Neural Computing & Applications最新文献_第10页

Performance analysis and comparison of Machine Learning and LoRa-based Healthcare model. 基于机器学习和LoRa的医疗保健模型的性能分析和比较。

IF 6 3区计算机科学

Neural Computing & Applications Pub Date : 2023-01-01 Epub Date: 2023-03-07 DOI: 10.1007/s00521-023-08411-5

Navneet Verma, Sukhdip Singh, Devendra Prasad

{"title":"Performance analysis and comparison of Machine Learning and LoRa-based Healthcare model.","authors":"Navneet Verma, Sukhdip Singh, Devendra Prasad","doi":"10.1007/s00521-023-08411-5","DOIUrl":"10.1007/s00521-023-08411-5","url":null,"abstract":"Diabetes Mellitus (DM) is a widespread condition that is one of the main causes of health disasters around the world, and health monitoring is one of the sustainable development topics. Currently, the Internet of Things (IoT) and Machine Learning (ML) technologies work together to provide a reliable method of monitoring and predicting Diabetes Mellitus. In this paper, we present the performance of a model for patient real-time data collection that employs the Hybrid Enhanced Adaptive Data Rate (HEADR) algorithm for the Long-Range (LoRa) protocol of the IoT. On the Contiki Cooja simulator, the LoRa protocol's performance is measured in terms of high dissemination and dynamic data transmission range allocation. Furthermore, by employing classification methods for the detection of diabetes severity levels on acquired data via the LoRa (HEADR) protocol, Machine Learning prediction takes place. For prediction, a variety of Machine Learning classifiers are employed, and the final results are compared with the already existing models where the Random Forest and Decision Tree classifiers outperform the others in terms of precision, recall, F-measure, and receiver operating curve (ROC) in the Python programming language. We also discovered that using k-fold cross-validation on k-neighbors, Logistic regression (LR), and Gaussian Nave Bayes (GNB) classifiers boosted the accuracy.","PeriodicalId":49766,"journal":{"name":"Neural Computing & Applications","volume":"35 17","pages":"12751-12761"},"PeriodicalIF":6.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9989556/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9479074","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Special issue on neuro, fuzzy and their hybridization. 神经、模糊及其杂交专刊。

IF 6 3区计算机科学

Neural Computing & Applications Pub Date : 2023-01-01 DOI: 10.1007/s00521-022-08181-6

Longzhi Yang, Vijayakumar Varadarajan, Yanpeng Qu

引用次数: 0

E-learningDJUST: E-learning dataset from Jordan university of science and technology toward investigating the impact of COVID-19 pandemic on education. E-learningDJUST：约旦科技大学的电子学习数据集，用于调查 COVID-19 大流行病对教育的影响。

IF 4.5 3区计算机科学

Neural Computing & Applications Pub Date : 2023-01-01 Epub Date: 2021-11-13 DOI: 10.1007/s00521-021-06712-1

Malak Abdullah, Mahmoud Al-Ayyoub, Saif AlRawashdeh, Farah Shatnawi

{"title":"E-learningDJUST: E-learning dataset from Jordan university of science and technology toward investigating the impact of COVID-19 pandemic on education.","authors":"Malak Abdullah, Mahmoud Al-Ayyoub, Saif AlRawashdeh, Farah Shatnawi","doi":"10.1007/s00521-021-06712-1","DOIUrl":"10.1007/s00521-021-06712-1","url":null,"abstract":"Recently, the COVID-19 pandemic has triggered different behaviors in education, especially during the lockdown, to contain the virus outbreak in the world. As a result, educational institutions worldwide are currently using online learning platforms to maintain their education presence. This research paper introduces and examines a dataset, E-LearningDJUST, that represents a sample of the student's study progress during the pandemic at Jordan University of Science and Technology (JUST). The dataset depicts a sample of the university's students as it includes 9,246 students from 11 faculties taking four courses in spring 2020, summer 2020, and fall 2021 semesters. To the best of our knowledge, it is the first collected dataset that reflects the students' study progress within a Jordanian institute using e-learning system records. One of this work's key findings is observing a high correlation between e-learning events and the final grades out of 100. Thus, the E-LearningDJUST dataset has been experimented with two robust machine learning models (Random Forest and XGBoost) and one simple deep learning model (Feed Forward Neural Network) to predict students' performances. Using RMSE as the primary evaluation criteria, the RMSE values range between 7 and 17. Among the other main findings, the application of feature selection with the random forest leads to better prediction results for all courses as the RMSE difference ranges between (0-0.20). Finally, a comparison study examined students' grades before and after the Coronavirus pandemic to understand how it impacted their grades. A high success rate has been observed during the pandemic compared to what it was before, and this is expected because the exams were online. However, the proportion of students with high marks remained similar to that of pre-pandemic courses.","PeriodicalId":49766,"journal":{"name":"Neural Computing & Applications","volume":"35 16","pages":"11481-11495"},"PeriodicalIF":4.5,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8590139/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9492167","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A neural network approach to optimising treatments for depression using data from specialist and community psychiatric services in Australia, New Zealand and Japan. 利用来自澳大利亚、新西兰和日本的专家和社区精神病学服务的数据，采用神经网络方法优化抑郁症治疗。

IF 6 3区计算机科学

Neural Computing & Applications Pub Date : 2023-01-01 DOI: 10.1007/s00521-021-06710-3

Aidan Cousins, Lucas Nakano, Emma Schofield, Rasa Kabaila

{"title":"A neural network approach to optimising treatments for depression using data from specialist and community psychiatric services in Australia, New Zealand and Japan.","authors":"Aidan Cousins, Lucas Nakano, Emma Schofield, Rasa Kabaila","doi":"10.1007/s00521-021-06710-3","DOIUrl":"https://doi.org/10.1007/s00521-021-06710-3","url":null,"abstract":"This study investigated the application of a recurrent neural network for optimising pharmacological treatment for depression. A clinical dataset of 458 participants from specialist and community psychiatric services in Australia, New Zealand and Japan were extracted from an existing custom-built, web-based tool called Psynary . This data, which included baseline and self-completed reviews, was used to train and refine a novel algorithm which was a fully connected network feature extractor and long short-term memory algorithm was firstly trained in isolation and then integrated and annealed using slow learning rates due to the low dimensionality of the data. The accuracy of predicting depression remission before processing patient review data was 49.8%. After processing only 2 reviews, the accuracy was 76.5%. When considering a change in medication, the precision of changing medications was 97.4% and the recall was 71.4% . The medications with predicted best results were antipsychotics (88%) and selective serotonin reuptake inhibitors (87.9%). This is the first study that has created an all-in-one algorithm for optimising treatments for all subtypes of depression. Reducing treatment optimisation time for patients suffering with depression may lead to earlier remission and hence reduce the high levels of disability associated with the condition. Furthermore, in a setting where mental health conditions are increasing strain on mental health services, the utilisation of web-based tools for remote monitoring and machine/deep learning algorithms may assist clinicians in both specialist and primary care in extending specialist mental healthcare to a larger patient community.","PeriodicalId":49766,"journal":{"name":"Neural Computing & Applications","volume":"35 16","pages":"11497-11516"},"PeriodicalIF":6.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8754538/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9503950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Toward real-time and efficient cardiovascular monitoring for COVID-19 patients by 5G-enabled wearable medical devices: a deep learning approach. 通过5G可穿戴医疗设备对新冠肺炎患者进行实时高效的心血管监测：一种深度学习方法。

IF 6 3区计算机科学

Neural Computing & Applications Pub Date : 2023-01-01 Epub Date: 2021-07-04 DOI: 10.1007/s00521-021-06219-9

Liang Tan, Keping Yu, Ali Kashif Bashir, Xiaofan Cheng, Fangpeng Ming, Liang Zhao, Xiaokang Zhou

{"title":"Toward real-time and efficient cardiovascular monitoring for COVID-19 patients by 5G-enabled wearable medical devices: a deep learning approach.","authors":"Liang Tan, Keping Yu, Ali Kashif Bashir, Xiaofan Cheng, Fangpeng Ming, Liang Zhao, Xiaokang Zhou","doi":"10.1007/s00521-021-06219-9","DOIUrl":"10.1007/s00521-021-06219-9","url":null,"abstract":"Patients with deaths from COVID-19 often have co-morbid cardiovascular disease. Real-time cardiovascular disease monitoring based on wearable medical devices may effectively reduce COVID-19 mortality rates. However, due to technical limitations, there are three main issues. First, the traditional wireless communication technology for wearable medical devices is difficult to satisfy the real-time requirements fully. Second, current monitoring platforms lack efficient streaming data processing mechanisms to cope with the large amount of cardiovascular data generated in real time. Third, the diagnosis of the monitoring platform is usually manual, which is challenging to ensure that enough doctors online to provide a timely, efficient, and accurate diagnosis. To address these issues, this paper proposes a 5G-enabled real-time cardiovascular monitoring system for COVID-19 patients using deep learning. Firstly, we employ 5G to send and receive data from wearable medical devices. Secondly, Flink streaming data processing framework is applied to access electrocardiogram data. Finally, we use convolutional neural networks and long short-term memory networks model to obtain automatically predict the COVID-19 patient's cardiovascular health. Theoretical analysis and experimental results show that our proposal can well solve the above issues and improve the prediction accuracy of cardiovascular disease to 99.29%.","PeriodicalId":49766,"journal":{"name":"Neural Computing & Applications","volume":"35 19","pages":"13921-13934"},"PeriodicalIF":6.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s00521-021-06219-9","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9526794","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 58

Machine learning-based diffusion model for prediction of coronavirus-19 outbreak. 基于机器学习的冠状病毒肺炎疫情扩散预测模型。

IF 4.5 3区计算机科学

Neural Computing & Applications Pub Date : 2023-01-01 Epub Date: 2021-08-12 DOI: 10.1007/s00521-021-06376-x

Supriya Raheja, Shreya Kasturia, Xiaochun Cheng, Manoj Kumar

{"title":"Machine learning-based diffusion model for prediction of coronavirus-19 outbreak.","authors":"Supriya Raheja, Shreya Kasturia, Xiaochun Cheng, Manoj Kumar","doi":"10.1007/s00521-021-06376-x","DOIUrl":"10.1007/s00521-021-06376-x","url":null,"abstract":"The coronavirus pandemic has been globally impacting the health and prosperity of people. A persistent increase in the number of positive cases has boost the stress among governments across the globe. There is a need of approach which gives more accurate predictions of outbreak. This paper presents a novel approach called diffusion prediction model for prediction of number of coronavirus cases in four countries: India, France, China and Nepal. Diffusion prediction model works on the diffusion process of the human contact. Model considers two forms of spread: when the spread takes time after infecting one person and when the spread is immediate after infecting one person. It makes the proposed model different over other state-of-the art models. It is giving more accurate results than other state-of-the art models. The proposed diffusion prediction model forecasts the number of new cases expected to occur in next 4 weeks. The model has predicted the number of confirmed cases, recovered cases, deaths and active cases. The model can facilitate government to be well prepared for any abrupt rise in this pandemic. The performance is evaluated in terms of accuracy and error rate and compared with the prediction results of support vector machine, logistic regression model and convolution neural network. The results prove the efficiency of the proposed model.","PeriodicalId":49766,"journal":{"name":"Neural Computing & Applications","volume":"35 19","pages":"13755-13774"},"PeriodicalIF":4.5,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8358916/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9526796","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

PA during the COVID-19 outbreak in China: a cross-sectional study. 新冠肺炎在中国爆发期间的PA：一项横断面研究。

IF 6 3区计算机科学

Neural Computing & Applications Pub Date : 2023-01-01 Epub Date: 2021-10-01 DOI: 10.1007/s00521-021-06538-x

Yingjun Nie, Yuanyan Ma, Xiaodong Li, Yankong Wu, Weixin Liu, Zhenke Tan, Jiahui Li, Ce Zhang, Chennan Lv, Ting Liu

{"title":"PA during the COVID-19 outbreak in China: a cross-sectional study.","authors":"Yingjun Nie, Yuanyan Ma, Xiaodong Li, Yankong Wu, Weixin Liu, Zhenke Tan, Jiahui Li, Ce Zhang, Chennan Lv, Ting Liu","doi":"10.1007/s00521-021-06538-x","DOIUrl":"10.1007/s00521-021-06538-x","url":null,"abstract":"COVID-19 has undergone several mutations and is still spreading in most countries now. PA has positive benefits in the prevention of COVID-19 infection and counteracting the negative physical and mental effects caused by COVID-19. However, relevant evidence has indicated a high prevalence of physical inactivity among the general population, which has worsened due to the outbreak of the pandemic, and there is a severe lack of exercise guidance and mitigation strategies to advance the knowledge and role of PA to improve physical and mental health in most countries during the epidemic. This study surveyed the effects of COVID-19 on PA in Chinese residents during the pandemic and provided important reference and evidence to inform policymakers and formulate policies and planning for health promotion and strengthening residents' PA during periods of public health emergencies. ANOVA, Kolmogorov-Smirnov, the chi-square test and Spearman correlation analysis were used for statistical analysis. A total of 14,715 participants were included. The results show that nearly 70% of Chinese residents had inadequate PA (95%CI 58.0%-82.19%) during the COVID-19 outbreak, which was more than double the global level (27.5%, 95%CI 25.0%-32.2%). The content, intensity, duration, and frequency of PA were all affected during the period of home isolation, and the types of PA may vary among different ages. The lack of physical facilities and cultural environment is the main factor affecting PA. However, there was no significant correlation between insufficient PA and the infection rate. During the period of home isolation and social distance of epidemic prevention, it is necessary to strengthen the scientific remote network monitoring and guidance for the process of PA in China.","PeriodicalId":49766,"journal":{"name":"Neural Computing & Applications","volume":"35 19","pages":"13739-13754"},"PeriodicalIF":6.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8485310/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9529747","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Topical collection on machine learning for big data analytics in smart healthcare systems. 智能医疗系统中用于大数据分析的机器学习专题集。

IF 6 3区计算机科学

Neural Computing & Applications Pub Date : 2023-01-01 Epub Date: 2023-05-09 DOI: 10.1007/s00521-023-08627-5

Mian Ahmad Jan, Houbing Song, Fazlullah Khan, Ateeq Ur Rehman, Lie-Liang Yang

引用次数: 1

Towards automated check-worthy sentence detection using Gated Recurrent Unit. 用门控循环单元实现句子的自动检出。

IF 6 3区计算机科学

Neural Computing & Applications Pub Date : 2023-01-01 DOI: 10.1007/s00521-023-08300-x

Ria Jha, Ena Motwani, Nivedita Singhal, Rishabh Kaushal

引用次数: 2

Enhanced balancing GAN: minority-class image generation. 增强平衡GAN:少数类图像生成。