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2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS)最新文献

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2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS) Pub Date : 2021-02-19 DOI: 10.1109/icccis51004.2021.9397150
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引用次数: 0
A Novel MIMO Microstrip Patch Antenna for 5G Applications 一种用于5G应用的新型MIMO微带贴片天线
2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS) Pub Date : 2021-02-19 DOI: 10.1109/ICCCIS51004.2021.9397137
Sweta Singh, A. Kumar Singh, Karunesh, Amrees Pandey, Rajeev Singh
{"title":"A Novel MIMO Microstrip Patch Antenna for 5G Applications","authors":"Sweta Singh, A. Kumar Singh, Karunesh, Amrees Pandey, Rajeev Singh","doi":"10.1109/ICCCIS51004.2021.9397137","DOIUrl":"https://doi.org/10.1109/ICCCIS51004.2021.9397137","url":null,"abstract":"In this paper a compact self complementary two port multiple-input multiple-output (MIMO) microstrip patch antenna is presented for the fifth generation (5G).The square shaped patch antenna (15×15 mm2) consist of circular slots and defected ground structure provide a wide frequency band (60.4-68.9GHz) and good isolation. Effect of slots and defected ground structure MIMO patch antenna is investigated in terms of returnloss and isolation. Simulated results of MIMO patch antenna has been investigated in terms of gain, return loss, radiation efficiency, radiation pattern, envelope correlation coefficient (ECC) and total active reflection coefficient (TARC).","PeriodicalId":316752,"journal":{"name":"2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125851133","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
News Classification from Microblogging Dataset using Supervised Learning 基于监督学习的微博数据集新闻分类
2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS) Pub Date : 2021-02-19 DOI: 10.1109/ICCCIS51004.2021.9397074
Md. Imran Hossain Showrov, Vikash Kumar Dubey, Khan Md Hasib, Md. Abu Awal Shameem
{"title":"News Classification from Microblogging Dataset using Supervised Learning","authors":"Md. Imran Hossain Showrov, Vikash Kumar Dubey, Khan Md Hasib, Md. Abu Awal Shameem","doi":"10.1109/ICCCIS51004.2021.9397074","DOIUrl":"https://doi.org/10.1109/ICCCIS51004.2021.9397074","url":null,"abstract":"Today, Microblog is one of the most popular social networks. There is a lot of data floating on this microblog that is useful for both general and unique purposes (e.g. business, current trend). From here, people can also get different types of news. We may use a supervised machine learning algorithm in order to reduce the search effort. One of the basic tasks for categorizing news is classification. We have proposed a model in this paper to identify news from the Twitter dataset and find the best outcome for the microblogging dataset. This task began with basic data crawling and after applying four supervised learning algorithms, ended with the selection of the best one. Eventually, we chose our best template for the crawled dataset.","PeriodicalId":316752,"journal":{"name":"2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128060710","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: 4
A comprehensive analysis of morphological process dependent retinal blood vessel segmentation 形态学过程依赖性视网膜血管分割的综合分析
2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS) Pub Date : 2021-02-19 DOI: 10.1109/ICCCIS51004.2021.9397095
Udayini Dikkala, M. Joseph, Mukil Alagirisamy
{"title":"A comprehensive analysis of morphological process dependent retinal blood vessel segmentation","authors":"Udayini Dikkala, M. Joseph, Mukil Alagirisamy","doi":"10.1109/ICCCIS51004.2021.9397095","DOIUrl":"https://doi.org/10.1109/ICCCIS51004.2021.9397095","url":null,"abstract":"The retinal vasculature is the source of nourishment for the retina through the flow of blood. Any disruption in this blood flow results in the deterioration of the working of the retina. Various techniques have been adopted to detect these disruptions by way of extraction of the vasculature structure. In this research work, an attempt has been made to implement a blood vessel segmentation method based on adaptive contrast enhancement for noise cancellation and morphological process for the extraction of features. The pre-processing also reduces the uneven illumination problem. The background noise pixels are removed through a post processing step to achieve well identified retinal blood vessels. The proposed segmentation method is evaluated on the available public database: DRIVE, which is commonly used. The higher specificity of 98% and lower FPR of about 2% based on the proposed algorithm leads to an improved detection of blood vessels with an accuracy of about 95%.","PeriodicalId":316752,"journal":{"name":"2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS)","volume":"118 45","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131913313","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
Shallow Neural Networks to Deep Neural Networks for Probabilistic Wind Forecasting 浅神经网络到深度神经网络的概率风预报
2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS) Pub Date : 2021-02-19 DOI: 10.1109/ICCCIS51004.2021.9397177
Parul Arora, B. K. Panigrahi, P. N. Suganthan
{"title":"Shallow Neural Networks to Deep Neural Networks for Probabilistic Wind Forecasting","authors":"Parul Arora, B. K. Panigrahi, P. N. Suganthan","doi":"10.1109/ICCCIS51004.2021.9397177","DOIUrl":"https://doi.org/10.1109/ICCCIS51004.2021.9397177","url":null,"abstract":"The uncertainty associated with wind forecasts is quantified through Neural networks. Comparison between Neural Networks from basic (Feed-Forward) to Deep Neural Networks (Auto-regressive Recurrent Neural Networks) is done. These neural networks are different in architecture as in MLP information flows unidirectionally, in RNN the output of the first time step is fed as input to the next time step whereas in Auto-regressive RNN, parameters are shared between multiple time-series. Auto-regressive RNN learns the trend and seasonality automatically with minimum feature extraction. These methods are used for probabilistic forecasting by addition of projection layer with distribution output. The accuracy and efficiency of these methods are tested on Australian wind power data with 5 min frequency. Prediction intervals with the confidence level of 80%, 85% and 90% are generated through quantiles. These methods prove to be better than other classical probabilistic forecasting methods.","PeriodicalId":316752,"journal":{"name":"2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125645404","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: 2
Noise Reduction of ECG using Chebyshev filter and Classification using Machine Learning Algorithms 基于切比雪夫滤波的心电降噪与基于机器学习算法的分类
2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS) Pub Date : 2021-02-19 DOI: 10.1109/ICCCIS51004.2021.9397163
M. Prakash, S. V., G. A, S. P.
{"title":"Noise Reduction of ECG using Chebyshev filter and Classification using Machine Learning Algorithms","authors":"M. Prakash, S. V., G. A, S. P.","doi":"10.1109/ICCCIS51004.2021.9397163","DOIUrl":"https://doi.org/10.1109/ICCCIS51004.2021.9397163","url":null,"abstract":"Cardiac disease detection is a tedious process. Classification of electrocardiogram (ECG) signals plays an important role in the diagnosis of heart diseases. The most important factor that limits the detection of cardiac disease is the rare availability of instances of the abnormal condition collected using ECG sensors. And if the signals contain noise, then the classification might become a challenging task. In this work, we address the problem of cardiac disease detection when the dataset has less number of noisy ECG sensor signals. Here, Chebyshev Type II filter and Chebyshev function, which is termed as Chebfun, are used. The Chebyshev filter is used for high-frequency noise removal and Chebfun is used to approximate the signal with its coefficients. These coefficients known as Chebfun coefficients are used as the features. These features are used for classification. In the proposed work, machine learning algorithms, like SVM, logistic regression, decision tree, and AdaBoost, are used for classifying the features extracted from Chebfun.","PeriodicalId":316752,"journal":{"name":"2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132565668","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: 6
Morphological and Characteristic Analysis of Upper Aero-Digestive Tract Tumour: Revealing Uncovered Facts in Digital Pathology* 上消化道肿瘤的形态学和特征分析:数字病理学揭示的事实*
2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS) Pub Date : 2021-02-19 DOI: 10.1109/ICCCIS51004.2021.9397133
Prabhakaran Mathialagan, Malathy Chidambaranathan
{"title":"Morphological and Characteristic Analysis of Upper Aero-Digestive Tract Tumour: Revealing Uncovered Facts in Digital Pathology*","authors":"Prabhakaran Mathialagan, Malathy Chidambaranathan","doi":"10.1109/ICCCIS51004.2021.9397133","DOIUrl":"https://doi.org/10.1109/ICCCIS51004.2021.9397133","url":null,"abstract":"Upper Aero Digestive Tract cancer is treated as the primary cancer type compared to other different cancers. Exploring the morphological behaviour and characteristics of biopsy tissue sample is very significant in tumour grade analysis for proper diagnosis. After all, the manual microscopic tissue analysis process is considered as the golden standard. Traditional pathological study is still challenging and tough to overcome the manual tissue analytical barriers. To develop an efficient automated computer aided system for1. cancer tissue analysis, 2. tumour grade classification and3. survival prediction of cancer patients. The combination of different image vision techniques and microscopic image analysis tools are used to develop the state-of-the-art frameworks which will be efficient to extract different morphological features from different UADT tumours. The extracted biopsy tissue morphological features will be taken for automatic tumour grade classification that helps in assisting the pathologists to overcome the manual microscopic cancer grade classification problems. The state-of-the-art automated tissue analysis framework is developed to extract the features from the tissue samples within the short period of time. The proposed framework will be efficient for automated tissue characteristic analysis from UADT biopsy tissue samples and that can assist the pathologists to solve the inter observer variability problems.","PeriodicalId":316752,"journal":{"name":"2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122340935","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
A Feasibility Approach in Diagnosing ASD with PIE via Machine Learning Classification Approach using BCI 基于脑机接口的机器学习分类方法诊断ASD的可行性研究
2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS) Pub Date : 2021-02-19 DOI: 10.1109/ICCCIS51004.2021.9397220
T. Roopa Rechal, P. Rajesh Kumar, Sk. Ebraheem Khaleelulla
{"title":"A Feasibility Approach in Diagnosing ASD with PIE via Machine Learning Classification Approach using BCI","authors":"T. Roopa Rechal, P. Rajesh Kumar, Sk. Ebraheem Khaleelulla","doi":"10.1109/ICCCIS51004.2021.9397220","DOIUrl":"https://doi.org/10.1109/ICCCIS51004.2021.9397220","url":null,"abstract":"Electroencephalogram (EEG)-based signal processing methods are essential clinical tools to determine and monitor neurological brain disorders such as autism. This article introduces a novel proposal to integrate various neuroimaging methods to characterize an autistic brain. In fact, it is challenging to diagnose and detect the disorder; therefore, it requires the most efficient algorithms for detection. A novel autism identification approach with a combination of VMD+PIE+supervised learning approach is propounded, which can fill the existing gap in the field. The EEG dataset is acquired via the Bonn University and Kaggle database to test the proposed method's performance. Firstly, the VMD technique is used for extracting features from each EEG signal. Then the Predictor Importance Estimates (PIE) have been employed to select the best features from the extracted features. Finally, using supervised learning algorithms (KNN, SVM and ANN), the signals are categorized into a normal or autistic group. The outcome illustrates that the proposed technique attains high accuracy, indicating a powerful way to diagnose and categorize autism.","PeriodicalId":316752,"journal":{"name":"2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115264992","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
Impact of Atmospheric Light on Haze Removal using Single Image 大气光对单幅图像去雾效果的影响
2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS) Pub Date : 2021-02-19 DOI: 10.1109/ICCCIS51004.2021.9397085
A. Shrivastava, Suryakant Yadav, S. Jain
{"title":"Impact of Atmospheric Light on Haze Removal using Single Image","authors":"A. Shrivastava, Suryakant Yadav, S. Jain","doi":"10.1109/ICCCIS51004.2021.9397085","DOIUrl":"https://doi.org/10.1109/ICCCIS51004.2021.9397085","url":null,"abstract":"Fog or haze are the common phenomenon which creates unpleasant weather condition. This unpleasant weather condition is not favorable for outdoor photography or imaging, because image characteristics are drastically deteriorated by fog or haze. To enhance the image features, fog or haze removal is required. Fog removal is also required in different applications like recognition, tracking and navigation and consumer electronics. During the fog removal process, estimation of atmospheric light plays main role. In this paper we performed an analysis on an estimation of atmospheric light by taking 0.01%, 0.02% and 0.05% most haze-opaque pixels from foggy image and its effect on results. To perform analysis, we used dark channel prior concept with guided filter for fog or haze removal.","PeriodicalId":316752,"journal":{"name":"2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115242004","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 Regression-based Hybrid Machine Learning Technique to Enhance the Database Migration in Cloud Environment 基于回归的混合机器学习技术增强云环境下的数据库迁移
2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS) Pub Date : 2021-02-19 DOI: 10.1109/ICCCIS51004.2021.9397123
Amit Kumar, M. Sivak Kumar, V. Namdeo
{"title":"A Regression-based Hybrid Machine Learning Technique to Enhance the Database Migration in Cloud Environment","authors":"Amit Kumar, M. Sivak Kumar, V. Namdeo","doi":"10.1109/ICCCIS51004.2021.9397123","DOIUrl":"https://doi.org/10.1109/ICCCIS51004.2021.9397123","url":null,"abstract":"The report of cloud computing in recent years has prompted circumstances that usually has lead to numerous advancements & novel mechanisms. The technologies available in the cloud have been prevalent for businesses as well as people who understand that cloud computing is a significant problem, even though they don't know why. We present a methodology that accurately assesses the migration cost, relocation length with cloud operating cost of the social databases, and upgraded the execution. The first step in our approach is to acquire workloads and structure models for moving the database from the database logs as well as from schemes. The second step uses these models to perform a discrete form of event simulation for estimated costs and time. We have implemented the software tools that simplify our approach in both phases. A comprehensive review contrasts our approach to the effects of real-world cloud data migration.","PeriodicalId":316752,"journal":{"name":"2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS)","volume":"122 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116213468","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
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