bluuebunny/crossref_metadata_2025 huggingface dataset:
Analysis and Research on Library Management Technology Based on the Integration of Virtual Reality and Management
Authors: Fengling Li
Published: May 2024

The library's commitment to being on the forefront of technology, it is able to provide sophisticated services to both its customers and the terminal. Due to the fact that they have access to a greater volume of information, they are in a better position than clients of the centralized digital reading room. In order for learning to be able to satisfy the varied data needs of its constituents, it is required for it to be able to supply knowledge services and carry out its duty of convergence. This is something that can only be done through effective value management that is based on innovative applications of technical solutions. Some of the problems that afflict digital libraries include a lack of forethought and organization, bad software, import constraints on technology, a dearth of experienced staff, a scarcity of standards, and an inability to collaborate with one another. This research proposes employing Eye Movement Technology (EMT) as an automated library administration system in order to assist smart libraries (SLs) in lowering their costs and increasing their output by taking advantage of the opportunities presented by virtual reality. In particular, the purpose of this study is to pave the way for SLs to be able to achieve this aim. Electronic medical technology (EMT) that may be worn on the body is part of the expanding subject of wearable computing, which is a subfield of computer technology. The positions of the eyes' focus sources, visual fields, and gaze vectors are examples of the information that is received from the eyes. The librarians are responsible for monitoring the vast majority of actions that take place inside the library, and this system keeps track of every book that is borrowed from and returned to the collection. As a direct consequence of the recent developments made by EMT-SL, an expanded number of users will soon be able to take advantage of the resources made available by the intelligent library. Users of both conventional virtual reality and augmented reality have the capacity to interact with computer-generated picture models, and both traditional virtual reality and augmented reality have the potential to make individuals visible in a wide range of different environments.
Construction of Logistics Information Management of Small and Medium-sized Enterprises Relying on Recursive Algorithm
Authors: Caihong Zhao
Published: May 2024

With the rapid development of information technology, the application of information technology in logistics management is more and more, which makes traditional logistics gradually move towards modern logistics, and realizes the optimal allocation of logistics management and resources; at the same time, the logistics industry is also faced with huge data flow. , the low level of informatization and many other challenges, these problems restrict the development of enterprises. In order to solve many problems existing in the construction of logistics management informatization, this paper analyzes logistics management and its informatization application. By understanding the current situation of logistics management in small and medium-sized enterprises, it studies how to construct the informatization application of logistics management in small and medium-sized enterprises, and adds recursive algorithm to the informatization application, and finally constructs a logistics management informatization system. The system is realized through the architecture of B/S model, and the function and performance of the system are tested. The test results show that the system functions well, the maximum load capacity of the system is 600, and the longest response time does not exceed about 20s. The test results are in line with expectations, indicating that the recursive algorithm constructed in this paper is effective in the application of logistics management informatization.
Simulation Analysis and Comparative Study of MOSGT with Other Techniques
Authors: Pramoda Medisetty
Published: May 2024

This paper presents a comparative study of the Multi-Objective Symmetric Game Theory (MOSGT) for task offloading in cloud robots with existing techniques such as Genetic Algorithm 4 Collaborative Computation Offloading (GA4CCO) and Approximation Collaborative Computation Offloading (ACCO). The study employs a simulation analysis to evaluate the performance of the proposed MOSGT model in a cloud robotics context. The results of the simulation study reveal that the MOSGT model reduces system cost by approximately 35% compared to GA4CCO and by 20% compared to ACCO. Furthermore, the MOSGT model exhibits a reduced rate of 30% less than GA4CCO and 25% minimal than ACCO in terms of iteration. These findings underscore the potential of MOSGT as a promising solution for efficient task offloading in cloud robots, offering significant improvements in computational complexity and service quality in edge computing.
Micro Grid Integration of Distributed Power Generation Optimal Operating Methods
Authors: Sunita Dixit
Published: May 2024

Microgrids, as localized energy systems comprising distributed power generation sources, offer resilience, flexibility, and efficiency to the modern power grid. Integrating various distributed energy resources (DERs) within microgrids poses challenges in optimizing their operation to ensure reliable and cost-effective energy supply. This paper presents a comprehensive review and analysis of optimal operating methods for the integration of distributed power generation within microgrids.The study explores various aspects of microgrid operation, including optimal dispatch strategies, demand-side management techniques, energy storage utilization, and renewable energy source integration. It investigates the application of advanced optimization algorithms, such as linear programming, mixed-integer linear programming, and metaheuristic algorithms, to address the complex optimization problems inherent in microgrid operation. Furthermore, the paper examines the role of smart grid technologies, advanced control systems, and communication infrastructure in facilitating the efficient operation of microgrids with distributed power generation. It discusses the importance of real-time monitoring, data analytics, and predictive modeling for enhancing decision-making processes and optimizing microgrid performance.
An Efficient Feature Selection and Extraction using Metaheuristic Technique for Diabetic Retinopathy
Authors: Anamika Raj
Published: May 2024

Diabetic retinopathy (DR) is associated with diabetes, which causes harm to the retina as a result of persistent elevated blood sugar levels, resulting in symptoms such as impaired vision. Regular eye examinations are crucial for the timely identification of any issues. The process of selecting characteristics in DR involves optimizing important features using a combination of Hybridized Weight-Optimized Particle Swarm Optimization and Whale Optimization Algorithm (HWOPSO-WOA). This leads to an enhancement in the accuracy of detection, which is crucial for appropriate diagnosis and therapy. Pre-processing encompasses the utilization of linear filters such as Sobel and Prewitt for image manipulation, mean filters for the reduction of noise, and Gaussian filters for the purpose of smoothing. Feature selection employs an objective function that considers significant performance measure, which utilizes binary encoding and conducts fitness evaluation. The methodology is utilized for the analysis of DR utilizing the Indian Diabetic Retinopathy Image Dataset. The results comprise convergence graphs, and a comparative analysis, which emphasize the greater accuracy and efficiency of the Proposed technique. Visual representations, such as fundus images with selected features, highlight the importance of the chosen features in detecting DR. The proposed HWOPSO-WOA achieves highest accuracy of 97.3% and minimal processing time 98.45seconds that outperforms the state-of-art techniques.
Modified wild Horse Herd Optimization based Bhattacharyya error constraint (BEC) based L2-norm Linear discriminant analysis (LDA) method for the sentiment analysis
Authors: S. Satheesh Kumar
Published: May 2024

People's opinions are analyzed via sentiment analysis in all fields. Reviews and tweets, among other formats, are used to express opinions. Irony, sarcasm, and other difficult-to-discern hidden meanings can occasionally be found in viewpoints. Artificial intelligence must be used to examine the sentiments as a result. We suggest a unique Bhattacharyya error constraint (BEC) based L2-norm linear discriminant analysis (LDA) because some of the earlier efforts lack optimization. There are some overfitting and class disparity issues in this. To address this, we used a brand-new method called Modified Wild Horse Herd Optimization (MHHO). The experiment is run to evaluate the performance of the suggested strategy and to compare it to other approaches already in use. We have used performance measures for comparison, and the results demonstrate that the suggested method successfully assesses the sentiment from the acquired dataset.
Emotion Analysis and Expression Algorithm of Dance Action Based on Machine Learning
Authors: Zhao Xinhui
Published: May 2024

Emotion analysis and expression algorithms represent a pivotal frontier in the intersection of artificial intelligence and human-computer interaction. These algorithms aim to decode and understand human emotions from various modalities such as text, speech, facial expressions, and physiological signals. This paper introduces the Context-Based Rough Sugeno Fuzzy (CBRSF) model tailored for emotion analysis and expression algorithms in the context of dance actions. With machine learning techniques, the CBRSF model integrates contextual information, rough set theory, and Sugeno fuzzy logic to accurately analyze and express emotions conveyed through dance movements. the power of machine learning techniques, the CBRSF model integrates various components, including contextual information, rough set theory, and Sugeno fuzzy logic, to provide a comprehensive framework for emotion analysis and expression. One of the key strengths of the CBRSF model lies in its ability to incorporate contextual information surrounding dance movements. Emotions conveyed through dance are often influenced by factors such as choreographic context, music, and cultural background. By integrating contextual cues into the analysis process, the CBRSF model can better capture the nuanced emotional nuances embedded within dance performances. The CBRSF model utilizes rough set theory to handle uncertainty and imprecision inherent in emotion analysis. Dance movements can be inherently ambiguous, making it challenging to accurately categorize the associated emotions. Rough set theory provides a principled framework for managing this uncertainty, allowing the CBRSF model to make informed decisions even in situations where data may be incomplete or inconsistent. Through comprehensive experimentation and evaluation, our proposed model achieves an emotion recognition accuracy of 98% across diverse dance action datasets, surpassing existing methods by 10.2%. Moreover, the CBRSF model enables nuanced emotion expression by dynamically adjusting dance movements based on real-time emotional cues.
A Blended Teaching Model of College English Based on Deep Learning
Authors: Feng Hongli
Published: May 2024

Blending traditional face-to-face instruction with online learning components, this model aims to enhance student engagement and improve learning outcomes. This paper introduces a novel blended teaching model for college English courses, integrating Conditional Random Field (CRF) techniques with deep learning frameworks. Blended learning, combining traditional face-to-face instruction with online learning components, has gained traction in higher education for its potential to enhance student engagement and learning outcomes. CRF blended teaching integrates the principles of conditional random fields, a powerful probabilistic graphical model, with traditional teaching methodologies to enhance learning outcomes. Unlike conventional teaching approaches that often rely on linear or static instructional methods, CRF blended teaching embraces a more dynamic and adaptive framework. By leveraging CRF, which models the dependencies between different instructional variables, educators can design personalized learning experiences tailored to individual student needs and learning styles. This approach enables the seamless integration of diverse instructional modalities, including face-to-face instruction, online resources, and interactive learning activities. The proposed model CRF algorithms to effectively capture the sequential dependencies inherent in language learning tasks, such as sentence parsing and part-of-speech tagging. By integrating deep learning architectures, including recurrent neural networks (RNNs) and transformers, model with large-scale linguistic corpora to improve accuracy and generalization. The model achieves a 15% increase in students' English proficiency scores and a 25% improvement in comprehension compared to traditional teaching methods, underscoring its effectiveness in enhancing learning outcomes.
Design and Application of English-Chinese Bilingual Teaching Model Based on Multimodal Learning
Authors: Zhang Yuanyuan
Published: May 2024

In the globalized landscape of education, the demand for effective bilingual teaching models has become increasingly prominent, particularly in English and Chinese instruction. this study introduces the Bilingual Teaching Random Conditional Field (BTRCF) as a novel approach to designing and implementing an English-Chinese Bilingual Teaching Model based on Multimodal Learning. The principles of multimodal learning, which integrates various modes of communication such as visual, auditory, and kinaesthetic, the BTRCF model aims to provide students with a comprehensive and immersive language learning experience. The BTRCF model capitalizes on the complementary strengths of both English and Chinese languages, aiming to cultivate bilingual proficiency while fostering cross-cultural understanding among students. By incorporating elements from each language and leveraging multimodal learning strategies, the BTRCF model provides students with a comprehensive language learning experience that extends beyond mere linguistic proficiency to encompass cultural appreciation and intercultural competence. By capitalizing on the complementary strengths of both languages, this model seeks to foster bilingual proficiency while promoting cross-cultural understanding among students. with the BTRCF model have shown promising results, with an average increase of 25% in students' bilingual proficiency scores compared to traditional teaching methods. Additionally, qualitative assessments reveal high levels of student engagement and satisfaction with the multimodal learning approach employed by the BTRCF model, indicating its potential to revolutionize bilingual education practices.
Development of Athlete Health Monitoring and Early Warning System Based on Sensor Data
Authors: Lv Fulu
Published: May 2024

An athlete health monitoring and early warning system based on sensor data utilizes wearable sensors to track various physiological parameters, such as heart rate, body temperature, oxygen saturation, and movement patterns. These sensors continuously collect real-time data during training sessions and competitions. Machine learning algorithms analyze the data to detect deviations from normal patterns and identify potential indicators of injury, fatigue, or overtraining. By monitoring athletes' health parameters over time and comparing them to established baseline values, the system can provide early warnings of potential health issues or performance declines. Coaches and medical staff can then intervene promptly with appropriate interventions, adjustments to training loads, or rest periods to prevent injuries and optimize athletes' performance and well-being. . The paper introduces the Reliable Fuzzy Sensor Optimized Automated Monitoring (RFSO-DL) system, a novel approach to athlete health monitoring leveraging fuzzy logic and sensor data optimization techniques. RFSO-DL offers a comprehensive solution for real-time assessment of athlete well-being by analyzing physiological measurements and activity levels. Through a series of experiments, the system demonstrates high accuracy, precision, recall, and F1 score in classifying athlete health status. Results indicate RFSO-DL's effectiveness in capturing subtle variations in health status and providing timely interventions. Through experiments, the system achieves impressive results, with accuracy ranging from 90.5% to 94.1%, precision from 85.8% to 91.2%, recall from 91.7% to 95.6%, and F1 score from 88.6% to 93.3%. These findings underscore RFSO-DL's potential to revolutionize athlete health monitoring by accurately classifying health status based on physiological measurements and activity levels.
Integration and Optimization of Educational Teaching Resources in Colleges and Universities Based on Clustering Algorithm
Authors: Ren Shiyuan
Published: May 2024

The paper presents Centroid Ranking Optimized Clustering (CROC), a novel approach aimed at enhancing educational teaching and resource optimization through advanced clustering techniques. CROC integrates centroid-based clustering with ranking optimization strategies to segment students or educational resources into meaningful clusters, facilitating personalized learning experiences and targeted interventions. Experimental analysis conducted on real-world educational datasets demonstrates that CROC outperforms traditional clustering algorithms in terms of clustering quality, accuracy, and interpretability. The findings suggest that CROC holds significant promise for informing data-driven decision-making in educational settings, enabling educators to better understand student performance patterns, identify at-risk students, and tailor instructional strategies to meet diverse learning needs. Experimental analysis on real-world educational datasets reveals that CROC achieves a Silhouette Score of 0.75 and a Davies-Bouldin Index of 0.40 on the MathScores dataset, outperforming traditional algorithms such as k-means (Silhouette Score: 0.68, Davies-Bouldin Index: 0.53), Hierarchical (Silhouette Score: 0.62, Davies-Bouldin Index: 0.57), and DBSCAN (Silhouette Score: 0.45, Davies-Bouldin Index: 0.78). Similarly, on the EnglishTest dataset, CROC attains a Silhouette Score of 0.82 and a Davies-Bouldin Index of 0.35, surpassing k-means (Silhouette Score: 0.74, Davies-Bouldin Index: 0.47), Hierarchical (Silhouette Score: 0.68, Davies-Bouldin Index: 0.52), and DBSCAN (Silhouette Score: 0.53, Davies-Bouldin Index: 0.68). These findings underscore the effectiveness of CROC in clustering educational data, enabling personalized learning experiences and data-driven decision-making in educational settings.
Music Tone Synthesis based Anti-Interference Dynamic Integral Neural Network optimized with Artificial Hummingbird Optimization Algorithm
Authors: Tao Chen
Published: May 2024

Music Tone Synthesis is an applied science or method that implies to identify and study of specific orchestral tone currently part of a music. It is especially effective in the area of oral music training system classes, somewhere be able to support in the practice and growth of musicians. Music Tone Synthesis provides singer and writer to produce large number of noises as well as imitate several tools and impact it cannot be flexible otherwise realistic for create over standard classical instrument. In this manuscript, Music Tone Synthesis based Anti-Interference Dynamic Integral Neural Network enhanced with artificial hummingbird Optimization algorithm (MTS-AIDINN-AHOA) is proposed. The input data are obtained from the audio signal. Then the data are pre-processing using Stein Particle Filtering (SPF) to remove the noise. The pre-processed data is given into the Two-sided Offset Quaternion Linear Canonical transform (TSOQLCT) for extracting the musical features such as melody, harmony, tempo, and dynamics. After this the extracted feature is provided to the Anti-Interference Dynamic Integral Neural Network (AIDINN) is used for the music tone synthesis and it is classified as pitch, chronaxie, volume, tone color. In general, the Anti-Interference Dynamic Integral Neural Network (AIDINN) does no express adapting optimization strategies to determine ideal parameters to assure precise prediction. Thus, it is proposed to utilize the Artificial Hummingbird Optimization Algorithm enhancement AIDINN for Music Tone Synthesis. The proposed MTS-AIDINN-AHOA method is implemented on MATLAB. Then performance of proposed technique is evaluated to other existing techniques. The proposed technique attains 26.36%, 20.69% and 35.29%  higher accuracy, 19.23%, 23.56%, and 33.96% higher precision, 26.28%, 31.26%, and 19.66%higher recall, 28.96%, 33.21% and 23.89%higher specificity comparing with the existing methods such as a research on Musical Tone Recognition Method Based on Improved RNN for Vocal Music Teaching Network Courses (MTS-RNN), Music Timbre Extracted from Audio Signal Features (MTS-BPNN)and Feature Extraction and Categorization of Music Content Based on Deep Learning(MTS-SMNN) respectively.
Optimization of Seating Arrangement in Sports Competition Venues Based on Recurrent Graph Neural Network
Authors: Zhong Wu
Published: May 2024

Seating in sports venues is a type of seating where rows are much more frequent than in other types of auditorium seating. Traditional seating looks to maximize capacity over fan experience. The stadium experience can be enhanced by recommending appropriate seating by viewer’s preferences and past behavior. Group customers would want to sit together when they go to a sports arena, but sometimes there are not enough seats available together. This study's methodology for improving viewing experience by optimizing seating arrangement using game type, historical ticket sales and opponent team is offered in this study as a way to get around this drawback. The data are collected from Los Angeles Memorial Sports Arena seating dataset. Afterward, the data are fed to pre-processing. In pre-processing segment, the noise is removed and data is enhanced using Adaptive Robust Cubature Kalman Filter (ARCKF). The outcome from the pre-processing data is transferred to the Recurrent Graph Neural Network (RecGNN).The reserved seating, bowl seating and stadium seating are successfully classified by using RecGNN. RecGNN's weight parameter is optimized by the Lotus Effect Optimization Algorithm (LEA). On the Python working platform, the proposed RecGNN-LEA is implemented. The proposed strategy is analyzed using performance measures including recall, f1-score, accuracy, precision, and computation time. RecGNN's weight parameter is optimized by the Lotus Effect Optimization Algorithm (LEA). On the Python working platform, the proposed RecGNN-LEA is implemented. The proposed method is analyzed using performance measures including recall, f1-score, accuracy, precision, and computation time. The gained results of the proposed RecGNN-LEA method attains higher accuracy of 16.61%, 18.89%, and 17.92%, higher sensitivity of 16.37%, 12.23%, and 18.56%  and higher precision of 14.81%, 16.79%, and 18.23%. The proposed SASCV-RecGNN-LEA method is compared with the existing methods such as SASCV-CNN, SASCV-LaRSA, and SASCV-TDNN models respectively.
Variational Neural Network Optimized with Lyrebird Optimization Algorithm for Analysis and Empirical Evidence of Using Data Mining in Financial Risk Prevention Research
Authors: Mengrui Bao
Published: May 2024

In the contemporary landscape of global finance, the effective prevention and mitigation of financial risks have become pivotal for the stability, resilience, and sustainable growth of financial institutions and markets. As financial instruments and markets continue to evolve in complexity, the need for a thorough analysis of risk prevention strategies has become increasingly apparent. In this manuscript, Variational Neural Network (VNN) optimized with lyrebird optimization algorithm (VNN-LOA) is proposed. Initially data is taken from machinehack-financial risk prediction. Afterward the data is fed to Adaptive Variational Bayesian Filter (VBF) based pre-processing process. The pre-processing output is provided to the Variational Neural Network (VNN) to effectively classify financial risk prevention as either involving risk or no risk. The learnable parameters of the VNN are optimized using lyrebird optimization algorithm (LOA). The proposed method is implemented in MATLAB and the efficiency of the proposed method DM-FRP-VNN-COA is estimated with the help of several performances evaluating metrics like, accuracy, precision, recall, f1-score, sensitivity, specificity, computational time, and ROC are analyzed. The proposed DM-FRP-VNN-LOA method attains 38.88%, 35.75%, and 33.16% higher accuracy for risk classification; 34.31%, 38.47% and 37.23% higher accuracy for no risk classification; 22.63%, 33.27% and 21.49% high precision for risk classification; 22.63%, 33.27% and 21.49% high precision for no risk; 35.136%, 39.04% and 38.81% lower computation Time compared with the existing method like data mining using financial risk prevention based back propagation neural network (DM-FRP-BPNN), data mining using financial risk prevention based machine learning (DM-FRP-ML), and data mining using financial risk prevention based convolutional neural network (DM-FRP-CNN) respectively.
Virtual Reality Animation Interaction Design using Bayesian Physics-Informed Neural Network with Archimedes Optimization Algorithm Based Scene Modelling
Authors: Pengyuan Zhu
Published: May 2024

Virtual Reality Animation Interaction Design is a dynamic field at the intersection of technology and creativity, where immersive experiences come to life through the fusion of animation and interactive design. There are some challenges involves in design of virtual Reality animation. The main challenge is animation image error involves in the design. To overcome this issue, present a Virtual Reality Animation Interaction Design Using Bayesian Physics-Informed Neural Network with Archimedes Optimization Algorithm Based scene modelling  (VRAID-BPINN-AOA) is proposed. Initially, the animation images are collected from animation dataset. Then, the animation images are fed to pre-processing segment.  In pre-processing segment, the noise of the image is removing layer by layer by utilizing adaptive-noise Augmented Kalman Filter (ANAKF). Then the pre-processed animation image is given for Feature extraction process. In Feature extraction, spatiotemporal features such as Object Position, velocity, Optical flow and Crowd Density are extracted by utilizing Multi-Level 2-D Quantum Wavelet Transform (ML2DQWT). Finally the extracted feature attributes are given to Bayesian physics-informed neural network (BPINN) for the prediction of error in the animation images. In general, BPINN does not express some adaption of optimization strategies for determining optimal parameters to promise accurate prediction of error. Therefore, Archimedes Optimization Algorithm (AOA) is proposed to optimize the parameter of BPINN .The proposed technique is implemented and efficacy of VRAID-BPINN-AOA technique is assessed by support of numerous performances such as Bit Error Rate, Design Rate, End to End Delay, Latency, and Transmission Rate. Proposed VRAID-BPINN-AOA method attains 28.56%, 26.67% and 25.67% lower Bit error rate and26.42%, 25.67% and 23.67% lower Design rate are compared with existing such as Virtual Interactive Animation Design with Restricted Boltzmann machine (VI-AD-RBM),Generative deep learning for visual animation in landscapes design using Generative adversarial network(GDL-VALD-GAN), and Hand interface using deep learning in immersive virtual reality using convolutional neural network(HID-LVIR-CNN)respectively.
Microwave Antenna Optimization for Low Latency and High Throughput Communication Systems
Authors: Sanjivani Munot
Published: May 2024

This paper presents a comprehensive exploration of microwave antenna optimization strategies aimed at achieving low latency and high throughput in modern communication systems. With the escalating demand for real-time applications and data-intensive services, the optimization of microwave antennas has become imperative to meet the stringent requirements of responsiveness and efficiency. The abstract delves into various facets of antenna optimization, including design parameters, signal processing techniques, and deployment considerations, all geared towards minimizing latency and maximizing throughput. Through a thorough review of existing literature and practical insights from real-world deployments, this research aims to provide valuable guidance for engineers and researchers in designing and implementing optimized microwave antennas for next-generation communication networks. By examining the intricate interplay between antenna design, signal processing algorithms, and deployment strategies, this paper elucidates the challenges and opportunities in achieving low latency and high throughput in microwave communication systems. Furthermore, it highlights the significance of factors such as gain, beamwidth, polarization, frequency band selection, multiple-input multiple-output (MIMO) systems, beamforming, adaptive coding and modulation (ACM), antenna placement, tower height, and line-of-sight considerations in optimizing antenna performance. The findings presented in this paper contribute to advancing the understanding of microwave antenna optimization and offer practical insights into enhancing the responsiveness, efficiency, and reliability of modern communication networks.
Enhance Image Quality with CAS-CNN: A Deep Convolutional Neural Network for Compression Artifact Suppression
Authors: Radha Seelaboyina
Published: May 2024

Objective: The goal is to outdo clumsy filtering algorithms and prevailing deep learning approaches in terms of the image quality metrics, computational efficiency, and memory usage by attaining better performance.
Methodology: Two-step approach has the methodology component including the formulation of the problem and review of the literature collections to detect known techniques. CAS-CNN's architecture is designed, and the entire experiment setup requires the preparation of an original and a compressed images dataset and also defining loss function for training and improving CAS-CNN parameters as a whole. Outputs of metrics are the PSNR, SSIM, MOS values, inference time, model size, training time, and hardware usage.
Results and Discussion: In terms of competitiveness, CAS-CNN is always ahead of all the latest algorithm levels and achieves the best results by standard metrics such as PSNR and SSIM, thus it is the method that gives the most undistorted recreated images with a high quality. It has shown its ability to compete closely with traditional methods by providing better performance, as well as consuming fewer computing resources, which makes it closer to the practical reality of real time applications. The CAS-CNN's model creates efficiency that enables cutting-edge design and testing into the market timelier than the existing methods do. Small progress is seen despite its quick performance; but still, important issues like artificial data and restrictions on given conditions are noticed, which means that we must not stop searching for scaling and generalizing power.
Conclusion: CAS-CNN is a next-level algorithm concerning removing distortion in compression, superior to prevailing techniques in terms of both effectiveness, efficiency, and accessibility. It holds an edge over other methods due to its high performance and efficiency, and as such, it is an applicable tool for a variety of situations where one must deal with limited resources in the need for high-quality images. More research is needed to account for the existing shortcomings and apply CAS-CNN to a range of different datasets, as well as the circumstances in the real world.
Intelligent and Automated Product Quality Assurance System using Machine Learning
Authors: Aashish Arora
Published: May 2024

The paper discusses the use of advanced technologies like the Internet of Things (IoT) and cyber-physical systems (CPS) to enhance product quality assurance in production industries. It proposes an intelligent approach using machine learning and termed the model as ETLViT, which integrates transfer learning models and vision transformers (ViT). The proposed model is a three-layered architecture integrated with a cyber-physical system (CPS). The first layer collects images of the processed products using a CPS, while the second layer transmits the data to a cloud server. The third layer analyzes the input image and predicts any faults in the product, with results also stored in the cloud server. The learning process involves noise removal, feature extraction, and classification, which are performed simultaneously by the ensemble model called ETLViT, designed using transfer learning and vision transformers. The result was presented with transfer learning models, ensemble model with machine learning and ETLViT. Among all of them, ETLViT outperforms the best.
Developing A Novel Load Balancing Approach for Mitigating Resource Contention in Multi-Cloud Environments
Authors: Akrati Sharma
Published: May 2024

In the rapidly evolving landscape of cloud computing, the management of resources and load balancing across multiple cloud environments presents significant challenges. This paper proposes a novel approach to address resource contention issues in multi-cloud environments through the development of an innovative load-balancing mechanism. By leveraging advanced algorithms and dynamic resource allocation strategies, our approach aims to effectively distribute workloads across diverse cloud infrastructures, thereby optimizing resource utilization and mitigating contention. Through extensive experimentation and evaluation, we demonstrate the effectiveness and efficiency of our proposed solution in enhancing performance and resilience in multi-cloud deployments. Our findings underscore the importance of tailored load-balancing mechanisms in enabling scalable and reliable operations in complex cloud environments.
Innovative Financial Management in Higher Education: A Multi-Scale Deep Learning Approach for Risk Reduction and Quality Enhancement
Authors: Hongbin Yue
Published: May 2024

This paper presents a novel university financial management system leveraging multi-scale deep learning. With rising college enrollment and teaching complexities, traditional financial models require adaptation to mitigate risks and improve management quality. The system integrates hardware and software innovations: multiple sensors enhance data scanning, coordinated by a central coordinator, ensuring comprehensive financial database coverage. Software-wise, a structured database establishes attribute-based financial connections, crucial for weight assignment. Employing a multilayer perceptual network topology, a full interconnection model based on multi-scale deep learning facilitates profound data extraction. Experimental evaluations demonstrate the system's superior financial risk assessment capabilities compared to traditional approaches, extracting a broader spectrum of financial parameters for comprehensive risk warnings. By embracing multi-scale deep learning, this system promises significant advancements in university financial management, enhancing adaptability and risk mitigation in college finance departments.
Machine Translation in EFL Learning: Chinese Undergraduates’ Use, Perceptions, and Machine Translation Literacy
Authors: Yuhua Deng
Published: May 2024

Machine translation (MT) has been recently experiencing an upsurge in popularity within the realm of second language (L2) and foreign language (FL) education.This paper aims to present the findings of a questionnaire survey that investigated the use and perception of MT, as well as MT literacy, in English as a foreign language (EFL) learning among undergraduate students in China. A total of 936 online questionnaires were collected from one university in China, with corresponding statistical data analyzed thereafter. The results indicate that MT is prevalently and frequently used in EFL learning by both low- and high-proficiency respondents from diverse disciplines, highlighting an urgent need to improve their MT literacy. This study holds significant pedagogical implications, shedding light on best practices for the efficient use of MT in realm of language education, particularly in EFL education.
Analysis of Teaching Mode of College Music Composition Theory Course Based on Data Analysis
Authors: Shuo Shen
Published: May 2024

After learning the Soviet model and developing it today, the rudiment of professional music education established in the 1930s has formed the burden of various historical problems. At the same time, in the ideological collision between China and the West, various concepts and viewpoints around music teaching also appeared, but this is not the main research object because there is no answer at all; therefore, it is necessary to make more rational improvements to the relevant experience, new ideas and new ideas. Based on the theoretical basis of in-depth algorithm and constraint solving, this research starts from the current music teaching in colleges. It explores the curriculum mode that is conducive to the development of music in the author’s country. The experimental results show that in the teaching mode of music composition theory courses in colleges and universities, the music composition optimization algorithm (MMC) has a profound impact on the teaching effect of the course. When solving Madcap’s, the current optimal solution significantly impacts the results. In the curriculum problem, due to the constraints of the curriculum schedule problem, MMC does not show a promising solution effect but has good performance on the curriculum scheduling problem with relatively few constraints, so when solving problems with fewer constraints, the reader can choose MMC.
Monitoring Fuel-Efficient Driving Patterns to Augment ADAS to regulate the fuel dynamically using Machine Learning
Authors: Manjunath T K
Published: May 2024

In the realm of enhancing Advanced Driver-Assistance Systems (ADAS) for optimal vehicle performance and sustainability, this paper introduces an innovative methodology that capitalizes on Machine Learning (ML) to scrutinize and learn from fuel-efficient driving patterns. Utilizing real-time driving cycle datasets collected through On-Board Diagnostics (OBD) tools, meticulously recorded the driving styles of 100 drivers on a predetermined route over duration of 35 minutes. The rich dataset includes an array of vehicle parameters and driving behavior, providing a comprehensive foundation for ML-based analysis. Proposed approach involves the application of state-of-the-art ML algorithms, with a specific focus on Machine learning and ensemble methods, to accurately model and predict fuel-efficient driving patterns. The algorithms XGB-regression, Random Forest regression and Gradient Boosting regressions were rigorously trained, validated, and tested on the collected data, ensuring robustness and reliability in their predictive capabilities. The scores and the Mean absolute Errors of these algorithms were estimated, most efficient algorithms are RF-regression and GB-regressions with a score of 99.93% and 99.93% with MAE 0.01219% and 0.01210%respectively. The integration of these predictive models into the ADAS framework mayshow promising results, significantly improving the system’s performance in real-time decision-making and driver assistance. The system demonstrates an enhanced capability to adapt to varying driving styles, offering personalized recommendations for fuel-efficient driving, and contributing to the reduction of fuel consumption and emissions.
Translating Gestures: Utilizing CNN to Enhance ASL Communication and Understanding
Authors: G. B. Sambare
Published: May 2024

This research introduces a system for translating American Sign Language (ASL) utilising Convolutional Neural Networks. (CNNs). The system under consideration has the capability to identify manual gestures executed by persons communicating in American Sign Language (ASL) and subsequently convert them into written language, thereby facilitating uninterrupted communication between the deaf and hearing populations. The CNN model was validated and trained using a set of data made up of 78,300 pictures of movements in the ASL Alphabet written in American Sign Language (ASL). In order to improve the performance of the model, the pre-processing of the data included a number of stages, such as converting the photos to grayscale, normalising the pixel values, and enhancing performance of the model. In order to ease classification, fully connected layers were added after a succession of pooling and convolutional layers in CNN's design. After 15 epochs of training, the model attained a validation accuracy of 99.85%. The findings of this research demonstrate the viability of employing Convolutional Neural Networks (CNNs) in the creation of precise and effective American Sign Language (ASL) translation systems, which can serve as a means of communication for people with auditory impairments.
Hybrid Framework Integrating Lexicon and Learning Methods for Enhancing Sentiment Analysis Based on Patients' Tweets on Medicines
Authors: Anuj Kumar
Published: May 2024

- Sentiment analysis is the act of locating and categorizing the emotions conveyed in text data using text analysis tools. Previous studies have demonstrated the great potential for sentiment analysis of medicine reviews to provide useful data that will aid healthcare professionals and businesses in assessing the safety of pharmaceuticals after they have been sold. These details protect patients and strengthen their confidence in healthcare professionals. Existing frameworks for opinion investigation within the restorative field either take two types of methods one is lexicon methods and the other one is machine learning models. Learning-based approaches need annotated data, whereas lexicon-based approaches are domain-specific and have a smaller range of applications. To improve outcomes, this study employs a hybrid methodology that fusion machine and deep learning models with lexicon techniques. The reviews are annotated using all-purpose emotion lexicons like TextBlob and SenticNet. To extract meaningful features, we are using feature engineering methods TF and TF- IDF. Last but not least, classification tasks are carried out using learning models such as machine learning models and deep learning models for biomedical text. The execution of the prospective combined technique is appraised using performance metrics. According to experimental findings, combining lexicon- and learning-based techniques yields superior outcomes over using them alone. Additionally, TextBlob has demonstrated impressive results, providing an accuracy of 97% with LSTM-CNN and the Bio Bert model when used to a dataset of medication reviews, as well as 95% accuracy when used with TF and the logistic regression model. Additionally, TextBlob provides an accuracy of 94% when combined with TF and LSTM, and provides an accuracy of 97% when using with Bio Bert Model on a dataset including tweets.
Advancements in Crop Disease Detection: Analytical Methods for Recognizing Disease Stages through Leaf Analysis
Authors: Sola Mohana Durga Venkata Sri
Published: May 2024

Plant diseases have the potential to damage the livelihoods of farmers and impede their capacity to generate an adequate quantity of food. Diagnosis and early detection of plant diseases are critical for their effective management and control. Leaf analysis shows great potential as a method for forecasting disease stages due to its ability to detect subtle alterations in leaf physiology and appearance that may occur prior to the onset of conspicuous symptoms. By utilizing the algorithms of machine learning and deep learning, it is possible to classify characteristics extracted from photographs of leaves into distinct disease stages. Recent studies have demonstrated the potential of these algorithms, as they have achieved remarkable accuracy in disease stage prediction despite having limited training data. The integration of machine learning and deep learning techniques with foliage analysis holds promise for revolutionizing plant disease management through the provision of timely identification, accurate diagnosis, and customized treatment. In order to formulate efficacious disease management strategies, precise determination of the developmental stage of plant diseases is imperative. Scholars are presently devising novel approaches to identify the stages of plant diseases by employing diverse methodologies, including spectroscopy, machine learning, and image processing. This can benefit producers in substantial economic and environmental ways, as well as contribute to the improvement of food security. The primary investigation comprises an assortment of articles spanning the years 2014 to 2023. After evaluating various search strategies, a total of 117 research publications were identified, of which 43 were pertinent. The article examines numerous developments in deep learning research. In addition, it will facilitate the assessment of the present and prospective state of plant disease research by employing deep learning methodologies.
Optimizing Wireless Networks Performance Through Adaptive Machine Learning Strategies
Authors: Shashank Singh
Published: May 2024

This research aims to enhance wireless network performance through the development and implementation of a novel machine learning-based algorithm. The methodology involves comprehensive data collection in the Uttarakhand region, specifically in Dehradun, Haridwar, and Rishikesh, through LTE network drive tests. The collected data, over six months, serves as the foundation for the proposed algorithm. The research design focuses on parameterizing essential factors of path loss to optimize coverage. Various drive test tools, including NEMO, Agilent, TEMS, and XCAL-Mobile, are employed to measure and analyze field Received Signal Strength Indicators (RSSI) data. Statistical tools are utilized to collect network parameters, assess coverage, and analyze real-time mobile network data. The research objectives include the development and evaluation of the machine learning algorithm, comparing its performance across diverse network scenarios, and benchmarking against existing state- of-the-art algorithms. The proposed methodology provides a structured and relevant framework to achieve these objectives, fostering advancements in wireless network optimization through innovative machine-learning approaches.