Pub Date : 2021-11-19DOI: 10.1109/CENTCON52345.2021.9687897
Shailendra Kumar Singh, M. Sachan
Code-mixing on social media sites has emerged as the new way of writing in a multilingual country. The researchers are already facing challenges on pre-processing (spell correction, POS tagging, stemming, language identification, etc. of code-mixed multilingual text. However, in this article some code-mixed bilingual phonetic text has been developed for sentiment analysis. The bilingual English-Punj abi phonetic word dictionary is developed using Gurmukhi to Roman transliteration (GRT) system. The training and testing code-mixed dataset has been developed using a bilingual word dictionary and GRT system. The English and bilingual code-mixed sentiment-bearing word dictionary has been developed manually using existing English SentiWordNet 3.0. This article focused on the development of code-mixed corpus and datasets for resource-scarce languages.
在这个多语言国家，社交媒体网站上的代码混合已经成为一种新的写作方式。研究人员已经面临着代码混合多语言文本的预处理(拼写校正、词性标注、词干提取、语言识别等)方面的挑战。然而，本文开发了一些用于情感分析的编码混合双语语音文本。采用Gurmukhi - to - Roman音译(GRT)系统开发了英旁遮普双语音标词词典。使用双语词字典和GRT系统开发了训练和测试代码混合数据集。使用现有的英语SentiWordNet 3.0，人工开发了英语和双语代码混合的包含情感的单词词典。本文的重点是为资源稀缺的语言开发代码混合语料库和数据集。
{"title":"Acquisition and Development of Code-Mixed Corpus for Sentiment Analysis of Resource-Scarce Languages","authors":"Shailendra Kumar Singh, M. Sachan","doi":"10.1109/CENTCON52345.2021.9687897","DOIUrl":"https://doi.org/10.1109/CENTCON52345.2021.9687897","url":null,"abstract":"Code-mixing on social media sites has emerged as the new way of writing in a multilingual country. The researchers are already facing challenges on pre-processing (spell correction, POS tagging, stemming, language identification, etc. of code-mixed multilingual text. However, in this article some code-mixed bilingual phonetic text has been developed for sentiment analysis. The bilingual English-Punj abi phonetic word dictionary is developed using Gurmukhi to Roman transliteration (GRT) system. The training and testing code-mixed dataset has been developed using a bilingual word dictionary and GRT system. The English and bilingual code-mixed sentiment-bearing word dictionary has been developed manually using existing English SentiWordNet 3.0. This article focused on the development of code-mixed corpus and datasets for resource-scarce languages.","PeriodicalId":103865,"journal":{"name":"2021 International Conference on Disruptive Technologies for Multi-Disciplinary Research and Applications (CENTCON)","volume":"21 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":"124536821","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}
Pub Date : 2021-11-19DOI: 10.1109/CENTCON52345.2021.9688151
Sudeep D. Thepade, Deepa Abin, Arati R. Dhake
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).
{"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}
Pub Date : 2021-11-19DOI: 10.1109/CENTCON52345.2021.9688197
Pranali Phulare, S. Deshmukh
The aim of this project is to create an algorithm that can accurately classify Twitter messages as positive or negative, in relation to a query term. Our hypothesis is that to have high accuracy in separating emotions in Twitter messages using machine learning methods. We propose an approach that analyses feeling of cricket fans and correlate sentiment to match play. We use data collected on twitter in the message form. To predict the outcome of a cricket match we are not going to rely on a single machine learning algorithm we are using at least two machine learning algorithms to compare the accuracy. We have applied modern classification techniques -Logistics Regression and Random Forest, and conducted a comparative study based on the overall cricket tweets. The project outcome is given in form of webpage giving both analysis and prediction of live tweets using Logistic Regression and older tweets using Random Forest.
{"title":"Cricket Twitter Data Sentiment Analysis and Prediction Exerted Machine Learning","authors":"Pranali Phulare, S. Deshmukh","doi":"10.1109/CENTCON52345.2021.9688197","DOIUrl":"https://doi.org/10.1109/CENTCON52345.2021.9688197","url":null,"abstract":"The aim of this project is to create an algorithm that can accurately classify Twitter messages as positive or negative, in relation to a query term. Our hypothesis is that to have high accuracy in separating emotions in Twitter messages using machine learning methods. We propose an approach that analyses feeling of cricket fans and correlate sentiment to match play. We use data collected on twitter in the message form. To predict the outcome of a cricket match we are not going to rely on a single machine learning algorithm we are using at least two machine learning algorithms to compare the accuracy. We have applied modern classification techniques -Logistics Regression and Random Forest, and conducted a comparative study based on the overall cricket tweets. The project outcome is given in form of webpage giving both analysis and prediction of live tweets using Logistic Regression and older tweets using Random Forest.","PeriodicalId":103865,"journal":{"name":"2021 International Conference on Disruptive Technologies for Multi-Disciplinary Research and Applications (CENTCON)","volume":"11 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":"121714312","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}
Pub Date : 2021-11-19DOI: 10.1109/CENTCON52345.2021.9687964
S. M, C. Nandini
Motif Discovery algorithms are used to identify meaningful patterns in gene sequences. The meaningful patterns called motifs are of significant biological importance. They are used in Bioinformatic applications like early detection of diseases, drug design, Environmental Health research, DNA Forensics and so on. Many different algorithms and tools have been developed for motif discovery. The algorithms developed have their own advantages and limitations. Motif discovery is still a complex challenge in the field of bio-informatics for biologists and computer researchers. This paper presents the working, advantages and limitations of the some of the important motif discovery algorithms and their subcategories. The paper also gives a comparison of different motif discovery algorithms and the direction of future work is also discussed
{"title":"A Survey on Motif Discovery Algorithms for analysis of Gene Sequences of Interest","authors":"S. M, C. Nandini","doi":"10.1109/CENTCON52345.2021.9687964","DOIUrl":"https://doi.org/10.1109/CENTCON52345.2021.9687964","url":null,"abstract":"Motif Discovery algorithms are used to identify meaningful patterns in gene sequences. The meaningful patterns called motifs are of significant biological importance. They are used in Bioinformatic applications like early detection of diseases, drug design, Environmental Health research, DNA Forensics and so on. Many different algorithms and tools have been developed for motif discovery. The algorithms developed have their own advantages and limitations. Motif discovery is still a complex challenge in the field of bio-informatics for biologists and computer researchers. This paper presents the working, advantages and limitations of the some of the important motif discovery algorithms and their subcategories. The paper also gives a comparison of different motif discovery algorithms and the direction of future work is also discussed","PeriodicalId":103865,"journal":{"name":"2021 International Conference on Disruptive Technologies for Multi-Disciplinary Research and Applications (CENTCON)","volume":"4 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":"121051033","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}
Pub Date : 2021-11-19DOI: 10.1109/CENTCON52345.2021.9687920
Priyanka Sarmah, R. Choudhury, Debashis Saikia
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.
{"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}
Pub Date : 2021-11-19DOI: 10.1109/CENTCON52345.2021.9688172
N. E, P. Dayananda
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.
内窥镜检查使医生能够通过观察肠道和人体消化系统的其他器官来识别炎症、溃疡和肿瘤。与x光相比，消化系统内的上消化道内窥镜检查在检测癌症和其他疾病引起的异常生长方面具有更好的准确性。医学图像处理的自动化增强了从图像源对受影响层的预测。因此，内窥镜医学领域的图像分析侧重于高层次的预测，并在患者压力较小的情况下，在早期评估患者的适当和更好的治疗。图像分析有几种方法来提取图像的特征并预测其类别。为了获得更好的预测模型，该图像的特征应该是旋转不变的，以找到最佳的匹配训练集。为了提高图像处理应用中特征提取的效率，利用纹理模式对图像进行分析。本文提出了一种基于纹理模式分析的内窥镜图像分类方法。这是通过基于卷积局部能量导向模式(Convoluted Local Energy Oriented Pattern, CLEOP)的特征提取方法实现的。为了验证所提出的工作的性能，在测试过程中使用了CVC-ClinicDB图像数据库。其中，数据库根据受影响组织区域的水平及其大小分为两大类。将CLEOP纹理模式提取方法的效率与现有的特征提取方法和分类模型方法进行了比较。这可以通过估计通过验证图像数据库的真值估计的统计参数来证明。
{"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}
Pub Date : 2021-11-19DOI: 10.1109/CENTCON52345.2021.9687944
Sheldon Mascarenhas, Mukul l Agarwal
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.
{"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}
Pub Date : 2021-11-19DOI: 10.1109/CENTCON52345.2021.9688165
Amrutha Varshini Rao, Guruswamy K.P
In order to increase the reliability of EVs, several technologies have been developed that include auxiliary power sources, like batteries. These auxiliary batteries can be charged from the main battery present in the batteries as the ratings of the auxiliary source are lower than the main battery. In this paper, a simulation study on auxiliary battery system is carried out for EV application.
{"title":"Analysis, Design and Simulation of Non-Inverting Buck-Boost DC-DC Converter for Battery Charging","authors":"Amrutha Varshini Rao, Guruswamy K.P","doi":"10.1109/CENTCON52345.2021.9688165","DOIUrl":"https://doi.org/10.1109/CENTCON52345.2021.9688165","url":null,"abstract":"In order to increase the reliability of EVs, several technologies have been developed that include auxiliary power sources, like batteries. These auxiliary batteries can be charged from the main battery present in the batteries as the ratings of the auxiliary source are lower than the main battery. In this paper, a simulation study on auxiliary battery system is carried out for EV application.","PeriodicalId":103865,"journal":{"name":"2021 International Conference on Disruptive Technologies for Multi-Disciplinary Research and Applications (CENTCON)","volume":"1 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":"130926596","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}
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.
{"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}
Pub Date : 2021-11-19DOI: 10.1109/CENTCON52345.2021.9688114
P. William, Pardeep Kumar, Gurpreet Singh Chhabra, K. Vengatesan
In the 21st century, agile software development (ASD) has emerged as one of the prominent software development techniques. Every major global company has moved to ASD as a means of reducing costs. In pursuit of huge markets and cheap cost of labour, the industry has shifted to a Distributed Agile Software Development (DASD) environment. As a consequence of improper job allocation, clients may refuse to accept the project, team members may be demonized, and the project may collapse. Numerous scholars have spent the past decade researching different techniques for work allocation in Distributed Agile settings, and the results have been promising. Ontologies and Bayesian networks were among the techniques they employed. This is a list of brute force techniques that may be useful in certain situations. Additionally, these methods have not been used to distributed Agile software development job allocation. The purpose of this article is to design and implement a method for job allocation in distributed Agile software development that is based on machine learning. The findings indicate that the suggested model is more accurate in terms of task assignment.
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