Satish C Sharma, Nathi Ram Chauhan, Manish Saraswat, Rahul Kumar
In this research a hole-entry hydrostatic journal bearings with couple stress and Newtonian lubricants have been examined analytically. The Newton Rapshon Method and Finite Element Method have been applied on Reynolds equation for couple stress and for the Newtonian lubricants to achieve the film pressure. The results of disparate factors of couple stress lubricants and the external load have been modeled. The outcome of the achieved results showed that the static and dynamic actions of bearings enhanced under a couple stress lubricants assessed the bearings performance with Newtonian lubricant.
{"title":"Theoretical Analysis of Orifice Compensated Symmetric and Asymmetric Hydrostatic Non-Recessedjournal Bearings Under Couple Stress Lubricants","authors":"Satish C Sharma, Nathi Ram Chauhan, Manish Saraswat, Rahul Kumar","doi":"10.5750/ijme.v1i1.1341","DOIUrl":"https://doi.org/10.5750/ijme.v1i1.1341","url":null,"abstract":"In this research a hole-entry hydrostatic journal bearings with couple stress and Newtonian lubricants have been examined analytically. The Newton Rapshon Method and Finite Element Method have been applied on Reynolds equation for couple stress and for the Newtonian lubricants to achieve the film pressure. The results of disparate factors of couple stress lubricants and the external load have been modeled. The outcome of the achieved results showed that the static and dynamic actions of bearings enhanced under a couple stress lubricants assessed the bearings performance with Newtonian lubricant.","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":"141797557","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}
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.
{"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}
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.
{"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}
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.
{"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}
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.
{"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}
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.
{"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}
Additive Manufacturing holds significant potential in influencing automotive sector and its supply chain however its effectiveness depends on variety of factors. This study aims to propose an analytic hierarchy process model to justify AM implementation to automotive sector for enhanced sustainability and resilience to its supply chain. This paper outlines benefits of AM over traditional manufacturing for automotive supply chain which leads to development of more sustainable and resilient supply chain. The AHP is used in this study to justify the advantages of the AM over Traditional Manufacturing for automotive supply chain. Major benefits of AM adoption have been identified through recent review of the literature on this topic and expert perspectives. AHP is used to calculate and compare the global desirability index of AMSC and TSC. Comparing AM-based supply chains to traditional manufacturing, it is found that the former have a higher global desirability index.
增材制造在影响汽车行业及其供应链方面具有巨大潜力，但其有效性取决于多种因素。本研究旨在提出一个分析层次过程模型，以证明在汽车行业实施增材制造的合理性，从而增强其供应链的可持续性和弹性。 本文概述了与传统制造相比，AM 为汽车供应链带来的好处，从而发展出更具可持续性和弹性的供应链。本研究采用了 AHP 方法，以证明在汽车供应链中，AM 比传统制造更具优势。通过最近对有关该主题的文献和专家观点的审查，确定了采用 AM 的主要好处。AHP 用于计算和比较 AMSC 和 TSC 的总体可取性指数。将基于 AM 的供应链与传统制造进行比较后发现，前者的总体可取性指数更高。
{"title":"Justification Framework for Adoption of Additive Manufacturing to Automotive Supply Chain: AHP Approach","authors":"Praveen Kumar Dwivedi, Girish Kumar, R. C. Singh","doi":"10.5750/ijme.v1i1.1377","DOIUrl":"https://doi.org/10.5750/ijme.v1i1.1377","url":null,"abstract":"Additive Manufacturing holds significant potential in influencing automotive sector and its supply chain however its effectiveness depends on variety of factors. This study aims to propose an analytic hierarchy process model to justify AM implementation to automotive sector for enhanced sustainability and resilience to its supply chain. This paper outlines benefits of AM over traditional manufacturing for automotive supply chain which leads to development of more sustainable and resilient supply chain. The AHP is used in this study to justify the advantages of the AM over Traditional Manufacturing for automotive supply chain. Major benefits of AM adoption have been identified through recent review of the literature on this topic and expert perspectives. AHP is used to calculate and compare the global desirability index of AMSC and TSC. Comparing AM-based supply chains to traditional manufacturing, it is found that the former have a higher global desirability index.","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":"141797729","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 study examines the impact of wear and turbulent flow on symmetric hole-entry hybrid journal bearings with orifice restrictors. Dufranes’s abrasive model for wear effect and Constantinescu’s lubrication model for turbulent flow have been used. A modification has been made to the Reynolds equationand utilized the finite element method to solvealong withflow equation of an orifice restrictor. For selected wear depth parameter values and Reynolds numbers,computed results have been acquired. The minimum fluid film thickness increases for worn bearings when operating under a turbulent regime rather than a laminar regime. Further, the stiffness coefficient decreases for constant external load when worn/unworn bearings function in a turbulent regime.
{"title":"Effect of Wear on Symmetric Hole-Entry Hybrid Journal Bearing Compensated by Orifice Restrictor Under Turbulent Regime","authors":"Nathi Ram Chauhan, Satish C Sharma, Manish Saraswat, Kuldeep Sharma, Rohit Sahu","doi":"10.5750/ijme.v1i1.1356","DOIUrl":"https://doi.org/10.5750/ijme.v1i1.1356","url":null,"abstract":"This study examines the impact of wear and turbulent flow on symmetric hole-entry hybrid journal bearings with orifice restrictors. Dufranes’s abrasive model for wear effect and Constantinescu’s lubrication model for turbulent flow have been used. A modification has been made to the Reynolds equationand utilized the finite element method to solvealong withflow equation of an orifice restrictor. For selected wear depth parameter values and Reynolds numbers,computed results have been acquired. The minimum fluid film thickness increases for worn bearings when operating under a turbulent regime rather than a laminar regime. Further, the stiffness coefficient decreases for constant external load when worn/unworn bearings function in a turbulent regime.","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":"141797795","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}
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.
{"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}
Pub Date : 2024-01-22DOI: 10.5750/ijme.v165ia3.1220
Hans Liwång, Jonas Kindgren, Johan Granholm, Therese Tärnholm
Development of autonomous vessels is expected to create a paradigm shift in how warfare is conducted. Therefore, there is need to explore the possibilities and limitations in developing integrated systems for defence at sea to support innovation. Fleet modelling can analyse functions and other design options such as autonomous platform’s and evaluate their added effect in naval operations. However, due to the complexity of naval operations, it is not feasible to create a tool that covers all aspects needed to mimic reality. This study, from the perspective of naval tactics, investigate the value of a tool that analyses potential fleet architectures including autonomous platforms. The study identifies that the tool creates relevant mental models for future naval fleets by identifying feasible fleet compositions. However, the proposed fleet combinations are only tested against a limited set of tactical needs and can only be seen as a starting point for development.
{"title":"Analyzing Naval Fleet Modelling with a Tactics Perspective – The Case of Implementation of Autonomous Vessels","authors":"Hans Liwång, Jonas Kindgren, Johan Granholm, Therese Tärnholm","doi":"10.5750/ijme.v165ia3.1220","DOIUrl":"https://doi.org/10.5750/ijme.v165ia3.1220","url":null,"abstract":"Development of autonomous vessels is expected to create a paradigm shift in how warfare is conducted. Therefore, there is need to explore the possibilities and limitations in developing integrated systems for defence at sea to support innovation. Fleet modelling can analyse functions and other design options such as autonomous platform’s and evaluate their added effect in naval operations. However, due to the complexity of naval operations, it is not feasible to create a tool that covers all aspects needed to mimic reality. This study, from the perspective of naval tactics, investigate the value of a tool that analyses potential fleet architectures including autonomous platforms. The study identifies that the tool creates relevant mental models for future naval fleets by identifying feasible fleet compositions. However, the proposed fleet combinations are only tested against a limited set of tactical needs and can only be seen as a starting point for development.","PeriodicalId":50313,"journal":{"name":"International Journal of Maritime Engineering","volume":null,"pages":null},"PeriodicalIF":0.4,"publicationDate":"2024-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140501041","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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