International Journal of Computer Science and Engineering最新文献_第3页

Leveraging Optical Character Recognition Technology for Enhanced Anti-Money Laundering (AML) Compliance 利用光学字符识别技术加强反洗钱(AML)合规

International Journal of Computer Science and Engineering Pub Date : 2023-05-25 DOI: 10.14445/23488387/ijcse-v10i5p102

Saikiran Subbagari

引用次数: 2

Fractional-Iterative BiLSTM Classifier : A Novel Approach to Predicting Student Attrition in Digital Academia 分数迭代BiLSTM分类器:数字学术中预测学生流失的新方法

International Journal of Computer Science and Engineering Pub Date : 2023-05-25 DOI: 10.14445/23488387/ijcse-v10i5p101

Gaurav Anand, S. Kumari, Ravi Pulle

{"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}

引用次数: 0

Investigating Tree-Based Classifiers and Selected Ensemble Learning on Iris Flower Species Classification 基于树的分类器和选择集成学习在鸢尾花分类中的应用研究

International Journal of Computer Science and Engineering Pub Date : 2023-05-25 DOI: 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

{"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}

引用次数: 0

Estimating Heart Disease Used by Data Mining and Artificial Intelligence Techniques 基于数据挖掘和人工智能技术的心脏病评估

International Journal of Computer Science and Engineering Pub Date : 2023-04-25 DOI: 10.14445/23488387/ijcse-v10i4p101

S. R

引用次数: 0

Robots and Artificial Intelligence’s Effects on Employ Prospects in the Future 机器人和人工智能对未来就业前景的影响

International Journal of Computer Science and Engineering Pub Date : 2023-04-25 DOI: 10.14445/23488387/ijcse-v10i4p102

L. M

{"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}

引用次数: 0

Machine Learning-Based Practical Social-Sensor Provision for Psychological Well-Being Intensive Care Consuming Twitter Data 基于机器学习的实用社会传感器提供心理健康重症监护使用Twitter数据

International Journal of Computer Science and Engineering Pub Date : 2023-03-25 DOI: 10.14445/23488387/ijcse-v10i3p101

A. S

引用次数: 0

Exploring Computer Vision's Deep Learning and Machine Learning Techniques 探索计算机视觉的深度学习和机器学习技术

International Journal of Computer Science and Engineering Pub Date : 2023-02-25 DOI: 10.14445/23488387/ijcse-v10i2p101

S. R

引用次数: 0

Label Projection based on Hadamard Codes for Online Hashing 基于Hadamard码的在线哈希标签投影

International Journal of Computer Science and Engineering Pub Date : 2023-01-31 DOI: 10.14445/23488387/ijcse-v10i1p101

Nannan Wu, Zhen Wang, Xiaohan Yang, Wenhao Liu, Xinyi Chang, Dongrui Fan

引用次数: 0

Seizure Prediction using Generative Adversarial Networks for EEG Data Synthesis 基于生成对抗网络的脑电图数据合成癫痫发作预测

International Journal of Computer Science and Engineering Pub Date : 2022-10-30 DOI: 10.14445/23488387/ijcse-v9i10p101

G. Agarwal, Sai Sanjeet, B. Sahoo

引用次数: 0

Detection of Brain Cancer using Machine Learning Techniques a Review 使用机器学习技术检测脑癌综述

International Journal of Computer Science and Engineering Pub Date : 2022-09-30 DOI: 10.14445/23488387/ijcse-v9i9p102

Meghana G R, Suresh Kumar Rudrahithlu, Shilpa K C

引用次数: 2