International Journal of Maritime Engineering最新文献_第7页

Theoretical Analysis of Orifice Compensated Symmetric and Asymmetric Hydrostatic Non-Recessedjournal Bearings Under Couple Stress Lubricants 耦合应力润滑剂作用下孔口补偿对称和非对称静压非凹陷轴颈轴承的理论分析

IF 0.7 4区工程技术

International Journal of Maritime Engineering Pub Date : 2024-07-27 DOI: 10.5750/ijme.v1i1.1341

Satish C Sharma, Nathi Ram Chauhan, Manish Saraswat, Rahul Kumar

引用次数: 0

Art Therapy to Promote College Students’ Mental Health Based on a Hierarchical Clustering Algorithm 基于层次聚类算法的促进大学生心理健康的艺术疗法

IF 0.7 4区工程技术

International Journal of Maritime Engineering Pub Date : 2024-07-27 DOI: 10.5750/ijme.v1i1.1398

J Y Zheng

{"title":"Art Therapy to Promote College Students’ Mental Health Based on a Hierarchical Clustering Algorithm","authors":"J Y Zheng","doi":"10.5750/ijme.v1i1.1398","DOIUrl":"https://doi.org/10.5750/ijme.v1i1.1398","url":null,"abstract":"Art therapy is a therapeutic approach that utilizes the creative process of making art to improve mental, emotional, and physical well-being. Art therapy is a form of expressive therapy that utilizes the creative process of making art to improve mental, emotional, and psychological well-being. It provides individuals with a non-verbal outlet for self-expression and exploration, allowing them to communicate and process their thoughts, feelings, and experiences in a safe and supportive environment. This paper proposed an efficient Weighted Hierarchical Clustering Deep Neural Network (WH-CDNN) to promote the mental health of college students. The proposed WH-CDNN model extracts the features of the art therapy to promote the mental health of students. The features considered for the analysis are color palette, texture, and therapy for the promotion of mental health assessment of students. The features associated with the weighted model are computed for the college student mental health assessment. The features with the WH-CDNN model use the hierarchical clustering model for the computation of the features in art therapy based on the assessment of mental health. The examination is based on the consideration of 10 features for the estimation with the 5 clusters for the evaluation of the mental health assessment. Experimental analysis of the results demonstrated that the proposed WH-CDNN model achieves significant improvement in the after the art therapy of the students with the mental health assessment. Through simulation and analysis, the study demonstrates the effectiveness of art therapy in improving mental health outcomes, with significant reductions observed in anxiety and depression levels post-therapy. Moreover, the WH-CDNN model accurately predicts students' mental health states and evaluates the efficacy of art therapy interventions. The findings highlight the potential of integrating advanced computational techniques with art therapy to support student well-being and inform targeted mental health interventions in educational settings.","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":"141798186","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}

引用次数: 0

Enhancing Support Vector Machine Performance: A Hybrid Approach with Davidon-Fletcher-Powell Algorithm and Elephant Herding Optimization (EHO-DFP) for Parameter Optimization 提高支持向量机性能：使用戴维顿-弗莱彻-鲍威尔算法和大象放牧优化（EHO-DFP）进行参数优化的混合方法

IF 0.7 4区工程技术

International Journal of Maritime Engineering Pub Date : 2024-07-27 DOI: 10.5750/ijme.v1i1.1345

Uttam Singh Bist, Nanhay Singh

{"title":"Enhancing Support Vector Machine Performance: A Hybrid Approach with Davidon-Fletcher-Powell Algorithm and Elephant Herding Optimization (EHO-DFP) for Parameter Optimization","authors":"Uttam Singh Bist, Nanhay Singh","doi":"10.5750/ijme.v1i1.1345","DOIUrl":"https://doi.org/10.5750/ijme.v1i1.1345","url":null,"abstract":"Support Vector Machines (SVMs) have gained prominence in machine learning for their capability to establish optimal decision boundaries in high-dimensional spaces. SVMs are powerful machine learning models but can encounter difficulties in achieving optimal performance due to challenges such as selecting appropriate kernel parameters, handling uncertain data, and adapting to complex decision boundaries.. This paper introduces a novel hybrid approach to enhance the performance of Support Vector Machines (SVM) through the integration of the Davidon-Fletcher-Powell (DFP) optimization algorithm and Elephant Herding Optimization (EHO) for parameter tuning. SVM, a robust machine learning algorithm, relies on effective hyperparameter selection for optimal performance. The proposed hybrid model synergistically leverages DFP's efficiency in unconstrained optimization and EHO's exploration-exploitation balance inspired by elephant herding behavior. The fusion of these algorithms address the challenges associated with traditional optimization methods. The hybrid model offers improved convergence towards the global optimum. Experimental results demonstrate the efficacy of the approach, showcasing enhanced SVM performance in terms of minimum 3.3% accuracy and 3.4% efficiency. This research contributes to advancing the field of metaheuristic optimization in machine learning, providing a promising avenue for effective parameter optimization in SVM applications.","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":"141798314","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}

引用次数: 0

English Sentiment Analysis and its Application in Translation Based on Decision Tree Algorithm 基于决策树算法的英语情感分析及其在翻译中的应用

IF 0.7 4区工程技术

International Journal of Maritime Engineering Pub Date : 2024-07-27 DOI: 10.5750/ijme.v1i1.1371

Meilan Yang

{"title":"English Sentiment Analysis and its Application in Translation Based on Decision Tree Algorithm","authors":"Meilan Yang","doi":"10.5750/ijme.v1i1.1371","DOIUrl":"https://doi.org/10.5750/ijme.v1i1.1371","url":null,"abstract":"Sentimental analysis belongs to the class of Natural Language Processing (NLP) based on the rule and machine model. The proposed model comprises of the pre-defined function for the estimation of the features in the English statements. This paper presents the Reflect Sentiment Translation Decision Tree (RSTDT), a novel model designed to integrate sentiment analysis and translation tasks for English text. The RSTDT model combines the strengths of decision tree algorithms with feature extraction techniques to accurately analyze sentiment and translate text across languages. The proposed RSTDT dataset comprises English sentences with annotated sentiment labels, the RSTDT model is trained to identify sentiment polarity and generate corresponding translations in Arabic. The proposed RSTDT model uses Traslation mapping for the estimation of the sentimental features. In order to estimate and classify the features in the neural network, the processes features are assessed using the decision tree model. The RSTDT model's efficacy in precisely capturing sentiment nuances and generating linguistically appropriate translations was shown through thorough testing and review. The model achieves high accuracy in sentiment analysis and exhibits proficiency in translating sentiment-rich content into Arabic while maintaining contextual relevance. Additionally, robust classification performance metrics underscore the model's efficacy in accurately classifying English words into sentiment categories. The RSTDT model offers a promising solution for multilingual sentiment analysis applications, with potential applications in social media monitoring, customer feedback analysis, and cross-cultural sentiment analysis.","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":"141797054","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}

引用次数: 0

Elastic Moduli, and Estimations of Some Physical, Thermal, and Optical Parameters of Ge-Se-As Glassy Systems with Improved Mechanical Strength 提高机械强度的 Ge-Se-As 玻璃体系的弹性模量以及一些物理、热和光学参数的估算

IF 0.7 4区工程技术

International Journal of Maritime Engineering Pub Date : 2024-07-27 DOI: 10.5750/ijme.v1i1.1354

Dipankar Biswas, Rittwick Mondal, Souvik Brahma Hota, Rahul Singh, Rishu Chabra

{"title":"Elastic Moduli, and Estimations of Some Physical, Thermal, and Optical Parameters of Ge-Se-As Glassy Systems with Improved Mechanical Strength","authors":"Dipankar Biswas, Rittwick Mondal, Souvik Brahma Hota, Rahul Singh, Rishu Chabra","doi":"10.5750/ijme.v1i1.1354","DOIUrl":"https://doi.org/10.5750/ijme.v1i1.1354","url":null,"abstract":"Employing the melt quench approach, glassy systems with the chemical composition Ge30Se(70-x)Asx have been synthesized. As the amount of arsenic increases, various physical, mechanical, thermal, optical parameters and some other aspects of elastic moduli have been assessed. The XRD pattern shows the amorphous characteristics of the inspected materials. The density of the glasses increases from 4.32 to 4.61 g-cm-3 whereas the molar volume declines from 19.32 to 18.62 cm3 mol-1 as the concentration of arsenic increases. The measured values of the ultrasonic velocities have been used to measure the elastic properties, such as the Shear, and longitudinal strains, Bulk modulus, Young's modulus, and Poisson's ratio of the synthesized glasses. The upsurge in the values of elastic moduli indicated the upgrading in the elastic properties of the materials. The outcomes are interpreted in terms of a profound structural change brought about by molecular rearrangement, which regulates the glass’s physical characteristics. The optical band gap energies are found to decrease from 2.17 to 1.86 eV due to the increase in Urbach energies from 040 to 0.64 eV with the incorporation of arsenic atom. The obtained results indicate the perspective of the as-synthesized thermally stable materials to be used in optoelectronic devices.","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":"141797364","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}

引用次数: 0

Automated English Teaching System Through Deep Belief Network for Human-Computer Interaction Experience 通过深度信念网络实现人机交互体验的自动化英语教学系统

IF 0.7 4区工程技术

International Journal of Maritime Engineering Pub Date : 2024-07-27 DOI: 10.5750/ijme.v1i1.1391

H W Huang

{"title":"Automated English Teaching System Through Deep Belief Network for Human-Computer Interaction Experience","authors":"H W Huang","doi":"10.5750/ijme.v1i1.1391","DOIUrl":"https://doi.org/10.5750/ijme.v1i1.1391","url":null,"abstract":"This paper presented the integration of Human-Computer Interaction (HCI) with the Automated Teaching Belief Network (ATBN) to enhance automated English teaching experiences. The proposed ATBN model implemented the Deep Belief Network for the estimation of the factors related to the HCI to promote the experience of the users. The ATBN model uses the deep learning model for the classification in English Teaching. Through the capabilities of deep learning and HCI principles, the ATBN system offers personalized and adaptive learning experiences tailored to individual student needs. The proposed ATBN model estimates the features in English teaching to improve the performance of the Students through HCI model. Simulation analysis expressed that proposed ATBN model improves the pre-test and post-test score by +15 for the English Teaching. The classification values are achieved with accuracy value of 94.8% with minimal loss of 0.12. The assessment of student performance through pre-test and post-test score is improved by 15 for the beginner, intermediate and advanced level. The findings expressed that proposed ATBN model achieves the higher teaching test performance for the HCI language level through the belief network those significantly improves the user experience.","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":"141797434","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}

引用次数: 0

Justification Framework for Adoption of Additive Manufacturing to Automotive Supply Chain: AHP Approach 汽车供应链采用增材制造的理由框架：AHP 方法

IF 0.7 4区工程技术

International Journal of Maritime Engineering Pub Date : 2024-07-27 DOI: 10.5750/ijme.v1i1.1377

Praveen Kumar Dwivedi, Girish Kumar, R. C. Singh

引用次数: 0

Effect of Wear on Symmetric Hole-Entry Hybrid Journal Bearing Compensated by Orifice Restrictor Under Turbulent Regime 湍流状态下通过孔口限制器补偿的对称孔-入口混合轴颈轴承的磨损效应

IF 0.7 4区工程技术

International Journal of Maritime Engineering Pub Date : 2024-07-27 DOI: 10.5750/ijme.v1i1.1356

Nathi Ram Chauhan, Satish C Sharma, Manish Saraswat, Kuldeep Sharma, Rohit Sahu

引用次数: 0

Application of Machine Learning- Based Sentiment Analysis in Packaging Design Style Prediction Modelling 基于机器学习的情感分析在包装设计风格预测建模中的应用

IF 0.7 4区工程技术

International Journal of Maritime Engineering Pub Date : 2024-07-27 DOI: 10.5750/ijme.v1i1.1337

MY Zhange

{"title":"Application of Machine Learning- Based Sentiment Analysis in Packaging Design Style Prediction Modelling","authors":"MY Zhange","doi":"10.5750/ijme.v1i1.1337","DOIUrl":"https://doi.org/10.5750/ijme.v1i1.1337","url":null,"abstract":"Machine learning-based sentiment analysis plays a pivotal role in the innovative realm of packaging design style prediction modeling. By harnessing advanced algorithms, this approach analyzes consumer sentiments towards various packaging designs, extracting valuable insights into preferences and trends. The model utilizes machine learning techniques to identify patterns in historical data, allowing it to predict and recommend packaging design styles likely to resonate positively with target audiences. This research introduces an innovative approach to packaging design style prediction modeling by incorporating a machine learning-based sentiment analysis technique known as the Conditional Random n-gram Classifier Sentimental (CRn-gCS). Focused on enhancing the intersection of design aesthetics and consumer sentiments, this model employs advanced algorithms to analyze historical data and predict packaging design styles that resonate positively with target audiences. The CRn-gCS, as a key component, refines sentiment analysis by considering conditional relationships between n-grams, contributing to a nuanced understanding of consumer preferences. By leveraging this sophisticated model, designers and marketers can make informed decisions, ensuring that packaging not only aligns with aesthetic trends but also elicits positive emotional responses from consumers. This research contributes to the advancement of predictive modeling in packaging design, offering a comprehensive and data-driven approach to create visually appealing and emotionally resonant packaging.","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":"141798195","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}

引用次数: 0

Analyzing Naval Fleet Modelling with a Tactics Perspective – The Case of Implementation of Autonomous Vessels 从战术角度分析海军舰队建模--自主舰艇的实施案例

IF 0.4 4区工程技术

International Journal of Maritime Engineering Pub Date : 2024-01-22 DOI: 10.5750/ijme.v165ia3.1220

Hans Liwång, Jonas Kindgren, Johan Granholm, Therese Tärnholm

引用次数: 0