Augmented Human Research最新文献_第6页

A Comparative Study of ECG Beats Variability Classification Based on Different Machine Learning Algorithms 基于不同机器学习算法的心电信号变异性分类的比较研究

Augmented Human Research Pub Date : 2020-04-20 DOI: 10.1007/s41133-020-00036-w

Agya Ram Verma, Bhumika Gupta, Chitra Bhandari

{"title":"A Comparative Study of ECG Beats Variability Classification Based on Different Machine Learning Algorithms","authors":"Agya Ram Verma, Bhumika Gupta, Chitra Bhandari","doi":"10.1007/s41133-020-00036-w","DOIUrl":"10.1007/s41133-020-00036-w","url":null,"abstract":"<div><p>The electrocardiogram (ECG) signal is a method that uses electrodes to record cardiac rates along with sensing minute electrical fluctuations for each cardiac rate. The information is utilized to analyze abrupt cardiac function like arrhythmias and conduction disturbance. The paper proposes strategy classifying ECG signal using various technique. The preprocessing stage includes filtering of input signal via low pass, high pass including Butterworth filter in order to remove clamour of high frequency. From signal, the excess clamour is sliced by Butterworth filter. The peak points are detected by peak detection algorithm, and the signal features are extracted using statistical parameters. At last, extracted feature classification is done via GWO-MSVM, SVM, Adaboost, ANN and Naive Bayes classifier to classify the ECG signal database into normal or abnormal ECG signal. The experimental result indicates the precision of the GWO-MSVM, SVM, Adaboost, ANN and Naive Bayes classifier is 99.9%, 94%, 93%,87.57% and 85.28%. When compared with other classifier, it was determined that precision of GWO-MSVM classifier is high.</p></div>","PeriodicalId":100147,"journal":{"name":"Augmented Human Research","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s41133-020-00036-w","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50088101","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

A Comprehensive Analysis Regarding Several Breakthroughs Based on Computer Intelligence Targeting Various Syndromes 综合分析基于计算机智能的针对不同证候的若干突破

Augmented Human Research Pub Date : 2020-03-30 DOI: 10.1007/s41133-020-00033-z

Darsh Shah, Rutvik Dixit, Aneri Shah, Priyam Shah, Manan Shah

{"title":"A Comprehensive Analysis Regarding Several Breakthroughs Based on Computer Intelligence Targeting Various Syndromes","authors":"Darsh Shah, Rutvik Dixit, Aneri Shah, Priyam Shah, Manan Shah","doi":"10.1007/s41133-020-00033-z","DOIUrl":"10.1007/s41133-020-00033-z","url":null,"abstract":"<div><p>Artificial intelligence (AI) is a broad field; this term signifies the application of a machine or computer to construct intelligent behaviour with insignificant human interruption or interference. AI is expressed as the combination of science and engineering for making intelligent computers. The term AI applies to a broad spectrum of matters in medicine and healthcare sectors like robotics, a medical diagnosis which concerns too many different types of diseases, human biology, and medical statistics. AI in medicine and health care is the main focus of this survey. Our goal is to highlight numerous algorithms based on the techniques which rely on artificially intelligent behaviour for detecting many diseases. We then review more precisely regarding AI applications in several categories of diseases such as hereditary diseases, physiological diseases, cancers, and infectious diseases. We have analysed the AI-based algorithms, and results for the same for the diseases included in the categories as mentioned above. Popular AI techniques include machine learning methods, along with the implementation of natural language processing. We have also discussed the impact of big data in the healthcare sector and how it has supported to improve the field of AI. An overview of various artificial intelligent methods is exhibited in this paper alongside the review of relevant important clinical applications.</p></div>","PeriodicalId":100147,"journal":{"name":"Augmented Human Research","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s41133-020-00033-z","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50104270","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: 26

Identification of Potential Task Shedding Events Using Brain Activity Data 利用大脑活动数据识别潜在的任务转移事件

Augmented Human Research Pub Date : 2020-03-30 DOI: 10.1007/s41133-020-00034-y

Danushka Bandara, Trevor Grant, Leanne Hirshfield, Senem Velipasalar

{"title":"Identification of Potential Task Shedding Events Using Brain Activity Data","authors":"Danushka Bandara, Trevor Grant, Leanne Hirshfield, Senem Velipasalar","doi":"10.1007/s41133-020-00034-y","DOIUrl":"10.1007/s41133-020-00034-y","url":null,"abstract":"<div><p>In Human–Machine Teaming environments, it is important to identify potential performance drops due to cognitive overload. If identified correctly, they can help improve the performance of the human–machine system by offloading some tasks to less cognitively overloaded users. This can help prevent user error that can result in critical failures. Also, it can improve productivity by keeping the human operators at an optimal performance state. This paper explores a new method for identifying user cognitive load by a three-class classification using brain activity data and by applying a convolutional neural network and long short-term memory model. The data collected from a set of cognitive benchmark experiments were used to train the model, which was then tested on two separate datasets consisting of more ecologically valid task environments. We experimented with various models built with different benchmark tasks to explore which benchmark tasks were better suited for the prediction of task shedding events in these compound tasks that are more representative of real-world scenarios. We also show that this method can be extended across-tasks and across-subject pools.</p></div>","PeriodicalId":100147,"journal":{"name":"Augmented Human Research","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s41133-020-00034-y","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50104269","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

Optimization of Smart Traffic Governance System Using Artificial Intelligence 基于人工智能的智能交通治理系统优化

Augmented Human Research Pub Date : 2020-03-29 DOI: 10.1007/s41133-020-00035-x

Aayush Sukhadia, Khush Upadhyay, Meghashree Gundeti, Smit Shah, Manan Shah

{"title":"Optimization of Smart Traffic Governance System Using Artificial Intelligence","authors":"Aayush Sukhadia, Khush Upadhyay, Meghashree Gundeti, Smit Shah, Manan Shah","doi":"10.1007/s41133-020-00035-x","DOIUrl":"10.1007/s41133-020-00035-x","url":null,"abstract":"<div><p>Traffic system shows a great scope of trade with the environment and is directly connected to it. Manual traffic systems are proving to be insufficient due to rapid urbanization. Central monitoring systems are facing scalability issues as they process increasing amounts of data received from hundreds of traffic cameras. Major traffic problems include congestion, safety, pollution (leading to various health issues) and increased need for mobility. A solution to most of them is the construction of newer and safer highways and additional lanes on existing ones, but it proves to be expensive and often not feasible. Cities are limited by space, and construction cannot keep up with ever-growing demand. Hence, a need for an improved system with a minimal manual interface is persisting. One of such methods is introduced and discussed in this paper; smart traffic governance system here used artificial intelligence to regulate and govern the course of transport and automated administration and implementation to make a difference in face of travel scenarios in urban cities suffering from such major traffic issues.</p></div>","PeriodicalId":100147,"journal":{"name":"Augmented Human Research","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s41133-020-00035-x","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50053265","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: 37

A Comparative Analysis of Logistic Regression, Random Forest and KNN Models for the Text Classification 逻辑回归、随机森林和KNN模型在文本分类中的比较分析

Augmented Human Research Pub Date : 2020-03-05 DOI: 10.1007/s41133-020-00032-0

Kanish Shah, Henil Patel, Devanshi Sanghvi, Manan Shah

{"title":"A Comparative Analysis of Logistic Regression, Random Forest and KNN Models for the Text Classification","authors":"Kanish Shah, Henil Patel, Devanshi Sanghvi, Manan Shah","doi":"10.1007/s41133-020-00032-0","DOIUrl":"10.1007/s41133-020-00032-0","url":null,"abstract":"<div><p>In the current generation, a huge amount of textual documents are generated and there is an urgent need to organize them in a proper structure so that classification can be performed and categories can be properly defined. The key technology for gaining the insights into a text information and organizing that information is known as text classification. The classes are then classified by determining the text types of the content. Based on different machine learning algorithms used in the current paper, the system of text classification is divided into four sections namely text pre-treatment, text representation, implementation of the classifier and classification. In this paper, a BBC news text classification system is designed. In the classifier implementation section, the authors separately chose and compared logistic regression, random forest and K-nearest neighbour as our classification algorithms. Then, these classifiers were tested, analysed and compared with each other and finally got a conclusion. The experimental conclusion shows that BBC news text classification model gets satisfying results on the basis of algorithms tested on the data set. The authors decided to show the comparison based on five parameters namely precision, accuracy, <i>F</i>1-score, support and confusion matrix. The classifier which gets the highest among all these parameters is termed as the best machine learning algorithm for the BBC news data set.</p></div>","PeriodicalId":100147,"journal":{"name":"Augmented Human Research","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s41133-020-00032-0","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50009524","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: 36

Multi-label Movie Genre Detection from a Movie Poster Using Knowledge Transfer Learning 基于知识转移学习的电影海报多标签电影类型检测

Augmented Human Research Pub Date : 2019-12-24 DOI: 10.1007/s41133-019-0029-y

Kaushil Kundalia, Yash Patel, Manan Shah

引用次数: 2

Preprocessing of Non-symmetrical Images for Edge Detection 用于边缘检测的非对称图像预处理

Augmented Human Research Pub Date : 2019-12-24 DOI: 10.1007/s41133-019-0030-5

Meet Gandhi, Juhi Kamdar, Manan Shah

引用次数: 47

Textural Measure for Medical Words Characterization Applied to Script Identification in Bilingual Context 医学词汇表征的结构度量在双语文本识别中的应用

Augmented Human Research Pub Date : 2019-12-24 DOI: 10.1007/s41133-019-0028-z

Nouf M. Alzahrani, Adil F. Alharthi

引用次数: 0

Implementation of Artificial Intelligence Techniques for Cancer Detection 人工智能技术在癌症检测中的应用

Augmented Human Research Pub Date : 2019-11-29 DOI: 10.1007/s41133-019-0024-3

Darshan Patel, Yash Shah, Nisarg Thakkar, Kush Shah, Manan Shah

{"title":"Implementation of Artificial Intelligence Techniques for Cancer Detection","authors":"Darshan Patel, Yash Shah, Nisarg Thakkar, Kush Shah, Manan Shah","doi":"10.1007/s41133-019-0024-3","DOIUrl":"10.1007/s41133-019-0024-3","url":null,"abstract":"<div><p>Diseases like cancer have been termed as chronic fatal disease because of its deadly nature. The reason why cancer is termed as fatal is cancer progresses faster, and in most of the cases, these cells are detected at an advance stage. It is found that early detection of cancer is the key to lower death rate. In this study, overviews of applying AI technology for diagnosis of three types of cancer, breast, lung and liver, have been demonstrated. Various studies are reviewed for the different types of systems which are used for early detection of cancer. Automated or computer-aided systems with AI are considered as they provide a perfect fit to process a large dataset with accuracy and efficiency in detecting cancer. Diagnosis and treatment can be carried out with the help of these systems. Breast, lung and liver cancer studies have shown that some of these systems provide accurate precision in diagnosis and thus can solve the problem if these systems are implemented. However, these systems have to face a lot of hurdles to be implemented on a large scale. Image preprocessing, data management and other technology also need enhancement to be compatible with AI and machine learning algorithms to be implemented. Considering the experimental results, this study shows there is no doubt that the AI-implemented neural networks would be the future in cancer diagnosis and treatment.</p></div>","PeriodicalId":100147,"journal":{"name":"Augmented Human Research","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s41133-019-0024-3","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50053995","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: 51

Application on Virtual Reality for Enhanced Education Learning, Military Training and Sports 虚拟现实技术在教育学习、军事训练和体育中的应用

Augmented Human Research Pub Date : 2019-11-29 DOI: 10.1007/s41133-019-0025-2

Kunjal Ahir, Kajal Govani, Rutvik Gajera, Manan Shah

{"title":"Application on Virtual Reality for Enhanced Education Learning, Military Training and Sports","authors":"Kunjal Ahir, Kajal Govani, Rutvik Gajera, Manan Shah","doi":"10.1007/s41133-019-0025-2","DOIUrl":"10.1007/s41133-019-0025-2","url":null,"abstract":"<div><p>Virtual reality is emerging freshly in the field of interdisciplinary research. In the past years, its area has grown over research and the industry has made important investments in the manufacturing of different VR products as well as in research. Virtual reality (VR) is developed by the union of technologies that are used to visualize and interact with virtual atmosphere. This atmosphere portrays a 3D space which may be imaginary, microscopic or macroscopic and based on practical laws of dynamics or imaginary dynamics. VR technology is getting supreme using computer hardware, software and virtual environment technology through which the real world can be simulated dynamically. The dynamical conditions can react according to the human language, form and so on rapidly that humans can communicate with virtual environment in true time. Therefore, VR technology can be put into application in education, military, sports training, and is portraying an important part in the evolution. The paper summarizes the developments in VR technology in the fields of education, military and sports, and then analyses the future trends of VR in these fields.</p></div>","PeriodicalId":100147,"journal":{"name":"Augmented Human Research","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s41133-019-0025-2","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50053997","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