2020 IEEE 1st International Conference for Convergence in Engineering (ICCE)最新文献_第5页

Health Care System for Home Quarantine People 居家隔离人员的卫生保健系统

2020 IEEE 1st International Conference for Convergence in Engineering (ICCE) Pub Date : 2020-09-05 DOI: 10.1109/ICCE50343.2020.9290557

John Colaco, R. Lohani

{"title":"Health Care System for Home Quarantine People","authors":"John Colaco, R. Lohani","doi":"10.1109/ICCE50343.2020.9290557","DOIUrl":"https://doi.org/10.1109/ICCE50343.2020.9290557","url":null,"abstract":"Due to the ongoing COVID-19 crisis, many people who are arriving in Goa are home quarantined. Therefore, to continuously check their health status, we authors have proposed Internet of Things based electronic wireless communication system which is monitoring the health parameters continuously by using biosensors such as Respiration sensor, Body temperature sensor, Heart rate sensor, and Oxygen sensor. The received values will be transmitted to the nearby medical center or COVID Hospital using wireless technology. The same will be displayed on the Adafruit server. Also, the messages are sent on doctor’s mobile devices dealing with quarantined people and Government authorities through server or GSM modem. If any of the health parameters such as the temperature of the human body, respiration rate, and Heartbeat rate are exceeding their normal rate or for health parameters such as oxygen level and respiration rate falls below the normal rate then the buzzer is ringing alarm and the preventive action is taken by receiving authorities. The above-proposed health monitoring system has analyzed using a soft computing technique called fuzzy logic.","PeriodicalId":421963,"journal":{"name":"2020 IEEE 1st International Conference for Convergence in Engineering (ICCE)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123133888","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

Model Selection for Predicting Breast Cancer using Supervised Machine Learning Algorithms 使用监督机器学习算法预测乳腺癌的模型选择

2020 IEEE 1st International Conference for Convergence in Engineering (ICCE) Pub Date : 2020-09-05 DOI: 10.1109/ICCE50343.2020.9290578

Ajit Kumar, Rajkumar Patra, A. Ghosh

{"title":"Model Selection for Predicting Breast Cancer using Supervised Machine Learning Algorithms","authors":"Ajit Kumar, Rajkumar Patra, A. Ghosh","doi":"10.1109/ICCE50343.2020.9290578","DOIUrl":"https://doi.org/10.1109/ICCE50343.2020.9290578","url":null,"abstract":"Breast Cancer is the most common malignancy in women affecting 2.1 million women every year and causing the maximum number of deaths in women due to cancer. It occurs as a result of the unusual development of cells in the breast tissue, which is generally referred to as a Tumor. A tumor does not signify cancer. It may be not cancerous (benign), pre-cancerous (pre-malignant), or cancerous (malignant). Various types of tests such as mammograms, MRIs, ultrasound, and biopsy are frequently used to identify breast cancer. Early detection and treatment will help to improve breast cancer outcomes as well as survival. Therefore, this paper consists of a relative study of the breast cancer prediction using different supervised machine learning algorithms like Logistics Regression, K-Nearest Neighbors, Decision Tree Classifier, Gaussian NB, and Support Vector Machine on the UCI repository dataset. Concerning the performance of all the models, the accuracy score, precision, recall, and F-score of each model have been compared. After using various models, we got to see that Logistic Regression is a well-suited algorithm for Breast cancer prediction and came up with better accuracy and other performance indices as compared with other models.","PeriodicalId":421963,"journal":{"name":"2020 IEEE 1st International Conference for Convergence in Engineering (ICCE)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129602431","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

Condition Monitoring of Three-Phase Induction Motor 三相感应电动机状态监测

2020 IEEE 1st International Conference for Convergence in Engineering (ICCE) Pub Date : 2020-09-05 DOI: 10.1109/ICCE50343.2020.9290540

Rakeshkumar A. Patel, B. Bhalja, Md. Aftab Alam

{"title":"Condition Monitoring of Three-Phase Induction Motor","authors":"Rakeshkumar A. Patel, B. Bhalja, Md. Aftab Alam","doi":"10.1109/ICCE50343.2020.9290540","DOIUrl":"https://doi.org/10.1109/ICCE50343.2020.9290540","url":null,"abstract":"Analysis of stator current and vibration of induction motor has long been used, in researches as well as industries, for the condition monitoring purpose. This paper presents the hardware results of three phase induction motor condition monitoring by current and vibration analysis, presenting one novel diagnosis method while analyzing the frequency spectra. Here current and vibration data are collected with the help of Digital Storage Oscilloscope and Vibration Analyzer respectively. These collected data are used to obtain frequency components of current and vibration with the help of MATLAB program. Later, the set of the most dominating frequency components of current and vibration are compared with that of healthy motor in order to establish the healthy/faulty condition. A 3 HP, 2.2 KW, three-phase induction motor has been used for analysis purpose. Three types of major faults, namely, stator inter turn short, rotor broken bar and bearing defect are considered. The proposed method proves to be quite successful in predicting rotor and bearing faults, while not being so successful in detecting stator inter-turn faults.","PeriodicalId":421963,"journal":{"name":"2020 IEEE 1st International Conference for Convergence in Engineering (ICCE)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134553029","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

Image Registration using Bio-inspired Algorithms 使用仿生算法的图像配准

2020 IEEE 1st International Conference for Convergence in Engineering (ICCE) Pub Date : 2020-09-05 DOI: 10.1109/ICCE50343.2020.9290541

Kaushik Shaw, Puja Pandey, Shyandeep Das, Debasmita Ghosh, Pratikshan Malakar, Supriya Dhabal

引用次数: 0

Study on Structure and Properties of Crystalline Hydroxyapatite obtained from Biological and Synthetic Sources 生物及人工合成羟基磷灰石晶体的结构与性质研究

2020 IEEE 1st International Conference for Convergence in Engineering (ICCE) Pub Date : 2020-09-05 DOI: 10.1109/ICCE50343.2020.9290655

Dalia Acharjee, M. Das, S. K. Samanta, Piyali Basak, Sukumar Roy

引用次数: 0

Forecasting Apple Inc. Stock Prices Using S&P500– An OLS Regression Approach with Structural Break 预测苹果公司基于标准普尔500指数的股票价格——一种具有结构突破的OLS回归方法

2020 IEEE 1st International Conference for Convergence in Engineering (ICCE) Pub Date : 2020-09-05 DOI: 10.1109/ICCE50343.2020.9290495

Trishit Banerjee

{"title":"Forecasting Apple Inc. Stock Prices Using S&P500– An OLS Regression Approach with Structural Break","authors":"Trishit Banerjee","doi":"10.1109/ICCE50343.2020.9290495","DOIUrl":"https://doi.org/10.1109/ICCE50343.2020.9290495","url":null,"abstract":"The study analyzes the impact of the S&P500 returns along with influence of S&P500 Information Technology stocks (S&P500-IT) on Apple Inc. daily returns. This study also gives an insight into the connection between S&P500 Composite (S&P500-C) and Apple Inc. and S&P500-IT. The constant fluctuation of S&P500-C was noted in the time period. However, the rapid variation in regular returns at the beginning of 2018 has also been a part of the observation. The variation of the S&P500 in the case of IT stocks in 2018 was scrutinized. For the S&P500-C index, two linear estimation models for the daily returns of Apple Inc. have been generated. The indexes of S&P markets were regarded as predictors and the variable effects were measured for daily returns of Apple Inc. The models were later modified into a multiple linear regression model including S&P500-IT and S&P500-C as mutual predictors. A structural break was examined with the Chow analysis. The index of S&P500-IT and S&P500-C in the complex-regression model exhibits a negative effect on the daily returns of Apple Inc., due to multi co-linearity of the daily returns with S&P500-IT stocks. The structural breaks were insignificant in the improved regression model.","PeriodicalId":421963,"journal":{"name":"2020 IEEE 1st International Conference for Convergence in Engineering (ICCE)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128401092","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

ICCE 2020 List Reviewer Page ICCE 2020名单审查页面

2020 IEEE 1st International Conference for Convergence in Engineering (ICCE) Pub Date : 2020-09-05 DOI: 10.1109/icce50343.2020.9290593

引用次数: 0

A Study on Event Identification on Social Media Data 基于社交媒体数据的事件识别研究

2020 IEEE 1st International Conference for Convergence in Engineering (ICCE) Pub Date : 2020-09-05 DOI: 10.1109/ICCE50343.2020.9290539

Rishov Nag, Soumik De, Nabhoneel Majumdar, Pratik Dutta

引用次数: 0

Application of Fuzzy Answer set Programming in Hybrid Deep Neural-Symbolic Architecture 模糊答案集规划在深度神经-符号混合体系结构中的应用

2020 IEEE 1st International Conference for Convergence in Engineering (ICCE) Pub Date : 2020-09-05 DOI: 10.1109/ICCE50343.2020.9290604

Sandip Paul, K. Ray, D. Saha

引用次数: 1

Deep Greedy Network: A Tool for Medical Diagnosis on Exiguous Dataset of COVID-19 深度贪婪网络：在 COVID-19 稀疏数据集上进行医学诊断的工具

2020 IEEE 1st International Conference for Convergence in Engineering (ICCE) Pub Date : 2020-09-05 DOI: 10.1109/ICCE50343.2020.9290715

Sumagna Dey, S. Biswas, Srija Nandi, Subhrapratim Nath, Indrajit Das

{"title":"Deep Greedy Network: A Tool for Medical Diagnosis on Exiguous Dataset of COVID-19","authors":"Sumagna Dey, S. Biswas, Srija Nandi, Subhrapratim Nath, Indrajit Das","doi":"10.1109/ICCE50343.2020.9290715","DOIUrl":"https://doi.org/10.1109/ICCE50343.2020.9290715","url":null,"abstract":"The extensive outbreak of COVID-19 has created a worldwide health crisis. Transmission of this disease occurs among people through droplets which causes severe respiratory distress and in turn can also lead to fatal death. At the pinnacle of this pandemic, scientists endeavor to discover the medication for the COVID-19 victims. Artificial Intelligence algorithms, especially, deep learning, on the other hand, is used for the diagnosis of the COVID-19 patients but this requires an enormous radiographic data set to effectively provide an optimized outcome for a particular scenario. This work presents a new technique called ‘Deep Greedy Network’ which will work efficiently with a finite number of datasets. In spite of peculiarity caused due to limited dataset, the anomaly of overfitting and underfitting could be effectively overcome using the proposed algorithm. This, in turn, is simultaneously going to be both cost-effective and efficient. The proposed architecture ensures the efficacious result after the proper judgement of the trained model on the given X-ray datasets of COVID-19 cases.","PeriodicalId":421963,"journal":{"name":"2020 IEEE 1st International Conference for Convergence in Engineering (ICCE)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126485197","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