{"title":"Recommendation System for Code Validation and Optimal Refactoring","authors":"Koteswara Rao Velpula, Hema Pavuluri, Poojitha Neeluri, Anushka Pappala, Mounika Narra","doi":"10.17148/ijarcce.2024.13313","DOIUrl":"https://doi.org/10.17148/ijarcce.2024.13313","url":null,"abstract":"","PeriodicalId":513159,"journal":{"name":"IJARCCE","volume":"230 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140445998","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 : 2024-02-20DOI: 10.17148/ijarcce.2024.13237
Subodh Pisal, Arya Kulkarni, Vaishnavi Karpe, Shreyas Pardeshi, M. Mujawar
{"title":"Assurance of Transparency In Charity Using Blockchain","authors":"Subodh Pisal, Arya Kulkarni, Vaishnavi Karpe, Shreyas Pardeshi, M. Mujawar","doi":"10.17148/ijarcce.2024.13237","DOIUrl":"https://doi.org/10.17148/ijarcce.2024.13237","url":null,"abstract":"","PeriodicalId":513159,"journal":{"name":"IJARCCE","volume":"59 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140445557","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 : 2024-02-20DOI: 10.17148/ijarcce.2024.13231
Santosh Reddy Addula, Karthik Meduri, Geeta Sandeep Nadella, Hari Gonaygunta
{"title":"AI and Blockchain in Finance: Opportunities and Challenges for the Banking Sector","authors":"Santosh Reddy Addula, Karthik Meduri, Geeta Sandeep Nadella, Hari Gonaygunta","doi":"10.17148/ijarcce.2024.13231","DOIUrl":"https://doi.org/10.17148/ijarcce.2024.13231","url":null,"abstract":"","PeriodicalId":513159,"journal":{"name":"IJARCCE","volume":"1120 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140445737","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}
{"title":"THE PERFORMANCE OF DEVELOPED INTENSITY HUE SATURATION FUSION OF MULTISPECTRAL AND PANCHROMATIC IMAGES USING PELICAN OPTIMIZATION ALGORITHM","authors":"Adeyemo Isiaka Akinkunmi, Tunbosun Oyewale Oladoyinbo, A. Gideon, Jeremiah Yetomiwa Sinat","doi":"10.17148/ijarcce.2024.13221","DOIUrl":"https://doi.org/10.17148/ijarcce.2024.13221","url":null,"abstract":"","PeriodicalId":513159,"journal":{"name":"IJARCCE","volume":"55 15","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140446053","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 : 2024-02-20DOI: 10.17148/ijarcce.2024.13315
Dr. Umesh Akare, Prof. Umme Ayeman Gani, Anushri Bhongade, Dhanashree Mure, Madhulika Chatterjee, Vanzuli Ramteke
The major killer cause of human death is Heart Disease (HD). Many people die due to this disease. Lots of researchers have been discovering new technologies to prognosticate the disease early before it ’ s too late for helping healthcare as well as people. These processes are still under research phase. Machine Learning (ML) is faster-emerging technology of Artificial Intelligence (AI) that contributes various algorithms for HD. Based on the proposed problem, ML provides different classification algorithms to divine the probability of patient having HD. For predicting HD, a lot of research scholars contributes their effort in this work using various techniques and algorithms such as Decision Tree (DT), Naïve Bayes (NB), Support Vector Machine (SVM), KNN (K-Nearest Neighbor), Neural Network (NN), etc. In order to give some effort on this work, we are going to develop a Web-based Heart Disease Prediction System (HDPS) by applying DT and NB ML algorithms. We are using the UCI repository HD dataset to train a model by comparing DT and NB algorithm for HDPS Web application. The dataset contains 303 instances with 14 attributes that help to train a prediction model that will be deployed into a web application for prediction. The main aim of this project is to build an efficient prediction model and deploy for prediction of disease. An HDP Model is built by using NB algorithm that provides 88.163% accuracy among others. A web-based HDPS application is developed through the waterfall model. Each phase is efficiently done. The project is successfully created with help of requirement analysis and project plan, system design, database design, testing plan, identifying features and functionalities, and system validation and deployment. The limitation of this project is to have only predicted the presence of heart disease but not identify which type of HD does have at patient. In future work, we can enhance the project by appending more detail prediction of HD at patient and incorporate with smart wear devices that integrate to Hospital Emergency System.
人类死亡的主要杀手是心脏病（HD）。许多人死于这种疾病。许多研究人员一直在探索新技术，以便在为时已晚之前及早预测疾病，为医疗保健和人类提供帮助。这些过程仍处于研究阶段。机器学习（ML）是人工智能（AI）中发展较快的技术，它为人类免疫缺陷病毒（HD）提供了各种算法。根据提出的问题，ML 提供了不同的分类算法，以预测患者患上 HD 的概率。为了预测 HD，许多研究学者在这项工作中使用了各种技术和算法，如决策树（DT）、奈夫贝叶斯（NB）、支持向量机（SVM）、KNN（K-近邻）、神经网络（NN）等。为了在这项工作上有所作为，我们将应用 DT 和 NB ML 算法开发基于网络的心脏病预测系统（HDPS）。我们使用 UCI 存储库中的 HD 数据集，通过比较 DT 和 NB 算法来训练 HDPS 网络应用模型。该数据集包含 303 个实例和 14 个属性，有助于训练预测模型，该模型将部署到网络应用程序中进行预测。该项目的主要目的是建立一个高效的预测模型，并将其部署到疾病预测中。我们使用 NB 算法建立了一个 HDP 模型，该模型的准确率高达 88.163%。通过瀑布模型开发了基于网络的 HDPS 应用程序。每个阶段都高效完成。在需求分析和项目计划、系统设计、数据库设计、测试计划、确定特征和功能以及系统验证和部署的帮助下，项目得以成功创建。本项目的局限性在于只能预测心脏病的存在，但不能确定患者患有哪种类型的心脏病。在今后的工作中，我们可以通过添加更详细的心脏病患者预测信息，并与集成到医院急救系统的智能穿戴设备相结合，来改进本项目。
{"title":"Heart Disease Prediction System Using Machine Learning","authors":"Dr. Umesh Akare, Prof. Umme Ayeman Gani, Anushri Bhongade, Dhanashree Mure, Madhulika Chatterjee, Vanzuli Ramteke","doi":"10.17148/ijarcce.2024.13315","DOIUrl":"https://doi.org/10.17148/ijarcce.2024.13315","url":null,"abstract":"The major killer cause of human death is Heart Disease (HD). Many people die due to this disease. Lots of researchers have been discovering new technologies to prognosticate the disease early before it ’ s too late for helping healthcare as well as people. These processes are still under research phase. Machine Learning (ML) is faster-emerging technology of Artificial Intelligence (AI) that contributes various algorithms for HD. Based on the proposed problem, ML provides different classification algorithms to divine the probability of patient having HD. For predicting HD, a lot of research scholars contributes their effort in this work using various techniques and algorithms such as Decision Tree (DT), Naïve Bayes (NB), Support Vector Machine (SVM), KNN (K-Nearest Neighbor), Neural Network (NN), etc. In order to give some effort on this work, we are going to develop a Web-based Heart Disease Prediction System (HDPS) by applying DT and NB ML algorithms. We are using the UCI repository HD dataset to train a model by comparing DT and NB algorithm for HDPS Web application. The dataset contains 303 instances with 14 attributes that help to train a prediction model that will be deployed into a web application for prediction. The main aim of this project is to build an efficient prediction model and deploy for prediction of disease. An HDP Model is built by using NB algorithm that provides 88.163% accuracy among others. A web-based HDPS application is developed through the waterfall model. Each phase is efficiently done. The project is successfully created with help of requirement analysis and project plan, system design, database design, testing plan, identifying features and functionalities, and system validation and deployment. The limitation of this project is to have only predicted the presence of heart disease but not identify which type of HD does have at patient. In future work, we can enhance the project by appending more detail prediction of HD at patient and incorporate with smart wear devices that integrate to Hospital Emergency System.","PeriodicalId":513159,"journal":{"name":"IJARCCE","volume":"147 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140447248","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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