Pub Date : 2023-05-25DOI: 10.14445/23488387/ijcse-v10i5p102
Saikiran Subbagari
{"title":"Leveraging Optical Character Recognition Technology for Enhanced Anti-Money Laundering (AML) Compliance","authors":"Saikiran Subbagari","doi":"10.14445/23488387/ijcse-v10i5p102","DOIUrl":"https://doi.org/10.14445/23488387/ijcse-v10i5p102","url":null,"abstract":"","PeriodicalId":186366,"journal":{"name":"International Journal of Computer Science and Engineering","volume":"399 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114002408","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 : 2023-05-25DOI: 10.14445/23488387/ijcse-v10i5p101
Gaurav Anand, S. Kumari, Ravi Pulle
- Virtual learning circumstances have been observed as consistent growth over the years. The widespread use of online learning leads to an emerging amount of enrollments, also from pupils who have quit the education scheme previously. However, it also earned an increased amount of withdrawal rate when compared to conventional classrooms. Quick identification of pupils is a difficult issue that can be alleviated with the help of previous models for data evaluation and machine learning. In this research, a fractional-Iterative BiLSTM is used for predicting the student's dropout from online courses with a high accuracy rate. The feature extraction is provided by utilizing the encoder layer that efficiently extracts the features based on Statistical features. The Fractional-Iterative BiLSTM classifier is employed in the decoder layer, which is effectively performed in the classification function to predict the student dropout. The accomplishment of the research is evaluated by calculating the enhancement, and the developed model achieved the increment of 96.71% accuracy, 95.31% sensitivity, and 97.01% specificity, which shows the method's efficiency, and the MSE is reduced by 0.11%.
{"title":"Fractional-Iterative BiLSTM Classifier : A Novel Approach to Predicting Student Attrition in Digital Academia","authors":"Gaurav Anand, S. Kumari, Ravi Pulle","doi":"10.14445/23488387/ijcse-v10i5p101","DOIUrl":"https://doi.org/10.14445/23488387/ijcse-v10i5p101","url":null,"abstract":"- Virtual learning circumstances have been observed as consistent growth over the years. The widespread use of online learning leads to an emerging amount of enrollments, also from pupils who have quit the education scheme previously. However, it also earned an increased amount of withdrawal rate when compared to conventional classrooms. Quick identification of pupils is a difficult issue that can be alleviated with the help of previous models for data evaluation and machine learning. In this research, a fractional-Iterative BiLSTM is used for predicting the student's dropout from online courses with a high accuracy rate. The feature extraction is provided by utilizing the encoder layer that efficiently extracts the features based on Statistical features. The Fractional-Iterative BiLSTM classifier is employed in the decoder layer, which is effectively performed in the classification function to predict the student dropout. The accomplishment of the research is evaluated by calculating the enhancement, and the developed model achieved the increment of 96.71% accuracy, 95.31% sensitivity, and 97.01% specificity, which shows the method's efficiency, and the MSE is reduced by 0.11%.","PeriodicalId":186366,"journal":{"name":"International Journal of Computer Science and Engineering","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114326660","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 : 2023-05-25DOI: 10.14445/23488387/ijcse-v10i5p105
Ramoni Tirimisiyu Amosa, Adekiigbe Adebanjo, Fabiyi Aderanti Alifat, Olorunlomerue Adam Biodun, Oni Esther Kemi, Adejola Aanu Adeyinka, Adigun Olajide Israel, Joseph Babatunde Isaac
- Eloquence, hope, knowledge, the ability to communicate effectively, and faith are some of the meanings associated with the iris flower in the language of flowers. Iris has different species types, and each type has its own medicinal purpose. Classifying the flower has become a serious task for researchers due to the high volume of datasets (big data), hence the introduction of machine learning algorithms for accurate and reliable classification. This paper focuses on the classification of the Iris flower using five tree-based algorithms; Best First Tree (BFTree), Least Absolute deviation Tree (LADTree), Cost-Sensitive Decision Forest (CSForest), Functional Tree (FT) and Random Tree (RT). Three selected ensemble learning (Bagging, Dagging and cascade generalisation) were equally implemented in the algorithm. The dataset that was utilised in this investigation is open source and may be downloaded without cost from a public repository (kaggle.com). The result of the classification showed that the FT classifiers outperform other tree-based classifiers with an accuracy of 96.67% and an AUC of 1.00. The ensemble algorithm has a significant impact on the performance of single classifiers (tree-based). Outperform tree based. AUC/ROC (Area Under Curve/Receiver Operating Characteristics) was used to evaluate the algorithm's performance. Bagging ensemble outperforms other ensembles (Dagging and Cascade) with an accuracy of 96.00% and AUC of 1.00.
{"title":"Investigating Tree-Based Classifiers and Selected Ensemble Learning on Iris Flower Species Classification","authors":"Ramoni Tirimisiyu Amosa, Adekiigbe Adebanjo, Fabiyi Aderanti Alifat, Olorunlomerue Adam Biodun, Oni Esther Kemi, Adejola Aanu Adeyinka, Adigun Olajide Israel, Joseph Babatunde Isaac","doi":"10.14445/23488387/ijcse-v10i5p105","DOIUrl":"https://doi.org/10.14445/23488387/ijcse-v10i5p105","url":null,"abstract":"- Eloquence, hope, knowledge, the ability to communicate effectively, and faith are some of the meanings associated with the iris flower in the language of flowers. Iris has different species types, and each type has its own medicinal purpose. Classifying the flower has become a serious task for researchers due to the high volume of datasets (big data), hence the introduction of machine learning algorithms for accurate and reliable classification. This paper focuses on the classification of the Iris flower using five tree-based algorithms; Best First Tree (BFTree), Least Absolute deviation Tree (LADTree), Cost-Sensitive Decision Forest (CSForest), Functional Tree (FT) and Random Tree (RT). Three selected ensemble learning (Bagging, Dagging and cascade generalisation) were equally implemented in the algorithm. The dataset that was utilised in this investigation is open source and may be downloaded without cost from a public repository (kaggle.com). The result of the classification showed that the FT classifiers outperform other tree-based classifiers with an accuracy of 96.67% and an AUC of 1.00. The ensemble algorithm has a significant impact on the performance of single classifiers (tree-based). Outperform tree based. AUC/ROC (Area Under Curve/Receiver Operating Characteristics) was used to evaluate the algorithm's performance. Bagging ensemble outperforms other ensembles (Dagging and Cascade) with an accuracy of 96.00% and AUC of 1.00.","PeriodicalId":186366,"journal":{"name":"International Journal of Computer Science and Engineering","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114064166","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 : 2023-04-25DOI: 10.14445/23488387/ijcse-v10i4p101
S. R
{"title":"Estimating Heart Disease Used by Data Mining and Artificial Intelligence Techniques","authors":"S. R","doi":"10.14445/23488387/ijcse-v10i4p101","DOIUrl":"https://doi.org/10.14445/23488387/ijcse-v10i4p101","url":null,"abstract":"","PeriodicalId":186366,"journal":{"name":"International Journal of Computer Science and Engineering","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132553481","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 : 2023-04-25DOI: 10.14445/23488387/ijcse-v10i4p102
L. M
- The prevalence of human-robot connection is expanding as robots have made everyone’s lives more relaxed and pleasant. This study examined the traits and behaviours of numerous robot kinds. They have also looked into how robotics and humans are evolving together. In addition to the many scientists and technicians who work in this field, they have included a few of their contributions in our study. By creating a working system that solves issues and produces good outcomes, they want to understand better how the human brain works. The field of artificial intelligence is enormous and is also making progress in business, healthcare, and quality control. According to several studies, the business sector collaborates with artificial intelligence to evaluate supply and demand. Design and systematize human resource management organizations. The public sector is also developing several intelligent devices for security observation and fault uncovering of nuclear reactors and other crucial systems. Robotics and Artificial Intelligence are also fantastic for safely enforcing law and order. Due to the massive need for intelligent robots across many industries, employment in this field and artificial intelligence are developing. Our main goal is to investigate how people and robots interact.
{"title":"Robots and Artificial Intelligence’s Effects on Employ Prospects in the Future","authors":"L. M","doi":"10.14445/23488387/ijcse-v10i4p102","DOIUrl":"https://doi.org/10.14445/23488387/ijcse-v10i4p102","url":null,"abstract":"- The prevalence of human-robot connection is expanding as robots have made everyone’s lives more relaxed and pleasant. This study examined the traits and behaviours of numerous robot kinds. They have also looked into how robotics and humans are evolving together. In addition to the many scientists and technicians who work in this field, they have included a few of their contributions in our study. By creating a working system that solves issues and produces good outcomes, they want to understand better how the human brain works. The field of artificial intelligence is enormous and is also making progress in business, healthcare, and quality control. According to several studies, the business sector collaborates with artificial intelligence to evaluate supply and demand. Design and systematize human resource management organizations. The public sector is also developing several intelligent devices for security observation and fault uncovering of nuclear reactors and other crucial systems. Robotics and Artificial Intelligence are also fantastic for safely enforcing law and order. Due to the massive need for intelligent robots across many industries, employment in this field and artificial intelligence are developing. Our main goal is to investigate how people and robots interact.","PeriodicalId":186366,"journal":{"name":"International Journal of Computer Science and Engineering","volume":"260 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129811038","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 : 2023-02-25DOI: 10.14445/23488387/ijcse-v10i2p101
S. R
{"title":"Exploring Computer Vision's Deep Learning and Machine Learning Techniques","authors":"S. R","doi":"10.14445/23488387/ijcse-v10i2p101","DOIUrl":"https://doi.org/10.14445/23488387/ijcse-v10i2p101","url":null,"abstract":"","PeriodicalId":186366,"journal":{"name":"International Journal of Computer Science and Engineering","volume":"107 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122228309","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 : 2022-10-30DOI: 10.14445/23488387/ijcse-v9i10p101
G. Agarwal, Sai Sanjeet, B. Sahoo
{"title":"Seizure Prediction using Generative Adversarial Networks for EEG Data Synthesis","authors":"G. Agarwal, Sai Sanjeet, B. Sahoo","doi":"10.14445/23488387/ijcse-v9i10p101","DOIUrl":"https://doi.org/10.14445/23488387/ijcse-v9i10p101","url":null,"abstract":"","PeriodicalId":186366,"journal":{"name":"International Journal of Computer Science and Engineering","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130071429","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 : 2022-09-30DOI: 10.14445/23488387/ijcse-v9i9p102
Meghana G R, Suresh Kumar Rudrahithlu, Shilpa K C
{"title":"Detection of Brain Cancer using Machine Learning Techniques a Review","authors":"Meghana G R, Suresh Kumar Rudrahithlu, Shilpa K C","doi":"10.14445/23488387/ijcse-v9i9p102","DOIUrl":"https://doi.org/10.14445/23488387/ijcse-v9i9p102","url":null,"abstract":"","PeriodicalId":186366,"journal":{"name":"International Journal of Computer Science and Engineering","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128462075","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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