Pub Date : 2022-12-15DOI: 10.1109/PuneCon55413.2022.10014939
Atharva Pansare, Prabhat Panwar, Pranali K. Kosamkar
As the modern IT revolution is booming at a rapid growth speed, organizations and recruiters are finding it increasingly challenging to select the ideal applicant from a large number of applicants with diverse skill sets and personalities. Hence, selecting a candidate with a suitable personality for respective job profiles is a very important and great challenge for the HR department nowadays. Out of various personality prediction methods available out there, Myers-Briggs Type Indicator or MBTI is famous and accurate for our purpose of creating a personality prediction system for selecting candidates based on their personality. This study took into account all sixteen MB-Model coordinates. A comparative study of Random Forest, Logistic Regression, SVM, XGBoost has been done to perform personality prediction, and accuracy and confusion matrix for performance measurement of the models. While using TF-IDF, for the personality categories like Introversion/Extroversion the accuracy is 80.46%, for Sensing/Intuition it is 88.70%, for Thinking/Feeling it is 81.21% and for Perceiving vs Judging it is 72.97% with the Logistic Regression algorithm. Using Count vectorization for tokenizing, the accuracy is 80.97% for Introversion/Extroversion, for Sensing/Intuition it is 88.93%, for Thinking/Feeling it is 77.92% and for Perceiving vs Judging it is 73.48% with XGBoost algorithm, which gave the best performance.
{"title":"Personality Prediction with Natural Language Processing using Questionnaire Responses","authors":"Atharva Pansare, Prabhat Panwar, Pranali K. Kosamkar","doi":"10.1109/PuneCon55413.2022.10014939","DOIUrl":"https://doi.org/10.1109/PuneCon55413.2022.10014939","url":null,"abstract":"As the modern IT revolution is booming at a rapid growth speed, organizations and recruiters are finding it increasingly challenging to select the ideal applicant from a large number of applicants with diverse skill sets and personalities. Hence, selecting a candidate with a suitable personality for respective job profiles is a very important and great challenge for the HR department nowadays. Out of various personality prediction methods available out there, Myers-Briggs Type Indicator or MBTI is famous and accurate for our purpose of creating a personality prediction system for selecting candidates based on their personality. This study took into account all sixteen MB-Model coordinates. A comparative study of Random Forest, Logistic Regression, SVM, XGBoost has been done to perform personality prediction, and accuracy and confusion matrix for performance measurement of the models. While using TF-IDF, for the personality categories like Introversion/Extroversion the accuracy is 80.46%, for Sensing/Intuition it is 88.70%, for Thinking/Feeling it is 81.21% and for Perceiving vs Judging it is 72.97% with the Logistic Regression algorithm. Using Count vectorization for tokenizing, the accuracy is 80.97% for Introversion/Extroversion, for Sensing/Intuition it is 88.93%, for Thinking/Feeling it is 77.92% and for Perceiving vs Judging it is 73.48% with XGBoost algorithm, which gave the best performance.","PeriodicalId":258640,"journal":{"name":"2022 IEEE Pune Section International Conference (PuneCon)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116976703","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 : 2022-12-15DOI: 10.1109/PuneCon55413.2022.10014857
D. Kumar, R. Singh, Deepak Kumar, Manoj Patkar, Shailendra Tiwari, Amita Singh
Increasing disposable income of society and the individual., time-saving attitude., health safety (during COVID-19)., and innovation have increased consumer inclination from offline to online food delivery (OFD). Before COVID-19., eating out the home was the fashion and hangout., but after COVID-19., consumers feel safe while sitting at home. This study aims to explore the pre and current publications on online food delivery., find out the most studied country in OFD., find out the top-cited research publications in OFD., and find out the most dominant research terms in OFD. The present study reviews previous research published during the last 11 years (2012-March 2022) extracted from the www.dimensions.ai free web app. A drastic increase in publications since 2020 explains researchers' inclination toward the OFD. Two hundred twenty research articles were published during the pandemic out of 253 published in the last 11 years. The maximum researched country in OFD is the United States., followed by India and the United Kingdom. The most cited research publication has 255 citations. The most visible keywords in the present study were “Zomato.,” followed by “SEM” and “OFD.” The present study has some limitations., like the database used in the study (dimensions) may not be as good as Scopus or WoS., which may give a better result. More study is required to understand the OFD topic and its survival. It is recommended that the catering industry take OFD as an opportunity along with the regular business generated through steady footfall of the customer/guest. It can be improved through proper logistics., software support., and merging with artificial intelligence.
{"title":"Bibliometric Analysis of Online Food Delivery: A Study on Pre (COVID-19) and Current Scenario","authors":"D. Kumar, R. Singh, Deepak Kumar, Manoj Patkar, Shailendra Tiwari, Amita Singh","doi":"10.1109/PuneCon55413.2022.10014857","DOIUrl":"https://doi.org/10.1109/PuneCon55413.2022.10014857","url":null,"abstract":"Increasing disposable income of society and the individual., time-saving attitude., health safety (during COVID-19)., and innovation have increased consumer inclination from offline to online food delivery (OFD). Before COVID-19., eating out the home was the fashion and hangout., but after COVID-19., consumers feel safe while sitting at home. This study aims to explore the pre and current publications on online food delivery., find out the most studied country in OFD., find out the top-cited research publications in OFD., and find out the most dominant research terms in OFD. The present study reviews previous research published during the last 11 years (2012-March 2022) extracted from the www.dimensions.ai free web app. A drastic increase in publications since 2020 explains researchers' inclination toward the OFD. Two hundred twenty research articles were published during the pandemic out of 253 published in the last 11 years. The maximum researched country in OFD is the United States., followed by India and the United Kingdom. The most cited research publication has 255 citations. The most visible keywords in the present study were “Zomato.,” followed by “SEM” and “OFD.” The present study has some limitations., like the database used in the study (dimensions) may not be as good as Scopus or WoS., which may give a better result. More study is required to understand the OFD topic and its survival. It is recommended that the catering industry take OFD as an opportunity along with the regular business generated through steady footfall of the customer/guest. It can be improved through proper logistics., software support., and merging with artificial intelligence.","PeriodicalId":258640,"journal":{"name":"2022 IEEE Pune Section International Conference (PuneCon)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122489038","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 : 2022-12-15DOI: 10.1109/PuneCon55413.2022.10014901
A. Sheshu, Prakash Tunga P, Sumukha M, Vineeth Kumar Kori
Wearable technology is gaining popularity, being employed in a variety of applications, and wearable safety devices have found high demand in the market as of late. This project work relates to an important area of application for wearable devices, which is road safety. The roads of developing and under-developed countries tend to be largely unsafe and vulnerable to accidents especially for two wheeler users. Apart from the riders own safety, the chaotic environment in roads and highways in such countries also poses safety concerns for the public which is often overlooked. Another key issue with the use of wearable devices is minimizing electronic waste. As environmental issues are a growing concern, it is crucial to use energy efficient methods wherever possible in developing technology. Our proposed device RAKSHAK (meaning ‘protector’ in Hindi) is a secure riding helmet that strives to strike an immaculate balance between incorporating several novel and thoughtful intelligent features involving Machine Learning and the Internet of Things for safety and convenience, as well as taking an environment friendly approach to consumer electronics by using a renewable energy source.
{"title":"RAKSHAK - An Energy Efficient Intelligent Helmet","authors":"A. Sheshu, Prakash Tunga P, Sumukha M, Vineeth Kumar Kori","doi":"10.1109/PuneCon55413.2022.10014901","DOIUrl":"https://doi.org/10.1109/PuneCon55413.2022.10014901","url":null,"abstract":"Wearable technology is gaining popularity, being employed in a variety of applications, and wearable safety devices have found high demand in the market as of late. This project work relates to an important area of application for wearable devices, which is road safety. The roads of developing and under-developed countries tend to be largely unsafe and vulnerable to accidents especially for two wheeler users. Apart from the riders own safety, the chaotic environment in roads and highways in such countries also poses safety concerns for the public which is often overlooked. Another key issue with the use of wearable devices is minimizing electronic waste. As environmental issues are a growing concern, it is crucial to use energy efficient methods wherever possible in developing technology. Our proposed device RAKSHAK (meaning ‘protector’ in Hindi) is a secure riding helmet that strives to strike an immaculate balance between incorporating several novel and thoughtful intelligent features involving Machine Learning and the Internet of Things for safety and convenience, as well as taking an environment friendly approach to consumer electronics by using a renewable energy source.","PeriodicalId":258640,"journal":{"name":"2022 IEEE Pune Section International Conference (PuneCon)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124702838","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 rise of multiple company competitors during the COVID-19 outbreak resulted in fierce competition among competing firms for new clients and the retention of current ones. As a result of the foregoing, exceptional customer service is required, regardless of the size of the organization. Furthermore, any company's ability to know each of its customers' desires will provide it an advantage when it comes to providing specialized customer care and establishing customized marketing plans for them. The term “Consumer Buying Behavior Analysis” refers to a comprehensive assessment of the company's ideal clients/customers. In this project, we're utilizing the K-Means Algorithm to divide clients into two groups: “Highly Active Customers” and “Least Active Customers.” Then, utilizing the Apriori Algorithm, we use Association Rule Mining to recommend the best goods to clients based on their purchasing history and associations. We take one step further and use Logistic Regression to validate our Clustering operation by doing Binary Classification with our clusters as the label, resulting in accuracy and an F1 score of 91%.
{"title":"Machine Learning Approach: Consumer Buying Behavior Analysis","authors":"Anjali Sharma, Aradhana Pratap, Kishan Vyas, Sashikala Mishra","doi":"10.1109/PuneCon55413.2022.10014928","DOIUrl":"https://doi.org/10.1109/PuneCon55413.2022.10014928","url":null,"abstract":"The rise of multiple company competitors during the COVID-19 outbreak resulted in fierce competition among competing firms for new clients and the retention of current ones. As a result of the foregoing, exceptional customer service is required, regardless of the size of the organization. Furthermore, any company's ability to know each of its customers' desires will provide it an advantage when it comes to providing specialized customer care and establishing customized marketing plans for them. The term “Consumer Buying Behavior Analysis” refers to a comprehensive assessment of the company's ideal clients/customers. In this project, we're utilizing the K-Means Algorithm to divide clients into two groups: “Highly Active Customers” and “Least Active Customers.” Then, utilizing the Apriori Algorithm, we use Association Rule Mining to recommend the best goods to clients based on their purchasing history and associations. We take one step further and use Logistic Regression to validate our Clustering operation by doing Binary Classification with our clusters as the label, resulting in accuracy and an F1 score of 91%.","PeriodicalId":258640,"journal":{"name":"2022 IEEE Pune Section International Conference (PuneCon)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126836104","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 : 2022-12-15DOI: 10.1109/PuneCon55413.2022.10014952
Rishabh Karmakar, Ketki Nirantar, Pooja Hiremath, Deptii D. Chaudhari
Blockchain technology as a foundation for distributed ledgers provides a cutting-edge foundation for a brand-new, transparent, decentralised transaction system across industries and businesses. The features of this technology that are intrinsic increase confidence by providing transparency and traceability in any data, products, or financial resource transaction. Real-time monitoring and tracking are critical for delivering a unified perspective of global supply chains management (SCM) that involves multiple stakeholders. This study suggests a blockchain-based supply chain platform that secures all the transactions with Identity Access Management (IAM) and works with multiple stakeholders while establishing transparency and traceability amongst them. This paper focuses on the platform that the authors have created to keep all the data secure pertaining to the parties involved with the supply chain like a producer, retailer, distributor, auditor, and customer at a single place whose access to that is being protected and private at the same time. We discuss Security Handling and Privacy, as well as how the smart contract plays a role in this SCM. We also look at the test outputs, implementation areas, and a brief discussion about the findings.
{"title":"High-Level Design and Rapid Implementation of Blockchain-Based Real Time Supply Chain Platform","authors":"Rishabh Karmakar, Ketki Nirantar, Pooja Hiremath, Deptii D. Chaudhari","doi":"10.1109/PuneCon55413.2022.10014952","DOIUrl":"https://doi.org/10.1109/PuneCon55413.2022.10014952","url":null,"abstract":"Blockchain technology as a foundation for distributed ledgers provides a cutting-edge foundation for a brand-new, transparent, decentralised transaction system across industries and businesses. The features of this technology that are intrinsic increase confidence by providing transparency and traceability in any data, products, or financial resource transaction. Real-time monitoring and tracking are critical for delivering a unified perspective of global supply chains management (SCM) that involves multiple stakeholders. This study suggests a blockchain-based supply chain platform that secures all the transactions with Identity Access Management (IAM) and works with multiple stakeholders while establishing transparency and traceability amongst them. This paper focuses on the platform that the authors have created to keep all the data secure pertaining to the parties involved with the supply chain like a producer, retailer, distributor, auditor, and customer at a single place whose access to that is being protected and private at the same time. We discuss Security Handling and Privacy, as well as how the smart contract plays a role in this SCM. We also look at the test outputs, implementation areas, and a brief discussion about the findings.","PeriodicalId":258640,"journal":{"name":"2022 IEEE Pune Section International Conference (PuneCon)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123189579","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 : 2022-12-15DOI: 10.1109/PuneCon55413.2022.10014811
Prajyot H. Mohite, P. Shingare
The technological gap between humans and machines has shrunk with the development of artificial intelligence and in many technological developments human emotion interfacing is in demand. Traditionally, emotions have been treated as an individual-level phenomenon. However, the recent advancement in the technology more generously looking at emotion recognition as an asset which can be useful for developing many advance techniques in variety of fields and for many applications. It is important fact that facial expressions are the major contributors to estimate overall emotion. Therefore, this review explains the face emotion recognition (FER) systems and focuses on the recent developments in group-based emotion recognition (GER). The key point in FER or GER system is, emotion-specified expressions have corresponding prototypic facial expressions. As the FER systems improved gradually, the advancement in the technology revealed the importance of GER. This review focus on all such developments and viewpoints which are extremely important to consider when the topic of emotion recognition and related technology development is under discussion. In essence this review is useful when background knowledge is to be gained for further development in this topic. The progression in this field is summarized to develop understanding of the different approaches which are used for the advancement of various GER systems.
{"title":"Various Aspects and Progression of Group-Based Emotion Recognition Methods: A Review","authors":"Prajyot H. Mohite, P. Shingare","doi":"10.1109/PuneCon55413.2022.10014811","DOIUrl":"https://doi.org/10.1109/PuneCon55413.2022.10014811","url":null,"abstract":"The technological gap between humans and machines has shrunk with the development of artificial intelligence and in many technological developments human emotion interfacing is in demand. Traditionally, emotions have been treated as an individual-level phenomenon. However, the recent advancement in the technology more generously looking at emotion recognition as an asset which can be useful for developing many advance techniques in variety of fields and for many applications. It is important fact that facial expressions are the major contributors to estimate overall emotion. Therefore, this review explains the face emotion recognition (FER) systems and focuses on the recent developments in group-based emotion recognition (GER). The key point in FER or GER system is, emotion-specified expressions have corresponding prototypic facial expressions. As the FER systems improved gradually, the advancement in the technology revealed the importance of GER. This review focus on all such developments and viewpoints which are extremely important to consider when the topic of emotion recognition and related technology development is under discussion. In essence this review is useful when background knowledge is to be gained for further development in this topic. The progression in this field is summarized to develop understanding of the different approaches which are used for the advancement of various GER systems.","PeriodicalId":258640,"journal":{"name":"2022 IEEE Pune Section International Conference (PuneCon)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114833854","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 : 2022-12-15DOI: 10.1109/PuneCon55413.2022.10014947
R. S. Kamath, R. K. Kamat
COVID-19 pandemic has resulted in the switching of educational organizations into online learning serving platforms. The moment online education moved from an optional to the only form of learning and that too long term, the issues, and challenges become evident. Online learning will be more sustainable while learners become part of the solution framework. This is possible with the adoption of Design Thinking (DT). Learners' inclusion in problem-solving opens up a lot of possibilities. This can transform challenges in online learning into opportunities. The present article portrays the research carried out to improve students' learning effectiveness in online classes. The authors have proposed a Design Thinking framework that is in line with the high-order thinking skills of Bloom's taxonomy. This research has showcased the application of the five phases of the DT framework for attaining the optimum solution to the general issues of the online paradigm for teaching-learning. The study recommends that peer collaboration, timely feedback, and taking the learners along for co-designing the learning content are the essence borrowed from the DT framework and help in increasing learning engagement.
{"title":"Leveraging the Fullest Potential of Online Teaching Learning: A Design Thinking Framework Approach","authors":"R. S. Kamath, R. K. Kamat","doi":"10.1109/PuneCon55413.2022.10014947","DOIUrl":"https://doi.org/10.1109/PuneCon55413.2022.10014947","url":null,"abstract":"COVID-19 pandemic has resulted in the switching of educational organizations into online learning serving platforms. The moment online education moved from an optional to the only form of learning and that too long term, the issues, and challenges become evident. Online learning will be more sustainable while learners become part of the solution framework. This is possible with the adoption of Design Thinking (DT). Learners' inclusion in problem-solving opens up a lot of possibilities. This can transform challenges in online learning into opportunities. The present article portrays the research carried out to improve students' learning effectiveness in online classes. The authors have proposed a Design Thinking framework that is in line with the high-order thinking skills of Bloom's taxonomy. This research has showcased the application of the five phases of the DT framework for attaining the optimum solution to the general issues of the online paradigm for teaching-learning. The study recommends that peer collaboration, timely feedback, and taking the learners along for co-designing the learning content are the essence borrowed from the DT framework and help in increasing learning engagement.","PeriodicalId":258640,"journal":{"name":"2022 IEEE Pune Section International Conference (PuneCon)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116847227","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 recent years, with the rapid growth of Artificial Intelligence, there is increasing interest in Text-to-Image models. High-quality images can be generated with state-of-art text-to-image AI models such as Imagen, DALL.E-2, Draw-Bench. However, these models struggle with generating well aligned images for conflict category and low database. Therefore, Image-dev is a Text-To-Image model that blends TF-IDF(Term Frequency - Inverse Document Frequency) model along with preposition model, to evaluate the relation between the data object. Proposed model output images have an unparalleled level of artistic finish and an added level of language understanding and interpretation further enhance model to produce conflict category images. Image-dev help user's to generate a high-quality, photorealistic images without any pre-context based on GANs, VAEs and diffusion model. Image-dev is based on diffusion model. Diffusion model is more relevant because of its high quality and realistic output generation capacity.
近年来，随着人工智能的快速发展，人们对文本到图像模型的兴趣越来越大。高质量的图像可以通过Imagen、DALL等最先进的文本到图像的人工智能模型生成。依照,拉丝。然而，这些模型难以为冲突类别和低数据库生成对齐良好的图像。因此，Image-dev是一个混合了TF-IDF(Term Frequency - Inverse Document Frequency)模型和介词模型的Text-To-Image模型，用来评估数据对象之间的关系。所提出的模型输出图像具有无与伦比的艺术完成水平，并且增加了语言理解和解释水平，进一步增强了模型产生冲突类别图像的能力。图像开发基于gan、VAEs和扩散模型，帮助用户在没有任何预先背景的情况下生成高质量、逼真的图像。图像开发是基于扩散模型的。扩散模型因其高质量和逼真的输出能力而更具有现实意义。
{"title":"Image-dev: An Advance Text to Image AI model","authors":"Manavkumar Patel, Sonal Fatangare, Aryaman Nasare, Abhijeet Pachpute","doi":"10.1109/PuneCon55413.2022.10014718","DOIUrl":"https://doi.org/10.1109/PuneCon55413.2022.10014718","url":null,"abstract":"In the recent years, with the rapid growth of Artificial Intelligence, there is increasing interest in Text-to-Image models. High-quality images can be generated with state-of-art text-to-image AI models such as Imagen, DALL.E-2, Draw-Bench. However, these models struggle with generating well aligned images for conflict category and low database. Therefore, Image-dev is a Text-To-Image model that blends TF-IDF(Term Frequency - Inverse Document Frequency) model along with preposition model, to evaluate the relation between the data object. Proposed model output images have an unparalleled level of artistic finish and an added level of language understanding and interpretation further enhance model to produce conflict category images. Image-dev help user's to generate a high-quality, photorealistic images without any pre-context based on GANs, VAEs and diffusion model. Image-dev is based on diffusion model. Diffusion model is more relevant because of its high quality and realistic output generation capacity.","PeriodicalId":258640,"journal":{"name":"2022 IEEE Pune Section International Conference (PuneCon)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128935186","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 : 2022-12-15DOI: 10.1109/PuneCon55413.2022.10014768
Rishabh Singh, P. Sonewar, Manish Kumar, Ashwini Shingare, Anand Deshpande, Kumar Satyam, Joseph Colorafi, S. Kakade, Karen Jiggins Colorafi
Many Nonprofit organizations (NPOs) have a mission to empower vulnerable populations by providing safety and support services to build a healthier social community. The critical success factor for these organizations is generous and consistent donations from individuals, organizations, businesses, and governments. To remain financially viable and effective in mission, NPOs must achieve donation objectives. This demands a better understanding of donation activities and more specifically propensity/churn of existing donors. An Artificial Intelligence (AI) technique, Machine Learning can play a vital role in gaining insight into patterns of donors' response over the time and for various campaigns. Such data driven insights can help organizations design effective and personalized campaigns that result in reduced donor churn, attract new donors, and increase per donor donation amount. In this paper, we present an innovative application of unsupervised machine learning technique (K-Means) used with a Recency, Frequency, and Monetary (RFM) model to help improve outcomes of a US-based NPO with a mission to help families in need.
{"title":"Empowering Nonprofit Organization to Reduce Donation Attrition with Machine Learning","authors":"Rishabh Singh, P. Sonewar, Manish Kumar, Ashwini Shingare, Anand Deshpande, Kumar Satyam, Joseph Colorafi, S. Kakade, Karen Jiggins Colorafi","doi":"10.1109/PuneCon55413.2022.10014768","DOIUrl":"https://doi.org/10.1109/PuneCon55413.2022.10014768","url":null,"abstract":"Many Nonprofit organizations (NPOs) have a mission to empower vulnerable populations by providing safety and support services to build a healthier social community. The critical success factor for these organizations is generous and consistent donations from individuals, organizations, businesses, and governments. To remain financially viable and effective in mission, NPOs must achieve donation objectives. This demands a better understanding of donation activities and more specifically propensity/churn of existing donors. An Artificial Intelligence (AI) technique, Machine Learning can play a vital role in gaining insight into patterns of donors' response over the time and for various campaigns. Such data driven insights can help organizations design effective and personalized campaigns that result in reduced donor churn, attract new donors, and increase per donor donation amount. In this paper, we present an innovative application of unsupervised machine learning technique (K-Means) used with a Recency, Frequency, and Monetary (RFM) model to help improve outcomes of a US-based NPO with a mission to help families in need.","PeriodicalId":258640,"journal":{"name":"2022 IEEE Pune Section International Conference (PuneCon)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130234569","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}
Nowadays, Online video streaming services are extremely popular. YouTube give facility to their content creators to spread their knowledge, thoughts, and interesting content with users. In YouTube there is a trending section which shows currently most popular videos, ensuring that a video reaches the widest possible audience. Other than those videos rest are unpredictable, with the exception of few viral videos having a large number of views and are guaranteed to be in the trending section. Data analysis and Data mining are critical in today's world, and businesses are improving their operations by using social media. The aim of paper is to investigate YouTube's trending videos data. Users in the app use Views, Comments, Likes, and Dislikes. Classification algorithms like Linear Regression, Decision Tree, many other Machine Learning models can be used by using Python libraries like pandas and matplotlib, to classify and analyze YouTube data, as well as collect useful information.
{"title":"YouTube Trend Analysis","authors":"Arushi Pathik, Saumya Patni, Vaibhav Patel, Jash Patel, Artika Singh","doi":"10.1109/PuneCon55413.2022.10014717","DOIUrl":"https://doi.org/10.1109/PuneCon55413.2022.10014717","url":null,"abstract":"Nowadays, Online video streaming services are extremely popular. YouTube give facility to their content creators to spread their knowledge, thoughts, and interesting content with users. In YouTube there is a trending section which shows currently most popular videos, ensuring that a video reaches the widest possible audience. Other than those videos rest are unpredictable, with the exception of few viral videos having a large number of views and are guaranteed to be in the trending section. Data analysis and Data mining are critical in today's world, and businesses are improving their operations by using social media. The aim of paper is to investigate YouTube's trending videos data. Users in the app use Views, Comments, Likes, and Dislikes. Classification algorithms like Linear Regression, Decision Tree, many other Machine Learning models can be used by using Python libraries like pandas and matplotlib, to classify and analyze YouTube data, as well as collect useful information.","PeriodicalId":258640,"journal":{"name":"2022 IEEE Pune Section International Conference (PuneCon)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122228361","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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