Significant progress has been made in the field of digital image processing in recent years through the utilization of machine learning and deep learning, surpassing previous methods by a large margin. Deep learning methods allow devices such as computers and mobile to automatically understand pattern characteristics. This review paper highlights challenges and issues in machine-deep learning applied to the domain of flower classification. in addition, the datasets were extracted that were found in the literature. The review offered in this article can encourage researchers in the domain of agriculture inspired techniques research society to further enhance the efficacy of the AI methods and to use the different AI techniques in other fields for solving complicated real-life challenges. In addition, the article provides an overview of the artificial intelligence techniques employed in the field of flower recognition, detection, segmentation, and other applications, delivering the most delinquent and recent literature for solving issues for researchers in the area of flowers.
{"title":"Flowers Images Classification with Deep Learning: A Review","authors":"Asia Kamal Mustfa, S. Abdulateef, Qabas A. Hameed","doi":"10.52783/cana.v31.851","DOIUrl":"https://doi.org/10.52783/cana.v31.851","url":null,"abstract":"Significant progress has been made in the field of digital image processing in recent years through the utilization of machine learning and deep learning, surpassing previous methods by a large margin. Deep learning methods allow devices such as computers and mobile to automatically understand pattern characteristics. This review paper highlights challenges and issues in machine-deep learning applied to the domain of flower classification. in addition, the datasets were extracted that were found in the literature. The review offered in this article can encourage researchers in the domain of agriculture inspired techniques research society to further enhance the efficacy of the AI methods and to use the different AI techniques in other fields for solving complicated real-life challenges. In addition, the article provides an overview of the artificial intelligence techniques employed in the field of flower recognition, detection, segmentation, and other applications, delivering the most delinquent and recent literature for solving issues for researchers in the area of flowers.","PeriodicalId":40036,"journal":{"name":"Communications on Applied Nonlinear Analysis","volume":" 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141674736","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 this research, data on general intelligence and emotional intelligence scores were analyzed and the difference between them was studied in terms of their application to students of the Mathematics Department in the four stages in the College of Computer Science and Mathematics at the University of Kufa for the academic year 2023-2024. Four statistical methods were used. The first was multiple regression analysis to study the extent to which each student's mathematics score was affected on the general intelligence and emotional intelligence tests (Y), by the student's gender (X1) and his type of residence (X2). The second statistical method is to analyze the design of a factorial experiment to study the presence or absence of significant differences between the grades of students in the four stages by classifying the students in the four stages into males and females, as well as studying the significant difference in the scores of the two intelligence tests (general and emotional) for students in the Mathematics Department in general and divided according to gender. requester. The third method of analysis was to analyze the design of a completely randomized experiment to study the significant differences in students’ intelligence scores between the four academic levels in the mathematics department. If there is a significant difference in the results of analyzing the data in the above methods, then we use the fourth statistical method in this research, which is the analysis of the least significant difference test to study the differences between each two groups separately. The results of the data analysis were numerous, the most important of which is that the intelligence scores in the two tests (general intelligence and emotional intelligence) do not depend on nor are affected by the student’s gender or type of residence. In addition, intelligence scores were not affected for the four educational levels.
{"title":"Significant Differences Between the IQ Scores of Mathematics Students based on Regression Analysis and Factorial Experimental Design","authors":"Hadeel Salim Alkutubi","doi":"10.52783/cana.v31.829","DOIUrl":"https://doi.org/10.52783/cana.v31.829","url":null,"abstract":"In this research, data on general intelligence and emotional intelligence scores were analyzed and the difference between them was studied in terms of their application to students of the Mathematics Department in the four stages in the College of Computer Science and Mathematics at the University of Kufa for the academic year 2023-2024. Four statistical methods were used. The first was multiple regression analysis to study the extent to which each student's mathematics score was affected on the general intelligence and emotional intelligence tests (Y), by the student's gender (X1) and his type of residence (X2). The second statistical method is to analyze the design of a factorial experiment to study the presence or absence of significant differences between the grades of students in the four stages by classifying the students in the four stages into males and females, as well as studying the significant difference in the scores of the two intelligence tests (general and emotional) for students in the Mathematics Department in general and divided according to gender. requester. The third method of analysis was to analyze the design of a completely randomized experiment to study the significant differences in students’ intelligence scores between the four academic levels in the mathematics department. If there is a significant difference in the results of analyzing the data in the above methods, then we use the fourth statistical method in this research, which is the analysis of the least significant difference test to study the differences between each two groups separately. The results of the data analysis were numerous, the most important of which is that the intelligence scores in the two tests (general intelligence and emotional intelligence) do not depend on nor are affected by the student’s gender or type of residence. In addition, intelligence scores were not affected for the four educational levels.","PeriodicalId":40036,"journal":{"name":"Communications on Applied Nonlinear Analysis","volume":" 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141674570","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}
Social network analysis (SNA) leverages graph theory to understand and visualize the complex relationships and structures within social networks. This research paper explores the optimization of graph theory algorithms tailored for SNA, focusing on efficiency improvements in handling large-scale networks. The study reviews key graph theory concepts, identifies common challenges in SNA, and evaluates various optimization techniques. Practical applications and case studies are presented to demonstrate the impact of these optimizations in real-world scenarios.
社会网络分析（SNA）利用图论来理解和可视化社会网络中的复杂关系和结构。本研究论文探讨了为 SNA 量身定制的图论算法的优化问题，重点是提高处理大规模网络的效率。研究回顾了关键图论概念，确定了 SNA 中的常见挑战，并评估了各种优化技术。论文还介绍了实际应用和案例研究，以展示这些优化技术在现实世界中的影响。
{"title":"Optimizing Graph Theory Algorithms for Social Network Analysis","authors":"S. Sahoo, Sasmita Mishra","doi":"10.52783/cana.v31.834","DOIUrl":"https://doi.org/10.52783/cana.v31.834","url":null,"abstract":"Social network analysis (SNA) leverages graph theory to understand and visualize the complex relationships and structures within social networks. This research paper explores the optimization of graph theory algorithms tailored for SNA, focusing on efficiency improvements in handling large-scale networks. The study reviews key graph theory concepts, identifies common challenges in SNA, and evaluates various optimization techniques. Practical applications and case studies are presented to demonstrate the impact of these optimizations in real-world scenarios.","PeriodicalId":40036,"journal":{"name":"Communications on Applied Nonlinear Analysis","volume":" 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141674776","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}
Iftekher S. Chowdhury, Dr. Eric Howard, Dr Nand Kumar
Fractional Quantum Mechanics (FQM) has emerged as a fascinating theoretical framework extending traditional quantum mechanics to describe physical systems with non-local or long-range interactions. In this paper, we delve into the realm of FQM, focusing on stability analysis and wave propagation in coupled Schrödinger equations. We begin with a comprehensive overview of FQM, elucidating its fundamental principles and mathematical formalism. Subsequently, we conduct stability analysis of coupled fractional Schrödinger equations, exploring the conditions under which these systems exhibit stable behavior. Furthermore, we investigate wave propagation phenomena within such systems, shedding light on the unique characteristics of fractional quantum waves. Our findings not only contribute to advancing the theoretical understanding of FQM but also offer insights into potential applications in diverse fields ranging from condensed matter physics to quantum information processing.
{"title":"Exploring Fractional Quantum Mechanics: Stability Analysis and Wave Propagation in Coupled Schrödinger Equations","authors":"Iftekher S. Chowdhury, Dr. Eric Howard, Dr Nand Kumar","doi":"10.52783/cana.v31.935","DOIUrl":"https://doi.org/10.52783/cana.v31.935","url":null,"abstract":"Fractional Quantum Mechanics (FQM) has emerged as a fascinating theoretical framework extending traditional quantum mechanics to describe physical systems with non-local or long-range interactions. In this paper, we delve into the realm of FQM, focusing on stability analysis and wave propagation in coupled Schrödinger equations. We begin with a comprehensive overview of FQM, elucidating its fundamental principles and mathematical formalism. Subsequently, we conduct stability analysis of coupled fractional Schrödinger equations, exploring the conditions under which these systems exhibit stable behavior. Furthermore, we investigate wave propagation phenomena within such systems, shedding light on the unique characteristics of fractional quantum waves. Our findings not only contribute to advancing the theoretical understanding of FQM but also offer insights into potential applications in diverse fields ranging from condensed matter physics to quantum information processing.","PeriodicalId":40036,"journal":{"name":"Communications on Applied Nonlinear Analysis","volume":" 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141673246","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 this study, the paper delves into precision challenges within traditional transportation problem solutions, which rigidly define cost, supply, and demand. Acknowledging the inherent vagueness in real world contexts, the research explores the efficacy of intuitive fuzzy sets as a potent tool. Organized into four distinct sections, this work utilizes decagonal intuitionistic fuzzy numbers for managing supply and demand, while upholding conventional approaches for cost considerations. Employing a fuzzy ordering method, optimal solutions are derived by adjusting the configuration of decagonal intuitive fuzzy numbers across each segment. Through a comparative analysis, the Study identifies the most effective solution, with initial sections addressing balanced geometric intuitionistic fuzzy transportation problems and the final part focusing on unbalanced scenarios, specifically emphasizing supply and demand complexities.
{"title":"Applying a Fuzzy Ordering Approach in Transportation Problems with Decagonal Intuitionistic Fuzzy Numbers","authors":"KR Balasubramanian","doi":"10.52783/cana.v31.975","DOIUrl":"https://doi.org/10.52783/cana.v31.975","url":null,"abstract":"In this study, the paper delves into precision challenges within traditional transportation problem solutions, which rigidly define cost, supply, and demand. Acknowledging the inherent vagueness in real world contexts, the research explores the efficacy of intuitive fuzzy sets as a potent tool. Organized into four distinct sections, this work utilizes decagonal intuitionistic fuzzy numbers for managing supply and demand, while upholding conventional approaches for cost considerations. \u0000Employing a fuzzy ordering method, optimal solutions are derived by adjusting the configuration of decagonal intuitive fuzzy numbers across each segment. Through a comparative analysis, the \u0000Study identifies the most effective solution, with initial sections addressing balanced geometric intuitionistic fuzzy transportation problems and the final part focusing on unbalanced scenarios, specifically emphasizing supply and demand complexities.","PeriodicalId":40036,"journal":{"name":"Communications on Applied Nonlinear Analysis","volume":" 19","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141673293","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}
Base stock is an amount of stock that a company requires to maintain in order to handle an unexpected huge demand. To lerant clients are the clients who are tolerant to the company’s delayed supply, who come back to the same company even if their demands are not met within their expected time. This implies that the clients are solely relied on that company. Marketing manager requires to maintain on hand to satisfy such tolerant clients’ demands with some delay, no longer than expected by that tolerant client.This paper diagnosis the performance measures of optimal base stock system for tolerant clients in fuzzy environment. The fuzzy numbers can be modified to crisp number with the help of fuzzy ranking methods. Here the used fuzzy ranking method is prominent for de-fuzzification. The famous Triangular fuzzy number and Trapezoidal fuzzy number methods are played a major role for de-fuzzification. At last, the optimization of base stock is verified with numerical examples in fuzzy environment.
{"title":"A Fuzzy Optimal Base Stock System for Tolerant Clients","authors":"Jagatheesan. R","doi":"10.52783/cana.v31.952","DOIUrl":"https://doi.org/10.52783/cana.v31.952","url":null,"abstract":"Base stock is an amount of stock that a company requires to maintain in order to handle an unexpected huge demand. To lerant clients are the clients who are tolerant to the company’s delayed supply, who come back to the same company even if their demands are not met within their expected time. This implies that the clients are solely relied on that company. Marketing manager requires to maintain on hand to satisfy such tolerant clients’ demands with some delay, no longer than expected by that tolerant client.This paper diagnosis the performance measures of optimal base stock system for tolerant clients in fuzzy environment. The fuzzy numbers can be modified to crisp number with the help of fuzzy ranking methods. Here the used fuzzy ranking method is prominent for de-fuzzification. The famous Triangular fuzzy number and Trapezoidal fuzzy number methods are played a major role for de-fuzzification. At last, the optimization of base stock is verified with numerical examples in fuzzy environment.","PeriodicalId":40036,"journal":{"name":"Communications on Applied Nonlinear Analysis","volume":" 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141674294","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}
Nitya Nand Jha, R. Singh, Sushila Sharma, Abhishek Kumar
In terms of impacts on ecosystems, industry, people, and flora and fauna, water quality is paramount. Contamination and pollution have degraded water quality in recent decades. Predicting WQC and Water Quality Index (WQI) is the problem of this article; WQI is an important measure of water validity. This research use machine learning approaches to forecast WQI and WQC, and it does so by optimizing and tweaking the parameters of several machine learning models. Parameter optimization and tuning for four classification models and four regression models both make use of grid search, an essential tool in both contexts. To forecast WQC, classification models such as Random Forest (RF), Extreme Gradient Boosting (Xgboost), Gradient Boosting (GB), and Adaptive Boosting (Ada-Boost) are used. Predicting WQI is done using regression models such as K-nearest neighbour (KNN), decision tree (DT), support vector regression (SVR), and multi-layer perceptron (MLP). Data normalization and data imputation (mean imputation) were also executed as pretreatment steps to suit the data and make it convenient for any further processing. Seven characteristics and ninety-one cases make up the dataset used for this research. Five evaluation measures were calculated to evaluate the classification systems' effectiveness: accuracy, recall, precision, Matthews' Correlation Coefficient (MCC), and F1 score. A total of four evaluation metrics were calculated to measure the efficacy of the regression models: MAE, MedAE,MSE, and R2. The results of the testing showed that the GB model yielded the most accurate predictions of WQC values (99.50%), making it the top performer in terms of categorization. The experimental findings show that the MLP regressor model got a value of 99.8 percent R2 when predicting WQI values, making it the best performing model in regression.
{"title":"Computational Machine Learning Analytics for Prediction of Water Quality","authors":"Nitya Nand Jha, R. Singh, Sushila Sharma, Abhishek Kumar","doi":"10.52783/cana.v31.942","DOIUrl":"https://doi.org/10.52783/cana.v31.942","url":null,"abstract":"In terms of impacts on ecosystems, industry, people, and flora and fauna, water quality is paramount. Contamination and pollution have degraded water quality in recent decades. Predicting WQC and Water Quality Index (WQI) is the problem of this article; WQI is an important measure of water validity. This research use machine learning approaches to forecast WQI and WQC, and it does so by optimizing and tweaking the parameters of several machine learning models. Parameter optimization and tuning for four classification models and four regression models both make use of grid search, an essential tool in both contexts. To forecast WQC, classification models such as Random Forest (RF), Extreme Gradient Boosting (Xgboost), Gradient Boosting (GB), and Adaptive Boosting (Ada-Boost) are used. Predicting WQI is done using regression models such as K-nearest neighbour (KNN), decision tree (DT), support vector regression (SVR), and multi-layer perceptron (MLP). Data normalization and data imputation (mean imputation) were also executed as pretreatment steps to suit the data and make it convenient for any further processing. Seven characteristics and ninety-one cases make up the dataset used for this research. Five evaluation measures were calculated to evaluate the classification systems' effectiveness: accuracy, recall, precision, Matthews' Correlation Coefficient (MCC), and F1 score. A total of four evaluation metrics were calculated to measure the efficacy of the regression models: MAE, MedAE,MSE, and R2. The results of the testing showed that the GB model yielded the most accurate predictions of WQC values (99.50%), making it the top performer in terms of categorization. The experimental findings show that the MLP regressor model got a value of 99.8 percent R2 when predicting WQI values, making it the best performing model in regression.","PeriodicalId":40036,"journal":{"name":"Communications on Applied Nonlinear Analysis","volume":" 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141677046","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}
Ahmed Ghanawi Jasim, Intisar Harbi, Ali Salim Mohammed
This paper presents definition to a fuzzy FF-smooth homotopy on FF-smooth fuzzy Fréchet manifold and proves that the fuzzy FF-smooth homotopy of a fuzzy path forms an equivalence relation. The researchers also expand the study to include three types of fuzzy FF-smooth homotopy of a fuzzy path, namely a maximal fuzzy FF-smooth homotopy, an internal fuzzy FF-smooth homotopy, and local fuzzy FF-smooth homotopy admitting equivalence relations and a structure of a group.
{"title":"Homotopy on Smooth Fuzzy Fréchet Manifold","authors":"Ahmed Ghanawi Jasim, Intisar Harbi, Ali Salim Mohammed","doi":"10.52783/cana.v31.859","DOIUrl":"https://doi.org/10.52783/cana.v31.859","url":null,"abstract":" This paper presents definition to a fuzzy FF-smooth homotopy on FF-smooth fuzzy Fréchet manifold and proves that the fuzzy FF-smooth homotopy of a fuzzy path forms an equivalence relation. The researchers also expand the study to include three types of fuzzy FF-smooth homotopy of a fuzzy path, namely a maximal fuzzy FF-smooth homotopy, an internal fuzzy FF-smooth homotopy, and local fuzzy FF-smooth homotopy admitting equivalence relations and a structure of a group.","PeriodicalId":40036,"journal":{"name":"Communications on Applied Nonlinear Analysis","volume":" 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141675983","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 recent times, the utilization of Statistical and Machine Learning techniques has gained prominence in the realm of financial data analysis. These methods are applied to various types of financial data, encompassing textual information, numerical data, and graphical representations. This study aims to compare the performance of two prominent forecasting methods, Hidden Markov Models and Facebook’s Prophet in the context of stock price prediction. Assessing the predictive accuracy, interpretability, and adaptability of both approaches through empirical experiments and case studies sheds light on their respective advantages and limitations. These experiments demonstrate that the predicted stock prices are in closer proximity to the actual price when compared to using a single data source. Furthermore, the achieved MAPE are 0.01, 0.025 and respectively, outperforming conventional methodologies. Our validation of effectiveness extends to real-world datasets encompassing the NIFTY50 Index. These findings offer valuable insights for researchers and practitioners seeking effective strategies for stock price prediction.
{"title":"Analysis and Prediction of Stock Price using HMM and Facebook’s Prophet Computational Models","authors":"K. Senthamarai Kannan","doi":"10.52783/cana.v31.813","DOIUrl":"https://doi.org/10.52783/cana.v31.813","url":null,"abstract":"In recent times, the utilization of Statistical and Machine Learning techniques has gained prominence in the realm of financial data analysis. These methods are applied to various types of financial data, encompassing textual information, numerical data, and graphical representations. This study aims to compare the performance of two prominent forecasting methods, Hidden Markov Models and Facebook’s Prophet in the context of stock price prediction. Assessing the predictive accuracy, interpretability, and adaptability of both approaches through empirical experiments and case studies sheds light on their respective advantages and limitations. These experiments demonstrate that the predicted stock prices are in closer proximity to the actual price when compared to using a single data source. Furthermore, the achieved MAPE are 0.01, 0.025 and respectively, outperforming conventional methodologies. Our validation of effectiveness extends to real-world datasets encompassing the NIFTY50 Index. These findings offer valuable insights for researchers and practitioners seeking effective strategies for stock price prediction.","PeriodicalId":40036,"journal":{"name":"Communications on Applied Nonlinear Analysis","volume":" 37","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141676676","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}
Dr.Kaushalya Thopate, Dr.Deepali S. Jadhav, Ms.Kalyani Ghuge, Dr.Virat V Giri, Dr. Ganesh B. Dongre, Mrs.Archana, Bhushan Burujwale
In a nation where the majority of transport is characterized by road transport and the large number of vehicles moving on the expansive road networks, the pressuring issues of potholes and pollution their stemming wear and tear, and the health hazards caused due to that have posed formidable challenges to the authorities as well as the individuals moving around also. To address these critical concerns of society we have introduced an IoT-based (Node-MCU) smart glove that continuously collects the real-time data of pollution and the exact locations of the potholes. This innovative solution not only provides enhanced pothole detection along the provided route but gives insights of the air quality along their routes. introducing the remembrance factor and managing the previous data on potholes and pollution along with the severity index to prioritize hazardous repairs on the route. All Together the smart glove ensures the driver's safety by delivering real-time data of the conditions of the routes.
{"title":"IoT-Based Smart Glove for Pollution Monitoring and Potholes mapping using Node-MCU","authors":"Dr.Kaushalya Thopate, Dr.Deepali S. Jadhav, Ms.Kalyani Ghuge, Dr.Virat V Giri, Dr. Ganesh B. Dongre, Mrs.Archana, Bhushan Burujwale","doi":"10.52783/cana.v31.947","DOIUrl":"https://doi.org/10.52783/cana.v31.947","url":null,"abstract":"In a nation where the majority of transport is characterized by road transport and the large number of vehicles moving on the expansive road networks, the pressuring issues of potholes and pollution their stemming wear and tear, and the health hazards caused due to that have posed formidable challenges to the authorities as well as the individuals moving around also. To address these critical concerns of society we have introduced an IoT-based (Node-MCU) smart glove that continuously collects the real-time data of pollution and the exact locations of the potholes. This innovative solution not only provides enhanced pothole detection along the provided route but gives insights of the air quality along their routes. introducing the remembrance factor and managing the previous data on potholes and pollution along with the severity index to prioritize hazardous repairs on the route. All Together the smart glove ensures the driver's safety by delivering real-time data of the conditions of the routes.","PeriodicalId":40036,"journal":{"name":"Communications on Applied Nonlinear Analysis","volume":" 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141674995","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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