Sentiment analysis, also known as opinion mining, plays a crucial role in understanding and extracting valuable insights from textual data in various domains, including social media, customer feedback, and product reviews. This research presents an in-depth examination of an improved machine learning-based sentiment analysis framework, focusing on its statistical analysis and accuracy assessment. The research begins by introducing the framework's architecture, which incorporates advanced machine learning algorithms and natural language processing techniques. These enhancements aim to provide a more nuanced and context-aware sentiment analysis, addressing the limitations of traditional approaches. To evaluate the performance of the proposed framework, a comprehensive statistical analysis is conducted. Various statistical metrics, such as precision, recall, F1-score, and accuracy, are employed to assess its effectiveness in classifying text sentiments accurately. Additionally, the study explores the impact of different feature engineering and pre-processing techniques on model performance. The results of this study demonstrate the significant improvements achieved by the enhanced sentiment analysis framework in terms of accuracy and reliability. The statistical analysis confirms its superior performance in capturing subtle sentiment nuances, making it a valuable tool for applications requiring precise sentiment understanding. In conclusion, this research contributes to the field of sentiment analysis by presenting an improved machine learning-based framework and conducting a rigorous statistical assessment of its accuracy. The findings provide valuable insights for researchers and practitioners seeking to enhance sentiment analysis techniques and apply them effectively in various domains..
{"title":"Statistical Analysis and Accuracy Assessment of Improved Machine Learning Based Opinion Mining Framework","authors":"Et al. Harshit Sharma","doi":"10.52783/anvi.v27.322","DOIUrl":"https://doi.org/10.52783/anvi.v27.322","url":null,"abstract":"Sentiment analysis, also known as opinion mining, plays a crucial role in understanding and extracting valuable insights from textual data in various domains, including social media, customer feedback, and product reviews. This research presents an in-depth examination of an improved machine learning-based sentiment analysis framework, focusing on its statistical analysis and accuracy assessment. The research begins by introducing the framework's architecture, which incorporates advanced machine learning algorithms and natural language processing techniques. These enhancements aim to provide a more nuanced and context-aware sentiment analysis, addressing the limitations of traditional approaches. To evaluate the performance of the proposed framework, a comprehensive statistical analysis is conducted. Various statistical metrics, such as precision, recall, F1-score, and accuracy, are employed to assess its effectiveness in classifying text sentiments accurately. Additionally, the study explores the impact of different feature engineering and pre-processing techniques on model performance. The results of this study demonstrate the significant improvements achieved by the enhanced sentiment analysis framework in terms of accuracy and reliability. The statistical analysis confirms its superior performance in capturing subtle sentiment nuances, making it a valuable tool for applications requiring precise sentiment understanding. In conclusion, this research contributes to the field of sentiment analysis by presenting an improved machine learning-based framework and conducting a rigorous statistical assessment of its accuracy. The findings provide valuable insights for researchers and practitioners seeking to enhance sentiment analysis techniques and apply them effectively in various domains..","PeriodicalId":40035,"journal":{"name":"Advances in Nonlinear Variational Inequalities","volume":"74 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140501642","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This research explores e-commerce dynamics, focusing on the challenge of predicting customer churn using deep learning [65]. It integrates and analyses both textual and transactional data, including social media posts and customer feedback [59]. The approach uses an advanced deep learning model, involving data collection, pre-processing, and feature extraction [40]. Novel methods fuse data to create a detailed customer profile combining sentiment analysis with behavioural insights derived from transaction data [25]. The deep learning architecture is designed to analyse and predict customer sentiments and purchasing behaviours, informed by the latest research [65]. This study is significant as it provides an innovative solution for predicting customer churn in e-commerce, aiding sustainability [45]. It also enables targeted retention strategies and personalized customer engagement [59]. Additionally, it contributes insights to big data analytics and customer relationship management in e-commerce, showcasing deep learning's potential in transforming business practices and enhancing customer experience [40].
{"title":"Mathematical Modelling and Deep Learning: Innovations in E-Commerce Sentiment Analysis","authors":"Et al. Ashish Suresh Awate","doi":"10.52783/anvi.v27.317","DOIUrl":"https://doi.org/10.52783/anvi.v27.317","url":null,"abstract":"This research explores e-commerce dynamics, focusing on the challenge of predicting customer churn using deep learning [65]. It integrates and analyses both textual and transactional data, including social media posts and customer feedback [59]. The approach uses an advanced deep learning model, involving data collection, pre-processing, and feature extraction [40]. Novel methods fuse data to create a detailed customer profile combining sentiment analysis with behavioural insights derived from transaction data [25]. The deep learning architecture is designed to analyse and predict customer sentiments and purchasing behaviours, informed by the latest research [65]. This study is significant as it provides an innovative solution for predicting customer churn in e-commerce, aiding sustainability [45]. It also enables targeted retention strategies and personalized customer engagement [59]. Additionally, it contributes insights to big data analytics and customer relationship management in e-commerce, showcasing deep learning's potential in transforming business practices and enhancing customer experience [40].","PeriodicalId":40035,"journal":{"name":"Advances in Nonlinear Variational Inequalities","volume":"12 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140511020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the field of medical diagnosis, combining different types of information like text, images, and audio is a big step forward in making patient assessments more accurate. This research introduces an innovative method to bring together and categorize these different types of data. This method fills an important gap in current research [50, 54]. Proposed approach focuses on turning each type of data—text, images, and audio—into useful numbers. Text data is processed to extract meaning and context, while images are analysed using advanced computer techniques to capture important visual details. We also carefully examine audio data to extract important sound features, which is often overlooked but can be a valuable source of diagnostic information [48]. What makes our method special is how we combine these different types of data. We designed a strategy to blend these diverse sets of numbers into a single, enriched representation. This approach keeps the unique characteristics of each data type intact while harnessing their combined power for diagnosis [22, 29]. After combining the data, we use a well-chosen classification model that's known for its ability to make sense of complex data, especially in medical diagnosis scenarios [67, 71]. Proposed approach is rigorously assessing our method using a set of strong metrics that measure not only how accurate it is but also how reliable and valid it is for diagnosis [90, 94]. The results of this study mark a significant step forward in the field of combining different types of data, showing how it can greatly improve medical diagnosis. This method has the potential to revolutionize healthcare, enabling more precise and comprehensive data-driven decisions [143, 156].
{"title":"Harnessing the Power of Multimodal Data: Medical Fusion and Classification","authors":"Et al. Bhushan Rajendra Nandwalkar","doi":"10.52783/anvi.v27.318","DOIUrl":"https://doi.org/10.52783/anvi.v27.318","url":null,"abstract":"In the field of medical diagnosis, combining different types of information like text, images, and audio is a big step forward in making patient assessments more accurate. This research introduces an innovative method to bring together and categorize these different types of data. This method fills an important gap in current research [50, 54]. Proposed approach focuses on turning each type of data—text, images, and audio—into useful numbers. Text data is processed to extract meaning and context, while images are analysed using advanced computer techniques to capture important visual details. We also carefully examine audio data to extract important sound features, which is often overlooked but can be a valuable source of diagnostic information [48]. What makes our method special is how we combine these different types of data. We designed a strategy to blend these diverse sets of numbers into a single, enriched representation. This approach keeps the unique characteristics of each data type intact while harnessing their combined power for diagnosis [22, 29]. After combining the data, we use a well-chosen classification model that's known for its ability to make sense of complex data, especially in medical diagnosis scenarios [67, 71]. Proposed approach is rigorously assessing our method using a set of strong metrics that measure not only how accurate it is but also how reliable and valid it is for diagnosis [90, 94]. The results of this study mark a significant step forward in the field of combining different types of data, showing how it can greatly improve medical diagnosis. This method has the potential to revolutionize healthcare, enabling more precise and comprehensive data-driven decisions [143, 156].","PeriodicalId":40035,"journal":{"name":"Advances in Nonlinear Variational Inequalities","volume":"24 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140510701","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
IAs the utilization of solar photovoltaic (PV) energy systems continues to expand, the efficient extraction of energy under non-linear operational conditions becomes paramount. This research focuses on the development and enhancement of a Maximum Power Point Tracking (MPPT) algorithm, specifically tailored for solar PV systems, through the integration of Improved Grey Wolf Optimization (IGWO) techniques. The study utilizes mathematical modeling and statistical analysis to evaluate the performance of the proposed IGWO-based MPPT algorithm. This research, we first establish a comprehensive mathematical model of a solar PV energy system that accurately represents its non-linear operational characteristics, taking into account factors such as temperature variations, shading effects, and changing environmental conditions. Subsequently, we introduce the Improved Grey Wolf Optimization algorithm to optimize the MPPT process, aiming to enhance energy extraction efficiency by dynamically adapting to varying conditions. The statistical analysis includes the comparison of the IGWO-based MPPT algorithm with conventional MPPT methods, such as Perturb and Observe (P&O) and Incremental Conductance (IncCond), under various non-linear operational scenarios. Key performance metrics, including energy conversion efficiency, response time, and tracking accuracy, are thoroughly evaluated to assess the algorithm's effectiveness in real-world conditions. The results of this study demonstrate the superior performance of the IGWO-based MPPT algorithm in enhancing the energy harvesting capabilities of solar PV systems under non-linear operational conditions. The proposed approach not only improves the overall energy conversion efficiency but also reduces the adverse effects of environmental variables on the system's performance. In conclusion, the integration of Improved Grey Wolf Optimization into the MPPT process represents a promising advancement in the field of solar photovoltaic energy systems. The mathematical modeling and statistical analysis conducted in this research provide valuable insights into the practical benefits of this approach, paving the way for more efficient and reliable solar energy utilization in the future.
{"title":"Mathematical Modelling and Statistical Analysis of Improved Grey Wolf Optimized Maximum Tracking for Solar Photovoltaic Energy System Under Non Linear Operational Conditions","authors":"Et al. Sunil Kumar Gupta","doi":"10.52783/anvi.v27.321","DOIUrl":"https://doi.org/10.52783/anvi.v27.321","url":null,"abstract":"IAs the utilization of solar photovoltaic (PV) energy systems continues to expand, the efficient extraction of energy under non-linear operational conditions becomes paramount. This research focuses on the development and enhancement of a Maximum Power Point Tracking (MPPT) algorithm, specifically tailored for solar PV systems, through the integration of Improved Grey Wolf Optimization (IGWO) techniques. The study utilizes mathematical modeling and statistical analysis to evaluate the performance of the proposed IGWO-based MPPT algorithm. This research, we first establish a comprehensive mathematical model of a solar PV energy system that accurately represents its non-linear operational characteristics, taking into account factors such as temperature variations, shading effects, and changing environmental conditions. Subsequently, we introduce the Improved Grey Wolf Optimization algorithm to optimize the MPPT process, aiming to enhance energy extraction efficiency by dynamically adapting to varying conditions. The statistical analysis includes the comparison of the IGWO-based MPPT algorithm with conventional MPPT methods, such as Perturb and Observe (P&O) and Incremental Conductance (IncCond), under various non-linear operational scenarios. Key performance metrics, including energy conversion efficiency, response time, and tracking accuracy, are thoroughly evaluated to assess the algorithm's effectiveness in real-world conditions. The results of this study demonstrate the superior performance of the IGWO-based MPPT algorithm in enhancing the energy harvesting capabilities of solar PV systems under non-linear operational conditions. The proposed approach not only improves the overall energy conversion efficiency but also reduces the adverse effects of environmental variables on the system's performance. In conclusion, the integration of Improved Grey Wolf Optimization into the MPPT process represents a promising advancement in the field of solar photovoltaic energy systems. The mathematical modeling and statistical analysis conducted in this research provide valuable insights into the practical benefits of this approach, paving the way for more efficient and reliable solar energy utilization in the future.","PeriodicalId":40035,"journal":{"name":"Advances in Nonlinear Variational Inequalities","volume":"36 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140511406","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A large number of people have been using social media platforms extensively to communicate their thoughts and feelings in the recent era of social networking. Both the user base and data volume on social networks are growing quickly every day. Any time an event or activity occurs nearby, nearby individuals express their thoughts and reactions on social media. When a new product is introduced, users on social media platforms also comment on it. Some people express their views or feelings using informal or complex language which makes it difficult to understand for another user. It is challenging to ascertain the true thoughts because different people express their opinions in complex ways. In this study, the various factors that affect these feelings are briefly discussed. In order to identify sarcasm on Twitter, a generic technique is also necessary in addition to the tweet's content. The proposed approach uses contents of tweet in association with important aspects like user behavior and context of tweet. By users’ behavior we can identify its influence on other users and context is required to identify user behavior while detecting sarcasm. Proposed approach uses user behavior pattern and personality features along with contextual data. This all information and the already known sarcasm prediction mechanism will help us to set up the generic approach to detect sarcasm on Twitter.
{"title":"Mathematical Analysis of Different Learning Approaches on User Behavior and Contextual Evaluation for Sarcasm Prediction","authors":"Et al. L.K. Ahire","doi":"10.52783/anvi.v27.316","DOIUrl":"https://doi.org/10.52783/anvi.v27.316","url":null,"abstract":"A large number of people have been using social media platforms extensively to communicate their thoughts and feelings in the recent era of social networking. Both the user base and data volume on social networks are growing quickly every day. Any time an event or activity occurs nearby, nearby individuals express their thoughts and reactions on social media. When a new product is introduced, users on social media platforms also comment on it. Some people express their views or feelings using informal or complex language which makes it difficult to understand for another user. It is challenging to ascertain the true thoughts because different people express their opinions in complex ways. In this study, the various factors that affect these feelings are briefly discussed. In order to identify sarcasm on Twitter, a generic technique is also necessary in addition to the tweet's content. The proposed approach uses contents of tweet in association with important aspects like user behavior and context of tweet. By users’ behavior we can identify its influence on other users and context is required to identify user behavior while detecting sarcasm. Proposed approach uses user behavior pattern and personality features along with contextual data. This all information and the already known sarcasm prediction mechanism will help us to set up the generic approach to detect sarcasm on Twitter.","PeriodicalId":40035,"journal":{"name":"Advances in Nonlinear Variational Inequalities","volume":"33 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140510753","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
An era of interconnected devices that exchange data has emerged due to machine-to-machine (M2M) communication, a key component of the Internet of Things (IoT). This study explains how dynamic knowledge graph construction improves knowledge management in M2Mcommunication networks. In M2M communication, devices continuously generate and exchange data, creating a complex and dynamic information network. A dynamic knowledge graph is a promising solution for managing and addressing this level of complexity. The knowledge graph evolves in real time to capture M2M network relationships, entities, and data flows. M2M communication with dynamic knowledge graphs has many benefits. It begins with a broad overview of network components and their relationships. The structured format helps understand and make decisions by representing devices, their attributes, and their contextual relationships. The knowledge graph can also scale easily to support the rapid growth of devices and data in M2M networks. A dynamic knowledge graph lets M2M networks route data intelligently. Context-aware decisions reduce latency and improve network efficiency. The knowledge graph helps M2M networks detect and analyze anomalies and patterns. Detecting deviations from expected behavior improves security and proactive network maintenance, ensuring its integrity and reliability. Efficient knowledge management requires dynamic knowledge graphs in M2M communication networks. The data used for the proposed work is collected from the World Wide Web Consortium (W3C). It provides valuable insights into using technologies to improve learning and knowledge management. The dataset is comprehensive and useful for studying dynamic knowledge graphs and clustering in M2M. This enhances M2M networks' reliability and intelligence in the IoT era..
{"title":"Analysis of Dynamic Knowledge Graph Construction and Clustering for Effective Knowledge Management in Machine-to-Machine Communication","authors":"Et al. Ganesh S. Pise","doi":"10.52783/anvi.v27.315","DOIUrl":"https://doi.org/10.52783/anvi.v27.315","url":null,"abstract":"An era of interconnected devices that exchange data has emerged due to machine-to-machine (M2M) communication, a key component of the Internet of Things (IoT). This study explains how dynamic knowledge graph construction improves knowledge management in M2Mcommunication networks. In M2M communication, devices continuously generate and exchange data, creating a complex and dynamic information network. A dynamic knowledge graph is a promising solution for managing and addressing this level of complexity. The knowledge graph evolves in real time to capture M2M network relationships, entities, and data flows. M2M communication with dynamic knowledge graphs has many benefits. It begins with a broad overview of network components and their relationships. The structured format helps understand and make decisions by representing devices, their attributes, and their contextual relationships. The knowledge graph can also scale easily to support the rapid growth of devices and data in M2M networks. A dynamic knowledge graph lets M2M networks route data intelligently. Context-aware decisions reduce latency and improve network efficiency. The knowledge graph helps M2M networks detect and analyze anomalies and patterns. Detecting deviations from expected behavior improves security and proactive network maintenance, ensuring its integrity and reliability. Efficient knowledge management requires dynamic knowledge graphs in M2M communication networks. The data used for the proposed work is collected from the World Wide Web Consortium (W3C). It provides valuable insights into using technologies to improve learning and knowledge management. The dataset is comprehensive and useful for studying dynamic knowledge graphs and clustering in M2M. This enhances M2M networks' reliability and intelligence in the IoT era..","PeriodicalId":40035,"journal":{"name":"Advances in Nonlinear Variational Inequalities","volume":"88 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140511222","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-01DOI: 10.52783/anvi.v26.i4.308
Et al. Manbir Kaur
A Spherical fuzzy model induced with teaching learning based optimization technique is developed for supporting the municipal solid waste management under fuzzy environment. Spherical fuzzy set’s ability to capture imprecise and contradictory information results in a substantial contribution to decision-making issues. Thus, we introduce SFLPP in a spherical fuzzy environ-ment in this article, which entails maximization of truthiness and minimization of indeterminacy and falsity membership functions.In present era TLBO is gaining the popularity of being less complex and only two algorithmic parameters based algorithm. This study introduced a mathematical model to include all of the major components of municipal solid waste management. To deal with uncertainty, the mathematical model of municipal solid waste management is defined using a spherical fuzzy environment.The goal of this research is to determine the current state of waste management in the Dinanagar area of Punjab, India. Finally,the mathematical model is in possession of long-term waste management in the study area, Dinanagar city in Punjab, India. The findings of comparing the suggested model to the current framework show that the new model provides better solutions in terms of sustainability.
{"title":"Mathematical Modelling Of Municipal Solid Waste Management In Spherical Fuzzy Environment","authors":"Et al. Manbir Kaur","doi":"10.52783/anvi.v26.i4.308","DOIUrl":"https://doi.org/10.52783/anvi.v26.i4.308","url":null,"abstract":"A Spherical fuzzy model induced with teaching learning based optimization technique is developed for supporting the municipal solid waste management under fuzzy environment. Spherical fuzzy set’s ability to capture imprecise and contradictory information results in a substantial contribution to decision-making issues. Thus, we introduce SFLPP in a spherical fuzzy environ-ment in this article, which entails maximization of truthiness and minimization of indeterminacy and falsity membership functions.In present era TLBO is gaining the popularity of being less complex and only two algorithmic parameters based algorithm. This study introduced a mathematical model to include all of the major components of municipal solid waste management. To deal with uncertainty, the mathematical model of municipal solid waste management is defined using a spherical fuzzy environment.The goal of this research is to determine the current state of waste management in the Dinanagar area of Punjab, India. Finally,the mathematical model is in possession of long-term waste management in the study area, Dinanagar city in Punjab, India. The findings of comparing the suggested model to the current framework show that the new model provides better solutions in terms of sustainability.","PeriodicalId":40035,"journal":{"name":"Advances in Nonlinear Variational Inequalities","volume":"8 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140516663","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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