Dr Subhadra P.S, Dr. A. Kalaivani, Dr. Rohit Markan, Ramesh Kumar, Dr Sundarapandiyan Natarajan, M. Rajalakshmi
The integration of artificial intelligence (AI) into business and industry is catalyzing a paradigm shift in how organizations operate, innovate, and interact with stakeholders. This abstract explores the multifaceted implications of AI across various domains, highlighting its role in automation, predictive analytics, personalized customer experiences, supply chain optimization, enhanced decision-making, natural language processing, product innovation, risk management, fraud detection, healthcare advancements, and workforce augmentation. By leveraging AI technologies, businesses can automate repetitive tasks, anticipate trends, tailor experiences, optimize operations, mitigate risks, and foster innovation. However, the widespread adoption of AI also poses ethical and societal challenges, including concerns about job displacement, data privacy, and algorithmic bias. Therefore, a holistic approach that balances technological advancement with ethical considerations is essential to harness the full potential of AI while ensuring its responsible and equitable deployment in business and industry.
{"title":"Rise of Artificial Intelligence in Business and Industry","authors":"Dr Subhadra P.S, Dr. A. Kalaivani, Dr. Rohit Markan, Ramesh Kumar, Dr Sundarapandiyan Natarajan, M. Rajalakshmi","doi":"10.52783/jier.v4i2.850","DOIUrl":"https://doi.org/10.52783/jier.v4i2.850","url":null,"abstract":"The integration of artificial intelligence (AI) into business and industry is catalyzing a paradigm shift in how organizations operate, innovate, and interact with stakeholders. This abstract explores the multifaceted implications of AI across various domains, highlighting its role in automation, predictive analytics, personalized customer experiences, supply chain optimization, enhanced decision-making, natural language processing, product innovation, risk management, fraud detection, healthcare advancements, and workforce augmentation. By leveraging AI technologies, businesses can automate repetitive tasks, anticipate trends, tailor experiences, optimize operations, mitigate risks, and foster innovation. However, the widespread adoption of AI also poses ethical and societal challenges, including concerns about job displacement, data privacy, and algorithmic bias. Therefore, a holistic approach that balances technological advancement with ethical considerations is essential to harness the full potential of AI while ensuring its responsible and equitable deployment in business and industry.","PeriodicalId":496224,"journal":{"name":"Journal of Informatics Education and Research","volume":"76 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141052532","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 Sundarapandiyan Natarajan, Dr. Korapu Sattibabu, Dr. Borugadda Subbaiah, Dr. D. Paul Dhinakaran, J. Rashmi Kumar, M. Rajalakshmi
In today's dynamic and competitive business landscape, effective talent management is paramount for organizational success. This paper explores the integration of artificial intelligence (AI) technologies into talent management practices to optimize recruitment, development, and retention processes. Through a comprehensive review of existing literature and case studies, we elucidate various AI-powered strategies for talent management optimization. These strategies encompass AI-driven recruitment, predictive analytics for talent acquisition, personalized learning and development initiatives, AI-enhanced performance management and feedback systems, retention strategies, succession planning, and diversity and inclusion initiatives. By harnessing AI capabilities, organizations can enhance decision-making, improve efficiency, mitigate bias, and foster a more inclusive and agile workforce. The implications of AI adoption in talent management are discussed, highlighting opportunities for innovation and potential challenges to address. Ultimately, this paper provides insights for HR professionals, business leaders, and researchers into leveraging AI for strategic talent management optimization in the digital age.
{"title":"AI-Powered Strategies for Talent Management Optimization","authors":"Dr Sundarapandiyan Natarajan, Dr. Korapu Sattibabu, Dr. Borugadda Subbaiah, Dr. D. Paul Dhinakaran, J. Rashmi Kumar, M. Rajalakshmi","doi":"10.52783/jier.v4i2.848","DOIUrl":"https://doi.org/10.52783/jier.v4i2.848","url":null,"abstract":"In today's dynamic and competitive business landscape, effective talent management is paramount for organizational success. This paper explores the integration of artificial intelligence (AI) technologies into talent management practices to optimize recruitment, development, and retention processes. Through a comprehensive review of existing literature and case studies, we elucidate various AI-powered strategies for talent management optimization. These strategies encompass AI-driven recruitment, predictive analytics for talent acquisition, personalized learning and development initiatives, AI-enhanced performance management and feedback systems, retention strategies, succession planning, and diversity and inclusion initiatives. By harnessing AI capabilities, organizations can enhance decision-making, improve efficiency, mitigate bias, and foster a more inclusive and agile workforce. The implications of AI adoption in talent management are discussed, highlighting opportunities for innovation and potential challenges to address. Ultimately, this paper provides insights for HR professionals, business leaders, and researchers into leveraging AI for strategic talent management optimization in the digital age.","PeriodicalId":496224,"journal":{"name":"Journal of Informatics Education and Research","volume":"12 23","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141054920","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. V. Arunkumar, Dr. V. Rengarajan, Dr. V. Vijay Anand, Dr. K. A. Shreenivasan, Prof. S. Thiyagarajan, Prof. R. Swaminathan
The evolution of smart technology has led to significant advancements in various aspects of urban infrastructure, including the management of educational campuses. Through the integration of comprehensive dashboards, decision-makers can access essential information related to building performance, occupancy rates, energy consumption, and maintenance needs. The study underscores the potential of smart campus management systems to optimize resource allocation, improve operational workflows, and create more responsive environments to meet the evolving needs of stakeholders within educational institutions. The usage of IoT in smart campuses, aiming to identify key trends, applications, challenges, and future directions in this domain. The proliferation of IoT technologies has sparked interest in their application within educational settings, leading to the emergence of smart campuses. By connecting physical objects and systems to the internet, IoT enables the collection, analysis, and utilization of real-time data to enhance campus efficiency, sustainability, and user experience. This systematic literature review aims to provide insights into the usage of IoT on smart campuses, shedding light on its applications, benefits, challenges, and future prospects.
{"title":"Impact of IOT On Campus; Smart Student Information System In The Educational Sector","authors":"Dr. V. Arunkumar, Dr. V. Rengarajan, Dr. V. Vijay Anand, Dr. K. A. Shreenivasan, Prof. S. Thiyagarajan, Prof. R. Swaminathan","doi":"10.52783/jier.v4i2.836","DOIUrl":"https://doi.org/10.52783/jier.v4i2.836","url":null,"abstract":"The evolution of smart technology has led to significant advancements in various aspects of urban infrastructure, including the management of educational campuses. Through the integration of comprehensive dashboards, decision-makers can access essential information related to building performance, occupancy rates, energy consumption, and maintenance needs. The study underscores the potential of smart campus management systems to optimize resource allocation, improve operational workflows, and create more responsive environments to meet the evolving needs of stakeholders within educational institutions. The usage of IoT in smart campuses, aiming to identify key trends, applications, challenges, and future directions in this domain. The proliferation of IoT technologies has sparked interest in their application within educational settings, leading to the emergence of smart campuses. By connecting physical objects and systems to the internet, IoT enables the collection, analysis, and utilization of real-time data to enhance campus efficiency, sustainability, and user experience. This systematic literature review aims to provide insights into the usage of IoT on smart campuses, shedding light on its applications, benefits, challenges, and future prospects.","PeriodicalId":496224,"journal":{"name":"Journal of Informatics Education and Research","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141039186","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. Ch Sudipta Kishore Nanda, Dr. R. Naveenkumar, Dr. Sameera Asif Siddiqui, Dr. Supriya Pathak, Uday Pratap Singh, Dr. Varsha Bihade
This research evaluates a company's development and performance based on its intelligence and BI utilization. A study using quantitative poll data from numerous firms examines the relationship between leveraging business knowledge, promoting innovation, and important performance metrics. The findings demonstrate that BI adoption and receptivity to new ideas improve corporate development. Companies that invest heavily in business intelligence (BI) programmes and foster innovation generally outperform their rivals in sales, earnings, and market share. Creative thinking and business knowledge are crucial to corporate success, according to the findings. These gives workers’ suggestions on how to work faster and more creatively. More research, sector-specific analysis, and continuing studies are needed to determine how innovation culture and BI adoption effect firm performance. Finally, this research adds to what is already known about how BI adoption and innovation culture effect firm performance in today's competitive business environment.
{"title":"Driving Business Growth from Research to Innovation in The Deployment of Business Intelligence","authors":"Dr. Ch Sudipta Kishore Nanda, Dr. R. Naveenkumar, Dr. Sameera Asif Siddiqui, Dr. Supriya Pathak, Uday Pratap Singh, Dr. Varsha Bihade","doi":"10.52783/jier.v4i2.840","DOIUrl":"https://doi.org/10.52783/jier.v4i2.840","url":null,"abstract":"This research evaluates a company's development and performance based on its intelligence and BI utilization. A study using quantitative poll data from numerous firms examines the relationship between leveraging business knowledge, promoting innovation, and important performance metrics. The findings demonstrate that BI adoption and receptivity to new ideas improve corporate development. Companies that invest heavily in business intelligence (BI) programmes and foster innovation generally outperform their rivals in sales, earnings, and market share. Creative thinking and business knowledge are crucial to corporate success, according to the findings. These gives workers’ suggestions on how to work faster and more creatively. More research, sector-specific analysis, and continuing studies are needed to determine how innovation culture and BI adoption effect firm performance. Finally, this research adds to what is already known about how BI adoption and innovation culture effect firm performance in today's competitive business environment.","PeriodicalId":496224,"journal":{"name":"Journal of Informatics Education and Research","volume":"13 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141054908","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. Manjula Shastri, Dr. Surajit Das, Akansh Garg, Mr. Gourab Dutta, Ms. Aneeqa, Dr. Abhishek Tripathi
This is a study that uses ML algorithms applications for effective credit risk prediction and management in small and mid-size businesses (SMBs). One of the ways this was achieved was by using comprehensive data sets, which consisted of historical credit sales transactions, customer demographics, and economic indicators. As a result, four specific ML algorithms, namely logistic regression, decision trees, random forest and gradient boosting, were assessed as the methodology. Findings show that gradient boosting yielded the best results, reaching an accuracy score of 90 %, precision of 89 %, recall value of 91 %, F1-score of 90 %, and area under the receiver operating characteristic curve is 0.95. Logistic regression has shown highly competitive results, in excess of 85% accuracy, and an AUC-ROC of 0.91. The findings demonstrate that credit history, the income level, and the age of the client are the most critical features in credit risk analysis of the SMBs.
这是一项利用 ML 算法应用来有效预测和管理中小型企业（SMB）信用风险的研究。实现这一目标的方法之一是使用综合数据集，其中包括历史信贷销售交易、客户人口统计和经济指标。因此，对四种特定的 ML 算法，即逻辑回归、决策树、随机森林和梯度提升进行了方法评估。研究结果表明，梯度提升算法取得了最好的结果，准确率达到 90%，精确度达到 89%，召回值达到 91%，F1 分数达到 90%，接收者工作特征曲线下面积达到 0.95。逻辑回归显示了极具竞争力的结果，准确率超过 85%，AUC-ROC 为 0.91。研究结果表明，信用记录、收入水平和客户年龄是中小型企业信用风险分析中最关键的特征。
{"title":"Machine Learning Based Risk Management of Credit Sales in Small and Mid-Size Business","authors":"Dr. Manjula Shastri, Dr. Surajit Das, Akansh Garg, Mr. Gourab Dutta, Ms. Aneeqa, Dr. Abhishek Tripathi","doi":"10.52783/jier.v4i2.842","DOIUrl":"https://doi.org/10.52783/jier.v4i2.842","url":null,"abstract":"This is a study that uses ML algorithms applications for effective credit risk prediction and management in small and mid-size businesses (SMBs). One of the ways this was achieved was by using comprehensive data sets, which consisted of historical credit sales transactions, customer demographics, and economic indicators. As a result, four specific ML algorithms, namely logistic regression, decision trees, random forest and gradient boosting, were assessed as the methodology. Findings show that gradient boosting yielded the best results, reaching an accuracy score of 90 %, precision of 89 %, recall value of 91 %, F1-score of 90 %, and area under the receiver operating characteristic curve is 0.95. Logistic regression has shown highly competitive results, in excess of 85% accuracy, and an AUC-ROC of 0.91. The findings demonstrate that credit history, the income level, and the age of the client are the most critical features in credit risk analysis of the SMBs.","PeriodicalId":496224,"journal":{"name":"Journal of Informatics Education and Research","volume":"18 S2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141051078","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}
Vidya Nayak, Dr. Shankar Chaudhary, Dr. Chitralekha Kumar
HR Analytics simplifies data collection, interpretation, measurement, and forecasting in organizations by combining statistical techniques for data collection, interpretation, measurement, and forecasting. It aims to enhance the utilization of data analytics in HR management actions, specifically in relation to tools and techniques with reference to employee attrition. Recent literature has reported that implementation of HR analytics helps in identifying employee attrition patterns, hiring timelines, productivity costs, and the impact of learning and development on performance. While the study suggests that a modern, innovative, and competitive workplace is being driven by performance expectations, which is why HR analytics is becoming more and more important in firms, it will also examine the advantages and challenges of HR analytics. This is a theoretical paper, and the purpose of this paper is to study the literature available on HR analytics tools and types of HR analytic techniques. The study is done based on secondary data from published research papers, journals, blogs, and websites from the period of 2017-2023.
{"title":"A Study on HR Analytical Tools and Techniques","authors":"Vidya Nayak, Dr. Shankar Chaudhary, Dr. Chitralekha Kumar","doi":"10.52783/jier.v4i2.839","DOIUrl":"https://doi.org/10.52783/jier.v4i2.839","url":null,"abstract":"HR Analytics simplifies data collection, interpretation, measurement, and forecasting in organizations by combining statistical techniques for data collection, interpretation, measurement, and forecasting. It aims to enhance the utilization of data analytics in HR management actions, specifically in relation to tools and techniques with reference to employee attrition. Recent literature has reported that implementation of HR analytics helps in identifying employee attrition patterns, hiring timelines, productivity costs, and the impact of learning and development on performance. While the study suggests that a modern, innovative, and competitive workplace is being driven by performance expectations, which is why HR analytics is becoming more and more important in firms, it will also examine the advantages and challenges of HR analytics. This is a theoretical paper, and the purpose of this paper is to study the literature available on HR analytics tools and types of HR analytic techniques. The study is done based on secondary data from published research papers, journals, blogs, and websites from the period of 2017-2023.","PeriodicalId":496224,"journal":{"name":"Journal of Informatics Education and Research","volume":"18 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141038369","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. Rajalakshmi Anantharaman, Dr. Badhusha M H N, Madhukumar. B, Dr. Subin Thomas, Dr. Prem Latha Soundarraj, Dr. Kumar Rahul
The study aim to explore and analyze the multifaceted relationship between digital marketing and e-commerce growth in the Indian context, considering various dimensions such as consumer behavior, technological advancements, regulatory frameworks, and market dynamics. However, while digital marketing is widely acknowledged as a critical driver of e-commerce growth, there exists a gap in understanding the specific mechanisms through which digital marketing influences the expansion of the e-commerce sector in India. The primary ways in which digital marketing influences consumer behavior in India is through personalized advertising and targeted messaging The role of digital marketing in driving the growth of e-commerce in India is a multifaceted phenomenon that requires in-depth analysis across various dimensions. By examining the impact of digital marketing on consumer behavior, technological innovations, regulatory frameworks, and market dynamics, this study aims to provide valuable insights for businesses, policymakers, and researchers seeking to understand and leverage the power of digital marketing in the Indian e-commerce landscape.
{"title":"Analyzing The Role of Digital Marketing in Growth of E-Commerce in India: A Multiple Holistic Approach","authors":"Dr. Rajalakshmi Anantharaman, Dr. Badhusha M H N, Madhukumar. B, Dr. Subin Thomas, Dr. Prem Latha Soundarraj, Dr. Kumar Rahul","doi":"10.52783/jier.v4i2.835","DOIUrl":"https://doi.org/10.52783/jier.v4i2.835","url":null,"abstract":"The study aim to explore and analyze the multifaceted relationship between digital marketing and e-commerce growth in the Indian context, considering various dimensions such as consumer behavior, technological advancements, regulatory frameworks, and market dynamics. However, while digital marketing is widely acknowledged as a critical driver of e-commerce growth, there exists a gap in understanding the specific mechanisms through which digital marketing influences the expansion of the e-commerce sector in India. The primary ways in which digital marketing influences consumer behavior in India is through personalized advertising and targeted messaging The role of digital marketing in driving the growth of e-commerce in India is a multifaceted phenomenon that requires in-depth analysis across various dimensions. By examining the impact of digital marketing on consumer behavior, technological innovations, regulatory frameworks, and market dynamics, this study aims to provide valuable insights for businesses, policymakers, and researchers seeking to understand and leverage the power of digital marketing in the Indian e-commerce landscape.","PeriodicalId":496224,"journal":{"name":"Journal of Informatics Education and Research","volume":"39 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141035644","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. Deepti Sharma, Dr. K. Sellvasundaram, Dr. Prasanta Chatterjee Biswas
The developments in human capital work that have occurred since machine intelligence (ML) was increased human resource management (HRM) are both good and bad. This essay looks at what HRM wealth, what questions it faces, and what potential it offers in this place age of AI and ML. In the beginning, we discuss how changes in data processing have transformed human resource management (HRM), focusing on in what way or manner AI and machine intelligence are becoming more influential in changeful HR processes. The goals concerning this study search out research what human capability administration is, how AI and ML influence it, how AI and ML will influence tasks from now on, and the pros and cons of utilizing ML in HRM. The composition review investigates excellent detail about the fundamental ideas of human property administration. It focuses on how the field has exchanged over opportunity from simple governmental tasks to crucial exertions to better member happiness, output, and the happiness of the association. In this part, we further talk about in what way or manner AI and ML have exchanged HR tasks like bringing in, directing act, and planning the trained workers. When people examine how AI and ML have transformed HRM, people can visualize that they may present family data-driven understandings, make HR tasks smooth, and manage smooth to handle operators and create decisions. But to catch the most out of machine intelligence in HRM, issues like partial data, bad data, and directing change need expected fixed.
{"title":"Machine Learning and HRM: A Path to Efficient Workforce Management","authors":"Dr. Deepti Sharma, Dr. K. Sellvasundaram, Dr. Prasanta Chatterjee Biswas","doi":"10.52783/jier.v4i2.841","DOIUrl":"https://doi.org/10.52783/jier.v4i2.841","url":null,"abstract":"The developments in human capital work that have occurred since machine intelligence (ML) was increased human resource management (HRM) are both good and bad. This essay looks at what HRM wealth, what questions it faces, and what potential it offers in this place age of AI and ML. In the beginning, we discuss how changes in data processing have transformed human resource management (HRM), focusing on in what way or manner AI and machine intelligence are becoming more influential in changeful HR processes. The goals concerning this study search out research what human capability administration is, how AI and ML influence it, how AI and ML will influence tasks from now on, and the pros and cons of utilizing ML in HRM. The composition review investigates excellent detail about the fundamental ideas of human property administration. It focuses on how the field has exchanged over opportunity from simple governmental tasks to crucial exertions to better member happiness, output, and the happiness of the association. In this part, we further talk about in what way or manner AI and ML have exchanged HR tasks like bringing in, directing act, and planning the trained workers. When people examine how AI and ML have transformed HRM, people can visualize that they may present family data-driven understandings, make HR tasks smooth, and manage smooth to handle operators and create decisions. But to catch the most out of machine intelligence in HRM, issues like partial data, bad data, and directing change need expected fixed.","PeriodicalId":496224,"journal":{"name":"Journal of Informatics Education and Research","volume":"16 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141042203","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}
Joyir Siram, Dr Gurmeet singh sikh, Dr Joel Osei-Asiamah, Dr. Chikati Srinu, Dr. Surendar Vaddepalli, Dr. Abhishek Tripathi
This paper proposes a new structure for management and machine learning in higher education institutions, which is designed to improve the efficiency of an organization and the success of the students at a whole. The framework brings about the enactment of several analytical techniques, like predictive modeling and data-driven decision making, which help to make accurate strategies for planning and providing continuous improvement. Four algorithms in machine learning- Linear Regression, Decision Tree, Random Forest and Multilayer Perceptron- are compared to see if they predict important performance markers for student success, faculty productivity and institutional efficiency. The results illustrate the Multilayer Perceptron algorithm as the best performer, getting MSE of 0.018 and MAE of 0.105, while R2 score of 0.842, showing the superiority of MLP over the others. Validation studies done comparing it with base line models or related models in the field are proof that the suggested model is widely applicable among the higher education spectrum in dealing with the involved issues. The imaginable framework seems to be a prospective tool for stimulating creativity, inclusion, and eminence in academia while adding to the knowledge acquisition and achieving institute objectives.
{"title":"Towards a Framework for Performance Management and Machine Learning in a Higher Education Institution","authors":"Joyir Siram, Dr Gurmeet singh sikh, Dr Joel Osei-Asiamah, Dr. Chikati Srinu, Dr. Surendar Vaddepalli, Dr. Abhishek Tripathi","doi":"10.52783/jier.v4i2.844","DOIUrl":"https://doi.org/10.52783/jier.v4i2.844","url":null,"abstract":"This paper proposes a new structure for management and machine learning in higher education institutions, which is designed to improve the efficiency of an organization and the success of the students at a whole. The framework brings about the enactment of several analytical techniques, like predictive modeling and data-driven decision making, which help to make accurate strategies for planning and providing continuous improvement. Four algorithms in machine learning- Linear Regression, Decision Tree, Random Forest and Multilayer Perceptron- are compared to see if they predict important performance markers for student success, faculty productivity and institutional efficiency. The results illustrate the Multilayer Perceptron algorithm as the best performer, getting MSE of 0.018 and MAE of 0.105, while R2 score of 0.842, showing the superiority of MLP over the others. Validation studies done comparing it with base line models or related models in the field are proof that the suggested model is widely applicable among the higher education spectrum in dealing with the involved issues. The imaginable framework seems to be a prospective tool for stimulating creativity, inclusion, and eminence in academia while adding to the knowledge acquisition and achieving institute objectives.","PeriodicalId":496224,"journal":{"name":"Journal of Informatics Education and Research","volume":"36 21","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141041583","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}
Swapna Datta Khan, Madhukumar. B, Maria Antony Raj M, Dr. Karthick R, Dr Shagufta Parween, Dr Balamurugan S
In the current era of technology, organizations encounter the crucial obstacle of efficiently utilizing digital marketing tactics to stimulate expansion, guarantee achievement, and improve client satisfaction. In today's digital era, companies face intense competitions in the online realm, where being visible, engaging, and converting customers are of utmost importance. Nevertheless, several firms have challenges in creating and implementing digital marketing strategies that are in line with their goals, appeal to their intended audience, and provide measurable outcomes. An important concern is the fast advancement of digital marketing platforms and technology, which may inundate firms. In order to be competitive and achieve sustainable development, companies must constantly adapt and modify their tactics due to the intricate and ever-changing nature of digital marketing. To tackle these difficulties, it is essential to adopt a complete strategy that combines data-driven analysis, innovative content creation, and a customer-focused attitude. This will enable the delivery of engaging experiences that connect with customers in the digital realm.
{"title":"Significant Role of Digital Marketing Strategies in Driving Business Growth, Success and Customer Experience","authors":"Swapna Datta Khan, Madhukumar. B, Maria Antony Raj M, Dr. Karthick R, Dr Shagufta Parween, Dr Balamurugan S","doi":"10.52783/jier.v4i2.837","DOIUrl":"https://doi.org/10.52783/jier.v4i2.837","url":null,"abstract":"In the current era of technology, organizations encounter the crucial obstacle of efficiently utilizing digital marketing tactics to stimulate expansion, guarantee achievement, and improve client satisfaction. In today's digital era, companies face intense competitions in the online realm, where being visible, engaging, and converting customers are of utmost importance. Nevertheless, several firms have challenges in creating and implementing digital marketing strategies that are in line with their goals, appeal to their intended audience, and provide measurable outcomes. An important concern is the fast advancement of digital marketing platforms and technology, which may inundate firms. In order to be competitive and achieve sustainable development, companies must constantly adapt and modify their tactics due to the intricate and ever-changing nature of digital marketing. To tackle these difficulties, it is essential to adopt a complete strategy that combines data-driven analysis, innovative content creation, and a customer-focused attitude. This will enable the delivery of engaging experiences that connect with customers in the digital realm.","PeriodicalId":496224,"journal":{"name":"Journal of Informatics Education and Research","volume":"4 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141058503","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：