Journal of Data Processing and Business Analytics最新文献

Semesters Simplified: A Comprehensive Approach to Academic Management 简化学期全面的教学管理方法

Journal of Data Processing and Business Analytics Pub Date : 2024-05-14 DOI: 10.48001/jodpba.2024.1131-37

Shantanu Sharma, Karan Negi, Sachin Rao, Anurag Goel, Gaurav Kumar Chaubey, Kalpana Kalpana

{"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}

引用次数: 0

Supply Chain Optimization through Real-Time Data Analysis and Visualization with Power BI 利用 Power BI 进行实时数据分析和可视化，优化供应链

Journal of Data Processing and Business Analytics Pub Date : 2024-05-08 DOI: 10.48001/jodpba.2024.1121-30

Muralidhar m, Banda Krishna Vaishnavi, Harish Knikhil S, Magizhan C B, Naveen Balaji S

引用次数: 0

Emotion Detection from Photos Using MobleNet-based Deep Learning 利用基于移动网络的深度学习从照片中进行情感检测

Journal of Data Processing and Business Analytics Pub Date : 2024-03-07 DOI: 10.48001/jodpba.2024.1113-20

Elizabeth Sunny, Therese Yamuna Mahesh

{"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}

引用次数: 0

Age, Cars, and Claims: Decoding the Insurance Landscape 年龄、汽车和索赔：解码保险业格局

Journal of Data Processing and Business Analytics Pub Date : 2024-02-29 DOI: 10.48001/jodpba.2024.119-12

M. Bharathi, T. A. Sai Srinivas

引用次数: 0

Data Visualization using a Powerful Tool – Power BI 使用强大工具的数据可视化- Power BI

Journal of Data Processing and Business Analytics Pub Date : 2023-06-26 DOI: 10.48001/jodpba.2023.111-4

Swati Mahajan

引用次数: 0

Crop Yield Prediction Data Analytics in Indian Agriculture Using Deep Learning 使用深度学习的印度农业作物产量预测数据分析

Journal of Data Processing and Business Analytics Pub Date : 2023-06-20 DOI: 10.48001/jodpba.2023.115-8

T. Jothilakshmi, R. Mohanabharathi, R. Tamilselvi

{"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}

引用次数: 0