2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS)最新文献

Disease Diagnosis Using Ayurvedic Pulse and Treatment Recommendation Engine 使用阿育吠陀脉搏和治疗推荐引擎进行疾病诊断

2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS) Pub Date : 2021-03-19 DOI: 10.1109/ICACCS51430.2021.9441843

Harshi Pogadadanda, U. Shwetha Shankar, K. Jansi

{"title":"Disease Diagnosis Using Ayurvedic Pulse and Treatment Recommendation Engine","authors":"Harshi Pogadadanda, U. Shwetha Shankar, K. Jansi","doi":"10.1109/ICACCS51430.2021.9441843","DOIUrl":"https://doi.org/10.1109/ICACCS51430.2021.9441843","url":null,"abstract":"The Ayurvedic practitioner places three lingers on the right hand below the wrist when taking the client’s pulse. This way he can feel the blood flowing in squirts during the expansion and contraction of the heart. This so-called “radial pulse” (Hasta Nadi) is the ideal pulse for complex pulse measurements. Ayurveda has determined that there are three types of pulse. The Vata pulse is felt under the index Unger. The Pitta pulse is taken under the middle finger, and the Kapha pulse is located under the ring finger. The Vata pulse can be determined by a thin, fast and irregular rhythm under the skin. In comparison, the Pitta pulse, which is stronger and more voluminous, and it may have either a regular or irregular rhythm. You can locate the Kapha pulse deeper down (under fatty tissue) and move slowly and regularly. The Ayurvedic practitioner can take the pulse at other parts of the body as well, thereby gaining a very complete picture of the state of health of any patient. This practice of reading pulse from patients requires highly experienced doctors and practitioners. Unfortunately, these days the ayurveda doctors and practitioners lack this ability to read the pulse accurately using the above stated method and hence wrongly diagnosing the patient. To overcome this problem and to treat patients appropriately this proposed system is used. This project deals with a remote patient diagnosis system built using IoT devices and predictive machine learning models that runs, time to time on patient data. A wearable device plugged on to the body of a patient which keeps collecting the data, time to time and that data will be uploaded to Amazon Web Services (AWS) server for storage and further analysis. The collected data will be ted to a machine learning models for creating a recommendations over treatment for the doctors/practitioners.","PeriodicalId":422546,"journal":{"name":"2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS)","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115611250","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

Prediction of Covid Disease using Advanced IoT and Support Vector Machine 利用先进物联网和支持向量机预测Covid - 19疾病

2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS) Pub Date : 2021-03-19 DOI: 10.1109/ICACCS51430.2021.9442040

R. Kanmani, S. Muthulakshmi, R. Radhapoorani, N. Suganya, K. S. Subitcha, M. Sriranjani

引用次数: 1

Dental Caries early detection using Convolutional Neural Network for Tele dentistry 基于卷积神经网络的远程牙科龋病早期检测

2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS) Pub Date : 2021-03-19 DOI: 10.1109/ICACCS51430.2021.9442001

Devesh Saini, Richa Jain, Anita Thakur

{"title":"Dental Caries early detection using Convolutional Neural Network for Tele dentistry","authors":"Devesh Saini, Richa Jain, Anita Thakur","doi":"10.1109/ICACCS51430.2021.9442001","DOIUrl":"https://doi.org/10.1109/ICACCS51430.2021.9442001","url":null,"abstract":"Dental caries is progressive bacterial infection which is the main cause of tooth lose. This occurs due to poor dental hygiene which also lead to various dental diseases. If caries can be diagnosis in early stage using tele-dentistry system it will great help for child oral care. Because severe form of caries produces infection and pain which may be result of tooth extraction. So early detection and diagnosis of these caries are the major concern for the researchers. Soft computing techniques are prominently used in dentistry which makes diagnosis easy and reduces the diagnosis time. This paper aims to detect dental caries using digital color image at an early stage so that the treatment can be performed easily and effectively. This classification also suitable for tele dentistry as tele informatic oral health care system for this proposed work we implemented the deep learning model is convolution neural network (CNN). We have trained deep learning different models of CNN; they are Visual Geometry Group (Vgg16 & Vgg19) Inception-V3 and Resnet50. Training, validation, and testing has been performed on binary dataset with caries and without caries images. The classification accuracy is achieved using Vgg16, Vgg19 Inception v3 and Resnet50 models and the highest accuracy is achieved among them is by Inception v3 with the training accuracy of 99.89% and validation accuracy of 98.95 with minimum loss compared to Vgg16 CNN models.","PeriodicalId":422546,"journal":{"name":"2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121022932","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: 8

Biometrically Secured ATM Vigilance System 生物识别安全的ATM警戒系统

2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS) Pub Date : 2021-03-19 DOI: 10.1109/ICACCS51430.2021.9441975

M. Navin Kumar, S. Raghul, K. Nirmal Prasad, P. Naveen Kumar

{"title":"Biometrically Secured ATM Vigilance System","authors":"M. Navin Kumar, S. Raghul, K. Nirmal Prasad, P. Naveen Kumar","doi":"10.1109/ICACCS51430.2021.9441975","DOIUrl":"https://doi.org/10.1109/ICACCS51430.2021.9441975","url":null,"abstract":"The increase in electronic transactions has contributed to a higher demand for fast and precise identification and authentication of users. In ATM’s, biometric-based authentication offers different benefits. Fingerprints and facial identification are used in biometrical authentication. The drawback of the current ATM authentication method is the use of password-PINs. Since PINs can be tracked and misused quickly. Our planned system is intended to provide greater protection for ATMs to achieve security and to resolve these criminal activities. Here, the PINs are replaced by randomly created OTP that are sent through the IoT. The goal of the work is to fully eradicate the use of ATM cards. The customer will be allowed to continue with the transaction after biometric and OTP pin authentication. The account will be blocked in the event of three consecutive incorrect attempts. This initiative also deals with ATM fraud prevention. In case of any suspicious activity is detected through the vibration sensor results in the closing of ATM doors followed by releasing of the fainting gas and alerting the surroundings. This will catch the perpetrator engaged in the crime and prevent the fraud from taking place.","PeriodicalId":422546,"journal":{"name":"2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS)","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124852433","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

Diabetes Prediction using Machine Learning Algorithms with Feature Selection and Dimensionality Reduction 用特征选择和降维的机器学习算法预测糖尿病

2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS) Pub Date : 2021-03-19 DOI: 10.1109/ICACCS51430.2021.9441935

S. Sivaranjani, S. Ananya, J. Aravinth, R. Karthika

{"title":"Diabetes Prediction using Machine Learning Algorithms with Feature Selection and Dimensionality Reduction","authors":"S. Sivaranjani, S. Ananya, J. Aravinth, R. Karthika","doi":"10.1109/ICACCS51430.2021.9441935","DOIUrl":"https://doi.org/10.1109/ICACCS51430.2021.9441935","url":null,"abstract":"In today’s world diabetes has become one of the most life threatening and at the same time most common diseases not only in India but around the world. Diabetes is seen in all age groups these days and they are attributed to lifestyle, genetic, stress and age factor. Whatever be the reasons for diabetics, the outcome could be severe if left unnoticed. Currently various methods are being used to predict diabetes and diabetic inflicted diseases. In the proposed work, we have used the Machine Learning algorithms Support Vector Machine (SVM) & Random Forest (RF) that would help to identify the potential chances of getting affected by Diabetes Related Diseases. After pre-processing the data, features which influences the prediction are selected by implementing step forward and backward feature selection. The Principle Component Analysis (PCA) dimensionality reduction method is analyzed after the selection of specific features and the accuracy of the prediction is 83% implementing Random Forest (RF) which is significant in comparison with Support Vector Machine (SVM) with accuracy of 81.4%.","PeriodicalId":422546,"journal":{"name":"2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125170375","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: 44

Analysis of the Measureable Results of Novel Structure Construction for Road Traffic 道路交通新型结构施工可测结果分析

2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS) Pub Date : 2021-03-19 DOI: 10.1109/ICACCS51430.2021.9441987

Sabbidi Venkatesham, Santhosh Kumar

引用次数: 0

A Review on Community Detection algorithms and Evaluation Measures in Social Networks 社交网络中社区检测算法与评价方法综述

2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS) Pub Date : 2021-03-19 DOI: 10.1109/ICACCS51430.2021.9441868

P. Ponveni, J. Visumathi

引用次数: 3

Empirical Analysis of Artificial Immune System Algorithms for Aging Related Bug Prediction 人工免疫系统算法在衰老相关Bug预测中的实证分析

2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS) Pub Date : 2021-03-19 DOI: 10.1109/ICACCS51430.2021.9441809

Megha Khanna, Mehak Aggarwal, Naman Singhal

{"title":"Empirical Analysis of Artificial Immune System Algorithms for Aging Related Bug Prediction","authors":"Megha Khanna, Mehak Aggarwal, Naman Singhal","doi":"10.1109/ICACCS51430.2021.9441809","DOIUrl":"https://doi.org/10.1109/ICACCS51430.2021.9441809","url":null,"abstract":"The complex nature of human immunology algorithms has motivated the research community to explore their practical applications in various other fields. As a result, Artificial Immune Systems (AISs) is one such class of algorithms that has found its way into software quality predictive modeling. In this paper, we evaluate AIS algorithms for developing Aging-Related Bug (ARB) prediction models. Software Aging, the gradual degradation and resource exhaustion in software systems, is said to be caused by ARBs, which may or may not be identified during software testing. Therefore, predicting ARBs before software release can help software managers in reducing their impact. This paper presents an empirical study that statistically analyzes the effectiveness of AIS classifiers for ARB prediction on five open-source software datasets. In order to account for the imbalanced nature of the investigated datasets, we used resampling and cost-sensitive classifiers. The results of the study indicate the effectiveness of AIS algorithms for developing ARB prediction models.","PeriodicalId":422546,"journal":{"name":"2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125476157","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 Survey On Detecting Android Malware Using Machine Learning Technique 基于机器学习技术的Android恶意软件检测研究

2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS) Pub Date : 2021-03-19 DOI: 10.1109/ICACCS51430.2021.9441712

S. Priyadharshini, S. Shanthi

{"title":"A Survey On Detecting Android Malware Using Machine Learning Technique","authors":"S. Priyadharshini, S. Shanthi","doi":"10.1109/ICACCS51430.2021.9441712","DOIUrl":"https://doi.org/10.1109/ICACCS51430.2021.9441712","url":null,"abstract":"Nowadays, internet connected smart phones devices usage are increasing steadily and also growth of Android application users are increasing. Mobile devices are used in daily activities such as, remote light control, goods and parking payment with the information of saved credit card details. Due to growth of Android application user, some intruder is creating malicious android application as a tool to steal the sensitive data and identity theft / fraud. There are numerous malicious applications detection tools and software are available. But an effective and efficient malware application detection tools needed to tackle and handle new complex malicious apps created by an intruder. In this paper machine learning approaches for detecting the malicious android application has been used. The datasets are taken by open source datasets (kaggle datasets). Here Data preprocessing and feature extraction technique are used for speeding up the calculation of an algorithm. Data Pre-Processing which is applied to features of data. It basically helps to normalize the information within a specific range. Feature extraction techniques like data pruning are used. The intention of this parchment is to trap the utilization of ML techniques for malware research and reveals that Data Science is employed for malware detection.","PeriodicalId":422546,"journal":{"name":"2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122498940","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

A Machine Learning Approach for offload Decision Making in Mobile Cloud Computing 移动云计算中卸载决策的机器学习方法

2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS) Pub Date : 2021-03-19 DOI: 10.1109/ICACCS51430.2021.9441795

R. Mathur, Manmohan Sharma

引用次数: 4