2021 International Conference on Science & Contemporary Technologies (ICSCT)最新文献

Zilla-64: A Bangla Handwritten Word Dataset Of 64 Districts` Name of Bangladesh and Recognition Using Holistic Approach Zilla-64:孟加拉国64个地区名称的孟加拉语手写词数据集及其整体识别方法

2021 International Conference on Science & Contemporary Technologies (ICSCT) Pub Date : 2021-08-05 DOI: 10.1109/icsct53883.2021.9642594

Md Ali Azad, H. Singha, Mahadi Hasan Nahid

{"title":"Zilla-64: A Bangla Handwritten Word Dataset Of 64 Districts` Name of Bangladesh and Recognition Using Holistic Approach","authors":"Md Ali Azad, H. Singha, Mahadi Hasan Nahid","doi":"10.1109/icsct53883.2021.9642594","DOIUrl":"https://doi.org/10.1109/icsct53883.2021.9642594","url":null,"abstract":"Bangla Handwritten Word Recognition (BHWR) is a very challenging task due to the high curvature nature of the character, overlapping between characters, and flourishes in the writing style of Bangla Handwritten Word (BHW). Despite the importance and challenges of BHWR, the handwritten word dataset of Bangla is very few. In this paper, a new challenging Bangla word dataset which, we called ‘Zilla-64’, is introduced, and also the preparation of the dataset is discussed. To the best of our knowledge, this is the first well-labeled Bangla word dataset. This dataset can be used for gender, age, and education level handwritten related researches. Deep learning shows tremendous success in the handwritten recognition area. Because of the popularity of deep learning methods and for testing the performance of the dataset, a holistic approach based, Deep Convolutional Neural Network (DCNN) is applied on the dataset and achieved 93.30% accuracy. The Zilla-64 dataset is made publicly available at this link https://github.com/MahadiHasanNahid/Zilla-64-Dataset.","PeriodicalId":320103,"journal":{"name":"2021 International Conference on Science & Contemporary Technologies (ICSCT)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124925590","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

A Comprehensive Exploration on Impact of Preprocessing for Prediction of Chronic Kidney Disease Using Multiple Machine Learning Approaches 综合探讨预处理对多重机器学习方法预测慢性肾病的影响

2021 International Conference on Science & Contemporary Technologies (ICSCT) Pub Date : 2021-08-05 DOI: 10.1109/icsct53883.2021.9642638

Nahid Hossain Taz, Abrar Islam, Ishrak Mahmud, Ehtashamul Haque, Md. Raqibur Rahman

{"title":"A Comprehensive Exploration on Impact of Preprocessing for Prediction of Chronic Kidney Disease Using Multiple Machine Learning Approaches","authors":"Nahid Hossain Taz, Abrar Islam, Ishrak Mahmud, Ehtashamul Haque, Md. Raqibur Rahman","doi":"10.1109/icsct53883.2021.9642638","DOIUrl":"https://doi.org/10.1109/icsct53883.2021.9642638","url":null,"abstract":"This manuscript aims to develop a framework to deliver the prediction of Chronic Kidney Disease (CKD) for a patient using the Machine Learning technique. The performance of five individual Machine Learning classifiers is analyzed for the purpose of clarifying the performance measures of CKD. According to average classification accuracy, precision, recall, and fl score, the decisions are estimated along with the ROC AUC score. For this investigation, Logistic Regression, K Nearest Neighbor, Support Vector Machine, Naive Bayes, and Random Forest classifiers are applied as distinct classifiers. In order to increase and stabilize the performance metrics of the classifiers, necessary data preprocessing is carried out on the CKD dataset. Observation of the corresponding performance metrics indicates that Random Forest has outperformed all the other classifiers by producing an accuracy score of 93.4% and ROC AUC of 94.4% before data preprocessing and an accuracy score of 95.6% and ROC AUC of 96.2% after necessary data preprocessing.","PeriodicalId":320103,"journal":{"name":"2021 International Conference on Science & Contemporary Technologies (ICSCT)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122519871","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

Modeling and Performance Analysis of Grounding System of a Grid Substation 电网变电站接地系统建模与性能分析

2021 International Conference on Science & Contemporary Technologies (ICSCT) Pub Date : 2021-08-05 DOI: 10.1109/icsct53883.2021.9642701

F. Amin, M. Rasel, Safkat Islam

引用次数: 0

Finite Element Prediction of Deformation of Closed-cell Cellular Materials for Sustainable Materials Characterisation 可持续材料表征中闭孔多孔材料变形的有限元预测

2021 International Conference on Science & Contemporary Technologies (ICSCT) Pub Date : 2021-08-05 DOI: 10.1109/icsct53883.2021.9642572

Md. Ashraful Islam, M. Hasan, S. M. Mahbub Hasan, H. Roy

引用次数: 0

Optimal Reactive Power or VAR Flow from Distributed Smart PV Inverters 分布式智能光伏逆变器的最优无功或无功流

2021 International Conference on Science & Contemporary Technologies (ICSCT) Pub Date : 2021-08-05 DOI: 10.1109/icsct53883.2021.9642496

A. M. Howlader, Md. Anwar Hossain, A. Saber, T. Senjyu

{"title":"Optimal Reactive Power or VAR Flow from Distributed Smart PV Inverters","authors":"A. M. Howlader, Md. Anwar Hossain, A. Saber, T. Senjyu","doi":"10.1109/icsct53883.2021.9642496","DOIUrl":"https://doi.org/10.1109/icsct53883.2021.9642496","url":null,"abstract":"Nowadays, smart photovoltaic (PV) inverters can perform advanced functionalities e.g., volt-var, volt-watt, frequency-watt, power curtailment controls which can support the voltage and frequency adjustment for an electric power grid. In order to maintain the voltage within the allowable region, the voltage regulator, FACTS devices, capacitor is used to provide reactive power or Var to the power grid. Smart PV inverters can provide the reactive power or Var to control voltage for a power grid using volt-var control command. The control signal of smart PV inverters may send over the cloud to generate Var requirement, but it needs to send a large number of control signals to several inverters at a time. It increases network congestion, and probability for missing commands. Therefore, an optimal switching command signal is important that will reduce total number of switching for inverters, the network congestion, and total control commands to inject Var for a distribution power network. This paper proposed an optimal reactive power or Var flow strategy from a bunch of smart PV inverters. The proposed method reduces the total numbers of switching commands for smart PV inverters, and consequently decreases the control commands, network congestion, and missing commands.","PeriodicalId":320103,"journal":{"name":"2021 International Conference on Science & Contemporary Technologies (ICSCT)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126293357","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

Stock Market Prediction: A Survey and Evaluation 股票市场预测:一项调查与评价

2021 International Conference on Science & Contemporary Technologies (ICSCT) Pub Date : 2021-08-05 DOI: 10.1109/icsct53883.2021.9642681

M. Biswas, Arafat Jahan Nova, Md. Kawsher Mahbub, S. Chaki, Shamim Ahmed, Md. Ashraful Islam

引用次数: 1

A Framework for LED Signboard Recognition for the Autonomous Vehicle Management System 面向自动驾驶车辆管理系统的LED标识识别框架

2021 International Conference on Science & Contemporary Technologies (ICSCT) Pub Date : 2021-08-05 DOI: 10.1109/icsct53883.2021.9642525

S. Chaki, Shamim Ahmed, Nagifa Nujhat Easha, M. Biswas, G. Sharif, Dipu Akter Shila

{"title":"A Framework for LED Signboard Recognition for the Autonomous Vehicle Management System","authors":"S. Chaki, Shamim Ahmed, Nagifa Nujhat Easha, M. Biswas, G. Sharif, Dipu Akter Shila","doi":"10.1109/icsct53883.2021.9642525","DOIUrl":"https://doi.org/10.1109/icsct53883.2021.9642525","url":null,"abstract":"An electronic road sign is an important instrument for providing real-time traffic-related information in the field of intelligent vehicle management systems. In most cases, electronic signboards present a piece of complex text information in which each character is made up of a matrix of light-emitting diodes lamps, referred to as LED text. LED dot matrix displays are also widely used to display notifications and content in a variety of applications. A matrix with a defined number of rows and columns is used to represent a single character. Since it demonstrates discontinuity, the LED text is difficult to detect. To do so, we have proposed a digital image processing-based recognition technique in this paper. Between classes, the variance technique is implemented for converting gray-scale images to binary images. We have used the Sobel masking operator to detect the cell region from the LED text. An improved optical character recognition (OCR) technique is then applied to the normalized LED text images for recognition purposes. The key contribution of this paper is to detect and recognize discontinuous LED texts from different environmental conditions that can be used to assist driver-less vehicle management systems. Our proposed framework has achieved a recognition rate of 84.4%.","PeriodicalId":320103,"journal":{"name":"2021 International Conference on Science & Contemporary Technologies (ICSCT)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115091026","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

Covid-19 and its impact on school closures: a predictive analysis using machine learning algorithms Covid-19及其对学校关闭的影响:使用机器学习算法的预测分析

2021 International Conference on Science & Contemporary Technologies (ICSCT) Pub Date : 2021-08-05 DOI: 10.1109/icsct53883.2021.9642617

Fahim Faisal, M. M. Nishat, Md. Ashif Mahbub, Md. Minhajul Islam Shawon, Md. Mahbub-Ul-Huq Alvi

{"title":"Covid-19 and its impact on school closures: a predictive analysis using machine learning algorithms","authors":"Fahim Faisal, M. M. Nishat, Md. Ashif Mahbub, Md. Minhajul Islam Shawon, Md. Mahbub-Ul-Huq Alvi","doi":"10.1109/icsct53883.2021.9642617","DOIUrl":"https://doi.org/10.1109/icsct53883.2021.9642617","url":null,"abstract":"This research presents an extensive point of reference for investigating the operation of several machine learning (ML) algorithms in postulating the multiclass classification problem regarding the forthcoming effects of Covid-19 on school closures. With the prompt closure of schools across the world in response to this pandemic, school-going children and teenagers are ruptured both mentally and physically. Hence, ML has come across to be a reliable component to forecast the scenario effectively. A dataset from UNESCO is trained and tested by ten supervised ML algorithms. A comprehensive analysis among the predictive ML models was executed which bought satisfactory results with regard to accuracy, precision, sensitivity, F1 score, ROC-AUC by hyper parameter optimization. In this regard, grid search cross validation (GridSearchCV) was utilized in order to obtain the optimal parameters. However, the performance of Artificial Neural Network (ANN) was also investigated and compared with the supervised ML models where ANN displayed maximum accuracy of 80.37%. After rigorous comparative analysis, Decision Tree (DT) portrayed the highest accuracy of 90.75%. Hence, it is evident that machine learning algorithm holds strong promise in forecasting the upcoming scenario of school closures due to Covid-19 and can contribute significantly in decision making for the welfare of the education system.","PeriodicalId":320103,"journal":{"name":"2021 International Conference on Science & Contemporary Technologies (ICSCT)","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116318528","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: 17

Comparative Analysis Between Gaussian Filter & Its Modified Version for High-Resolution Image Compression 高斯滤波器及其改进版本在高分辨率图像压缩中的比较分析

2021 International Conference on Science & Contemporary Technologies (ICSCT) Pub Date : 2021-08-05 DOI: 10.1109/icsct53883.2021.9642495

Sk. Hasibul Alam, Md. Neyamul Hasan, Fahim, Md. Anish Sarker, Md. Rakibul Islam Suvo, Md Mehedi Hasan

引用次数: 0

A Wideband Microstrip Patch Antenna with Slotted Ground Plane for 5G Application 面向5G的开槽接地宽带微带贴片天线

2021 International Conference on Science & Contemporary Technologies (ICSCT) Pub Date : 2021-08-05 DOI: 10.1109/icsct53883.2021.9642597

L. Paul, Sajeeb Chandra Das, Nayan Sarker, R. Azim, Md. Fatin Ishraque, S. Shezan

引用次数: 5