2021 International Conference on Computing, Electronic and Electrical Engineering (ICE Cube)最新文献

Customizable Digital Control System for Domestic Application with IoT Capability 可定制的具有物联网功能的国内应用数字控制系统

2021 International Conference on Computing, Electronic and Electrical Engineering (ICE Cube) Pub Date : 2021-10-26 DOI: 10.1109/ICECube53880.2021.9628319

Muhammad Naeem, Y. Salam, Ahmad Azeem, Ashar Sattar, Ali Sufyan, Faisal Salam

{"title":"Customizable Digital Control System for Domestic Application with IoT Capability","authors":"Muhammad Naeem, Y. Salam, Ahmad Azeem, Ashar Sattar, Ali Sufyan, Faisal Salam","doi":"10.1109/ICECube53880.2021.9628319","DOIUrl":"https://doi.org/10.1109/ICECube53880.2021.9628319","url":null,"abstract":"In this era, digitization and automation has made the life of human beings simpler. Automation saves money, time, and reduces the human efforts. Traditional appliances control systems are less reliable, require more human effort, and lag from safety measures. We designed a sophisticated but very intuitive and low-cost mechanized touch screen control panel system for home appliances and security with the Internet of Things (IoT) capability. This work includes a standard web server and Wi-Fi technology to control the home appliances. The appliances can be switched ON and OFF through an Android device and touch screen board mounted on the wall. To control the speed of a fan, a TRIAC-based digital dimmer has been designed. The concept is based on controlling the ON time of TRIAC to control the AC power of the fan. Furthermore, different sensors, e.g., Temperature, Humidity, and Voltage have been deployed for security reasons that take the values and display them on the android device. The ESP8266 module with microcontroller and Wi-Fi capability has been used for wireless control of appliances. Similarly, ESP8266 takes the data from sensors and transmits it to the server using the client-server protocol. Each appliance is connected with an ESP8266 and one ESP8266 is connected to touch modules through a client-server connection. We aim to make the interface look very presentable, efficient, customizable, and user-friendly.","PeriodicalId":308227,"journal":{"name":"2021 International Conference on Computing, Electronic and Electrical Engineering (ICE Cube)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122639787","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

Performance Analysis of Droop Control with Virtual Impedance in Parallel Operation of Microgrid in Islanded Mode 孤岛状态下微电网并联运行虚阻抗下垂控制性能分析

2021 International Conference on Computing, Electronic and Electrical Engineering (ICE Cube) Pub Date : 2021-10-26 DOI: 10.1109/ICECube53880.2021.9628235

Muhammad Muzammil Farooqi, A. Ulasyar, Waleed Ali, Haris Sheh Zad, A. Khattak

引用次数: 1

A Survey on AI-based ECG, PPG, and PCG Signals Based Biometric Authentication System 基于人工智能的心电、PPG和PCG信号生物识别认证系统研究

2021 International Conference on Computing, Electronic and Electrical Engineering (ICE Cube) Pub Date : 2021-10-26 DOI: 10.1109/ICECube53880.2021.9628307

H. Shahid, Afeefa Aymin, A. Remete, Sumair Aziz, Muhammad Umar Khan

引用次数: 0

Two-step Feature Selection for Predicting Mortality Risk in COVID-19 Patients 两步特征选择预测COVID-19患者死亡风险

2021 International Conference on Computing, Electronic and Electrical Engineering (ICE Cube) Pub Date : 2021-10-26 DOI: 10.1109/ICECube53880.2021.9628327

Saira Mustafa, Aatka Ali, Huma Salahuddin, Muhammad Umar Chaudhry

{"title":"Two-step Feature Selection for Predicting Mortality Risk in COVID-19 Patients","authors":"Saira Mustafa, Aatka Ali, Huma Salahuddin, Muhammad Umar Chaudhry","doi":"10.1109/ICECube53880.2021.9628327","DOIUrl":"https://doi.org/10.1109/ICECube53880.2021.9628327","url":null,"abstract":"COVID-19 pandemic is causing serious impact on our society. The whole world is suffering from financial, social, psychological, and other health crisis. One of the various challenges faced is the lack of health and medical facilities around the globe. It is very crucial to properly manage the available resources to save the lives of COVID-19 affected patients. This study proposes an intelligent model to facilitate the hospitals and medical facilities to diagnose which patients are in serious conditions and needs priority health services. The proposed model is based on feature selection-based mechanism, where most dominating features are identified to best discriminate among the serious patients and the less affected patients. We adopted two-step strategy, where filter measure is applied to rank the features according to their relevance in the first step, and Genetic Algorithm is applied with Decision Tree classifier to find the best feature subset in the second step. The results are reported in terms of classification accuracy and the most dominating features are also identified to help the medical practitioners.","PeriodicalId":308227,"journal":{"name":"2021 International Conference on Computing, Electronic and Electrical Engineering (ICE Cube)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114155710","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

Visible Light Communication (Li-Fi Technology) 可见光通信(Li-Fi技术)

2021 International Conference on Computing, Electronic and Electrical Engineering (ICE Cube) Pub Date : 2021-10-26 DOI: 10.1109/ICECube53880.2021.9628334

Asjad Raza, H. Mehdi, Zakir Hussain, Muhammad Arif, Shabbir Hussain

引用次数: 3

Prediction and Analysis of Covid-19 Positive Cases Using Deep Learning Model 基于深度学习模型的新型冠状病毒阳性病例预测与分析

2021 International Conference on Computing, Electronic and Electrical Engineering (ICE Cube) Pub Date : 2021-10-26 DOI: 10.1109/ICECube53880.2021.9628335

M. Farhan, Sohail Jabbar, M. R. Shahid

引用次数: 0

A Compact Study of Recent Trends of Challenges and Opportunities in Integrating Internet of Things (IoT) and Cloud Computing 整合物联网(IoT)和云计算的挑战和机遇的最新趋势的紧凑研究

2021 International Conference on Computing, Electronic and Electrical Engineering (ICE Cube) Pub Date : 2021-10-26 DOI: 10.1109/ICECube53880.2021.9628191

M. Ishaq, M. Afzal, Shehla Tahir, K. Ullah

引用次数: 4

Learning Predictive Models for Underground Coal Mine Environment Using Sensor Data 利用传感器数据学习煤矿井下环境预测模型

2021 International Conference on Computing, Electronic and Electrical Engineering (ICE Cube) Pub Date : 2021-10-26 DOI: 10.1109/ICECube53880.2021.9628259

A. Gul, Waheed Noor, Junaid Babar, Ali Nawaz, Syed Owais Athar

引用次数: 0

A Bidirectional DC-DC Bipolar Converter for Power Transmission Network 输电网用双向DC-DC双极变换器

2021 International Conference on Computing, Electronic and Electrical Engineering (ICE Cube) Pub Date : 2021-10-26 DOI: 10.1109/ICECube53880.2021.9628367

Laraib Saeed, Muhammad Yousaf Ali Khan, Hamid Karim, Emad Alhani

引用次数: 0

Using Machine Learning to Predict Patient’s Admission Trends in Hospital 利用机器学习预测住院患者入院趋势

2021 International Conference on Computing, Electronic and Electrical Engineering (ICE Cube) Pub Date : 2021-10-26 DOI: 10.1109/ICECube53880.2021.9628249

Qaisar Khan, Syed Attique Shah

{"title":"Using Machine Learning to Predict Patient’s Admission Trends in Hospital","authors":"Qaisar Khan, Syed Attique Shah","doi":"10.1109/ICECube53880.2021.9628249","DOIUrl":"https://doi.org/10.1109/ICECube53880.2021.9628249","url":null,"abstract":"In this era of advanced healthcare facilities, where health and technology are immensely integrated, the role of big data analytics is evolving in every aspect of healthcare. The large influx of patients in hospitals is a fatal problem for the hospital management systems. The patient crowding problem can cause potential consequences, including increased mortality rate, unnecessary labor cost, poor customer service, ambulance divergence, and cancelation of equipment. Therefore, it is crucial to utilize machine learning to prevent overcrowding and enhance resource allocation. This research study has used the openly available dataset of patients and a combination of machine learning algorithms such as logistic regression, support vector machine, k-nearest neighbor, and decision tree to analyze patient admission trends for effective decision making. According to the simulation results, the decision tree algorithm achieved the best accuracy with a score of (0.89). While on the parameter of the area under the receiver operating characteristics (AUROC), the logistic regression algorithm performed best with an AUROC score of (0.74), followed by the support vector machine with a score of (0.73). The least performers are KNN and decision tree with AUROC of (0.67) and (0.53), respectively.","PeriodicalId":308227,"journal":{"name":"2021 International Conference on Computing, Electronic and Electrical Engineering (ICE Cube)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114217807","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