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Int. J. E Health Medical Commun.最新文献

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Automated Glucose Control: A Review 自动血糖控制:综述
Int. J. E Health Medical Commun. Pub Date : 2021-11-01 DOI: 10.4018/IJEHMC.20211101.OA12
Owais Bhat, D. Khan, R. Yousuf
{"title":"Automated Glucose Control: A Review","authors":"Owais Bhat, D. Khan, R. Yousuf","doi":"10.4018/IJEHMC.20211101.OA12","DOIUrl":"https://doi.org/10.4018/IJEHMC.20211101.OA12","url":null,"abstract":"","PeriodicalId":375617,"journal":{"name":"Int. J. E Health Medical Commun.","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129092618","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
Improving Performance During Camera Surveillance by Integration of Edge Detection in IoT System 集成物联网系统边缘检测提高摄像机监控性能
Int. J. E Health Medical Commun. Pub Date : 2021-09-01 DOI: 10.4018/IJEHMC.20210901.OA6
Sonal Beniwal, Usha Saini, P. Garg, R. Joon
{"title":"Improving Performance During Camera Surveillance by Integration of Edge Detection in IoT System","authors":"Sonal Beniwal, Usha Saini, P. Garg, R. Joon","doi":"10.4018/IJEHMC.20210901.OA6","DOIUrl":"https://doi.org/10.4018/IJEHMC.20210901.OA6","url":null,"abstract":"This paper is proposing an IoT-based camera surveillance system. The objective of research is to detect suspicious activities by camera automatically and take decision by comparing current frame to previous frame. Major motivation behind research work is to enhance the performance of IoT-based system by integration of edge detection mechanism. Research is making use of numerous cameras, canny edge detection-based compression module, picture database, picture comparator. Canny edge detection has been used to minimize size of graphical content to enhancing the performance system. Simulation of output of this work is made in MATLAB simulation tool. Moreover, MATLAB has been used to give comparative analysis among IoT-based camera surveillance system and traditional system. Such system requires less space, and it takes less time to inform regarding any suspicious activities. KEywoRDS Camera Surveillance, Canny Edge Detection, Cloud Server, IoT, MATLAB, NET Platform","PeriodicalId":375617,"journal":{"name":"Int. J. E Health Medical Commun.","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123513142","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: 6
On Performance of Big Data Storage on Cloud Mechanics in Mobile Digital Healthcare 移动数字医疗云力学大数据存储性能研究
Int. J. E Health Medical Commun. Pub Date : 2021-09-01 DOI: 10.4018/IJEHMC.20210901.OA3
Abhilasha Rangra, V. Sehgal
{"title":"On Performance of Big Data Storage on Cloud Mechanics in Mobile Digital Healthcare","authors":"Abhilasha Rangra, V. Sehgal","doi":"10.4018/IJEHMC.20210901.OA3","DOIUrl":"https://doi.org/10.4018/IJEHMC.20210901.OA3","url":null,"abstract":"In recent years, the concept of cloud computing and big data analysis are considered as two major problems. It empowers the resources of computing to be maintained as the service of information technology with high effectiveness and efficiency. In the present scenario, big data is treated as one of the issues that the experts are trying to solve and finding ways to tackle the problem of handling big data analytics, how it could be managed with the technology of cloud computing and handled in the recent systems, and apart from this, the most significant issue is how to have perfect safety of big data in the cloud computing environment. In this paper, the authors mainly improve the performance of big data storage on cloud mechanics as the integration of mobile digital healthcare. The proposed framework involves the process of refining the sensitivity by using a deep learning approach. After this, it involves the step of computing or storage in the cloud-based server in an optimized manner. The experimental analysis provides a significant improvement in terms of cost, time, and accuracy.","PeriodicalId":375617,"journal":{"name":"Int. J. E Health Medical Commun.","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131792457","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
Artificial Bee Colony Optimized Deep Neural Network Model for Handling Imbalanced Stroke Data: ABC-DNN for Prediction of Stroke 处理不平衡中风数据的人工蜂群优化深度神经网络模型:ABC-DNN预测中风
Int. J. E Health Medical Commun. Pub Date : 2021-09-01 DOI: 10.4018/IJEHMC.20210901.OA5
Ajay Dev, S. K. Malik
{"title":"Artificial Bee Colony Optimized Deep Neural Network Model for Handling Imbalanced Stroke Data: ABC-DNN for Prediction of Stroke","authors":"Ajay Dev, S. K. Malik","doi":"10.4018/IJEHMC.20210901.OA5","DOIUrl":"https://doi.org/10.4018/IJEHMC.20210901.OA5","url":null,"abstract":"The healthcare domain gets wide attention among the research community due to incremental data growth, advanced diagnostic tools, medical imaging processes, and many more. Enormous healthcare data is generated through diagnostic tool and medical imaging process, but handling of these data is a tough task due to its nature. A large number of machine learning techniques are presented for handling the healthcare data and right diagnosis of disease. However, the accuracy is one of primary concerns regarding the disease diagnosis. Hence, this study explores the applicability of deep neural network (DNN) technique for handling the imbalance of healthcare data. An artificial bee colony technique is adopted to determine the relevant features of stroke disease called ABC-FS-optimized DNN. The performance of proposed ABC-FS-optimized DNN model is evaluated using accuracy, precision, and recall parameters and compared with state of art existing techniques. The simulation results showed that proposed model obtains 87.09%, 84.28%, and 85.72% accuracy, precision, and recall rates, respectively.","PeriodicalId":375617,"journal":{"name":"Int. J. E Health Medical Commun.","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125696804","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: 5
Noise Removal in Lung LDCT Images by Novel Discrete Wavelet-Based Denoising With Adaptive Thresholding Technique 基于自适应阈值的离散小波去噪方法在肺LDCT图像中的应用
Int. J. E Health Medical Commun. Pub Date : 2021-09-01 DOI: 10.4018/IJEHMC.20210901.OA1
S. Ziyad, V. Radha, Thavavel Vaiyapuri
{"title":"Noise Removal in Lung LDCT Images by Novel Discrete Wavelet-Based Denoising With Adaptive Thresholding Technique","authors":"S. Ziyad, V. Radha, Thavavel Vaiyapuri","doi":"10.4018/IJEHMC.20210901.OA1","DOIUrl":"https://doi.org/10.4018/IJEHMC.20210901.OA1","url":null,"abstract":"Cancer is presently one of the prominent causes of death in the world. Early cancer detection, which can improve the prognosis and survival of cancer patients, is challenging for radiologists. Low-dose computed tomography, a commonly used imaging test for screening lung cancer, has a risk of exposure of patients to ionizing radiations. Increased radiation exposure can cause lung cancer development. However, reduced radiation dose results in noisy LDCT images. Efficient preprocessing techniques with computer-aided diagnosis tools can remove noise from LDCT images. Such tools can increase the survival of lung cancer patients by an accurate delineation of the lung nodules. This study aims to develop a framework for preprocessing LDCT images. The authors propose a noise removal technique of discrete wavelet transforms with adaptive thresholding by computing the threshold with a genetic algorithm. The performance of the proposed technique is evaluated by comparing with mean, median, and Gaussian noise filters.","PeriodicalId":375617,"journal":{"name":"Int. J. E Health Medical Commun.","volume":"2022 13","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132879593","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: 5
Data Mining-Based Privacy Preservation Technique for Medical Dataset Over Horizontal Partitioned 基于数据挖掘的水平分区医疗数据集隐私保护技术
Int. J. E Health Medical Commun. Pub Date : 2021-09-01 DOI: 10.4018/IJEHMC.20210901.OA4
Shivlal Mewada
{"title":"Data Mining-Based Privacy Preservation Technique for Medical Dataset Over Horizontal Partitioned","authors":"Shivlal Mewada","doi":"10.4018/IJEHMC.20210901.OA4","DOIUrl":"https://doi.org/10.4018/IJEHMC.20210901.OA4","url":null,"abstract":"The valuable information is extracted through data mining techniques. Recently, privacy preserving data mining techniques are widely adopted for securing and protecting the information and data. These techniques convert the original dataset into protected dataset through swapping, modification, and deletion functions. This technique works in two steps. In the first step, cloud computing considers a service platform to determine the optimum horizontal partitioning in given data. In this work, K-Means++ algorithm is implemented to determine the horizontal partitioning on the cloud platform without disclosing the cluster centers information. The second steps contain data protection and recover phases. In the second step, noise is incorporated in the database to maintain the privacy and semantic of the data. Moreover, the seed function is used for protecting the original databases. The effectiveness of the proposed technique is evaluated using several benchmark medical datasets. The results are evaluated using encryption time, execution time, accuracy, and f-measure parameters.","PeriodicalId":375617,"journal":{"name":"Int. J. E Health Medical Commun.","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114857355","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
A Novel Method for Despeckling of Ultrasound Images Using Cellular Automata-Based Despeckling Filter 一种基于元胞自动机的超声图像去斑滤波方法
Int. J. E Health Medical Commun. Pub Date : 2021-09-01 DOI: 10.4018/IJEHMC.20210901.OA2
Ankur Bhardwaj, Sanmukh Kaur, A. P. Shukla, M. Shukla
{"title":"A Novel Method for Despeckling of Ultrasound Images Using Cellular Automata-Based Despeckling Filter","authors":"Ankur Bhardwaj, Sanmukh Kaur, A. P. Shukla, M. Shukla","doi":"10.4018/IJEHMC.20210901.OA2","DOIUrl":"https://doi.org/10.4018/IJEHMC.20210901.OA2","url":null,"abstract":"Ultrasound images have an inherent property termed as speckle noise that is the outcome of interference between incident and reflected ultrasound waves which reduce image resolution and contrast and could lead to improper diagnosis of any disease. In different approaches for reducing the speckle noise, there exists a class of filters that convert multiplicative noise into additive noise by using algorithmic functions. The current study proposes a cellular automata-based despeckling filter (CABDF) that implements a local spatial filtering framework for the restoration of the noisy image. In the proposed CABDF filter, a dual transition function has been designed which emphasizes the calculation of nearby weighted separation whose loads originate from the CABDF filtered image, including spatial separation, extend inconsistency, and statistical dispersion. The proposed filter found efficient both in terms of filtering and restoration of the original structure of the ultrasound images.","PeriodicalId":375617,"journal":{"name":"Int. J. E Health Medical Commun.","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124456673","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: 5
Performance Analysis of Machine Learning Algorithms for Big Data Classification: ML and AI-Based Algorithms for Big Data Analysis 大数据分类中机器学习算法的性能分析:大数据分析中基于ML和ai的算法
Int. J. E Health Medical Commun. Pub Date : 2021-07-01 DOI: 10.4018/IJEHMC.20210701.OA4
S. Punia, Manoj Kumar, Thompson Stephan, G. Deverajan, Rizwan Patan
{"title":"Performance Analysis of Machine Learning Algorithms for Big Data Classification: ML and AI-Based Algorithms for Big Data Analysis","authors":"S. Punia, Manoj Kumar, Thompson Stephan, G. Deverajan, Rizwan Patan","doi":"10.4018/IJEHMC.20210701.OA4","DOIUrl":"https://doi.org/10.4018/IJEHMC.20210701.OA4","url":null,"abstract":"In broad, three machine learning classification algorithms are used to discover correlations, hidden patterns, and other useful information from different data sets known as big data. Today, Twitter, Facebook, Instagram, and many other social media networks are used to collect the unstructured data. The conversion of unstructured data into structured data or meaningful information is a very tedious task. The different machine learning classification algorithms are used to convert unstructured data into structured data. In this paper, the authors first collect the unstructured research data from a frequently used social media network (i.e., Twitter) by using a Twitter application program interface (API) stream. Secondly, they implement different machine classification algorithms (supervised, unsupervised, and reinforcement) like decision trees (DT), neural networks (NN), support vector machines (SVM), naive Bayes (NB), linear regression (LR), and k-nearest neighbor (K-NN) from the collected research data set. The comparison of different machine learning classification algorithms is concluded.","PeriodicalId":375617,"journal":{"name":"Int. J. E Health Medical Commun.","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121664371","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: 28
Kernel Parameter Tuning to Tweak the Performance of Classifiers for Identification of Heart Diseases 核参数调优调整心脏疾病分类器的性能
Int. J. E Health Medical Commun. Pub Date : 2021-07-01 DOI: 10.4018/IJEHMC.20210701.OA1
Anurag Dhankhar, Sapna Juneja, Abhinav Juneja, V. Bali
{"title":"Kernel Parameter Tuning to Tweak the Performance of Classifiers for Identification of Heart Diseases","authors":"Anurag Dhankhar, Sapna Juneja, Abhinav Juneja, V. Bali","doi":"10.4018/IJEHMC.20210701.OA1","DOIUrl":"https://doi.org/10.4018/IJEHMC.20210701.OA1","url":null,"abstract":"Medical data analysis is being recognized as a field of enormous research possibilities due to the fact there is a huge amount of data available and prediction in initial stage may save patient lives with timely intervention. With machine learning, a particular algorithm may be created through which any disease may be predicted well in advance on the basis of its feature sets or its symptoms can be detected. With respect to this research work, heart disease will be predicted with support vector machine that falls under the category of supervised machine learning algorithm. The main idea of this study is to focus on the significance of parameter tuning to elevate the performance of classifier. The results achieved were then compared with normal classifier SVM before tuning the parameters. Results depict that the hyperparameters tuning enhances the performance of the model. Finally, results were calculated by using various validation metrics.","PeriodicalId":375617,"journal":{"name":"Int. J. E Health Medical Commun.","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115268723","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: 15
Lung Tumor Segmentation Using Marker-Controlled Watershed and Support Vector Machine 基于标记控制分水岭和支持向量机的肺肿瘤分割
Int. J. E Health Medical Commun. Pub Date : 2021-07-01 DOI: 10.4018/ijehmc.2021030103
Surbhi Vijh, Rituparna Sarma, Sumit Kumar
{"title":"Lung Tumor Segmentation Using Marker-Controlled Watershed and Support Vector Machine","authors":"Surbhi Vijh, Rituparna Sarma, Sumit Kumar","doi":"10.4018/ijehmc.2021030103","DOIUrl":"https://doi.org/10.4018/ijehmc.2021030103","url":null,"abstract":"The medical imaging technique showed remarkable improvement in interventional treatment of computer-aided medical diagnosis system. Image processing techniques are broadly applied in detection and exploring the abnormalities issues in tumor detection. The early stage of lung tumor detection is extremely important in medical research field. The proposed work uses image processing segmentation technique for detection of lung tumor and the support vector classifier learning technique for predicting stage of tumor. After performing preprocessing and segmentation the features are extracted from region of lung nodule. The classification is performed on dataset acquired from national cancer institute for the evaluation of lung cancer diagnosis. The multi-class machine learning classification technique SVM (support vector machine) identifies the tumor stage of lung dataset. The proposed methodology provides classification of tumor stages and improves the decision-making process. The performance is evaluated by measuring the parameters namely accuracy, sensitivity, and specificity.","PeriodicalId":375617,"journal":{"name":"Int. J. E Health Medical Commun.","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114924126","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
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