Intelligent Automation and Soft Computing最新文献_第9页

Low Cost Autonomous Learning and Advising Smart Home Automation System 低成本自主学习和建议智能家居自动化系统

IF 2 4区计算机科学

Intelligent Automation and Soft Computing Pub Date : 2022-01-01 DOI: 10.32604/iasc.2022.020649

Daniel Chioran, H. Valean

{"title":"Low Cost Autonomous Learning and Advising Smart Home Automation System","authors":"Daniel Chioran, H. Valean","doi":"10.32604/iasc.2022.020649","DOIUrl":"https://doi.org/10.32604/iasc.2022.020649","url":null,"abstract":"In today’s world, more than ever before, we are fascinated and drawn towards smart autonomous devices that make our lives safer and more comfortable. Devices that aid in reducing our energy consumption are also highly appreciated but often quite expensive to buy. This context is favorable for developing an autonomous smart home automation system (SHAS) with energy-saving potential and low price, making it widely accessible. This paper presents the design and prototype implementation of such a low-cost micro-controller based autonomous SHAS that learns the resident’s work schedule and integrates a wide array of sensors and actuators to automatically control the lights, temperature, humidity and power sockets. The proposed automation system also monitors the home environment for potential energy-saving opportunities, gas leaks, or unauthorized entry. For reliability purposes and to limit the risk of signal interference, the proposed system design uses a wired inter-module communication method. To enhance the home’s security, both personal identification number (PIN) protection and Global System for Mobile Communications (GSM) communication are added, making the proposed system design less vulnerable to cyberattacks when compared to other wireless alternatives. The hardware and software architectures, the prototype test results and the cost analysis are presented in detail, validating the system’s design and efficient operation as an autonomous smart home automation system.","PeriodicalId":50357,"journal":{"name":"Intelligent Automation and Soft Computing","volume":"18 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73975791","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Intelligent Computing and Control Framework for Smart Automated System 智能自动化系统的智能计算与控制框架

IF 2 4区计算机科学

Intelligent Automation and Soft Computing Pub Date : 2022-01-01 DOI: 10.32604/iasc.2022.023922

R. Manikandan, G. Ranganathan, V. Bindhu

{"title":"Intelligent Computing and Control Framework for Smart Automated System","authors":"R. Manikandan, G. Ranganathan, V. Bindhu","doi":"10.32604/iasc.2022.023922","DOIUrl":"https://doi.org/10.32604/iasc.2022.023922","url":null,"abstract":"This paper presents development and analysis of different control strategies for smart automated system. The dynamic role of an electrical motor and sensor interfacing with wireless module becomes an essential element in a smart agriculture system to monitor various environmental parameters. The various key parameters such as temperature, humidity, air pressure, soil health and solar radiation are widely used to analyze the growth of plants and soil health based on different climate conditions. However, the smart development of an automatic system to measure these vital parameters provides a feasible approach and helps the farmers to monitor their crops productivity. In this paper, a smart sensor based intelligent and automatic control strategies such as fuzzy logic controller and PID (Proportional Integral Derivative) controller is developed to collect the real time environmental parameters and to adapt any environmental conditions by updating their membership functions automatically with the help of sensor outputs. This paper targets to bring the usage of sensor-based intelligent and automatic control methods in the field of an agriculture system which includes automatic solar panel tracking and control, environmental vital parameters measurement, monitoring and implementation of intelligent control methods. The different experiments have been carried out with the support of graphical and microcontroller programming environment. Experimental results provide that the proposed system effectively measure the environmental parameters and provide an accurate transmission of data for continuous monitoring. The advantage of the proposed system is simple, easy to implement and maintain the dynamic environment for plants with respect to any climate conditions.","PeriodicalId":50357,"journal":{"name":"Intelligent Automation and Soft Computing","volume":"180 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80165469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Determination of COVID-19 Patients Using Machine Learning Algorithms 使用机器学习算法确定COVID-19患者

IF 2 4区计算机科学

Intelligent Automation and Soft Computing Pub Date : 2022-01-01 DOI: 10.32604/iasc.2022.018753

M. Malik, M. W. Iqbal, S. Shahzad, M. T. Mushtaq, M.R Naqvi, Maira Kamran, Babar Ayub Khan, M. Tahir

{"title":"Determination of COVID-19 Patients Using Machine Learning Algorithms","authors":"M. Malik, M. W. Iqbal, S. Shahzad, M. T. Mushtaq, M.R Naqvi, Maira Kamran, Babar Ayub Khan, M. Tahir","doi":"10.32604/iasc.2022.018753","DOIUrl":"https://doi.org/10.32604/iasc.2022.018753","url":null,"abstract":"Coronavirus disease (COVID-19), also known as Severe acute respiratory syndrome (SARS-COV2) and it has imposed deep concern on public health globally. Based on its fast-spreading breakout among the people exposed to the wet animal market in Wuhan city of China, the city was indicated as its origin. The symptoms, reactions, and the rate of recovery shown in the coronavirus cases worldwide have been varied. The number of patients is still rising exponentially, and some countries are now battling the third wave. Since the most effective treatment of this disease has not been discovered so far, early detection of potential COVID-19 patients can help isolate them socially to decrease the spread and flatten the curve. In this study, we explore state-of-the-art research on coronavirus disease to determine the impact of this illness among various age groups. Moreover, we analyze the performance of the Decision tree (DT), K-nearest neighbors (KNN), Naive bayes (NB), Support vector machine (SVM), and Logistic regression (LR) to determine COVID-19 in the patients based on their symptoms. A dataset obtained from a public repository was collected and pre-processed, before applying the selected Machine learning (ML) algorithms on them. The results demonstrate that all the ML algorithms incorporated perform well in determining COVID-19 in potential patients. NB and DT classifiers show the best performance with an accuracy of 93.70%, whereas other algorithms, such as SVM, KNN, and LR, demonstrate an accuracy of 93.60%, 93.50%, and 92.80% respectively. Hence, we determine that ML models have a significant role in detecting COVID-19 in patients based on their symptoms.","PeriodicalId":50357,"journal":{"name":"Intelligent Automation and Soft Computing","volume":"112 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78822537","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9

Computation of Aortic Geometry Using MR and CT 3D Images 利用MR和CT三维图像计算主动脉几何形状

IF 2 4区计算机科学

Intelligent Automation and Soft Computing Pub Date : 2022-01-01 DOI: 10.32604/iasc.2022.020607

Maryam Altalhi, S. Rehman, Fakhre Alam, A. Alarood, A. Rehman, M. Irfan Uddin

{"title":"Computation of Aortic Geometry Using MR and CT 3D Images","authors":"Maryam Altalhi, S. Rehman, Fakhre Alam, A. Alarood, A. Rehman, M. Irfan Uddin","doi":"10.32604/iasc.2022.020607","DOIUrl":"https://doi.org/10.32604/iasc.2022.020607","url":null,"abstract":"The proper computation of geometric parameters of the aorta and coronary arteries are very important for surgery planning, disease diagnoses, and age-related changes observation in the vessels. The accurate knowledge about the geometry of aorta and coronary arteries is required for the proper investigation of heart related diseases. The geometry of aorta and coronary arteries includes the diameter of the ascending and descending aorta and coronary arteries, length of the coronary arteries, branching angles of the coronary arteries and branching points. These geometric parameters from arteries can be computed from the 3D image data. In this paper, we propose an approach for calculating geometric parameters such as length, diameter of the aorta and angles of the coronary arteries. The proposed method automatically computes the geometry of aorta and left and right coronary arteries. The geometry is computed by logically dividing the aorta, calculating the centerline and extracting the features of aorta and coronary arteries. The method has been tested on different 3D CT/MR image data. The results of the proposed method are tested on different data sets to check its accuracy. The results show more accuracy and less computation time on noisy image data as compared to the already developed method. The obtained results are visualized and compared using visualization toolkit (VTK).","PeriodicalId":50357,"journal":{"name":"Intelligent Automation and Soft Computing","volume":"3 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78854274","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

COVID-19 Pandemic Prediction and Forecasting Using Machine Learning Classifiers 使用机器学习分类器的COVID-19大流行预测和预测

IF 2 4区计算机科学

Intelligent Automation and Soft Computing Pub Date : 2022-01-01 DOI: 10.32604/iasc.2022.021507

Jabeen Sultana, Anjani Kumar Singha, Shams Tabrez Siddiqui, G. Nagalaxmi, Anil Kumar Sriram, Nitish Pathak

{"title":"COVID-19 Pandemic Prediction and Forecasting Using Machine Learning Classifiers","authors":"Jabeen Sultana, Anjani Kumar Singha, Shams Tabrez Siddiqui, G. Nagalaxmi, Anil Kumar Sriram, Nitish Pathak","doi":"10.32604/iasc.2022.021507","DOIUrl":"https://doi.org/10.32604/iasc.2022.021507","url":null,"abstract":"COVID-19 is a novel virus that spreads in multiple chains from one person to the next. When a person is infected with this virus, they experience respiratory problems as well as rise in body temperature. Heavy breathlessness is the most severe sign of this COVID-19, which can lead to serious illness in some people. However, not everyone who has been infected with this virus will experience the same symptoms. Some people develop cold and cough, while others suffer from severe headaches and fatigue. This virus freezes the entire world as each country is fighting against COVID-19 and endures vaccination doses. Worldwide epidemic has been caused by this unusual virus. Several researchers use a variety of statistical methodologies to create models that examine the present stage of the pandemic and the losses incurred, as well as considered other factors that vary by location. The obtained statistical models depend on diverse aspects, and the studies are purely based on possible preferences, the pattern in which the virus spreads and infects people. Machine Learning classifiers such as Linear regression, Multi-Layer Perception and Vector Auto Regression are applied in this study to predict the various COVID-19 blowouts. The data comes from the COVID-19 data repository at Johns Hopkins University, and it focuses on the dissemination of different effect patterns of Covid-19 cases throughout Asian countries. © 2022, Tech Science Press. All rights reserved.","PeriodicalId":50357,"journal":{"name":"Intelligent Automation and Soft Computing","volume":"6 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80480124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

H-infinity Controller Based Disturbance Rejection in Continuous Stirred-Tank Reactor 基于h∞控制器的连续搅拌槽反应器扰动抑制

IF 2 4区计算机科学

Intelligent Automation and Soft Computing Pub Date : 2022-01-01 DOI: 10.32604/iasc.2022.019525

Sikander Hans, Smarajit Ghosh

引用次数: 0

Automated Learning of ECG Streaming Data Through Machine Learning Internet of Things 通过机器学习物联网实现心电流数据的自动学习

IF 2 4区计算机科学

Intelligent Automation and Soft Computing Pub Date : 2022-01-01 DOI: 10.32604/iasc.2022.021426

Mwaffaq Abu-Alhaija, Nidal M. Turab

{"title":"Automated Learning of ECG Streaming Data Through Machine Learning Internet of Things","authors":"Mwaffaq Abu-Alhaija, Nidal M. Turab","doi":"10.32604/iasc.2022.021426","DOIUrl":"https://doi.org/10.32604/iasc.2022.021426","url":null,"abstract":"Applying machine learning techniques on Internet of Things (IoT) data streams will help achieve better understanding, predict future perceptions, and make crucial decisions based on those analytics. The collaboration between IoT, Big Data and machine learning can be found in different domains such as Health care, Smart cities, and Telecommunications. The aim of this paper is to develop a method for automated learning of electrocardiogram (ECG) streaming data to detect any heart beat anomalies. A promising solution is to use medical sensors that transfer vital signs to medical care computer systems, combined with machine learning, such that clinicians can get alerted about patient’s critical condition and act accordingly. Since the probability of false alarms pose serious impact to the accuracy of cardiac arrhythmia detection, it is the most important factor to keep false alarms to the lowest level. The proposed method in this paper demonstrates an example of how machine learning can contribute to health technologies with in detecting heart disease through minimizing negative false alarms. Stages of heartbeat learning model are proposed and explained besides the stages heartbeat anomalies detection stages.","PeriodicalId":50357,"journal":{"name":"Intelligent Automation and Soft Computing","volume":"14 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78183098","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

A Wireless ECG Monitoring and Analysis System Using the IoT Cloud 基于物联网云的无线心电监测与分析系统

IF 2 4区计算机科学

Intelligent Automation and Soft Computing Pub Date : 2022-01-01 DOI: 10.32604/iasc.2022.024005

Anas Bushnag

{"title":"A Wireless ECG Monitoring and Analysis System Using the IoT Cloud","authors":"Anas Bushnag","doi":"10.32604/iasc.2022.024005","DOIUrl":"https://doi.org/10.32604/iasc.2022.024005","url":null,"abstract":"A portable electrocardiogram (ECG) monitoring system is essential for elderly and remote patients who are not able to visit the hospital regularly. The system connects a patient to his/her doctor through an Internet of Things (IoT) cloud server that provides all the information needed to diagnose heart diseases. Patients use an ECG monitoring device to collect and upload information regarding their current medical situation via the Message Queue Telemetry Transport (MQTT) protocol to the server. The IoT cloud server performs further analysis that can be useful for both the doctor and the patient. Moreover, the proposed system has an alert mechanism that sends notifications when a certain threshold is reached. The monitoring system accepts two types of input data: real-time data that are collected by an ECG device and benchmark data from the PhysioNet ECG-ID database. The system framework has four components: input, embedded device, IoT cloud server, and interface. Herein, two experiments are conducted using both types of input data. The results show that the proposed system provides reliable and trusted results that might reduce the number of required hospital visits. A comparison between the proposed system and several techniques previously reported in the literature is conducted. Finally, an implementation of the proposed system is presented to illustrate its operation.","PeriodicalId":50357,"journal":{"name":"Intelligent Automation and Soft Computing","volume":"13 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82356796","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Security and Privacy Aspects of Cloud Computing: A Smart Campus Case Study 云计算的安全和隐私方面:一个智能校园案例研究

IF 2 4区计算机科学

Intelligent Automation and Soft Computing Pub Date : 2022-01-01 DOI: 10.32604/iasc.2022.016597

Sajid Habib Gill, Mirza Abdur Razzaq, Muneer Ahmad, Fahad M. Almansour, Ikram Ul Haq, Noor Zaman Jhanjhi, Malik Zaib Alam, Mehedi Masud

{"title":"Security and Privacy Aspects of Cloud Computing: A Smart Campus Case Study","authors":"Sajid Habib Gill, Mirza Abdur Razzaq, Muneer Ahmad, Fahad M. Almansour, Ikram Ul Haq, Noor Zaman Jhanjhi, Malik Zaib Alam, Mehedi Masud","doi":"10.32604/iasc.2022.016597","DOIUrl":"https://doi.org/10.32604/iasc.2022.016597","url":null,"abstract":"The trend of cloud computing is accelerating along with emerging technologies such as utility computing, grid computing, and distributed computing. Cloud computing is showing remarkable potential to provide flexible, costeffective, and powerful resources across the internet, and is a driving force in today’s most prominent computing technologies. The cloud offers the means to remotely access and store data while virtual machines access data over a network resource. Furthermore, cloud computing plays a leading role in the fourth industrial revolution. Everyone uses the cloud daily life when accessing Dropbox, various Google services, and Microsoft Office 365. While there are many advantages in such an environment, security issues such as data privacy, data security, access control, cyber-attacks, and data availability, along with performance and reliability issues, exist. Efficient security and privacy measures should be implemented by cloud service providers to ensure the privacy, confidentiality, integrity, and availability of data services. However, cloud service providers have not been providing enough secure and reliable services to end users. Blockchain is a technology that is improving cloud computing. This revolutionary technology offers persuasive data integrity properties and is used to tackle security problems. This research presents a detailed analysis of privacy and security challenges in the cloud. We demonstrate the importance of security challenges in a case study in the context of smart campus security, which will encourage researchers to examine security issues in cloud computing in the future.","PeriodicalId":50357,"journal":{"name":"Intelligent Automation and Soft Computing","volume":"46 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83354598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 12

Federated Learning for Privacy-Preserved Medical Internet of Things 隐私保护医疗物联网的联邦学习

IF 2 4区计算机科学

Intelligent Automation and Soft Computing Pub Date : 2022-01-01 DOI: 10.32604/iasc.2022.023763

Navod Neranjan Thilakarathne, G. Muneeswari, V. Parthasarathy, Fawaz Alassery, Habib Hamam, Rakesh Kumar Mahendran, M. Shafiq

引用次数: 5