Cause-related marketing (CRM) has been widely acknowledged as one of the major types of promotional initiatives under the broad head of CSR. Indian business organizations are under constant pressure to deliver products that focus on environment or eco-friendly offerings. Prior literature have claimed that environmental issues are deeply rooted in human value-orientation. The data was collected from 467 retail shoppers in two prominent cities in the western part of India. In the first phase, this study assessed the role of three value-orientations (egoistic, altruistic and biospheric) on attitude toward eco-friendly products. In the second phase, the impact of attitude toward eco-friendly products on CRM purchase intention was investigated. The results of our study revealed that value-orientation has significant impact on attitude toward eco-friendly products and subsequently, has positive influence on CRM purchase intention. This research provides valuable insights for CRM marketers to develop promotional strategies exclusively for CRM- linked eco-friendly products.
{"title":"An Investigation into the Impact of Value Orientation on Attitude and CRM Purchase Intention towards Eco-Friendly Products: Evidence from Gujarat State in India","authors":"Neha Upadhyay, Dr. Hitesh Parmar","doi":"10.52783/cana.v31.1012","DOIUrl":"https://doi.org/10.52783/cana.v31.1012","url":null,"abstract":"Cause-related marketing (CRM) has been widely acknowledged as one of the major types of promotional initiatives under the broad head of CSR. Indian business organizations are under constant pressure to deliver products that focus on environment or eco-friendly offerings. Prior literature have claimed that environmental issues are deeply rooted in human value-orientation. The data was collected from 467 retail shoppers in two prominent cities in the western part of India. In the first phase, this study assessed the role of three value-orientations (egoistic, altruistic and biospheric) on attitude toward eco-friendly products. In the second phase, the impact of attitude toward eco-friendly products on CRM purchase intention was investigated. The results of our study revealed that value-orientation has significant impact on attitude toward eco-friendly products and subsequently, has positive influence on CRM purchase intention. This research provides valuable insights for CRM marketers to develop promotional strategies exclusively for CRM- linked eco-friendly products.","PeriodicalId":40036,"journal":{"name":"Communications on Applied Nonlinear Analysis","volume":" 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141829884","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}
The continuous rise of green start-ups worldwide has undoubtedly addressed certain environmental challenges, yet several unresolved issues regarding their feasibility and performance persist. Throughout this journey, entrepreneurs have assumed a pivotal role in generating wealth, subsequently fostering economic development for both institutions and the overall country. Their primary focus centers on practical knowledge related to entrepreneurship and the creation of employment opportunities, aiming to mitigate income disparity and contribute to balanced regional development. Given the current environmental crises confronting the world, business leaders are compelled to explore novel approaches to conducting business. While green entrepreneurship is experiencing growth, it offers only a glimmer of hope towards sustainable development. This movement instills a sense of consumer awareness and promotes the production of environmentally friendly products. Incorporating green concepts into business models propels organizations towards a reasonable lead in the market. The investigation in this direction is inherently exploratory, offering insights into the realm of green entrepreneurship and playing a pivotal part in the Indian perspective. The study also sets the stage for an exploration of existing green entrepreneurship models, shedding light on successful green ventures within the country. This paper probes into the state of the art in literature, discussing various significant contributions found in previously published works. Furthermore, it serves as a call to action for entrepreneurs to research deeper into the dominion of green entrepreneurship, ultimately contributing to the sustainable future we collectively aspire to achieve.
{"title":"Growing a Sustainable Future: Exploring the Benefits and Challenges of Green Entrepreneurship","authors":"Thaya Madhavi, Priya Todwal, Divya Bhatt","doi":"10.52783/cana.v31.1000","DOIUrl":"https://doi.org/10.52783/cana.v31.1000","url":null,"abstract":"The continuous rise of green start-ups worldwide has undoubtedly addressed certain environmental challenges, yet several unresolved issues regarding their feasibility and performance persist. Throughout this journey, entrepreneurs have assumed a pivotal role in generating wealth, subsequently fostering economic development for both institutions and the overall country. Their primary focus centers on practical knowledge related to entrepreneurship and the creation of employment opportunities, aiming to mitigate income disparity and contribute to balanced regional development. Given the current environmental crises confronting the world, business leaders are compelled to explore novel approaches to conducting business. While green entrepreneurship is experiencing growth, it offers only a glimmer of hope towards sustainable development. This movement instills a sense of consumer awareness and promotes the production of environmentally friendly products. Incorporating green concepts into business models propels organizations towards a reasonable lead in the market. The investigation in this direction is inherently exploratory, offering insights into the realm of green entrepreneurship and playing a pivotal part in the Indian perspective. The study also sets the stage for an exploration of existing green entrepreneurship models, shedding light on successful green ventures within the country. This paper probes into the state of the art in literature, discussing various significant contributions found in previously published works. Furthermore, it serves as a call to action for entrepreneurs to research deeper into the dominion of green entrepreneurship, ultimately contributing to the sustainable future we collectively aspire to achieve.","PeriodicalId":40036,"journal":{"name":"Communications on Applied Nonlinear Analysis","volume":" 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141831044","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 presents an innovative class of Beta weakly semi-CS, namely Compactness and Connectedness in Beta weakly semi-CS in TS. Throughout this paper, bws-Compactness and bws-Connectedness were examined to get the fundamental facts in the Beta weakly semi-CS. In this paper, the notion of countable βws- compact in TS were explored and bws – Connectedness ( in TS were also studied to get results. The bws - and bws – Compactness fulfilled most of the connectedness and compactness properties in TS. Here, many characterizations were obtained along with some of their features. The paper concludes on how it relates to other kinds of functions and beta ws-Compactness in TS and its characteristics were studied to obtain results theoretically.
{"title":"Compactness and Connectedness in Beta Weakly Semi – Closed Sets in Topological Spaces","authors":"S. Saranya, V. E. Sasikala, Research Scholar","doi":"10.52783/cana.v31.1033","DOIUrl":"https://doi.org/10.52783/cana.v31.1033","url":null,"abstract":"This research presents an innovative class of Beta weakly semi-CS, namely Compactness and Connectedness in Beta weakly semi-CS in TS. Throughout this paper, bws-Compactness and bws-Connectedness were examined to get the fundamental facts in the Beta weakly semi-CS. In this paper, the notion of countable βws- compact in TS were explored and bws – Connectedness ( in TS were also studied to get results. The bws - and bws – Compactness fulfilled most of the connectedness and compactness properties in TS. Here, many characterizations were obtained along with some of their features. The paper concludes on how it relates to other kinds of functions and beta ws-Compactness in TS and its characteristics were studied to obtain results theoretically.","PeriodicalId":40036,"journal":{"name":"Communications on Applied Nonlinear Analysis","volume":" 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141829833","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}
Merdin Shamal, Salih, Rowaida Khalil, Subhi R. M. Zeebaree, D. A. Zebari, L. M. Abdulrahman, Nasiba Mahdi
Diabetes mellitus, a chronic condition, causes disruptions in the metabolic processes of carbohydrates, lipids, and proteins. Hyperglycemia, characterised by elevated blood sugar levels, is the primary distinguishing characteristic of all forms of diabetes. Diabetes is a disease that has significantly increased in prevalence due to the contemporary lifestyle. Consequently, it is essential to get an early-stage diagnosis of the illness. When constructing classification models, data pre-processing is a crucial step. The Pima Indian Diabetes dataset, available in the University of California Irvine (UCI) repository, is a challenging dataset with a higher proportion of missing values (48%) compared to comparable datasets. To improve the accuracy of the classification model, many rounds of data pre-processing are conducted on the Pima Diabetes dataset. The proposed approach consists of two stages: outlier removal and imputation in the first stage, and normalisation in the second stage. Regarding the feature aspect, we used a method called principal component analysis (PCA). Ultimately, to classify the PIMA dataset, we used many classifiers such as Support Vector Machine (SVM), Random Forest (RF), Naïve Bayes (NB), and Decision Tree (DT). The testing revealed that the maximum achievable accuracy was 89.86% when 80% of the data was used for training. This was accomplished by integrating the feature selection technique with the classifier.
{"title":"Diabetic Prediction based on Machine Learning Using PIMA Indian Dataset","authors":"Merdin Shamal, Salih, Rowaida Khalil, Subhi R. M. Zeebaree, D. A. Zebari, L. M. Abdulrahman, Nasiba Mahdi","doi":"10.52783/cana.v31.1008","DOIUrl":"https://doi.org/10.52783/cana.v31.1008","url":null,"abstract":"Diabetes mellitus, a chronic condition, causes disruptions in the metabolic processes of carbohydrates, lipids, and proteins. Hyperglycemia, characterised by elevated blood sugar levels, is the primary distinguishing characteristic of all forms of diabetes. Diabetes is a disease that has significantly increased in prevalence due to the contemporary lifestyle. Consequently, it is essential to get an early-stage diagnosis of the illness. When constructing classification models, data pre-processing is a crucial step. The Pima Indian Diabetes dataset, available in the University of California Irvine (UCI) repository, is a challenging dataset with a higher proportion of missing values (48%) compared to comparable datasets. To improve the accuracy of the classification model, many rounds of data pre-processing are conducted on the Pima Diabetes dataset. The proposed approach consists of two stages: outlier removal and imputation in the first stage, and normalisation in the second stage. Regarding the feature aspect, we used a method called principal component analysis (PCA). Ultimately, to classify the PIMA dataset, we used many classifiers such as Support Vector Machine (SVM), Random Forest (RF), Naïve Bayes (NB), and Decision Tree (DT). The testing revealed that the maximum achievable accuracy was 89.86% when 80% of the data was used for training. This was accomplished by integrating the feature selection technique with the classifier.","PeriodicalId":40036,"journal":{"name":"Communications on Applied Nonlinear Analysis","volume":" 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141830308","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}
Putting emotional labels on music, or "music mood classification," is important for use in recommendation systems and music therapy. Using fine-tuned machine learning methods, this study aims to improve the accuracy and performance of classification. We used a large dataset with names for different types of music and moods to make sure that the model training was strong. Advanced feature extraction methods picked up both the traits of the audio stream and the lyrics. For audio features, color features, spectral contrast, and mel-frequency cepstral coefficients (MFCCs) were recovered. For poetry analysis, TF-IDF and word embeddings were used, along with natural language processing (NLP) methods. Logistic Regression, SGD Classifier, Gaussian Naive Bayes, Decision Tree, Random Forest, XGB Classifier, SVM Linear, and K-Nearest Neighbors (KNN) were some of the machine learning classification methods we used. Random Forest, XGB Classifier, and SVM Linear all did better than the others. We used grid search and random search to fine-tune the hyperparameters of these top-performing models in order to make them even better. Cross-validation made sure that the models were stable and could be used in other situations. Our results show that the highly tuned Random Forest, XGB, and SVM models greatly improved the accuracy of classification, with the XGB Classifier performing the best. This study adds to music information retrieval by creating a useful method for mood classification that can be used in real-life situations to improve user experiences and create more personalized music services.
{"title":"Enhancing Accuracy and Performance in Music Mood Classification through Fine-Tuned Machine Learning Methods","authors":"Shital Shankar Gujar, Dr. Ali Yawar Reha","doi":"10.52783/cana.v31.1019","DOIUrl":"https://doi.org/10.52783/cana.v31.1019","url":null,"abstract":"Putting emotional labels on music, or \"music mood classification,\" is important for use in recommendation systems and music therapy. Using fine-tuned machine learning methods, this study aims to improve the accuracy and performance of classification. We used a large dataset with names for different types of music and moods to make sure that the model training was strong. Advanced feature extraction methods picked up both the traits of the audio stream and the lyrics. For audio features, color features, spectral contrast, and mel-frequency cepstral coefficients (MFCCs) were recovered. For poetry analysis, TF-IDF and word embeddings were used, along with natural language processing (NLP) methods. Logistic Regression, SGD Classifier, Gaussian Naive Bayes, Decision Tree, Random Forest, XGB Classifier, SVM Linear, and K-Nearest Neighbors (KNN) were some of the machine learning classification methods we used. Random Forest, XGB Classifier, and SVM Linear all did better than the others. We used grid search and random search to fine-tune the hyperparameters of these top-performing models in order to make them even better. Cross-validation made sure that the models were stable and could be used in other situations. Our results show that the highly tuned Random Forest, XGB, and SVM models greatly improved the accuracy of classification, with the XGB Classifier performing the best. This study adds to music information retrieval by creating a useful method for mood classification that can be used in real-life situations to improve user experiences and create more personalized music services.","PeriodicalId":40036,"journal":{"name":"Communications on Applied Nonlinear Analysis","volume":" 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141828672","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 rapidly expanding domain of the Internet of Things (IoT), Wireless Sensor Networks (WSNs) have become indispensable, supporting applications ranging from environmental monitoring to industrial automation. However, as the IoT ecosystem continues to burgeon with an array of devices and applications, the effective management of data transmission and congestion control within these networks presents an escalating challenge. To address this, this paper introduces a ground-breaking Optimal Congestion Control Mechanism tailored explicitly for IoT-enabled Wireless Sensor Networks. This innovative mechanism incorporates a Hybrid Aggregation and Scheduling technique to tackle the dual hurdles of congestion relief and energy efficiency in WSNs. By seamlessly blending data aggregation with dynamic scheduling, this approach endeavors to optimize network resources and alleviate congestion-related issues. Data aggregation intelligently consolidates multiple data packets into a single transmission, reducing overhead and maximizing the con- strained bandwidth of wireless channels. Concurrently, dynamic scheduling adapts the transmission schedule in real-time based on network conditions, ensuring the timely delivery of critical data while minimizing congestion. To achieve an optimal configuration, the mechanism employs an intelligent decision-making algorithm that considers factors like data priority, network traffic, and energy constraints. Furthermore, machine learning techniques, notably reinforcement learning, can be leveraged to enhance the algorithm’s adaptability over time. The efficacy of the proposed mechanism undergoes rigorous assessment through simulations and real-world experiments, validating its ability to diminish congestion, enhance data delivery, and prolong the operational life of the network. The outcomes underscore the significant potential of this Optimal Congestion Control Mechanism to elevate the reliability and efficiency of IoT-enabled Wireless Sensor Networks. By harnessing the combined advantages of data aggregation and dynamic scheduling, the proposed mechanism offers a comprehensive solution for efficiently managing congestion and optimizing network resource utilization.
{"title":"Enhancing IoT-Enabled Wireless Sensor Network Performance through Adaptive Congestion Control: Investigation of Hybrid Aggregation and Scheduling Techniques","authors":"Shiv H. Sutar, Y. Jinila","doi":"10.52783/cana.v31.1017","DOIUrl":"https://doi.org/10.52783/cana.v31.1017","url":null,"abstract":"In the rapidly expanding domain of the Internet of Things (IoT), Wireless Sensor Networks (WSNs) have become indispensable, supporting applications ranging from environmental monitoring to industrial automation. However, as the IoT ecosystem continues to burgeon with an array of devices and applications, the effective management of data transmission and congestion control within these networks presents an escalating challenge. To address this, this paper introduces a ground-breaking Optimal Congestion Control Mechanism tailored explicitly for IoT-enabled Wireless Sensor Networks. This innovative mechanism incorporates a Hybrid Aggregation and Scheduling technique to tackle the dual hurdles of congestion relief and energy efficiency in WSNs. By seamlessly blending data aggregation with dynamic scheduling, this approach endeavors to optimize network resources and alleviate congestion-related issues. Data aggregation intelligently consolidates multiple data packets into a single transmission, reducing overhead and maximizing the con- strained bandwidth of wireless channels. Concurrently, dynamic scheduling adapts the transmission schedule in real-time based on network conditions, ensuring the timely delivery of critical data while minimizing congestion. To achieve an optimal configuration, the mechanism employs an intelligent decision-making algorithm that considers factors like data priority, network traffic, and energy constraints. Furthermore, machine learning techniques, notably reinforcement learning, can be leveraged to enhance the algorithm’s adaptability over time. The efficacy of the proposed mechanism undergoes rigorous assessment through simulations and real-world experiments, validating its ability to diminish congestion, enhance data delivery, and prolong the operational life of the network. The outcomes underscore the significant potential of this Optimal Congestion Control Mechanism to elevate the reliability and efficiency of IoT-enabled Wireless Sensor Networks. By harnessing the combined advantages of data aggregation and dynamic scheduling, the proposed mechanism offers a comprehensive solution for efficiently managing congestion and optimizing network resource utilization.","PeriodicalId":40036,"journal":{"name":"Communications on Applied Nonlinear Analysis","volume":" 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141830881","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}
Medical imaging has a significant challenge in accurately classifying skin lesions into benign and malignant classifications. To solve this issue, we have developed a technique that utilizes a custom convolutional neural network classifier with a support vector machine. Our customized CNN architecture is designed to address the core issue of skin cancer categorization. DenseNet121, DenseNet201, InceptionV3, InceptionResNetV2, MobileNet, ResNet50V2, ResNet101, VGG16, VGG19, and Xception are among the most prominent pre-trained models evaluated in our study. The customized CNN exceeds existing models on an average basis, displaying greater accuracy, recall, precision, and F1-Score for both benign and malignant cases. This technique has significant prospects for enhancing early skin cancer diagnosis, perhaps leading to better patient results and more efficient medical treatments.
{"title":"Skin Cancer Diagnosis with a Customized CNN Model using Deep Learning Approaches","authors":"Kiran Likhar, Dr. Sonali Ridhorkar","doi":"10.52783/cana.v31.1016","DOIUrl":"https://doi.org/10.52783/cana.v31.1016","url":null,"abstract":"Medical imaging has a significant challenge in accurately classifying skin lesions into benign and malignant classifications. To solve this issue, we have developed a technique that utilizes a custom convolutional neural network classifier with a support vector machine. Our customized CNN architecture is designed to address the core issue of skin cancer categorization. DenseNet121, DenseNet201, InceptionV3, InceptionResNetV2, MobileNet, ResNet50V2, ResNet101, VGG16, VGG19, and Xception are among the most prominent pre-trained models evaluated in our study. The customized CNN exceeds existing models on an average basis, displaying greater accuracy, recall, precision, and F1-Score for both benign and malignant cases. This technique has significant prospects for enhancing early skin cancer diagnosis, perhaps leading to better patient results and more efficient medical treatments.","PeriodicalId":40036,"journal":{"name":"Communications on Applied Nonlinear Analysis","volume":" 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141828986","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. K. Abraham, K.Nagendra, Dr. D. Venkatesh, Dr.Devendra Malapati, Dr M Rama
Low-income consumers are the people who leads their life by satisfying their essential needs with their limited resources. Majority of the Indian population more or less related to this category, that’s why the present study has been taken up in the selected area. To find out the average consumption expenditure of low-income consumers in the proposed study area. The objectives of the study are to know the pattern of consumption expenditure of low-income consumers on different items and to know the variation in the consumption expenditure of low-income consumers on essential commodities, durable goods and non-durable goods. The other objectives are to know the difference in the consumption expenditure of low-income consumers in respect of their literacy level and employment. In this regard the hypotheses are Ho: There is no difference in the average consumption expenditure of essential commodities and the average consumption expenditure of durable and non-durable goods. Ho2: The average consumption expenditure on essential commodities is same as durable goods. H03: The average consumption expenditure on durable goods is same as non-durable goods etc. Multi stage disproportionate non-random sampling technique was employed for selecting the sample in the proposed study area. Out of four districts in the Rayalaseema region of Andhra Pradesh we have selected two districts that is Kadapa and Chittoor. Five families each were selected from 50 mandal of Kadapa district. And out of 50 mandals of Chittoor district we have selected five families from each mandal. Hence, altogether it becomes 500 families for the present study. One-way Anova post hoc test multiple comparisons and two - way Anova Univariate, Mean and Standard deviation were used in the present study. The low-income consumers’ consumption expenditure is not the same in respect of all the items that is their average consumption expenditure on essential commodities is different from durable and non- durable goods. In the present study it is clear that the low-income consumers’ consumption expenditure on essential commodities is high next followed by durable goods and non-durable goods. It is suggested that the producers and marketers have to concentrate on essential commodities where they can encash the demand of the low-income consumers.
{"title":"“Are All Low-Income Consumers are Stereotype in Respect of Their Consumption Expendituere on Various Items?” A Comparative Study","authors":"Dr. K. Abraham, K.Nagendra, Dr. D. Venkatesh, Dr.Devendra Malapati, Dr M Rama","doi":"10.52783/cana.v31.995","DOIUrl":"https://doi.org/10.52783/cana.v31.995","url":null,"abstract":"Low-income consumers are the people who leads their life by satisfying their essential needs with their limited resources. Majority of the Indian population more or less related to this category, that’s why the present study has been taken up in the selected area. To find out the average consumption expenditure of low-income consumers in the proposed study area. The objectives of the study are to know the pattern of consumption expenditure of low-income consumers on different items and to know the variation in the consumption expenditure of low-income consumers on essential commodities, durable goods and non-durable goods. The other objectives are to know the difference in the consumption expenditure of low-income consumers in respect of their literacy level and employment. In this regard the hypotheses are Ho: There is no difference in the average consumption expenditure of essential commodities and the average consumption expenditure of durable and non-durable goods. Ho2: The average consumption expenditure on essential commodities is same as durable goods. H03: The average consumption expenditure on durable goods is same as non-durable goods etc. Multi stage disproportionate non-random sampling technique was employed for selecting the sample in the proposed study area. Out of four districts in the Rayalaseema region of Andhra Pradesh we have selected two districts that is Kadapa and Chittoor. Five families each were selected from 50 mandal of Kadapa district. And out of 50 mandals of Chittoor district we have selected five families from each mandal. Hence, altogether it becomes 500 families for the present study. One-way Anova post hoc test multiple comparisons and two - way Anova Univariate, Mean and Standard deviation were used in the present study. The low-income consumers’ consumption expenditure is not the same in respect of all the items that is their average consumption expenditure on essential commodities is different from durable and non- durable goods. In the present study it is clear that the low-income consumers’ consumption expenditure on essential commodities is high next followed by durable goods and non-durable goods. It is suggested that the producers and marketers have to concentrate on essential commodities where they can encash the demand of the low-income consumers.","PeriodicalId":40036,"journal":{"name":"Communications on Applied Nonlinear Analysis","volume":" 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141829949","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}
T.Ragunathan, Dr. Shweta Choudhary, G. V. Narayanan, S. Jagadeesh
Determinants have played an important role in many areas of mathematics. As an example, they are extremely useful in the research and resolution of linear equation and system problems. The study of determinants can be approached from several distinct angles. Throughout the course of this inquiry, we discover a large number of determinant identities involving Jacobsthal and Lucas numbers.
{"title":"Numerous Determinants Identities Involving Jacobsthal and Jacobsthal Lucas Numbers","authors":"T.Ragunathan, Dr. Shweta Choudhary, G. V. Narayanan, S. Jagadeesh","doi":"10.52783/cana.v31.1037","DOIUrl":"https://doi.org/10.52783/cana.v31.1037","url":null,"abstract":"Determinants have played an important role in many areas of mathematics. As an example, they are extremely useful in the research and resolution of linear equation and system problems. The study of determinants can be approached from several distinct angles. Throughout the course of this inquiry, we discover a large number of determinant identities involving Jacobsthal and Lucas numbers.","PeriodicalId":40036,"journal":{"name":"Communications on Applied Nonlinear Analysis","volume":" 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141828286","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 topological representation of a molecule is called molecular graph. A molecular graph is a collection of points representing the atoms in the molecule and set of lines represent the covalent bonds. Topological indices gather data from the graph of molecule and help to foresee properties of the concealing molecule. All the degree based topological indices have been defined through classical degree concept. In this paper, we define a novel degree concept for a vertex of a simple connected graph: Extended Reverse R degree and also, we define Extended Reverse R indices of a simple connected graph by using the Extended Reverse R degree concept. We compute the Extended Reverse R indices using the above contemporary degree concept for well-known simple connected graphs such as complete bipartite graph, Wheel graph, Generalized Peterson graph, Crown graph, Double star graph, and Windmill graph.
分子的拓扑表示法称为分子图。分子图由代表分子中原子的点和代表共价键的线组成。拓扑指数从分子图中收集数据，有助于预测隐藏分子的特性。所有基于度数的拓扑指数都是通过经典的度数概念定义的。在本文中，我们为简单连通图的顶点定义了一种新的度数概念：同时，我们还使用扩展反向 R 阶数概念定义了简单连通图的扩展反向 R 指数。我们使用上述当代度数概念计算了著名简单连通图的扩展反向 R 指数，如完整二方图、车轮图、广义彼得森图、皇冠图、双星图和风车图。
{"title":"Extended Reverse R Degrees of Vertices and Extended Reverse R indices of Graphs","authors":"T. Lavanya","doi":"10.52783/cana.v31.1005","DOIUrl":"https://doi.org/10.52783/cana.v31.1005","url":null,"abstract":"A topological representation of a molecule is called molecular graph. A molecular graph is a collection of points representing the atoms in the molecule and set of lines represent the covalent bonds. Topological indices gather data from the graph of molecule and help to foresee properties of the concealing molecule. All the degree based topological indices have been defined through classical degree concept. In this paper, we define a novel degree concept for a vertex of a simple connected graph: Extended Reverse R degree and also, we define Extended Reverse R indices of a simple connected graph by using the Extended Reverse R degree concept. We compute the Extended Reverse R indices using the above contemporary degree concept for well-known simple connected graphs such as complete bipartite graph, Wheel graph, Generalized Peterson graph, Crown graph, Double star graph, and Windmill graph.","PeriodicalId":40036,"journal":{"name":"Communications on Applied Nonlinear Analysis","volume":" 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141828932","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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