2021 International Conference on Disruptive Technologies for Multi-Disciplinary Research and Applications (CENTCON)最新文献_第5页

A Survey on Motif Discovery Algorithms for analysis of Gene Sequences of Interest 用于感兴趣基因序列分析的Motif发现算法综述

2021 International Conference on Disruptive Technologies for Multi-Disciplinary Research and Applications (CENTCON) Pub Date : 2021-11-19 DOI: 10.1109/CENTCON52345.2021.9687964

S. M, C. Nandini

引用次数: 0

Cricket Twitter Data Sentiment Analysis and Prediction Exerted Machine Learning 板球推特数据情感分析和预测运用机器学习

2021 International Conference on Disruptive Technologies for Multi-Disciplinary Research and Applications (CENTCON) Pub Date : 2021-11-19 DOI: 10.1109/CENTCON52345.2021.9688197

Pranali Phulare, S. Deshmukh

引用次数: 2

Machine Learning Based Melanoma Skin Cancer Detection using Fusion of Thepade's SBTC and GLCM Features 基于机器学习的黑色素瘤皮肤癌检测，融合Thepade的SBTC和GLCM特征

2021 International Conference on Disruptive Technologies for Multi-Disciplinary Research and Applications (CENTCON) Pub Date : 2021-11-19 DOI: 10.1109/CENTCON52345.2021.9688151

Sudeep D. Thepade, Deepa Abin, Arati R. Dhake

{"title":"Machine Learning Based Melanoma Skin Cancer Detection using Fusion of Thepade's SBTC and GLCM Features","authors":"Sudeep D. Thepade, Deepa Abin, Arati R. Dhake","doi":"10.1109/CENTCON52345.2021.9688151","DOIUrl":"https://doi.org/10.1109/CENTCON52345.2021.9688151","url":null,"abstract":"Various types and forms of diseases are caused due to deficiencies obtained by inheritance, infection or physical disorders. In all the diseases it is found that cancer is caused due to abnormal cell divide, unmanageable and demolish body tissue. Melanoma is one of the type of cancer. Melanoma detection at an early stage is very important as it mostly spread to different parts of body if it is not diagnosed and cared earlier. This is the very dangerous cancer since it spreads at a very faster rate. Melanoma has the highest cure rate when diagnosed at its early stage and however if detection is done late the cancer can spread and then it becomes uncontrollable and incurable. There are some factors which make it difficult for identification of melanoma visually: melanoma is very similar in appearance to a mole or sun burn at the body surface during its early to middle stages; melanoma can have different shapes and various forms; there are many techniques for the detection of melanoma. The paper proposes fusion of Thepade SBTC and GLCM features for melanoma identification from dermoscopic images using machine learning classifiers. The experimentation done using ISIC dataset with seven assorted machine learning classifiers have shown fusion performs better for identification of melanoma. Overall best performance is observed by fusion of Thepade's SBTC 4-ary with GLCM for J48 machine learning algorithm (as 86.83 % accuracy).","PeriodicalId":103865,"journal":{"name":"2021 International Conference on Disruptive Technologies for Multi-Disciplinary Research and Applications (CENTCON)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117251830","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

Acquisition and Development of Code-Mixed Corpus for Sentiment Analysis of Resource-Scarce Languages 资源稀缺语言情感分析的混码语料库获取与开发

2021 International Conference on Disruptive Technologies for Multi-Disciplinary Research and Applications (CENTCON) Pub Date : 2021-11-19 DOI: 10.1109/CENTCON52345.2021.9687897

Shailendra Kumar Singh, M. Sachan

引用次数: 1

A comparison between VGG16, VGG19 and ResNet50 architecture frameworks for Image Classification VGG16、VGG19和ResNet50图像分类体系结构的比较

2021 International Conference on Disruptive Technologies for Multi-Disciplinary Research and Applications (CENTCON) Pub Date : 2021-11-19 DOI: 10.1109/CENTCON52345.2021.9687944

Sheldon Mascarenhas, Mukul l Agarwal

{"title":"A comparison between VGG16, VGG19 and ResNet50 architecture frameworks for Image Classification","authors":"Sheldon Mascarenhas, Mukul l Agarwal","doi":"10.1109/CENTCON52345.2021.9687944","DOIUrl":"https://doi.org/10.1109/CENTCON52345.2021.9687944","url":null,"abstract":"Artificial Intelligence advancements have come a long way over the past twenty years. Rapid developments in AI have given birth to a trending topic called machine learning. Machine learning enables us to use algorithms and programming techniques to extract, understand and train data. Machine learning led to the creation of a concept called deep learning which uses algorithms to create an artificial neural network and use it to develop and learn, based on which it makes intuitive decisions by itself. Image classification is a task where we classify the images into sets of different categories, which when performed using deep learning increases business productivity by saving time and manpower. In this paper, we intend to determine which model of the architecture of the Convoluted Neural Network (CNN) can be used to solve a real-life problem of product classification to help optimize pricing comparison. We have compared the VGG16, VGG19, and ResNet50 architectures based on their accuracy while all three of these models solve the same image classification problem. We have concluded that the ResNet50 is the best architecture based on the comparison. These models have provided accuracies of 0.9667, 0.9707, and 0.9733 for VGG16, VGG19, and ResNet50 at epoch 20. The data provided is a real-life data set, sourced from a regional retailer.","PeriodicalId":103865,"journal":{"name":"2021 International Conference on Disruptive Technologies for Multi-Disciplinary Research and Applications (CENTCON)","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130150031","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: 70

Novel Convoluted Local Energy Oriented Patten(CLEOP) for the Classification of Wireless Capsule Endoscopy images 用于无线胶囊内镜图像分类的新型卷积局部能量导向模式(CLEOP)

2021 International Conference on Disruptive Technologies for Multi-Disciplinary Research and Applications (CENTCON) Pub Date : 2021-11-19 DOI: 10.1109/CENTCON52345.2021.9688172

N. E, P. Dayananda

{"title":"Novel Convoluted Local Energy Oriented Patten(CLEOP) for the Classification of Wireless Capsule Endoscopy images","authors":"N. E, P. Dayananda","doi":"10.1109/CENTCON52345.2021.9688172","DOIUrl":"https://doi.org/10.1109/CENTCON52345.2021.9688172","url":null,"abstract":"Endoscopy enables Physician to identify inflammation, ulcers, and tumors by viewing the intestines and other organs of the digestive systems of a human being. Upper endoscopy examination inside digestive system gives better accuracy when compared to X-rays in detecting abnormal growths caused by cancer and other diseases. Automation in medical image processing has enhanced the prediction of affected layer from the source of image. In that, image analysis in endoscopic medical field was focused on high level prediction and to estimate proper and better treatment for patients at earlier stage with less amount of stress. There are several methods of image analysis to extract the features of the image and predict its category. For better prediction model, the features of that image should be rotational invariant to find the best match training set. To improve the feature extraction in image processing application, texture patterns performed with better efficiency to analyze an image. In this proposed work, a novel method of texture pattern analysis method employed to classify the endoscopic image. This was achieved by using the Convoluted Local Energy Oriented Pattern (CLEOP) based feature extraction method. For validating the performance of proposed work, the CVC-ClinicDB image database was used in the testing process. In that, the database was separated into two major types based on the level of affected tissue region and its size. The efficiency of CLEOP texture pattern extraction method is compared with other state-of-the-art methods of feature extraction and classification model. This can be justified by estimating the statistical parameters estimated by validating the ground-truth of image database.","PeriodicalId":103865,"journal":{"name":"2021 International Conference on Disruptive Technologies for Multi-Disciplinary Research and Applications (CENTCON)","volume":"310 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131921776","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

Implementation of Machine Learning Techniques to Predict Briskness and Brightness of Tea Liquor using Factory Data 利用工厂数据预测茶酒亮度的机器学习技术的实现

2021 International Conference on Disruptive Technologies for Multi-Disciplinary Research and Applications (CENTCON) Pub Date : 2021-11-19 DOI: 10.1109/CENTCON52345.2021.9687920

Priyanka Sarmah, R. Choudhury, Debashis Saikia

{"title":"Implementation of Machine Learning Techniques to Predict Briskness and Brightness of Tea Liquor using Factory Data","authors":"Priyanka Sarmah, R. Choudhury, Debashis Saikia","doi":"10.1109/CENTCON52345.2021.9687920","DOIUrl":"https://doi.org/10.1109/CENTCON52345.2021.9687920","url":null,"abstract":"This paper documents the prediction of two important quality attributes of tea liquor viz. briskness and brightness using process parameters of fermentation room of tea factory. Different machine learning techniques are implemented for this work. Temperature and relative humidity are two important parameters of the fermentation room. These data are collected from the tea factory by the developed instrument during factory hours. Corresponding tea samples are also collected and tea quality is evaluated by tea tasters. This tea quality is a numeric value called overall liquor rating which is a function of different tea quality attributes like briskness, body, brightness etc. This work is an attempt to predict briskness and brightness from the collected factory data by implementing different machine learning techniques viz. k- Nearest Neighbor, decision tree and random forest. Average accuracy, f1-score and recall are found more than 95% for k-Nearest Neighbour, decision tree and random forest in training and validation for both the attributes.","PeriodicalId":103865,"journal":{"name":"2021 International Conference on Disruptive Technologies for Multi-Disciplinary Research and Applications (CENTCON)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131868267","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

Artificial Intelligence enabled Web-Based Prediction of Diabetes using Machine Learning Approach 人工智能利用机器学习方法实现基于web的糖尿病预测

2021 International Conference on Disruptive Technologies for Multi-Disciplinary Research and Applications (CENTCON) Pub Date : 2021-11-19 DOI: 10.1109/CENTCON52345.2021.9688236

Chongtham Pankaj, Konjengbam Vivekananda Singh, Khoirom Rajib Singh

{"title":"Artificial Intelligence enabled Web-Based Prediction of Diabetes using Machine Learning Approach","authors":"Chongtham Pankaj, Konjengbam Vivekananda Singh, Khoirom Rajib Singh","doi":"10.1109/CENTCON52345.2021.9688236","DOIUrl":"https://doi.org/10.1109/CENTCON52345.2021.9688236","url":null,"abstract":"Healthcare is the preservation of health that includes diagnosis, surgery, therapy, cure and other related to health of the people. With the advancement of technology, healthcare is enhanced by practicing smart healthcare, e-health and m-Health. In recent years, computerized physician consultant has become popular for the improvement of people's health and this requires a wide range of study of disease that emerges to the clinical decision support system. In this work, an early stage diabetes risk prediction dataset is trained with supervised machine learning and categorized with unsupervised machine learning. The diabetes is classified by best accuracy among supervised machine learning algorithms for a new patient examination. A Web application is created to predict early stage risk of diabetes by classifying results based on the questionnaire of the patient without testing kit using machine learning. Also, the result is analyzed and predicted the chance of having positive or negative diabetes based on the examination falls into clusters by unsupervised machine learning. The evaluation of the prediction is observed to improve the accuracy further with a deep learning approach.","PeriodicalId":103865,"journal":{"name":"2021 International Conference on Disruptive Technologies for Multi-Disciplinary Research and Applications (CENTCON)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133994810","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

Analysis, Design and Simulation of Non-Inverting Buck-Boost DC-DC Converter for Battery Charging 电池充电用非反相Buck-Boost DC-DC变换器的分析、设计与仿真

2021 International Conference on Disruptive Technologies for Multi-Disciplinary Research and Applications (CENTCON) Pub Date : 2021-11-19 DOI: 10.1109/CENTCON52345.2021.9688165

Amrutha Varshini Rao, Guruswamy K.P

引用次数: 1

Image Processing Based Vehicle Detection and Tracking: A Comparative Study 基于图像处理的车辆检测与跟踪的比较研究

2021 International Conference on Disruptive Technologies for Multi-Disciplinary Research and Applications (CENTCON) Pub Date : 2021-11-19 DOI: 10.1109/CENTCON52345.2021.9687988

A. V, S. Kulkarni

引用次数: 1