to businesses and consumers alike. This paper explores emerging technologies and trends designed to mitigate cyber fraud in e-commerce. With the increasing sophistication of cybercriminals, traditional security measures such as encryption and two-factor authentication are no longer sufficient. The study highlights the role of cutting-edge technologies like artificial intelligence (AI), machine learning (ML), blockchain, and biometrics in detecting and preventing fraudulent activities. AI and ML algorithms can analyze vast amounts of data in real-time, identifying anomalous patterns and predicting fraudulent behavior with high accuracy. Blockchain technology, with its decentralized nature and cryptographic security, offers transparency and immutability in transactions, reducing the risk of fraud. Biometrics, such as fingerprint and facial recognition, add an additional layer of security for identity verification. Furthermore, the paper discusses emerging trends, including the integration of AI-driven Chatbots, behavioural analytics, and tokenization, which enhance fraud detection and prevention. The study concludes that a multi-layered approach combining these technologies is essential for ensuring secure and fraud-free e-commerce environments, safeguarding user trust and protecting businesses from financial losses.
Automated Model Management for Microservices: A CI/CD Approach
Authors: Wangdong Wu
Published: January 2025

The rapid advancement of microservices architecture in software and internet applications has introduced significant challenges in managing distributed systems, particularly in maintaining operational efficiency and real-time responsiveness. This paper presents an innovative automated encapsulation and deployment framework tailored to address the frequent updates required by data-driven models in dynamic microservices environments. By integrating Docker containerization with Continuous Integration/Continuous Deployment (CI/CD) practices, the framework streamlines model management processes, enhancing both adaptability and operational efficiency. Leveraging modern tools such as Kubernetes and GitLab, we developed and tested a prototype system that facilitates rapid updates and robust management of intelligent models, ensuring their responsiveness to evolving data patterns. Empirical evaluations demonstrated substantial reductions in model deployment times and improved responsiveness to business requirements, while also identifying critical bottlenecks in traditional packaging and deployment methods. Our findings underscore significant improvements in operational efficiency and reduced manual intervention for model updates, with further optimizations to minimize deployment times and resource consumption. This research contributes a scalable and efficient solution for managing the lifecycle of models in microservices environments, addressing key challenges such as service granularity, dependency management, and rapid deployment needs, thereby enhancing overall system performance and reliability.
Multi Objective Fuzzy Energy Management Strategy of Hybrid Electric Vehicles
Authors: Bo Li
Published: January 2025

To address the shortcomings of the current lithium battery-supercapacitor composite energy electric vehicle in a single fuzzy control measure, a multi-fuzzy joint control energy management measure is put forward and designed. Combining with the actual parameters of the experimental rig, the whole system model is constructed in MATLAB context, and the fuzzy square wave regulation control strategy, the power distribution factor fuzzy control strategy and the improved fuzzy square wave regulation based joint control strategy are compared and analyzed by ECE and UDDS operating conditions, and finally the fuzzy square wave regulation based joint control strategy with the best effect is selected to be embedded in the experimental rig for verification. According to the experimental simulation outcomes, the control strategy herein can achieve smooth control of lithium battery charging and discharging current within 1C under different SOC in both test conditions, so it is profit to the safe operation of lithium battery pack and effectively reduce the driving cost of the whole vehicle control system.
A Transfer Learning Based Approach to Automatic SQL Translation
Authors: Wensheng Tang
Published: January 2025

Limitations of Oracle database in business support, development, operation, and maintenance hinder marketing 2.0's growth. Translating Oracle SQL statements to domestic databases is vital. A neural machine translation model based on migration learning resolves the lack of parallel corpus between Oracle and domestic databases. Encoder and decoder parameters of both databases' models are initialized using a trained Oracle database-side encoder and domestic database-side decoder. Fine-tuning and optimization produce high-quality parameters, improving translation performance. Results showed that the bilingual evaluation understudy (BLEU) of model reached 31.20 and an execution accuracy value of 84.3, outperforming the Transformer model by 13.28 and 13.3, respectively. Demonstrate support for database migration.
Research of Infant Voice Pitch Based on Speech Analysis Technology
Authors: Lei Guo
Published: January 2025

This paper takes the pitch of infants' speech as the research object, chooses 4 infants as the research target, studies the pronunciation of infants before the age of two. The infant speech database was established, and the infant speech analysis platform was developed, including automatic segment segmentation of speech samples and analysis of related acoustic parameters. After two years of tracking and recording, the characteristics and rules of phonetic pitch pronunciation of infants in pre-language stage and language stage were studied by the method of speech experiment. This study expands the vision of infant phonetics research and summarizes the phased characteristics and rules of infant phonetics development on the basis of large corpus analysis.
A Fast Pattern Matching Algorithm Based on Middle Characters of Pattern String
Authors: Weiqiang Ma
Published: January 2025

String pattern matching is one of the important string operation. At present, the pattern matching algorithm of strings mainly includes BF algorithm, KMP algorithm, and improved KMP algorithm, BM algorithm etc. Drawing from the study of current pattern matching algorithms and practical programming experience, this paper presents a refined pattern matching algorithm that leverages the middle character of the pattern string. The enhancement offered by this pattern matching algorithm lies in its departure from the traditional approach, which typically involves sequential comparison and matching starting from the first character. This paper introduces a fast pattern matching algorithm that focuses on the middle character of the pattern string. By locating this central character within the target string and initiating the comparison and matching process from there, the algorithm significantly decreases the number of character comparisons required, thereby substantially enhancing the efficiency of the matching process. After repeated verification by program practice, The algorithm reduces the quantity of character comparisons while achieving correct matching, improves the matching efficiency, and effectively reduces the time complexity of the algorithm.
Application of Computerized 3D Human Model Training Technology in Sports Movement Correction
Authors: Li Baofeng
Published: January 2025

In order to improve the ability of judging and correcting wrong movements in sports training, a 3d modeling and detection method of bad movements based on computer-aided 3d human body model feature decomposition is proposed. The motion behavior is analyzed by using machine vision image processing method, and the image acquisition model of motion error actions is constructed. The multi-dimensional wavelet scale decomposition method is used to decompose the collected motion error action images at pixel level, and the gray outline model of motion error actions is constructed, and the three-dimensional feature quantity of motion error actions is extracted. The three-dimensional modeling detection of motion errors is realized according to the three-dimensional human model feature decomposition method. The simulation results show that the accuracy of the 3d modeling detection of sports error actions is high, the detection error rate is low, and the real-time correction capability of the error actions is strong.
Research on Computer Algorithm for Drawing Pencil Drawing of Cold Landscape Based on Improved Neural Network Algorithm
Authors: JiaJia Li
Published: January 2025

In order to face the cold landscape image, the pencil sketch image is drawn by computer algorithm. This article uses an improved neural network to complete the above process. First, we first propose a pencil drawing image generation method based on multi-level visual saliency maps. Subsequently, the corresponding stroke generation, tone mapping and texture synthesis methods are proposed to form an automatic pencil drawing generation framework, which has universal practical significance. Finally, we improved the cold landscape pencil sketch simulation method based on line integral convolution, and proposed a A new mixed noise model that replaces the random binary white noise used in the traditional pencil sketch simulation method based on line integral convolution, and also proposes a new objective evaluation measure to evaluate different noise models; experimental results It shows that the image generated by the method proposed in this paper is more layered and artistic, and closer to the painter’s paintings. Experiments show that the composite frame obtained by this method can quickly convert the photo into a pencil drawing artistic style image, and the effect is vivid and lifelike.