2022 International Conference on Emerging Trends in Engineering and Medical Sciences (ICETEMS)最新文献

Comparative Analysis of Normal Washroom Vs. IoT-Based WPS Washroom 普通厕所与基于物联网的WPS厕所的对比分析

2022 International Conference on Emerging Trends in Engineering and Medical Sciences (ICETEMS) Pub Date : 2022-11-18 DOI: 10.1109/ICETEMS56252.2022.10093610

P. Gawande, Y. Kale, Prachi Dabhade

引用次数: 0

Postoperative Discharge Destination Classification via Extreme Gradient Boosting 通过极端梯度增强进行术后出院目的地分类

2022 International Conference on Emerging Trends in Engineering and Medical Sciences (ICETEMS) Pub Date : 2022-11-18 DOI: 10.1109/ICETEMS56252.2022.10093582

Gernel S. Lumacad, Aliah Alpha A Micaroz, Junar Paolo A Gabia

{"title":"Postoperative Discharge Destination Classification via Extreme Gradient Boosting","authors":"Gernel S. Lumacad, Aliah Alpha A Micaroz, Junar Paolo A Gabia","doi":"10.1109/ICETEMS56252.2022.10093582","DOIUrl":"https://doi.org/10.1109/ICETEMS56252.2022.10093582","url":null,"abstract":"Hypothermia is a medical crisis which arises when a patient’s body significantly loses heat rather than producing heat. It is a vital concern after surgery, exerting multiple effects such as impairment of innate immunity leading to an increase complication and mortality risk. Postoperative care received after a surgical procedure includes discharge destination whether the patient is to be delivered to an intensive care unit (ICU), to be delivered to the general hospital floor or the patient is already allowed to be sent home. Since postoperative hypothermia is a serious risk, discharge decision corresponds roughly to patient’s body temperature measurements. In this paper, we discuss the utilization of extreme gradient boosting (XGBoost) algorithm–a variant of gradient boosted algorithm and an ensemble of classification and regression tree, for classifying patient’s postoperative discharge destination. Patients’ data, including core and surface temperatures, blood pressure, blood oxygen level, stability of patient’s internal and surface temperatures, stability of patient’s blood pressure and postoperative perceived comfort are used as input features in formulating the XGBoost model. Experimental results show high performance of the formulated XGBoost model (accuracy =0.S947, kappa coefficient =0.S407, f-score =0.90) in classifying postoperative patients’ discharge destination compared to the methods used as discussed in section II. A ranking of feature importance is presented in the latter part of this paper.","PeriodicalId":170905,"journal":{"name":"2022 International Conference on Emerging Trends in Engineering and Medical Sciences (ICETEMS)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115077120","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

Precision Diagnosis to Discriminate Covid-19 and Pneumonia using Mixed-data Model based on Custom Neural Networks 基于自定义神经网络的混合数据模型精确诊断Covid-19和肺炎

2022 International Conference on Emerging Trends in Engineering and Medical Sciences (ICETEMS) Pub Date : 2022-11-18 DOI: 10.1109/ICETEMS56252.2022.10093240

S. Gore, Neelam D Gaikwad

{"title":"Precision Diagnosis to Discriminate Covid-19 and Pneumonia using Mixed-data Model based on Custom Neural Networks","authors":"S. Gore, Neelam D Gaikwad","doi":"10.1109/ICETEMS56252.2022.10093240","DOIUrl":"https://doi.org/10.1109/ICETEMS56252.2022.10093240","url":null,"abstract":"Classifying Covid-19 and Pneumonia is one of the most important and challenging tasks in the field of the medical sector since manual classification with human assistance can lead to incorrect prediction and diagnosis. Additionally, it is a difficult operation when there is a lot of data that need to be analyzed thoroughly. Due to the similarity in symptoms as well as in chest X-ray images of Covid-19 and Pneumonia diseases, it is difficult to distinguish those. The study presents a technological solution to build a mixed-data model using customized neural networks to discriminate between Covid-19 and Pneumonia. The proposed method is applied to the chest X-ray images and symptoms of patients of Covid-19 and Pneumonia. This helps to perform immediate prediction of Covid-19 and Pneumonia providing fast and specialized treatment to the patients appropriately. This prediction also helps the radiologist or doctors in making quick decisions. In this work, imaging data (such as Chest X-ray images) and text data (such as disease symptoms like cough, body pain, short breathing, fever, etc.) are taken for detecting Covid-19, Pneumonia and Normal patients. Data Synthesis is carried out due to the unavailability of mixed data and it has created dataset of 450 entries of Covid-19, Normal and Pneumonia cases. The goal is to design a system that accurately classifies Covid19, Pneumonia, and Normal patients by utilizing convolutional neural networks (CNN) and multi-layer perceptron (MLP) algorithms. An accuracy of 93.33% is obtained for the mixed-data model using a deep neural network, that is designed by combining custom CNN and MLP architectures.","PeriodicalId":170905,"journal":{"name":"2022 International Conference on Emerging Trends in Engineering and Medical Sciences (ICETEMS)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116727962","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

Laptop Price Prediction using Machine Learning Algorithms 使用机器学习算法预测笔记本电脑价格

2022 International Conference on Emerging Trends in Engineering and Medical Sciences (ICETEMS) Pub Date : 2022-11-18 DOI: 10.1109/ICETEMS56252.2022.10093357

M. Shaik, Medicherla Varshith, Sanka SriVyshnavi, N.L. Sanjana, Rama Sujith

{"title":"Laptop Price Prediction using Machine Learning Algorithms","authors":"M. Shaik, Medicherla Varshith, Sanka SriVyshnavi, N.L. Sanjana, Rama Sujith","doi":"10.1109/ICETEMS56252.2022.10093357","DOIUrl":"https://doi.org/10.1109/ICETEMS56252.2022.10093357","url":null,"abstract":"The laptop has grown to be one of the most essential and used gadgets in our day-to-day existence for different activities. We will be supplied with many specs and company names in the market, it will become difficult for laptop computer makers to sell their merchandise and for customers to pick out one. Machine learning (ML) is high quality in assisting in making decisions and predictions from the large volume of facts produced. We have additionally viewed ML strategies being used in recent developments in the Internet of Things (IoT) areas. Various studies supply solely a glimpse into predicting the price of the laptop with ML techniques as in this paper, we suggest a novel technique that targets identification process through tremendous elements using making use of desktop getting to know fashions resulting in improving the accuracy in the prediction of laptop price. The prediction model is delivered with one-of-a-kind combos of features and several regarded computing device learning models. We are the use of a one-of-a-kind laptop to gain knowledge of fashions like Decision trees, Multiple linear regression, KNN, and Random forest to test which desktop mastering model is more accurate in predicting the rate of the laptop.","PeriodicalId":170905,"journal":{"name":"2022 International Conference on Emerging Trends in Engineering and Medical Sciences (ICETEMS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129808365","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

Design of Universal Set of CMOS Gates using Multiple Valued Logic 基于多值逻辑的通用CMOS门设计

2022 International Conference on Emerging Trends in Engineering and Medical Sciences (ICETEMS) Pub Date : 2022-11-18 DOI: 10.1109/ICETEMS56252.2022.10093527

Y. Gaidhani, Ashutosh Bagade, Monica Kalbande, Archana Kishorrao Vaidya

{"title":"Design of Universal Set of CMOS Gates using Multiple Valued Logic","authors":"Y. Gaidhani, Ashutosh Bagade, Monica Kalbande, Archana Kishorrao Vaidya","doi":"10.1109/ICETEMS56252.2022.10093527","DOIUrl":"https://doi.org/10.1109/ICETEMS56252.2022.10093527","url":null,"abstract":"The design and implementation of digital circuits are performed in the binary logic switching algebra. The binary logic switching algebra is used to design and build digital circuits. We are developing a generic collection of gates for use in the synthesis and simplification of digital MV Logic circuits. Using digital circuits with multiple-valued logic, we can reduce the number of connections. MVL digital circuits can be designed by expanding the representation domain from the binary level (N=2) switching algebra to N > 2 levels. The implementation of an MVL digital circuit uses universal sets of MVL CMOS gates. The design and implementation of a universal set of CMOS gates are the topics of this work. In this paper we are dealing with the design and implementation of a universal set of CMOS gates, which includes extended AND operators: eANDl, eAND2,eAND3, Maximum operator and Successor operators for designing of any MVL digital circuits. Implemented circuits gives the proper functionality of the MVL combinational and sequential circuit design. In this MVL digital circuit uses voltage mode for operation having different range of voltages.","PeriodicalId":170905,"journal":{"name":"2022 International Conference on Emerging Trends in Engineering and Medical Sciences (ICETEMS)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129090971","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

Predictive Analysis of Cardiovascular Diseases 心血管疾病的预测分析

2022 International Conference on Emerging Trends in Engineering and Medical Sciences (ICETEMS) Pub Date : 2022-11-18 DOI: 10.1109/ICETEMS56252.2022.10093374

Hrishikesh Vinzey, Aditya Tidke, P. Palsodkar, Soham Kottawar, Yogita K. Dubey, P. Fulzele

{"title":"Predictive Analysis of Cardiovascular Diseases","authors":"Hrishikesh Vinzey, Aditya Tidke, P. Palsodkar, Soham Kottawar, Yogita K. Dubey, P. Fulzele","doi":"10.1109/ICETEMS56252.2022.10093374","DOIUrl":"https://doi.org/10.1109/ICETEMS56252.2022.10093374","url":null,"abstract":"This paper is a short summary on the results of research on Heart Disease prediction from a data analytics point of view. Application of Machine learning algorithms on data analyzed from a patient provides reliable performance as that achieved by diagnosing Heart disease. The growth in technology has improved the information or data which can be extracted from a patient to help pinpoint the cause of illness. Using fourteen of such attributes or data from the medical profile of a patient can predict the chance of a patient developing a heart condition. In simple terms, these attributes are loaded into logistic regression, Decision Tree, Random Forest, SVM, KNN and Naive bayes, that is, Machine learning (ML) algorithms for the analysis and further prediction of heart disease. There are many other techniques, methods used by other researchers, however we have stuck to data analytics and the three algorithms mentioned earlier. By using this method, the standards in the medical industries are elevated and rose as they can provide better diagnostics and treatment of the patient, resulting in providing an overall good quality service. This paper has its main focus towards: Using Data analysis, creating prediction Models to provide early detection of Heart Diseases, Also by creating a reliable/cost efficient method to predict heart disease","PeriodicalId":170905,"journal":{"name":"2022 International Conference on Emerging Trends in Engineering and Medical Sciences (ICETEMS)","volume":"169 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115914949","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

Dynamic Performance Analysis & Sizing of Vehicle Body & Powertrain for 48V Electric 2-Wheeler System 48V电动两轮车系统车身与动力系统动态性能分析及尺寸确定

2022 International Conference on Emerging Trends in Engineering and Medical Sciences (ICETEMS) Pub Date : 2022-11-18 DOI: 10.1109/ICETEMS56252.2022.10093293

S. Mopidevi, Kiran Sai Dasari, Sk. Alla Bakshu, B. S. Reddy

引用次数: 0

A Study of Real-Time Human Machine Interface for Scheduling Smart Microgrids 智能微电网调度实时人机界面研究

2022 International Conference on Emerging Trends in Engineering and Medical Sciences (ICETEMS) Pub Date : 2022-11-18 DOI: 10.1109/ICETEMS56252.2022.10093329

A. Gupta, R. Singh, Uliya Mitra

{"title":"A Study of Real-Time Human Machine Interface for Scheduling Smart Microgrids","authors":"A. Gupta, R. Singh, Uliya Mitra","doi":"10.1109/ICETEMS56252.2022.10093329","DOIUrl":"https://doi.org/10.1109/ICETEMS56252.2022.10093329","url":null,"abstract":"Due to the growing energy demand it is necessary to move towards renewable or green sources for power generation. Nowadays local production through renewable sources like PV, and wind is increasing every day. Microgrids are small power systems that are connected to such local networks. The concept of a smart microgrid is introduced to monitor and control the microgrid with help of advanced technologies. The commercial aspects regarding power exchanges are extremely important. A microgrid can operate in both grid-connected and islanded modes. The optimal performance of the microgrid can be achieved only by generation scheduling. A microgrid also supplies ancillary services (power services) to the grid. Depending on the selected model the microgrid management is rarely influenced by these activities. This control and management is easy with Human Machine Interface (HMI) as it is user-friendly and it can give a lot of information. This paper presents a real-time HMI that is industrial-based and is designed for scheduling smart microgrids. The panel for different functions of microgrid has been explained and a pictorial view has been presented. Also, various work done in scheduling smart microgrids has also been discussed.","PeriodicalId":170905,"journal":{"name":"2022 International Conference on Emerging Trends in Engineering and Medical Sciences (ICETEMS)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114984618","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 Comprehensive Review on Access Control Systems amid Global Pandemic 全球流行病背景下门禁系统综述

2022 International Conference on Emerging Trends in Engineering and Medical Sciences (ICETEMS) Pub Date : 2022-11-18 DOI: 10.1109/ICETEMS56252.2022.10093551

Gagandeep Kaur, A. Singh, Dhawan Singh

引用次数: 0

A Machine Learning Approach for Early Detection and Diagnosis of Autism and Normal Controls and Estimating Severity Levels Based on Face Recognition 基于人脸识别的自闭症和正常对照早期检测诊断和严重程度估计的机器学习方法

2022 International Conference on Emerging Trends in Engineering and Medical Sciences (ICETEMS) Pub Date : 2022-11-18 DOI: 10.1109/ICETEMS56252.2022.10093412

Shubhangi D.C, Baswaraj Gadgay, Shaista Farheen, M. A. Waheed

{"title":"A Machine Learning Approach for Early Detection and Diagnosis of Autism and Normal Controls and Estimating Severity Levels Based on Face Recognition","authors":"Shubhangi D.C, Baswaraj Gadgay, Shaista Farheen, M. A. Waheed","doi":"10.1109/ICETEMS56252.2022.10093412","DOIUrl":"https://doi.org/10.1109/ICETEMS56252.2022.10093412","url":null,"abstract":"Autism is unique among the numerous brain disorders in that it typically affects children at a young age. For people with autism, the most difficult element is expressing their sentiments and emotions to others. Autism spectrum disorder(ASD) is another name for autism is a chronic developmental impairment, difficult and complex, marked by recurring actions, non-verbal communication, and lack of concentration. Although autism cannot be cured, early diagnosis can assist in reducing its symptoms. ASDs have varying degrees of symptoms and severity. This study uses the most well-known machine learning techniques to discriminate between autistic people and healthy controls. Thermal-face photos are used to extract features using GLCM. This was accomplished using autocorrelations, contrast, cluster, prominence, cluster shadow, difference, entropy, squared sum variance, homogeneity, and the maximum probability. For example, the Support-Vector Machine Classifier, K-Nearest Neighbor Classifier, Nave Bayes Classifier, and Random-Forest Classifier have been utilised for classification.","PeriodicalId":170905,"journal":{"name":"2022 International Conference on Emerging Trends in Engineering and Medical Sciences (ICETEMS)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128385545","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