2021 4th International Conference on Computing and Communications Technologies (ICCCT)最新文献_第5页

Cloud Computing's Use in Medical Science 云计算在医学科学中的应用

2021 4th International Conference on Computing and Communications Technologies (ICCCT) Pub Date : 2021-12-16 DOI: 10.1109/ICCCT53315.2021.9711867

D. Thamizhselvi, M. Ragavi, K. Nivetha

引用次数: 0

Automated Data Driven Preprocessing and Training of Classification Models 自动数据驱动预处理与分类模型训练

2021 4th International Conference on Computing and Communications Technologies (ICCCT) Pub Date : 2021-12-16 DOI: 10.1109/ICCCT53315.2021.9711766

Vatsal Chheda, Samit Kapadia, Bhavya Lakhani, Pratik Kanani

引用次数: 0

Descriptive and Predictive Analytics of Groundwater 地下水的描述和预测分析

2021 4th International Conference on Computing and Communications Technologies (ICCCT) Pub Date : 2021-12-16 DOI: 10.1109/ICCCT53315.2021.9711907

S. Madhumithaa, S. Mannish, J. Justus

引用次数: 0

Character Recognition Tamil Language in Printed Images using Convolutional Neural Network (CNN) analysis 使用卷积神经网络(CNN)分析打印图像中的泰米尔语字符识别

2021 4th International Conference on Computing and Communications Technologies (ICCCT) Pub Date : 2021-12-16 DOI: 10.1109/ICCCT53315.2021.9711893

M. Chithambarathanu, D. Ganesh

{"title":"Character Recognition Tamil Language in Printed Images using Convolutional Neural Network (CNN) analysis","authors":"M. Chithambarathanu, D. Ganesh","doi":"10.1109/ICCCT53315.2021.9711893","DOIUrl":"https://doi.org/10.1109/ICCCT53315.2021.9711893","url":null,"abstract":"In this paper, we suggested a system for handwritten character recognition in printed images of the Tamil language. The current work is being implemented using Optical character Recognition (OCR) in step one of the projects. The most recognized issues are poor print and paper quality and unknown font faces. OCR is also not accurate in acknowledging the handwritten text and the fonts. Also, the implementation is carried out using the Convolutional Neural Network (CNN) model with handwritten digit recognition. CNN has the potential to recognize handwritten picture characters clearly and robustly. For Tamil handwritten character classification, we have considered the CNN in this paper without any feature collection. In terms of test accuracy, the proposed approach provides comparable output with the other existing methods. And it was checked on a major data set as well. For Tamil handwritten character recognition, experiments on a large data set showed the robustness of this model. The outcome of the proposed model for handwritten Tamil character recognition using CNN gives an accuracy of 98.00%","PeriodicalId":162171,"journal":{"name":"2021 4th International Conference on Computing and Communications Technologies (ICCCT)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126485222","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: 1

Design of Porous Core Fiber for Terahertz Regime using Zeonex 利用Zeonex设计太赫兹区多孔芯光纤

2021 4th International Conference on Computing and Communications Technologies (ICCCT) Pub Date : 2021-12-16 DOI: 10.1109/ICCCT53315.2021.9711841

Md. Tabil Ahammed, M. Das, Md. Anwar Sadath, M. Hossain, Mehedi Hasan Kaium, Md. Ahasan Ali, MD. Shamsul Islam, C. Das

引用次数: 11

Credit Risk Analysis using LightGBM and a comparative study of popular algorithms 基于LightGBM的信用风险分析与常用算法的比较研究

2021 4th International Conference on Computing and Communications Technologies (ICCCT) Pub Date : 2021-12-16 DOI: 10.1109/ICCCT53315.2021.9711896

J. Ponsam, S.V. Juno Bella Gracia, G. Geetha, S. Karpaselvi, K. Nimala

{"title":"Credit Risk Analysis using LightGBM and a comparative study of popular algorithms","authors":"J. Ponsam, S.V. Juno Bella Gracia, G. Geetha, S. Karpaselvi, K. Nimala","doi":"10.1109/ICCCT53315.2021.9711896","DOIUrl":"https://doi.org/10.1109/ICCCT53315.2021.9711896","url":null,"abstract":"Credit Risk analysis and mitigation have been an area of concern since the 07–08 Financial Crisis. One of the main reasons for the collapse was the high default rates of low-income security loans. Calculating credit scores can be a complicated process for people with thin credit histories or non-existent credit histories. Banks may refuse to give loans if the scores don't satisfy their requirements. Lack of a credit score is considered as an indicator for potential default and hence banks avoid sanctioning loans for people who come under this category. However, banks still offer loans if people are willing to offer securities. Credit Scoring can be done by using state-of-the-art Machine Learning models. Machine Learning and Data Science are becoming increasingly crucial in the fin-tech world. Popular machine learning algorithms such as Random Forest and Linear Support Vector Machines are being used currently. We're looking to explore further into credit risk analysis with LightGBM as our algorithm of choice. It is an open source framework developed by Microsoft in 2017. It is an ensemble model which has several advantages such as better prediction and higher stability. Predictions aggregated from multiple models tend to be less noisy than a single model, this is one of the main reasons why an ensemble model such as LightGBM can perform better than Logistic Regression and other algorithms like SVMs for this use case.","PeriodicalId":162171,"journal":{"name":"2021 4th International Conference on Computing and Communications Technologies (ICCCT)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123119540","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: 5

Convolutional Recommended Neural Network system based on user reviews for movies 基于用户评论的卷积推荐神经网络系统

2021 4th International Conference on Computing and Communications Technologies (ICCCT) Pub Date : 2021-12-16 DOI: 10.1109/ICCCT53315.2021.9711772

P. Kirubanantham, A. Saranya, D. Kumar

引用次数: 0

Tuning Hyperparameters of Machine Learning Methods for Afan Oromo Hate Speech Text Detection for Social Media 面向社交媒体的Afan Oromo仇恨言论文本检测机器学习方法的超参数调优

2021 4th International Conference on Computing and Communications Technologies (ICCCT) Pub Date : 2021-12-16 DOI: 10.1109/ICCCT53315.2021.9711850

Naol Bakala Defersha, Kula Kekeba, K. Kaliyaperumal

{"title":"Tuning Hyperparameters of Machine Learning Methods for Afan Oromo Hate Speech Text Detection for Social Media","authors":"Naol Bakala Defersha, Kula Kekeba, K. Kaliyaperumal","doi":"10.1109/ICCCT53315.2021.9711850","DOIUrl":"https://doi.org/10.1109/ICCCT53315.2021.9711850","url":null,"abstract":"With the rapidly growing penetration of social media networks in linguistically diverse and multicultural developing nations like Ethiopia, the conversations of online users have increasingly become more casual and multilingual. The emergency of hate speech text system. To this end, various automated hate speech detection and classification systems have been developed for resource-rich languages such as English and French even though online users are using many other languages on different social media platforms. Afan Oromo is one natural language used by social media users to express feelings, emotions and share messages. Hence, there is an urgent need for the development of an intelligent system that can automatically detect and classify hate speech, especially for resource-scarce indigenous Ethiopian languages like Afan Oromo. This work is about the identification of hate speech text from comments and posts generated in resource scary poor language Afan Oromo. We prepared first hate speech text detection dataset of Afan Oromo that containing comments and posts from social media. Then, n-gram and TF-IDF feature selection approaches were employed to select features. After the important feature selected Natural language processing tasks applied on the dataset. We applied six machine learning classifiers from default and tuned parameters to detect hate speech text posts and comments. The experiment show that Support Vector Machine outperform 92% values of F-measure than classifiers Afan Oromo hate speech text detection dataset. This Afan Oromo hate speech text dataset publicly available on https://www.naolinfo.info/for further research.","PeriodicalId":162171,"journal":{"name":"2021 4th International Conference on Computing and Communications Technologies (ICCCT)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134614020","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: 3

Optimal Placement of Distribution Generator by Incorporating Demand Response Strategy for Residential and Industrial User 结合住宅和工业用户需求响应策略的配电发电机优化配置

2021 4th International Conference on Computing and Communications Technologies (ICCCT) Pub Date : 2021-12-16 DOI: 10.1109/ICCCT53315.2021.9711824

P. Shanmugapriya, M. Kumaran, J. Baskaran, C. Nayanatara, P. Sharmila

引用次数: 0

Credit Sanction Forecasting 信用制裁预测

2021 4th International Conference on Computing and Communications Technologies (ICCCT) Pub Date : 2021-12-16 DOI: 10.1109/ICCCT53315.2021.9711790

P. Kirubanantham, A. Saranya, D. S. Kumar

{"title":"Credit Sanction Forecasting","authors":"P. Kirubanantham, A. Saranya, D. S. Kumar","doi":"10.1109/ICCCT53315.2021.9711790","DOIUrl":"https://doi.org/10.1109/ICCCT53315.2021.9711790","url":null,"abstract":"In today's banking sector, there is a lot of enhancement because of the advancement of technology. The number of applicants for the loan approval is also increasing every day, and it is difficult for the banking sector to verify each applicant manually and then recommend for the loan approval. The banking sector still needs a more precise method for forecasting the safe customer before approving the loan. One of the quality metrics of the loan is the status of the loan. It doesn't instantly reveal anything, though it is a foremost step in the process of loan approval. To obtain a defaulter and also valid user, the Credit Sanction Forecasting framework is used for precise analysis of the credit data. A customer's loan repayment capacity is more reliably estimated using the random forest classifier technique. Therefore, the efficiency of this projection is based on the multiple factors of the Random Forest method. The aim is to show that parameter optimization outcomes in high accuracy for the estimation of loan repayments capacity by customers. The primary aim has implemented using a software package of python and machine learning algorithms. The combination Min-Max standardization, Logistic Regression, Random Forest classifier, and deep learning model created using tensor flow are used to predict the safe customers for the loan approval. CSF offers important details with high accuracy and is also mainly used to forecast the loan status of the bank with help of a classification algorithm of ML and deep learning.","PeriodicalId":162171,"journal":{"name":"2021 4th International Conference on Computing and Communications Technologies (ICCCT)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130108065","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