Graphic design colour schemes play a fundamental role in shaping the visual identity and impact of a design. This paper proposed an efficient Feature Extraction Madhami Fuzzy Clustering Probabilistic Corrosion (FEMFcPC) for the graphic design. The proposed FEMFcPC model comprises the CMYK color scheme for the estimation of the features in the images. With the estimation of the FEMFcPC model corrosion algorithm is implemented for the computation of the graphic design features. Once the features are estimated the related features for the graphic design are designed with the mamdhami fuzzy clustering model. The estimated features are clustered with the CMYK color schemes. The proposed FEMFcPC model estimates the patterns and relationships among graphic designs to provide valuable insights for design categorization and recommendation systems. A dataset comprising various graphic design examples with their corresponding CMYK color schemes is utilized for analysis. Through the application of the FEMFcPC algorithm, clustering results are obtained, revealing distinct grouping patterns and similarities among the designs. The simulation results stated that the proposed FEMFcPC model achieves a significant color scheme for the graphic design. Through the clustering process, the proposed FEMFcPC model with the values of the probabilistic features for the graphic design. The Key findings include the identification of dominant clusters, even distribution of designs across multiple clusters, detection of outliers, and assessment of cluster density. These insights offer significant implications for design categorization, recommendation systems, and targeted marketing strategies within the graphic design domain. The study contributes to advancing our understanding of graphic design analysis and provides a foundation for future research in this field.
{"title":"Generation of Graphic Design Color Schemes Based on CMYK Color Model and Corrosion Algorithms","authors":"H L Yuan","doi":"10.5750/ijme.v1i1.1397","DOIUrl":"https://doi.org/10.5750/ijme.v1i1.1397","url":null,"abstract":"Graphic design colour schemes play a fundamental role in shaping the visual identity and impact of a design. This paper proposed an efficient Feature Extraction Madhami Fuzzy Clustering Probabilistic Corrosion (FEMFcPC) for the graphic design. The proposed FEMFcPC model comprises the CMYK color scheme for the estimation of the features in the images. With the estimation of the FEMFcPC model corrosion algorithm is implemented for the computation of the graphic design features. Once the features are estimated the related features for the graphic design are designed with the mamdhami fuzzy clustering model. The estimated features are clustered with the CMYK color schemes. The proposed FEMFcPC model estimates the patterns and relationships among graphic designs to provide valuable insights for design categorization and recommendation systems. A dataset comprising various graphic design examples with their corresponding CMYK color schemes is utilized for analysis. Through the application of the FEMFcPC algorithm, clustering results are obtained, revealing distinct grouping patterns and similarities among the designs. The simulation results stated that the proposed FEMFcPC model achieves a significant color scheme for the graphic design. Through the clustering process, the proposed FEMFcPC model with the values of the probabilistic features for the graphic design. The Key findings include the identification of dominant clusters, even distribution of designs across multiple clusters, detection of outliers, and assessment of cluster density. These insights offer significant implications for design categorization, recommendation systems, and targeted marketing strategies within the graphic design domain. The study contributes to advancing our understanding of graphic design analysis and provides a foundation for future research in this field.","PeriodicalId":50313,"journal":{"name":"International Journal of Maritime Engineering","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141797098","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The primary synthesis and secondary treatment of aluminum matrix composites are thoroughly reviewed in this work. further treatments that are intended to improve the properties of the synthesized composites—such as heat treatment, forging, and other thermomechanical processes—are covered. An overview of the benefits and limitations of several main synthesis pathways and secondary treatments for the production of ceramic-reinforced AMCs is provided in a clear and comprehensive manner through a synthesis of previous investigations. A noteworthy vacuum exists in the literature regarding the synergistic application of several synthesis pathways and secondary treatment procedures for the production of AMCs, despite substantial research efforts in this area.
{"title":"Exploring the Heat Treatment of Aluminium Matrix Composites: A Review","authors":"Varun Singhal, Lavish Kumar Singh, Devender Kumar, Yadaiah Nirsanametla","doi":"10.5750/ijme.v1i1.1372","DOIUrl":"https://doi.org/10.5750/ijme.v1i1.1372","url":null,"abstract":"The primary synthesis and secondary treatment of aluminum matrix composites are thoroughly reviewed in this work. further treatments that are intended to improve the properties of the synthesized composites—such as heat treatment, forging, and other thermomechanical processes—are covered. An overview of the benefits and limitations of several main synthesis pathways and secondary treatments for the production of ceramic-reinforced AMCs is provided in a clear and comprehensive manner through a synthesis of previous investigations. A noteworthy vacuum exists in the literature regarding the synergistic application of several synthesis pathways and secondary treatment procedures for the production of AMCs, despite substantial research efforts in this area.","PeriodicalId":50313,"journal":{"name":"International Journal of Maritime Engineering","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141797471","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Rural revitalization refers to efforts aimed at renewing and invigorating rural communities to enhance their economic, social, and environmental well-being. To promote the appropriate development in rural revitalization it is necessary to select and promote the appropriate scheme with effective planning, this paper presented effective rural revitalization strategies with the advanced methodology of Genetic Ant Swarm Fuzzy (GAsF), to assess and optimize interventions across various dimensions. The constructed GAsF model implementation of the genetic algorithm-based ant swarm optimization the key features related to the revitalization are estimated. The estimated features are categorized with the fuzzy dynamic planning model for the classification of the program in rural revitalization with the path model implementation in the fuzzy dynamic planning system. Through the computed features classification is performed for the estimation of the features in the programs. Results stated that strategies such as the "Ecotourism Development Strategy" and the "Community Empowerment Program" as particularly promising, demonstrating strong performance in economic, social, and environmental dimensions. Simulation analysis demonstrated that the propose GAsF model achieved the classification accuracy of 0.98 with significant green development values. Through the fuzzy dynamic modelling the GAsF optimize the best fitness value for the ecotourism development value of 0.93.
{"title":"Selection and Promotion of Rural Revitalization Path Mode Incorporating Fuzzy Dynamic Planning","authors":"H M Hao","doi":"10.5750/ijme.v1i1.1390","DOIUrl":"https://doi.org/10.5750/ijme.v1i1.1390","url":null,"abstract":"Rural revitalization refers to efforts aimed at renewing and invigorating rural communities to enhance their economic, social, and environmental well-being. To promote the appropriate development in rural revitalization it is necessary to select and promote the appropriate scheme with effective planning, this paper presented effective rural revitalization strategies with the advanced methodology of Genetic Ant Swarm Fuzzy (GAsF), to assess and optimize interventions across various dimensions. The constructed GAsF model implementation of the genetic algorithm-based ant swarm optimization the key features related to the revitalization are estimated. The estimated features are categorized with the fuzzy dynamic planning model for the classification of the program in rural revitalization with the path model implementation in the fuzzy dynamic planning system. Through the computed features classification is performed for the estimation of the features in the programs. Results stated that strategies such as the \"Ecotourism Development Strategy\" and the \"Community Empowerment Program\" as particularly promising, demonstrating strong performance in economic, social, and environmental dimensions. Simulation analysis demonstrated that the propose GAsF model achieved the classification accuracy of 0.98 with significant green development values. Through the fuzzy dynamic modelling the GAsF optimize the best fitness value for the ecotourism development value of 0.93.","PeriodicalId":50313,"journal":{"name":"International Journal of Maritime Engineering","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141797816","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Music therapy, enriched by the integration of AI technology, represents a cutting-edge approach to harnessing the therapeutic power of music for mental and emotional well-being. AI algorithms are employed to analyze individual preferences, emotional states, and physiological responses, enabling the creation of personalized music interventions. These interventions can range from mood-enhancing playlists to dynamically generated compositions tailored to the specific needs of the listener. This paper introduces an Optimized Sentimental n-gram Classifier (OSC) model tailored for application in the context of music therapy. Leveraging artificial intelligence (AI) technology and sentiment analysis techniques, the OSC model aims to enhance the understanding and classification of sentiments expressed during music therapy sessions. The OSC model uses the n-gram classifier for the estimation of the feature vector in the music speech signal. The classifier model comprises of the Artificial Intelligence (AI) for the evaluation of the music therapy for the sentimental analysis. Through extensive experimentation and evaluation, the OSC model demonstrates high accuracy, precision, recall, and F1 scores across multiple iterations, indicating its effectiveness in accurately predicting sentiments and classifying sessions. The model's robust performance suggests its potential to assist therapists in better understanding participants' emotional states and tailoring interventions accordingly. By providing a valuable tool for sentiment analysis in music therapy, the OSC model contributes to advancing the integration of AI technology into healthcare practices, with implications for improving patient outcomes and well-being.
{"title":"Music Sentiment Analysis and its Application in Music Therapy Based on AI Technology","authors":"JY Zheng","doi":"10.5750/ijme.v1i1.1358","DOIUrl":"https://doi.org/10.5750/ijme.v1i1.1358","url":null,"abstract":"Music therapy, enriched by the integration of AI technology, represents a cutting-edge approach to harnessing the therapeutic power of music for mental and emotional well-being. AI algorithms are employed to analyze individual preferences, emotional states, and physiological responses, enabling the creation of personalized music interventions. These interventions can range from mood-enhancing playlists to dynamically generated compositions tailored to the specific needs of the listener. This paper introduces an Optimized Sentimental n-gram Classifier (OSC) model tailored for application in the context of music therapy. Leveraging artificial intelligence (AI) technology and sentiment analysis techniques, the OSC model aims to enhance the understanding and classification of sentiments expressed during music therapy sessions. The OSC model uses the n-gram classifier for the estimation of the feature vector in the music speech signal. The classifier model comprises of the Artificial Intelligence (AI) for the evaluation of the music therapy for the sentimental analysis. Through extensive experimentation and evaluation, the OSC model demonstrates high accuracy, precision, recall, and F1 scores across multiple iterations, indicating its effectiveness in accurately predicting sentiments and classifying sessions. The model's robust performance suggests its potential to assist therapists in better understanding participants' emotional states and tailoring interventions accordingly. By providing a valuable tool for sentiment analysis in music therapy, the OSC model contributes to advancing the integration of AI technology into healthcare practices, with implications for improving patient outcomes and well-being.","PeriodicalId":50313,"journal":{"name":"International Journal of Maritime Engineering","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141797935","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Nanostructured magnetic nanoparticles and nanocomposites possess exceptional properties and serve and act as an interface between physics and engineering. Innovations in nano-scale material science can be utilized to solve the issue of marine biofouling by creating anti-corrosion coatings that are not harmful to wildlife and the environment. In this paper, the author has studied the impact of size, dimension, and shape on the Saturation magnetisation of magnetic nanomaterials having wide applications using a qualitative model. It is known that Curie temperature and saturation magnetisation of magnetic materials are linearly related and also the Curie temperature varies linearly with melting temperature. A qualitative model is proposed in the present study extending the relation between Curie temperature and Saturation magnetisation to study the size and shape effect on magnetisation (MS) in nano solids. The nanomaterials considered to study the size and shape impact on Saturation magnetisation with size are Fe, Ni, Co, Fe3O4, MnFe2O4, and CoFe2O4.The Saturation magnetisation is found to reduce with size reduction at nano level due to the drastic increase in the surface area to volume ratio in nano solids with size reduction to nanoregime. The results obtained using the model are compared with the available experimental data. The variation in magnetisation is studied for shapes viz. nanowires, thin films, spherical, tetrahedral, octahedral, dodecahedral and icosahedral nanosolids. A good consistency is obtained between the present model results and the experimental results available that justify the validity of the model proposed.
{"title":"Study of Magnetization Variation in Magnetic Nanomaterials Having Wide Applications in Making Anti-Corrosion Coatings Using Qualitative Approach","authors":"Monika Goyal, Rahul Kumar, Rupali Gill","doi":"10.5750/ijme.v1i1.1343","DOIUrl":"https://doi.org/10.5750/ijme.v1i1.1343","url":null,"abstract":"Nanostructured magnetic nanoparticles and nanocomposites possess exceptional properties and serve and act as an interface between physics and engineering. Innovations in nano-scale material science can be utilized to solve the issue of marine biofouling by creating anti-corrosion coatings that are not harmful to wildlife and the environment. In this paper, the author has studied the impact of size, dimension, and shape on the Saturation magnetisation of magnetic nanomaterials having wide applications using a qualitative model. It is known that Curie temperature and saturation magnetisation of magnetic materials are linearly related and also the Curie temperature varies linearly with melting temperature. A qualitative model is proposed in the present study extending the relation between Curie temperature and Saturation magnetisation to study the size and shape effect on magnetisation (MS) in nano solids. The nanomaterials considered to study the size and shape impact on Saturation magnetisation with size are Fe, Ni, Co, Fe3O4, MnFe2O4, and CoFe2O4.The Saturation magnetisation is found to reduce with size reduction at nano level due to the drastic increase in the surface area to volume ratio in nano solids with size reduction to nanoregime. The results obtained using the model are compared with the available experimental data. The variation in magnetisation is studied for shapes viz. nanowires, thin films, spherical, tetrahedral, octahedral, dodecahedral and icosahedral nanosolids. A good consistency is obtained between the present model results and the experimental results available that justify the validity of the model proposed.","PeriodicalId":50313,"journal":{"name":"International Journal of Maritime Engineering","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141798463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This review examines recent studies investigating the impact of TiO2 nanoparticles (NPs) on marine bivalves, shedding light on potential threats and ecotoxicological implications. As TiO2 NPs become ubiquitous in industrial and consumer products, concerns arise about their effects on crucial marine ecosystem components. The analysis delves into bioaccumulation, cellular responses and its consequences, emphasizing the need for understanding the intricate interactions between TiO2 NPs and marine bivalves. By unveiling emerging threats and ecotoxicological implications, this article aims to inform scientists, policymakers, and stakeholders, guiding future research and facilitating measures to mitigate potential risks to marine ecosystems.
{"title":"Recent Studies on the Effect of TiO₂-NPS on Marine Bivalves: Unveiling Potential Threats and Ecotoxicological Implications","authors":"Ranjay Shaw, Himanshu Kumar, Monit Kapoor","doi":"10.5750/ijme.v1i1.1373","DOIUrl":"https://doi.org/10.5750/ijme.v1i1.1373","url":null,"abstract":"This review examines recent studies investigating the impact of TiO2 nanoparticles (NPs) on marine bivalves, shedding light on potential threats and ecotoxicological implications. As TiO2 NPs become ubiquitous in industrial and consumer products, concerns arise about their effects on crucial marine ecosystem components. The analysis delves into bioaccumulation, cellular responses and its consequences, emphasizing the need for understanding the intricate interactions between TiO2 NPs and marine bivalves. By unveiling emerging threats and ecotoxicological implications, this article aims to inform scientists, policymakers, and stakeholders, guiding future research and facilitating measures to mitigate potential risks to marine ecosystems. \u0000 ","PeriodicalId":50313,"journal":{"name":"International Journal of Maritime Engineering","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141798517","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mechanical friction in marine environments poses significant challenges, leading to resource depletion and energy consumption. Tribocorrosion, combining electrochemical corrosion and friction wear, jeopardizes metal components, necessitating robust protective measures. Despite minimal wear of marine equipment, even slight mass reductions can trigger catastrophic failures, resulting in substantial maintenance expenses. Steel structures in marine settings are vulnerable to corrosion from aggressive external and internal factors. ASTM AH36 steel, widely used in marine construction, faces wear challenges despite corrosion resistance, prompting the need for surface treatments and coatings to enhance longevity. Previous research emphasizes thermal spray methods, particularly HVOF-applied coatings, as eco-friendly solutions for improving wear resistance on marine steels. Fe-based coatings, characterized by improved hardness and corrosion resistance, offer promising solutions in abrasive marine environments, igniting fervent research efforts. This study conclusively demonstrates that AH36 steel samples coated with Fe-based amorphous layers exhibit substantially reduced wear rates.
{"title":"Enhancing Wear Resistance of Marine Steel with Fe Based Amorphous Coating Via HVOF Spraying","authors":"Varsha Pathak, Ranganath MS, R S Mishra","doi":"10.5750/ijme.v1i1.1374","DOIUrl":"https://doi.org/10.5750/ijme.v1i1.1374","url":null,"abstract":"Mechanical friction in marine environments poses significant challenges, leading to resource depletion and energy consumption. Tribocorrosion, combining electrochemical corrosion and friction wear, jeopardizes metal components, necessitating robust protective measures. Despite minimal wear of marine equipment, even slight mass reductions can trigger catastrophic failures, resulting in substantial maintenance expenses. Steel structures in marine settings are vulnerable to corrosion from aggressive external and internal factors. ASTM AH36 steel, widely used in marine construction, faces wear challenges despite corrosion resistance, prompting the need for surface treatments and coatings to enhance longevity. Previous research emphasizes thermal spray methods, particularly HVOF-applied coatings, as eco-friendly solutions for improving wear resistance on marine steels. Fe-based coatings, characterized by improved hardness and corrosion resistance, offer promising solutions in abrasive marine environments, igniting fervent research efforts. This study conclusively demonstrates that AH36 steel samples coated with Fe-based amorphous layers exhibit substantially reduced wear rates.","PeriodicalId":50313,"journal":{"name":"International Journal of Maritime Engineering","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141797569","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Alzheimer's is one of the most well-known reasons for Dementia. Alzheimer's sickness (AD) is an irreversible, moderate cerebrum issue that gradually obliterates memory and thinking abilities. Alzheimer's and different types of dementia positioned seventh driving reason for death by WHO. Picture handling is broadly utilized in the clinical field to recognize illness and help specialists in dynamics dependent on perception. The paper means to recognize the Alzheimer's sickness at the most punctual with the goal that patients can be forestalled before irreversible changes happen in the mind. We propose the picture handling method to deal with the Magnetic Resonance Imaging (MRI) of the cerebrum from the pivotal plane, coronal plane, and sagittal plane. The picture division is utilized to feature the impacted locale in cerebrum MRI. The analyzed area in mind MRI incorporates the hippocampus and volume of the cerebrum. A similar ID of people impacted with the Alzheimer's illness, Healthy partner, and Mild Cognitive weakness is finished.
{"title":"Innovative Approaches for Early Alzheimer’s Disease Detection through Novel Analysis of Brain MRI Images","authors":"Rekha Gangula, A Manjula, Raghuram Bhukya, Rajesh Saturi, Nandam Gayatri, Ravula Rajesh","doi":"10.5750/ijme.v1i1.1402","DOIUrl":"https://doi.org/10.5750/ijme.v1i1.1402","url":null,"abstract":"Alzheimer's is one of the most well-known reasons for Dementia. Alzheimer's sickness (AD) is an irreversible, moderate cerebrum issue that gradually obliterates memory and thinking abilities. Alzheimer's and different types of dementia positioned seventh driving reason for death by WHO. Picture handling is broadly utilized in the clinical field to recognize illness and help specialists in dynamics dependent on perception. The paper means to recognize the Alzheimer's sickness at the most punctual with the goal that patients can be forestalled before irreversible changes happen in the mind. We propose the picture handling method to deal with the Magnetic Resonance Imaging (MRI) of the cerebrum from the pivotal plane, coronal plane, and sagittal plane. The picture division is utilized to feature the impacted locale in cerebrum MRI. The analyzed area in mind MRI incorporates the hippocampus and volume of the cerebrum. A similar ID of people impacted with the Alzheimer's illness, Healthy partner, and Mild Cognitive weakness is finished.","PeriodicalId":50313,"journal":{"name":"International Journal of Maritime Engineering","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141797576","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A three-dimensional auxiliary system serves as a foundational framework for spatial analysis and modeling in various fields. This system serves as a fundamental tool for visualizing and manipulating three-dimensional data, allowing researchers, designers, and engineers to accurately represent and analyze complex structures and environments. Dance creation is a multifaceted artistic process that involves choreographing movements, sequences, and gestures to convey ideas, emotions, and narratives through bodily expression. This paper uses the advanced automated application model for the dance creation with the 3D-auxiliary system for the choreography. The constructed model incorporates statistically integrated Principal Component Analysis (PCA) for the computation of features in the dance creation movement prediction. Finally, the estimation of the statistically integrated PCA model is applied over neural network modeling for the classification of features in the dance creation. With the estimated PCA model values statistical correlation between the PCA features are estimated and classified for the different dance types. The examination is based on the classification of dance movement dynamics, patterns, and stylistic elements for the dance creation. Simulation estimation demonstrated that a constructed statistical 3D auxiliary system was effectively involved in the dance movement prediction with the classification of features through a neural network for the dance movement prediction. The PCA model uses the 5 features to evaluate the auxiliary points of the dance movement in the reference human video. Through the analysis of the PCA features with the statistical values the outline sketch of the dance is framed and dance movement are created.
{"title":"Dance Creation Based on the Development and Application of a Computer Three-Dimensional Auxiliary System","authors":"L X Gao","doi":"10.5750/ijme.v1i1.1367","DOIUrl":"https://doi.org/10.5750/ijme.v1i1.1367","url":null,"abstract":"A three-dimensional auxiliary system serves as a foundational framework for spatial analysis and modeling in various fields. This system serves as a fundamental tool for visualizing and manipulating three-dimensional data, allowing researchers, designers, and engineers to accurately represent and analyze complex structures and environments. Dance creation is a multifaceted artistic process that involves choreographing movements, sequences, and gestures to convey ideas, emotions, and narratives through bodily expression. This paper uses the advanced automated application model for the dance creation with the 3D-auxiliary system for the choreography. The constructed model incorporates statistically integrated Principal Component Analysis (PCA) for the computation of features in the dance creation movement prediction. Finally, the estimation of the statistically integrated PCA model is applied over neural network modeling for the classification of features in the dance creation. With the estimated PCA model values statistical correlation between the PCA features are estimated and classified for the different dance types. The examination is based on the classification of dance movement dynamics, patterns, and stylistic elements for the dance creation. Simulation estimation demonstrated that a constructed statistical 3D auxiliary system was effectively involved in the dance movement prediction with the classification of features through a neural network for the dance movement prediction. The PCA model uses the 5 features to evaluate the auxiliary points of the dance movement in the reference human video. Through the analysis of the PCA features with the statistical values the outline sketch of the dance is framed and dance movement are created.","PeriodicalId":50313,"journal":{"name":"International Journal of Maritime Engineering","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141797760","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Music education plays a vital role in fostering creativity, expression, and cognitive development among students in university settings. Moodle is the learning management platform to promotes significant knowledge sharing among the students in the Universities. In this paper, introduce the Federated Deep Learning Moodle Hidden Chain (FDLMHc) with the Moodle platform for music education experiences. The FDLMHc system combines the power of federated learning with the flexibility of Moodle to provide personalized feedback and adaptive learning pathways for students. The FDLMHc model uses the Music signal pitch estimation with the consideration of the different pitches in the signal frequencies. The signal of the music signal is estimated for the different SNR rates of -10dB, 0dB, 10 dB, and 20 dB. The proposed FDLMHc model computes and processes the music signal with the hidden chain process for the estimation of the pitches in the music signal. The estimated hidden chain model is applied over the federated learning network for the classification of the signal in the Music. The findings reveal promising results, demonstrating the system's ability to accurately classify musical elements, such as pitch, rhythm, and dynamics, while providing personalized feedback tailored to individual student needs. The accuracy for the estimation of the Music pitch is estimated as the 95% with a convergence rate of 91% for the estimation of the signal in the Music signal.
在大学环境中，音乐教育在培养学生的创造力、表现力和认知发展方面发挥着至关重要的作用。Moodle 是一个学习管理平台，可促进大学学生之间的知识共享。本文将结合 Moodle 平台，介绍用于音乐教育体验的 Federated Deep Learning Moodle Hidden Chain（FDLMHc）。FDLMHc 系统将联合学习的强大功能与 Moodle 的灵活性相结合，为学生提供个性化反馈和自适应学习途径。FDLMHc 模型使用音乐信号音高估计，并考虑到信号频率中的不同音高。在-10dB、0dB、10 dB 和 20 dB 的不同信噪比率下对音乐信号进行估计。所提出的 FDLMHc 模型利用隐链过程计算和处理音乐信号，以估算音乐信号中的音高。估计出的隐链模型被应用于联合学习网络，用于音乐信号的分类。研究结果表明，该系统能够准确地对音高、节奏和动态等音乐元素进行分类，同时提供符合学生个人需求的个性化反馈。估计音乐音高的准确率为 95%，音乐信号中信号估计的收敛率为 91%。
{"title":"Designing a Deep Learning-Enabled Music Teaching System in Universities Using the Moodle Platform","authors":"X F Chen","doi":"10.5750/ijme.v1i1.1349","DOIUrl":"https://doi.org/10.5750/ijme.v1i1.1349","url":null,"abstract":"Music education plays a vital role in fostering creativity, expression, and cognitive development among students in university settings. Moodle is the learning management platform to promotes significant knowledge sharing among the students in the Universities. In this paper, introduce the Federated Deep Learning Moodle Hidden Chain (FDLMHc) with the Moodle platform for music education experiences. The FDLMHc system combines the power of federated learning with the flexibility of Moodle to provide personalized feedback and adaptive learning pathways for students. The FDLMHc model uses the Music signal pitch estimation with the consideration of the different pitches in the signal frequencies. The signal of the music signal is estimated for the different SNR rates of -10dB, 0dB, 10 dB, and 20 dB. The proposed FDLMHc model computes and processes the music signal with the hidden chain process for the estimation of the pitches in the music signal. The estimated hidden chain model is applied over the federated learning network for the classification of the signal in the Music. The findings reveal promising results, demonstrating the system's ability to accurately classify musical elements, such as pitch, rhythm, and dynamics, while providing personalized feedback tailored to individual student needs. The accuracy for the estimation of the Music pitch is estimated as the 95% with a convergence rate of 91% for the estimation of the signal in the Music signal.","PeriodicalId":50313,"journal":{"name":"International Journal of Maritime Engineering","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141798325","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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