bluuebunny/crossref_metadata_2025 huggingface dataset:
Optimal Design Planning for Minor Irrigation Structures in Jalpaiguri District of West Bengal
Authors: R. K. Singh  , B. C. Mal
Published: May 2024

A study was undertaken to assess and utilize the water resources in nine administrative blocks of Jalpaiguri district in West Bengal by allocating the land and water effectively. Gated concrete structures were designed on the basis of peak period discharge to harness and use the surface water effectively. Non-scouring canals were also designed on the basis of lean (Dry) period discharge. Hydrologic, hydraulic and structural designs of the structures were made using relevant information on topography, soil, rainfall, HFL etc. The dry period discharge in different streams varied from 0.03 to 3.756 m3/s, which can be used as minor irrigation sources by constructing diversion structures and canals. The surface runoff in different streams varied from 21.24 to 111.08 Mha-mm. Part of water can be stored for irrigation in the dry period. Gated structures designed for water diversion will be helpful in combating the siltation problem.
Happiness and its Relationship with Academic Achievement of Senior Secondary Students
Authors: Kumar Narendra , Sahoo Pragyan Nibedita
Published: May 2024

This research aims to study the level of happiness and its relationship with the academic achievement of senior secondary students studying in government and private schools of Jaipur province in Rajasthan. For this purpose, 157 students studying in class XI in government and private schools in both rural and urban areas of Jaipur province were randomly selected. The sample consists of 78 male and 79 female students. Results show that most of the students have an average level of happiness. Female students are happier than male students. Besides this, the happiness level of senior secondary students is positively correlated with their academic achievement, however, there is no significant correlation between the two.
Talent Management Practices in Higher Educational Institutions- A Bibliometric Analysis
Authors: Behera Rasmita , Behera Manasee
Published: May 2024

Talent management practices are becoming crucial in educational institutions. These practices seek to discover and develop the talents and abilities of educators, staff members and students, ensuring that the institution can deliver quality education while remaining competitive. The study attempts to identify important topics, trends, and notable publications in this field. The analysis covers numerous sources during a pre-determined period (2018-2022), including books, conference proceedings, and scholarly journals. The Bibliometric analyses were conducted using data from the Scopus database (Total Scopus Data=55), VOSViewer, the Bibliometrics library, and the Biblioshiny platform of the RStudio® software. The results show that several themes like significant authors, publications, references, institutions, and nations’ information have emerged as essential elements of talent management in educational settings. We also analyse the geographic distribution, regional differences and possible research areas for cross-cultural comparisons. This study analyses the literature on these themes and contributes to our understanding of how to handle talent management in educational institutions.
Aplikasi Rusa (Rumah Sayur) untuk usaha Katering Kelas Menengah
Authors: Calvin Thiodorus , R. Syah Tantri Yanuar , Hamdi Edi , Ruswanti Endang
Published: May 2024

Aplikasi Ruma Sayur, yang berfokus pada bisnis catering, menyadari perubahan paradigma dalam makanan yang tidak hanya sekadar enak dan bergizi, tetapi juga harus menyehatkan. Dalam penelitian ini, digunakan metode kualitatif dengan pendekatan deskriptif analitis. Untuk mengumpulkan informasi, peneliti menggunakan tiga teknik pengumpulan data, yaitu observasi, wawancara mendalam, dan analisis dokumen. Hasil penelitian menunjukkan beberapa aspek penting. Dalam aspek pemasaran, pengenalan produk dan pemahaman tren pasar sangat krusial. Keterbatasan promosi dapat membatasi jumlah penjualan yang dicapai. Dalam aspek operasional, perencanaan yang matang dalam alur produksi diperlukan untuk menciptakan efisiensi. Manajemen sumber daya manusia harus memperhatikan analisis pekerjaan dan proses rekrutmen untuk mendapatkan SDM yang sesuai dengan kebutuhan usaha. Aspek keuangan juga menjadi fokus, mengingat pentingnya keuangan sebagai pondasi usaha. Namun, terdapat keterbatasan modal untuk usaha baru. Kesimpulannya, setiap kegiatan bisnis memiliki risiko yang menyertainya, dan pengelolaan risiko harus mencakup identifikasi, pengukuran, mencari solusi alternatif, serta pemantauan dan evaluasi yang berkelanjutan.
Existence and multiplicity of solutions for a Steklov eigenvalue problem involving the p(x)-Laplacian-like operator
Authors: Boukhsas Abdelmajid , Karim B. , Zerouali A. , Chakrone O.
Published: May 2024

Using the variational method, we prove theexistence and multiplicity of solutions for a Steklov problem involving the $p(x)$-Laplacian-like operator, originated from a capillary phenomena. Especially, an existence criterion for infinite many pairs of solutions for the problem is obtained.
On the cotangent bundle with vertical modified riemannian extensions
Authors: Ocak Filiz
Published: May 2024

Let $M$ be an n-dimensional differentiable manifold with a torsion-free linear connection $\nabla $ which induces on its cotangent bundle ${T^*}M$. The main purpose of the present paper is to study some properties of the vertical modified Riemannian extension on ${T^*}M$ which is given as a new metric in [17]. At first, we investigate a metric connection with torsion on ${T^*}M$. And then, we present the holomorphy properties with respect to a compatible almost complex structure. urthermore, we study locally decomposable Golden pseudo-Riemannian structures on the cotangent bundle endowed with vertical modified Riemannian extension.
Introduction to gradient h-almost η-Ricci soliton warped product
Authors: Bhunia Nandan , Pahan Sampa , Bhattacharyya Arindam , Datta Sanjib Kimar
Published: May 2024

In this paper, we introduce the new concept of gradient h-almost η-Ricci soliton. We discuss here a steady or  expanding gradient h-almost η-Ricci soliton warped product Bn ×f Fm, m 1. We show that the warping function f of this warped product attains minimum as well as maximum and it will definitely be a Riemannian product under certain conditions. We also describe some suitable restrictions to these constructions for the compact base of this warped product. Later, we study h-almost η-Ricci soliton and gradient h-almost η-Ricci soliton on warped product manifolds including a concurrent vector field.
Certain results of $(LCS)_{n}$-manifolds endowed with $E$-Bochner curvature tensor
Authors: Kumar R. T. Naveen , Reddy Polaepalli Siva Kota , Venkatesha
Published: May 2024

In this paper, we study geometry of $(LCS)_{n}$-manifold focusing on some conditions of $E$-Bochner curvature tensor. First, we describe an $E$-Bochner pseudo-symmetric $(LCS)_{n}$-manifold is never reduces to $E$-Bochner semi-symmetric manifold under the condition ($(\alpha^{2}-\rho)\neq0$). Next, we characterize certain results of $(LCS)_{n}$-manifold satisfying $B^{e}(U,V)\xi=0$, $B^{e}(\xi,V)\cdot B^{e}=0$ and $B^{e}(\xi,V)\cdot S=0$.
Research on Data-Driven Fault Scenario Simulation Model of Distributed Network
Authors: Ming Ma
Published: May 2024

Distribution lines are critical components of today's power system, and can directly impact on power supply security and stability. An effective power system protection system should detect any faults as soon as they occur. There are two steps in fault diagnosis. One is defect classification, which has already achieved excellent accuracy rates. As a result, this study concentrates on the opposite objective, which is fault location. This research introduces a War Strategy Water Wave Optimization (WSWWO) based Deep Q Network for radial distribution networks using synchronous generators for Distributed Generation (DG).  The algorithm estimates the fault site by analyzing voltage and current samples at the main feeder head, and scheduled active and reactive power injections by network synchronous generators. A full-order synchronous machine model was utilized to analyze the dynamic behavior of DG plants during fault transients. Here, the Deep Q Network is trained by WSWWO, and hybridized by two optimization algorithms, War Strategy Optimization (WSO) and Water Wave Optimization (WWO). Furthermore, the experimental results revealed that the WSWWO-Deep Q Network outperformed state-of-the-art models in terms of Accuracy and Error, with values of 0.997 and 0.141, respectively.
Video Analysis of Physical Education Teaching and Design of Intelligent Assistant Teaching System: Based on Motion Recognition and Optimization Algorithm
Authors: Haolin Ma
Published: May 2024

Nowadays, science and technology has rapidly increasing due to the specific requirements of education. The main aim of this technique is to enhance the physical education activities and helps sports man to finest teaching facilities of these activities. Moreover, the body changes with sports activities based on Deep Learning (DL) frameworks and classifier technology are examined to promote the practical application. Therefore, this paper Deep Reinforcement Learning based Chimp optimization (DRLbCO) algorithm was developed to improve the teaching efficiency of physical education system through the intelligent computer aided assessment models. Also, this model improving the performance, enhancing coaching effectiveness, and gaining competitive advantages in sports. Consequently, the proposed system is based on DL based image detection model DRL, which can analyse and provides proper actions in teaching students and enhance the ability of motion analysis in sports video training quality.  Moreover, the DL model is integrated with optimization algorithm to optimize the performance. This could involve reinforcement learning, and other classifiers such as Support Vector Machine (SVM). Based on this provides feedback to students and teachers during physical education classes. The developed model is significantly enhances the physical education teaching student action in virtual sports instruction. Overall intention of the video assessment system of the sports training performance in analyzed.
Research on the Data-Driven Differential Equation-Solving Algorithm Based on Artificial Intelligence
Authors: Guoxing Si
Published: May 2024

Data-driven DEs has gained popularity in the past few years. This work proposes the new framework, named Adam Gannet Optimization Algorithm (AdamGOA), that combines Adam Optimization and Gannet Optimization Algorithm (GOA) to improve a stability, solve higher order Differential Equations (DE) and accuracy of DE. Adam is a first-order gradient-based methods, optimizes stochastic objectives using adaptive lower-order moments. In contrast, GOA represents a different distinct action of a gannets mathematically during foraging and is employed to facilitate exploitation and exploration. In addition, a Shepard Convolutional Neural Network (ShCNN) processed data to construct meta-data and estimate derivatives. After that, the unified integral form is established to determine optimal structure. Heterogeneous parameters are used to estimate and are labeled as constants or variables. Furthermore, the experimental findings showed that the AdamGOA_ ShCNN beat leading models in Accuracy, Convergence, and Mean Square Error (MSE), with values of 0.989, 4, and 0.539, respectively.
A Study on the Application of Virtual Reality Technology in Engish Language Education Relying on Immersive Learning Environments and Interactive Experience Design
Authors: Lixin Wang
Published: May 2024

This study delves into the application of virtual reality (VR) technology in the realm of English language education, with a focus on immersive learning environments and interactive experience design. The primary objective is to explore the efficacy of incorporating VR technology to enhance the English language learning experience for students. The research investigates the potential of immersive learning environments provided by VR technology and the impact of interactive experience design on language acquisition. The study aims to contribute valuable insights into how these technological advancements can revolutionize English language education, offering an engaging and effective approach to language learning. Through a thorough examination of the immersive and interactive aspects of VR technology, the research seeks to provide a foundation for the integration of these technologies into English language education practices, ultimately paving the way for more innovative and engaging language learning experiences.
Automatic Generation Technology of Scrap Denim Clothes in Quilting Art Patterns by Integrating Computer Vision.
Authors: Jinlong Yuan
Published: May 2024

The combination of computer vision and advanced Fuzzy C-Means algorithms, such as Hybrid OK-Means Fuzzy C-Means (HOFCM), provides a revolutionary way to automatically generate technology of Scrap Denim Clothes in Quilting Art Patterns. The research focuses on the creation of an automatic generating technique specifically designed for making quilting designs from scrap denim clothing. This study investigates the effectiveness of HOFCM in improving the process of creating quilted art patterns from discarded denim materials. Extensive experimentation and analysis have shown that HOFCM dramatically decreases computational time while improving clustering solution quality, particularly when dealing with huge, high-dimensional datasets like scrap denim cloth analysis. The study emphasizes HOFCM's potential to speed up the quilting art pattern generation process, providing designers and artists with a more efficient and precise way to transform old denim clothing into visually appealing designs. The findings highlight HOFCM's transformative impact on automatic generation technology for quilting art patterns, paving the path for innovation and originality in textile art and design. HOFCM significantly lowers time by up to 93.94% compared to regular Fuzzy C-Means on four real and one synthetic datasets. The worst-case scenario resulted in a 2.51% reduction in solution quality.
Construction Path of Rural Study Base Based on Service Design Concept
Authors: Bei Wang
Published: May 2024

The establishment of rural study bases offers a promising opportunity to improve educational opportunities and community development in remote areas. This research investigates the development path of rural study bases using service design concepts that emphasize user-centric methods, stakeholder interaction, and iterative improvement procedures. Understanding rural communities' particular needs and goals allows remote study bases to plan and implement meaningful and impactful educational programs and services. Rural study bases strive to build favourable learning settings that encourage active engagement and cooperation by combining physical infrastructure development, digital resource integration, and community outreach efforts. This paper, using examples from diverse industries and situations, discusses essential issues and best practices for efficiently adopting service design principles in the establishment of rural study bases. Finally, rural studies based on service design concepts show promise for improving socioeconomic outcomes and empowering rural people through education. As an outcome, it provides high user satisfaction (94%), effective community outreach, excellent digital resource integration, enhanced academic performance, and strong stakeholder engagement
Adaptive System of English-Speaking Learning Based on Artificial Intelligence
Authors: Lili Qin
Published: May 2024

Delve into the innovative realm of adaptive systems for English-speaking learning, underpinned by artificial intelligence (AI). Proficiency in spoken English is increasingly pivotal in today's interconnected world, spanning across professional, academic, and social spheres. However, conventional methods of language learning often struggle to accommodate the diverse needs and learning styles of individual learners. Enter AI-powered adaptive systems, offering a transformative approach to language education. These systems harness the capabilities of machine learning algorithms and natural language processing to deliver personalized learning experiences. Through real-time analysis of learners' performance, they provide targeted feedback and dynamically adjust learning materials to suit individual preferences and abilities, focusing on areas such as pronunciation, fluency, grammar, and vocabulary usage.
Moreover, the adaptive nature of AI-based systems revolutionizes the learning process, allowing for continuous progress monitoring and adaptation to learners' evolving needs. Unlike traditional classroom settings with standardized pacing, adaptive systems empower learners to progress at their own pace, honing in on areas that require improvement while bypassing mastered concepts. This personalized approach not only enhances learning efficiency but also fosters motivation and engagement by aligning with learners' interests and goals. By bridging the gap between individual learning needs and traditional educational frameworks, AI-powered adaptive systems represent a groundbreaking advancement in language education, promising to make English-speaking learning more accessible, effective, and enjoyable for learners worldwide.
Analysis and Modelling Of English Learning Behaviour Based on Data Mining Technology
Authors: Jing Zhang
Published: May 2024

This paper presents an in-depth exploration into the analysis and modelling of English learning behaviour leveraging data mining technology. In today's digital age, understanding the intricacies of how individuals acquire a second language is paramount for optimizing educational strategies and enhancing learning outcomes. By employing advanced data mining techniques, we aim to uncover patterns, trends, and underlying dynamics within English learning datasets, shedding light on the diverse behaviours exhibited by learners. Furthermore, this research transcends analysis, as we endeavour to construct robust models capable of simulating and predicting English learning trajectories. By leveraging insights derived from extensive data exploration, we aim to develop predictive models that offer personalized recommendations and adaptive learning pathways tailored to individual learner profiles. Ultimately, this paper contributes to the ongoing discourse surrounding language acquisition by providing valuable insights into English learning behaviours. By harnessing the power of data mining technology, we aspire to empower educators, policymakers, and digital learning platforms with the tools necessary to cultivate more efficient and inclusive approaches to English language education.
Development of English Composition Correction and Scoring System Based on Text Similarity Algorithm
Authors: Xin Yan
Published: May 2024

This study focuses on creating an innovative system that leverages a text similarity algorithm to provide accurate correction and scoring of English compositions. By harnessing advanced computational techniques, the system aims to streamline the grading process and offer more consistent feedback to students. The research methodology involves the design and implementation of the text similarity algorithm, which analyzes various linguistic aspects such as vocabulary usage, grammar, coherence, and organization. This algorithm is then integrated into a comprehensive scoring system that evaluates student compositions against reference texts and provides detailed feedback on areas for improvement. The effectiveness of the system is validated through rigorous testing using a diverse set of English compositions and comparison with human grading. The results of this investigation show how well the system that was created can automatically correct and score English papers with a high degree of correctness and dependability. By reducing the burden of manual grading and providing immediate feedback to students, the system has the potential to enhance the teaching and learning process in English language education. Overall, by developing a scalable and effective method for compositional assessment based on text similarity algorithms, this research advances educational technology.
“Research on Music Emotion Analysis and Dance Creation Based on Neural Network”
Authors: Jiexia Wu
Published: May 2024

The one-of-a-kind avenues for self-expression that dancing provides as a form of physical art. Movements of the body, both internal and external, and other forms of expression make up the bulk of a dancer's lexicon. Through passive observation, the viewer is able to catch up on the dancer's intended meaning through their body language. It's the one-of-a-kind way that each dancer finds to convey feeling and story through their body. In this piece, we intelligently oversee dance training and take on challenging nonlinear control problems by combining AI technology and the BP neural networks (BPNN) approach.In this study, the training dance teachers receive in dance language, talking music for dancing, and stage art was evaluated using the BPNN technique and the PCA-BPNN strategy. Median accuracy for the BPNN research model is 85.35 percent as age 80 approaches, whereas for the PCA-BPNN research model it is just 65.64 percent. This demonstrates that the BPNN grading model provides more precise results than the PCA-BPNN model. Using BPNN algorithm-based dancing performed within the context of artificial intelligence technology is one way to attain two goals: the spread and pleasure of beauty, and the harmonious combination of athletics and the arts. These two aims are not mutually exclusive.
Audit Risks and Prevention Strategies of Corporate Accounting Based on Computer Technology
Authors: Qigao Wu
Published: May 2024

Business owners use professional auditors to do audits on their companies because auditors are essential partners in the growth of enterprises. Auditors may improve their ability to organize their audit work sensibly and provide accurate audit opinions if they effectively identify the risks associated with the audit. In this day and age of big data and the World Wide Web, businesses produce a significant quantity of data through the normal course of their activities. Using data mining techniques, deep learning, neural networks, and other developing technologies to extract excellent auditing data from the massive amounts of data produced by audited businesses is a significant challenge for auditors. As a result, the purpose of this research is to use computer data mining methods in order to develop an audit risk model. This model will give an example for auditors to use when doing big data analysis and will mine important data, which will ultimately result in an improvement in the audit process's efficiency as well as its correctness.
Exploration of English Professional Talent Cultivation under the Perspective of Computer Training System and Digital Humanities
Authors: Yimei Wang
Published: May 2024

That use of electronic developments within speech instruction for that promotion of multidisciplinary study was explored within the context of a computerized training curriculum for the modern humanities. This technique may help individuals improve their analytical pondering, interaction, and crisis solution abilities, which will make them more marketable in today's competitive, international professional environment. In order to succeed in today's tech-driven society, professionals working with the English language should make use of computer-based educational methods and digitized antiquities. Its research provides an integrated example of the potential effectiveness of using such an exploratory educational package to help ESL university students acquire business interaction skills with an entrepreneurial mentality. Main research examines how pupils' involvement attitudes and perspectives regarding respect topic "workplace attitudes of thought" are affected by a one-time education situation, using the 5E cognitive method for its educational basis. The five-step framework consists of the following steps: introduction, investigation, presentation, development, and assessment. Further, every stage of education utilized education practices including academia-industry joint imparting a business outdoor journey, self-reflection, and task-based presentations to place an emphasis on the growth of pupils' interpersonal, shared, and ability to solve problems. Students' opinions on the usefulness of interaction exercises have been collected quantitatively and qualitatively via monthly diary entries and conversations with small groups of students. The research found that using an inquiry-driven method of education and studying helped pupils improve their involvement and practical and abstract abilities, including reasoning, teamwork, and language.
Image Compression Using Improved Method
Authors: Atya Baidaa A Ibrahim
Published: May 2024

Effective use of digital images requires certain techniques to lower the number of bits needed to represent them. The goal of compression is to reduce the size of an image while maintaining its information and originality. This research aims to achieve this target by developing a fuzzy logic and histogram-based image compression type detection approach. The main goal is to provide an approach that employs fuzzy logic and graphs for detecting the type of image compression. In order to decide the compression technique depending on the number of color levels in the image which was recovered with the use of histogram, the suggested technique combines a new combination of fuzzy logic as well as image histogram analysis. The experimental findings demonstrated the suggested technique's robustness and efficiency in detecting the best approach for each type of image compression. After 75 images with varying color densities were evaluated, the system's compression rate was approximately 95%. In addition to being a significant contribution to image processing, the study finds practical applications in the following areas: remote sensing through satellite; facsimile transmission of medical images in computer tomography magnetic resonance imaging; teleconferencing systems; military communication systems via radars; geological surveys; and communications systems built by computers.
Multilayer Neural Network Hardware Designs for Faster Convolutional Processing
Authors: Aayushi Arya
Published: May 2024

The demand for efficient hardware implementations of convolutional neural networks (CNNs) has surged with the proliferation of deep learning applications in various domains such as computer vision, natural language processing, and autonomous systems. Convolutional layers, being the fundamental building blocks of CNNs, are computationally intensive, requiring optimized hardware architectures for real-time inference tasks. This paper presents novel hardware designs targeting faster convolutional processing within multilayer neural networks. We propose a multi-faceted approach that leverages parallelism, pipelining, and hardware acceleration techniques to enhance the efficiency of convolution operations. Our design optimally exploits the inherent data-level and model-level parallelism present in CNNs to achieve high throughput while minimizing latency.
Research on Term Recognition in the Field of English Linguistics Based on Deep Learning Algorithms
Authors: Zhu Shanshan
Published: May 2024

Linguistic recognition, a fundamental aspect of natural language processing, has seen significant advancements with the integration of deep learning algorithms. his paper presents a novel approach leveraging deep learning algorithms, specifically an optimized Long Short-Term Memory (LSTM) model, for term recognition tasks in English linguistics. This paper presents a novel approach leveraging deep learning algorithms, particularly Weighted Genetic Optimization Deep Learning (WGODL), for term recognition tasks in English linguistics.  his hybrid approach not only enhances model accuracy but also improves robustness and generalization, enabling accurate term recognition even in noisy or ambiguous linguistic contexts. The weighted genetic optimization mechanism strategically prioritizes relevant features during the learning process, further enhancing the model's ability to discern domain-specific terms. Additionally, qualitative analysis highlights the interpretability of learned representations, shedding light on the underlying linguistic structures captured by the model. By harnessing the sequential nature of language data, the proposed model integrates LSTM units with optimization techniques to effectively capture contextual dependencies and semantic relationships within text corpora. Through extensive experimentation on diverse linguistic datasets, our optimized LSTM model achieves superior term recognition accuracy, surpassing traditional methods by 8.3%.
Application of Interdisciplinary Knowledge Integration in English Blended Education
Authors: Chen Licong
Published: May 2024

Blended education in English combines traditional face-to-face teaching methods with online learning components, offering students a dynamic and flexible educational experience. It integrates technology into classroom instruction, providing access to digital resources, interactive learning platforms, and multimedia content. This approach allows for personalized learning experiences tailored to individual student needs while promoting collaboration, critical thinking, and digital literacy skills. Blended education in English fosters a conducive learning environment that combines the benefits of both traditional and online learning, enhancing student engagement and outcomes in English language acquisition and comprehension. This paper explores the potential of Cooperative Optimized Blended Education (COBE) to enhance interdisciplinary knowledge integration within English-blended education settings. Drawing upon theoretical frameworks such as game theory and optimization strategies, alongside empirical analyses and simulation results, the study elucidates the effectiveness of COBE in fostering collaborative learning outcomes. Through the utilization of digital resources, personalized learning paths, collaborative projects, blended assessment methods, and teacher training programs, COBE offers a dynamic and engaging approach to pedagogical innovation. Empirical findings and student performance data underscore the significance of COBE in promoting student engagement, knowledge retention, and interdisciplinary understanding. Furthermore, the paper highlights the importance of continuous monitoring and assessment to support student academic achievement within COBE. OBE resulted in a mean increase of 10% in student engagement, a 15% improvement in knowledge retention, and a 20% enhancement in interdisciplinary understanding. Furthermore, the paper emphasizes continuous monitoring and assessment to support student academic achievement within COBE.
Design and Adjustment of Optimizing Athletes' Training Programs Using Machine Learning Algorithms
Authors: Li Zhang
Published: May 2024

The adjustment of optimizing athletes' training programs using machine learning involves leveraging data-driven approaches to enhance training regimens and performance outcomes for athletes. By analyzing various factors such as athletes' physiological data, training logs, performance metrics, and external conditions, machine learning algorithms can identify patterns, correlations, and optimal training strategies. These insights enable coaches and sports scientists to tailor training programs more effectively to individual athletes' needs, goals, and abilities. By continuously adapting and refining training plans based on real-time feedback and data analysis, machine learning helps optimize athletes' preparation, recovery, and overall performance, ultimately maximizing their potential and success in competitive sports. This paper explores novel methodologies and machine-learning techniques aimed at optimizing athletes' training programs. With the increasing demand for peak performance and injury prevention in sports, there is a growing need for data-driven approaches to tailor training regimens effectively. One such methodology, the Optimized Adjustment Evolutionary Computing Feature Selection (OA-EC-FS), is investigated for its ability to select relevant features crucial for enhancing athletes' performance across various sports disciplines. Additionally, machine learning algorithms are employed to classify athletes' training programs based on selected features, enabling coaches and trainers to make informed decisions to maximize performance outcomes.
Construction and Optimization of Sports Athlete Selection and Talent Cultivation System Based on Data Analysis
Authors: Zhuoxiao Liu
Published: May 2024

Talent cultivation is the deliberate and systematic process of identifying, nurturing, and developing individuals' innate abilities and potential across various domains. It involves providing structured opportunities, resources, and guidance to help individuals flourish and excel in their chosen fields. This process encompasses education, mentorship, training programs, and practical experiences tailored to the specific needs and interests of each individual. This paper presents a novel approach to the construction and optimization of a Sports Athlete Selection and Talent Cultivation System through the integration of data analysis techniques, with a particular emphasis on the Anthropometric Invariance Gait Recognition for Pattern (AIGRP) system. Leveraging advanced algorithms, the AIGRP system enables precise characterization of athletes' biomechanical profiles based on anthropometric measurements and gait characteristics. By analyzing data collected from athletes, including age, gender, height, weight, and specific performance metrics, such as speed, strength, and endurance, the system facilitates personalized athlete selection and talent cultivation strategies. Furthermore, the objective and data-driven nature of the AIGRP system reduces biases inherent in traditional selection methods, leading to more efficient and effective talent identification. By analyzing data collected from athletes, including age, gender, height (mean: 175 cm), weight (mean: 70 kg), and specific performance metrics, such as speed (mean: 6.5 m/s), strength (mean: 140 kg), and endurance (mean: 47 min), the system facilitates personalized athlete selection and talent cultivation strategies.
Forecasting Student Employment Trends in Colleges and Universities Education Using Time Series Analysis Algorithms
Authors: Xu Xiaojun
Published: May 2024

Student employment trends in colleges have been evolving, with a notable shift towards internships, co-op programs, and other experiential learning opportunities. Colleges are increasingly emphasizing the importance of gaining practical work experience alongside academic studies to enhance students' employability. This trend reflects the growing demand from employers for graduates who possess not only theoretical knowledge but also relevant skills and hands-on experience. This study investigates student employment trends within Colleges and Universities education through the application of time series analysis algorithms. Utilizing historical data spanning five years from 2019 to 2023, the study examines the dynamics of student employment rates and explores the effectiveness of forecasting methodologies, including ARIMA and Ranking with ARIMA (R-ARIMA). The findings reveal a consistent upward trend in student employment rates over the study period, indicating a favorable environment for student career opportunities within higher education institutions. Moreover, the accuracy of forecasting models is demonstrated through the close alignment between predicted and observed employment rates. Optimal ARIMA configurations are identified, providing insights for strategic planning and resource allocation to support student career development and success. The findings reveal a consistent upward trend in student employment rates over the study period, with the percentage of employed students increasing annually. For instance, the student employment rate rises from 65% in 2019 to 77% in 2023. This indicates a favourable environment for student career opportunities within higher education institutions. Moreover, the accuracy of forecasting models is demonstrated through the close alignment between predicted and observed employment rates. Optimal ARIMA configurations are identified, with the best-performing model achieving an AIC score of 95.3 and a BIC score of 99.8. These configurations provide insights for strategic planning and resource allocation to support student career development and success.
Design and Implementation of English Writing Aids Based on Natural Language Processing
Authors: Huang Ruixia
Published: May 2024

The design and implementation of English writing aids based on natural language processing (NLP) involve leveraging advanced algorithms and techniques to assist users in improving their writing skills. These aids can encompass various functionalities such as grammar and spell checking, style suggestions, vocabulary enhancement, and plagiarism detection. By analyzing the context, structure, and semantics of the text, NLP models can provide intelligent feedback and recommendations to help users express themselves more effectively. Additionally, interactive features such as real-time editing and personalized suggestions contribute to a seamless user experience. The paper presents a comprehensive exploration of the Sentimental Random Field Point (SRFP) approach in the domain of natural language processing (NLP). Through a series of experiments and analyses, we investigate the effectiveness and versatility of SRFP across various NLP tasks, including sentiment analysis, named entity recognition, text classification, and machine translation. Our findings demonstrate the robustness of SRFP-based algorithms and models in accurately classifying sentiments, enhancing grammatical correctness, expanding vocabulary, improving writing style, and achieving high performance in classification tasks. Through results it is observed that an accuracy of 92.7% for sentiment analysis using the BERT model, a precision of 85.3% and recall of 88.9% for vocabulary enhancement, and an accuracy of 93.5% for machine translation using the Transformer model.
Cross-Language Translation and Comparative Study of English Literature Using Machine Translation Algorithms
Authors: Shanshan Wang
Published: May 2024

Cross-language processing in English literature involves the translation and analysis of literary texts from English into other languages or vice versa. This multidimensional task encompasses various aspects, including language translation, cultural adaptation, and literary interpretation. Through cross-language processing, literary works originally written in English can reach a wider audience, enabling individuals from diverse linguistic backgrounds to access and appreciate the richness of English literature. This paper presents an innovative approach to language processing tasks through the integration of Ant Swarm Domain Statistical Machine Learning (ASDS-ML). Leveraging principles of swarm intelligence and statistical learning techniques, ASDS-ML offers a robust framework for addressing challenges in language translation and classification. In the domain of translation, ASDS-ML demonstrates promising results in achieving accurate and nuanced translations across diverse language pairs, while also exhibiting adaptability to varying linguistic contexts. Furthermore, ASDS-ML showcases its effectiveness in text classification tasks, accurately categorizing instances across multiple classes with high precision and recall. In language translation tasks, ASDS-ML achieves an average BLEU score of 0.85 across multiple language pairs, outperforming baseline methods by 10%. Additionally, in text classification tasks, ASDS-ML achieves an average accuracy of 0.92 across ten different classes, surpassing existing approaches by 5%.
Exploring the Relationship Between Key Factors and Victory and Defeat in Volleyball Matches using Bayesian Network Modeling
Authors: Yang Ge
Published: May 2024

Identifying key factors contributing to victory and defeat in volleyball matches using Bayesian network modeling involves probabilistic graphical modeling to analyze the interrelationships among various factors and their influence on match outcomes. By integrating data on player performance, team strategies, match conditions, and opponent characteristics, Bayesian networks can uncover complex dependencies and predict the likelihood of winning or losing based on different scenarios. The research conducted on exploring the relationship between key factors and victory and defeat in volleyball matches using Bayesian network modeling. Through the utilization of Bayesian network modeling, this study aims to elucidate the intricate interplay between different variables and their impact on match outcomes in the context of volleyball. The research employs a Hidden Markov Process Bayesian Network (HMPBN) to model the temporal dependencies and uncertainties inherent in volleyball matches. The dataset comprises a comprehensive collection of volleyball match data, encompassing various observable variables such as points scored, successful serves, defensive plays, and more. The simulation setup involves training the HMPBN model on a dataset consisting of 1000 instances and evaluating its performance on a separate test dataset of 500 instances. The model parameters, including the transition matrix, emission matrix, and initial state distribution, are determined through rigorous statistical estimation techniques. The results of the simulation experiments provide valuable insights into the probabilities associated with different actions and their influence on match outcomes. The study reveals that successful serves have a high probability of contributing to match victories, with an average success rate of 80% in the dataset. Conversely, defensive plays exhibit a lower success rate, with an average probability of success of 60%. The Hidden Markov Process Bayesian Network (HMPBN) achieves an overall classification accuracy of 85% on the test dataset, demonstrating its effectiveness in predicting match outcomes.
Emotion Analysis of Literary Works Based on Qutrit-inspired Fully Self-supervised Quantum Neural Network
Authors: Lirong Wang
Published: May 2024

The development of foreign literature, embodiment of emotional value in modern, contemporary foreign literature is more attentive on experience, through reading it, one can understand humanistic, personal feelings embedded in the work and grasp the author’s personality, spiritual experience. To analyze sentiment data of foreign literary works, proposed Emotion Analysis of Literary Works Based on Qutrit-inspired Fully Self-supervised Quantum Neural Network Method (EA-LW- QIFSQN). Initially input data are collected from NLP-dataset. Afterward, the input data provided to preprocessing. In preprocessing segment Federated Neural Collaborative Filtering (FNCF) is used to clean the unwanted data. Then preprocessed data is fed to feature extraction, synchro spline-kernelled chirplet extracting transform (SKCET) is used to extract two features such as textual features and lexical features. Afterwards QIFSQN is used to classify the emotions likes joy, sadness, anger, fear. Generally, QIFSQN doesn’t show some optimization adaption techniques to determine optimum parameter to offer accurate detection. Polar Coordinate Bald Eagle Search Algorithm (PCBSOA) is proposed to enhance QICCN classifies the emotions accurately. The proposed technique is executed and efficacy of EA-LW- QIFSQN technique is assessed with support of numerous performances like accuracy, recall, precision and F1-scorce is analyzed. Then, performance of EA-LW- QIFSQN technique is analyzed with existing techniques like emotion analysis of literary works depend on attention mechanisms with fusion of two-channel features (EA-LW-CNN), integrative improvement of modern literary works with traditional culture combined by semantic association network modeling(EA-LW-SAN) and emotion expression in modern literary appreciation: emotion-depend analysis (EA-LW-RFA) respectively.
Optimization of Digital Media Art Creation Process Based on Dual Transformer Residual Network (DTRN) and Lotus Effect Optimization Algorithm (LEA)
Authors: Zhifang He
Published: May 2024

In response to the problems of lack of intelligence, low recognition, loss of brightness, and unclear design objects in the comprehensive material painting of installation art, when materials enter the field of artistic creation, they can assist in artistic creation, but constantly updating materials is also a challenge for artists. This manuscript proposes an image processing technology in the digital media art creation based on dual transformer residual network (DTRN) and lotus effect optimization algorithm (LEA) (DMAC-DTRN-LEA) is proposed. Initially, the extracted images from the multimedia are collected from MSCOCO2014 dataset. Collected images are preprocessed to resize the image using federated neural collaborative filter (FedNCF). Later, resized images are given to feature extraction; morphological features like shape, structure, colour, pattern, and size are extracted based on synchro spline-kernelled chirplet extracting transform (SSCET). Finally, the extracted features are fed to Dual Transformer Residual Network (DTRN) for effectively classify the art images. In, general Dual Transformer Residual Network classifier does not express adapting optimization strategies to determine optimal parameters to ensure accurate art images detection system. Hence, the proposed method examined utilizing performance metrics like accuracy, precision, fl-score, sensitivity, peak signal to noise ratio (PSNR), error rate, and Structural similarity index (SSIM). Proposed DMAC-DTRN-LEA method attains 97% higher accuracy and 0.7% low error rate analysed to the existing methods respectively.