The "Semesters Simplified" project addresses challenges in academic management at Dr. A.P.J. Abdul Kalam Technical University (AKTU) by introducing a dedicated website and mobile application. This technological initiative aims to streamline information dissemination, enhance communication, and foster an engaging learning environment for both students and educators within semester-based academic structures. Grounded in educational technology principles, the project prioritizes user engagement, personalized learning, and inclusivity. The chosen technology stack employs modern tools for front-end and back-end development, ensuring a seamless and responsive user experience. The methodology encompasses needs analysis, user-centric design, development, testing, deployment, and continuous improvement. The proposed facilities, spanning software and hardware requirements, are crucial for the project's success. The literature review provides a solid foundation, aligning the project with established principles and best practices in educational technology. Ultimately, "Semesters Simplified" aspires to contribute significantly to the advancement of academic management, fostering accessibility, engagement, and inclusivity at AKTU.
{"title":"Semesters Simplified: A Comprehensive Approach to Academic Management","authors":"Shantanu Sharma, Karan Negi, Sachin Rao, Anurag Goel, Gaurav Kumar Chaubey, Kalpana Kalpana","doi":"10.48001/jodpba.2024.1131-37","DOIUrl":"https://doi.org/10.48001/jodpba.2024.1131-37","url":null,"abstract":"The \"Semesters Simplified\" project addresses challenges in academic management at Dr. A.P.J. Abdul Kalam Technical University (AKTU) by introducing a dedicated website and mobile application. This technological initiative aims to streamline information dissemination, enhance communication, and foster an engaging learning environment for both students and educators within semester-based academic structures. Grounded in educational technology principles, the project prioritizes user engagement, personalized learning, and inclusivity. The chosen technology stack employs modern tools for front-end and back-end development, ensuring a seamless and responsive user experience. The methodology encompasses needs analysis, user-centric design, development, testing, deployment, and continuous improvement. The proposed facilities, spanning software and hardware requirements, are crucial for the project's success. The literature review provides a solid foundation, aligning the project with established principles and best practices in educational technology. Ultimately, \"Semesters Simplified\" aspires to contribute significantly to the advancement of academic management, fostering accessibility, engagement, and inclusivity at AKTU.","PeriodicalId":283934,"journal":{"name":"Journal of Data Processing and Business Analytics","volume":"29 39","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140980297","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-08DOI: 10.48001/jodpba.2024.1121-30
Muralidhar m, Banda Krishna Vaishnavi, Harish Knikhil S, Magizhan C B, Naveen Balaji S
Supply chain optimization is a critical aspect of modern business operations, aiming to enhance efficiency, reduce costs, and improve customer satisfaction. In today's dynamic business environment, supply chains face increasing complexity and uncertainty. The ability to access and analyse data in real-time is paramount for organizations to stay competitive and responsive to market dynamics. Effective management requires timely access to accurate data and actionable insights. By harnessing the power of real-time data and integration of real-time data analysis techniques, coupled with visualization and Business Intelligence tools like Power BI, organizations can make informed decisions promptly, leading to agile and responsive supply chain management. Raw data has been transformed into meaningful insights through interactive dashboards and reports by using Power BI.
供应链优化是现代企业运营的一个重要方面，旨在提高效率、降低成本和提高客户满意度。在当今多变的商业环境中，供应链面临着日益增加的复杂性和不确定性。企业要想保持竞争力并对市场动态做出快速反应，实时访问和分析数据的能力至关重要。有效的管理需要及时获取准确的数据和可行的见解。通过利用实时数据的力量和整合实时数据分析技术，再加上 Power BI 等可视化和商业智能工具，企业可以迅速做出明智的决策，从而实现敏捷和反应迅速的供应链管理。通过使用 Power BI 的交互式仪表盘和报告，原始数据已转化为有意义的见解。
{"title":"Supply Chain Optimization through Real-Time Data Analysis and Visualization with Power BI","authors":"Muralidhar m, Banda Krishna Vaishnavi, Harish Knikhil S, Magizhan C B, Naveen Balaji S","doi":"10.48001/jodpba.2024.1121-30","DOIUrl":"https://doi.org/10.48001/jodpba.2024.1121-30","url":null,"abstract":"Supply chain optimization is a critical aspect of modern business operations, aiming to enhance efficiency, reduce costs, and improve customer satisfaction. In today's dynamic business environment, supply chains face increasing complexity and uncertainty. The ability to access and analyse data in real-time is paramount for organizations to stay competitive and responsive to market dynamics. Effective management requires timely access to accurate data and actionable insights. By harnessing the power of real-time data and integration of real-time data analysis techniques, coupled with visualization and Business Intelligence tools like Power BI, organizations can make informed decisions promptly, leading to agile and responsive supply chain management. Raw data has been transformed into meaningful insights through interactive dashboards and reports by using Power BI.","PeriodicalId":283934,"journal":{"name":"Journal of Data Processing and Business Analytics","volume":" 94","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141000425","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-07DOI: 10.48001/jodpba.2024.1113-20
Elizabeth Sunny, Therese Yamuna Mahesh
In the era of twenty-first century, an era characterized by the proliferation of digital technology, big data and so on, the ability to identify human emotions through visual content from images has gained much importance and its popularity is increasing worldwide. This project deals with the task of detecting emotions from images using deep learning techniques with a specific emphasis on Mobile Net-based architectures. We start the project by preparing the dataset of various images showing diverse emotions. The Mobile Net architecture, a powerful convolutional neural network is fine-tuned with a custom dense layer to classify emotions into seven distinct categories. Data argumentation techniques such as zooming, shearing and horizontal flipping are incorporated to enhance robustness and prevent overfitting. The training dataset is preprocessed and normalized while a segregated validation dataset ensures stringent evaluation. During training we implemented early stopping and model checkpoint mechanisms to get optimal performance while avoiding overfitting. After training the analysis of accuracy and loss metrics provides an insight into the model’s trajectory. In practical applicability we use the trained model to predict emotion from single images, showcasing its potential in various domains, including digital marketing, healthcare, and user experience design. In today’s digital landscape the project findings hold relevance for a wide spectrum of applications, promising advancements in human computer interactions and emotion aware systems.
{"title":"Emotion Detection from Photos Using MobleNet-based Deep Learning","authors":"Elizabeth Sunny, Therese Yamuna Mahesh","doi":"10.48001/jodpba.2024.1113-20","DOIUrl":"https://doi.org/10.48001/jodpba.2024.1113-20","url":null,"abstract":"In the era of twenty-first century, an era characterized by the proliferation of digital technology, big data and so on, the ability to identify human emotions through visual content from images has gained much importance and its popularity is increasing worldwide. This project deals with the task of detecting emotions from images using deep learning techniques with a specific emphasis on Mobile Net-based architectures. We start the project by preparing the dataset of various images showing diverse emotions. The Mobile Net architecture, a powerful convolutional neural network is fine-tuned with a custom dense layer to classify emotions into seven distinct categories. Data argumentation techniques such as zooming, shearing and horizontal flipping are incorporated to enhance robustness and prevent overfitting. The training dataset is preprocessed and normalized while a segregated validation dataset ensures stringent evaluation. During training we implemented early stopping and model checkpoint mechanisms to get optimal performance while avoiding overfitting. After training the analysis of accuracy and loss metrics provides an insight into the model’s trajectory. In practical applicability we use the trained model to predict emotion from single images, showcasing its potential in various domains, including digital marketing, healthcare, and user experience design. In today’s digital landscape the project findings hold relevance for a wide spectrum of applications, promising advancements in human computer interactions and emotion aware systems.","PeriodicalId":283934,"journal":{"name":"Journal of Data Processing and Business Analytics","volume":"1 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140258612","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-29DOI: 10.48001/jodpba.2024.119-12
M. Bharathi, T. A. Sai Srinivas
Embark on a journey through the realms of car insurance modeling, where the fusion of statistical and mathematical prowess unveils the secrets behind predicting claim frequency, severity, and overall costs. This enchanted exploration not only guides you through the wizardry of Python but also empowers you with the art of crafting insurance products, navigating risk, and orchestrating business strategies. If the arcane world of Car Insurance Modeling beckons you, join this mystical narrative, where algorithms and Python spells converge, weaving a tale of predictive mastery. Illuminate your path and delve into the enchantment of modeling automotive destinies with code as your guide
{"title":"Age, Cars, and Claims: Decoding the Insurance Landscape","authors":"M. Bharathi, T. A. Sai Srinivas","doi":"10.48001/jodpba.2024.119-12","DOIUrl":"https://doi.org/10.48001/jodpba.2024.119-12","url":null,"abstract":"Embark on a journey through the realms of car insurance modeling, where the fusion of statistical and mathematical prowess unveils the secrets behind predicting claim frequency, severity, and overall costs. This enchanted exploration not only guides you through the wizardry of Python but also empowers you with the art of crafting insurance products, navigating risk, and orchestrating business strategies. If the arcane world of Car Insurance Modeling beckons you, join this mystical narrative, where algorithms and Python spells converge, weaving a tale of predictive mastery. Illuminate your path and delve into the enchantment of modeling automotive destinies with code as your guide","PeriodicalId":283934,"journal":{"name":"Journal of Data Processing and Business Analytics","volume":"32 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140415522","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 : 2023-06-26DOI: 10.48001/jodpba.2023.111-4
Swati Mahajan
In today’s world, where data becomes a crucial thing in daily life it is hard to manage large amounts of data. And to manage data and to take relevant data we need data visualization and business intelligence. To understand Data Visualization, we first need to understand Business Intelligence (BI). BI is taking raw data and provides meaningful business insights which will help to make business decisions. BI is what makes your business smarter or to make your business intelligent. Data Visualization is the concept in which the data is represented by common graphics such as charts, plots, animations and many more. It is used for various purposes. It is used for data-driven insights and decisions. The data is represented by dashboards which are built by using tableau and power bi. The main purpose/goal of DV is making it easier to identify trends, patterns, and outliers in a large dataset because a single outlier can lead to wrong decisions.
{"title":"Data Visualization using a Powerful Tool – Power BI","authors":"Swati Mahajan","doi":"10.48001/jodpba.2023.111-4","DOIUrl":"https://doi.org/10.48001/jodpba.2023.111-4","url":null,"abstract":"In today’s world, where data becomes a crucial thing in daily life it is hard to manage large amounts of data. And to manage data and to take relevant data we need data visualization and business intelligence. To understand Data Visualization, we first need to understand Business Intelligence (BI). BI is taking raw data and provides meaningful business insights which will help to make business decisions. BI is what makes your business smarter or to make your business intelligent. Data Visualization is the concept in which the data is represented by common graphics such as charts, plots, animations and many more. It is used for various purposes. It is used for data-driven insights and decisions. The data is represented by dashboards which are built by using tableau and power bi. The main purpose/goal of DV is making it easier to identify trends, patterns, and outliers in a large dataset because a single outlier can lead to wrong decisions.","PeriodicalId":283934,"journal":{"name":"Journal of Data Processing and Business Analytics","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127260694","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 : 2023-06-20DOI: 10.48001/jodpba.2023.115-8
T. Jothilakshmi, R. Mohanabharathi, R. Tamilselvi
India is a nation where agriculture and industries associated with it are the main sources of income for the populace. The country's economy primarily depends on agriculture. It is also one of the nations that experience severe natural disasters like floods or droughts, which ruin the crops. The current system uses regression approaches to estimate yield, such as Kernel Ridge, Lasso, and ENet algorithms, and it also employs the idea of stacking regression to improve the algorithms' performance. Utilise technology like data analytics and machine learning to analyse and mine this agricultural data to produce results that will be valuable to farmers for more productive and efficient crop yields. We suggest creating efficient methods to forecast agricultural yield under various climatic situations, which can assist farmers and other stakeholders in making knowledgeable decisions regarding agronomy and crop selection. The DNN algorithm, Multilayer Perceptrons (MLP), was employed. Additionally, the DL (Deep Learning) model's time and space complexity will increase with the addition of new characteristics that have minimal impact on the model's performance. The findings show that compared to the current classification technique, an ensemble technique provides more accurate prediction.
{"title":"Crop Yield Prediction Data Analytics in Indian Agriculture Using Deep Learning","authors":"T. Jothilakshmi, R. Mohanabharathi, R. Tamilselvi","doi":"10.48001/jodpba.2023.115-8","DOIUrl":"https://doi.org/10.48001/jodpba.2023.115-8","url":null,"abstract":"India is a nation where agriculture and industries associated with it are the main sources of income for the populace. The country's economy primarily depends on agriculture. It is also one of the nations that experience severe natural disasters like floods or droughts, which ruin the crops. The current system uses regression approaches to estimate yield, such as Kernel Ridge, Lasso, and ENet algorithms, and it also employs the idea of stacking regression to improve the algorithms' performance. Utilise technology like data analytics and machine learning to analyse and mine this agricultural data to produce results that will be valuable to farmers for more productive and efficient crop yields. We suggest creating efficient methods to forecast agricultural yield under various climatic situations, which can assist farmers and other stakeholders in making knowledgeable decisions regarding agronomy and crop selection. The DNN algorithm, Multilayer Perceptrons (MLP), was employed. Additionally, the DL (Deep Learning) model's time and space complexity will increase with the addition of new characteristics that have minimal impact on the model's performance. The findings show that compared to the current classification technique, an ensemble technique provides more accurate prediction.","PeriodicalId":283934,"journal":{"name":"Journal of Data Processing and Business Analytics","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126771403","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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